Geo-informatics in Sustainable Ecosystem and Society: 6th International Conference, GSES 2018, Handan, China, September 25–26, 2018, Revised Selected Papers [1st ed.] 978-981-13-7024-3, 978-981-13-7025-0

Table of contents :
Front Matter ....Pages I-XII
Soil Property Surface Modeling Based on Ensemble Learning for Complex Landforms (Wei Liu, Yongkun Liu, Mengyuan Yang, Meng Xie)....Pages 1-14
Enhancement of Class Separability for Polarimetric TerraSAR-X Data and Its Application to Crop Classification in Leizhou Peninsula, Southern China (Hongzhong Li, Yu Han, Jinsong Chen, Shanxin Guo)....Pages 15-25
Mapping the Distribution of Exotic Mangrove Species in Shenzhen Bay Using Worldview-2 Imagery (Hongzhong Li, Yu Han, Jinsong Chen, Shanxin Guo)....Pages 26-42
Vortex Extraction Method Based on Compact Ratio (Ya-ru Xu, Min Ji, Zhi-wei Lu)....Pages 43-50
Optimized Data Organization of Land Cover Survey Based on Redis Memory Database (Jia Liu, Min Ji)....Pages 51-61
A Dynamic Switching Technique for Virtual Network in SDN Environment (Haifeng Fang, Yachan Zhao, Rong Tan, Tao Wang)....Pages 62-70
Multi-mode Control Strategy for Dual Active Bridge Bidirectional DC-DC Converters (Yaguang Zhang, Yong Du)....Pages 71-78
Spatial Distribution and Source Identification of Loess Heavy Metal Pollution in Northern Baoji, China (Ling Han, Zhiheng Liu, Yuming Ning, Zhongyang Zhao)....Pages 79-92
Analysis and Comparison of Uncertain Means Clustering Algorithm (Nini Zhang, Lihua Qi, Xiaomei Qin)....Pages 93-99
Research on Matrix Multiplication Based on the Combination of OpenACC and CUDA (Yuexing Wang)....Pages 100-108
Research on ICS Intrusion Success Rate Algorithm Based on Attack and Defense Countermeasures (Wending Wang, Kaixing Wu)....Pages 109-118
The Review of Task Scheduling in Cloud Computing (Fengjun Xin, Lina Zhang)....Pages 119-126
Prediction Model of River Water Quality Time Series Based on ARIMA Model (Lina Zhang, Fengjun Xin)....Pages 127-133
A Review of Gait Behavior Recognition Methods Based on Wearable Devices (Chang Liu, Jijun Zhao, Zhongcheng Wei)....Pages 134-145
K-Means Optimization Algorithm Based on Tightness Mutation (Tie Fei Li, Jian Fei Ma, Rui Xin Yang, Di Wu, Yan Guang Shen)....Pages 146-156
Study of Coal Integrated Network Decision Support System Based on GIS (Haixin Liu, Wei Wang, Tao Jiang, Yuling Zhao, Xiuyun Sun)....Pages 157-164
Analysis on Spatio-Temporal Changes of the Land Covers in Shenyang (Dayong Yang, Zhiwei Xie, Hua Ding)....Pages 165-175
Hierarchical Decision Tree for Change Detection Using High Resolution Remote Sensing Images (Zhiwei Xie, Min Wang, Yaohui Han, Dayong Yang)....Pages 176-184
Surface Features Classification of Airborne Lidar Data Based on TerraScan (Maohua Liu, Xiubo Sun, Yue Shao, Yingchun You)....Pages 185-190
The Regionalization of Eco-Geological Environment System and Brief Function Evaluation of Luoyang City (Liu Yang, Jian-yu Zhang, Chang-li Liu, Li-xin Pei)....Pages 191-200
Cloud Detection in Landsat Imagery Using the Fractal Summation Method and Spatial Point-Pattern Analysis (Ling Han, Tingting Wu, Zhiheng Liu, Qing Liu)....Pages 201-207
Extraction of Target Geological Hazard Areas in Loess Cover Areas Based on Mixed Total Sieving Algorithm (Ling Han, Tingting Wu, Qing Liu, Zhiheng Liu, Tingyu Zhang)....Pages 208-214
Research on Heat and Humidity Transfer Performance Evaluation of Spraying Mine Exhaust Air Heat Exchanger (Lingling Bao, Yang Zhao, Xiu Su, Ziyong Wang, Yajing Rong)....Pages 215-228
Land Cover Change Analysis in Wuhan, China Using Google Earth Engine Platform and Ancillary Knowledge (Yahya Ali Khan, Yuwei Wang, Zongyao Sha)....Pages 229-239
Overview of Speed Sensorless Control of Permanent Magnet Synchronous Motors (Yuhang Zhang, Wangyu Qin, Dawei Zheng, Chongxia Zhou, Jianhui Liu)....Pages 240-251
Experimental Study on Lateral Compaction Characteristics of Filled Gangue Under Limited Roof Condition (Xin-wang Li, Xin-yuan Zhao, Li Li, Jian-gong Liu, Li-chao Cheng, Yi-ling Qin)....Pages 252-264
Using Improved Genetic Algorithm to Solve the Equations (Yifan Zhang, Dekang Zhao)....Pages 265-271
Vector Control of Three Phase Permanent Magnet Synchronous Motor Based on Neural Network Sliding Mode Speed Controller (Jingli Miao, Wangyu Qin, Dawei Zheng)....Pages 272-279
Design of Security Alarm System Based on LoRa Technology (Yafei Chen, Peng Gao, Zhihua Li)....Pages 280-288
Study on Relationship Between Filling Rate and Ground Settlement in Strip Mining (Ming Li, Zhao-jiang Zhang, Yu-lin Li)....Pages 289-301
Division of “Three Zones” of Gas in U Type Ventilation Goaf Under Different Seam Inclination Angle (Yong-chen Yang, Shao-fang Cao, Hong-yuan Mao)....Pages 302-307
High-Precision Dynamic Deformation Monitoring Model of GPS/Pseudolites Integrated System (Xi Zhang, Zhao-jiang Zhang, Yu-lin Li)....Pages 308-322
Research on PM2.5 Concentration in Shenyang Based on MODIS Data (Wang Xin, Ding Hua, Liu Yumei)....Pages 323-331
The Review of Recommendation System (Ning Wang, Hui Zhao, Xue Zhu, Nan Li)....Pages 332-342
Visibility Analysis of Core Urban Landscape Based on Grasshopper (Shaofeng Hou, Yike Hu, Fengyun Yang)....Pages 343-351
Edge Computing Resource Allocation Algorithm Based on Auction Game (Zuopeng Li, Haoxiang Wang)....Pages 352-359
Analysis of Spatiotemporal Characteristics of Drought and Flood in the Haihe River Basin from 1965 to 2015 (Qianqian Fan, Anzhou Zhao, Anbing Zhang, Lili Feng, Yuling Zhao, Haixin Liu)....Pages 360-373
Research on 3D Reconstruction of Transmission Linesnd Identification of Hidden Dangers of Tree Barriers Based on Airborne Lidar Point Cloud (Chuanxun Yang, Yong Li, Xia Zhou, Ji Yang, Chen Zhang, Hongkai Liu)....Pages 374-384
Research and Application of Automatic Classification Method for Patrol Targets of Transmission Lines Based on LiDAR Point Cloud (Chen Zhang, Yong Li, Xia Zhou, Ji Yang, Chuanxun Yang, Hongkai Liu)....Pages 385-393
A Model Base Framework for the Risk Assessment and Prevention of Geological Disasters in Coal Mines (Yong Sun, Fengxiang Jin, Min Ji, Huimeng Wang, Ting Li)....Pages 394-411
Comparison of the Inversion Methods for Probability Integral Parameters (Jingyu Yang, Shuai Yu, Chao Liu)....Pages 412-421
An Outlier Recognition Method Based on Improved CUSUM for GPS Time Series (Hao Wu, Mengmeng Li, Chao Liu)....Pages 422-433
Research on GNSS Multi-system Relative Positioning Algorithm (Yongchun Deng, Shuaipeng Wang, Chao Liu)....Pages 434-447
Gross Error Elimination of ICESat/GLAS Data in Typical Land Areas (RuRen Li, ChongYang Zhang, Zhen Yang, GuoYuan Li, HuiJie Liu)....Pages 448-462
Remote Sensing Evaluation of Environmental Quality – A Case Study of Cixian County in Handan City (Honghong Li, Anbing Zhang, Yuling Zhao, Jiabao Li)....Pages 463-474
Building Information Extraction Based on Electronic Map Points of Interest (Yifei Wang, Hefeng Wang, Yuan Cao)....Pages 475-484
Back Matter ....Pages 485-486

Citation preview

Yichun Xie · Anbing Zhang · Haixin Liu · Lili Feng (Eds.)

Communications in Computer and Information Science

980

Geo-informatics in Sustainable Ecosystem and Society 6th International Conference, GSES 2018 Handan, China, September 25–26, 2018 Revised Selected Papers

123

Communications in Computer and Information Science Commenced Publication in 2007 Founding and Former Series Editors: Phoebe Chen, Alfredo Cuzzocrea, Xiaoyong Du, Orhun Kara, Ting Liu, Krishna M. Sivalingam, Dominik Ślęzak, and Xiaokang Yang

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Yichun Xie Anbing Zhang Haixin Liu Lili Feng (Eds.) •

•

•

Geo-informatics in Sustainable Ecosystem and Society 6th International Conference, GSES 2018 Handan, China, September 25–26, 2018 Revised Selected Papers

123

Editors Yichun Xie Eastern Michigan University Ypsilanti, MI, USA

Anbing Zhang Hebei University of Engineering Handan, China

Haixin Liu Hebei University of Engineering Handan, Hebei, China

Lili Feng Hebei University of Engineering Handan, China

ISSN 1865-0929 ISSN 1865-0937 (electronic) Communications in Computer and Information Science ISBN 978-981-13-7024-3 ISBN 978-981-13-7025-0 (eBook) https://doi.org/10.1007/978-981-13-7025-0 Library of Congress Control Number: 2019933336 © Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

GSES (Geo-informatics in Sustainable Ecosystemand Society) is an annual academic conference in a series held in China or in the USA and sponsored by multiple universities located in the two countries. A sustainable ecosystem and society has been a hot topic in recent years as more and more environmental and ecological issues continue to arise around the world. GSES 2018 was an important forum for researchers, engineers, and students from research institutes, industries, and universities to share their ideas, research results, and experiences, which we hope will promote the research and technical innovation in these fields domestically and internationally. The papers contained in these proceedings address challenging issues in spatial data acquisition, processing and management, modeling and analysis, and recent applications in the context of building a healthier ecology and resource management using advanced remote sensing technology and spatial data modeling and analysis. This year, GSES 2018 was held in Handan, China, in September 2018. We received 153 submissions. After a thorough reviewing process, 46 English papers were selected for this volume and the acceptance rate is 30.07%. The high-quality program would not have been possible without the authors who chose GSES 2018 as a venue for their publications. We are also very grateful to the Program Committee members and Organizing Committee members, who put a tremendous amount of effort into soliciting and selecting research papers with a balance of high quality and new ideas and new applications. We hope that you enjoy reading and benefit from the proceedings of GSES 2018. December 2018

Anbing Zhang Yichun Xie

Organization

Program Committee Yichun Xie Zhongyao Sha Xinyue Ye Anbing Zhang Ruren Li Min Ji

Eastern Michigan University, USA Wuhan University, China New Jersey Institute of Technology, USA Hebei University of Engineering, China Shenyang Jianzhu University, China Shandong University of Science and Technology, China

Additional Reviewers Lichao Cheng Andrew Crooks Siyu Fan Lili Feng Yuanbin Han Min Ji Hai Lan Ruren Li Weiwei Li Xiaomeng Li Haitao Lian Michael Batty Chao Liu Haixin Liu Xinxia Liu Feng Luo Xiaoliang Meng Jiaguo Qi Danping Ren Zongyao Sha Hongli Song Qi Sun Fang Tian Chao Wang Dongli Wang Hefeng Wang Huaming Wang Liping Wang Suna Wang Wei Wang

Hebei University of Engineering, China George Mason University, USA Eastern Michigan University, USA Hebei University of Engineering, China Hebei University of Engineering, China Shandong University of Science and Technology, China London University, China Shenyang Jianzhu University, China Hebei University of Engineering, China Hebei University of Engineering, China Hebei University of Engineering, China University College London, UK Anhui University of Science and Technology, China Hebei University of Engineering, China Hebei University of Engineering, China Hebei University of Engineering, China Wuhan University, China Hebei University of Engineering, China Hebei University of Engineering, China Wuhan University, China Hebei University of Engineering, China Hebei University of Engineering, China Hebei University of Engineering, China Hebei University of Engineering, China Hebei University of Engineering, China Hebei University of Engineering, China Wuhan University, China Hebei University of Engineering, China Hebei University of Engineering, China Hebei University of Engineering, China

VIII

Organization

Zhongcheng Wei Yichun Xie Yifei Yang Zongliang Yang Xinyue Ye Anbing Zhang Anzhou Zhao Zhibo Zhai Guobin Zhu

Hebei University of Engineering, China Eastern Michigan University, USA Hebei University of Engineering, China Wuhan University, China New Jersey Institute of Technology, USA Hebei University of Engineering, China Hebei University of Engineering, China Hebei University of Engineering, China Hebei University of Engineering, China

Organizer Anbing Zhang

Hebei University of Engineering, China

Contents

Soil Property Surface Modeling Based on Ensemble Learning for Complex Landforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Liu, Yongkun Liu, Mengyuan Yang, and Meng Xie Enhancement of Class Separability for Polarimetric TerraSAR-X Data and Its Application to Crop Classification in Leizhou Peninsula, Southern China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongzhong Li, Yu Han, Jinsong Chen, and Shanxin Guo Mapping the Distribution of Exotic Mangrove Species in Shenzhen Bay Using Worldview-2 Imagery. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongzhong Li, Yu Han, Jinsong Chen, and Shanxin Guo Vortex Extraction Method Based on Compact Ratio . . . . . . . . . . . . . . . . . . Ya-ru Xu, Min Ji, and Zhi-wei Lu

1

15

26 43

Optimized Data Organization of Land Cover Survey Based on Redis Memory Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jia Liu and Min Ji

51

A Dynamic Switching Technique for Virtual Network in SDN Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haifeng Fang, Yachan Zhao, Rong Tan, and Tao Wang

62

Multi-mode Control Strategy for Dual Active Bridge Bidirectional DC-DC Converters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yaguang Zhang and Yong Du

71

Spatial Distribution and Source Identification of Loess Heavy Metal Pollution in Northern Baoji, China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ling Han, Zhiheng Liu, Yuming Ning, and Zhongyang Zhao

79

Analysis and Comparison of Uncertain Means Clustering Algorithm . . . . . . . Nini Zhang, Lihua Qi, and Xiaomei Qin

93

Research on Matrix Multiplication Based on the Combination of OpenACC and CUDA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuexing Wang

100

Research on ICS Intrusion Success Rate Algorithm Based on Attack and Defense Countermeasures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wending Wang and Kaixing Wu

109

X

Contents

The Review of Task Scheduling in Cloud Computing . . . . . . . . . . . . . . . . . Fengjun Xin and Lina Zhang

119

Prediction Model of River Water Quality Time Series Based on ARIMA Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lina Zhang and Fengjun Xin

127

A Review of Gait Behavior Recognition Methods Based on Wearable Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chang Liu, Jijun Zhao, and Zhongcheng Wei

134

K-Means Optimization Algorithm Based on Tightness Mutation . . . . . . . . . . Tie Fei Li, Jian Fei Ma, Rui Xin Yang, Di Wu, and Yan Guang Shen Study of Coal Integrated Network Decision Support System Based on GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haixin Liu, Wei Wang, Tao Jiang, Yuling Zhao, and Xiuyun Sun Analysis on Spatio-Temporal Changes of the Land Covers in Shenyang . . . . Dayong Yang, Zhiwei Xie, and Hua Ding

146

157 165

Hierarchical Decision Tree for Change Detection Using High Resolution Remote Sensing Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhiwei Xie, Min Wang, Yaohui Han, and Dayong Yang

176

Surface Features Classification of Airborne Lidar Data Based on TerraScan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maohua Liu, Xiubo Sun, Yue Shao, and Yingchun You

185

The Regionalization of Eco-Geological Environment System and Brief Function Evaluation of Luoyang City . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liu Yang, Jian-yu Zhang, Chang-li Liu, and Li-xin Pei

191

Cloud Detection in Landsat Imagery Using the Fractal Summation Method and Spatial Point-Pattern Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ling Han, Tingting Wu, Zhiheng Liu, and Qing Liu

201

Extraction of Target Geological Hazard Areas in Loess Cover Areas Based on Mixed Total Sieving Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ling Han, Tingting Wu, Qing Liu, Zhiheng Liu, and Tingyu Zhang

208

Research on Heat and Humidity Transfer Performance Evaluation of Spraying Mine Exhaust Air Heat Exchanger . . . . . . . . . . . . . . . . . . . . . . Lingling Bao, Yang Zhao, Xiu Su, Ziyong Wang, and Yajing Rong

215

Land Cover Change Analysis in Wuhan, China Using Google Earth Engine Platform and Ancillary Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yahya Ali Khan, Yuwei Wang, and Zongyao Sha

229

Contents

Overview of Speed Sensorless Control of Permanent Magnet Synchronous Motors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuhang Zhang, Wangyu Qin, Dawei Zheng, Chongxia Zhou, and Jianhui Liu Experimental Study on Lateral Compaction Characteristics of Filled Gangue Under Limited Roof Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin-wang Li, Xin-yuan Zhao, Li Li, Jian-gong Liu, Li-chao Cheng, and Yi-ling Qin Using Improved Genetic Algorithm to Solve the Equations . . . . . . . . . . . . . Yifan Zhang and Dekang Zhao Vector Control of Three Phase Permanent Magnet Synchronous Motor Based on Neural Network Sliding Mode Speed Controller . . . . . . . . . . . . . . Jingli Miao, Wangyu Qin, and Dawei Zheng Design of Security Alarm System Based on LoRa Technology . . . . . . . . . . . Yafei Chen, Peng Gao, and Zhihua Li

XI

240

252

265

272 280

Study on Relationship Between Filling Rate and Ground Settlement in Strip Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ming Li, Zhao-jiang Zhang, and Yu-lin Li

289

Division of “Three Zones” of Gas in U Type Ventilation Goaf Under Different Seam Inclination Angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yong-chen Yang, Shao-fang Cao, and Hong-yuan Mao

302

High-Precision Dynamic Deformation Monitoring Model of GPS/Pseudolites Integrated System . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xi Zhang, Zhao-jiang Zhang, and Yu-lin Li

308

Research on PM2.5 Concentration in Shenyang Based on MODIS Data. . . . . Wang Xin, Ding Hua, and Liu Yumei

323

The Review of Recommendation System . . . . . . . . . . . . . . . . . . . . . . . . . . Ning Wang, Hui Zhao, Xue Zhu, and Nan Li

332

Visibility Analysis of Core Urban Landscape Based on Grasshopper . . . . . . . Shaofeng Hou, Yike Hu, and Fengyun Yang

343

Edge Computing Resource Allocation Algorithm Based on Auction Game . . . Zuopeng Li and Haoxiang Wang

352

Analysis of Spatiotemporal Characteristics of Drought and Flood in the Haihe River Basin from 1965 to 2015 . . . . . . . . . . . . . . . . . . . . . . . Qianqian Fan, Anzhou Zhao, Anbing Zhang, Lili Feng, Yuling Zhao, and Haixin Liu

360

XII

Contents

Research on 3D Reconstruction of Transmission Linesnd Identification of Hidden Dangers of Tree Barriers Based on Airborne Lidar Point Cloud . . . Chuanxun Yang, Yong Li, Xia Zhou, Ji Yang, Chen Zhang, and Hongkai Liu Research and Application of Automatic Classification Method for Patrol Targets of Transmission Lines Based on LiDAR Point Cloud . . . . . . . . . . . . Chen Zhang, Yong Li, Xia Zhou, Ji Yang, Chuanxun Yang, and Hongkai Liu A Model Base Framework for the Risk Assessment and Prevention of Geological Disasters in Coal Mines . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yong Sun, Fengxiang Jin, Min Ji, Huimeng Wang, and Ting Li Comparison of the Inversion Methods for Probability Integral Parameters . . . Jingyu Yang, Shuai Yu, and Chao Liu An Outlier Recognition Method Based on Improved CUSUM for GPS Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hao Wu, Mengmeng Li, and Chao Liu

374

385

394 412

422

Research on GNSS Multi-system Relative Positioning Algorithm . . . . . . . . . Yongchun Deng, Shuaipeng Wang, and Chao Liu

434

Gross Error Elimination of ICESat/GLAS Data in Typical Land Areas . . . . . RuRen Li, ChongYang Zhang, Zhen Yang, GuoYuan Li, and HuiJie Liu

448

Remote Sensing Evaluation of Environmental Quality – A Case Study of Cixian County in Handan City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Honghong Li, Anbing Zhang, Yuling Zhao, and Jiabao Li

463

Building Information Extraction Based on Electronic Map Points of Interest. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yifei Wang, Hefeng Wang, and Yuan Cao

475

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

485

Soil Property Surface Modeling Based on Ensemble Learning for Complex Landforms Wei Liu(&), Yongkun Liu, Mengyuan Yang, and Meng Xie School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China [email protected]

Abstract. It is difficult to simulate soil property with a single global interpolation model. For the characteristics of spatial discontinuity, limited precision of global interpolation model and poor adaptability, a high accuracy surface modeling for soil property based on ensemble learning and fusion geographical environment variables was proposed (HASMSP-EL). The simulation accuracy of different interpolation methods was evaluated by using Mean Error (ME), Mean Relative Error (MRE), Root Mean Square Error (RMSE) and Accuracy (AC). The results showed that: (1) In the interpolation method of fusion geographical environment variables, the estimation deviation of HASMSP-EL was lower. Compared with other interpolation methods, ME, MRE, RMSE and AC of HASMSP-EL were better. HASMSP-EL had more advantages in describing spatial variation and local detail information of soil potassium content, and its accuracy was 6.42%, 7.28%, 11.56% and 9.38% higher than that of Regression Kriging (RK), Bayesian Kriging (BK), Inverse Distance Weighting (IDW) and Ordinary Kriging (OK), respectively. (2) The HASMSP-EL can provide more details in the geographical boundary, which made the simulation results consistent with the real auxiliary variables. HASMSP-EL not only considered the nonlinear relationship between geographical environmental variables and soil property, but also combined the adaptive advantages of multiple models. It is a new method to simulate soil property in complex geomorphological regions with higher precision. Keywords: Ensemble learning
Spatial interpolation Geographical environment variable
Soil property




1 Introduction The continuous change of soil property is the premise of scientific management and use of soil resources. Usually, the observation of soil property is more expensive, especially for complex geomorphologic type regions, the sample data of soil property can only be obtained by sample distribution, however, scientists and managers usually require spatially distributed soil property data for decision support [1]. As a time-space quantitative detection method for continuous soil change, the spatial interpolation method of soil property and its accuracy is an important research direction in the field © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 1–14, 2019. https://doi.org/10.1007/978-981-13-7025-0_1

2

W. Liu et al.

of digital soil [2], which is also the main way to obtain the continuous change of soil property [3]. The current spatial interpolation methods are mainly derived from discrete modern mathematical theories (function theory and differential geometry). Most of them are global interpolation models, that is, the whole study region is interpolated by one model [4, 5]. However, for complex geomorphological regions, the spatial distribution of soil property is influenced by the elements of geological types, soil types, land use types and geomorphologic types etc., and its spatial differentiation is very obvious. So it is difficult to meet the assumptions of the existing model, and the single global interpolation model, due to its own shortcomings, restricts the improvement of the prediction accuracy. The selection of spatial interpolation method is one of the most important factors affecting the prediction effect of soil property [6]. Among all kinds of interpolation methods, kriging and variant kriging derived from kriging have been applied to soil property spatial interpolation with high accuracy [7]. However, kriging is essentially a sliding weighted average method based on linear theory framework, belongs to category of linear geo-statistics, inevitably lead to smooth data, which cannot describe the nonlinear characteristics of the soil property spatial distribution. In addition, some traditional non-geostatistical interpolation methods, such as inverse distance weighted (IDW), spline and radial basis function (RBF), do not need to make other assumptions about the data and are simple to operate, but they do not have some statistical advantages of kriging method. The academic community does not have a unified understanding of which interpolation method is the best. For example, for the three common methods of soil property interpolation, kriging, IDW and Spline, researchers have done a lot of experiments and comparisons, some of the results show that the prediction effect of kriging is better than that of IDW [8–11] and Spline [11], while the other part of the study shows the opposite [12–14]. For the same interpolation method, different varieties and different scholars have also obtained different results. Triantafilis et al. compared the results of ordinary kriging, regression kriging, 3D-kriging and cooperative kriging for soil salinity interpolation. The results showed that the precision of regression kriging was the best, but the synergistic kriging was the best in terms of average value and standard deviation [15]. Spatial interpolation is an important method to obtain the continuous variation of soil property. However, in complex geomorphologic regions, different interpolation methods have their advantages and disadvantages under the combined influence of interpolation models, environmental variables and sampling. It is not difficult to find out from the existing research that the adaptability of each interpolation method is different and there is no absolute optimal spatial interpolation model. If multi-model integration can be carried out according to the adaptability of different interpolation models, the simulation accuracy can be improved theoretically. In order to solve the problem of global interpolation model and auxiliary variables, our previous study [16] used linear interpolation to construct error surface and obtain the spatial range of each interpolation model. However, the method of constructing error surface by linear interpolation to realizing partition, will inevitably introduce errors. In this paper, taking the complex geomorphologic type regions of Qinghai Lake Basin as an example, the interpolation surface was scanned by using 110 soil potassium

Soil Property Surface Modeling Based on Ensemble Learning

3

content samples collected in 2013 and using the scanning line algorithm as the partition learning machine of ensemble learning. The scanning lines with higher partition accuracy and greater difference were selected to partition each interpolation surface adaptively to realize the high precision interpolation of soil property. Experiments shows that using the HASMSP-EL can well solve the single model interpolation accuracy difficult to improve problem in complex geomorphologic regions.

2 Study Regions and Soil Sampling 2.1

Study Regions

The study region (36°38′–37°29′N, 99°52′–100°50′E) is located in the southeast of the Qinghai Lake basin (Fig. 1). In the long-term effects of tectonic movement and the formation of exogenic landforms, the landform is complex and diverse, the total regions is about 2100 km2, the altitude is from 3007 m to 5285 m, there are a large amount of agricultural and animal husbandry activities, also including mountain, hills, plateaus and plains, which is a typical complex geomorphologic regions.

Fig. 1. Location of the study area, showing sample sites (circles) and elevation (shading)

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Soil Sampling

According to the research results of our project team, soil sampling was carried out by spatial stratification combination [17]. At the same time of soil sampling, the geoenvironmental information related to soil property, such as longitude and latitude, altitude, land use types, soil types, grassland types and geology types, were recorded. Each sample was sampled third times at 0–5 cm, 5–15 cm and 15–30 cm. In September 2013, with the assistance of the Qinghai Environmental Monitoring Center, 110 samples were collected from the soil surface of the study regions. Soil samples were taken back to the laboratory for analysis. After flow path of drying, grinding, and screening through a 2 mm sieve, we taken the average value of soil potassium content in three times sampling as sample value.

3 Methods 3.1

Geographical Environment Variables

The driving factors of soil potassium content spatial variability mainly include: land use types, soil types, grassland types, geology types, slope, and fertilization et al. According to the existing research’s conclusions [18, 19], combined with the study of the natural landscape type regions, we excluded the two driving factors: fertilization and land management measures, and selected the land use types, soil types, grassland types, geology types as auxiliary variables. The study regions is characterized by 8 land use types, including cropland, meadow land, and scrubland et al. (Fig. 2a); 6 soil types, including alpine meadow soil, and chestnut soil et al. (Fig. 2b); and 33 grassland types, including leymus, and achnatherum splendens et al. (Fig. 2c). The geology can be divided into 13 types, mainly including alluvial terrace, vally plain, and sand hill et al. (Fig. 2d). In order to further screen the geographical environments which have significant influence on the spatial distribution of soil potassium content, we used SPSS to analyze the variance between the soil potassium content and the four geographical environments mentioned above, and then we selected the geographical environments with significant characteristics as auxiliary environmental variables. The results of variance analysis in Table 1 showed that the landscape fragmentation of grassland types is too high in this study region. There is only one sampling point in many regions and only one or two sampling points in some subtypes of grassland, so the simulation effect is not satisfactory. Therefore, the grassland types were excluded.

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5

Fig. 2. Feature of study regions: (a) land use types; (b) soil types; (c) grassland types; (d) geology types

3.2

Modeling Method

3.2.1 Interpolation Models In this paper, four methods of spatial interpolation of soil potassium content are implemented based on ArcGIS10.2 platform. Below is a typical two interpolation models. (1) Regression Kriging, RK RK establishes the optimal linear relationship between dependent variables and independent variables by ordinary least square method [20], obtains the trend terms

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Table 1. Variance analysis of soil potassium content between different geographical environments Geo-factors

Sources of variance Land use Between type groups In-group Total Soil type Between groups In-group Total Grassland Between type groups In-group Total Geology Between type groups In-group Total Note: *0.05 conspicuous level;

Degree of freedom 106

Sum of variance 4.631

4 110 106

0.462 5.093 4.371

4 110 94

0.722 5.093 4.159

16 110 101

0.934 5.093 4.060

Mean variance 0.116 0.044

F value 2.645

P value

0.181 0.041

4.378

0.003**

0.058 0.044

1.319

0.202

0.115 0.04

2.856

0.005**

0.038*

9 1.033 110 5.093 **0.01 conspicuous level

representing deterministic parts and the residual terms representing random parts, then interpolates the residuals with OK. Finally, the result of RK interpolation is obtained by adding the trend term and residual interpolation term. Using the expression: zðxÞ ¼ mðxÞ þ dðxÞ

ð1Þ

Where, zðxÞ is the prediction value of the dependent variable at point x, mðxÞ is the trend item of OLS fitting, dðxÞ is the residual term of OK interpolation. (2) Bayesian Kriging, BK Assuming random function: Z T ðxÞ ¼ ZðxÞ  lMðxÞ

ð2Þ

Where, Z(x) is a regional variables in regional D, referred to as “hard data”, M(x) is a forecast variables in regional D, referred to as “soft data”, lMðxÞ is the mathematical expectation of MðxÞ. For any set of observations (fZ T ðxi Þ ¼ Zðxi Þ  lMðxi Þ; i ¼ 1; 2; . . .; Ng), the estimate Z  ðx0 Þ of the BK takes the following function: Z  ðx0 Þ ¼

N X i¼1

ki Z T ðxi Þ þ lMðxo Þ

ð3Þ

Soil Property Surface Modeling Based on Ensemble Learning

7

Where, x0 is a point in region D, ki ði ¼ 1; 2; . . .; NÞ is the weighting coefficient to be determined. 3.2.2 High Accuracy Surface Modeling Method Based on Ensemble Learning 3.2.2.1 Basic Interpolation Model Based on the theory of regression kriging and spatial correlation and variation, the spatial variation of any variable can be expressed by the sum of the following two main components: one is structural components related to trends; the second is residuals related to local variations [21, 22]. The corresponding spatial distribution model of soil potassium can be expressed as follows: Sðx; yÞ ¼ trendðGeox;y Þ þ rðx; yÞ

ð4Þ

Where Sðx; yÞ is the prediction value of soil potassium sampling point, in which ðx; yÞ is the coordinate of sampling point. trendðGeox;y Þ is the trend value to describe S in ðx; yÞ, where Geox;y is the geographical environment information which is closely related to soil potassium in ðx; yÞ; rðx; yÞ is the residual value describing S in ðx; yÞ. The multivariate regression model based on spatial dissimilarity theory is used to get the trend function trendðGeox;y Þ and the trend surface which is fitted to describe the structure component, so that the trend separation can be realized. According to the spatial characteristics of soil potassium content, this paper chosen OK algorithm to further deal with residual rðx; yÞ, thus, a series of basic interpolation models such as OK-Landuse, OK-Soil and OK-Geology etc. are generated. 3.2.2.2 Method for Adaptive Partitioning According to the basic interpolation model established in Sect. 3.2.2.1, a series of soil potassium interpolation surfaces were generated, and the predication errors of soil potassium sample points were calculated. The interpolation surface was scanned by scanning line algorithm as an ensemble learning classifier. The scan lines with higher classification accuracy and greater difference were selected for integration, and each interpolation surface was automatically partitioned (automatically adapted to the preset precision threshold), and the applicable spatial range of each interpolation model was obtained. ‘+’ and ‘−’ respectively represent the sample points that meet the interpolation accuracy requirements and those that do not meet the interpolation accuracy requirements (Fig. 3). Step one: A new sample distribution D2 (the weight distribution of each sample point in the samples) and a subclassifier h1 are obtained according to the correct rate of partitioning interpolation surfaces. The larger ‘+’ means that the sample is weighted. Step two: Obtain a new sample distribution D3 according to the correct rate of partitioning. A subclassifier h2 has three ‘−’ symbol classification errors in the weak classifier, and the partition error rate is obtained (e3 ¼ 0:14), and the weight to be assigned is obtained (a3 ¼ 0:91).

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Fig. 3. The partition process of scanline algorithm

Step three: Obtain a subclassifier h3 . In the weak classifier, there are two ‘+’ symbols and a ‘−’ symbol classification error, and the partition error rate e3 ¼ 0:14 and the weight assigned are obtained (a3 ¼ 0:91). Step four: All subclassifiers are integrated to get the final Hfinal. Through the above three steps, we can extract all the partitions that meet the accuracy threshold, which category each region belongs to is determined by the weights of the classifier in which the region is located. The spatial distribution map of the HASMSP-EL is obtained through the integration all the meet accuracy threshold partition. 3.3

Assessment of Performance

The interpolation accuracy is evaluated by independent verification. The 110 sampling points were randomly divided into 90 interpolation points and 20 verification points for many times. The evaluation indicators include ME, MRE, RMSE and AC. Their mathematical formulas are as follows: ME ¼

MRE ¼

n 1X ½zðxi Þ  z ðxi Þ n i¼1

n 1X jzðxi Þ  z ðxi Þ=zðxi Þj n i¼1

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n 1X RMSE ¼ ½zðxi Þ  z ðxi Þ2 n i¼1

ð5Þ

ð6Þ

ð7Þ

Where zðxi Þ is the predicted value of soil potassium content, z ðxi Þ is the measured value of soil potassium content, n is the number of verification points.

Soil Property Surface Modeling Based on Ensemble Learning

nRMSE 2 PE

ð8Þ

½jz  oj  jz  oj2

ð9Þ

AC ¼ 1  PE ¼

n X

9

j¼1

Where PE is a potential error variance, z and z are the predicted values and the measured values, respectively, O is the mean of the measured value. The range of AC values is from 0 to 1, and the predicted results are proportional to the magnitude of AC values. MRE can overcome the influence of dimension, and the accuracy is inversely proportional to the values of ME and RMSE.

4 Results 4.1

HASMSP-EL

According to the interpolation model constructed in Sect. 3.2.2.1, a series of interpolation surfaces were simulated, such as OK-Landuse, OK-Soil and OK-Geology etc. The interpolation surface was extracted adaptively by using ensemble learning model. Finally, the optimal combination of interpolation surfaces was carried out. HASMSPEL is as follows: 4.1.1 Adaptive Partitioning of Interpolation Surfaces Based on the method of constructing error surfaces described in Sect. 3.2.2.2, the error surfaces of different interpolation models were obtained (Fig. 4), and the applicable range of each interpolation model was determined.

Fig. 4. Error surface of basic interpolation model: (a) land use types; (b) soil types; (c) geology types

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4.1.2 Optimal Combination of Interpolation Surfaces We selected raster cells with minimum error as optimal interpolation results. Figure 5 shows the optimal partition combination results corresponding to different interpolation models.

Fig. 5. Regional distribution of HASMSP-EL model optimization

4.2

Comparison of Interpolated Accuracy

In order to evaluate the spatial distribution pattern of soil potassium content predicted by HASMSP-EL, the accuracy of five interpolation methods RK, BK, IDW, OK and HASMSP-EL were compared in this paper. As shown in Table 2, the interpolation accuracy of RK, BK and HASMSP-EL is higher than that of IDW and OK, which means that the interpolation accuracy can be improved by merging appropriate

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Table 2. Accuracy comparison between RK, BK, IDW, IDW, OK and HASMSP-EL Evaluation index RK BK IDW ME 0.0062 −0.0041 0.0074 MRE 96.08% 96.58% 96.01% RMSE 0.1001 0.0674 0.1638 AC 0.9273 0.9187 0.8759

OK 0.0094 95.33% 0.1068 0.8977

HASMSP-EL −0.0011 89.67% 0.0561 0.9600

auxiliary variables. The HASMSP-EL reached −0.0011, 89.67% and 0.0561% for ME, MRE and RMSE, respectively, which is better than the other 4 interpolation methods, its ME value is closer to 0 than that of OK and IDW, and has better unbiased property. The AC of HASMSP-EL is 0.9600, which fully indicates that it can better simulate the spatial distribution pattern of soil potassium content in complex geomorphologic regions. In general, the interpolation accuracy of HASMSP-EL is superior to that of other methods for two main reasons. Firstly, the method combines auxiliary variables, which can more accurately depict the mutation boundary of soil potassium content with the geographical environmental factors. Secondly, the ensemble learning model is used to extract the optimal simulation regions of each interpolation model for optimal combination, which greatly improves the interpolation accuracy to a great extent. 4.3

Comparison of Interpolated Maps

In order to obtain the prediction effect of five interpolation methods, we compared the prediction map of five interpolation models (Fig. 6). It can be seen from Fig. 6 that the spatial variation of soil potassium content in RK, BK and OK is lower than the true value, especially OK. The results of RK, BK and OK interpolation showed different degrees of weak “bull’s eye” effect. The effect of RK and BK interpolation is better than that of OK, which can better present the spatial distribution pattern of soil potassium content in the study regions, but it is difficult to show the local variation of soil potassium content. IDW interpolation effect is the worst, and IDW interpolation’s result shows a strong “bull’s eye” effect. HASMSP-EL can best depict the spatial variation pattern of soil potassium content, and generate a moderate interpolation range of 1.30–2.32. HASMSP-EL can reflect more details of the spatial distribution of soil potassium content; especially can accurately depict the abrupt change of soil potassium content. HASMSP-EL has strong adaptability to spatial interpolation of soil potassium content in complex geomorphological regions; it also can more accurately depict the spatial variation pattern of soil potassium content in the study regions.

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Fig. 6. Comparison of soil potassium content interpolation map with different methods: (a) RK; (b) BK; (c) IDW; (d) OK; (e) HASMSP-EL

5 Conclusions Two main problems that restrict the precision of the interpolation of the soil property in the complex geomorphic region. One is that there is a nonlinear relationship between soil properties and geographical environment variables, and the fitting accuracy of conventional linear model is limited; the other is that the selected interpolation model must be the optimal interpolation model. In reality, every interpolation model has its own advantages and disadvantages. Even if we have enough data to explore the analysis (such as covariance, stability and estimation of variogram, et al.), we can find a globally optimal interpolation model. But the spatial instability between soil property spatial variables cannot be explained by one interpolation model. In order to improve the accuracy of soil property interpolation in complex geomorphological regions, this paper studied the construction of base interpolation model, the adaptive partition of interpolation surface and the integration of interpolation surfaces. Firstly, based on the geomorphological characteristics of the study regions, and integrating many geographical environmental variables closely related to the spatial differentiation of soil potassium content, a series of spatial interpolation models of soil potassium content were constructed as the basic interpolation model of ensemble learning. Then, we

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explored the classification learner with the scan line algorithm as ensemble learning to realize the adaptive partition of the interpolated surface. Finally, the results of partition were optimized and combined to predicate the soil potassium content in complex geomorphological regions with high precision. We compared the interpolation models with no geographical auxiliary variables such as IDW, OK, and those using geographical auxiliary variables such as RK, BK. The results showed that HASMSP-EL can more accurately describe the spatial differentiation of soil potassium content and reduce the prediction error effectively. In addition, HASMSP-EL fusion of geographical environment variables can accurately describe the boundary of spatial variation of soil potassium content with the variation of surrounding geographical environment, and the prediction results were more in line with geographical laws. It is convenient to physically explain the spatial differentiation characteristics of soil potassium content. HASMSP-EL is a new method for high accuracy prediction of soil property in complex geomorphologic regions and provides a new method for the future study of soil property mapping. Acknowledgments. This study was supported by the National Natural Science Foundation of China (Grant No. 41601405). We are grateful to the Qinghai Environmental Monitoring Center for providing topsoil sampling approval. Thanks to the China Soil Investigation Office and the Bureau of Geological Exploration & Development of Qinghai Province for providing secondary datasets.

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11. Panagopoulos, T., Jesus, J., Antunes, M., Beltrao, J.: Analysis of spatial interpolation for optimising management of a salinized field cultivated with lettuce. Eur. J. Agron. 24, 1–10 (2016) 12. Gotway, C.A., Ferguson, R.B., Hergert, G.W., Peterson, T.A.: Comparison of kriging and inverse-distance methods for mapping soil parameters. Soil Sci. Soc. Am. J. 60, 1237–1247 (1996) 13. Montealegre, A., Lamelas, M., Riva, J.: Interpolation routines assessment in ALS-derived digital elevation models for forestry applications. Remote Sens. 7, 8631–8654 (2015) 14. Xie, Y.F., Chen, T.B., Lei, M., Zheng, G.D., Song, B., Li, X.Y.: Impact of spatial interpolation methods on the estimation of regional soil cd. Acta Sci. Circum. 30, 847–854 (2010) 15. Triantafilis, J., Odeh, I., McBratney, A.: Five geostatistical models to predict soil salinity from electromagnetic induction data across irrigated cotton. Soil Sci. Soc. Am. J. 65, 869– 878 (2001) 16. Liu, W., Zhang, H.R., Yan, D.P., Wang, S.L.: Adaptive surface modeling of soil properties in complex landforms. ISPRS Int. J. Geo Inf. 6, 178 (2017) 17. Zhang, H., Lu, L., Liu, Y., Liu, W.: Spatial sampling strategies for the effect of interpolation accuracy. ISPRS Int. J. Geo Inf. 4, 2742–2768 (2015) 18. Liu, W., Du, P.J., Wang, D.C.: Ensemble learning for spatial interpolation of soil potassium content based on environmental information. PLoS ONE 10, e0124383 (2015) 19. Shi, W.J., Liu, J.Y., Du, Z.P., Yue, T.X.: High accuracy surface modeling of soil properties based on geographic information. Acta Geogr. Sin. 66, 1574–1581 (2011) 20. Collins, F.C., Bolstad, P.V.: A comparison of spatial interpolation techniques in temperature estimation (1996) 21. Asli, M., Marcotte, D.: Comparison of approaches to spatial estimation in a bivariate context. Math. Geol. 27, 641–658 (1995) 22. Odeh, I.O., McBratney, A., Chittleborough, D.: Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging. Geoderma 67, 215–226 (1995)

Enhancement of Class Separability for Polarimetric TerraSAR-X Data and Its Application to Crop Classification in Leizhou Peninsula, Southern China Hongzhong Li1,2(&), Yu Han1, Jinsong Chen1, and Shanxin Guo1 1

Shenzhen Institute of Advanced Technology, CAS, Xueyuan Avenue, Shenzhen 1068, People’s Republic of China {hz.li,yu.han,js.chen,sx.guo}@siat.ac.cn 2 Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing, People’s Republic of China

Abstract. In this paper, an enhanced class separability is proposed for multilook fully polarimetric SAR classification. Instead of measuring the Wishart distance between two classes directly, we apply the deorientation procedure to eliminate the fluctuation of polarization orientation angle, which is an extrinsic property of targets and might result in larger inner class distance. Then the Barnes-Holm decomposition is used to factorize the deorientationed coherency matrix into a pure target and a distributed target, and the enhanced class separability, which is proved strictly, is measured by the sum of two Wishart distances based on pure targets and distributed targets respectively. The effectiveness of the proposed measure is demonstrated with the TerraSAR X-band PolSAR data in crop classification, in Leizhou Peninsula, southern China. Keywords: Class separability Barnes-Holm decomposition


Crop classification
Wishart distance


1 Introduction Land-cover classification is an important and traditional application of remote sensing image analysis. Due to its ability to penetrate cloud cover and its night sensing capabilities, Synthetic Aperture Radar (SAR) has many advantages over optical data. Polarimetric SAR (PolSAR) images provide more polarimetric variables than singlechannel SAR, such as scattering entropy, scattering angle, orientation angle, etc. PolSAR has been proved useful in the extraction of bio- and geo- information, which is helpful to improve the land-cover classification accuracy. Class separability is the measure of the similarity between different classes. For single look PolSAR data, the complex scattering vector was assumed [4] to have a zero-mean complex multivariate Gaussian distribution. For multi-look data represented in covariance or coherency matrices, Lee derived the Wishart distance measure based on the complex Wishart distribution, which is widely used to measure PolSAR class © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 15–25, 2019. https://doi.org/10.1007/978-981-13-7025-0_2

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separability [6]. Based on the Wishart distance, fuzzy c-mean classification, dynamic learning and fuzzy neural network techniques are introduced in PolSAR classification. In recent years, target decomposition theories have been widely used to extract physical scattering characteristics, and combined with statistical properties for terrain classification, such as Cloude and Pottier decomposition [2, 7], Freeman and Durden decomposition [12]. The main role of the decomposition methods is to divide image pixels into several initial clusters. The cluster merging and iteratively classifying criterion are still based on the Wishart distance measure. This paper presents an enhanced Wishart distance to measure class separability. Barnes-Holm decomposition [9], which factorizes the measured coherency matrix into a rank 1 pure target and a distributed N-target, is used to enhance the inter-class separability by the sum of two Wishart distance for pure target and distributed target, respectively. A deorientation procedure [1, 7, 8], eliminates the fluctuation of polarimetric orientation angle, thus decreasing the inner-class distance. Both theoretical analysis and experiment have verified the validity of the enhanced distance measure. The remainder of this paper is organized as follows. In Sect. 2, the background theories are briefly introduced, including class separability, Barnes-Holm decomposition and deorientation. Then, the enhanced class separability measure for PolSAR data is proposed in Sect. 3. The experimental results and conclusions are given in Sects. 4 and 5. At last, the appendix presents the proof of class separability enhancement based on Barnes-Holm decomposition.

2 Background Theory 2.1

Class Separability for Polarimetric SAR Data

Class separability is the measure of the similarity between different classes. For multilook PolSAR data, the class separability measurement is Wishart distance [3, 7, 8]. Take two classes fx1 g and fx2 g, with class covariance matrices C1 and C2 , the Wishart distance is dðx1 ; x2 Þ ¼



1  1 lnðjC1 jÞ þ lnðjC2 jÞ þ Tr C1 1 C2 þ Tr C2 C1 2

ð1Þ

Wang et al. [6] quantified the inter-class separability by take the following difference:

1  dðx1 ; x2 Þ ¼ Tr C1 1 C2 þ Tr C2 C1  2q ¼ 2dðx1 ; x2 Þ  dðx1 ; x1 Þ  dðx2 ; x2 Þ

ð2Þ

Enhancement of Class Separability for Polarimetric TerraSAR-X Data

2.2

17

Barnes-Holm Decompositions

Barnes-Holm decompositions are two forms of dichotomy decomposition, 2

2A0 T ¼ 4 C þ jD H  jG

C  jD B0 þ B E  jF

3 H þ jG E þ jF 5 ¼ T0 þ TN B0  B

ð3Þ

where the measured coherency matrix T is factorized into a rank 1 pure target T0 and a distributed N-target TN which has its rank r [ 1 and is roll invariant, and the parameters in T are called “Huynen parameters”. The pure single target T0 is rank 1 that there exists an equivalent vector k0 , T0 ¼ k0
k0 . Holm showed that dichotomy decomposition (3) has three solutions, of which the last two ones correspond to Barnes-Holm decompositions [9]. In this paper, one of the Barnes-Holm decompositions is used and the normalized vectors k0 is 2 3 C  G þ jH  jD 1 k0 ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 4 B0 þ B  F þ jE 5 2ðB0  FÞ E þ jB  jB  jF

ð4Þ

0

2.3

Deorientation

Polarization orientation angle (POA) is the angle of rotation about the line of sight [5, 10]. The POA can be derived as  h¼

  1 g; if g\p=4 1 E ; g ¼ tan þp g  p=2; if g [ p=4 4 B

ð5Þ

where E and B are Huynen parameters in (3). The deorientation process is to minimize T33 of the matrix T by a rotation [1]. In other words, it is to make the Huynen parameter E ¼ 0, which represents target local twist (torsion). The deorientation process can be expressed as 0

T ¼ QTQH

ð6Þ

where 2

1 Q ¼ 40 0 The superscript

H

3 0 0 cosð2hÞ  sinð2hÞ 5 sinð2hÞ cosð2hÞ

denotes the conjugate transpose.

ð7Þ

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3 Enhancement of Class Separability In the past, the Wishart distance is used directly to measure class separability as formulas (1) and (2) [2, 3, 7, 11]. However, in radar remote sensing, many pixel targets of interest are coherent sum of random vector scattering from surface and volume, in this case, the Wishart distance between two sets of mixed scattering units might degrade the class separability. Moreover, the extrinsic property (such as POA) might increase the inner class distance, which in turn decrease the inter class separability. In this paper, two strategies are adopted to enhance class separability. 3.1

Wishart Distance Based on Barnes-Holm Decomposition

As noted in 2.2, Barnes-Holm decomposition divides each pixel into a single target T0 and a distributed target TN . To evaluate the difference between two classes accurately, it is much to be preferred to measure distances between their single targets and their distributed targets respectively. A reasonable conjecture is that whether the sum of Wishart distance between single targets Tl0 ; Tm0 and the one between distributed targets TlN ; TmN is greater than the one between Tl0 þ TlN and Tm0 þ TmN , the answer is yes. The proof of the conjecture is given in the Appendix. In a word, the Wishart distance based on Barnes-Holm decomposition can enhance class separability. 3.2

Impact of POA on Inner Class Distance

POA is the angle of rotation about the line of sight, the POA shifts are induced by surfaces with nonzero azimuth slopes [5], so it is an extrinsic property of targets. For the distributed media in X-band TerraSAR data, the POA is not sensitive to the surface slopes due to the high radar frequency. In this case, the POA vary from p=4 to p=4. One crucial property of target parameters is roll invariant, which is independence of POA. However, the Wishart distance is not roll invariant, The POA fluctuation might result in larger inner class distance, which in turn weaken the inter-class separability. 3.3

Enhanced Class Separability and Its Application on Supervised Classification

  Based on Barnes-Holm decomposition and deorientation process, two classes x0 , fxN g are generated from class fxg, and the enhanced class separability is dEnhanced ðx1 ; x2 Þ ¼ dðx01 ; x02 Þ þ dðxN1 ; xN2 Þ

ð8Þ

An improved supervised classification based on the enhanced class separability is (1) Initial Class Characterizing (a) The training samples obtained from a ground truth measurement map is available. In the absence of ground truth map, training samples have to be selected from images based on scattering characteristics. (b) Estimate the class covariance matrices by the center of training samples.

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(c) For each class fxi g, apply the deorientation to coherency matrix Ti , and then decompose the deorientation matrix based on Barnes-Holm decomposition. Class fxi g is characterized by a pure single target Ti0 with POA 0 and a distributed target TiN . (2) Wishart Classification (a) For each pixel, apply the deorientation and Barnes-Holm decomposition, and then characterize it by T0 and TN as in step 1(c). (b) For each pixel, compute the enhanced Wishart distances between the pixel and each class fxi g, then classify it to the class with minimum distance. (3) Iterative Clustering Iteratively apply the Wishart classifier for two to four iterations, in which the class covariance matrices are re-estimated based on the classification results.

4 Experimental Results An experiment on TerraSAR X-band data is given in this section to illustrate the effectiveness of this class separability enhancement method. The area chosen for analysis is Leizhou Peninsula in southern China, and the data was acquired on Apr. 26, 2010. Figure 1 shows the location of the study area. These data were originally SSC (Single Look Slant Range Complex) format of size 9944 * 23601, and the spatial resolution is 6.6 m (azimuth) * 2.18 m (slant range). A 9 * 5 multi-look is processed to averaged coherent matrix of square pixel. The test site is covered by many tropical crops, such as banana, sugarcane, sisal, and some other land covers, such as rice, eucalyptus forest, village, watermelon in greenhouse. Two areas from the data are chosen for class sample selection, as shown in Fig. 2(A) and (B), with Pauli matrix components: jHH  VV j, 2jHV j, and jHH þ VV j, for the three composite colors: red, green, and blue, respectively. The ground survey of the test site was carried out on Apr. 2011. We carried out detailed investigation in these two areas, and got corresponding relationships between the land covers and the PolSAR data. The photographs of the typical land covers are shown in Fig. 2(C)–(H).

Fig. 1. The location of study area.

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Fig. 2. Test areas displayed with Pauli components: |HH-VV|, 2|HV|, and |HH-VV|, for red, green, and blue, respectively, and photos of typical land-covers. (A) Test site 1. (B) Test site 2. (C) Eucalyptus. (D) Rice. (E) Greenhouse Watermelon. (F) Banana. (G) Sugarcane. (H) Sisal. (Color figure online)

For most of the classes of TSX data, lnjCj\0, and the Wishart distance in (1) might be negative, which does not meet the non-negative property of distance measure, so in this paper we use the Eq. (2) to quantify inter-class separability with inner-class separability 0. The six typical land covers in Fig. 2 and water are chosen to illuminate the effectiveness of the enhanced class separability, as shown in Tables 1 and 2, for the class separability and the enhanced one respectively. Comparing the two Tables, it can be concluded that the sum of two Wishart distance based on Barnes-Holm decomposition does enhance the inter class separability. The ratio of enhancement varies greatly, from 1.1 (Greenhouse watermelon VS Rice) to 3.24 (Greenhouse watermelon VS Banana), which is related to scattering mechanisms Table 1. Inter-class separability for original Wishart distance. Sugarcane Sisal Rice Banana Sugarcane 0 1.81 2.39 0.23 Sisal 0 5.19 3.49 Rice 0 2.11 Banana 0 G-W Eucalyptus Water

G-W 0.33 2.29 1.19 0.21 0

Eucalyptus 0.81 3.60 1.25 0.56 0.25 0

Water 19.7 39.4 12.4 14.2 14.3 9.67 0

0 45/45 0/0 0/0 1/1 0/0 0/0 46

97.8/97.8 100/100

48/49 0/0 0/0 7/7 4/4 8/7 0/0 67

71.6/73.1 82.7/83 0.8039/0.8407 83.64/86.73

Sisal

Rice T0 2.01 3.75 0 TN 0.87 3.12 0

Banana T0 0.38 4.86 3.0 0 TN 0.22 2.29 0.13 0

G-W T0 0.19 1.54 1.14 0.48 0

85.9/92.2 93.2/93.7

0/0 0/0 55/59 3/1 6/4 0/0 0/0 64

Rice

74.5/80.4 71.7/78.8

8/8 0/0 0/0 38/41 4/1 1/1 0/0 51

Banana

75/83.3 54.5/66.7

0/0 0/0 4/4 2/0 18/20 0/0 0/0 24

G-W

Table 3. Comparison of classification accuracy.

Sugarcane Sisal Rice Banana G-W Eucalyptus Water Sum. Accuracy Producer (%) User (%) Kappa Over accuracy (%)

TN 1.69 0

Reference data Sugarcane

Sisal T0 2.47 0

Classified data

Sugarcane Sisal Rice Banana G-W Eucalyptus Water

Sugarcane T0 TN 0 0 TN 0.25 1.96 0.18 0.2 0

91.9/91.9 86.4/87.7

2/2 0/0 0/0 3/3 0/0 57/57 0/0 62

Eucalyptus

Water T0 19.8 44.3 15.6 14.5 17.9 10.9 0

100/100 100/100

0/0 0/0 0/0 0/0 0/0 0/0 10/10 10

Water

Eucalyptus T0 TN 1.07 0.84 3.70 3.86 5.3 0.01 1.19 0.7 1.31 0.1 0 0

Table 2. Enhanced inter-class separability based on Barnes-Holm decomposition.

58/59 45/45 59/63 53/52 33/30 66/65 10/10 324

Sum.

TN 13.4 26.6 8.2 8.6 12.4 3.5 0

Enhancement of Class Separability for Polarimetric TerraSAR-X Data 21

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of the two compared land covers. From Table 2, it should be mentioned that the Wishart distance based on T0 component is larger than that on TN component, for most cases. As we know, X-band has short wavelength, which is sensitive to the surface roughness. For most land covers of vegetation, the surface roughness is greater than X-band wavelength. In this case, the distributed component TN in Barnes-Holm decomposition is dominated by the Bragg surface scattering, which result that the disparity in TN component is small, while the majority disparity of different vegetation land covers is ascribed to T0 component. As shown in Table 2, the TN component Wishart distance for Eucalyptus VS Rice is 0.01, and for Eucalyptus VS Greenhouse Watermelon is 0.1, which means that their Bragg surface scattering are almost identical. Further, the quantitative evaluations of the enhanced class separability measure on supervised classification are shown in Table 3. A total number of 324 reference fields are used for accuracy assessment, which result in an overall accuracy of 83.64 and Kappa coefficient 0.8039 for the original Wishart distance measure. When applying the enhanced Wishart distance measure, the two indexes rise to 86.73% and 0.8407.

5 Conclusion The proposed distance measure combines the Wishart distance, the Barnes-Holm target decomposition, and the deorientation method to perform an enhanced class separability measurement, and it is applied in supervised classification. Theoretical proof and experiment on TerraSAR X-band data in Leizhou Peninsula, southern China demonstrate the effectiveness of the proposed distance measure. The inherent scattering characteristics of X-band PolSAR and the fundamental properties of Barnes-Holm decomposition affect the results of supervised classification. The proposed distance measure still has some limitations and disadvantages, which need to be solved in the further work. (1) For Barnes-Holm decomposition, the coherency matrix of single pure target is rank 1, and the determinant of distributed target is close to 0 even if its rank is 3, which led to abnormally large or negative values when calculating Wishart distance. So for each class, selecting enough samples to get the class covariance center is the only way to make sure the enhanced Wishart distance measure and supervised classification method available. (2) In the experiment, the repaired Wishart distance measure in (2) is used to avoid having to use the negative value of lnjCj. However, the inner-class separability, which measures classes compactness, is set to be 0 for all classes. (3) For high frequency X-band data, the disparity between two different land-cover classes is ascribed to single pure target component, while for C-band or L-band data, along with the increase of wavelength, the disparities in distributed target component might become wider.

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Acknowledgments. The work was funded by the Fundamental Research Foundation of Shenzhen Technology and Innovation Council (JCYJ20170818155853672, JCYJ20160429191127529), Natural science foundation of China project 41771403, research project from the Chinese Academy of Sciences (XDA05050107-03, XDA19030301), and the Agricultural Scientific Research Outstanding Talent Fund, Agricultural Information Technology Key Laboratory Opening Fund of Ministry of Agriculture (2016006). We wish to take this opportunity to express their sincere acknowledgment to them.

Appendix Conjecture: Let Tl and Tm be the coherency matrix centers of two classes flg and fmg, respectively. Based on Barnes-Holm decomposition, flg and fmg are expressed as fl0 g þ flN g, fm0 g þ fmN g. Whether d ðl; mÞ  d ðl0 ; m0 Þ þ d ðlN ; mN Þ

ð9Þ

Proof: For an arbitrary sample z 2 flg with coherency matrix Z, based on Barnes-Holm decomposition, z is expressed as z0 þ zN , with coherency matrix Z ¼ Z0 þ ZN . According to Probability theory, ðz0 2 fm0 gÞ [ ðzN 2 fmN gÞðz 2 fmgÞ

ð10Þ

which means that on the assumption of z0 belongs to the class fm0 g and zN belongs to the class fmN g, it can be deduced that z belongs to the class fmg. In probability inequality, (10) can be expressed as p ð z 0 2 fm 0 gÞ
pð z N 2 fm N gÞ  pð z 2 fm gÞ

ð11Þ

pðz0 jm0 ÞPðm0 Þ  pðzN jmN ÞPðmN Þ  pðzjmÞPðmÞ

ð12Þ

and thus

where PðmÞ is the priori probability of class fmg. For classes fmg, fm0 g, fmN g, their probability density functions follow Wishart distribution. According to the distance measure derived by Lee et al. [7], by taking the natural logarithm of (12) and changing its sign, we have d ðz0 ; m0 Þ þ d ðzN ; mN Þ  d ðz; mÞ

ð13Þ

The average Wishart distance from class flg to center of class fmg can be evaluated as dðljmÞ ¼

1 X dðz; mÞ Nl z2flg

ð14Þ

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where Nl is the sample number of class flg. Combining Eqs. (13) and (14), we have dðl0 jm0 Þ þ dðlN jmN Þ  dðljmÞ

ð15Þ

dðm0 jl0 Þ þ dðmN jlN Þ  dðmjlÞ

ð16Þ

and vice versa, that

By substituting Eqs. (15) and (16) into dðl; mÞ ¼ dðljmÞ þ dðmjlÞ

ð17Þ

the proof of Eq. (9) is completed. Equation (1) is a simplified expression by deleting the constant terms and it is assumed that n [ [ q, so it can be concluded that the Wishart distance based on Barnes-Holm decomposition enhance class separability in relative terms, as well as in absolute terms.

References 1. An, W.T., Cui, Y., Yang, J.: Three-component model-based decomposition for polarimetric SAR data. IEEE Trans. Geosci. Remote Sens. 48(6), 2732–2739 (2010) 2. Cao, F., Hong, W., Wu, Y.R., Pottier, E.: An unsupervised segmentation with an adaptive number of clusters using the SPAN/H/a/A space and the complex wishart clustering for fully polarimetric SAR data analysis. IEEE Trans. Geosci. Remote Sens. 45(11), 3454–3467 (2007) 3. Ferro-Famil, L., Pottier, E., Lee, J.S.: Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha- Wishart classifier. IEEE Trans. Geosci. Remote. Sens. 39(11), 2332–2342 (2002) 4. Kong, J.A., Swartz, A.A., Yueh, H.A., Novak, L.M., Shin, R.T.: Identification of terrain cover using the optimum polarimetric classifier. J. Electromagn. Waves Appl. 2(2), 171–194 (1988) 5. Lee, J.S., Ainsworth, T.L.: The effect of orientation angle compensation on coherency matrix and polarimetric target decompositions. IEEE Trans. Geosci. Remote Sens. 49(1), 53–64 (2011) 6. Lee, J.S., Grunes, M.R.: Classification of multi-look polarimetric SAR data based on complex Wishart distribution. Int. J. Remote Sens. 15(11), 2299–2311 (1994) 7. Lee, J.S., Grunes, M.R., Ainsworth, T.L., Du, L.J., Schuler, D.L., Cloude, S.R.: Unsupervised classification using polarimetric decomposition and the complex Wishart classifier. IEEE Trans. Geosci. Remote Sens. 37(5), 2249–2258 (1999) 8. Lee, J.S., Grunes, M.R., Pottier, E., Ferro-Famil, L.: Unsupervised terrain classification preserving polarimetric scattering characteristics. IEEE Trans. Geosci. Remote Sens. 42(4), 722–731 (2004) 9. Barnes, R.M., Holm, W.A.: On radar polarization mixed target state decomposition techniques. In: IEEE National Proceedings of the Radar Conference, Ann Arbor, MI, 20–21 April 1988

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10. Lee, J.S., Schuler, D.L., Ainsworth, T.L.: Polarimetric SAR data compensation for terrain azimuth slope variation. IEEE Trans. Geosci. Remote Sens. 38(5), 2153–2163 (2000) 11. Wang, Y.T., Ainsworth, T.L., Lee, J.S.: Estimation of the orientation and shape parameters of canopy scatterers from POLSAR observations. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(3), 835–847 (2012) 12. Freeman, A., Durden, S.L.: A three component scattering model for polarimetric SAR data. IEEE Trans. Geosci. Remote Sens. 36(3), 963–973 (1998)

Mapping the Distribution of Exotic Mangrove Species in Shenzhen Bay Using Worldview-2 Imagery Hongzhong Li1,2(&), Yu Han1, Jinsong Chen1, and Shanxin Guo1 1 Shenzhen Institute of Advanced Technology, CAS, Xueyuan Avenue 1068, Shenzhen, People’s Republic of China {hz.li,yu.han,js.chen,sx.guo}@siat.ac.cn 2 Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen, People’s Republic of China

Abstract. Shenzhen Bay is an important distribution area of mangrove, with Futian Reserve and Mai Po Reserve. Sonneratia (including Sonneratia caseolaris and Sonneratia apetala) are exotic mangrove species in Shenzhen Bay. Study on the Sonneratia potential impact on native mangrove species requires accurate and repeatable mapping of Sonneratia distribution. Several previous studies have mapped mangrove extent and species, but Sonneratia have been largely ignored. In this study, high resolution Worldview-2 (WV2) imagery was used in Shenzhen Bay Sonneratia mapping. Separability analyses with spectral and textural features were conducted based on Jeffries-Matusita (JM) distance. Results showed that Sonneratia caseolaris and Sonneratia apetala were inseparable in WV2 imagery, and therefore the two species were merged into a single species in the study. Maximum likelihood (ML) classifier, neural net (NN) classifier and support vector machine (SVM) classifier were applied to spectral and textural features, and six mangrove species classification results were obtained. Considering the six classification results together, the distribution of Sonneratia was mapped based on the criteria that, for each polygon, it was categorized as Sonneratia if and only if it was classified as Sonneratia in at least four classification results. The distribution results of Sonneratia showed an agreement with distribution characteristic based on field survey and past literatures. The producer’s accuracy was 79.24% and the user’s accuracy was 92.14%, which indicated great potential of using high-resolution multi-spectral data for distinguishing and mapping Sonneratia. Keywords: Mangrove


Exotic species
Sonneratia
Worldview-2

1 Introduction Mangroves are salt tolerant woody plants that form highly productive intertidal ecosystems in tropical and subtropical regions. Mangroves are a significant habitat for sustaining biodiversity and also provide direct and indirect benefits to human activities [9]. Their dense root system reduces coastal erosion, and protects the coastline from flood, waves and storms. These roots also filter and trap pollutants, thereby decreasing coastal pollution. Mangrove forests serve as a nursery area for shrimps, fish and crustaceans [10]. © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 26–42, 2019. https://doi.org/10.1007/978-981-13-7025-0_3

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Sonneratia caseolaris and Sonneratia apetala are fast-growing high-yield arbor species of mangrove, and are main species in mangrove afforestation [33]. During the early 1990s, they were both introduced to Futian Mangrove Forest Nature Reserve, Shenzhen, China, from Dongzhaigang Mangrove nature Reserve of Hainan, China, as a part of a national key project in The Eight of Five-year Plan to afforest Shenzhen Bay. After 20 years, by natural dispersion, they have become natural-forest communities, and have been one of the dominant populations in the Futian Reserve. In addition, due to the drift by the water current, they have been diffused to the Mai Po Nature Reserve in Hong Kong across the Shenzhen River, with a trend to further diffusion. The two exotic species were proliferating in several sites of Shenzhen Bay in recent years. Whether the two species would cause ecological invasion in Shenzhen Bay has been being attended by scholars and government. Zan et al. stated that while the two species grew faster than indigenous species, at the same time they promoted the growth of indigenous species, and they could not replace indigenous mangrove species [32]. Zhou et al. used the two exotic mangrove species to control invasive Spartina alterniflora Loisel through replacement control for five years, which concurrently promoted the restoration of native mangroves [34]. No evidence has been found to show that the two species were invasive species competing with the native species. On the contrary, they acted primarily as pioneer species in the coastal environments [31]. Although the potential impact of Sonneratia on the native mangrove communities was still unknown, the Hong Kong Wetland Advisory Committee agreed to the precautionary measure to remove the exotic mangrove from the Mai Po Reserve in 2001. Since then, the Hong Kong Agriculture, Fisheries and Conservation Department (AFCD) has conducted Sonneratia removal exercises regularly [17]. Accurate Sonneratia maps are a fundamental requirement for the study of their spread rate and potential impact. Generally speaking, mapping Sonneratia distribution is a special kind of species discrimination. Traditional field work for species mapping is costly, time consuming, and sometimes inapplicable due to the poor accessibility of mangrove areas [15]. Alternatively, remote sensing technology has been applied widely [9, 16, 24]. Satellite data with medium spatial resolution such as ALOS PALSAR [4], Landsat Thematic Mapper (TM) [14], SPOT XS [6] can provide adequate details for mapping mangrove distribution. But they are often inadequate for mangrove discrimination at the species level [16]. The commercial availability of high-spatialresolution satellite imagery has created new opportunities for mangrove species discrimination. SPOT-5 [18], IKONOS [26], Worldview-2/3 [9], QuickBird [5] satellite imagery have been used to map mangroves to a species level. For mangrove community in Shenzhen Bay, mangrove species classification has been studied using various remote sensing data. It was proved that hyperspectral Hymap data was a good source to derive data of mangrove community, and four mangrove species, including Sonneratia caseolaris and Sonneratia apetala, were extracted in Futian Reserve [22]. By fusion of ALOS-AVNIR multi-spectral and ALOS-PALSAR data, mangroves in Futian Reserve were classified to five species, in which Sonneratia caseolaris and Sonneratia apetala were merged into a single species [29]. In Mai Po Reserve,

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by combining EO-1 Hyperion and Envisat ASAR data, Wong and Fung [28] discriminated seven mangrove species, in which the Sonneratia caseolaris and Sonneratia apetala were also merged into a single species. Worldview-3 imagery [27], EO1 Hyperion and SPOT-5 data [13] were also used in Mai Po mangrove species mapping, while in these studies, the Sonneratia caseolaris and Sonneratia apetala were not mentioned. The Worldview-2 (WV2) satellite imaging sensor provides remote sensing data with narrow spectral bands lying within the ideal spectral range for mangrove species identification. Its combination of narrow spectral bands and high spatial resolution has increased potential for accurately mapping the distribution of Sonneratia. The objectives of this study are: (1) to test the potential of Worldview-2 imagery to distinguish Sonneratia; (2) to establish an accurate and repeatable methodology to map the distribution of Sonneratia in Shenzhen Bay, China.

2 Materials and Methods 2.1

Study Area

This study focused on the mangrove forests within Shenzhen Bay, China (Fig. 1). The Shenzhen Bay (also known as Deep Bay) is located between the northwestern New Territories of Hong Kong and the southeastern Nanshan of Shenzhen, centered at latitude 22 29′ N and longitude 113 58′. In the area, there are two mangrove reserves, the Futian Mangrove Nature Reserve and the Mai Po Marshes nature Reserve.

Fig. 1. The location of the study area.

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The Futian Mangrove Nature Reserve was officially created in 1984. It covers an area of 367 ha. It is the only nature reserves that located in the urban area and the smallest one in China. The Mai Po Nature Reserve has been managing by the World Wide Fund for Nature Hong Kong since 1983, and added to Wetland of International Importance under the Ramsar Convention in 1995. It covers an area of 1540 ha. The reserve was divided into five different management zones by Agriculture, Fisheries and Conservation Department (AFCD) Hong Kong based on the habitat types, ecological values and existing land uses, i.e. the Core Zone (CZ), the Biodiversity Management Zone (BMZ), the Wise Use Zone (WUZ), the Public Access Zone (PA), and the Private Land Zone (PL). The two reserves are across the Shenzhen Bay, as shown in Fig. 1. 2.2

Two Exotic Mangrove Species

In the study area, a total of eight species of mangroves have been recorded [27, 29, 30]. Table 1 shows the main information of the eight species. Table 1. Main information of the eight mangrove species Speices Bruguiera gymnorhiza Kandelia obovato Excoecaria agallocha L. Aegiceras corniculatum Acanthus ilicfolius Avicennia marina Sonneratia caseolaris Sonneratia apetala

Genius Bruguiera Kandelia Excoecaria Aegiceras Acanthus L. Aricennia Sonneratia Sonneratia

Family Rhizophoraceae Rhizophoraceae Euphorbiaceae Myrsinaceae Acanthaceae Verbenaceae Sonneratiaceae Sonneratiaceae

Class True mangroves True mangroves Mangrove associates True mangroves True mangroves True mangroves True mangroves True mangroves

Life form Small trees Small trees Small trees Shrubs Shrubs Shrubs Small trees Small trees

Sonneratia caseolaris and Sonneratia apetala are exotic species for the study area. During the early 1990s, they were both introduced to Futian Mangrove Forest Nature Reserve, Shenzhen, China, from Dongzhaigang Mangrove nature Reserve of Hainan, China. S. caseolaris was native to Dongzhaigang, while S. apetala in Dongzhaigang was originally introduced from Sundarban, the southwest of Bangladesh in 1985. Photographs of the two species taken during different phonological periods are shown in Fig. 2. Table 2 shows the morphological characteristics of the two species [17]. After 20 years, by natural dispersion, they have become natural-forest communities, and have been one of the dominant populations in Futian Reserve. Due to the drift by the water current, they have been diffused to the Mai Po Nature Reserve in Hong Kong across the Shenzhen River, with a trend to further diffusion. To prevent the two exotic species’ invasion, AFCD has conducted Sonneratia removal exercises regularly since 2001. Figure 3 shows the Sonneratia removal in dual-time Google Earth images.

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Fig. 2. The two exotic species during different phonological periods. (a–c) Sonneratia caseolaris; (d–f) Sonneratia apetala. Table 2. The morphological characteristics of Sonneratia caseolaris and Sonneratia apetala. Height Natural distribution

Leaves

Flowers

Fruits

Sonneratia caseolaris Up to 15 m South East Asia to the northern Australia. Native to China where they are naturally found on Hainan Island Broad, ovate, opposite leaf, incompletely unrolled to show venation. Apex acute in young plants which becomes round at later stage. Length to width ratio is less than two. Petiole short (0.5 cm) and red Relatively larger (*5 cm). Bisexual. Stamens red and white distally, standing erect. Style greenish and long, twice the length of stamens, topped with capitate stigma. Usually have 6 sepals. Petals red and oblong. Flowers appear all year round Compressed and edible fleshy. Large (up to 8.5 cm). Green when young and turns yellowish green and aromatic when mature. Produces 800–1,300 seeds per fruit. Fruits appear all year round

Sonneratia apetala Up to 20 m South Asia such as India, Bangladesh and Malaysia

Narrow, elliptical, opposite, gradually taper toward the apex. Petiole is longer (*1 cm)

Flower small (1.5–2 cm) and stalked, single or clustered at branch ends. 4 green calyx lobes. Bisexual. Stamens white, standing erect. Style yellow topped with mushroomshaped or peltate stigma. Flowers appear from May to December Oval berry. Distinctively smaller (1.5–2.5 cm) than S. caseolaris. 4–5 calyx lobes. Green when young and becomes grayish when mature. Each fruit produces 100–130 seeds. Fruits appear from August to early Spring

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Fig. 3. The removal of Sonneratia spp. shown by Google Earth images. (a) 2013; (b) 2015.

2.3

Remote Sensing Data and Pre-processing

A WV2 image was selected as the remote sensing data source for this study. The image was acquired on 9th Jan. 2015, with eight multispectral bands and one panchromatic band. Details of the WV2 imagery are shown in Table 3. The image was radiometrically corrected by converting digital numbers to radiance values by multiplying the metadata-based gain with the pixel value and adding the offset with ENVI’s Worldview Radiance tool. In this study, the atmospheric correction was not processed as Harris [8] confirmed that WV2 radiance images without atmospheric correction are useful to observe objects surrounded by water, and that atmospheric correction images using Dark Object Subtraction and Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) markedly decreases the signal to noise ratio of the data. Table 3. Worldview-2 band specifications. Band name Coastal Blue Green Yellow Red Red-Edge NIR1 NIR2 Panchromatic

Band number 1 2 3 4 5 6 7 8 9

Bandwidth (nm) 396–458 442–515 506–586 584–632 624–694 699–749 765–901 856–1043 447–808

Center wavelength (nm) 427 478 546 608 659 724 833 949 627

Spatial resolution (m) 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.5

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Mangrove Distribution, Field Survey and Sample Collection

To avoid the confusion between mangroves and non-mangroves, mangroves areas were extracted using the object based image segmentation and decision tree classification method. In addition, accurate manual post-processing was conducted according to historical data, programming, and field survey. The distribution of mangroves can be seen in Fig. 4.

Fig. 4. Distribution of mangroves and samples.

The field works were conducted in the Futian Reserve in December 2014. A differential GPS (Global Positioning System) coordinated in the UTM system was used to match the geographical location between field survey data and image pixel data. Because of the limited accessibility of mangrove area and the politically sensitivity of Mai Po Reserve to mainland Chinese, the ground reference points collected in the field were very limited. In this study, we collected reference data via the past literatures [27–29], programming [1, 30] and Google Earth images. As a result, 667 reference points were selected, as shown in Table 4. It should be noted that there was no reference points for E. agallocha and B. gymnorhiza as they are fairly small in the area. S. caseolaris and S. apetala were merged into a single mangrove species as their inseparability by WV2 data. Four other types, water, mudflat, reed, and shadow were added into the classification system. Reed was mainly distributed in the Biodiversity Management Zone of Mai Po Reserve. Water and mudflat were distributed in the rim of mangrove forests. Shadow areas were generated due to the high resolution of WV2 and height difference between neighboring mangrove forests. For convenience, coded names were given for each of the mangrove species. To validate the results in this paper, the plots of each class were randomly split into two groups: training samples and testing samples. The distribution of the samples was shown in Fig. 4.

Mapping the Distribution of Exotic Mangrove Species in Shenzhen Bay

2.5

33

Class Separability

Class separability is the measure of the similarity between different classes and determines the degree to which those classes can be distinguished. The idea of using a measure of class separability to select features has been used in remote sensing landcover classification [25] and mangrove classification [11]. In this paper, the JeffriesMatusita (JM) distance was used to measure the class separability. The JM distance between a pair of probability functions is the measure of the average distance between the two class density functions. For normally distributed classes, this distance becomes the Bhattacharyya (BH) distance (1) [23]. J ¼ 2ð1  eB Þ   1 1 det R T 1 B ¼ ðl1  l2 Þ R ðl1  l2 Þ þ ln pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R ¼ R1 þ R2 8 2 det R1 det R2

ð1Þ ð2Þ

where li and Ri are the means and covariances of the two distributions. The J-M distance measures the separability on a scale ½0; 2. Complete separability of the two classes with respect to the analyzed feature is indicated by J ¼ 2. That is to say, on the basis of the training samples used, there will be no misclassifications. The lower J is, the worse is the separability and the higher the number of misclassified samples [21]. 2.6

Textural Features Extraction

The introduction of texture features, which has been widely proven to be an effective method of improve the accuracy of classification for remote sensing imagery [12], was used for Sonneratia extraction. Textural features can reflect the local spatial changes of intensity or color brightness. Wang et al. [27] found that with the Grey-Level CoOccurrence Matrix (GLCM) texture features, the overall accuracy of mangrove species classification was 90.92%, higher than the classification results with only original spectra. In this study, the texture features of GLCM were extracted based on the 0.5 m panchromatic image. In the Texture category, only four texture features based on the GLCM were considered among the 14 originally proposed by Haralick et al. [7] due to the strong correlation frequently reported between many of the features [2]. The four selected features were contrast (CON), entropy (ENT), correlation (COR), and homogeneity (HOM). The window size of textural features is important to the mapping results, as an undersized window will fail to fully exploit the textural arrangement of the objects of interest, whereas an oversized window will result in blurring of the object boundaries [3, 27]. In addition, as S. caseolaris and S. apetala were exotic species in the study area, they were not always distributed in large community. In Mai Po Reserve, as shown in Fig. 3(a), they were always distributed as isolated high tree with the other low mangrove species around. In this case, an oversized window will result in instability of the texture features among Sonneratia. To determine the optimal window size, we conducted a stability analysis. The window size was set to be 5  5, 7  7, 9  9,

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11  11, 13  13, 15  15, 17  17, and 19  19. The standard deviations of the textural features among the 83 Sonneratia training samples were calculated. 2.7

Mangrove Species Classification

We used three classifiers to conduct mangrove species classifications, maximum likelihood (ML), neural net (NN), and support vector machine (SVM). ML classifier is a statistical decision criterion to assist in the classification of overlapping signatures. It is assumed that the training data follows the Gaussian distribution. Pixels are assigned to the class of highest probability [20]. NN classifiers are modeled after the constructs of the human brain, wherein intelligence is stored in neural pathways as well as in memory. In an artificial neural network, knowledge is stored in the form of weights applied to a node, that is, as multiplicative values to be applied to an input. Instead of algorithms to determine values, a supervised network is presented with repeated examples of inputs and corresponding correct outputs, and allowed to “learn” for itself [19]. SVM is a machine-learning technique that is well adapted to solving non-linear, high dimensional space classifications. Different from traditional classification approaches, SVM classifier identifies the boundary between classes in n-dimensional spectral-space rather that assigning points to a class based on mean values. SVM creates a hyperplane through n-dimensional spectral-space that separates classes based on a user defined kernel function and parameters that are optimized using machinelearning to maximize the margin from the closest point to the hyperplane [11].

Worldview-2

Preprocessing

SVM

Multi-spectral

Panchromatic

Spectra

Spectra+Texture

ML

NN

SVM

ML

Sonneratia mapping

Accuracy accessment

Fig. 5. Flowchart of the method used.

NN

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35

In this study, the classifications were processed with ENVI 5.0. The original spectral features and textural features were adopted for input of the three classifiers. We applied the three classifiers with two groups of features, respectively. They are: (i) features from Original eight bands Spectral data (OS); (ii) features combining Original Spectra and calculated Textures (OST). Then we got six mangrove species classification results: SVM+OS, ML+OS, NN+OS, SVM+OST, ML+OST, NN+OST. A flow chart of the methodology adopted in this study is shown in Fig. 5. It should be noted that when combining the original spectra and calculated textures, the Layer Staking tool in ENVI 5.0 was used to unify the resolution. 2.8

Post-processing, Sonneratia Mapping and Accuracy Assessment

The six classification results were exported to polygon shapefile, and then the postprocessing was conducted in ArcGIS 10.0 and eCognition Developer 8.64. For each classification results, polygons less than 36 m2 were removed using the Eliminate tool in ArcGIS’s Data Management Tools. This is done because according to the field works and Google Earth 0.3 m imagery, the canopy diameters of isolated Sonneratia are not less than 6 m, and therefore, the polygons less than 36 m2 are likely to be erroneous in classification maps. The shadow class was removed using the Assign Class tool in eCognition’s Basic Classification Tools. The shadow polygons were converted to Sonneratia class if and only if the borders to other classes were 0, that is, they were surrounded by Sonneratia polygons. This is done because Sonneratia trees are higher than the other mangrove species, and shadows adjacent to both Sonneratia and other species are likely to be generated due to the height difference neighboring Sonneratia and other species. Then the distribution of Sonneratia was mapped based on the six mangrove species classification results. The six classification results were intersected by the ArcGIS’s Intersect tool. For each polygon, it was categorized as Sonneratia if and only if it was classified as Sonneratia in at least four classification results. At last, the Eliminate tool in ArcGIS was also adopted to remove the polygons with area less than 36 m2. Table 4. Samples and their coded names used for image classification Classes S. caseolaris S. apetala A. ilicifolius A. marina K. obovata A. corniculatum Reed Water Shadow Mudflat Total

Coded name Training samples Testing samples Total SN(SC) 50 53 103 SN(SA) 33 31 64 AI 22 24 46 AM 22 21 43 KO 96 72 168 AC 44 35 79 Reed 11 8 19 Water 24 20 44 Shadow 22 19 41 Mudflat 37 23 60 361 306 667

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Accuracy assessment was conducted using confusion matrix. The overall accuracy, user’s accuracy, producer’s accuracy, and the Kappa coefficient of the classification results of Sonneratia and non-Sonneratia were generated using all the testing samples described in Fig. 4(b) and Table 4.

3 Results and Discussion 3.1

Spectral Separability Analysis

Class spectral separability at the pixel level for all training samples is shown in Table 5 for WV-2 imagery. The spectral separability between S. caseolaris (SC) and S. apelata (SA) was 1.236, which indicated that the two species of Sonneratia were inseparability based on the original 8 bands spectra of WV2. In this study, the two species were merged as one species, Sonneratia (SN). The remaining of Table 5 shows the spectral separability for Sonneratia and non-Sonneratia. The spectral separability between Sonneratia and non-vegetation classes, such as water, mudflat, and shadow were found to be greater than the empirical threshold 1.9 [11]. However, the spectral separability between Sonneratia and the other mangrove species were moderate to poor, especially between Sonneratia and Kandelia obovata (KO). This indicates that with only spectral features, it is impossible to discriminate Sonneratia accurately. Table 5. Spectral separability (JM distance) between Sonneratia and non-Sonneratia SN vs AI SN vs AM SN vs KO SN vs AC 1.849 1.920 1.650 1.838 SN vs Water SN vs Mudflat SN vs Shadow 1.998 1.996 1.965

3.2

SN vs Reed 1.932 SC vs SA 1.236

Analysis Textural Features Extraction

Table 6 shows the variety of texture separability with window size. The window size had little effect on the texture separability between Sonneratia and Acanthus ilicifolius (AI), Sonneratia and Aegiceras corniculatum (AC). The JM distance was always larger than 1.9. However, for the other pair separations, with the increase of window size, the JM distance decreased. When the window size increased up to a certain level, the JM distance was less than 1.9. It should be noted that the texture separability between S. caseolaris (SC) and S. apelata (SA) was around 1.6, and the two Sonneratia species were still inseparable based on the textural features. Table 7 shows the variety of texture stability for Sonneratia with window size. For the four textural features, with the increase of window size, the standard deviations decreased, that is, the stability of the textural features for Sonneratia was improved. Considering the texture separability and texture stability together, the window size for calculation of the GLCM textures was set to be 9  9.

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Table 6. Texture separability (JM distance) with window size SN vs AC SN vs AI SN vs AM SN vs KO SN vs Mudflat SN vs Reed SN vs Shadow SN vs Water SC vs SA

55 1.966 1.959 1.935 1.96 1.979 1.977 1.939 1.944 1.582

77 1.963 1.957 1.988 1.931 1.954 1.957 1.94 1.895 1.557

99 1.952 1.937 1.885 1.925 1.945 1.931 1.924 1.835 1.601

11  11 1.955 1.93 1.861 1.928 1.947 1.896 1.911 1.833 1.582

13  13 1.966 1.938 1.847 1.916 1.958 1.881 1.895 1.78 1.591

15  15 1.975 1.945 1.859 1.914 1.966 1.851 1.896 1.533 1.573

17  17 1.978 1.942 1.851 1.908 1.855 1.827 1.888 1.235 1.586

19  19 1.981 1.934 1.862 1.899 1.815 1.537 1.885 1.232 1.594

Table 7. Texture stability (standard deviation) for Sonneratia with window size 55 77 HOM 0.108 0.086 CON 17.84 13.08 ENT 0.415 0.382 COR 1.005 0.218

3.3

99 0.072 9.606 0.356 0.106

11  11 0.067 7.576 0.347 0.079

13  13 0.061 6.274 0.340 0.071

15  15 0.058 6.144 0.332 0.068

17  17 0.056 5.289 0.327 0.068

19  19 0.054 6.037 0.321 0.082

Classification Results

The classification results were illustrated in Fig. 6. As the polygons with area less than 36 m2 were eliminated, the classification maps of mangrove species were very smooth, with no speckle point. The overall accuracy and the kappa coefficient of the six mapping results were shown in Table 8. Comparing to the original spectral features, the inclusion of textural features improves the overall accuracy of the distribution of mangrove species. Take ML classifier for example, the overall accuracy was improved from 89.35% to 90.85%, and the Kappa coefficient was improved from 0.8725 to 0.8907. Among the three classifiers, the ML classifier performed the best mapping results, while the SVM classifier was the worst. The producer’s and user’s accuracy of Sonneratia were also illustrated in Table 8. The introduction of textural features was also helpful to the Sonneratia’s mapping accuracy. Take SVM classifier for example, the user’s accuracy was improved from 71.15% to 83.72%, and the producer’s accuracy was improved from 66.47% to 86.23%. Among the six classification results, the ML classifier with original spectral features and textural features performed best in Sonneratia user’s accuracy, 100%, and the SVM classifier with original spectral features and textural features performed best in Sonneratia producer’s accuracy, 86.23%.

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(a) ML+OS

(d) ML+OST

(b) NN+OS

(c) SVM+OS

(e) NN+OST

(f) SVM+OST

Fig. 6. Classification results. (a) ML+OS; (b) NN+OS; (c) SVM+OS; (d) ML+OST; (e) NN+OST; (f) SVM+OST.

Table 8. Accuracy assessment for the six classification results and Sonneratia mapping result Overall accuracy ML+OS 89.35% NN+OS 79.16% SVM+OS 75.11% ML+OST 90.85% NN+OST 85.64% SVM+OST 83.81% Final Sonneratia mapping

Kappa coefficient 0.8725 0.7492 0.7008 0.8907 0.8169 0.8052

User’s accuracy 95.68% 76.73% 71.15% 100% 79.35% 83.72% 92.14%

Producer’s accuracy 74.64% 73.05% 66.47% 77.65% 74.43% 86.23% 79.24%

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39

Distribution of Sonneratia

The distribution of Sonneratia based on the six classification results was shown in Fig. 7. The total area of Sonneratia in Shenzhen Bay is about 42.69 ha. The areas in Futian Reserve and Mai Po Reserve are 27.69 ha and 15.0 ha, respectively. As shown in Fig. 7, the distribution results of Sonneratia showed an agreement with distribution characteristic based on field survey and past literatures. In the northwest and southeast of Futian Reserve, Sonneratia have become natural-forest communities, and is one of the dominant populations. In Mai Po Reserve, Sonneratia mainly grow in the outermost region of the seashore due to the drift by the water current. At the mouth of Shenzhen River, that is between Futian Reserve and Mai Po Reserve, the newly formed mangrove forests on the mudflat are Sonneratia, which can be deduced that the seeds are also diffused by water.

Fig. 7. The distribution of Sonneratia spp. in Shenzhen Bay.

The producer’s and user’s accuracy of the final Sonneratia mapping result were also assessed, as shown in the last row of Table 8. The producer’s accuracy was 79.24%, and the user’s accuracy was 92.14%. According to the definition of the user’s accuracy, the commission error of Sonneratia was very low at just 7.86%. By checking the properties of the testing samples, we found that the commission error was mainly from mudflat and shadow. The misclassified mudflats were originally covered by Sonneratia, and they were set to be mudflat samples as the Sonneratia had been removed. The misclassified shadow samples were related to Sonneratia boundaries as the shadows were generated due to the height difference between Sonneratia and neighboring lower

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mangrove species. According to the definition of the producer’s accuracy, the omission error of Sonneratia was 20.76%. By checking the locations of the testing samples, we found that the omission Sonneratia samples were mainly located on the rim of Sonneratia canopy in Mai Po Reserve. This is because that the selected window size is larger than the distance between Sonneratia sample and Sonneratia canopy boundary. Heumann [11] also demonstrated that the accurate demarcation of the precise community boundaries of mangrove species was one of the challenges in mangrove species mapping using high-spatial-resolution data. The results with producers’ accuracy 79.24% and user’s accuracy 92.14% indicated that our Sonneratia mapping results are consistent with those results obtained in ground-truth sites and past literatures, and they can be used as a foundation in the study of Sonneratia potential impact on native mangrove.

4 Conclusion Sonneratia caseolaris and Sonneratia apetala are two exotic mangrove species in Shenzhen Bay, China. While previous studies have mapped mangrove extent and species, these studies have largely ignored the Sonneratia. The goals of this research were to test the applicability of Worldview-2 imagery to map the distribution of Sonneratia. The two exotic species were merged into a single species due to their inseparability with spectral and textural features. Separability analysis revealed that textural features were promising complementary to spectral features in Sonneratia identification. Three classifiers including ML, NN, and SVM were applied to the textural and spectral features, and six mangrove species classification results were obtained. Then the distribution of Sonneratia was mapped based on the criteria that, for each polygon, it was categorized as Sonneratia if and only if it was classified as Sonneratia in at least four classification results. The results showed that Sonneratia in Shenzhen Bay was 42.69 ha. The high classification accuracy (producer’s accuracy of 79.24% and user’s accuracy of 92.14%) indicated great potential of the data and methodology used in this study. The Sonneratia distribution map serves as a foundation for the study of Sonneratia potential impact on native mangrove in Shenzhen Bay. Acknowledgments. The work was funded by the Fundamental Research Foundation of Shenzhen Technology and Innovation Council (JCYJ20170818155853672, JCYJ20160429191127529), Natural science foundation of China project 41771403, research project from the Chinese Academy of Sciences (XDA05050107-03, XDA19030301), and the Open Fund of Key laboratory of Urban land Resources Monitoring and Simulation, Ministry of Land and Resources (KF-2016-02-019). We wish to take this opportunity to express their sincere acknowledgment to them.

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Vortex Extraction Method Based on Compact Ratio Ya-ru Xu1, Min Ji1(&), and Zhi-wei Lu2 1

2

School of Surveying Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China [email protected], [email protected] Planning and Design Institute of Forestry Products Industry, State Forestry Administration, Beijing 100010, China [email protected]

Abstract. As a typical feature of the ocean flow field, the vortex plays an important role in the transmission and distribution of marine matter and energy, and thus becomes a hot spot of feature extraction. In this paper, the vortex extraction is carried out based on the algorithm of compact ratio. In order to further verify its rationality and accuracy, the sea surface height data in the same area is used to test, which proves the feasibility of this method in vortex extraction. In addition, the compact ratio method for extracting vortex is less affected by data resolution, and incorporates the idea of mathematical morphology to provide a new idea for ocean vortex extraction. Keywords: Vortex


Recognition
Compact ratio

1 Introduction As early as 1972, Lugt proposed that the vortex is a phenomenon in which a plurality of material particles rotate around a common central region [1]. Although it is vague, it gives the original theoretical definition of vortex to some extent. Jeong proposed in his literature that the vortex core is the negative region of the second eigenvalue of the velocity gradient tensor, and the existence of the vortex core determines whether there is a vortex at the location [2]. Cucitore then defined the vortex core in more detail. First, the core of the vortex in the flow field must be a stationary vortex, a stationary cycle, and the potential flow region excludes the vortex core, which is a potential zero. Section vortex, secondly, the geometry of the vortex core that satisfies the condition needs to be verified by Galileo inequality. At the same time, the above two conditions can be called the vortex core [3], and the mathematical method is used to quantitatively identify the vortex. The vortex is defined in Baidu Encyclopedia as “a flow phenomenon in which a cylinder with a small radius rotates in a stationary fluid to cause a circular motion of the surrounding fluid.” Based on the above definition, it is not difficult to find that a common feature of the vortex is: A vortex has a vortex core, and other parts around the vortex will rotate around the vortex core [4]. As people’s definition of vortex becomes clearer, the extraction of vortex has become a hot research © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 43–50, 2019. https://doi.org/10.1007/978-981-13-7025-0_4

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topic. The current vortex extraction mainly includes remote sensing image-based extraction methods. Changming introduced several major vortex detection methods developed in the past decade, such as Euler type based on sea surface height anomaly, sea surface temperature and global drifting. Based on the Lagrangian type of buoy data, the application of the vortex database is further explored [5]. Based on remote sensing images, Guangrong proposed a computer automatic detection method for the scale vortex of the oceanic remote sensing image from coarse to fine [6].

2 Research Area and Data Source Introduction There are many reasons for the vortex, which is also very complicated. The vortex is usually accompanied by the arrival of typhoons and tornadoes. The typhoon is an atmospheric vortex with a wind speed of 32.7 m/s or more on the tropical ocean. Its radius can reach hundreds of kilometers. In the North Atlantic, the Caribbean, the Gulf of Mexico and the Northeast Pacific, known as hurricanes. Because the vortex is closely related to the typhoon, strong eddy currents are often observed in the Gulf Stream of the South Atlantic Gulf [7], which usually forms east of 70°W. Therefore, this paper selects part of the Gulf of Mexico as the study area, the Gulf Stream. It is a strong western boundary flow in the northern hemisphere with the Kuroshio. It interacts strongly with the topography of the southeast coast of the United States. The interaction between flow and terrain affects the energy budget, average flow and life cycle of the vortex. NetCDF (Network Common Data Format) is a file format for storing multidimensional scientific variables such as temperature, humidity, air pressure, wind speed, and velocity. The data in the file is stored as an array containing dimensions, variables, and attributes. The flow field data selected in this paper is a sea current data (NCOM_amseas_latest3d) provided by the ESR Research Institute’s Ocean Current Analysis Data (OSCAR) and NOAA (National Oceanic and Atmospheric Administration) official website, and selects the American Seas part of the sea (4.98°N–25.61°N, 54.89°W–83.70°W)1. The 500 m depth current is the study data with a resolution of 1/30°  1/30° and a size of 865  529. The variables include the zonal flow water_u and the radial flow water_v, the storage format is NetCDF, with high precision and spatiotemporal resolution. The latitudinal flow rate water_u and the radial flow rate pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi water_v are calculated according to the formula u ¼ water u2 þ water v2 to obtain the superimposed flow velocity layer, and the result is shown in Fig. 1. Considering the shortcomings of the existing methods and considering the morphological similarity, in order to more intuitively display the instantaneous motion state of the vortex, this paper uses ArcGIS to symbolize the stored velocity vectors u and v and calculate its settings. The angle of rotation and the flow rate are used to more easily show the speed and direction of the water flow. The figure below shows the partial area of the flow field velocity (Fig. 2).

1

Data from: NOAA’s National Centers For Environment Information(NCEI), Web site: https://www. ncdc.noaa.gov/.

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Fig. 1. Current data of April 5, 2013 in the American Seas part of the Sea Area

Fig. 2. Partial flow diagram of the study area

3 Theoretical Introduction 3.1

The Concept of Compact Ratio

The original compact ratio idea was derived from Mathematical Morphology, which was established in 1964 as an image processing method based on set theory and lattice theory. The goal is to quantitatively describe the geometric structure of the image. The basic method is reasonable. By selecting the structural elements with specific shapes,

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the relationship between the structural elements and the images can effectively obtain the relationship between the various parts of the digital image, and finally extract and measure the morphological structure of the image to be processed. Mathematical morphology can roughly divide the image processing into the following three steps: (1) extracting the geometric features of the image, that is, finding the corresponding geometric structure pattern for the image to be processed; (2) selecting the appropriate geometric pattern for the image. The structural element, the selection criterion is to be able to display the geometrical pattern most effectively, and secondly the geometrical mode should be as simple as possible; (3) in order to make the image more prominent than the original image, the image needs to be The target performs a corresponding mathematical morphological transformation to quantitatively represent the geometric pattern of the target. Of course, the structural elements selected here are more than just a feature of shape. Structural elements can contain a lot of information, such as size, orientation, gray value and color. As a special form phenomenon in the ocean flow field, vortex can be applied to the identification of vortex by the idea of mathematical morphology because its geometry is close to ellipse or circle [8]. The idea of compact ratio comes qffiffiffi from this. The idea can be expressed as follows: in the formula Cj ¼ ABii , Cj is compact ratio, Ai current polygon area, Bi is the area of a circle that has the same perimeter as the polygon. The Jacobi matrix of each critical point in the twodimensional flow field corresponds to two eigenvalues, and the basis for dividing the critical point type is based on the real part and the imaginary part of the eigenvalue [9]. I1 and I2 respectively represent the real part of the two eigenvalues of the critical point, and R1 and R2 represent the imaginary part of the two eigenvalues, the specific classification criteria are summarized in Table 1. Table 1. Two-dimensional flow field critical point classification Critical point type Eigenvalues Real I Saddle points I1 = −I2 Attracting nodes I1 < 0, I2 < 0 Repulse nodes I1 > 0, I2 > 0 Attract focus I1 < 0, I2 < 0 Rejection focus I1 > 0, I2 > 0 Center points I 1 = I2 = 0

3.2

Imaginary R R1 = R2 = 0 R1 = R2 = 0 R1 = R2 = 0 R1 = R2 6¼ 0 R1 = R2 6¼ 0

Critical Point Extraction Example

Reading current data in NetCDF format in ArcGIS software. Since there may be abnormal values in the metadata, the data needs to be filtered to propose an outlier; Then reclassify the raster data to extract a critical point with a speed close to zero (Fig. 3). Because the ocean has a lower productivity sea area and less suspended

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matter, it has a higher transparency and is known as the “marine desert”. In order to avoid the confusion between the critical point and the desertification area, the latter needs to be eliminated. The critical point after screening is shown in Fig. 4.

Fig. 3. Before screening

Fig. 4. After screening

In order to further divide the critical point type, the eigenvalues of the extracted critical points are calculated by Matlab, and five parameters are defined, which are water_u (radial flow velocity), water_v (latitude flow velocity), c (combination velocity), e (resolution).) and t (flow rate critical threshold). According to the

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discriminant criterion, the 186 critical points extracted are divided into the following types: including 14 saddle points, 5 attracting nodes, 8 repulse nodes, 10 attract focus and 17 rejection focus, no center points (Table 2, Figs. 5 and 6). Table 2. Critical point eigenvalue calculation Number

1

2

3

…

84

85

86

Line number

45

89

94

…

223

387

127

Column number

50

78

66

…

426

632

859

k1

−0.0775 + 0.0000i

−0.0060 + 0.0377i

0.0140 + 0.0000i

…

0.0210 + 0.0000i

0.0065 + 0.0274i

0.0000 + 0.0000i

k2

0.0000 + 0.0000 i

−0.0060 − 0.0377i

−0.0140 − 0.0000i

…

0.0000 + 0.0000i

0.0065 − 0.0274i

0.0390 + 0.0000i

Critical point type

–

Attract focus

Saddle points

…

–

Rejection focus

–

From the calculation results, the compact ratios of the 19 vector planes formed are all above 0.75, and the vortex center is determined according to the definition of the vortex core of Jeong [2]. In order to further determine the accuracy of the extraction vortex position, the sea surface height of the same research data was downloaded and verified and filtered to more intuitively display the extracted vortex, the effect diagram is shown in Fig. 7.

Fig. 5. The vector surface after turning point

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Fig. 6. Compact ratio calculation

Fig. 7. Vortex core position after filtering

4 Conclusion Based on the significance of the study of ocean vortex extraction, with the idea of mathematical morphology, the vortex geometry is combined with the area of the circle of the same circumference, and the compact ratio is realized by the principle that the number of critical points is included in the circle of the same area. The calculation of the vortex center is determined according to the type of critical point in the twodimensional flow field, and the feasibility of extracting the vortex based on the compact

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ratio method is also verified by the sea surface height data and curvature. Experiments show that the proposed method has a strong universality, simple and fast operation, can realize the identification of the vast majority of vortices in the study area, but also from the morphology of the existing vortex extraction methods effectively complement. Acknowledgments. This work was supported in part by a grant from the National Science Foundation of China (41471330).

References 1. Lugt, H.: Vortex Flow in Nature and Technology. Wiley, New York (1972) 2. Jeong, J., Hussain, F.: On the identification of a vortex. J. Fluid Mech. 285, 69–94 (1995) 3. Charola, A.E., Henriques, M.A.: Hydraulicity in lime mortars revisited. In: International R1LEM Workshop on Historic Mortars: Characteristics and Tests, Paisley, Scotland, 1999, pp. 95–104 (2000) 4. Wang, P., Wang, J.: Vortex extraction and tracking algorithm. Sci. Technol. Eng. 13(26), 7716–7719 (2013) 5. Dong, C., Jiang, X., Xu, G., et al.: Automated eddy detection using geometric approach, eddy datasets and their application. Adv. Mar. Sci. 35(04), 439–453 (2017) 6. Ji, G., Chen, X., Huo, Y., et al.: An automatic detecting method of the marine meso-scale eddy in remote sensing image. Oceanologia Et Limnologia Sinica 02, 139–144 (2002) 7. Gula, J., Molemaker, M.J., McWilliams, J.C.: Topographic vorticity generation, submesoscale instability and vortex street formation in the Gulf Stream. Geophys. Res. Lett. 42(10), 4054–4062 (2015) 8. Fecko, M.: A generalization of vortex lines. J. Geom. Phys. 124, 64–73 (2018) 9. Xu, H.: Extraction and visualization for complex flow field features. National University of Defense Technology (2011)

Optimized Data Organization of Land Cover Survey Based on Redis Memory Database Jia Liu and Min Ji(&) School of Surveying Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, Shan Dong, China [email protected], [email protected]

Abstract. Land cover change survey plays an important role in the sustainable development of the national economy. In view of the increasing amount of land change survey data, the service response time increases. At the same time, the size of data cannot satisfy scale of distributed GIS. According to the characteristics of the land change survey data, this paper studies the change survey data storage strategy based on Redis memory database, and puts forward a traditional server framework, use Redis as a buffer layer of the back-end service framework, and validated it. The results show that the data organization strategy based on Redis significantly improve the response speed of the back-end service, and has better ability to deal with concurrency, the use of a certain significance in the land change survey. Compared with several memory substitution strategies, LRU (Least Recently Used) can make the cache layer have higher cache hit ratio. Keywords: Redis


Land cover change
Response time
Cache hit rate

1 Introduction After the accumulation of experience in recent years, the land change investigation system has been substantially improved [1–4]. However, the emergence of new concepts such as Internet+, cloud computing, and big data has stimulated the development of geographical information technology from geographic information systems to geographic information services. The land change investigation tends to be informatized, and the requirements for the survey’s real-time and accuracy are getting higher and higher. In addition, with the development of remote sensing technology, the resolution of satellite sensors has been significantly improved, the data on land change surveys issued has become increasingly sophisticated, and the data volume has also been accelerating. Most of the current land survey data are stored in relational databases [5–7]. These traditional data organization strategies have gradually been unable to meet the needs of land cover change investigations, and there have been limited disk I/O and long service response times for back office services. Due to the characteristics

J. Liu—Male, Master degree candidate, studies in the application of GIS. Foundation support: Shandong province key research and development project (2016GSF117017). © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 51–61, 2019. https://doi.org/10.1007/978-981-13-7025-0_5

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of the change survey data itself, it is difficult to meet the requirements for the construction of distributed clusters for a short period of time. Compared to traditional relational databases, NoSQL databases can be highly efficient when reading and writing large amounts of data. Li and others proposed a spatial data distributed storage strategy based on NoSQL and developed a prototype system [8]. Chen and others combined a NoSQL database to propose a vector grid integrated distributed storage strategy and service access interface design scheme, which improved the query efficiency [9]; domestic scholars have also applied NoSQL to store various types of data and verified the feasibility [10, 11]. Redis is currently a popular No-SQL database of key-value type, or a high-performance in-memory database. Compared with databases stored on disk, in-memory databases have better read/write performance and have a certain degree of certainty. The ability to handle high concurrency and rich data structures. Currently, many large projects of companies or companies use Redis in-memory databases as their cache data layers, and cache data in Redis memory databases. The high performance of memory data can greatly improve the response time of background services and effectively cope with high concurrency scenarios [12, 13]. Zhang proposed a vector data organization structure based on Redis and verified the feasibility [14]; Zhu and others constructed a grid index in the Redis database and compared it with the Oracle Spatial database to verify the efficiency of Redis to store vector data [15]. There are many other scholars who have done research on other types of data and verified it [16–18]. The vector data is stored in the memory data, which greatly improves the speed of data reading and writing. To some extent, it solves the problem of high concurrency. How to deal with the storage and management of land change data, reduce service response time, and improve the efficiency of land change investigations are issues that need to be resolved. This paper analyzes the characteristics and status quo of land change survey data, proposes an organizational strategy for land change survey data based on Redis, and validates it in a prototype system.

2 Land Change Survey Data Characteristics and Storage Status According to the requirements of the National Geographic National Conditions Survey, related departments need to conduct surveys and statistics on the areas where changes have occurred. Currently, the current satellite imagery is mainly used to compare with the previous period to extract the suspected changeable lands, which are then distributed as vector files. To relevant departments in various regions. In addition, there are checkpoints issued by the provincial departments. Figure 1 shows the flow of the spot between the levels. Its characteristics are as follows: (1) Small file characteristics: It is stored in the form of maps, each block occupies less memory, and has more fields and a larger number. As the land change investigation becomes more comprehensive, the data of land change surveys show a trend of accelerated growth;

Optimized Data Organization of Land Cover Survey

SERVICE

data schedule

transacƟon

CACHE LAYER

Cache strategy

cache replacement strategy

DATA LAYER

annual survey

quarterly survey

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log management

MMDB

VerificaƟon

RDB

Fig. 1. Diagram of the system of land change data storage

(2) Multi-level nature: The work of investigation requires multiple levels of coordination, and is usually issued at the national, provincial, and municipal levels. The subordinate departments will survey within its administrative area. The data will submit to the audit level by level. (3) Multi-source: The source of map spot data is diverse and comes from different types of change investigations. In addition to the annual change survey, there are also some provincial and municipal organizations. The quarterly change surveys, etc., therefore, there are problems such as differences in the projection coordinate system and the difference in the description form. At present, most of the applications for land change surveys use a three-tier structure of data tier, service tier, and application tier. The data layer is often stored using a relational database. Such a storage strategy can solve data management difficulties and the logical complexity of land surveys. With the progress of change investigation, the requirements for background services are also getting higher and higher. The resulting limitations mainly include: (1) The background service has a long response time. With the gradual accumulation of data, the query efficiency of the relational database is reduced, resulting in an increase in the response time of the service layer. (2) The ability to handle concurrency is limited. Although the use of relational databases in the data layer has the ability to deal with concurrency, but with the cloud services applied to the field of land change investigation, the increase in the number of concurrent, still can not meet the needs of users.

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This paper combines the advantages of traditional relational database and NOSQL in-memory database, and designs a hybrid data storage strategy and prototype system, which can not only satisfy the complicated land change investigation work, but also improve the efficiency of back-end service.

3 Land Change Survey Data Storage Strategy The data storage strategy proposed in this paper is for the storage and management of land change survey data, including the annual change survey data issued by the state, and the provinces and cities to develop map inspection data and regional autonomous quarterly change survey data. A storage structure based on traditional relational database and in-memory database is designed, a data cache layer is added on the data layer, and some hotspot data is cached to a cache layer, which is based on an in-memory database (Redis). The application framework based on this storage strategy is shown in Fig. 1: (1) Relational database storage strategy: The land change investigation work is multilevel and multi-source, and it will flow between multiple levels. It has more complicated logic and it is more reasonable to select relational database as the persistence layer. At present, the country issues surveys of land change investigations to provinces and cities, and the issued data are complex planar elements. Surface features mainly include spatial attributes and other attributes. The spatial attributes of map spots are complex planar features, including island polygons and holed polygons. Other attributes include the coding of map spots, in-situ classes, area and other basic attributes, as well as survey feedback information. The other attributes of the map spot are stored in the main table, and the spatial attributes are stored in the boundary point table. At the same time, in order to facilitate the development of business work, it is also necessary to design a land change investigation task information table. (2) In-memory database storage structure: Although the in-memory database has a high read/write speed, due to memory limitations, the system stores some hotspot data in an in-memory database, which is used as a data caching layer. Nowadays, the memory of mainstream computers has reached more than 4G. Combined with the small file nature of the land change survey data, a large amount of map data is stored in the in-memory database, which greatly improves the efficiency of the overall framework. Compared with other in-memory databases, Redis supports operations on data types such as strings, hashes, and linked lists. This feature facilitates the storage of land-change data. Since the Redis database itself is a huge hash table, try to use hash to store data when storing data. To correspond to a relational database, use “:” as a separator to split the table name and field. For example, a certain type of spot is stored in the memory data, and Jctb:ID is stored as a key and value in a hash structure. The field includes a spot number, a task number, and a space attribute (Table 1).

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Table 1. Design of data structure in Redis Key

Hash Field Jctb:ID Geometry Code xzqdm tblx jcmj

Value ([], [], []..) 001 3701 1A 0.5

4 Key Technology (1) The Design of land cover data cache database: Most of the spatial databases are divided into different levels to store spatial data. The land change survey data is highly operational. This paper also hierarchically stores the land change survey data, which are the task layer and the map information layer. Because there are many business information items in the land change survey data, dividing the map into spatial information and business information is conducive to the management of map data. In a relational database, logical models can be mapped into different tables. There is no concept of tables in Redis. These models need to be mapped to a series of data structures. The spatial information and business information of maps can be merged. Redis management. The Redis-based land change survey database is structured as follows (Fig. 2).

Key

Value

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Set of Task：A

Task

Key

Value

Key

TbList

Set of Tb：A

TbID

Field Name Type Descript Date

Value ... ... ... ...

Field Code Geometry ... Remark

Value ... ... ... ...

Fig. 2. Structure of land change database in Redis

(2) Data Scheduling: In this framework, all land change data is stored in a relational database, the data layer. Store some data with high access frequency in the inmemory database, the cached data layer. When the application terminal sends a request, the request enters the web service layer, and the service layer forwards the request from the application layer, preferentially inquires whether the data exists

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in the cache layer, if there is direct return data, otherwise it enters the relational database for query, at the same time, the data cache enters the cache layer, and the next time the map data is queried, the cache layer will directly return the data. As shown in Fig. 3.

Request 1 Response 1 Request 2

Response 2 Request 2

Redis Cache

If Redis does not exist this data cache

Write Cache

RDB Fig. 3. Structure of cache process

(3) Data consistency policy in the in-memory database: In the process of updating data, whether it is to write the library first, and then delete the cache; or delete the cache first, and then write the database, there may be data inconsistency. Because writing and reading are concurrent, there is no way to guarantee the order. If you delete the cache, you haven’t had time to write the library yet. Another thread reads it and finds that the cache is empty, then reads the data into the database and writes to the cache. Dirty data in the cache. If you first write the library, and then delete the cache before writing the thread of the library down, without deleting the cache, there will be data inconsistencies. In order to completely solve the problem of data inconsistency, the data is specifically synchronized by publishing a service in the project. The service is actually a service that clears the cached data and is used to clear the data in the Redis database corresponding to the piece of data. When the management background updates the content information and invokes the service to clear the cached content, when a user requests the data again, it will re-take cache logic and write it to the Redis database again.

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(4) Cache replacement algorithm: The land change survey data is stored in the form of map spots. The data amount of each record is not large. When the memory is sufficient, these data can exist in large amounts in the memory database, greatly improving the entire System performance. However, the physical memory has limited storage space. When the cached data reaches a preset memory limit, the cache needs to be replaced. At present, the widely used memory replacement strategies include LFU (Least Frequently Used), LRU (Least Recently Used), FIFO (First In First Out), Random (random) Wait. According to the use scenario of the land change data, the FIFO strategy obviously is not suitable, and the probability that each survey data is accessed in the course of use is not equal to the probability, so it is not consistent to use all the randomness directly. The LRU essentially replaces the unused data for the longest period of time. It only considers the last access time and does not consider the access frequency. The LFU prioritizes the least recently used data. Because it does not take into account the weights of recent historical information, some of the older data is still stored in the cache. This article compares the cache hit ratios using LFU, LRU, and Random respectively.

5 Test Results In the eclipse environment, this paper uses JAVA to develop the land change investigation backend service framework based on Redis. The service layer adopts the currently popular SSM framework (Spring, SpringMVC, MyBatis), the data layer adopts MYSQL storage, and the pure SSM framework. Traditional relational database storage for comparison. Both the database server and the cache server are Intel(R) Core (TM) [email protected] GHz. The hard disk is 1T storage, the memory is 8G, the operating system is windows 10 64 bits, the Redis database version is 3.2, and the MySQL database version is 5.7. The experimental data set is the land change survey data of Qingdao City, including 16681 map spots with a data size of 47.3M. These data include spatial information of map spots, basic attribute information, and change investigation information. First, query tasks with different number of spots, compare the cold query response time of the cache miss, the response time of the hot query service with cache hits, and the survey task with the same number of spots, under different data totals. Hot and cold query service response time. Then, simulate the quarterly land change survey and the annual change survey process, that is, from field surveys of map spots to step-by-level audits, and finally generate reports. Compare the cache hit rate under different cache strategies under this process. a. Comparison of response time between hot and cold queries and traditional frameworks According to the actual situation in the land change survey, we constructed different query targets, designed five different query test cases, and corresponded to five tasks with different plots, and the map data volume were 10, 100, 300, 500, 1000.

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Each test case is executed 10 times and its average value is taken. The comparison between hot and cold query and traditional background service response time is shown in Fig. 4 below.

Fig. 4. Query time comparison in different query count

From the Fig. 5 above, the background query time of the cold frame based on Redis is not much different from that of the traditional frame. However, the response time of the hot query is about 5–10 times shorter than the traditional SSM frame, mainly due to the traditional SSM. Frames and cold queries are data queries to the disk, hot queries directly to the Redis in-memory database queries in the cache layer, performance is far greater than disk IO. From Fig. 7, it can be seen that with the increase of the total amount of data, the query response time of the cold query and the traditional SSM increase rapidly, while the query time of the hot query does not increase significantly, reflecting the superior performance of the Redis in-memory database as a cache layer. b. Comparison of Cache Hit Ratios for Different Replacement Strategies Adjust the cache ratio by changing the cache size of Redis and simulate a land change investigation. Use the “info” command to get the number of cache hits and cache misses, and calculate the cache hit rate. The experimental results are shown in Fig. 6 below. As a result of the experiment, the LRU replacement strategy has a higher cache hit rate under three different strategies, which is more suitable for land change investigation scenarios. c. Comparison of Concurrent Tests The concurrency test compares the performance difference between the traditional backend framework and the Redis cache framework in the face of concurrent query data. Concurrency tests use the LoadRunner tool for testing.

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Fig. 5. Query time comparison in total count

Fig. 6. Cache hit rate comparison

Since the cold query does not go through the Redis cache layer and directly enters the SSM framework query, the test only needs to compare the query response time of hot and cold queries. Figure 7 compares the query time of cold data query and hot data query at different concurrent amounts. It can be seen that the cold query increases with the increase of the concurrent amount. The query time has been greatly increased, and the average query length has been longer. In contrast, there has been no large fluctuation in the hot query. The query time has been within 1 s. Once again, the efficiency of the in-memory database is proved.

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Fig. 7. Query time comparison in diff concurrent volume

6 Conclusion In this paper, we propose a Redis-based land change survey data organization model, designed and implemented a background framework based on Redis and traditional relational database, compared the query response time between the framework and the traditional SSM framework, and compared the different replacements. Cache hit rate under strategy. Experiments have shown that storing the land change survey data in the Redis cache framework can effectively increase the response time of query data, improve the background service quality and the efficiency of land change investigation. Compared with the traditional three cache replacement strategies, LRU has better performance. The effect is the highest cache hit rate. In addition, the ability of hot queries to cope with concurrency far exceeds cold queries. The efficiency of the cold query is not much different from the efficiency of the traditional framework query, so a large amount of land change survey data is stored in Redis, which can greatly improve the overall performance of the service. However, because Redis acts as a caching layer, its stored data is limited. As the data volume increases, the data volume of the real data storage layer increases, the proportion of the cache is small, and there is also a long response time or the query crashes. In the future, with the gradual improvement of the quality of the land change survey, the data will increase to a certain extent. When using Redis as a cache layer, a distributed structure may be introduced as a data storage layer to enhance the ability to process large data. Acknowledgments. This work was supported in part by a grant from the National Science Foundation of China (41471330).

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References 1. Gui, D.Z., Zhang, Y., et al.: Reunderstanding the connotation of normalization geographical conditions monitoring. Bull. Surveying Mapp. 2, 133–137 (2017) 2. Li, D.R., et al.: Reflections on issues in National Geographical Conditions monitoring. Geomatics Inf. Sci. Wuhan Univ. 41(2), 143–147 (2016) 3. Chen, J.Y., et al.: Reflections on the National Geographic Conditions census. Geospatial Inf. 2, 1–3 (2014) 4. Zhou, X., Ruan, Y.Z., Gui, D.Z., et al.: Research on long-term mechanism of National Geographic Condition monitoring. Sci. Surveying Mapp. 39(4), 46–49 (2014) 5. Zhou, Y., Qian, P., Xie, G.S., et al.: Design and implementation of the provincial inspection system of land change survey in Guangdong Province. Bull. Surveying Mapp. 2, 124–128 (2017) 6. Duan, H.R., Wang, L.Y., et al.: On key technology of satellite image based on land change survey. J. Southwest China Normal Univ. (Nat. Sci. Ed.) 7, 165–169 (2015) 7. Huang, R., Zhang, X., Chang, F.Q., et al.: Study on physical storage strategy of land and resources data of province: a case study of Shaanxi Province. Geogr. Geo-information Sci. 28(3), 36–39 (2012) 8. Li, S.J., Yang, H.J., et al.: Geo-spatial big data storage based on NoSQL database. Geomatics Inf. Sci. Wuhan Univ. 42(2), 163–169 (2017) 9. Chen, Z.C., Yang, J.F., et al.: Massive geo-spatial data cloud storage and services based on NoSQL database technique. Geo-Information Sci. 15(2), 166–174 (2013) 10. Hu, Y.L., et al.: Application of NoSQL spatial data management in provincial water conservancy data sharing service platform. Bull. Surveying Mapp. 12, 88–92 (2015) 11. Wang, X.R., Yang, Q.G., et al.: Storage model design and implementation of high resolution and hyperspectral remote sensing image based on NoSQL. Earth Sci. 8, 1420–1426 (2015) 12. Pan, S., Xiong, L., Xu, Z., et al.: A dynamic replication management strategy in distributed GIS. Comput. Geosci. 112, 1–8 (2018) 13. Li, M., Zhang, H.J., Wu, Y.J., et al.: MemSC: a scan-resistant and compact cache replacement framework for memory-based key-value cache systems. J. Comput. Sci. Technol. 32(1), 55–67 (2017) 14. Zhang, J.Y.: Vector data organization research based on Redis. Nanjing Normal University (2013) 15. Zhu, J., Hu, B., et al.: Research of lightweight vector geographic data management based on main memory database Redis. Geo-Information Sci. 16(2), 165–172 (2014) 16. Wang, J.P.: Design and implementation of structured data cache system based on Redis. Huazhong University of Science and Technology (2016) 17. Min, M.Q., Wang, Z.H., et al.: Large-scale trajectory data storage model based on Redis. Microcomput. Appl. 33(4), 9–11 (2017) 18. Jiao, J., Li, Y.: SVG spatial visualization database based on Redis. J. Chin. Mini-Micro Comput. Syst. 36(6), 1193–1198 (2015)

A Dynamic Switching Technique for Virtual Network in SDN Environment Haifeng Fang, Yachan Zhao(&), Rong Tan, and Tao Wang School of Information and Electrical Engineering, Hebei University of Engineering, Handan, China [email protected]

Abstract. Nowadays, the emergence of software defined network has realized the further transformation of computer virtual network model, and realized the function of separating control level from data forwarding. The OpenFlow protocol enables communication between the controller and the switch. In our study, two virtual machines carry out data transmission through two routes, and select whether to use optical or electrical switches through the size of traffic. Through tests of Ping and Netperf, we can determine that the data transmission will not be affected when switching, proving the feasibility of the platform. Keywords: SDN


Virtual network
OpenFlow
Ping
Netperf

1 Introduction Software defined network (SDN) gets a new architecture of computer network model, which realizes the virtualization of network structure. The purpose of software defined network is to separate control from forwarding function. The controller is mainly responsible for decision-making, while the switch is only liable for forwarding. SDN architecture is divided into three parts: network application, OpenFlow controller and OpenFlow switch. The controller uses RYU technology. RYU is a component-based SDN framework that supports OpenFlow and is deployed in Python. Switches use the Open vSwitch with mobility, the dynamic response, maintaining logic tags and hardware integration advantages [1], therefore has been transplanted into multiple chipset of virtualization platform and switches, the default for many platforms switches. Network applications and controllers communicate through the north interface, and controllers and switches communicate through the OpenFlow protocol. In this paper, the virtual network switching technology in SDN environment is introduced. SDN adopted separating the data plane and control plane, the architecture of the controller is responsible for issued an order to relay messages between switches [12]. In this system, we designed two paths, one for the optical switch and the other for the electric switch. We distinguished large and small flows depending upon the set flow threshold value. When the flow exceeds the threshold, we select the optical switch, and when the threshold is lower than that, we select the electric switch. Our system realizes the function of the path of the virtual network switches, light switches and electrical switches, each has advantages and disadvantages, implementation © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 62–70, 2019. https://doi.org/10.1007/978-981-13-7025-0_6

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of switching system greatly reduces the energy consumption, improve the utilization rate of resources and the feasibility is high. This paper consists of five parts. The first part mainly introduces SDN, OpenFlow and Open vSwitch. The second part describes in the design and components of the system. The third part is the main part of the article, which describes the process of system implementation and switching. In the fourth part, we concentrated on the platform’s two tests, Ping and Netperf. The last part is the conclusion.

2 Background 2.1

Technology of SDN

The software definition network is called SDN for short which was first proposed by Stanford university in the United States and it is a new network model. The emergence of SDN is to simplify the network management and control [2], will control forward and implement separation and control of the controller. The controller is responsible for the instructions. Switches only responsible for packet forwarding. This trims the network smarter and more flexible. Hardware responsible for forwarding in SDN includes flow tables and transport layer protocols that contain flow records. As showed in Fig. 1, SDN is divided into three layers, namely the application layer, control layer and network infrastructure layer. The application layer interacts with the control layer from the north interface, while the communication between the control layer and the network infrastructure layer relies on the south interface. So far, the north

Fig. 1. SDN architecture

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interface has not been clearly defined, and the south interface USES the OpenFlow protocol for communication. SDN web applications running in the application layer, application layer told the controller itself by north to interface to the demand of the network resources and the need for network behavior, through the north to the interface at the same time we can be read from the controller of the current state and various statistical data of the network. Control layer is the most critical part of SDN architecture, the controller will run on control layer, which through the south to interface to the network infrastructure to send instructions, to achieve the purpose of control and through the north to interface to control network behavior and strategy. The switch runs on the network infrastructure layer, where data exchange and processing can be taken. 2.2

Technology of RYU

RYU is a component-based software defined network framework developed and implemented in python by NTT of Japan. Ryu provides a set of software components and an efficient API that makes it easier and faster for web developers to develop network management control applications. Ryu supports various southward interfaces to control network devices such as OpenFlow, Netconf, OF-config, etc. The RYU framework mainly consists of three layers. The first layer is SDN Apps. This layer is divided into three types. The first type is operator, who controls and manages the SDN framework through the RESTful management API. The second type is OpenStack cloud orchestration. The REST API for Quantum is used to manage and control the network. The third type is User Apps. The second layer is controlled and managed through user-defined API via REST or RPC. The second layer is RYU SDN framework, and this layer is the framework layer of RYU, which is used to realize the functions of SDN controller such as flow table sending and topology discovery. The third layer is OpenFlow switch, which contains virtual switches and supports the OpenFlow protocol. 2.3

Virtual Machine Switching Technology

2.3.1 OpenFlow The OpenFlow protocol is used to communicate between the controller and the switch, and the messages are delivered through the secure channel, which is the interface to connect the switch to the controller. When the switch is turned on, a transport layer security connection is initiated to the controller, whose TCP default port is 6633. After receiving the packet, as shown in Fig. 2, switches to parse of baotou, then carries on the look-up table, starting from the flow chart of the first record, in turn, query, when found that match the flow of records, switch to perform the corresponding action, when there is no matching flow record, send packets through the security channel packet - in a message to a controller, the controller to send packet - to switch out a message to feedback the corresponding action [3].

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Fig. 2. Packet processing flows in the OpenFlow switch

The OpenFlow protocol defines three message types: controller to switch messages, symmetric messages, and asynchronous messages. Controller to switch messages are initiated by the controller to manage the switch or view its state. The switch can respond to this message or not. The sub-types of such messages are: Features message, Configuration message, modify-state message, readstate message, send-packet message, and Barrier message. Symmetric messages can be sent either by the controller or by the switch. The subtypes of such messages are: Hello message, echo message, Vender message. Asynchronous messages are submitted by the switch on its own initiative to tell the controller whether the packet has arrived. The state of the switch has changed, or an error has occurred. Such messages are primarily divided into the Packet-in message, flow-removal message, port-status message and error message. 2.3.2 Open vSwitch Open vSwitch [10], or OVS for short, is a multi-layer virtual switch with product quality authorized under the Open source Apache 2.0 license. The switch was developed by Nicira Networks, Supports standard network interfaces and protocols (such as NetFlow, sFlow, SPAN, RSPAN, CLI, LACP, 802.1ag), it also supports large-scale network automation through programming extensions. Open vSwitch is used in a virtual environment. Support Xen/XenServer, KVM, Proxmox VE, VirtualBox and other virtualization technologies. Open vSwitch is further integrated into the virtualization management system. Examples: OpenStack, OpenQRM, oVirt, etc. Open vSwitch consists of three parts, the first part is ovsdb-sever, which is the database server of OVS, it is mainly used to store switch configuration information.

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The second part of the ovs-vswitchd is the core of ovs, communicating with the controller through the OpenFlow protocol, communication with ovsdb-sever through ovsdb protocol, communication with ovs kernel module is carried out through netlink. The third part is the ovs kernel module, the kernel module of OVS, in this part, the function of table lookup and forwarding is realized.

3 Related Work Multi-path transmission mechanism (OFMT) [5] calculates the path by convection and divides the traffic into multiple paths. At the same time, the balanced load is achieved through periodic polling and dynamic scheduling, which improve the network throughput and reduces the transmission time. The paper [6] is a technical paper. Previous studies have focused on network performance such as latency, throughput, and packet loss. This paper mainly introduces the collection characteristics of the flow statistics of OpenFlow protocol in the SDN environment. By capturing and analyzing the request information and reply messages collected by the controller, it calculates the parameters of other traffic engineering. Opennetmon [7] is a network monitoring based on SDN, which is a method to monitor traffic indicators such as throughput, delay and packet loss in OpenFlow network. OpenFlow provides TE interface and the method proposed in this paper provides monitoring and supports fine-grained flow engineering. OpenTM [8] is a matrix system used to measure network traffic. This system selects the switch to obtain the traffic through the routing information obtained. By comparison, OpenTM is found to be more accurate than the OpenFlow protocol’s functions for matching and querying the number of switches and bytes. However, this system has high requirements on switches, so it is necessary to select appropriate switches.

4 System Design In this system, we need to realize is two virtual machine when sending a message to choose according to the volume of traffic switch, when traffic hours, in order to save costs and time, we choose our electrical switches, when large flow, need to switch to a light switch. In Fig. 3, virtual machine 1 sends data to virtual machine 2. We can see that there are two paths. The first is the path from switch 1 to optical switch. Optical communication speed is relatively fast, but the cost is high. When the data flow is greater than the threshold set by the controller, we use the optical switch to transmit data, which is called elephant flow. When the traffic flow is less than the threshold set by the controller, we choose the second path for data transmission, that is, virtual machine 1 transmits data to virtual machine 2 through an electrical switch. This data transfer is called mouse flow [9]. Selecting a path based on traffic can reduce energy and time waste.

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Fig. 3. Switching topology diagram of SDN switch

5 Implementation 5.1

Process of the System Operation

Before the system runs, we need to prepare the environment. Before starting the SDN controller, first of all, the node virtual machine should not be started to prevent network traffic. Second, the initial value of the virtual switch’s flow table should be zero. Finally, the physical network topology is determined. After starting the controller, we configure the information according to the physical network topology. Start the monitor thread again, check the status information of the port, and record the information in the database. The program realizes the configuration of VXLAN path-related flow table. According to the configuration of the flow table, the flow table item is configured on the virtual switch related to the specified communication path. The communication path specified in the activation before the need to check whether there is in the loop of port, the disabled, to specify the inlet side of the communication path CF flow virtual switch configuration, easy to catch exceptions. When two virtual machines are started, we perform two tests, ping and Netperf (more on that in the next section). After the test, we analyzed the derivative data in the database. 5.2

System Switching Process

We configure the initial environment so that the virtual machine is not started and the flow table is empty, and then let the SDN controller start. Then, the SDN controller

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starts traffic monitoring to obtain port status information, update path information and ARP information. Start after the virtual machine SDN controller start switch monitor, monitor traffic according to a threshold value to judge whether to start switching, if the startup switch, you need to find paths, such as: closed path 1–2, start the path 3–4. Finally, the output path packet loss information completes the path switch. When the path switches from 1–2 to 3–4, turning off path 1–2 requires sending cflow along the data channel in the path, then deleting the original flow, vxlan flow table and ARP information in the middle of the path, and finally disabling port in the data path at the endpoint. When switching to path 3–4, we need to configure vxlan flow table and ARP information in the middle of the path, send flow to the data channel in the middle of the path, and delete c-flow of the data channel in the middle of the path, and the interface of the data channel at the endpoint is enabled. Finally, we test whether the KVM is available in the path 3–4. The same goes for switching from the path 3–4 to the path 1–2.

Fig. 4. System switching flow chart

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6 Evaluation 6.1

Ready Test Environment

Before testing this system, we need to prepare the test environment and backup node-1kvm and node-2-kvm images. The virtual machine is then prepared for snapshot, running the RYU controller, and restoring the Open vSwitch in node-1 and node-2 to the initial state. Finally, the pre-test environment is described: The switcher is installed in the controller and the db database is ready. The network topology of node-1 and node-2 has been configured and all ports have been started. All data path in node-1/2 have no flow tables. KVM in node-1 and 2 does not start Netperf and Ping. 6.2

Start Testing the New KVM

Start node-1 and node-2 to kill the currently running KVM and enable the new KVM. Kvm-1 and kvm-2 are respectively started by node-1 and node-2, and KVM-1 and kvm-2 need to be restarted during each test. Start the controller, upload the RYU, and start the RYU. Go to kvm-1 and kvm-2, respectively, and run two network test tools: Ping and Netperf. Finally, start the test program and initiate the task. After the test, the data are differentiated by MySQL workbench. 6.3

Ping

Ping is a communication protocol that tests whether a network is connected between two clients. By default, the Ping test has a default package of 64 bytes. The Ping test format is: Ping + space + IP address. The first is the Request Timed Out. The reason for the timeout is that the IP address may be incorrect or the gateway may be set incorrectly. The second for the Destination Host Unreachable, namely, unable to access the target virtual machine error factor is the network equipment. IP addresses need to be configured during Ping testing, and firewalls of kvm-1 and kvm-2 need to be turned off. When performing the ping test, we adjust the parameters as needed. With the ping test, we can analyze the loss rate and time delay. 6.4

Netperf

Netperf is a tool that tests network performance primarily for TCP/UDP protocol [4] transfers. The test results from Netperf show us that one system is sending data like another. How fast can the first system transmit data, and how fast can the other system receive data [11]. Netperf works in five modes: TCP_STREAM, TCP_RR, TCP_CRR, UDP_STREAM, and UDP_RR. The firewall needs to be closed during the Netperf test. Enter ‘netserver’ to start the server in kvm-1 (2), and conduct Netperf test in kvm-2 (1). Through the test results, we can analyze the traffic fluctuations.

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7 Conclusion This paper introduces the dynamic switching technology of virtual network under SDN environment. The system is divided into two paths. The first path is the optical switch and the second is the electrical switch. After the controller starts up, the flow is monitored and the current path information is obtained. The SDN monitor determines whether to start the switch according to the flow threshold. The appearance of this system makes the virtual machine greatly reduce the energy consumption when transmitting data. This system implements the only path to the switch, the next step we plan optimization system, through test to determine the threshold. The virtual machine transmission of data in the switch point is less packet loss rate and maximum throughput. Acknowledgments. The work was supported by the education department of Hebei province (NO. QN2016142, YQ2014014) and the natural science foundation of Hebei province (NO. F2015402119). Thanks to my mentor and team, as well as those who have contributed to this design.

References 1. Azodolmolky, S.: Software Definition Network: Disclosure of OpenFlow Based SDN Technology. Machinery Industry Press, Beijing (2014) 2. Nadeau, T.D., Gery, K.: Software definition network: SDN and OpenFlow analysis, no. 11– 18, 84. People’s Post and Telecommunications Press, Beijing (2014). Translated by Bi Jun, Shan Ye, Zhang Shaoyu, Yao Guang, et al. 3. Tong, H., Shu, G.Y., Tian, C.Y.: Illustration OpenFlow, pp. 5–10. People’s Post and Telecommunications Press, Beijing (2016). Translated by Li Zhanjun, Xue Wenling 4. Wang, L., Qian, L.: Classification method and practice of network traffic, pp. 4–7. People’s Post and Telecommunications Press, Beijing (2013) 5. Chen, M., Hui, H., Liu, B., et al.: A multipath transmission mechanism based on OpenFlow. J. Electron. Inf. Technol. 38(5), 1242–1248 (2016) 6. Hamad, D.J., Yalda, K.G., Okumus, I.T.: Getting traffic statistics from network devices in an SDN environment using OpenFlow. ITaS 951–956 (2015) 7. Van Adrichem, N.L.M., Doerr, C., Kuipers, F.A.: OpenNetMon: network monitoring in OpenFlow software-defined networks. In: 2014 IEEE Network Operations and Management Symposium (NOMS), pp. 1–8. IEEE (2014) 8. Tootoonchian, A., Ghobadi, M., Ganjali, Y.: OpenTM: traffic matrix estimator for OpenFlow networks. In: Krishnamurthy, A., Plattner, B. (eds.) PAM 2010. LNCS, vol. 6032, pp. 201– 210. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12334-4_21 9. Zuo, Q., Chen, M., Wang, X., Liu, B.: An online traffic anomaly detection method based on SDN. J. Xi’an Univ. Electron. Sci. Technol. 42(01), 155–160 (2015) 10. Song, P., Liu, Y., Liu, C., Zhang, J., Qian, D., Hao, Q.: A virtualized programming framework of SDN that supports fine-grained parallelism. Softw. J. 25(10), 2220–2234 (2014) 11. Afek, Y., Bremler-Barr, A., Feibish, S.L., Schiff, L.: Detecting heavy flows in the SDN match and action model. Comput. Netw. 136, 1–12 (2018) 12. Bakhshi, T.: State of the art and recent research advances in software defined networking. Wirel. Commun. Mob. Comput. 2017, 1–35 (2017)

Multi-mode Control Strategy for Dual Active Bridge Bidirectional DC-DC Converters Yaguang Zhang(&) and Yong Du School of Information and Electric Engineering, Hebei University of Engineering, Handan Hebei 056038, China [email protected]

Abstract. Dual Active Bridge (DAB) bi-directional DC-DC converters can transmit power in both directions, realize zero voltage switching (ZVS), and have high power density, which can be well applied to power electronic transformers. Three common control methods for DAB converters are described in detail in this paper. They are phase-shift control, single PWM, and dual PWM control. Aiming at the shortcomings of large loop current and limited range of zero-voltage switching when the converter has low-load operation, a multi-mode control is proposed, which broadens the range of zero-voltage switching, reduces the effective value and peak value of the current, and makes converter efficiency improve. Finally, MATLAB software was used to simulate the feasibility of the program. Keywords: DAB converter
Bidirectional transmission Zero-voltage switching
Multi-mode control




1 Introduction The dual active bridge (DAB) is particularly suitable for high-power isolated DC-DC converters. Its advantages are high power density, zero voltage switching (ZVS), bidirectional transmission power, symmetrical structure, and control simple. Dualactive bridge converters are widely used in electric vehicles, aerospace technologies, and renewable energy power generation. However, in wide voltage range applications, the DAB converter has a limited range of zero voltage switching and a high loop current at low loads. In order to solve this problem. Demetriades G D reduces the loop current by improving the traditional phase-shifting control method, so that the transmission efficiency is slightly improved [1]. Krismer et al. considered the importance of application in low-voltage, highcurrent applications. Zero-current switching (ZCS) was used for both H-bridges of the converter [2]. The literature [3, 4] proposes to use the pulse width modulation strategy (PWM) to expand the zero-voltage switching range of the DAB converter, but only use the PWM on the front or rear axle. A further study was made in [5]. PWM control was used for both H-bridges, but the effect of PWM on current rms and peak values was not considered. Literature [6] proposed an optimized single PWM strategy to reduce losses. The literature [7, 8] uses different small-signal models to define the DAB’s dynamic © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 71–78, 2019. https://doi.org/10.1007/978-981-13-7025-0_7

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performance, because the analysis process is especially complex considering the influence of parasitic parameters. In order to solve the problem of low transmission efficiency and high reactive power loss when the DAB is under low load, this paper proposes a multi-mode control method for DAB converters, which makes full use of the advantages of phase shift control, dual phase shifting and triple phase shifting at low loads. When using a triple phase shift, the load changes to a double phase shift, and after the load increases further, it switches to traditional phase shift control. Zero-voltage switching can also be achieved at no-load under multi-mode control methods, and the loop current is reduced. At the same time, the core loss of the transformer is reduced, so that the low-load efficiency is improved. Verified by MATLAB modeling simulation.

2 DAB Converter Figures 1(a) and (b) are the schematic and working waveforms of the DAB converter, respectively. The converter consists of two H-bridges (HB1 and HB2) and a transformer with leakage inductance L. Since the value of the pair inductance is higher, an inductor can be connected in series externally. HB1 and HB2 work with a duty cycle of 50% and the phase difference between them is a controlled phase angle /. From Fig. 1 we can find the expression of the transmission power:
 V2 juj P0 ¼ ku 1   1 p 2pfs L

ð1Þ

Among them, switching frequency is fs, L is the leakage inductance, the voltage ratio is set K = NV2/V1, N is the ratio of turns of the original secondary side of the transformer, and the reference value of the defined power is Pbase ¼ V12 =XL ; where the standard value of the transmission power is XL = 2pfsL, then the standard value of transmission power is: P0


 ju j ¼ ku 1  p

ð2Þ

The formula (2) shows that the size and direction of the transmission power are determined to /. When / > 0, HB1 ahead of HB2, Power from V1 to transmission V2. At that time / < 0, power is reversed. This method of changing the size and direction of power transmission by changing /, it is called phase shift control. Due to the presence of leakage inductance, the output current of each bridge leg lags behind the output voltage, and the switched capacitor charges and discharges during the dead time, thereby achieving zero voltage switching.

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Fig. 1. DAB converter

3 DAB Converter PWM Control 3.1

Single PWM Control

Based on the phase shift control, the duty cycle of the front axle or the rear axle is controlled again. Such a control method is generally called a single PWM plus phase shift control method. The operating characteristics of DAB bidirectional DC-DC converters in forward and reverse power transmission are similar. When the voltage ratio k  1, adjust the primary voltage duty cycle D1; when k > 1, adjust the secondary voltage duty cycle D2, while the two switch tubes on each bridge are 180° complementary conduction, in both cases, the working principle is similar. Therefore, the following uses the converter’s forward transmission power as an example to

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analyze the operating characteristics of the single-PWM plus phase-shift control and define the duty cycle Du corresponding to the phase-shift angle / between HB1 and HB2. The control variables at this time are: D1 and Du. Different transmission time and different trigger times can be used to derive the corresponding transmission power expressions, where a1 ¼ ð1  D1 Þp.  8  k/ 1  ap1 > > >   < 4/2 þ a2 P0 ¼ k /  4p 1 >   > > : kðp  a1 Þ 1  / p



0/

a 

1

2

a1 2



\/  p  a21



p  a21  /  p



ð3Þ

Similarly, the power expression of the reverse transmission of the converter can be expressed. Equation (3) shows that the positive and negative angle / determine the direction of the transmission power, and the magnitude of the angle / and a1 the sum of the transmission power. When / = p/2, a1 = 0, the maximum transmission power is the same as the phase shift control. When / ∊ [−p/2, p/2], the power is in a monotonously increasing relationship / and the same power is being transmitted, the current RMS and peak values in this range are relatively small. The following begins to analyze the range of zero voltage switching of the converter. When using HB1 with PWM control, advanced bridge arm Q1 and Q2 zero voltage switching requires ia > 0, Lag bridges Q3 and Q4 requires ib > 0, ic > 0. These can be obtained by setting the corresponding vertex current. This gives the optimal value a1 = p(1 − k) of the control variable at the maximum range of the zero voltage switch. In addition to the widening of the zero-voltage switching range, the angle / can also be used to minimize the effective value of the transformer current, thereby reducing the transmission loss of the winding and the switch. Here, in order to achieve a simpler implementation, directly use a1 = p(1 − k) without considering the load. 3.2

Dual PWM Control

On the basis of phase shift control, the duty cycle of two H-bridges is controlled at the same time. This control method is generally called dual-PWM plus phase-shift control. It is necessary to adjust the primary-side voltage duty ratio D1, the secondary-side voltage duty ratio D2, and the phase shift angle / between HB1 and HB2 at the same time. So there are three control variables D1, D2 and /. In the same way as in the previous section, taking the converter’s forward transmission power as an example, the operating characteristics of dual-PWM plus phase-shift control are analyzed. In order to achieve soft switching, the literature [13] proposed that the duty cycle of the primary voltage should be less than the duty cycle of the secondary side, ie D1 < D2. Dual PWM plus phase shift control has three control variables D1, D2 and /, depending on the trigger time of the three variables, it can derive the corresponding transmission power expression, in a1 = p(1 − D1), a1 = p(1 − D2).

Multi-mode Control Strategy

P0 ¼

 i 8 h  /ða1 þ a2 Þ ða1 a2 Þ2 / > k / 1    > 8p 2p 2p > > > h   i > 2 2 > a þ a / >k / 1 1 2 > > p  4p > > > > < kðpa1 Þðpa2 Þ 2p

h2 2 i > > ðp/Þða1 þ a2 Þ ða1 a2 Þ2 p / > > k   > 2p 8p 2p > > > > kðpa1 Þðp/Þ > > > 2p > > : k/ð1  ap1 Þ

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A B C

ð4Þ

D E F

Similarly, the power expression of the reverse transmission of the converter can be introduced. The positive and negative angle / determines the direction of the transmission power. The size of the angle /, a1 and a2 determines the magnitude of the transmission power. When a1 and a2 are fixed, the larger the angle, the greater the transmission power. When / is fixed, the smaller the angle a1 and a2, the greater the transmission power, when / = p/2, a1 = 0, the maximum transmission power. (1) Zero voltage switching range and current RMS Similar to single PWM, when / ∊ [−p/2, p/2], the current rms and peak current are relatively small. Modes C and D are not within this range, so consider the zero voltage switching range below to analyze modes A, B, C, and F only. With dual PWM control, the HB1 leading-edge arm and zero-voltage switching requires ia > 0, lagging arm and requires ib > 0, HB2 leading-edge arm and requires ic > 0, lagging arm and requires id > 0. You can set the range of control variables for all switches to achieve soft switching by setting the corresponding peak current. In mode A, there is no guarantee that all switches can achieve zero-voltage switching, so it is not desirable. In mode B, the conditions for all switches to achieve soft switching are: ð1  kÞp  2/\a1  ka2 \ð1  kÞp þ 2k/

ð5Þ

Obviously, this inequality can only be satisfied / > 0. With a simple choice, let this a1 − ka2 = (1 − k)p be similar to the case of / > 0 a single PWM, but the current rms is greater than the rms current of a single PWM, and mode C has no advantage at this time. From Eq. (4), it can be seen that in mode C, only the angle is changed and no change in power occurs. Therefore, this model is not desirable. In mode F, all switches can achieve zero voltage switching at low loads. In contrast, Mode F has clear advantages. Combining zero voltage switching conditions, by setting the vertex current selection: a1 ¼ p

2k 2 /; a2 ¼ p  / 1k 1k

ð6Þ

This option theoretically guarantees zero-voltage switching while minimizing the rms current.

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(2) Multi-mode solution Dual PWM operation is only feasible for low-load operation and does not have any advantage when the load is increased. Therefore, a multi-mode solution is proposed. When the load is continuously increased, the operation of the transformer is switched from dual PWM in mode E to / > 0 single PWM in time and then to phase shift control. In order to restore the full power capability of the converter, three operating states can be smoothly switched by setting the value. When a1 = p(1 − k), also say / = p(1 − k)/2, It is at the critical point of single PWM and dual PWM conversion. When / = p/2 is reduced to zero, it is naturally switched to PWM-free phase-shift control. The simplest solution is to have a linear change between the two points. The entire multi-mode scheme is given by the following equation: ( a1 ¼

2k / p  1k

0\/\ pð1kÞ 2

ðp  2/Þ 1k k

pð1kÞ \/\ p2 2

( a2 ¼

2 / p  1k

0\/\ pð1kÞ 2

0

pð1kÞ \/\ p2 2

ð7Þ

ð8Þ

4 Simulation Verification and Results This article uses MATLAB software to simulate the circuit. The main parameters of the DAB bidirectional DC-DC converter are as follows: rated power P0 = 3 kW; switching frequency fs = 5 kHz; input voltage V1 = 360 V; output voltage V2 = 216 V; original secondary side turns ratio N=1. Like HB2, HB1 uses an IGBT module for each switch, and an RC snubber circuit with a capacitor and resistor connected in series. In the simulation process, the inductor currents in both PWM-less and PWM-based cases are first compared at low load. At the same power level, it can be seen that the double-PWM at the low load is further reduced compared to the phase shift control and the single H-bridge PWM current peak, and the transformer core loss is reduced, and the double PWM efficiency is improved. Figure 2 shows the current rms and converter efficiency in phase-shifted, single PWM, and dual PWM modes. Figure 2 shows that at low load (100 W) the efficiency increases from 21% without PWM to 50% with single H-bridge PWM and then to 78% with dual PWM. As the rms current and peak value decrease, the efficiency of the converter rises significantly. However, as the load increases, the efficiency of a single PWM gradually exceeds that of a dual PWM, so it is more advantageous. As the load increases further, the RMS current of the single PWM starts to increase sharply. At this time, the phase shift control with a low rms current should be selected.

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Fig. 2. Current RMS and Efficiency in Different Modes

5 Conclusion In this paper, the PWM control DAB converter is deeply analyzed and the advantages of dual PWM, single PWM and PWM-free phase-shift control are integrated. A multimode scheme is proposed. Dual PWM is suitable for operation at low loads, and the advantages of the inverter in processing a wide range of input and output voltages are especially obvious. When the load is increased, the dual PWM has little effect, so a single PWM is used. After the load is further increased, compared with the conventional phase shift control, the single PWM maximum power transmission capability is not high, and the current RMS is higher, so the phase shift control is directly adopted. The multi-mode solution addresses these limitations: 1 using dual PWM at low load; 2 switching to single PWM when the load is increased; 3 further increasing the load, increasing the duty cycle of the PWM output bridge, and dual PWM at the maximum basic phase shift And single PWM naturally switches to phase shift control without PWM. The simulation results show that under low load conditions, the converter has high efficiency under double PWM control. After the load increases, the dual PWM loses its advantage. At this time, the single PWM with higher efficiency is used. After the load is further increased, the efficiency of the single PWM and phase shift control approaches However, the latter’s current RMS is relatively low, so phase-shift control is used, thus demonstrating the feasibility of a multi-mode solution.

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References 1. Demetriades, G.D.: On small-signal analysis and control of the single-and the dual-active bridge topologies. KTH (2005) 2. Krismer, F., Round, S., Kolar, J.W.: Performance optimization of a high current dual active bridge with a wide operating voltage range. In: 37th IEEE Power Electronics Specialists Conference. PESC 2006, pp. 1–7. IEEE (2006) 3. Vangen, K., Melaa, T., Bergsmark, S., et al.: Efficient high-frequency soft-switched power converter with signal processor control. In: 13th International Telecommunications Energy Conference. INTELEC 1991, pp. 631–639. IEEE (1991) 4. Tao, H., Kotsopoulos, A., Duarte, J.L., et al.: Transformer-coupled multiport ZVS bidirectional DC–DC converter with wide input range. IEEE Trans. Power Electron. 23(2), 771–781 (2008) 5. Oggier, G.G., Garcia, G.O., Oliva, A.R.: Switching control strategy to minimize dual active bridge converter losses. IEEE Trans. Power Electron. 24(7), 1826–1838 (2009) 6. Costinett, D., Zane, R., Maksimović, D.: Discrete-time small-signal modeling of a 1 MHz efficiency-optimized dual active bridge converter with varying load. In: 2012 IEEE 13th Workshop on Control and Modeling for Power Electronics (COMPEL), pp. 1–7. IEEE (2012) 7. Qin, H., Kimball, J.W.: Closed-loop control of DC–DC dual-active-bridge converters driving single-phase inverters. IEEE Trans. Power Electron. 29(2), 1006–1017 (2014) 8. Krismer, F., Kolar, J.W.: Accurate power loss model derivation of a high-current dual active bridge converter for an automotive application. IEEE Trans. Industr. Electron. 57(3), 881– 891 (2010) 9. Alonso, A.R., Sebastian, J., Lamar, D.G., et al.: An overall study of a Dual Active Bridge for bidirectional DC/DC conversion. In: 2010 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 1129–1135. IEEE (2010) 10. Xu, H.: Topology and analysis theory of high power bidirectional DC-DC Converter. Graduate School of Chinese Academy of Sciences (Electrical Institute) (2005)

Spatial Distribution and Source Identification of Loess Heavy Metal Pollution in Northern Baoji, China Ling Han, Zhiheng Liu(&), Yuming Ning, and Zhongyang Zhao Ministry of Land and Resources Key Laboratory of Degradation and Unused Land Remediation, School of Geology Engineering and Geomatics, Chang’An University, Xi’an 710054, China [email protected]

Abstract. The spatial distribution of heavy metal pollution in loess is an essential prerequisite and scientific basis for detecting and evaluating the quality of loess ecosystem and the sustainable development of regional environment, but lack of research in special soil such as loess. Distribution characteristics and pollution sources of heavy metals in the northern loess area of Baoji is studied by means of geo-statistics and cluster analysis. The results showed that heavy metal pollution still existed in this area, and the pollution in Changqing town was obviously higher than the surrounding area. Consequently, different management strategies can be adopted in the loess area to reduce the contamination load of heavy metal, and attention should be paid to human activities, such as mining, transportation and gas emission, especially. Keywords: Loess area Cluster analysis
GIS




Heavy metal pollution




Geo-statistics analysis




1 Introduction Soil heavy metal pollution has always been a hot issue affecting soil ecological environment and restricting regional sustainable development. It is also an essential reason for the decline of soil ecological quality. However, human activity in the improvement request of environmental quality also prominent day by day. Therefore, it is imperative to take a reasonable and effective way to quickly grasp the spatial distribution and pollution sources for controlling and precision agriculture. As an important basis for the survival and development of human beings, soil provides nutrients and moisture for crops, and is another important medium for crop roots extension and fixation. It is the carrier of pollutants and the purification field of pollutants. Toxic substances can inhibit soil function, poison plants, and pollute food chain [1]. In recent years, with the rapid development of our economy, the demand for resources, such as the mine and gas, has increased day and day, which increasing threats to ecosystems. The main reason for this phenomenon is mainly due to the slow migration of Cd, Cr, As, Hg, Pb, Cu, Zn and Ni, which have the characteristics of gradual migration, difficult leaching and degrading, irreversible and so on. At the same © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 79–92, 2019. https://doi.org/10.1007/978-981-13-7025-0_8

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time, it has also accelerated the degradation of soil quality and the deterioration of the ecological environment in mining areas, and has a serious inhibitory effect on regional sustainable development. It has been placed as a priority pollutant by the United States Environmental Protection Agency (USEPA), and has attracted more and more attention in many parts of the world [2, 3]. Previous study mostly concentrated on the evaluation and assessment of soil quality, while the spatial distribution of heavy metal pollution in spatial area remains relatively small. The environmental improvement of special soil is always the focus of current research, so it is necessary to further analyze this hot spot [4–6]. There are two kinds of research about this issue. On the one hand, the pollution status of the region is obtained through field observation and multivariate linear fitting in the laboratory, and then predicted the heavy metal content by using Kriging interpolation. On the other hand, the spectral characteristics of loess are mainly analyzed by hyperspectral images, that is, by obtaining a particular spectral band range corresponding to heavy metal elements. The applicability of the two models is limited, because the former mainly depends on the distribution and interval of sampling points, while the latter has significant differences in prediction and verification. The purpose of this study is to investigate the spatial distribution characteristics and identify the source of heavy metals in loess area. By using geo-statistical analysis and cluster analysis, we not only determine the source of pollutants, but also provide sustainable management advice to the environmental protection part of the region.

2 Study Area and Materials 2.1

Study Area

The research area is located in northern Baoji. The geographical coordinates are N34° 25′00′′–N34°50′00′′, and E107°00′00′′–E107°45′00′′. It is about 160 km from Xi’an, and 30 km from Baoji, with a total area of 2400 km2. According to the landform type, the whole area can be divided into: loess hilly area, loess platform area, bedrock low mountain area, loess hilly area and valley area (Fig. 1). As the national key production base of apple, pear and other crops, the quality and heavy metal content of loess in the region play an important role in crop growth and nutrient condition. This area belongs to the warm temperate sub-humid climate and is controlled by the East Asian monsoon. The heavy metal content is easy to be influenced by the gas emission from the development of industry and mining, and the monsoon climate. It is deposited on the loess surface, which results in serious pollution of heavy metals in loess. At the same time, different geomorphological units often produce different types and degrees of land damage or land use change, such as the loose degree of soil, the parent rock of mineralization, the use of chemical fertilizer in agriculture and so on, all of which will affect the content of heavy metal elements in the topsoil loess. Combined with the previous research results, the heavy metal pollution of loess in this area mainly occurred in the loess platform, where industrial activities were concentrated, that is, Qianyang County and Fengxiang County.

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Fig. 1. Study area. The first row is the Landsat 8 OLI images in study area clipped from the regional vector; the second row are the factory and the administrative boundaries in Baoji, China. (Color figure online)

2.2

Data Collection

According to the geomorphological characteristics of the area and the spatial position of the industrial and mining enterprises, the sampling is located in tableland of Fengxiang County and Qianyang County, where leading wind-direction gathering. From August 12 to 17, 2017, a total of 19 sampling sites were collected for the human population in the study area (Fig. 1, the red points). The sampling depth was 0–20 cm, and 3 soil samples of the control group were used for model verification and correction at each sampling point. After air drying and grinding, the contents of 5 heavy metal elements in the samples of Cu, Pb, Zn, Cr and Cd were given in atomic absorption spectrophotometer (AAS) Z-5000, and their characteristics were calculated. The results are as follows (Table 1): From the point of view of test results and background values, the content of heavy metals in loess in this region is not more than the standard of heavy metal pollution, but

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L. Han et al. Table 1. Statistical characteristics of heavy metal content in loess area. Parameters Cu Pb Zn Cr Cd Max 47.800 294.000 526.000 85.000 7.430 Min 23.600 21.800 35.000 60.700 0.140 Mean 31.563 42.889 97.495 69.221 0.646 Standard deviation 6.500 60.977 105.200 5.942 1.648 Variation 0.206 1.422 1.079 0.086 2.550 Skewness 1.723 4.319 4.167 0.999 4.313 Kurtosis 2.521 18.755 17.808 1.470 18.714 Background level 21.400 21.400 69.400 62.500 0.094 Sample number 19 19 19 19 19 National standards  250  80  100  300  0.3 Ps: the background level is the value of heavy metals in Guanzhong loess of Shaanxi province, China, 1990. The national standard is GB15618-2008, second degree.

the minimum value of Cd has obviously exceeded, so it is urgent to study the heavy metal pollution of loess.

3 Method 3.1

Geo-Statistics Analysis

Geo-statistical analysis is a kind of science which is based on regionalized variables and studies natural phenomena with randomness and structure, or spatial correlation and dependence, with the help of variation function. It has a good effect on discretization and volatility of simulation data. Geo-statistical methods usually use semivariance function to represent the regionalized variables and estimate the data with optimal unbiased interpolation. Therefore, geo-statistics method can be used to calculate the characteristic distance with physical significance, and the spatial distribution information of heavy metal pollution in loess can be obtained by using Kriging interpolation method. Not only the prediction results but also the errors can be obtained, which is useful to evaluate the uncertainty of the prediction results. And the first thing is to test the data. Testing the normal distribution of the data is a prerequisite for the spatial analysis of soil properties using the geo-statistical Kriging method, because even if there are no strict requirements, the value of high bias, kurtosis and outliers will affect the structure of the variogram and the Kriging interpolation results [7–9]. After calculating the actual half-square difference r(h) and drawing the curve of variation function by using the normal distribution, and the analysis of soil heavy metal content in different spatial positions, we can carry out Kriging interpolation and obtained the distribution of heavy metals in the region. The semi-variance function r(h) is the basis of geo-statistics to explain the soil spatial variation structure, which can describe the spatial characteristics of the coexistence of regionalization

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variable structure and randomness, and reflect the variation intensity of regionalized variable in the scope of study [8, 9]. The formula is as follows: 1 X ½Zðxi Þ  Zðxi þ hÞ2 2NðhÞ i¼1 NðhÞ

rðhÞ ¼

ð1Þ

Where N(h) is the number of samples with a distance of step h; Z(xi) and Z(xi + h) are the sample value at xi and xi + h. The spatial distribution of geographical parameters can influence the correlation, and the closer the distance is, the stronger the correlation is. This reflects the spatial correlation between the observed values of all pairs of soil heavy metal contents in distance and direction, the shape characteristics of the semi-variogram undulating, the origin, the trend, and different directions provide abundant spatial structure information. After analyzing spatial autocorrelation, using Kriging interpolation can quickly control the content of heavy metals in the loess. Kriging interpolation, also known as spatial local interpolation, is a method of unbiased optimal estimation of regionalized variables in a finite region based on the theory of variogram and structural analysis. It is also one of the principal contents of geo-statistics. It is a highly efficient interpolation method which was first used in gold prospecting by D. R. Krigeer 1951 and was theorized and systematized by G. Matheron. According to the spatial position of the point to be interpolated and near point, the linear unbiased optimal estimation of the height value of the interpolated point is carried out, and the advanced geo-statistical process of the estimated surface is generated by a set of scattered points with z value [10–12]. The formula is as follows: Zðx0 Þ ¼

N X

ki Zðxi Þ

ð2Þ

i¼1

Where Z(x0) is an unknown sample value, Z(xi) is the known value around the unknown sample point, N is the number of sample points, and ki is the weight of the ith sample point, which is obtained by semi-variance graph analysis. 3.2

Source Identification of Heavy Metals

The spatial distribution of heavy metal pollution in soil has obtained the geographical range of heavy metal in this area, but it is difficult to analyze the source of pollution, so the source of pollution is further analyzed. Cluster analysis (CA) is a multivariate statistical method for classification based on distance or similarity of objects [13–15]. The basic idea is to determine the distance between samples, and define similarity coefficients among variables. The distance or similarity coefficient represents the degree of similarity between samples or variables. According to the size of similarity, the samples (or variables) are classified one by one. Until all samples (or variables) have been assembled to form a pedigree that represents kinship. The principle is that the individuals in the same class have greater similarity, and the individual differences among different classes are great.

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There are N sample data with M-dimensional: Xij (i = 1, 2, …, N; j = 1, 2,. M). First, the square of the standard Euclidean distance between samples is calculated: M P

D2ik ¼

ðXij  Xkj Þ2

j¼1

ð3Þ

S2j

Where, S2j is the variance of the jth variable. Considering each sample as a class, and a new class can be merged with the nearest two classes. The distance between the new and original classes is the measure rule to judge the “closest” of the two samples. In other words, the difference between the classification objects can be calculated by the distance between the points in the ndimensional space they correspond to. For example, the shortest distance method, the longest distance method, the intermediate distance method, the barycenter method and the sum of deviation square method are used to help determine the distance between classes. In summary, the technological methods of this study are shown below (Fig. 2). Data collect Measurement of heavy metal content Data pre-statistics

Variance analysis Kriging interpolation

Determine the scope of pollution

Spatial distribution

Cluster analysis

Determine the source of pollution

Resolution

Spatial Distribution and Source Identification of Heavy Metal Pollution in Loess Fig. 2. The flow chart of main method.

4 Results and Discussion 4.1

Spatial Distribution Patterns of Loess Heavy Metals in Northern Baoji

In the experiment, SPSS 24.0 and ArcGIS 10.2 were used for correlation analysis and visual mapping. According to the content of heavy metal, the heavy metal was classified into different grades and presented in different colors to distinguish different heavy metal regions. Finally, the spatial distribution of heavy metals in loess was obtained by GIS.

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(a) Histogram

(b) Normal QQ diagram

(c) Trend analysis

(d) Semi-variogram

Fig. 3. Geo-statistics analysis of loess heavy metals pollution.

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From the histogram of Cr (Fig. 3a), the test data is basically normal distribution. The Geo-statistics tool of ArcGIS is used to display the normal QQ diagram of Cr element (Fig. 3b), the result shows that the content is concentrated near the standard normal value curve. It is considered that the experimental data are approximately from normal distribution. The trend analysis is used to describe the trend of the sample point data, and the sample points in the plane are transformed into a three-dimensional view of the value of the property of interest as the height. It can make the subsequent surface fitting more objective, and fitting results are more reliable. However, due to the small number of samples, the trend analysis results are not really obvious (Fig. 3c), but it can still be seen that the degree of pollution is relatively uniform in the east and west directions of the study area, while the north and south directions show a more obvious curve, which is related to the city plan. And the result show that the polynomial fitting of order 2. Most of the semi-variogram points are concentrated near zero (Fig. 3d), some points are still scattered beyond zero, which will directly affect the root mean square (RMS) and the average standard error of the final model. Table 2 shows that the mean square root standardized prediction error in this example is next to 1, which indicates that the standard error is accurate. The average prediction error is next to 0, which indicates that the prediction is unbiased. Although the difference between the root mean square error and the average standard error is small, and the deviation between the prediction and the measured value is insignificant, it is also found that the value is relatively large. Therefore, in order to further enhance the prediction accuracy, it is necessary to encrypt the sampling points.

Table 2. Prediction errors. Parameters Index Numbers 19 Mean 0.617 Root mean square 6.765 Mean standardized 0.067 Root mean square standardized 1.163 Average standard error 5.828

According to the result of semi-variogram, the optimal linear unbiased estimation is carried out by using Kriging interpolation method, and the spatial distribution of heavy metal content in loess in northern Baoji is obtained, as showed in the following Fig. 4. According to the results of spatial distribution, there is still some heavy metal pollution in the study area, among which the content of Pb, Zn, Cd is most serious, while the content of Cu, Cr is relatively low in the study area. It is inseparable with the degree of waste discharge and the enrichment of polluting elements in loess. The content of Cr in Zhaoxian Town is obviously higher than the surrounding area, which is related to the fraction of vehicle tires on the highway and domestic waste. Based on the spatial distribution and the predicted values, the pollution degree of heavy metal in this

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Fig. 4. Spatial distribution of loess heavy metals pollution.

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Fig. 4. (continued)

area is analyzed preliminarily and get the pollution sequence are Cd, Pb, Zn, Cu, Cr, which is consistent with the results of previous studies on the assessment of heavy metal pollution in Baoji. Although the calculation of the model is simple and the sample distribution is relatively uniform, the number of samples is relatively small, so the root mean square error and the average standard error have a certain deviation from the standard value. It is necessary to further encrypt the sampling points and modify the model to achieve adaptive optimization. At the same time, it is of great significance to examine the distribution characteristics of data and to understand and understand the data before geo-statistical analysis.

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Pollution Source Identification

Before clustering analysis, the possibility of heavy metal elements coming from the same pollution source can be preliminarily grasped by the correlation analysis. Although the correlation coefficient is only a measure of linear correlation between two variables, it does not need to causality. However, to some extent, it can be speculated whether the source of heavy metal is the same. The higher the correlation, the greater the probability. And the less the correlation, the less the probability. Using SPSS 24.0 software, the correlation analysis of five heavy metals (Cu, Pb, Zn, Cr and Cd) in loess covering areas from northern Baoji was carried out. The results are as follows. Table 3. Correlation coefficient of loess heavy metals in northern Baoji. Elements Cu Pb Zn Cr Cd

Cu 1 0.332 0.375 0.362 0.321

Pb

Zn

Cr

Cd

1 0.993 1 0.651 0.679 1 0.998 0.992 0.638 1

According to Table 3, there is a high positive correlation among some elements, such as the absolute value of the correlation coefficient of Pb-Cd, Cn-Pb and Cd-Zn is 0.993–0.998. It is strongly correlated, and the possibility of the same pollution source is very high, and the three elements tend to gather each other. However, the absolute value of correlation coefficient between Cu and other four elements is 0.30–0.40, which belongs to low correlation, and the possibility of coming from the same pollution source is very small. It also indicates that the accumulation degree between Cu and other pollution elements is low. The rest of the elements are moderately correlated. Then the systematic clustering method is used to analyze L1, L4, L9 and L14. The spectrum of cluster analysis tree is shown as follows (Fig. 5). The cluster analysis results of the four points can be further divided into different categories. As shown in Table 4, point L1 is located at 50 m in the northwest of Dongling lead zinc factory, and the content of lead and zinc is relatively high, which is related to the discharge of moving vehicles and solid waste on the eastern side of the power plant. The main solid emissions and wind direction are mostly NW-SE, so the east side of the pollution is more than the west side. Although L9 and L14 are both located at road junctions, and are closely related to vehicle tire friction and domestic garbage pollution, the L14 is more mechanized mining and urban pollution. On the whole, Cd pollution is the most serious in the study area, followed by Zn and Pb. Most of Fengxiang County are machined and irrigated with waste water in farmland, so the accumulation of heavy metal still exists in the soil in some regions.

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5

10

15

20

25

Cu

Cd

Pb

Cr

Cd

Pb

Zn

Zn

0

5

10

20

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Cr

(a)L1 Cu

15

0

5

10

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(b)L4 20

25

0

5

10

15

20

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Cu

Pb

Pb

Cr

Cd

Cd

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Zn

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c L9

d L14

Fig. 5. Dendrogram of heavy metals in loess of Northern Baoji.

Table 4. Group of cluster analysis. Number L1 L13 L25 L31

Group 1 Cu, Cr Cu, Pb, Cd Cu, Pb, Cr Cu, Pb, Cd

Group 2 Group 3 Cd Pb, Zn Zn Cr Cd Zn Zn Cr

5 Conclusion Based on geo-statistics and cluster analysis, the spatial distribution and pollution sources of soil heavy metal contents in loess in northern Baoji were studied in this paper. (1) The results of geo-statistics show that the model is simple and easy to calculate, but the spatial distribution of soil characteristics in the region cannot be well applied to the agricultural production in the field when there are not enough sampling points.

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Therefore, in order to obtain more accurate models, we need to use a larger scale in standard grid acquisition function, and make full use of the control group and the predicted value to test and modify, and obtain a more applicable model. The panel data model can consider the relationship between soil heavy metal elements and the influence of spectral band eigenvalue on each soil heavy metal, so panel data model is an important inversion model that needs to be dealt with the next step. (2) From the clustering results, the main causes of heavy metal pollution in loess in northern Baoji are as follows: the spatial distribution of large and medium-sized enterprises such as mechanical processing and non-ferrous metal processing, etc. The geomorphological features of the area are not conducive to the diffusion of pollutants. In particular, the spread of various types of dust. The results of this study provide support for the evaluation and management of loess heavy metal, and are conducive to promoting regional agricultural and animal husbandry management and sustainable development. At the same time, it provides important scientific basis and decision support for government departments to evaluate and improve the regional soil ecological environment. Acknowledgments. Thanks to the staff of Xi’an Center of China Geological Survey for the determination and inspection of heavy metal contents. This work was financially supported by the open fund for key laboratory of land and resources degenerate and unused land remediation, under Grant [SXDJ2017-7]; the 1:50, 000 geological mapping in the loess covered region of the map sheets: Caobizhen (I48E008021), Liangting (I48E008022), Zhaoxian (I48E008023), Qianyang (I48E009021), Fengxiang (I48E009022), & Yaojiagou (I48E009023) in Shaanxi Province, China, under Grant [DD-20160060].

References 1. Li, F., Cai, Y., Zhang, J.: Spatial characteristics, health risk assessment and sustainable management of heavy metals and metalloids in soils from Central China. Sustainability 10, 1–24 (2018) 2. Giller, K.E., Mcgrath, S.P.: Pollution by toxic metals on agricultural soils. Nature 335(6192), 676 (1988) 3. Rodrigues, S.M., Cruz, N., Coelho, C., et al.: Risk assessment for Cd, Cu, Pb and Zn in urban soils: chemical availability as the central concept. Environ. Pollut. 183(4), 234–242 (2013) 4. Wang, Lujun, Fan, Shuanxi: Risk assessment of heavy metals in farmland soil in the outskirts of Baoji City. Chin. Agric. Sci. Bull. 31(3), 179–185 (2015). (in Chinese) 5. Ren, C.H., Xin-Wei, L.U., Wang, L.J., et al.: Human health risk related to pollution level of lead in dust around a lead-zinc plant in Changqing Town, Baoji City. Arid Zone Res. 29(1), 155–160 (2012). (in Chinese) 6. Wang, Lijun, Xinwei, Lu, Jing, Qi, et al.: Heavy metals pollution in soil around the lead-zinc smelting plant in Changqing Town of Baoji City, China. J. Agro-Environ. Sci. 31(2), 325– 330 (2012). (in Chinese) 7. Shaheen, A., Iqbal, J.: Spatial distribution and mobility assessment of carcinogenic heavy metals in soil profiles using geostatistics and random forest, boruta algorithm. Sustainability 10(3), 799 (2018)

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8. Cortés, J.L., Bautista, F., Delgado, C., et al.: Spatial distribution of heavy metals in urban dust from Ensenada, Baja California, Mexico. Revista Chapingo Serie Ciencias Forestales Y Del Ambiente 23(1), 235–248 (2017) 9. Chen, J., Zhang, H., Liu, W., et al.: Spatial distribution patterns of ammonia-oxidizing archaea abundance in subtropical forests at early and late successional stages. Scientific Reports 5, 16587 (2015) 10. Xie, Y., Chen, T., Lei, M., Yang, J., Guo, Q., Song, B., Zhou, X.: Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: accuracy and uncertainty analysis. Chemosphere 82(3), 468–476 (2011) 11. Saito, H., Goovaerts, P.: Geostatistical interpolation of positively skewed and censored data in a dioxin-contaminated site. Environ. Sci. Technol. 34(19), 4228–4235 (2000) 12. Fu, C., Zhang, H., Tu, C., et al.: Geostatistical interpolation of available copper in orchard soil as influenced by planting duration. Environ. Sci. Pollut. Res. 25, 52–63 (2018) 13. Chen, T., Chang, Q., Liu, J., et al.: Identification of soil heavy metal sources and improvement in spatial mapping based on soil spectral information: a case study in northwest China. Sci. Total Environ. 565, 155–164 (2016) 14. Huang, S., Tu, J., Jin, Y., et al.: Contamination assessment and source identification of heavy metals in river sediments in Nantong, Eastern China. Int J Environ Res 12(3), 1–17 (2018) 15. Basatnia, N., Hossein, S.A., Rodrigo-Comino, J., et al.: Assessment of temporal and spatial water quality in international Gomishan Lagoon, Iran, using multivariate analysis. Environ. Monit. Assess. 190(5), 1–17 (2018)

Analysis and Comparison of Uncertain Means Clustering Algorithm Nini Zhang(&), Lihua Qi, and Xiaomei Qin School of Information Electrical Engineering, University of Engineering Hebei, Handan, China [email protected]

Abstract. Clustering analysis is an important method of multivariate statistical analysis. It has important applications in pattern recognition, artificial intelligence, automatic control and other fields. An iterative algorithm called uncertain means clustering is defined by analyzing the contribution of the features to the sample and calculating the degree of membership based on the weight. In this paper, we use the uncertain means clustering algorithm to cluster IRIS data to test the clustering accuracy, convergence speed and robustness of the algorithm. At the same time, compared with the traditional clustering algorithm, which KMeans clustering algorithm and KNN clustering algorithm, the experimental results show that the uncertain means clustering algorithm has good performance in the accuracy and convergence speed of the sample data sets, and is an effective unsupervised clustering algorithm. Keywords: Uncertain means clustering
K-Means clustering KNN clustering
Unsupervised clustering




1 Introduction Clustering analysis is one of the important research contents in data mining and pattern recognition. The clustering method is mainly applied to speech recognition and character recognition in pattern recognition. And it is applied to image segmentation and machine vision processing in machine learning. The clustering of image recognition can be used for data compression and information retrieval. At the same time, clustering can also be applied to multi relational data mining, spatiotemporal data application (such as geographic information system), sequence and heterogeneous data analysis and processing [1–3]. Traditional clustering algorithms include K-Means clustering algorithm, KNN clustering algorithm and fuzzy mean clustering algorithm. The unascertained clustering theory is based on the unascertained system theory. Professor Liu Kaidi is the biggest contributor to this theory. The limitation of precise mathematics is that it sometimes fails to describe objective reality accurately. For example, when we are watching TV, we want to make the image clearer. This is very difficult for the computer because it is a vague concept without clear boundary information. In addition, it is difficult to play a greater role in the fields of biology, psychology and social sciences. It is not that these subjects are too simple to use © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 93–99, 2019. https://doi.org/10.1007/978-981-13-7025-0_9

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computer technology, but the rules of these disciplines are too complex and accurate mathematics can’t accurately reflect their features. [4–6] The uncertain means clustering theory holds that the sample points can be divided into different categories because the same characteristic values of different samples are different. The closer the characteristic observations of different samples are, the smaller the contribution of the feature to the separation of sample categories. [7] The degree of concentration and divergence of the sample set about the same feature reflects the contribution of the feature to the classification, which is an objective fact that “exists at the same time” with the classification information. Uncertain means clustering is used to analyze the contribution of the sample classification, define the weight of the feature classification, and get the iterative algorithm of the sample to all kinds of membership, and cluster analysis for the sample. [8–10] In this paper, we first describe the idea of uncertain means clustering algorithm, verify the effectiveness of the algorithm by experiments, and compare with the running results of several traditional clustering algorithms, and prove the practicability of the algorithm from the point of view of the program running.

2 Uncertain Means Clustering 2.1

The Concept of Uncertain Means Clustering

The nonempty finite set U consisting of research object x is called discourse domain. (F1, F2, … Fp) is a division of the property field F on U. Any object x in U has a property Fk(k = 1,2, … p). The topology E of F is generated by divisions. For any x 2 U, uA ðxÞ ¼ uðx 2 AÞ, it is an unascertained measure on the topological space (E, F). Using uF1 ðxÞ; uF2 ðxÞ; . . .; uFp ðxÞ as membership functions, the P unascertained subsets C1 ; C2 ; . . .; Cp on the discourse domain U is determined, then the P unascertained subsets give an unascertained classification of the discourse domain U, and Ck ðk ¼ 1; 2; . . .; pÞ is the k class. Obviously, the unascertained classification is different from the usual deterministic classification. It is a kind of uncertainty classification. For example, a deterministic classification of U is given arbitrarily, Ck ðk ¼ 1; 2; . . .; pÞ is the K class. In any two classes, there is no common element. The union of P classes is the discourse domain U. The elements in class K belong to class K by defining the degree of membership equal to 1, and belong to class jðj 6¼ kÞ by defining the degree of membership equal to 0. The degree of membership of element X in discourse domain U takes only 0 or 1 values. For unascertained classification, any element X in discourse domain U belongs to Ck class with degree of membership equal to uFk ðxÞ. uFk ðxÞ is a real number on [0, 1] intervals and needs to satisfy the following conditions. p X

uFk ðxÞ ¼ 1

k¼1

Therefore, unascertained classification is a kind of soft classification.

ð1Þ

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The Principle of Uncertain Means Clustering

The characteristics of the influence sample classification are D, and the observed value of sample xi about characteristic j is xij, and every one dimension data ({x1j, x2j,, …, xNj} (j = 1,2, …, d)) is standardized. In this way, the sample xi can be expressed as a point in the D dimensional feature space. xi ¼ ðxi1 ; xi2 ; . . .; xid Þ; ði ¼ 1; 2; . . .; NÞ If the N sample points in the D dimensional feature space are divided into C classes, Ck (k = 1, 2, …, C) represents the class k, and mk is the class central vector of the class Ck : mk ¼ ðmk1 ; mk2 ; . . .; mkd ÞT ; ðk ¼ 1; 2; . . .; CÞ. It is obvious that this is a deterministic classification. However, when mk is used as a representative to approximate the samples in the class of Ck , the “certain distance” from the sample x to the class centre mk is used as an approximation measure between the X and the Ck class, in fact the deterministic classification has been indeterminate. Usually, this uncertainty is closer to the actual classification. However, in order to make this uncertainty classification useful, we must be able to reasonably determine the membership degree of samples. If lCk (x) is used to indicate that x belongs to the membership of class Ck , the truth value of lCk (x) can’t be known due to the existence of uncertain. Since the size of lCk (x) is relative, it is a basic research content of unascertained classification that how to determine lCk (x) is more reasonable. 2.3

The Description of Uncertain Clustering Algorithm

The computational process of unascertained clustering algorithm is as follows: (1) Data standardization. The values of each dimension component are defined between 0 and 1. yij ¼ ðxij  min fxij gÞ=ðmaxfxij g  min fxij gÞ 1iN

1iN

ð2Þ

Definition of the class center of the initial classification. J ¼ ðC  1ÞðsumðiÞ  Mi Þ=ðMa  Mi Þ In which sumðiÞ ¼

d P j¼1

ð3Þ

yij ; Ma ¼ max sumðiÞ; Mi ¼ min sumðiÞ. Assuming that K is i

i

the nearest positive number of J + 1, and assign yi to class k, and the result is that N samples are divided into C classes. According to an initial classification, all kinds of ð0Þ class centers are calculated by mk ð1  k  CÞ. ð0Þ

ð0Þ

ð0Þ

(2) According to the class center m1 ; m2 ; . . .; mC of the C initial classes, the mean class of the initial classification can be calculated. In this step, on the basis of the given initial classification, the classification contribution of J can be obtained.

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rj

¼

aj X ð0Þ ð0Þ  j Þ2 ð1  j  dÞ ðmkj  m C

ð4Þ

The classification weight is calculated from the above formula. ð0Þ wj

¼

ð0Þ2 rj

, d X

ð0Þ2

rl

ð5Þ

l¼1

Calculate the weighted distance of each feature based on the following formula. ð0Þ

jjyi  mk jj2 ¼

d X

ð0Þ

ð0Þ

wl ðyil  mkl Þ2

ð6Þ

l¼1

After obtaining the weighted distance, we need to calculate the basic degree of membership of the corresponding initial classification. ð0Þ uCk ðyi Þ

¼

,

1 jjyi 

ð0Þ mk jj2

þe

C X

1

l¼1

jjyi  ml jj2 þ e

ð0Þ

ð7Þ

In which e ¼ 0:01. (3) After obtaining the unascertained degree of membership of N samples, we can calculate the classification of the N samples corresponding to each feature. The ð0Þ specific method is to assign the basic degree of membership uCk ðyi Þ as the point mass to the point yi , where the point mass is the point yi about Ck class. In this way, the centroid of the mass point group fðy1 ; uCk ðy1 ÞÞ; ðy2 ; uCk ðy2 ÞÞ; . . .; ðyN ; uCk ðyN ÞÞg composed of N mass points can be determined by physical method, that is, the corresponding degree of membership vector of each mass point is ð1Þ mk

¼

N X

ð0Þ uCk ðyi Þyi

k¼1 ð1Þ

, N X

ð0Þ

uCk ðyi Þ

ð8Þ

k¼1 ð1Þ

ð1Þ

In this step, the class center m1 ; m2 ; . . .; mC is obtained after the first iteration. ð0Þ mk

ð1Þ with mk ð1  k  CÞ return to step 3 again, and continue to iterate. When Replace ðtÞ ðt1Þ the result is maxjjmk  mk jj\d after t iterations, the iteration is stopped. The ðtÞ ðtÞ ðtÞ ðtÞ output is the class center m1 ; m2 ; . . .; mC ; mk of the C classes, which is also the class

center of Ck . Finally, N points in D dimensional feature spaces are clustered without any other classification information. At the same time, the central vector of the C class center is also obtained.

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The Flowchart of Uncertain Clustering Algorithm

According to the description of the unascertained clustering algorithm, the flow chart of the algorithm is as follows (Fig. 1):

Data standardization

Analyzing conditions: Determine whether the difference between the new class center and the old class center is less than a certain number

Definition of the class center of the initial classification Yes Calculate the mean class

Calculate the determined classification of each of the N samples for each feature

Get a new class center

output class center

Fig. 1. The flowchart of uncertain clustering algorithm.

Unascertained clustering algorithm overcomes the shortcomings of the traditional clustering algorithm, and makes the classification results show its membership degree, rather than simply let a sample belong to a class with the degree of membership 1. Unascertained clustering algorithm considers that the samples of d dimensional space are divided into C classes because the same observation values of different classes of samples are different, and their different features have different contributions to the classification of samples. Uncertain clustering algorithm acknowledges this point and gives a quantitative description. By analyzing the heuristic information about classification provided by the input data, the unascertained mean clustering can satisfy the measurement criteria. The process of clustering is not the contribution of human to the classification of each attribute, but rather objective to the process of data clustering, which is a major advantage of the unascertained clustering algorithm compared with the traditional clustering algorithm

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3 Benchmark Test In order to analyze the uncertain means clustering algorithm, 2 traditional algorithms are selected for comparison, and the dataset uses the IRIS dataset selected from the UCI machine learning dataset. Computer configuration: the processor is Intel Core (TM) i72.6 GHz, memory 16 GB, hard disk 160G, operation system is Windows10, programming language is python3.6. 3.1

Data Set

In this paper, IRIS data set is used as test data set. It is a commonly used well-known data set. Its clustering results are reliable and suitable for clustering benchmark data sets. The dataset contains three classes, each of which has 50 elements. According to the four properties of calyx length, calyx width, petal length and petal width, it can be predicted which categories (Setosa, Versicolour, Virginica) of the flower belongs to. Each representing one type of Luan tail flower and 150 samples evenly distributed in 3 classes. One class is linearly separable from the other two, while the other two have some overlap. 3.2

Computing Process

The experimental steps are as follows: 1. First of all, input IRIS data sets and randomly disrupt the order of the data set. 2. Clustering the data sets by Uncertain-Means algorithm, K-Means algorithm and KNN algorithm respectively. According to the four attributes of calyx length, calyx width, petal length and petal width in the data, which Luan tail flower belong to one category and all data are divided into three categories. 3. Restore the order of the dataset and compare the results with the classification of the original data, and calculate the running time of the program. (4) Output the accuracy and program running time. 3.3

Test Result

Using three clustering algorithms which include K-Means, KNN and Uncertain-Means, to calculate the average accuracy and average running time of IRIS data sets (Table 1). Table 1. Clustering results of three clustering algorithms on IRIS dataset Algorithm

Accuracy and time Average accuracy(%) K-Means 81.33 KNN 86.66 Uncertain-Means 90.66

Average running time(s) 0.01 0.13 0.05

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From the above test results, the Uncertain-Means algorithm has an effective increase in accuracy and running time compared with the K-Means and KNN algorithms under the same data set.

4 Conclusion The Uncertain-Means clustering algorithm, based on the internal relationship between the membership degree and the class center, directly uses the iterative method to find the cluster center, avoiding the establishment of criterion function, so that the class center and membership degree involved in each step of the algorithm have physical interpretability. In this paper, the clustering results of IRIS data sets show that the Uncertain-Means clustering algorithm has the advantages of higher accuracy and faster convergence than the traditional K-Means clustering algorithm and KNN clustering algorithm, and Uncertain-Means clustering algorithm is an effective unsupervised clustering algorithm. The data set selected in this paper is only IRIS data set, and future research direction can be compared with more data sets and clustering algorithms, so as to better study the properties of Uncertain-Means algorithm.

References 1. Jain, A.K., Flynn, P.J.: Image segmentation using clustering. In: Ahuja, N., Bowyer, K. (eds.) Advances in Image Understanding: A Festchrift for Azriel Rosenfeld, pp. 65–83. IEEE Press, Piscataway (1996) 2. Cades, I., Smyth, P., Mannila, H.: Probabilistic modeling of transactional data with applications to profiling, visualization and prediction, sigmod. In: Proceedings of the 7th ACM SIGKDD, pp. 37–46. ACM Press, San Francisco (2001). http://www.sigkdd.org/ kdd2001 3. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Comput. Surv. 31(3), 264–323 (1999) 4. 刘开第, 王义闹, 吴和琴. 四种不确定性信息概念与联系. 华中理工大学学报 (4), 68–70 (1999) 5. 刘开第, 李万庆, 庞彦军. 未确知集. 数学的实践与认识, 36(10), 197–204 (2006) 6. 刘开第, 庞彦军, 吴和琴等. 信息及其数学表达. 系统工程理论与实践 (8), 91–93 (1999) 7. 管祥兵, 刘历波, 代兰, 任向阳. 基于未确聚类的动态联盟伙伴选择研究. 河北建筑科技 学院学报 23(1), 44–45 (2006) 8. 曹庆奎, 任向阳, 刘琛, 等. 基于粗集-未确知测度模型的企业技术创新能力评价研究. 系 统工程理论与实践, 26(4), 32–34 (2006) 9. 刘开第, 庞彦军, 孙光勇. 城市环境质量的未确知测度评价. 系统工程理论与实践, 19 (12), 31–32 (1999) 10. 庞彦军, 刘立民, 刘开第. 未确知均值聚类. 河北工程大学学报(自然科学版) 27(4), 98– 100 (2010)

Research on Matrix Multiplication Based on the Combination of OpenACC and CUDA Yuexing Wang(&) Hebei University of Engineering, Han Dan 056000, Hebei, China [email protected]

Abstract. With the improvement of GPU’s general computing capacity, the use of parallel computing to solve some difficult problems with large amount of data and intensive computing tasks has become the trend of the times. In GPU general computing, CUDA and OpenCL have been widely used and studied. However, the two parallel programming models generally exist the weakness that whose API is too close to the underlying hardware, which makes programming inefficient and is not suitable for the large-scale parallel tasks that require rapid implementation. OpenACC is a relatively advanced and simple programming language, which can achieve rapid parallelization, but the computing effect of the program is relatively low (generally lower than CUDA). Therefore, this paper tries to combine CUDA and OpenACC for mixed parallelization. This way not only greatly reduces the workload of code conversion, but also has a computing performance no less than a pure CUDA program. Keywords: CUDA OpenACC matrix multiplication

1 Introduction GPU general computing refers to the use of GPU, which is originally used for image processing. To carry out general use of calculation tasks, Nvidia Corp launched CUDA. There are many cases about the research and application of GPUGPU, such as those mentioned in paper [1–5]. There are a large number of computing intensive tasks in scientific computing, which can be decomposed into multiple small tasks, and the data dependence between them is very low. GPU is a multi computing core processor on the hardware. GPU is a multi computing core processor on the hardware. The computing ability of a single processor is inferior to a single CPU core, but the core number of GPU is more than that of a CPU processor. As it can has hundreds of cores, GPU has obvious advantages in dealing with relatively simple but parallel tasks. Therefore, a large number of studies have focused on how to use GPU for general computing, especially the mathematical tools that are widely used in scientific computing, such as matrix multiplication. In these studies, CUDA and OpenCL is widely used because of its very high parallelization effect and rich API programming based on C language and Fortran. This greatly facilitates the scientific research personnel who use these two programming languages. But the API provided by CUDA is relatively low level, which makes programming task very heavy and easy to make mistakes. A simple data migration task will cost more than ten lines of code. This is very challenging for © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 100–108, 2019. https://doi.org/10.1007/978-981-13-7025-0_10

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parallel tasks with large code size. The latest OpenACC parallelization architecture has a relatively high level of API, similar to OpenMP, a programming language based on a compiled guidance statement that allows programmers to make very simple code conversion. Many scholars have carried out research and application of OpenACC, as mentioned in papers [6–11]. However, the parallel code converted by OpenACC is relatively inefficient. In this experiment, the computing efficiency of OpenACC code is much lower than that of CUDA, which creates such a problem, how to implement parallelization quickly and have the same computing performance as that of CUDA. After many attempts, this paper finally proposes a method combining CUDA and OpenACC to solve this problem well. We would like to draw your attention to the fact that it is not possible to modify a paper in any way, once it has been published. This applies to both the printed book and the online version of the publication. Every detail, including the order of the names of the authors, should be checked before the paper is sent to the Volume Editors. The following contents are arranged as follows: the first part is about OpenACC and CUDA. The second part is the theoretical analysis and specific methods of combining OpenACC and CUDA to realize large matrix multiplication parallelization. In the third part, the effect of the combined method is verified by multi group comparison test. The fourth part, the conclusion and the analysis.

2 OpenACC and CUDA 2.1

CUDA

CUDA (Compute Unified Device Architecture) was introduced by NVIDIA in June 2007. The calculation of CUDA does not have to be mapped to graphic API (OpenGL or Direct 3D), and to a certain extent it reduces the difficulty of development. CUDA consists of three parts: the development library, the running environment, and the driver. The running environment provides application development interfaces and runtime components, including the definition of basic data types, the management of various computation and the management of memory. CUDA assigns parallel computing tasks to different threads, and the organization of threads can be divided into three levels, grid, block and bundle. Thread bundles are the basic units of thread scheduling. In actual programming, the layout and setting of thread grid and thread block are mainly carried out. For the sake of better memory access efficiency, the size of thread bundles must also be considered. CUDA uses thread block and thread grid to manage threads, the relationship between the grid and the thread block, as shown in Fig. 1, the thread block in the grid can be multidimensional, and the thread inside the thread block can be multidimensional, but the number of the threads in the block is restricted by the hardware. In the block, 32 threads are used as a group called thread bundles. In order to achieve memory efficiently, the number of threads is often a multiple of 32.

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Fig. 1. This shows a figure of the Grid and the Block.

2.2

OpenACC

OpenACC is the abbreviation of Open Accelerators. It is a programming standard issued by PGI, NVIDIA and other companies in November 2011. They want to jointly create a set of standards that can be used for Fortran, C, and C++ applications to help the compiler find parallel parts of the code and put them on accelerators, such as GPU, to speed up the code. OpenACC hopes that compiler directive statements can be used in multi-core CPU and various accelerators, not just Nvidia GPU. Through the OpenACC instruction statement, the compiler can automatically parallelize the code, and can also move data between CPU and GPU. It is important that the indicative statements added to the code span a variety of architectures, and if the underlying hardware changes, the code needs to be recompiled to adapt to the new hardware platform. NVIDIA’s CUDA programming environment or the OpenCL framework used by AMD accelerator will inevitably introduce redundant parallel computing and data mobile code. Another purpose of OpenACC is to let programmers avoid them. OpenACC mainly provides guidance statements and computation management statements for data mobility management. It provides a variety of data components and computing components. Common data components include input data for setting up and revoking data areas, exiting data, and so on. Computing components, such as parallel components and kernel components, can automatically extend the for loop. OpenACC divides the parallel level into three layers: Gang, Worker and Vector, which are similar to CUDA’s mesh and thread blocks, but are easier to configure than CUDA. OpenACC can only specify the number of them, and do not allow for further configuration, such as thread index references. This also makes the parallel control of programs unpredictable. The duplicated code omitted by OpenACC is basically the platform and device initialization code required by the programmer in CUDA and OpenCL, which is

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automatically (implicitly) processed by the OpenACC. CUDA and OpenCL always have tasks other than kernel code to complete. Programmers not only need to write kernel code, but also need to write these initialization processes. Programmers also need to write code to start the kernel. Additional code costs are about 50% to 150%. “However, because OpenACC’s API is not as close to the hardware as CUDA, it also makes OpenACC lose some parallel performance, and because OpenACC does not provide a reference to the thread ID, parallelization is not flexible enough, and the events included in the thread are not controlled, and many things are completed by the compiler. The calculation efficiency is not satisfactory. Based on the respective characteristics of OpenACC and CUDA, we try to parallelize matrix multiplication in these two languages respectively, and compare their performances. Finally, we take their respective advantages to abandon their shortcomings and combine the two flexibly to get a new parallel way that can not only simplify the programming task, but also improve the computing speed.

3 The Method of Realizing Matrix Multiplication by Combining CUDA with OpenACC Suppose there are three matrices A, B, C. Where A is the matrix of M * N, B is N * P matrix. As C = AB, C is M* P.cij is one element of the matrix. Then it can be calculated by the Eq. 1. cij ¼

k1 X

ais bsj

ð1Þ

s¼0

For the calculation process of CUDA, the three matrices are first stored in three one-dimensional arrays according to the line priority principle, and then the elements of the matrix are mapped to the two-dimensional thread layout of the GPU. So that each thread corresponds to an element of the matrix, as shown in Fig. 2.

Fig. 2. This shows a figure of the map from the matrix to the net of threads.

The acquisition of CUDA thread ID is based on the configuration of grid and thread blocks. There are several inherent variables used to represent thread configuration in API of CUDA. For example, ThreadIdx.x is used to represent the X direction index in

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thread thread blocks. ThreadIdx.y is used to represent the Y direction index in thread thread blocks. BlockIdx.x is used to represent the X direction of the thread block in the grid, and BlockIdx.y is used to represent the Y direction of the thread block in the grid. BlockDim.x and BlockDim.y are used to represent the size of thread blocks. Using the above variables to find thread ID is shown in Fig. 3.

Fig. 3. This shows a figure of how to find the ID from the net of threads.

The matrix elements can then be read and calculated according to the mapping rules. The specific mapping method is as follows: set the matrix A to N * M matrix, assume x = N, y = M, first set the thread layout of each thread block. It must be noted that the number of threads in the block is restricted by hardware, not a random value. Then we assume that the upper limit is L, the X direction is set to s, and the Y direction is q, then s * q < L. The next step is to deduce the grid layout, assuming that the number of threads in the X direction of the grid is NX and the Y direction is NY. bx = (NX + block.x − 1)/block.x; by = (NY + block.y − 1)/block.y; so the element of the matrix basically mirrors the logical thread layout of GPU (non hardware actual layout). Because the number of threads is larger than the number of matrix elements at this time, we need to determine whether a thread is redundant. Redundant threads will then be excluded from the matrix. When calculating matrix elements, the locations of matrix elements are derived from thread indexes, and then they can be taken out of memory. The method is as follows: set the row coordinates of the corresponding matrix elements of the thread to i, and set j as a column. Then i = threadIdx.x + blockIdx.x * blockDim.x, j = threadIdx.y + blockIdx.y * blockDim.y. OpenACC is used for the movement of data. First, we have to define the host array variable, which is used to store the initial value of the matrix, and assign the memory address to it. Then, we declare that it assigns the corresponding device variable on the GPU with the OpenACC statement and assigns the memory address, and specifies the next CUDA function to use the addressed that has been specified by OpenACC. Finally, OpenACC tells the compiler that the computation result is to sent back to the host after the CUDA function is calculated.

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4 Experiment The environment condition is shown in Table 1. Table 1. Experimental conditions. Experimental conditions CPU GPU System Compiling software

List Intel Core i5-4210 M @2.60 GHz NVIDIA GeForce GTX 960 M Ubuntu 16.04 PGI CUDA 8.0

OpenACC provides support for invoking CUDA, but it needs to compile separately and encapsulate CUDA functions. CUDA can also call the encapsulated OpenACC function. Considering the inefficiency of OpenACC in the matrix multiplication, the CUDA is used to calculate the process. CUDA data mobility is not as convenient as OpenACC, so OpenACC is used for data movement. This paper designs OpenACC as the main file and encapsulates it into a.c file, in which the function of data movement between host and device is stored, and CUDA is encapsulated into a.cu file as a function of GPU function of calculation process. The function of allocation of threads, thread blocks and grid configurations are also included in the .c file. In order to simplify the programming process, we choose two N  N matrices A, B, multiplication result C is N  N matrix. When N takes 6 different sets of values, the time consumption (unit seconds) of pure CUDA, pure OpenACC and two combination methods is listed in Table 2.

Table 2. This shows a table of the experimental result. N 2000 2900 3500 4000 4500 5000

OpenACC 0.43 1.36 2.43 2.61 4.97 5.69

CUDA 0.29 0.73 1.22 1.70 2.50 3.26

Openacc + CUDA 0.28 0.70 1.18 1.65 2.45 3.22

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Fig. 4. This shows figure of the experimental result.

From Fig. 4 and Table 2, we can see that the computational efficiency of the method combining CUDA with OpenACC is basically equal to the pure CUDA program. Then we can compare the amount of code used for data transfer between host and GPU. Here is CUDA’s: cudaMalloc((void **)&dev_a,sizeof(float)*N*N); cudaMalloc((void **)&dev_b,sizeof(float)*N*N); cudaMalloc((void **)&dev_c,sizeof(float)*N*N); cudaMemcpy(dev_a,a,sizeof(float)*N*N,cudaMemcpyHostToD evice); cudaMemcpy(dev_b,b,sizeof(float)*N*N,cudaMemcpyHostToD evice); cudaMemcpy(c,dev_c,sizeof(float)*N*N,cudaMemcpyDeviceT oHost); cudaFree(dev_a); cudaFree(dev_b); cudaFree(dev_c);

The follow is OpenACC’s: #pragma acc data create(a[0:N*N],b[0:N*N],c[0:N*N]) copyout(c[0:N*N]) #pragma acc host_data use_device(a,b,c)

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5 Conclusion Experimental results show that the code conversion efficiency of matrix multiplication of pure OpenACC is higher than that of CUDA, but its computation efficiency is not as efficient as that of pure CUDA program. The computation efficiency of pure CUDA program is much higher than that of OpenACC, but its code quantity is much higher than that of OpenACC. The combined matrix multiplication of CUDA and OpenACC is very close to the pure CUDA, even a little better than pure CUDA code, and the number of code is far less than the pure CUDA code. Therefore, the combination of the two methods has taken advantage of both sides and has given up their shortcomings. So, in the parallelization of some computing task that similar to matrix multiplication, we can consider the combination of CUDA and OpenACC. The core idea of this combination method is to replace CUDA redundant memory management and data movement statements with lightweight statements from similar data in OpenACC.

References 1. Harris, M., et al.: GPGPU: general purpose computation on graphics hardware. In: ACM SIGGRAPH 2004 Course Notes, p. 33. ACM (2004) 2. Yang, Y., et al.: An optimizing compiler for GPGPU programs with input-data sharing. In: ACM Sigplan Symposium on Principles & Practice of Parallel Programming, pp. 343–344. ACM (2010) 3. Giunta, G., Montella, R., Agrillo, G., Coviello, G.: A GPGPU transparent virtualization component for high performance computing clouds. In: D’Ambra, P., Guarracino, M., Talia, D. (eds.) Euro-Par 2010. LNCS, vol. 6271, pp. 379–391. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15277-1_37 4. Lee, S., Min, S.J., Eigenmann, R.: OpenMP to GPGPU: a compiler framework for automatic translation and optimization. In: ACM Sigplan Symposium on Principles and Practice of Parallel Programming, pp. 101–110. ACM (2009) 5. Han, T.D., Abdelrahman, T.S.: hiCUDA: high-Level GPGPU programming. IEEE Trans. Parallel Distrib. Syst. 22(1), 78–90 (2010) 6. Kessler, C., et al.: Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: programming productivity, performance, and energy consumption. In: The Workshop on Adaptive Resource Management & Scheduling for Cloud Computing, pp. 1–6. ACM (2017) 7. Komatsu, K., et al.: Translation of large-scale simulation codes for an OpenACC platform using the xevolver framework. Int. J. Networking Comput. 6(2), 167–180 (2017) 8. Rostami, R.M., Ghaffari-Miab, M.: Fast computation of finite difference generated timedomain Green’s functions of layered media using OpenAcc on graphics processors. In: Iranian Conference on Electrical Engineering (2017) 9. Pereira, A.D., et al.: Enabling efficient stencil code generation in OpenACC. Procedia Comput. Sci. 108, 2333–2337 (2017)

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10. Calore, E., Kraus, J., Schifano, S.F., Tripiccione, R.: Accelerating lattice boltzmann applications with OpenACC. In: Träff, J.L., Hunold, S., Versaci, F. (eds.) Euro-Par 2015. LNCS, vol. 9233, pp. 613–624. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3662-48096-0_47 11. Feki, S., Al-Jarro, A., Bagci, H.: Multi-GPU-based acceleration of the explicit time domain volume integral equation solver using MPI-OpenACC. Radio Science Meeting, p. 90. IEEE (2013)

Research on ICS Intrusion Success Rate Algorithm Based on Attack and Defense Countermeasures Wending Wang1,2(&) and Kaixing Wu1,2 1 School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056000, China [email protected] 2 Hebei Engineering Laboratory of Comprehensive Informatization of Coal Mine, Handan, China

Abstract. According to the existing ICS, the research on ICS intrusion success rate algorithm does not consider the deficiency. In this paper, it proposes an ICS intrusion success rate algorithm based on ADT model. Firstly,according to common attack attributes to build a complete index system, and introduce attack part of ADT model to get the success rate of invasion of each path. Secondly, introducing the intrusion alarm rate to achieve passive defense, and using active scanning’s method to achieve active defense. Finally, combined with the above research, the final success rate of invasion is obtained. And a case study is carried out what is based on ICS of a chemical enterprise. This method reduces the success rate of invasion of the optimal attack path by 27%. And it improves the accuracy of the traditional model evaluation. Keywords: ICS Defense system


ADT model
Invasion success rate
Attack path


1 Preface From the outbreak of the “Stokes Network” virus in 2009 to Black Industrial’s attack on the industrial control system (ICS) of the Ukrainian power grid in 2016, ICS has suffered more and more losses. Scientifically and reasonably preventing attackers from invading is an important precondition and guarantee for the safe operation of ICS [1]. Accurate assessment of the success rate of ICS intrusion has become a key issue that must be addressed in the industry. Therefore, domestic and foreign scholars have done extensive research on the success rate of invasion. For example, Ye Qiru et al. used the attack tree model to evaluate the probability that ICS was successfully invaded. But the model did don’t consider the defender’s defense measures, that could not accurately reflect the assessment results [2]; Chen-Ching Liu et al. used the Markov model to calculate the possibility that the attacker can reach the target but the probability is easily disturbed by human factors, and the evaluation results are lack of scientificity and rationality [3].

© Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 109–118, 2019. https://doi.org/10.1007/978-981-13-7025-0_11

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Aiming at the deficiencies of the above thesis research, the paper proposes an attack and defense countermeasures (ADT). The model evolved from the game of attack and defense in game theory. Considering the issue from both the attacker and the defender, it not only reflects the attacker’s attack process, but also shows the defense measures of the defender. Compared with [2], the success rate of intrusion is more fully. Compared with [3], the success rate of intrusion is more objectively. Therefore, the algorithm improves the accuracy and objectivity of the intrusion success rate.

2 Attack and Defense Countermeasures ADT model Game theory from Nash [4]. In 2009, Jiang Wei et al. applied the method to network security evaluation [5]. In 2014, Kordy et al. first introduced the method to the industrial field. The model solves the traditional attack tree model without considering defenders’ defense measures [6]. The ADT model transforms the focus of research from the traditional attack behavior to the interaction between attackers and defenders. Second, the model includes key elements of the confrontation process, such as attack costs, attack methods, protection measures, system statues, and many others. Finally, using the ADT model to evaluate the invasion success rate of ICS [7]. The basic model of the ADT model is shown in Fig. 1. The model consists of four types of nodes: terminal, server, port, and defense measures. The attacker launches an attack through each port. When the port is broken, it will threaten the server and even the terminal. For port-to-server impact, using logic gates to represent the impact of the port.

Terminal

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Fig. 1. The top level represents (terminal), M1 represents (server), N1 represents (port), C1 represents (defense measures), and finally is linked (And or Or) by logic gates.

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“AND” means that the same leaf node must be completely compromised to move to the final target; “OR” means that if only one leaf node in the same leaf node is breached, it can be moved to the final target. Defenders propose defenses measures (e.g., C1, etc.) the port being attacked (e.g., N1, etc.).

3 ICS Intrusion Success Rate Algorithm Based on ADT Model The concrete steps of the ICS intrusion success rate algorithm based on the ADT model proposed in the paper are as follows, and the flow chart is shown in Fig. 2. (1) Determine the target of attack and establish a model; (2) Select appropriate evaluation indicators, quantify the indicators of child nodes in the ADT model, and build an index system; (3) Calculate the probability of occurrence of each attacking child node; (4) Analyze each attack path and calculate the probability of occurrence of each attack path; (5) Analyze the result of intrusion alarm of ICS, calculate the probability of passive defense of each path of ICS; (6) Analyze the results of the system’s active scanning and calculate the probabilities of active defense of each ICS path; (7) Combine active defense and passive defense to obtain the final intrusion success rate of each path.

Select target

Create ADT model

Attacker angle

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Calculate the quantification of each node Calculate the probability of occurrence of each attack path

Proactive defense yields final intrusion probability

Passive defense yields intrusion alarm rate

Get the probability of successful invasion

Fig. 2. The figure shows overall flow chart.

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Quantifying Attack Attributes of Attacking Nodes

The quantitative indicators of the leaf nodes in the ADT model are the basis for the calculation of the success rate of the invasion. Based on the attack properties such as attack cost, attacker level [8], attack’s detected probability and attack frequency [3], the attribute value of each attack node is obtained through the expert system. According to the above four attribute values, develop objective and reasonable classification criteria, as shown in Table 1. (1) Attack Cost: It refers to the cost of the attacker in each attack. When the setting is  ¥100, the attack cost is very low; when ¥100–¥1000 is between, the attack cost is low; when ¥1000–¥5000, the attack cost is general; when ¥5000–¥20000, the attack cost is high;  ¥20000, the cost of attack is very high. (2) Attacker level: because the identity of the attacker cannot be judged, the attacker’s level can only be judged based on the impact of the attack on the ICS. Five kinds of situations from small to large are: no rights, unauthorized access rights, normal user rights, administrator rights, and super administrator rights. According to these five conditions, the five levels of the attacker’s evaluation were very low, low, normal, high, and very high. (3) The probability that the attack is detected: The detected probability is correspondingly tested with the protection measures deployed in the system. Defense measures include firewalls, intrusion detection systems, etc. Industrial firewalls generally consist of four technologies: packet filtering, state packet inspection, proxy service, and application network management. When the attack is detected by all four technologies, the probability of being detected is very high; when it is detected by three technologies, it is high; when it is detected by two technologies, it is normal; when it is detected by a technology, it is difficult; when the attack is not detected by any kind, it is very difficult. (4) Attack frequency: According to [9] in the attack frequency division. When an attacker attacks ICS at least once per week, the attack frequency is set to be very high; When an attacker attacks an ICS at least once per month, it is high. When an attacker attacks an ICS at least once every six months, it is normal. When an attacker attacks an ICS at least once per year, it is low. When an attacker launches an attack for more than two years, it is very low. Based on four common attack attributes combined with Delphi method [10], the attribute values of the quantified attack leaf nodes are obtained. Based on the above attribute values, develop objective and reasonable classification criteria, as shown in Table 1.

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Table 1. Classification of attribute values Attack cost

Attack detection probability Options Grade Options Grade Options Grade Very high 5 Very high 5 Very high 5 High 4 High 4 High 4 Normal 3 Normal 3 Normal 3 Low 2 Low 2 Difficult 2 Very low 1 Very low 1 Very difficult 1

3.2

Attacker level

Attack frequency Options Very high High Normal Low Very low

Grade 5 4 3 2 1

Calculating Attack Success Rate of Attacking Nodes

Using multi-attribute utility theory to convert these attributes to utility values. The invasion success rate of leaf nodes is calculated as (1). mðLi Þ ¼ Wc  Uðci Þ þ Wn  Uðni Þ þ Wd  Uðdi Þ þ Wr  Uðri Þ

ð1Þ

Let Li represent a leaf node, and vðLi Þ is the probability of attack event Li .ni is the attacker’s level to complete attack Li ; di is the possibility that the attack event Li is detected; ri is the number of occurrences of the attack event Li . Uðni Þ, Uðdi Þ and Uðri Þ represent the utility value of three attributes. wn ,wd and wr are the weighting coefficients of the three attributes respectively, and their sum is [1]. (The weighting coefficients are wn ¼ 0:6, wd ¼ 0:3; wr ¼ 0:1) Through the above research analysis, ni , di , ri and Uðni Þ, Uðdi Þ, Uðri Þ are inversely related. So, normalized by UðxÞ ¼ c=x, which is the C normalization factor (c ¼ 1). 3.3

Attack Path Analysis

The attack path is the set of nodes in the root node G from the subleaf node N of the basic event to the final target. The attack path Yi consists of Li1


Lin nodes. The attack probability is (2). According to the above analysis, the invasion success rate of each attack path is obtained. V ðYi Þ ¼ vðLi1 Þ  vðLi2 Þ  . . .  vðLin Þ

3.4

ð2Þ

Defense System

The defense system means,when the ICS is attacked or there is a potential threat, the system will make corresponding defense measures. The two main defense measures are passive defense and active defense.

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Passive Defense. Passive defense, when the system alarm occurs, the defender remediate in time. When an attacker cracks a vulnerability in ICS, the alarm system will issue an alert, and the defender will remedy the situation. In [7], the intrusion alarm rate considered only false alarm events and did not consider missed events. However, in the real ICS, missed events occur frequently. Based on the missed events, the new intruder alarm rate is given as (3). PðI j AÞ ¼

PðIÞPðAjI Þ PðIÞPðAjI Þ þ Pð:IÞPðAj:I Þ þ PðIÞPð:AjI Þ

ð3Þ

I and A represent intrusions and alarms, PðIÞ represents the probability of an invasion of ICS; PðAjI Þ represents the conditional probability of an alert when an intrusion occurs; Pð:IÞ ¼ 1  PðIÞ. represents the probability of no intrusion. PðAj:I Þ represents the false alarm rate, the false alarm rate represents the probability of an alarm when an intrusion event does not occur. e represents the missed rate, and the missed rate represents the probability that no alarm occurred when an intrusion occurred. P ¼ ð:AjIÞ represents the missed rate, and the missed rate represents the probability that no alarm occurred when an intrusion occurred. Therefore, the probability of passive defense is (4). Where aD represents the action taken by the defender, when aD is 0, it takes no action; when aD is 1, it takes action. PPass ¼ aD  PðI j AÞ

ð4Þ

Active Defense. Active defense can accurately and timely warn before the intrusion can attack ICS. Through vulnerability scanning, penetration testing etc., the probability of finding a new vulnerability and repairing it in Dt period of time is active defense, which can be expressed by (5). PAct ¼ PI PII

ð5Þ

PI ; PII respectively represents the probability of discovering the vulnerability discovery rate and the vulnerability repair rate, which are (6) and (7). PI ¼

Nv  nv Ncv  nv

PII ¼ aD PðnAct ¼ 1Þ ¼ aD eDkv

ð6Þ ð7Þ

In (6), Nv is the number of vulnerabilities in ICS, Ncv is the size of the public vulnerability set constructed by the attacker, and nv is the reduction in the number of vulnerabilities discovered by the attacker. Because the vulnerability repair rate follows

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the three laws of Poisson distribution, the vulnerability repair rate is achieved through the Poisson distribution. In (7), scanning for system vulnerabilities, the defender may increase the number of intrusion vulnerabilities during the round of Dt, and the probability of repairing e vulnerabilities is (8). Dkv ¼

rs kv Ts  rs

ð8Þ

rs represents the vulnerability detection rate of the completed system scan, Ts is the number of actions required for the completed system scan. According to the probability of intrusion of each path, the attacker chooses the optimal path Lmax as (9). Lmax ¼ MaxfL1 ; L2 ;


Lk ; k 2 k g

ð9Þ

The final invasion success rate P is (10). P ¼ PPassðMaxfL1 ;L2 ;


Lk gÞ þ PAct

ð10Þ

4 Case Studies Taking the ICS of a chemical company as the experimental background, an ADT model was constructed and an accurate ICS invasion success rate was obtained [3]. 4.1

Invasion Success Rate

In the ADT model, the root node is the ICS of a chemical company. The ADT model is shown in Fig. 1, and the attributes of each node are shown in Table 2. Table 2. Attributes of each node Node G M1 M2 M3

Attributes Send ICS error message to make ICS Send signals through the front end Access HMI in ICS Get identity information authentication

Node N3

Attributes ICS shared server

N4 N5 N6

Local ICS access Web server access FTP server access (continued)

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Node M4

Node N7

Attributes Database server access

M5

Attributes Influence State Estimation Module Connect to the database server

N8

M6

Spurious data injection

N9

N1

Scan Control Center History N10 Server Get Control Center Application Server

Intercept measurement or status packets Eavesdropping information in the network Crack message encryption algorithm

N2

The properties of each leaf node deserve a corresponding attack probability, as shown in Table 3. Table 3. Attribute scores and probabilities of leaf nodes Attack event N1 N2 N3 N4 N5 N6 N7 N8 N9 N10

Attack cost 3 4 3 3 3 2 2 3 2 2

Attacker level 4 5 3 4 2 3 4 3 2 2

Attack detection probability 5 5 3 3 2 2 2 4 2 3

Attack frequency 3 3 5 4 4 4 4 2 3 1

Attack probability 0.272 0.222 0.327 0.288 0.438 0.404 0.363 0.329 0.492 0.500

Using the attack success rate of each node in Table 3, the attack success rate for each attack path can be calculated, as shown in Table 4. Table 4. Attack path and probability No. L1 L2 L3 L4 L5 L6

Leaf node attack path Probability N1N2 0.06 N3 0.327 N4 0.288 N5N7 0.159 N6N7 0.147 N8N9N10 0.081

Finally, the intrusion success rate of each path is obtained.

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Defense System

Defense measures are based on intrusion alarm rate to achieve passive defense and active defense through active scanning to achieve. Finally, the combination of the two achieves a defensive effect. Passive Defense. According to the survey, there are 54 false events and 421 missed events in the most recent 3,000 exception logs [11]. The intrusion success rate of each path after the passive defense is obtained by (3) and (4), as shown in Table 5. Table 5. Intrusion success rate of each path after passive defense No. L1 L2 L3 L4 L5 L6

Leaf node attack path Probability N1N2 0.020 N3 0.058 N4 0.052 N5N7 0.035 N6N7 0.039 N8N9N10 0.024

Active Defense. Knowing that Nv ¼ 200, Ncv ¼ 6787 [4] and set Ts ¼ 200, rs ¼ 0:08, kv ¼ 10 and nv ¼ 30 to get the probability of active defense PAct . The probability of finding a hole in the scan can be obtained by (6) PI ¼ 0:025. An increase of Dkv ¼ 0:004 in time Dt is obtained by (8), and PII ¼ 0:996 is obtained by (7). In the end, the probability of active defense is PAct ¼ 0:025  0:996  0:025. Therefore, the final intrusion success rate of each attack path is compared with the traditional attack model (without defense measures), as shown in Fig. 3.

Fig. 3. The dark bar is (the traditional attack model), the light bar is (the traditional attack + AHP model), and the white bar is (the ADT model). Figure 3 shows the comparison of each method on each attack path

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From Fig. 3, we can see that L2 has the highest intrusion success rate. Therefore, the attacker must choose the optimal path Lmax as L2. Compared with pre defense, the success rate has reduced by 27% (PLmax ¼ 0:057). Through experiments, it can be proved that the algorithm can more accurately evaluate the ICS intrusion success rate than the traditional model.

5 Conclusion For the existing methods, the ICS intrusion success rate cannot be assessed objectively and accurately. This paper proposes an ICS intrusion success rate algorithm based on the ADT model, and introduces related concepts such as intrusion success rate, active defense and passive defense to evaluate ICS intrusion success rate. Finally, the ICS of a chemical enterprise is taken as the background. The results show that the algorithm evaluates the ICS invasion success rate more objectively and accurately. However, the determination of quantitative indicators in this algorithm has a certain subjectivity, and further research will be conducted on the inadequacies in the future.

References 1. Jiang, W., Fang, B.X., Tian, Z.H.: Network security measurement and optimal active defense based on offense and defense game model. J. Comput. 32(04), 817–827 (2009) 2. Peng, Y., Jiang, C.Q., Xie, F.: Research progress of information security in industrial control system. J. Tsinghua Univ. (Nat. Sci.) 52(10), 1396–1408 (2012) 3. Ru, Y., Wang, Y., Li, J.: Risk assessment of cyber attacks in ECPS based on attack tree and AHP. In: 2016 12th International Conference Natural Computation, Fuzzy Systems and Knowledge Discovery, pp. 465–470. IEEE, USA (2016) 4. Chen, Y., Hong, J., Liu, C.C.: Modeling of Intrusion and defense for assessment of cyber security at power substations. IEEE Trans. Smart Grid 9(4), 2541–2552 (2016) 5. Arghavani, A., Arghavani, M., Ahmadi, M.: Attacker-manager game tree (AMGT): a new framework for visualizing and analysing the interactions between attacker and network security manager. Comput. Netw. 133, 42–58 (2018) 6. Kordy, B., Pietre, L., Schweitzer, P.: DAG-based attack and defense modeling: don’t miss the forest for the attack trees. Comput. Sci. Rev. 13, 1–38 (2014) 7. Cherdantseva, Y., Bumap, P., Blyth, A.: A review of cyber security risk assessment methods for SCADA systems. Comput. Secur. 56, 1–27 (2016) 8. Huang, J.H., Feng, D.Q., Wang, H.J.: Quantification method of industrial control system vulnerability based on attack graph. Autom. J. 42(05), 792–798 (2016) 9. GB/T 33009.3-2016, Industrial automation and control systems network security distributed control system (DCS) part 3: evaluation guide 10. Okil, C., Pawlowski, S.D.: The delphi method as a research tool: an example, design considerations and applications. Inf. Manage. 42(1), 15–29 (2004) 11. Liu, F.F.: Process industrial data analytics for alarm analysis. Beijing University of Chemical Technology, pp. 1–77 (2015)

The Review of Task Scheduling in Cloud Computing Fengjun Xin(&) and Lina Zhang School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056000, China [email protected]

Abstract. Cloud computing is based on the calculation model of the internet platform, which model can access through the network to share the storage resources of network, service, storage and to reduce the workload of people. In order to meet the requirements of quality services, economic principles, and other requirements to allocate a large number of data tasks reasonably, many experts and scholars regard task scheduling strategies as an important research object for cloud computing. In the process of task scheduling, many issues are considered, such as cost, time, resource utilization, etc. In order to reasonably schedule and manage virtual machines, a task scheduling model was proposed. This paper mainly discusses the problems encountered in the process of resource management, and discusses the existing scheduling strategies and the problems in the research. In order to balance the influence of various factors on the scheduling algorithm, a task scheduling multi-objective task optimization was proposed. Keywords: Cloud computing
Task scheduling
Multi-objective optimization

1 Introduction The term cloud computing [1] was proposed by Google CEO Eric Schmidt in 2006. In October 2017, Google and IBM launched cloud computing promotion plan in American universities, which further deepened people’s understanding of cloud computing. The origin of cloud computing and China dates back to 2008. This year, IBM announced that it will establish the world’s first cloud computing center for China in Taihu, Wuxi. Cloud computing has undergone several stages: power plant model, utility calculation, grid computing [2] and cloud computing: from the initial prototype to the present maturity. The people’s initial idea was that humans could use network-sharing resources as easily as electricity and water, instead of spending a lot of money on the original platform. After McKinsey, the father of artificial intelligence, proposed the concept of “utility computing,” people intended to integrate distributed servers, storage systems, and applications into multiple users. However, due to limited technology at the time, this plan was not implemented. The grid computing proposed later did not develop in the absence of business models, technologies, and security. With the explosion of data, people’s demand for computing is also increasing. At this time, technology is relatively mature, which makes cloud computing develop reasonably and rapidly. © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 119–126, 2019. https://doi.org/10.1007/978-981-13-7025-0_12

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Although the impact of the cloud is global, less than half of the companies use public cloud platforms. In 2018, the three public cloud providers Amazon Web Services (AWS), Google and Microsoft will account for 76% of all cloud platform revenue. Major companies are scrambling to develop cloud computing to capture a larger market share. AWS will open up more markets in 2018. As a rising star in the cloud market, Google’s cloud platform GCP (Google Cloud Plateform) now has more than 100 nodes in 35 countries. Google’s current cloud strategy is to continue investing in emerging technologies and potential companies. The most compelling acquisition should be Microsoft’s recent $7.5 billion in revenue for GitHub, which may be linked to Microsoft’s big business. In addition to the acquisition, a large number of patent applications have become a big strategy for Microsoft to catch up with Amazon. From the above, the development of cloud computing is unstoppable, and it is of great research significance. In the actual work of cloud computing, it is necessary to consider not only the needs of users, but also the benefits of cloud computing providers. How to solve these problems can start with cloud computing service classification. Cloud computing services are mainly divided into three layers: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) [3]. In recent years, the global cloud computing market has steadily grown. In 2017, the global public cloud market represented in IaaS, PaaS, and SaaS reached 110 billion dollars, a growth rate of 22.92%. It is estimated that the average market growth rate will be around 22% in the next few years, and the market size will reach $246.1 billion by 2021. The improvement of infrastructure will greatly improve the working efficiency of hardware, and the software performance will affect the computing efficiency of cloud computing. On the surface, there is no direct connection among the three layers, but they are reflected in the same concerns about their efficiency improvement, that is, the allocation of resources and task scheduling, and then into task scheduling problems [4]. The scheduling problem involves the service quality of cloud computing at three levels, which directly affects the customer satisfaction, operator benefits and resource utilization of cloud computing. Therefore, in-depth study on task scheduling in cloud computing is useful and academic.

2 Task Scheduling Model in Cloud Computing The task scheduling process in the cloud computing system involves the user level, task scheduling mechanism, virtual machine level, and data center level. The user submits the task, and the data center allocates the number of virtual machines and the task execution sequence according to the task scheduling mechanism. The virtual machine completes the task scheduling as required. The process is shown in Fig. 1.

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Fig. 1. User acquisition service flow chart in cloud computing

Task scheduling is the mapping of user tasks and data center resources, and through a certain algorithm to achieve. The cloud computing task scheduling process can be divided into two levels of task scheduling based on the process of virtual machines performing tasks: primary scheduling is the scheduling between users and virtual machines, and secondary scheduling is the scheduling between hosts and virtual machines [5]. The first-level scheduling needs to solve the problem of matching resource processing and user tasks, and the second-level scheduling needs to consider the system load balancing in addition to the mapping between host and virtual machine. Common classical task scheduling algorithms such as genetic algorithm, particle swarm optimization [6], ant colony algorithm, simulated annealing algorithm and so on. These algorithms are modified and can then be used for task scheduling in cloud computing. In the process of assigning a virtual machine to perform tasks, it is necessary to consider the time consumption and resource utilization, and also consider the user request such as completion time and cost, so the task scheduling process meets multiple target requirements [7]. Therefore, in the task scheduling process, the multiobjective optimization problem also appears to be particularly important.

3 Status of Task Scheduling 3.1

Task Scheduling Based on a Single Problem

The task scheduling algorithm in cloud computing has different calculation models based on different requirements. Most of the early task scheduling algorithms are designed based on the optimization of a single problem. From the user’s point of view, they always hopes to complete his work in less time. Zhang et al. [8] proposed a task

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scheduling model based on genetic ant colony algorithm in cloud computing environment. In the design process, this algorithm overcomes the lack of information in the initial period of the ant colony, and it also takes advantage of the global search capability of the genetic algorithm. The experimental results show that by reducing the ratio of effective nodes in the experiment, the advantage of the algorithm can be increased to reduce the average task completion time, and the gap between the effective nodes becomes zero when the ratio of the effective nodes approaches zero. Zhou et al. [9] proposed a prediction-based ant colony algorithm to meet the dynamic allocation of resources in the cloud environment based on the task completion time minimization problem. Wang et al. [10] proposed a data placement and task scheduling algorithm that dynamically adjusts the number of copies and uses time as a standard for measuring data transmission. The algorithm effectively reduces the task completion time by reducing the data transmission time, especially in the case of a large number of task sets and network nodes, it can reduce transmission time by nearly 50%. Tan et al. [11] proposed a particle swarm optimization cloud computing task scheduling algorithm. This algorithm proposes the correlation of extreme disturbances to avoid the algorithm falling into a local optimum, experiments show that the total completion time of characters is greatly reduced. Cha’an et al. [12] combined particle swarm optimization with ant colony algorithm and proposed a cloud computing task scheduling algorithm for particle swarm and ant colony. The algorithm uses the particle swarm algorithm earlier and later uses the ant colony algorithm. The experimental results show that the proposed algorithm outperforms the other two comparison algorithms in single use, and it is an efficient and feasible task scheduling algorithm. The cloud computing task scheduling process also needs to consider the priority issue during the actual operation process. In this regard, Biao Wei and others [13] proposed a cloud computing task scheduling strategy based on user priorities. This scheduling strategy is more in line with the global optimal task scheduling scheme in a more complex cloud environment. From the perspective of the operator, only considering the optimization task completion time will suddenly drop the problem of resource allocation and load. Operators always want to reduce costs with fewer resources and lower loads. Therefore, Jin et al. [14] proposed using genetic algorithms to reduce energy consumption. The algorithm is based on the genetic algorithm to add a reservation option to optimize the scheduling algorithm. Simulation results show that this algorithm can reduce energy consumption. Hameed et al. in document [15] gave some surveys of the system’s resource allocation techniques. Feng et al. [16] proposed a cloud computing task scheduling algorithm based on resource pre-classification. This algorithm takes resource attributes into consideration in resource classification. From the experimental results, this algorithm improves the resource utilization while taking into account load balancing. Zhang et al. [17] proposed an ant colony optimization algorithm based on load balancing. A pheromone adjustment factor (PAF) was proposed in this algorithm to improve the pheromone update rules. The results show that the algorithm can effectively maintain the cloud computing center virtual Machine balance. There are sometimes interdependencies among tasks in task scheduling. Therefore, in order to prevent deadlock in the scheduling process, Qi, Zhang et al. [18] proposed an improved ant colony system (ACS)-based algorithm. Simulation experimental results show that this algorithm is effective in preventing deadlock.

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The security issue is not only related to whether the user’s privacy is safe, but also relates to the operator’s reputation. Therefore, during the task scheduling process, the security issue is very important. Zhu et al. [19] proposed a fusion-safe grid-dependent task scheduling dual-objective optimization model and algorithm, in which the security benefit membership relationship and the grid task scheduling security model are defined. Experimental results show that the algorithm has good convergence for singleobjective optimization and multi-objective optimization, which is of great help for the implementation of cloud computing security in complex network environments. Chen et al. [20] in the article mentioned the observation of data flow through selective repetition. 3.2

Task Scheduling Based on Multi-objective Optimization

In order to meet the requirements of users and service providers in the cloud computing task scheduling process, people have repeatedly put forward many multi-objective optimization task scheduling algorithms. For example, people integrate a single optimization goal completion time, load, cost, and resource utilization, and propose a new task scheduling algorithm. Cha et al. [21] proposed an improved ant colony algorithm, which innovatively used the assignment of tasks on virtual machines as an ant’s search object. The simulation results show that the algorithm’s task execution time and load balance performance are better than the improved ant colony algorithm. Feng et al. [22] proposed a task scheduling algorithm based on improved particle swarm optimization in cloud computing environment. In this algorithm, the time and cost functions are considered and the improved particle swarm algorithm is used to update the position and velocity of particles. The experimental results show that the algorithm is better than the results obtained before the improvement. Duan et al. [23] proposed a QoS constrained task scheduling algorithm that combines genetic algorithm and ant colony algorithm in cloud computing environment. The algorithm mainly achieves the global optimization feature through the genetic algorithm and improves the accuracy of the solution through the ant colony algorithm. The experimental results show that the algorithm can meet the service quality and load balance to some extent.

4 Task Scheduling Trends Resources in cloud computing environments vary widely and are unstable. Task content is not the same, and there is a preference in the task scheduling process [24]. In order to use resources in the cloud computing environment more efficiently, people have proposed several algorithms and continuously improved them. Task scheduling in cloud computing environments evolves from single-objective optimization to multi-objective optimization. The applied algorithms are also from traditional algorithms such as Min-Min, Max-Min, etc. to simple swarm intelligence algorithms, as well as algorithm improvements, Algorithm fusion and algorithm innovation [25]. Although there are many existing algorithms, there are still many areas for improvement in the current algorithms.

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Task scheduling in cloud computing To meet the needs of various parties and improve the efficiency of task scheduling, its development trend can be divided into the following aspects: (1) Develop A Task Scheduling Model Based on Different Users Different users have different requirements for the massive data provided in the cloud computing. In order to make reasonable use of resources, and to improve user’s working efficiency, the task scheduling model can be formulated for different users’ different work attributes and expected costs and time. When the user’s needs change, the task scheduling model will be adjusted accordingly to avoid wasting resources. (2) On-demand Allocation of Resources in Cloud Computing The amount of equipment and other resources needed in cloud computing is set according to the initial requirements at the beginning of the environment setup. However, in the actual operation of cloud computing, the uncertainty of the cloud computing environment will change the resource demand. In real life, even large companies like IBM have not yet achieved dynamic allocation of resources. Once the dynamic allocation of resources is achieved, not only can the utilization rate of resources be reduced, service costs can be reduced, but new research fields can also be developed. Therefore, it is of great research value to realize the dynamic allocation of virtual resources from a commercial and academic perspective.

5 Conclusion This paper starts from the main work of cloud computing, studies the main work process of cloud computing, and summarizes the importance of task scheduling according to its process. From the development of cloud computing, it can be seen that cloud computing task scheduling continuously improves in order to meet the needs of different user suppliers. This article briefly introduced the task scheduling model, and learned during the task set-up process that the task scheduling process needs to optimize not only a time goal, but also costs, user budgets, and resource utilization. Although there are many researches on task scheduling algorithms in cloud computing at home and abroad, people still need to continuously research new task scheduling algorithms to meet the public for increasingly complicated cloud environments and people’s changing needs. At the end of the article, the outlook of several cloud computing task scheduling studies is given.

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References 1. Dustdar, S.: Cloud computing. Comput. 49(2), 12–13 (2016) 2. Mishra, B.S.P., Dehuri, S., Kim, E.: Techniques and Environments for Big Data Analysis: Parallel, Cloud, and Grid Computing. Springer, Switzerland (2016). https://doi.org/10.1007/ 978-3-319-27520-8 3. Alvertis, I., Koussouris, S., Papaspyros, D.: User involvement in software development processes. Procedia Comput. Sci. 97, 73–83 (2016) 4. Gabi, D., Ismail, A.S., Zainal, A.: Orthogonal taguchi-based cat algorithm for solving task scheduling problem in cloud computing. Neural Comput. Appl., 1–19 (2016) 5. Abdullahi, M., Ngadi, M.A., Abdulhamid, S.M.: Symbiotic organism search optimization based task scheduling in cloud computing environment. Future Gener. Comput. Syst. 56, 640–650 (2016) 6. Zhao, S.: Research on cloud computing task scheduling based on improved particle swarm optimization. Int. J. Performability Eng. 13(7), 1063 (2017) 7. Gabi, D., Ismail, A.S., Zainal, A.: Cloud scalable multi-objective task scheduling algorithm for cloud computing using cat swarm optimization and simulated annealing. In: International Conference on Information Technology, pp. 1007–1012. IEEE (2017) 8. Zhang, J., Li, F., Zhou, T.: Task scheduling algorithm based on genetic ant colony algorithm in cloud computing environment. Comput. Eng. Appl. 50(6), 51–55 (2014) 9. Zhou, W.J., Cao, J.: Cloud computing resource scheduling strategy based on prediction and ACO algorithm. Comput. Simul. 29(9), 239–242 (2012) 10. Wang, Q., Li, X.F., Wang, J.: A data placement and task scheduling algorithm in cloud computing. J. Comput. Res. Develop. 51(11), 2416–2426 (2014) 11. Tan, W.A., Zha, A.M., Chen, S.B.: Task scheduling algorithm of cloud computing based on particle swarm optimization. Comput. Technol. Develop. 26(7), 6–10 (2016) 12. Zha, A.M., Tan, W.A.: A task scheduling algorithm of cloud computing merging particle swarm optimization and ant colony optimization. Comput. Technol. Develop. 26(8), 24–29 (2016) 13. Bo, X., Du, J., Lu, X.M.: Task scheduling policy for cloud computing based on user priority level. Comput. Eng. 39(8), 64–68 (2013) 14. Jin, H.Z., Yang, L., Hao, O.: Scheduling strategy based on genetic algorithm for cloud computer energy optimization. In: IEEE International Conference on Communication Problem-Solving, pp. 516–519. IEEE (2016) 15. Hameed, A., Khoshkbarforoushha, A., Ranjan, R.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. J. Comput. 98(7), 751– 774 (2016) 16. Feng, L.L., Xia, X.Y., Jia, Z.H.: Task scheduling algorithm based on improved particle swarm optimization algorithm in cloud computing environment. Comput. Simul. 30(10), 363–367 (2013) 17. Zhang, H.Q., Zhang, X.P., Wang, H.T.: Task scheduling algorithm based on load balancing ant colony optimization in cloud computing. Microelectron. Comput. 32(5), 31–35 (2015) 18. Zhang, J., Qi, C.: ACS-based resource assignment and task scheduling in grid. J. Southeast Univ. 23(3), 451–454 (2007) 19. Zhu, H., Wang, Y.P.: Integration of security grid dependent tasks scheduling doubleobjective optimization model and algorithm. J. Softw. 22(11), 2729–2748 (2011) 20. Chen, H., Zhu, X., Qiu, D.: Scheduling for workflows with security-sensitive intermediate data by selective tasks duplication in clouds. IEEE Trans. Parallel Distrib. Syst. 28(9), 2674– 2688 (2017)

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21. Zha, Y.H., Yang, J.L.: Task scheduling in cloud computing based on improved ant colony optimization. Comput. Eng. Des. 34(5), 1716–1719 (2013) 22. Feng, L.L., Zhang, T., Jia, Z.H.: Task schedule algorithm based on improved particle swarm under cloud computing environment. Comput. Eng. 39(5), 183–186 (2013) 23. Duan, W.J., Fu, X.L., Wang, F.: QoS constraints task scheduling based on genetic algorithm and ant colony algorithm under cloud computing environment. J. Comput. Appl. 34(S2), 66– 69 (2014) 24. Wang, J., Li, F., Zhang, L.Q.: Apply PSO into cloud storage task scheduling with QoS preference awareness. J. Commun. 3, 027 (2014) 25. Safwat, A., Fatma, A.: Genetic-based task scheduling algorithm in cloud computing environment. Int. J. Adv. Comput. Sci. Appl. 7(4), 550–556 (2016)

Prediction Model of River Water Quality Time Series Based on ARIMA Model Lina Zhang(&) and Fengjun Xin School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056000, China [email protected]

Abstract. Water quality prediction is one of the main research contents in water quality management. According to the historical data of the monitored water quality factors, the analysis of the laws and predictions is of great significance to water quality early warning. In this paper, the time series prediction method ARIMA was used to analyze and model the water quality factor NH4 concentration in Zhuyi River. The results show that ARIMA has a high degree of accuracy in short-term water quality predictions. Keywords: Time series analysis


Water quality data
ARIMA
Prediction

1 Introduction With the continuous development of science and technology, scholars have deepened the study of river water quality management [1, 2]. In the research process, a large amount of water quality data is often collected, mainly including factors such as plankton, debris, dissolved oxygen, silicates, phosphates, ammonia salts, and nitrates that affect the quality of water. Behind these data are a large number of Value information. Nowadays, the actual demand for river water quality management is to dig out the implied regularity information from these data and predict the trend of future water quality changes accordingly [3]. Combined with the time-series characteristics of collected water quality data, the use of time series analysis method to establish a corresponding prediction model model is the current popular river water quality prediction method. In various data sets, there is a time-ordered relationship between data in one type of data set, and such data is called time-series data. In the process of analyzing such data sets, it is important that there is a chronological relationship between the data in the data set. Time series prediction refers to constructing a corresponding time series model that changes according to time t according to the characteristics of past observations of predicted things, and then uses the analysis rules to speculate the future. This kind of forecasting method is highly applicable. It should be that it doesn’t care about the background of the data. The information represented by any available time series can be predicted by using this prediction model. In 1968 Box and Jenkins proposed a set of

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well-established time series modeling theory and analysis methods [4, 5]. These classical mathematical methods predict by establishing stochastic models such as autoregressive (AR) models, moving average (MA) models, autoregressive moving average (ARMA) models, differential autoregressive moving average (ARIMA) models, and seasonal adjustment models [6]. Many scholars have applied these models to real life, such as forecasting stock prices, network flows, and river runoff [7, 8]. Some scholars have also improved these prediction models and put forward prediction models that are more in line with practical applications [9, 10]. Based on the time series autocorrelation analysis, this paper uses the ARlMA model which has significant advantages in short-term prediction. This model not only considers the time series of time series data but also considers the random fluctuation disturbance in the prediction process. It is one of the most widely used methods. Based on the monitored NH4 concentration data of Zhuyi River in 2014, this paper analyzes and establishes the ARIMA prediction model.

2 ARIMA Model In the early 1970s, some scholars proposed the famous ARIMA model of time series prediction, also known as the Box-Jenkins model. The basic idea of the ARIMA model is that some time series are a set of random variables that depend on time t. Although the individual sequence values that make up the time series are uncertain, the whole sequence changes have a certain degree of regularity. The fluctuations of the series can all be regarded as the combined effects of both the deterministic and stochastic factors. The ARIMA model consists of three parts: AR, MA, and I. AR (auto regression) represents an autoregressive model, which is a sequence of regressions of the values of itself at different times in the past. In this model, the value Xi of the arbitrary point i of the time series is represented by the linear combination of the values of p sequence points before point i. The model is abbreviated as AR(p). The mathematical form is: Xi ¼ u1 Xi1 þ u2 Xi2 þ . . . þ up Xip þ ai :

ð1Þ

u1 ; u2 ; . . .; up represent autoregressive coefficients. ai is a normally distributed white noise with mean zero variance of d2a . MA (moving average model) represents a moving average model. The value Xi of an arbitrary point i in the model sequence is weighted by the white noise of the point i and the first q sequence point values and their lag entries. The model is abbreviated as MA(q). The mathematical form is: Xi ¼ ai þ hi1 a1 þ h2 ai2 þ . . . þ hq aiq :

ð2Þ

h1 ; h2 ; . . .; hq are smooth coefficients. fai g is a normal distribution white noise with mean square 0.

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I (integration) represents the difference order, the time series model must be the stationarity sequence to establish the econometric model, and the ARIMA model as the time series model is no exception. The most common way to make the time series smooth is the difference. The operation that subtracts the current value of the time series variable from its lag value is called differential. The difference formula is: DXi ¼Xi  Xi1 D2 Xi ¼DXi  DXi1 .. .

ð3Þ

Dd Xi ¼Dd1 Xi  Dd1 Xi1 d represents the difference order. D called differential operator. From the above, it can be seen that the ARIMA model is actually a combination of an AR model and a MA model in which a non-stationary time series is transformed into a stationary sequence after difference. The mathematical expression of this model is: w ¼ Dd X wt ¼

Xp j¼1

uj wtj þ at þ

ð4Þ Xq j¼1

hj atj

ð5Þ

w is the stationary sequence obtained by the difference of the original sequence, and then an ARMA (p, q) model is established for wt. Therefore, the obtained model is called Xt * ARIMA (p, d, q). According to the formula of the ARIMA model, the construction of the model mainly consists of an autoregressive term p, a difference order d, and a moving average term q. When the general ARIMA model is built, it is necessary to determine the stationarity of the sequence through the ADF unit root test. If it is not smooth, the difference processing is performed, and the difference order d is determined by trial and error. Generally, d does not exceed 2. After the time series is smoothed, the ARIMA (p, d, q) model is transformed into an ARMA (p, q) model. In the time series analysis, the autocorrelation function (ACF) and partial autocorrelation function (PACF) are usually used to identify the p and q of the model. Then the order is determined according to the minimum information criterion AIC. After the model order is determined, the ARMA model is estimated. In this paper, the least square method is used to estimate the parameters. Finally, inspections and diagnostics are still needed to verify the suitability of the selected model.

3 Experimental Modeling According to the collected 365-day NH4 concentration data of Zhuyi River in 2014, this article uses Eviews software to analyze and model the previous 360 days of data. The last 5 days of data are used to detect the prediction accuracy.

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Experimental Procedure

(1) Data preprocessing. Outliers are detected and replaced with the average of the week before the outliers. The 360-day NH4 concentration change trend was obtained as shown in Fig. 1.

NH4 concentration (mmol/m3)

14 12 10 8 6 4 2 0

50

100

150

200

250

300

350

Fig. 1. Trend of NH4 concentration in Zhuyi River

Observing the trend of changes in Fig. 1, it was found that the concentration sequence fluctuates greatly. Then use the unit root method to test the stationarity and compare the ADF values. The specific results are shown in Table 1. Table 1. Augmented Dickey-Fuller unit root test on NH4. Augmented Dickey-Fuller test statistic Test critical values: 1% level 5% level 10% level

t-Statistic

Prob.*

–2.075694 –3.448414 –2.869396 –2.571023

0.2548

As can be seen from the results in Table 1, ADF = –2.075694 > –3.448414 indicates that the sequence did not pass the ADF test, and the sequence was not stable. In order to obtain a stationary sequence, the NH4 concentration sequence is differentially processed. Different differential order d is selected for testing, and it is found that d = 1 can obtain a stationary sequence, so the original sequence is first-order differential.

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Fig. 2. Correlation analysis

(2) Model identification. An autocorrelation function and a partial correlation function analysis were performed on the first-order differentially stationary sequence. Observing Fig. 2, we find that the autocorrelation coefficient and the partial correlation coefficient have tailing phenomena, so the NH4 concentration time series is suitable for the ARIMA (p, 1, q) model. From the figure, we can see that when the lag order is 1, 2, there is a significant truncation phenomenon, and the correlation coefficient falls to the edge of the double standard deviation when the lag is 2 orders, so p takes 1 or 2 and q takes 2. Then AR(1) AR(2) MA(2) was analyzed and the results are shown in Table 2. Table 2. Model analysis. Variable Coefficient Std. error t-Statistic AR(1) 1.555544 0.029935 51.96382 AR(2) –0.561144 0.029684 –18.90422 MA(2) –0.104100 0.036101 –2.883594 SIGMASQ 0.164740 0.006350 25.94281

Prob. 0.0000 0.0000 0.0042 0.0000

From the above table, the final selected model is ARIMA (2, 1, 2). (3) Model test. In order to ensure the reliability of the established model, the residual sequence of the model needs to be tested to see whether it meets the white noise process and the model is reasonable. Otherwise, the type of the selected model must be re-identified. Q-statistical tests were performed on the residuals to find that the residuals satisfied the white noise process and the model passed the test. (4) Prediction of the model. Based on the model obtained in the above steps, the NH4 concentration of the Zhuyi River in the next 10 days is predicted.

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3.2

Experimental Results

Compare the predicted results of ARIMA (2, 1, 2) models with the actual values obtained in Table 3. Table 3. Forecast analysis table Time(day) 1 2 3 4 5

Actual value 0.0959 0.1041 0.1021 0.1049 0.1066

Predictive value Relative error 0.0907 0.052 0.0901 0.134 0.0897 0.121 0.0894 0.147 0.0891 0.164

From the above table, we can see that when the prediction time t = 1, the predicted value is quite close to the actual value, and the relative error is only 5.2%. It can be seen that the prediction accuracy of the model in the short-term is very good. However, as the prediction period increases, the accuracy of the prediction gradually declines. Therefore, this model is only suitable for short-term prediction of water quality time series.

4 Conclusion In this paper, the traditional time series prediction model ARIMA is used to predict the NH4 concentration of Zhuyi River. After analyzing the historical data of NH4 concentration, the model parameters are determined and the data of the next 5 days are predicted. The analysis found that ARIMA has a high degree of accuracy in short-term forecasting but is not suitable for long-term forecasting. Based on this deficiency, we will improve it in combination with other forecasting models in future work.

References 1. Xia, J., Zhang, X.W.: A grey nonlinear programming applied to river water quality management. J. Hydraul. Eng. 12, 121–131 (1993) 2. Inyim, N., Liengcharernsit, W.: A linear programmimg model for tidal river water quality management. Lowland Technol. Int. 14(2), 38–49 (2012) 3. Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. 45(1), 1–34 (2012) 4. Box, G.E.P., Jenkins, G.M., Bacon, D.W.: Models for Forecasting Seasonal and NonSeasonal Time Series. Models for Forecasting Seasonal and Non-Seasonal Time S. (1967) 5. Brockwell, P.J., Davis, R.A.: Stationary Time Series. Time Series: Theory and Methods. Springer, New York (1987). https://doi.org/10.1007/978-1-4899-0004-3 6. Janacek, G.: Time series analysis forecasting and control. J. Time 31(4), 303 (2010)

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7. Contreras, J., Espinola, R., Nogales, F.J.: ARIMA models to predict next-day electricity prices. IEEE Trans. Power Syst. 18(3), 1014–1020 (2003) 8. Shan, R., Shi, S., Liu, W.: Thesis on combination stock forecasting model with wavelet denoising and optimized arima method. Icic Express Lett. 8(8), 2315–2320 (2014) 9. He, J., Si, B.: The application of ARIMA-RBF model in urban rail traffic volume forecast. ICCSEE-13 (2013) 10. Kusumaningrum, O.: PeramalanKebutuhan Bahan Bakar Premium di Depot Ampenan dengan Metode Hibrida Arima-Neural Network untuk Optimasi Persediaan. Paper & Presentation of Statistics Rsst Kus P. 1(1) (2012)

A Review of Gait Behavior Recognition Methods Based on Wearable Devices Chang Liu(&), Jijun Zhao, and Zhongcheng Wei School of Information and Electric Engineering, Hebei University of Engineering, Handan 056038, Hebei, China [email protected]

Abstract. As a new biometric recognition technique, gait behavior recognition is mainly based on the individual behavior analysis of human walking. Among them, the recognition of gait classification is a key step and an important task in the process of gait behavior recognition. Firstly, this paper analyzes the factors of data noise, and summarizes the methods of data preprocessing. Secondly, it analyzes and discusses the classification of gait features. Then it compares the algorithms of gait behavior classification and recognition; The gait classification recognition method based on Hidden Markov is reviewed, which has certain theoretical guiding significance and application value. Keywords: HMM
Gait behavior recognition Classification recognition


Feature extraction


1 Introduction In recent years, as one of the key technologies for human behavior recognition, gait behavior recognition technology has attracted widespread attention in the academic community. Its goal is to distinguish individuals through individual walking postures. Gait is the inherent bio-characteristics of the human body [1]. It mainly refers to the behavioral characteristics of the human body when walking in a normal walking state. It is not only ubiquitous in a body weight, but also can not easily be stolen or imitated [2]. Studies in Medicine [3] and Psychology [4] have shown that the gait of the human body involves coordinated movements between various parts of the human body and joints [5]. Differences in gait characteristics generally include height, weight, step, and step speed factors Moreover, it has the advantages of far-reaching perception, difficulty in disguise, non-invasiveness, and small impact on the environment, and it has increasingly attracted the attention of researchers. Based on the above advantages, gait behavior recognition has a wide range of application prospects in fields such as mobile health, telemedicine, security monitoring, smart home, and smart city. The gait behavior recognition technology utilizes the attitude of people walking to recognize the behavior of people, and through the classification and identification of gait behavior, it can further analyze the behavior of the moving human body. Since Niyogi and Adelson [6] used machine vision and pattern recognition theory to conduct

© Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 134–145, 2019. https://doi.org/10.1007/978-981-13-7025-0_14

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research on gait behavior recognition technology in 1994, more and more researchers have devoted a lot of time and energy to gait behavior recognition technology. A lot of research work has started. Mantyjarvi et al. proposed the use of an acceleration sensor to perceive gait behavior data [7], which opened up a new way to obtain gait data. Sabatini et al. proposed a wearable sensor network centered on a smartphone to assess the risk of falls for the elderly. By collecting gait data from different sensor sources, a step-based gait stability index was evaluated in real time. Evaluate the fall risk, extract the accelerometer data using time and frequency domain features, and use the radial basis function of the support vector machine as the classifier algorithm. The support vector machine shows advantages in solving small sample, nonlinear and high dimensional pattern recognition. The method used in [8] is to first determine an acceleration threshold. If the acceleration of an axis exceeds this threshold, a dynamic time warping (DTW) algorithm is used to perform the discrimination. Fan Lin and others used smartphones to collect acceleration data of people’s motions such as motionlessness, walking, running, and going downstairs. In order to eliminate the influence of mobile phone carrying position on behavioral recognition efficiency, feature sets highly relevant to behaviors were extracted from different positional data. Build position-independent behavior recognition [9]. Second, there are still some studies that classify out-of-sync states through acceleration signals, such as upstairs, downstairs, walking, running, and other daily activities. In [10], five 5-axis accelerometers were fixed on the soles, thighs, wrists, arms, and buttocks of the human body, respectively, to obtain human behavior data. Although the behavioral data it obtains is more comprehensive, the system consumes a lot of energy, has a large amount of calculation, and the wearer’s comfort is poor, which seriously hampers human daily life. In addition, there are gait behavior recognition methods based on angles, pressures, etc. Professor Zhang Daqing proposed a gait calculation framework for distinguishing between Parkinson’s patients and normal people in 2016, gait phase identification, feature extraction and selection, and mode. The three parts of the classification constitute the framework, first proposed a sliding window to distinguish the four gait stages from the plantar data, and the second step to extract and select the gait features describe the gait stability, symmetry and coordination. Finally, a mixed classification model was used to identify the Parkinson’s gait [11]. Through a brief overview of the above literature, we can understand that the key technologies involved in gait behavior recognition systems include: data processing, feature value extraction, data classification, and so on. The gait behavior recognition mainly analyzes and deals with the time series containing human gait movements. Generally, the gait behavior recognition process includes data preprocessing, gait feature extraction, and gait classification and identification. The following section outlines the methods and algorithms for gait behavior recognition.

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2 Gait Behavior Recognition Process 2.1

Data Preprocessing

The following sources of noise may be present when collecting human gait information: (1) In walking, the sensor will produce vibration and noise as the body shakes; (2) Associated noise occurs during the conversion of a signal from an analog signal to a digital signal; (3) Electromagnetic interference exists in the process of data transmission from the acquisition device to the host computer; (4) Power frequency interference caused by working power in the acquisition system. The noise generated in the process of collecting data may be very close to the real signal. Therefore, the collected gait data must be denoised and preprocessed. The denoising preprocessing can be performed in the acquisition circuit, for example, by using hardware filtering methods such as inductance, capacitance, and LC composite filter circuit, and also by using MATLAB, FIR filter, wavelet filter, etc. in the upper computer [12–15] Software filtering method. 2.2

Gait Behavior Feature Extraction

The main task of feature extraction is to select the most effective features for classification and identification, which is the key to gait behavior identification. It can be divided into spatio-temporal parameters feature extraction methods [16] and [17], angle parameters feature extraction methods [18] and lower limb movement mechanics. Parameter feature extraction method. 2.2.1 Temporal and Spatial Parameter Characteristics Spatio-temporal parameters refer to features such as gait cycle, support period, pace, pace, and step length. Gait cycle: refers to the period of time between the landing of one leg from the foot to the next leg. According to the movement of the gait, the gait cycle can be divided into the support period and the swing period. The stance phase (stance phase) refers to the period from the heel touchdown to the toe off the ground. The heel strikes the ground as the starting point of the gait cycle and lands on the heel and toe to support the mid-term. After that, the heel began to leave the ground, the body gradually moved forward, finally to the tiptoe off the ground, the end of the support period. Swing phase: It refers to the period from the toe to the next heel strike after the support period. When the support period is over, that is, when the tiptoe is off the ground, the lower limbs stop by accelerating forward to reduce the swing speed, and the next support begins.

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The support period lasts about 60% of the gait cycle. It can be divided into three stages: early support, middle support, and late support. The early support refers to the heel strike from the ground to the toe, and the middle support refers to the full support of the foot. The late period refers to the process from the heel off the ground to the toes off the ground. Walking speed: The ratio of the distance traveled in the walking cycle to the duration of the gait cycle, ie .pace = step/gait cycle. Stride length: It refers to the linear distance between two adjacent landings on the same side of the heel. Step length: A linear distance between the measured heel and the other heel. 2.2.2 Angle Parameter Characteristics The angle parameter features mainly include the angle changes of the three joints of the hip, knee, and ankle, such as the angle, angular velocity, and angular acceleration of each joint. The definition of lower limb joint angle [18] is as follows: Hip angle parameter: The angle between the longitudinal trunk axis and the femur longitudinal axis. Knee angle parameter: The angle between the extension of the femur longitudinal axis and the parallel line of the longitudinal axis of the tibia. Ankle angle parameter: The angle between the fifth metatarsal and the midline of the lateral tibia is reduced by 90°. 2.2.3 Characteristics of Mechanical Parameters of Lower Limbs The parameters of lower limb movement mechanics mainly include plantar pressure and joint torque. Through the above description of the characteristics of gait parameters, it is clear that space-time parameters are relatively intuitive, and angle parameters and kinematics parameters are more detailed in describing gait details. 2.3

Recognition of Gait Behavior

The gait classification recognition process is to match the gait recognition after feature extraction with the known sequences trained in the sample database, and determine the category of the gait recognition classification criteria and the discrimination conditions. The gait classification recognition algorithm has a very close relationship with the construction of the entire gait behavior recognition system. Selecting an appropriate classification recognition algorithm and matching strategy is a key issue. Common gait behavior classifier algorithms are: k-Nearest Neighbor (KNN), Bayesian classifiers, Support Vector Machines (SVM), neural network classifiers, and hidden Mars Hidden Markov Model (HMM), etc. The common gait behavior classifier principle and characteristics are shown in Table 1.

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Name

Principle

Features

K-nearest neighbor method classifier

Classifying by measuring the distance between different feature values

Bayesian classifier

Sorting based on the probability of an extracted feature value

Support vector machines

A classification decision scheme combining optimal linear classification and kernel functions is proposed.

Neural network classifier

Non-linear mapping between the incoming and outgoing sample sets to achieve

Hidden Markov Model

Use a double stochastic process to describe statistical relationships between states and between states and observation sequences

No data input preset, high accuracy, but insensitive to outliers, computationally complex, high space complexity Can handle multi-category classification problems and still use it when there is less data It is sensitive to the choice of kernel function and parameter adjustment, the error rate of generalization is low, the computational overhead is small, and the result is easy to understand Parallel processing, computation and distributed representation, and selflearning, self-adaptation Strong ability to learn and handle unsegmented continuous data sequences

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For the multi-perspective classification and identification research, when the identification is a big lock, a decision-based information fusion classification method is used. This method firstly classifies individual different features and obtains their own independent decision results. Association and comprehensive matching according to the fusion strategy, and finally get the classification recognition result. Decision-based fusion methods include Bayesian decision theory, artificial neural networks, genetic algorithms, and rough theory. In recent years, with the gradual deepening of research on gait behavior classification, a variety of algorithms combined with research methods and some improved algorithms based on classical algorithms have emerged gradually to improve the recognition of gait classification.

3 HMM The Hidden Markov Model (HMM) is a statistical model used to describe a Markov process with implicit unknown parameters. Founded in the 1970 s, it was proposed by Baum et al. [41] and conducted rigorous mathematical arguments by Rabiner et al. [42].

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It was first used to solve continuous speech recognition problems [43]. Bell Lab’s Rabiner et al. [44] made the HMM become a research hotspot in the field of voice processing that researchers are interested in through the promotion of HMM theory and the successful use of vocabulary recognition reports. The Hidden Markov Model is based on the Markov model. In the Markov model, observations and states are in a one-to-one correspondence. However, practical problems are often more complex than the problems described. The observed events do not correspond to the states one by one. They are related by a set of observed probabilities. Such a model is called HMM [42]. It consists of two stochastic processes, one of which is the Markov model, which is a basic random process that describes the transitions between states; the other is the correspondence between states and observations. In other words, observers can only see observations and cannot see the state directly. Instead, they perceive the existence of states through a random process. Therefore, they are called “hidden” Markov models. The following is a simple description of the HMM parameter description model: (1) Q: The number of states in the model of state sequences is Q ¼ fq1 ; q2 ; . . .; qN g (2) V: The set of observations corresponding to each state, denoted as V ¼ fv1 ; v2 ; . . .; vM g (3) p: The initial state probability distribution vector, denoted as p ¼ fp1 ; p2 ; . . .; pN g,among them p ¼ Pði1 ¼ hi Þ; 0  pi  1;

X

pi ¼ 1; 1  i  N:

ð1Þ

i

It is the probability qi of being in the state at time t = 1.   (4) A: State transition probability matrix, A ¼ aij NN , among them   ai;j ¼ P ii þ 1 ¼ qj jii ¼ qi ; i ¼ 1; 2; . . .; N; j ¼ 1; 2; . . .; N:

ð2Þ

It is the probability qi of transitioning to the state at the next time t+1 under the condition of the state at time t.   (5) B: Observation probability matrix, B ¼ bj ðkÞ NM ,among them bj ðkÞ is   P ot ¼ vk jit ¼ qj ; k ¼ 1; 2; . . .; M; j ¼ 1; 2; . . .; N:

ð3Þ

It is the probability qj of generating observations vk under the condition of state at time t.

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The Hidden Markov Model is determined by the initial state probability distribution vector p, the state transition probability matrix A, and the observation probability matrix B. A and p determine the state sequence and B determines the sequence of observations. So Hidden Markov Models can be represented by ternary symbols, i.e. k ¼ ðA; B; pÞ:

ð4Þ

A, B, p are called the three elements of the hidden Markov model. Among them, the state transition probability matrix A and the initial state probability distribution vector p determine the hidden Markov chain, thereby generating an unobserved state sequence. The observation probability matrix B determines how to generate the observation sequence from the state, and the state sequence determines The resulting observation sequence. The Hidden Markov Model mainly solves three basic problems. For this purpose, three basic algorithms are proposed. The questions are as follows: Question I: Probability Calculation of Hidden Markov Models Given the sequence of observations Q ¼ fq1 ; q2 ; . . .; qN g and the model k ¼ ðA; B; pÞ, calculate the probability PðOjkÞ of occurrence of the sequence Q of observations under a given model. Baum corresponds to the problem of a forward-backward algorithm. According to the composition of the HMM, the solution method is as follows [30]: For a given state sequence S ¼ fq1 ; q2 ; . . .; qN g, There have PðOjS; kÞ ¼

t Y

Pðot jqt ; kÞ ¼ bq1 ðo1 Þbq2 ðo2 Þ. . .bqt ðot Þ:

ð5Þ

t¼1

Among them, bqt ðot Þ ¼ bjk jqt ¼ hj ; ot ¼ vk ; 1  t  N:

ð6Þ

For a given k, the probability of producing S is pðSjkÞ ¼ pq1 aq1q2 . . .aqT1qT :

ð7Þ

So the probability is pðOjkÞ ¼

X

PðOjS; kÞpðSjkÞ:

ð8Þ

s

Question 2: Prediction (Decoding) of Hidden Markov Models Given the sequence of observations Q ¼ fq1 ; q2 ; . . .; qN g and the model k ¼ ðA; B; pÞ, the probability of the observed sequence PðOjkÞ is the largest under the given model, and the corresponding state sequence is obtained. That is, given the sequence of observations, find the corresponding most likely sequence of states.

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The second problem is to seek the “optimal” state sequence [42]. Refers to the state sequence determined when the maximum value is taken. The Viterbi algorithm actually uses dynamic programming to solve the prediction problem of Hidden Markov Models, that is, using dynamic programming to solve the optimal path. This process can be implemented using the Viterbi algorithm: Input: model k ¼ ðA; B; pÞ And observation sequences O ¼ ðo1 ; o2 ; . . .; oT Þ; Output: Optimal path I ¼ ði1 ; i2 ; . . .; iT Þ. Initialization d1 ðiÞ ¼ pi bi ðoI Þ; i ¼ 1; 2; . . .; N:

ð9Þ

w1 ðiÞ ¼ 0; i ¼ 1; 2; . . .; N:

ð10Þ

Recursive, for t ¼ 2; 3; . . .; T   dt ðiÞ ¼ max dt1 ð jÞaji bi ðot Þ; i ¼ 1; 2; . . .; N:

ð11Þ

  w1 ðiÞ ¼ arg max dt1 ð jÞaji ; i ¼ 1; 2; . . .; N:

ð12Þ

P ¼ max dT ðiÞ:

ð13Þ

iT ¼ arg max ½dT ðiÞ:

ð14Þ

1jN

1jN

Termination 1jN

1jN

Best path backtracking. For t ¼ T  1; T  2; . . .1 it ¼ wi þ 1 ðit þ 1 Þ:

ð15Þ

Finding the optimal path I ¼ ði1 ; i2 ; . . .; iT Þ Question 3: Learning Problems of Hidden Markov Models Given the sequence of observations Q ¼ fq1 ; q2 ; . . .; qN g, the parameters of the model k ¼ ðA; B; pÞ, so that the probability of the sequence of observations under the given model is maximum, ie, the parameters of the model PðOjkÞ are estimated using the method of maximum likelihood estimation. The Baum-Welch algorithm is a hidden Markov model for learning.

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Input: Observation sequence O ¼ ðo1 ; o2 ; . . .; oT Þ; Output: Hidden Markov Model Parameters Initialization   ð0Þ ð0Þ for n = 0,select aij ; bj ðkÞð0Þ ; pi , Get the model kð0Þ ¼ Að0Þ ; Bð0Þ ; pð0Þ Recursive. For n = 1, 2, … , aij ¼

T 1 X

nt ði; jÞ=

t¼1

t¼1;ot ¼vk ðn þ 1Þ

pi

nt ðiÞ:

ð16Þ

t¼1

T X

bj ð k Þ ¼

T 1 X

ct ð jÞ=

T X

ct ð jÞ:

ð17Þ

t¼1

¼ c1 ðiÞ:

ð18Þ

  Termination. Get model parameters kðn þ 1Þ ¼ Aðn þ 1Þ ; Bðn þ 1Þ ; pðn þ 1Þ : As a statistical model of dynamic time series, Hidden Markov Model is the most effective recognition method for human gait behavior recognition [40]. The gait behavior movement can be regarded as a series of posture changes of the human body, determined by the structure of the human body itself, and has the characteristics of probability and statistics. The structure behind the gait behavior can become the hidden state of the hidden Markov model, and the walking posture Seen as the output of the structure, the observed vector value. Theoretical and practical experiments have shown that Hidden Markov Model can not only represent gait behavior well, but also has a good classification effect.

4 Conclusion This article mainly describes the progress of gait behavior recognition research. The gait behavior recognition is mainly divided into three parts: gait data preprocessing, gait behavior feature extraction and gait behavior classification and identification. The classification and recognition is extremely important and the focus of this study. Firstly, the causes of the gait behavior data noise and the research status of the data preprocessing measures are analyzed. Secondly, the characteristic parameters of the gait behavior are evaluated from three angles: space-time, angular velocity, and motion mechanics. Finally, the gait behavior is classified. The recognition algorithm is characterized and compared, and the hidden Markov model with good classification effect is analyzed. This article describes the gait behavior recognition process intuitively, and provides valuable reference for related researchers; it facilitates researchers to better understand the characteristics of gait behavior recognition and broadens the research ideas; and identifies the gait behavior recognition system. The application has a certain theoretical guidance and practical value.

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Acknowledgments. This paper is found by Science and Technology Research and Development Plan Project of Handan (No. 1721203048).

References 1. Dawson, M.R.: “Gait recognition.” Final Thesis Report, Department of Computing, Imperial College of Science, Technology & Medicine, London (2002) 2. Xingzhi, Z., Chensheng, W., Feng, L.: Gait recognition review. Software 34(4), 160–164 (2013). (in Chinese) 3. Cho, C.W., Chao, W.H., Lin, S.H., Chen, Y.Y.: A vision-based analysis system for gait recognition in patients with Parkinson’s disease. Expert Syst. Appl. 36(3), 7033–7039 (2009) 4. Stevenage, S.V., Nixon, M.S., Vince, K.: Visual analysis of gait as a cue to identity. Appl. Cogn. Psychol. Official J. Soc. Appl. Res. Mem. Cogn. 13(6), 513–526 (1999) 5. Liang, W., Weiming, H.: Gait based identification. J. Comput. Sci. 26(3), 353–360 (2003). (in Chinese) 6. Niyogi, S.A., Adelson, E.H.: Analyzing gait with spatiotemporal surfaces. In: Proceedings of the 1994 IEEE Workshop on Motion of Non-Rigid and Articulated Objects, pp. 64–69. IEEE (1994) 7. Mantyjarvi, J., Lindholm, M., Vildjiounaite, E., et al.: Identifying users of portable devices from gait pattern with accelerometers. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2005), vol. 2, pp. 973–976. IEEE (2005) 8. Mannini, A., Sabatini, A.M.: A smartphone-centered wearable sensor network for fall risk assessment in the elderly. In: Proceedings of the 10th EAI International Conference on Body Area Networks. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 167–172 (2015) 9. Lin, W., Zhongmin, W.: User behavior recognition model for location independent mobile phone. Comput. Appl. Res. 32(01), 63–66 (2015). (in Chinese) 10. Bao, L., Stephen, I.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) Pervasive 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24646-6_1 11. Wang, T., Wang, Z., Zhang, D., et al.: Recognizing Parkinsonian gait pattern by exploiting fine-grained movement function features. ACM Trans. Intell. Syst. Technol. (TIST) 8(1), 6 (2016) 12. Joy, J., Peter, S., John, N.: Denoising using soft thresholding. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 2(3), 1027–1032 (2013) 13. Donoho, D.L., Johnstone, I.M.: Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90(432), 1200–1224 (1995) 14. Xianbing, P.: Performance analysis of an improved wavelet threshold denoising method. Microcomput. Inf. (03S), 112–113 (2006). (in Chinese) 15. Haofan, D., Shuang, C.: Research on de-noising method based on MATLAB wavelet. Comput. Simul. 20(7), 119–122 (2003). (in Chinese) 16. Xiaona, Q., Tengyu, Z., Xitai, W.: Experimental study on the influence of walking speed on gait parameters. Chin. J. Rehabil. Med. 27(3), 257–259 (2012). (in Chinese) 17. Xueyan, H., Xiaoping, H.: Gait characteristics in normal adults. Chin. Rehabil. Theor. Pract. 12(10), 855–857 (2006). (in Chinese)

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K-Means Optimization Algorithm Based on Tightness Mutation Tie Fei Li(&), Jian Fei Ma, Rui Xin Yang, Di Wu, and Yan Guang Shen School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, Hebei, China [email protected]

Abstract. The random initial clustering center is K - means algorithm to determine the center of traditional way, there will be a clustering result is not stable, optimized to determine the initial clustering center K - means algorithm requires some parameter values, artificial subjective makes the clustering results. Therefore, based on the compactness information of the sample distribution in space, this paper proposes a k-means algorithm to optimize the initial clustering center by using the compactness mutation. The algorithm using the sample space distribution information, through calculating the tightness of the spatial distribution of the sample whether mutations sample information intensity, and based on class sample tightness mutation segmentation principle of cluster center, puts forward the tightness mutation to optimize the initial clustering center of the K - Means algorithm, through the optimization of K - Means clustering algorithm in the UCI machine learning database data set Sentiment labelled sentences and Sentence experiments on Corpus show that the algorithm not only can get better clustering results, The clustering results have high stability. Keywords: Clustering Initial cluster center


K - means algorithm
Density mutation


1 Introduction Cluster analysis is an important technique to divide research objects into groups of relatively homogeneous groups [1], It is a method of unsupervised machine learning, which analyzes and summarizes the internal relations of things based on the common principles of similarity. In the process of cluster analysis, based on similarity, the elements in the same cluster have high similarity, while those in different clusters have low similarity. Cluster analysis is applied to text analysis, data classification, image processing and other fields [2]. The commonly used clustering methods are partition method, hierarchical method, and density algorithm, graph theory clustering method, model algorithm and grid algorithm. K - means algorithm is a kind of typical classification method of clustering algorithm, this algorithm will be given a N set or record data sets, split structure K group, each group represents a cluster, K < N. Each group contains at least one data record, every data record belongs to and only to a group, in © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 146–156, 2019. https://doi.org/10.1007/978-981-13-7025-0_15

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which each child collection represents a kind of cluster, samples of the same class of cluster similarity is high, not low similarity of similar clusters in the sample. K-means algorithm is also simple and efficient, time complexity and space complexity are low for large data sets, and it has become a widely used clustering algorithm. However, when the data set of traditional k-means algorithm is large, the results are easy to be locally optimized. K value needs to be preset, which is sensitive to the selection of the first K points. Sensitive to noise and outliers, non-convex data cannot be solved. K-means algorithm is a local search process, and its clustering results depend on the initial clustering center and the initial partition [3]. And K - means algorithm, the final result is only relative to the initial classification is better, is not necessarily the global optimal partition [4]. If the initial cluster center is well selected, the initial partition will distribute samples to each initial cluster center according to the most similar method to generate the initial cluster, and the clustering results will reach the global optimum [5], Therefore, many researches are devoted to the optimal initial clustering center selection of k-means algorithm. Literature [6] proposed an initial cluster center selection method, and K initial cluster centers were selected according to the local density of samples. Literature [7] proposed that the initial center of the cluster was selected by calculation rather than random selection. Literature [8, 9] studies the k-harmonic mean clustering algorithm for mixed data sets. Literature [10] et al. used spectral method to estimate the feature center to obtain the initial clustering center of k-means algorithm. Literature [11–14] used different sample density calculation methods to estimate the density of sample points, and finally got the clustering center. Literature [15] used the local density adaptive measurement method to generate the initial clustering center of k-means algorithm. Literature [16] according to the initial clustering center of k-means clustering algorithm based on SIFT feature distribution; Literature [17] k-means clustering based on cell space optimization. These optimized k-mean algorithms improve the clustering performance of k-mean to a certain extent, but lack objectivity to a certain extent due to the subjective selection of some parameters. How to determine the initial clustering center and the appropriate K value for the study of k-means algorithm, Based on the analysis of K - means to optimize the initial clustering center, on the basis of proposed based on the tightness of the spatial distribution of sample a mutation to optimize K - means clustering algorithm of the initial clustering center, in order to overcome the existing optimized K - means the initial clustering center need some algorithms based on experience to select certain parameters, the clustering results depend on the artificial parameter selection of faults. According to the distribution characteristics of the samples, the similarity, average similarity and compactness of the samples were calculated. Choosing mutation data sample data before most of the K samples as K - means algorithm of the initial clustering center, avoid the traditional K - means algorithm randomly selected from the initial clustering center clustering results caused by unstable phenomenon, and the optimization of the initial clustering center existing K - means algorithm depend on the experience of parameter selection problem. Therefore, based on the principle of every sample tightness mutation, this paper proposes a k-means algorithm for optimizing the initial clustering center based on sample distribution tightness mutation.

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2 Introduction to K-Means Algorithm K-means algorithm is a kind of algorithm that input the number of clustering K, as well as the database containing n data objects, and the output satisfies the minimum variance standard of K clustering. K-means algorithm receives input k; then, n data objects are divided into k clusters to satisfy the obtained clustering: the object similarity in the

Fig. 1. K-means algorithm flow chart

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same cluster is high; the object similarity in different clusters is small. The commonly used similarity judgment is to calculate the Euclidean distance, cosine distance and Gerard similarity measurement between samples. K-Means algorithm the basic idea is: first, select any K objects from n data objects as the initial clustering center, according to the mean of each object clustering objects (center), and calculate the distance form the centre of each object and the object; the corresponding objects are reclassified according to the minimum distance. The mean value (central object) of each (variable) cluster is recalculated. The standard measure function is calculated. When certain conditions are met, such as convergence, the algorithm is terminated. If the condition does not satisfy the requirement to continue the iteration until the original sample set of K intersecting stable class clusters. The flow diagram is shown in Fig. 1.

3 K-Means Optimization Algorithm Based on Tightness Mutation In the traditional k-means algorithm, an initial partition should be determined according to the initial clustering center, and then the initial partition should be optimized. The selection of the initial clustering center has a great influence on the clustering results. Once the initial value is not set satisfactorily, the effective clustering results may not be obtained. As a result, the clustering result may deviate from the real distribution of data set samples and get the wrong clustering result. Physical distance, each sample as the initial clustering center, can avoid the initial clustering center may reside in the same class clusters of defects, but is sensitive to noise and may cause the deviation of clustering results. [11–14] respectively based on density are studied to optimize the initial clustering center of the K - means algorithm, in order to solve K - means algorithm choose distance each other samples for initial clustering center, may choose to noise and random clustering center problems. Makes these studies this problem with a certain degree of improvement, to improve the accuracy of clustering results, good stability, but the literature involves adjustment coefficient or density radius fixed value problem, the clustering results directly depends on the selection of these parameters or the density of different text is not complete for a fixed radius, parameter selection, clustering results will become very poor. Therefore, these researches have a great degree of subjectivity and immobility, which requires a certain prior knowledge and experience value for the original data set. In this paper, the sample tightness mutation is used as the information to select the initial cluster center of k-means. In this paper, the sample tightness segmentation principle of cluster center is used to determine whether the tightness is mutated. Select data samples before tightness mutation data at most K samples as K - means algorithm of the initial clustering centers don’t need other parameters choice, in this paper, the optimization of the K - means algorithm selected the initial clustering center for high intensity, and around the sample distribution is more dense sample, thus ensuring K - means algorithm quickly converge to global optimal solution rather than the local optimal solution, and there’s no need to enter any parameters, guarantee the objectivity of the K - means algorithm clustering results and stability.

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Algorithm Principle

By calculating the degree of each sample is similar to other samples from big to small, define (N) with (N - 1) the difference of two adjacent tightness value, and then calculate the sample average value intensity, and when the sample of tightness appeared in the process of calculation is less than the average value, the intensity and the tightness of mutation, this mutation point as the reference point, statistics the number of mutations samples before M, contains the biggest M the number of samples as a center of the first, and then from the first central point mutations tightness before the center of the data sample averaging get updated, delete the first central point and the related points, Then calculate the second center point successively and so on, until K center points are found and k-means clustering is carried out. 3.2

Basic Concepts

Suppose the data set of the pending classes is: A ¼ fai jai 2 R; i ¼ 1; 2;


; ng, The initial cluster centers of K are: B1 ; B2 ;


; Bk , 用 C1 ; C2 ;


; Ck Represents the sample set contained in K class clusters, and the set of all samples is C. The calculation formula of text distance is as follows: dðai ; aj Þ ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  ffi ðai  aj ÞT  ai  aj

ð1Þ

The sum of squared error of clustering is as follows: E¼

Xk X i1

p2cj

  p  mj 2

ð2Þ

Where, E represents the sum of the squared values of each group; P refers to the data item in class j cluster Cj . The average value of data items in class Cj is calculated as mj The word frequency formula is as follows: ni;j tf i;j ¼ P k nk;j Type of ni;j said in the case of a sample I j word occurrences, in all in the total number of samples. Reverse file frequency formula: idf i ¼ loge 

jD j  j : ti2dj 

ð3Þ P k

nk;j as the j word

ð4Þ

  Where, |D| is the total number of documents in the corpus, and  j : ti2dj  contains the number of documents containing words.

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Define the similarity between sample ai and aj of sample 1: Sim dðai ; aj Þ ¼ cosðai; aj Þ ¼

ai :aj   jai j:aj 

cosðAai; aj Þ ¼ cosðai; aj Þ

ð5Þ ð6Þ

Definition 2 samples of the ith a N the tightness of the ith a sample with other samples from big to small sorted intensity, and the N + 1 and N term similarity value of the difference of two adjacent tightness value: value gradientiN ¼ value(N + 1)  value(N)

ð7Þ

Definition 3 average sample tightness value: Value average gradient ¼

3.3

Xn 1  ðvalue(i þ 1Þ  valueðiÞÞ i¼1 n1

ð8Þ

Algorithm Steps

The algorithm steps are described as follows: (1) Preprocessing English text 1. Split words according to Spaces; 2. Exclude stop words; 3. Extract word stem; 4. According to formula (3), the number of occurrence of words will be converted into weight through tf-idf. (2) Determine the initial clustering center According to definition 1 to calculate the similarity of each sample with other sample matrix, and generate the definition through each row of the matrix of defined four smallest to get defined matrix Q, through the calculation of matrix Q value gradientN gradient values of each sample, when value gradientN [ Value average gradient kindly tell gradient transition, in order to sample as the reference point, after the sample and the sample for this kind of elements of the clusters, statistical gradient transition before sample number M, The M values of each sample are obtained successively. According to the definition, the average gradient Value_ average_ gradient of data set samples is calculated: looks for the largest sample ai1 of M in dataset C, and takes it as the initial cluster center B1 of the first class cluster. C1 ¼ faj jvalue gradienti1j \Value average gradient; j ¼ 1; 2;


; n  1g C ¼ C  C1 :

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looks for the largest sample of M again in the remaining data set C, ai2 , as the initial cluster center B2 of the second class cluster. Cn ¼ faj jvalue gradienti2j \Value average gradient; j ¼ 1; 2;


; n  1g C ¼ C  Cn . . .. . . looks for the largest sample aik of M again in the remaining data set C, and takes it as the initial cluster center Bk of the second class cluster. Cn ¼ faj jvalue gradientikj \Value average gradient; j ¼ 1; 2;


; n  1gC ¼ C  Cn : iterates successively to determine all the initial clustering centers B1 − Bk when C or Cn are empty sets (3) initial division 1 calculation data set according to the definition of each sample to the initial clustering center distance, according to the principle of similarity to the nearest the sample distribution, that is the most similar type of cluster, the initial partition. calculates the mean value of all samples in each class cluster as the new center of the class cluster. calculates the sum of squares and E of the current clustering results according to definition 5. (4) update the clustering center calculates the distance from each sample in the data set to the center of each class cluster according to definition 1, and distributes samples to the nearest class cluster according to the similarity principle. calculates the mean value of all samples in each class cluster as the new center of the class cluster. calculates the sum of squares and E of the current clustering results according to definition 5. if E converges, that is, the clustering center will not change, then the algorithm will end and output the clustering results. Otherwise, make the steering step .

4 Simulation Experiment To verify the effect of clustering algorithm in this paper and its stability, respectively on two UCI data sets to test, and compared with literature, 13, 15, 16 [11] as well as the traditional K - means algorithm clustering result comparison, literature, 13, 15 [11] the result of the experiment is to adjust parameters of optimal results. The evaluation

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criteria of the clustering results are the sum of squared error and the accuracy of clustering. The accuracy of each class cluster is defined as follows: Pi ¼

li  100% mi

li represents the number of text marked as category I in the result of the classification and the correct number of text marked as category I, while mi represents the number of text marked as class I in the result. Global accuracy is defined as follows: Pk

li MP ¼ Pki¼1 i¼1 mi K is the number of clustering centers. 4.1

Experimental Data Set

The descriptions of the two commonly used data sets used in the unlabelled I machine learning database and the sentences Corpus are shown in Table 1. Table 1. Description of UCI data set used in the experiment Data set Number of samples Class number Sentiment labelled sentences 3000 2 Sentence Corpus 91 5

4.2

Experimental Results and Analysis

The following are the experimental results and analysis of this algorithm on UCI data set labelled sentences and Sentence Corpus respectively. The optimized initial clustering center k-means algorithm for experimental comparison is the operation result under the optimal parameter setting. The comparison algorithm relies on parameter adjustment. In this paper, the best experimental results of other algorithms are compared with the experimental results of this algorithm to demonstrate the superiority of this algorithm. 4.2.1 Clustering Results and Analysis of UCI Data Set Table 2 shows the sum of squares of clustering error of each algorithm on two sets of UCI data sets. Table 2. Sum of squares of clustering error of each algorithm in UCI data set UCI data set

Sentiment labelled sentences Sentence Corpus

Literature [11] algorithm

Literature [12] algorithm

Literature [13] algorithm

In this paper, algorithm

63.2543

42.8798

46.8976

43.6529

42.6539

323.5683

288.9008

289.9087

286.9098

286.8909

Traditional kmeans algorithm

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Clustering error sum of squares is visible from Table 2, the traditional K - means algorithm running 50 times the average clustering error sum of squares is bigger, this algorithm under the condition of don’t need any initial parameters adjustment in the clustering error sum of squares of the data set is obviously superior to the result of literature [12], and bad is not the best clustering error sum of squares of [11, 13]. Clustering analysis of the error variance is visible above, this algorithm under the condition of does not require any parameter adjustment, got very good initial clustering center, is a kind of do not rely on any parameter adjustment to optimize the initial clustering center of the K means clustering algorithm. Figures. 2 and 3 are the comparison of the clustering result accuracy of each algorithm.

Fig. 2. Stability comparison of data set 1 clustering results

From Figs. 2 and 3, comparison of clustering accuracy in this paper, the clustering accuracy of algorithm is optimal on two data sets, and based on the sample space distribution tightness inspired the optimal initial clustering center K-means algorithm achieved good experimental results on data sets by comparing shows that this algorithm has the best clustering effect. The above results show that compared with other optimization initial clustering center of the K - means the clustering results depend on the choice of parameters of algorithm; this algorithm does not depend on any parameter adjustment, can obtain better clustering effect, and relatively stable clustering results.

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Fig. 3. Stability comparison of data set 2 clustering results

5 Conclusions In view of the traditional K - means algorithm, the initial clustering center randomly selected from the clustering result is not stable, and the optimization of initial clustering center of the existing algorithms need to select parameters or select parameters are not suitable for all sample problem, in this paper, using the kind of tightness in the center of the cluster segmentation principle, proposed the tightness mutation to optimize the initial clustering center of the K means clustering algorithm. Data sets from UCI machine learning database of the experimental results show that this algorithm and the traditional K - means algorithm and the optimization of the initial clustering center existing K - means algorithm, improves the clustering accuracy and clustering result is stable and can objectively reflect the distribution of the sample. Acknowledgments. This work is supported by Research Projects of Science and Technology in Hebei Higher Education Institutions (ZD2018087, ZD2016017). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

References 1. Lu, Y.: Research and application of clustering analysis data mining methods. Anhui University (2007) 2. Dongming, T.: Cluster analysis and its application research. University of electronic science and technology (2010) 3. Zhang, J.P., Liu, X.Y.: Research and application of k-means algorithm based on cluster analysis. Comput. Appl. Res. 24(5), 166–168 (2007)

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4. Hartigan, J.A.: A k-means clustering algorithm. Appl. Stat. 28(1), 100–108 (1979) 5. Shen, Y., Xu, H., Liu, H., et al.: Plant depth detection image restoration based on k-means and k-nearest neighbor algorithm. Trans. Chin. Soc. Agric. Eng. 32, 188–194 (2016) 6. Li, M., Zhu, Y., Chen, G., et al.: Kernel k-means clustering algorithm based on local density, a Kernel k-means algorithm based on local density. Comput. Appl. Res. 28(1), 78–80 (2011) 7. Lee, W.H., Lee, S., An, D.U.: Study of a reasonable initial center selection method applied to a k-means clustering. IEICE Trans. Inf. Syst. 96(8), 1727–1733 (2013) 8. Khan, S., Ahmad, A.: Cluster center initialization algorithm for k-means clustering. Elsevier Science Inc. (2004) 9. Ahmad, A., Hashmi, S.: K-harmonic means type clustering algorithm for mixed datasets. Appl. Soft Comput. 48, 39–49 (2016) 10. Qian, X., Huang, X.J., Wu, L.D., et al.: A spectral method of K - means initialization initialize K - means of spectrum method. J. Autom. 33(4), 342–346 (2007) 11. Fu ming Si: Research on an incremental k-means clustering algorithm based on density. J. Changchun Inst. Eng. (Nat. Sci. Edn.) 21(2), 114–117 (2016) 12. Xue, W., Yang, R., Zhao, N., et al.: K-means algorithm for spatial density similarity measurement. Small Microcomput. Syst. 39(1), 53–57 (2018) 13. Xiong, K., Peng, J., Yang, X., et al.: K-means clustering optimization based on kernel density estimation. Comput. Technol. Dev. 27(2), 1–5 (2017) 14. Qi, H.L., Gu, L.: Initial central method of k-means algorithm based on density and minimum distance. Comput. Technol. Dev. 27(9), 60–63, 69 (2017) 15. Ma, F.M., Lu, R.Q., Zhang, T.F.: Rough k-means clustering algorithm based on local density adaptive measurement. Comput. Eng. Sci. 40(1), 184–190 (2018) 16. Lv, H., Huang, X.L., Yang, L.F., et al.: A k-means clustering algorithm based on SIFT feature distribution. In: National Doctoral Academic Conference (2012) 17. Chen, D.N., Cui, X.: K-means initial clustering center selection algorithm based on cell space. Digit. Technol. Appl. (10), 118–119 (2011)

Study of Coal Integrated Network Decision Support System Based on GIS Haixin Liu1,2(&), Wei Wang2, Tao Jiang1, Yuling Zhao2, and Xiuyun Sun2 1

College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China [email protected] 2 College of Mining and Geomatics, Hebei University of Engineering, Handan 056038, China

Abstract. In order to scientifically and safely develop coal resources, the paper designed and developed Coal integrated network decision support system with popular program and GIS. By adopting SQL Server 2005 and Geographical Information System software SuperMap Objects 5.3 combined with relevant program languages, the system is achieved. It discusses the data input, searching, and modifying of system, the functions of graphics editing, thematic mapping and the symbols library management, In particular, the functions of ventilation network analysis, transport of underground tunnel, supply and drainage analysis underground water, analysis of underground power lines and security contingency analysis. It preliminary discusses how to build digital mine. Keywords: Integrated network of coal


GIS
Decision support system

1 Introduction Coal resource is an important resource for the development of national economy in China. The safety production in coal mine is a political, economic, and technical issue related to the whole national economy and the people’s livelihood. As the coal resources are stored in the underground, most of the underground coal mine production is carried out by people, so a large number of scholars have carried on the underground complex network. Liu et al. (2001) put forward the use of GIS technology to build coal mine ventilation network and express; Yang (2007a, 2007b) summed up and analyzed the ventilation of the mine, and realized the mine ventilation information system based on GIS; Qi (2007) also studied the dynamic ventilation management of the mine, and completed the corresponding software system based on MapX component; Sun proposed a solution to the mine ventilation system for space analysis and visualization, and used ArcEngine to achieve the mine ventilation management information system; Cao (2012) elaborated coal mine ventilation information management system design based on GIS for the safety of coal mine ventilation, including the management of electronic map editing module, ventilation information management module, management module of ventilation network, security detection module and the operation control module and so on. Nie (2013) eveloped coal mine electric power monitoring and © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 157–164, 2019. https://doi.org/10.1007/978-981-13-7025-0_16

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management system based on GIS in accordance with mines networking unified spatiotemporal model. In a word, all of the above documents are the research and development of the coal mine ventilation network or power network by GIS technology. However, the concept of digital mine requires the whole mine as one research object. This paper mainly from the perspective of GIS spatial data modeling, the coal mine production environment abstract as a spatial network data model, and on this basis, the use of GIS spatial network analysis function (path analysis, accessibility analysis, logistics distribution, etc.) to achieve coal mine underground transportation, water supply and drainage, mine ventilation, mine power supply, safety emergency (such as avoiding disaster path) and other production link of the management and analysis of coal mine safety production.

2 System Structure and Function 2.1

System Framework

System architecture uses a hierarchical design idea, from the program logic is divided into data layer, software layer, application layer. The data layer is based on the data source and the relational database type data source two kinds of parallel mode, as the data source of all kinds of spatial data. Using SDB SuperMap, SDX+ data engine to optimize storage and call. The software layer is composed of the function module, the various kinds of mine production operation algorithm, based on the third party plug-in, Objects SuperMap and some public classes. Application layer design is based on the specific business types, to achieve the application logic of the display function. System frame structure as shown in Fig. 1. 2.2

System Function Structure

After the comprehensive analysis and study of this system, the system is divided into three subsystems: data management subsystem, graphics management subsystem and network analysis subsystem. The system function structure was shown in Fig. 2. These three subsystems can work independently, and the internal data and the relationship between them, and the data management subsystem is the foundation, which provides data support for the graphics management subsystem and network analysis decision support subsystem. 2.3

System Module Function

(1) Data management subsystem ① User management For the safety of the system, the use of the system of personnel must be mine safety production dispatching room professional business personnel. Through this module, you can use this system to manage personnel, including the addition of personnel, password changes, and so on.

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Fig. 1. The structure of system frame

② Data input The module mainly realizes the input of attribute data and graphic data. Graphic data mainly from the existing mining engineering plan, ventilation system, water supply and drainage system, power line layout, the main data from the keyboard input, and other coal mine safety related data (such as: air, gas concentration, etc.) from all kinds of underground coal mine monitoring station, and then stored in the database. ③ Information retrieval This module can realize the interactive query between attribute information and spatial information, including attribute to spatial query and spatial query, and query of spatial analysis result. ④ Data modification The module mainly realizes to add, modify and delete the data and attribute data in the database of the database, and other basic operations. ⑤ Data backup The module mainly realizes the manual backup and automatic backup of data, and the data backup is very important in data management.

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Fig. 2. The structure of system function

(2) graphics management subsystem ① Layer management This function is mainly to realize the hierarchical management of spatial data, which aims to manage, query, display and analyze the spatial data. The main function of the management of the layers is the upper and lower order of the management layer, if the layer is displayed, whether it can be edited, the choice of the layer and the addition and deletion of the layer. ② Map operation The function mainly includes the basic operation, such as zoom in, zoom out, translation, distance measurement, area measurement, map reduction, etc. ③ Graphics editing The function is mainly used to increase, modify, delete points, lines, surfaces, text labels and other graphics of the map. ④ Thematic map production The function mainly makes all kinds of thematic maps for the different needs of the user, including the production of statistical thematic map, single valued thematic map, the scope of the thematic map, The purpose is to use different colors, graphics, symbols to show the different sub regions of the air volume and other data.

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⑤ The editor of Symbol library The function mainly includes the creation, modification and deletion of the symbol, such as point, line and fills. With the support of the symbol library, the graphics display is beautiful, vivid, and the different symbols can express different gas hazard levels appropriately. (3) Network analysis assistant decision subsystem Network analysis subsystem is the core function of the system, is also the highlight of the system, the specific functions are as follows: ① Ventilation network analysis module Through the design and the use of various algorithms to calculate and estimate the mine ventilation data, such as the use of “from inside to outside” method to estimate the required air quantity of the mine; According to air volume to predict mine production conditions; and to simulate the ventilation network with the form of thematic map; at the same time, according to the regional air demand to choice appropriate fan. (a) Calculation of mine air quantity According to the corresponding coefficient, the model used the air volume forecast model to calculate the air quantity of coal mining face, the air quantity of spare capacity, the air quantity of the development and the air quantity of the tunnel, and the total demand of the coal mine. Finally, the GIS technology is used to simulate and express. (b) Calculation of new mine According to the new mine, this system first judged the new mine is high gas or low gas, then chose the new mine layout method, and set the air reserve coefficient, the average daily output of coal mine and the maximum number of simultaneous work and other related factors, finally, the system calculate the air quantity. (c) Generating conditions by air volume calculation This function is mainly based on the situation of coal mine roadway to determine the generation of the roadway. The system first needs to select a tunnel in the graph, and then input the air flow coefficient which is uniform, after calculation, the maximum allowable working of the roadway, the maximum allowable gas quantity, the wind speed and the temperature range of the current roadway can be obtained. Finally, the graphic visualization function of GIS technology is used to analyze and express. ② The analysis module of underground roadway transportation The module mainly includes the dynamic management of the roadway, the connectivity of the roadway and the selection of the best transportation route for the underground transportation. This system is mainly on the basis of existing roadway, to achieve a comprehensive analysis, reasonable arrangements for various routes, Its main purpose is to mine safety production smoothly, improve the efficiency of coal mining, reduce the safety accident.

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(a) Analysis of roadway connectivity This function mainly uses GIS software to construct the topological relationship of each node in the underground roadway and analysis the connectivity of all nodes. The system first needs to select the start node and the end node in the transportation graph. After the analysis, the connection of the two nodes will be displayed in the form of the dialog box. (b) Transport line analysis This function uses the SuperMap network analysis function to achieve the choice of the shortest path between two points; it can save the transportation cost effectively. (c) Transportation line maintenance The function is to maintain the topology of the network data, including adding nodes and adding line and so on. ③ Underground water supply and drainage analysis module The module can select the water well and choose the best way to lay the water pipe, and can also select the best drainage route for the underground drainage and drainage. Two functions similar to the transport line analysis, the difference is the choice of objects. For water supply analysis, first, select the need for water and water supply locations, and then set the length of the water supply, water supply point of the maximum water supply and other limiting factors; However, for the drainage, we should choose the place of the water and dibhole Which the wateris discharged. ④ Power line analysis This function is mainly for the wiring and management of power line. (a) The search for Substation The function used the unique buffer search function of GIS to select the nearest substation, the default search range is 50 m, and the 5 m can be added to the search range. We can also enter the search range to find. (b) The location of power supply line The function is also provided by the network analysis function of SuperMap, which can provide assistant decision support for the reasonable and most provincial expenses of the power line. ⑤ Safety emergency analysis In the past coal mine accidents, underground workers usually selected escape route according to the experience, but this approach is not desirable. The system provided this module to avoid the disaster more scientific and more reasonable. At the same time, we can also conclude the causes, process flow, method of all kinds of accidents, and make template. And this function can use the analysis function of GIS to generate the route map to avoid disaster, which is displayed in the template, and make the accident report. (a) The analysis of the avoidance disaster road This function provided timely and accurate information and guided their rapid disaster avoidance through the analysis of terrain information and wind direction information. (b) Disaster relief plan The function provides a variety of disaster relief plan, for workers and their technical personnel to learn.

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3 System Development 3.1

System Development Method

There are three kinds of development methods of GIS (Liu 2008), including the development of independent, the second simple development and the integrated secondary development, the integrated secondary development contains two methods, one is DDE and OLE, the other is the component GIS. Through the comprehensive analysis, the development of this system uses the component GIS technology. 3.2

System Development Process

According to the system construction of the short-term goals and principles, system development will be used development process as shown in Fig. 3.

Fig. 3. Development process of system

4 Conclusion GIS technology is introduced into the integrated management system of underground network, and a comprehensive network analysis and decision support system is established, which provides integrated data environment and visual analysis platform for coal mine safety production and management of various pipelines. The establishment of the

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system can improve the production efficiency of coal mine, provide the basis for the analysis and decision of the coal mine ventilation network, transportation network, water supply and drainage network and power network. The system is a great promoting function for the comprehensive management of coal mine and the establishment of the digital mine. Acknowledgments. This work was supported by grants from Natural Science Foundation of Hebei Province of China (D2015402134; D2017402159; 15967663D), and the Education Department of Hebei Province (BJ2018043 and ZD2018230), Handan Municipal Science and Technology Bureau (1723209055-2; 1721203048-2 and 1724230057-1).

References Yang, Y.C., Zhang, Y.H., Gao, Y.G., et al.: Analyzed on heat power of fire damp explosion of Chenjiashan mine. J. Hebei Univ. Eng. (Nat. Sci. Ed.) 24(4), 89–90 (2007a) Liu, H.D., Cui, X.M., Zhang, X.: Expression of coal mine ventilation network based on GIS. Saf. Coal Mines 08, 43–45 (2001) Yang, Y.L., Wang, Z.J., Cheng, L., Zhang, Q.T.: Development of mine ventilation information system based on GIS. Min. Saf. Environ. Prot. 04, 35–37 (2007b) Qi, M.J., Gao, G.F.: Study on dynamic ventilation management system basing on MapX module technique. Min. Eng. 02, 62–63 (2007) Cao, Z., Li, J.X.: Based on GIS of coal mine ventilation information management system design. Coal Mine Mach. 09, 289–291 (2012) Nie, L.G., Zhang, H.J.: Underground power monitoring and management system based on GIS. Ind. Minc Autom. 11, 93–95 (2013) Liu, H.X., Shi, C.M., et al.: Design of Handan public transport inquiry system based on ComGIS. J. Hebei Univ. Eng. (Nat. Sci. Ed.) 25(3), 76–78 (2008)

Analysis on Spatio-Temporal Changes of the Land Covers in Shenyang Dayong Yang(&), Zhiwei Xie, and Hua Ding Shenyang Jianzhu University, No 9 Hunnan East Road, Hunnan New District, Shenyang, China [email protected]

Abstract. Taking Shenyang as the research object, the temporal and spatial changes of land cover in Shenyang city were studied by using four Landsat series remote sensing images in 1989, 2000, 2010 and 2016. The maximum likelihood method is used to classify the land cover in the study area, and the classification data of all kinds of land are analyzed. A Markov model is set up to predict the land cover change in Shenyang in the next 14 years. The results show that the land cover change in Shenyang is significant, in which the area of construction land is increased in 2000–2010 years, the area of cropland is increased and the area of woodland is reduced. The result of Markov forecast shows that the land cover pattern of the inner Shenyang city will be constantly changing in the next 15 years. Keywords: Land cover changes Markov model
Shenyang


Maximum likelihood
Dynamic change


1 Introduction With the rapid development of science and technology and rapid economic development, many countries and regions have plundered resources, resulting in serious ecological problems. Therefore, the effective and rational use of land resources has become more and more important, related to social stability and the sustainable development of the economy. Since twentieth Century, regional and global environmental changes have paid more attention to the impact and response of land change, and land use/Cover Change (land use and land cover change, LUCC) has become one of the hotspots of global change research [1–5]. Taking Shenyang as the research object, using the technology of RS and GIS, this paper discusses the spatial and temporal changes of the land cover in the 1989–2016 years of Shenyang, statistics the information of various land types, calculates the dynamic degree of land cover, and studies the mutual transformation between the types of land, and divides it into three aspects from the structure, the dynamic degree and the transfer matrix. The change rule of land cover in Shenyang is analyzed. Finally, the Markov model is used to predict the future land cover status. This study can provide decision support for land rational planning in Shenyang, and has certain practical significance for improving the ecological environment. © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 165–175, 2019. https://doi.org/10.1007/978-981-13-7025-0_17

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2 The General Situation and Data Processing of the Research Area 2.1

A Survey of the Research Area

Shenyang is located in the middle of Liaoning province. It is located in the Changbai Mountain foothills, south of the Liaodong Peninsula, and within the economic circle of the Bohai. The geographical coordinates are located in the 41°48′11.75′′ of the north latitude, 123°25′31.18′′ in the East. Shenyang is located in the middle of the Liaohe plain, the eastern part of Liaodong hilly and mountains region, the northern part of the hilly region in the north of Liaoning Province. The terrain is broadened and broadened to the West and south. The water system is developed in the region, the river is vertical and horizontal, the species is diverse and the resources are rich [6]. 2.2

Data Preprocessing

In this study, in 1989, 2000, 2010 Landsat5 satellite images in Shenyang and 2016 Landsat8 satellite image as the basic data source, the data preprocessing under the ERDAS IMAGINE 9.2 software platform. Firstly, the standard false color image of the band is selected. Then, the polynomial model is used to calibrate the remote sensing image of Shenyang. The correction error is less than 0.5 pixels. Finally, the remote sensing image of the study area is obtained according to the range of the study area. 2.3

Land Covers Classification

According to the national standard of the classification of land use status and the characteristics of remote sensing images in the study area, the land cover types of the study area are divided into five types: water area, construction land, cropland, woodland and other lands. Under the ERDAS software platform, the maximum likelihood method is used to supervise and classify the remote sensing images of four phases. The accuracy of the classification results is evaluated, the overall accuracy is better than 85%, and the Kappa coefficient is better than 0.8, which satisfies the classification precision requirements. Finally, cluster analysis and removal analysis are used to classify the post-processing, and the result of the study area is shown in Fig. 1.

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Fig. 1. The land cover classification of each time phase in the study area. (a) The results of the land cover classification in the 1989 research area. (b) The results of the land cover classification in the 2000 research area. (c) Land cover classification results in the 2010 research area. (d) Land cover classification results in the 2016 research area.

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3 Analysis of Land Covers Change 3.1

Analysis of Land Covers Structure

The change range of the land cover structure is the change range of the land cover type in the area, reflecting the overall development trend of the land cover change and the change of the land cover structure [7]. Statistical analysis was carried out under the ArcGIS 10.2 software platform, and land cover data in 1989, 2000, 2010 and 2016 were obtained, as shown in Table 1. Table 1. Land cover and proportion of each time phase in the study area Waters Construction land Cropland Woodland Other lands Total 1989 Area (hectare) Proportion (%) 2000 Area (hectare) Proportion (%) 2010 Area (hectare) Proportion (%) 2016 Area (hectare) Proportion (%)

23.59 0.69 52.65 1.54 55.51 1.62 48.40 1.41

346.80 10.13 448.24 13.09 1311.02 38.30 1365.18 39.88

895.09 26.14 1045.12 30.53 972.33 28.40 1181.82 34.52

1321.01 38.59 1292.90 37.77 967.87 28.27 738.02 21.56

836.97 24.45 584.55 17.07 116.73 3.41 90.04 2.63

3423.46 100 3423.46 100 3423.46 100 3423.46 100

From the above table, it can be seen that the overall change of the area of water area is not large, the area of construction land has increased in 2000–2010 years, the area of cropland is increasing, the area of woodland and other land is gradually reduced, and the other land has been reduced more during the 2000–2010 period. 3.2

Analysis of the Dynamic Degree of Land Cover

Land cover dynamic degree can quantitatively describe the rate of regional land cover change. Land cover dynamic degree can be divided into two types: single land cover dynamic degree and comprehensive land cover dynamic degree [8]. Based on the remote sensing data of each time phase land cover, the land cover dynamics of the city of Shenyang for 1989–2000, 2000–2016 and 1989–2016 years was obtained, as shown in Table 2. Table 2. Land cover dynamic degree of Shenyang in different periods (%) Time interval

Waters Construction land

Cropland Woodland Other land use

1989–2000 2000–2010 2010–2016

11.21 0.54 −2.13

1.52 −0.70 3.59

2.66 19.25 0.69

−0.19 −2.51 −3.96

−2.74 −8.00 −3.81

Comprehensive dynamic degree 0.74 2.53 1.28

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From Table 2, it is known that Shenyang city has the maximum comprehensive dynamic degree of 2000–2010 years in the three period, reaching 2.53%. This is in accordance with the land change caused by the real estate heat and a large number of population influx after the 2000. The dynamic degree of the 2010–2016 period is 1.28%, indicating that the land change speed is compared with the previous period. The speed of urban economic development has slowed down somewhat; the dynamic degree of the 1989–2000 period is only 0.74%, indicating that the economy of Shenyang has not yet entered the period of high speed development. The variation of single dynamic degree in each period is: 1989–2000 period, other lands > construction land > cropland > woodland; 2000–2010 period, construction land > other lands > woodland > Cropland > waters; construction land > other land > woodland > cropland > water in the 2010–2016 period. 3.3

Analysis of Land Cover Transfer Matrix

The land cover transfer matrix can quantitatively reflect the interconversion between different areas in the study area, and explain the main source and direction of the various categories. According to the land use taxonomy of Shenyang in 1989, 2000, 2010 and 2016, the superposition analysis function of Arcgis was applied to superimposition the images of the adjacent two phases, and the 1989–2000, 2000–2010 and 2010–2016 land cover state transfer information Atlas (as shown in Fig. 2) were obtained, and the transfer moment of land cover state was established. The array, as shown in Tables 3, 4 and 5. Table 3. The transfer matrix of land cover in Shenyang in 1989–2000 years Unit: hectare Land cover type

Waters Construction land Cropland Woodland Other land use 1989 total

Waters 14.92 Construction land 0.72 Cropland 6.26 Woodland 4.31 Other land use 26.45 2000 total 52.65

0.75 253.12 19.39 35.26 139.71 448.24

2.12 1.25 679.82 273.57 88.36 1045.12

3.55 11.12 145.43 904.93 227.88 1292.90

2.24 80.59 44.19 102.95 354.57 584.54

23.58 346.80 895.09 1321.01 836.97 3423.46

Table 4. The transfer matrix of land cover in Shenyang in 2000–2010 years Unit: hectare Land cover type

Waters Construction land Cropland Woodland Other land use 2000 total

Waters 23.78 Construction land 4.19 Cropland 6.60 Woodland 8.07 Other land use 12.87 2010 total 55.51

16.83 415.06 186.79 267.77 424.57 1311.02

3.50 14.51 605.29 296.42 52.61 972.33

1.41 13.72 211.19 684.37 57.19 967.87

7.13 0.75 35.26 36.28 37.30 116.73

52.65 448.24 1045.12 1292.90 584.54 3423.46

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Table 5. The transfer matrix of land cover in Shenyang in 2010-2016 years Unit: hectare Land cover type

Waters Construction land Cropland Woodland Other land use 2010 total

Waters 36.46 Construction land 6.13 Cropland 1.91 Woodland 1.34 Other landuse 2.56 2016 total 48.41

10.64 1046.28 148.97 143.80 15.48 1365.17

5.36 211.36 625.50 310.65 28.95 1181.82

3.02 45.82 190.04 485.05 14.09 738.02

0.04 1.43 5.90 27.04 55.64 90.04

55.51 1311.02 972.33 967.87 116.73 3423.46

As can be seen from the chart above, there is a transformation between different classes in different periods. (1) During the 1989–2000 years, all categories were transferred and transferred out. Among them, 63.28% of the waters were not transferred, and the area increased mainly from other lands; 72.99% of the construction land had not been transferred, the increase of the area was mainly from other lands, and the main flow to other land was reduced; 75.95% of the cropland had not been transferred, and the increase of the area was mainly derived from woodland. The area mainly flows to the woodland, the total area is increased; 68.50% of the woodland has not been transferred, the area mainly flows to the cropland, the increase of the area mainly comes from the cropland and other land; the other land has not been transferred, the total area is reduced, mainly to the construction land, cropland and woodland. (2) During the 2000–2010 years, 45.16% of the water was not transferred. The area increased mainly from other lands, reducing the main flow of the area to the construction land; 92.60% of the construction land had not been transferred, the cropland, the woodland and other lands to be transferred to the cropland and the woodland, and 57.92% of the cropland were not in the field. There was 57.92% of the cropland. The area is mainly derived from woodland, and the area mainly flows to the construction land and forest land. 52.93% of the woodland has not been transferred, and the area mainly flows to the construction land and cropland, the increase of the area is mainly from the cropland; 6.38% of the other land has not been transferred, the overall area is greatly reduced, the main area is greatly reduced. Flow to construction land. (3) During the 2010–2016 years, 65.68% of the waters were not transferred, and the area was reduced mainly to the construction land; 79.81% of the construction land had not been transferred, the cropland and the woodland were transferred to the cropland, and the decrease was mainly to the cropland; 64.33% of the cropland had not been transferred, and the increase of the area was mainly from the construction land and woodland and reduced surface. The product mainly flows to the construction land and woodland; 50.11% of the woodland has not been transferred, and the area mainly flows to the cropland, the increase of the area mainly comes from the cropland, the total area is reduced, and 47.67% of the other land is not transferred, mainly to the cropland, mainly to the type of woodland.

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Fig. 2. Information transfer map of land cover status in Shenyang. (a) Land covers state transfer information of 1989–2000 years. (b) Land covers state transfer information of 2000–2010 years. (c) Land covers state transfer information of 2010–2016 years. Note: 1, 2, 3, 4, 5 of the atlas represent water, construction land, cropland, woodland, and other lands. The map code XY represents the class X of land type Y (X to Y), such as 11 represents the unchanged waters, 23 represents the construction land to the cropland, that is, the construction land to the cropland, and 54 to represent his land to the woodland.

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4 Forecast of Land Cover Change In this study, the Markov model was used to predict the land cover change, and the 2010–2016 land cover transfer matrix was used to establish the Markov prediction model. The data of the land cover in 2016 was retrieved to determine the accuracy of the prediction model, and then the future land cover status of the research area was predicted. The Markov prediction model formula is as follows: PðnÞ ¼ Pðn  1ÞPij ¼ Pð0ÞPnij

ð1Þ

P(0) represents the state probability vector of the land cover type at the beginning of the study, and P(n) represents the state probability vector of the land cover type in the future, and represents the transition probability matrix of the land type i to the land type j. The n is the number of transfer steps [9]. 4.1

The Determination of the Initial State Matrix

In this study, the percentage of land cover types in 2010 was taken as the initial state matrix, as shown in Table 6. Table 6. Initial state matrix Land cover type Waters Construction land Cropland Woodland Other lands

4.2

Initial probability 1.62% 38.30% 28.40% 28.27% 3.41%

The Determination of the Transfer Probability Matrix

The annual average transfer probability of each land cover type in initial condition is as follows:  Uij n Pij ¼  100% Ui þ

ð2Þ

In the study, the amount of land cover type i is converted to the amount of land cover type j during the study period; n represents the period of study, the unit is year, and the number of land cover type i in the initial time. M transfer probability vectors constitute the transition probability matrix, and m is the total [10] of land cover types. According to the data and formula of the 2010–2016 land cover state transfer matrix (2), the annual transfer probability matrix of the land cover type area of Shenyang in 2010–2016 years is obtained, as shown in Table 7.

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Table 7. Annual average transfer probability matrix of land cover in Shenyang in 2010-2016 years 2010

2016 Waters Waters 0.9428 Construction land 0.0008 Cropland 0.0003 Woodland 0.0002 Other land use 0.0037

4.3

Construction land Cropland 0.0319 0.0161 0.9663 0.0269 0.0255 0.9406 0.0248 0.0535 0.0221 0.0413

Woodland 0.0091 0.0058 0.0326 0.9168 0.0201

Other land use 0.0001 0.0002 0.0010 0.0047 0.9128

The Establishment and Test of the Prediction Model

Under the Matlab software platform, the Markov prediction model is established according to formula (1). In order to verify the accuracy of the model prediction, the Markov model is used to retrieve the land type coverage in 2016, and the results of 2016 are compared with the actual results. The results are shown in Table 8. Table 8. Markov prediction results test (%) Land cover type

Actual data

Waters Construction land Cropland Woodland Other land

0.0141 0.3988 0.3452 0.2156 0.0263

Prediction results 0.0143 0.3967 0.3300 0.2316 0.0274

The difference between the forecast result and the actual data 0.0002 −0.0021 −0.0152 0.0160 0.0011

As can be seen from the above table, the predicted results are in good agreement with the actual data. The prediction results of water, construction land and other lands are better. It shows that the Markov prediction model established by the 2010–2016 land cover state transfer matrix is of better accuracy and can predict the land cover state in the future.4. Forecast of change trend. The percentage of land cover types in Shenyang in 2020, 2025 and 2030 was calculated by the Markov prediction model based on the 2010–2016 land cover state transfer matrix, as shown in Table 9. Table 9. Forecast results of land cover change in Shenyang (%) Land cover type Waters Construction land Cropland Woodland Other land

2016 1.41 39.88 34.52 21.56 2.63

2020 1.34 40.35 34.61 21.29 2.42

2025 1.25 41.00 35.75 19.88 2.12

2030 1.17 41.49 36.33 19.08 1.92

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As can be seen from Table 9, in theory, since 2016, the area of construction land and cropland has increased, water, woodland and other land are decreasing, and the change speed of various kinds of land gradually slows down. By 2030, the proportion of construction land and cropland is 41.49% and 36.33%, respectively, and the proportion of water and other land is small, only 1.17% and 1.92%, and the proportion of forest land is 19.08%. This data can provide the basis for Shenyang government to formulate the future land use policy.

5 Conclusion Using four Landsat series remote sensing image data of 1989, 2000, 2010 and 2016, with the help of GIS and RS technology, based on the interpretation of land cover remote sensing images, the land cover change analysis method, such as single dynamic degree, comprehensive dynamic degree, transfer matrix and so on, is used to analyze Shenyang City 1 from multi angle and multi-level. The structure, dynamics, main source and direction of land cover change during the 989–2016 period, and based on the land cover change data of 2010 and 2016, the Markov model was established, and the land cover status of the three years in the 2016–2030 period was predicted. The conclusions are as follows: (1) By 2016, the area of construction land occupied the largest proportion, which was 39.88%. The proportion of the area in the rest of the area was cropland > woodland > other lands > waters. The dynamic degree of comprehensive land cover in three periods was 0.74%, 2.53% and 1.28%, indicating that the urban expansion rate of Shenyang was faster in the 2000–2010 years. (2) Use the Markov model to forecast the land cover situation in 2020, 2025 and 2030. The forecast results show that, compared with 2010–2016 years, 2016–2030 years in Shenyang will keep the same change trend, but the change will gradually slow, concrete is the construction land and the increase of cropland, woodland and other land decreasing trend, the water area is slightly reduced. The prediction results can provide data for future government land resource planning.

References 1. Ma, Z., Zeng, Y., Yan, L.: Changsha Zhuzhou Xiangtan city cluster core area of the land use/cover change driving mechanism quantitative research. J. Survey. 10, 41–44 (2012). (in Chinese) 2. Rawat, J.S., Biswas, V., Kumar, M.: Changes in land use/cover using geospatial techniques: a case study of Ramnagar town area, district Nainital, Uttarakhand, India. Egypt. J. Remote. Sens. Space Sci. 16, 111–117 (2013) 3. Butt, A., Shabbir, R., Ahmad, S.S., Aziz, N.: Land use change mapping and analysis using Remote Sensing and GIS: a case study of Simly watershed, Islamabad, Pakistan. Egypt. J. Remote. Sens. Space Sci. 18, 251–259 (2015)

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4. Hegazy, I.R., Kaloop, M.R.: Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. Int. J. Sustain. Built Environ. 4, 117–124 (2015) 5. Wang, G., He, G., Liu, J., Wang, M., Cheng, B.: Land cover change monitoring of mine city using multi-temporal satellite remote sensing images. J. Adv. Inf. Technol. 1, 17–22 (2017). (in Chinese) 6. Guo, W.: Analysis of living suitability of human settlements in Liaoning based on ecological factors. Liaoning Normal University, Dalian (2010). (in Chinese) 7. Liu, L. (editor in chief): Land Resources Science, pp. 98–101. China Agricultural University press, Beijing (2000). (in Chinese) 8. Zhao, L.: Land use/land cover change and driving mechanism in Daqing. Jilin University, Jilin (2006). (in Chinese) 9. Cao, J., Wu, J., Li, C.: Prediction of LUCC Trend in Suzhou District of Jiuquan Based on Markov Model. Land and Natural Resources Research, Heilongjiang, vol. 1, pp. 45–47 (2008). (in Chinese) 10. Wang, Y.: Research on land use change in Fuxin based on RS and GIS. Liaoning Technical University, Fuxin (2009). (in Chinese)

Hierarchical Decision Tree for Change Detection Using High Resolution Remote Sensing Images Zhiwei Xie1(&), Min Wang2, Yaohui Han1, and Dayong Yang1 1

School of Transportation Engineering, Shenyang Jianzhu University, Shenyang, China [email protected], [email protected], [email protected] 2 Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China [email protected]

Abstract. In order to improve the classification accuracy of high resolution change detection, the key technologies such as segmentation scale determination, features selection and classifier use are studied, and a change detection method using hierarchical decision tree is proposed. Firstly, fractal net evolution approach was used to obtain image objects, and the optimal scales of vegetation, water, and man-made objects were determined by evaluation index based on classification accuracy. Second, the feature spaces of man-made, water and vegetation objects are constructed. Then, the hierarchical decision tree classification method with the optimal segmentation scales is applied to multitemporal high resolution remote sensing images. Finally, change detection was implemented by comparing the multi-temporal classification results. The multitemporal high resolution remote sensing images in Wuhan Lujiazhuang were chosen as the experimental data. The experiments show that the method is effective. Keywords: Image objects
Optimal segmentation scale Hierarchical decision tree
Change detection


Ratio index


1 Introduction The traditional geographical situation monitoring method cannot meet the goal of rapid monitoring gradually, since its low efficiency and high cost of manpower and material resources. With the continuous improvement of the spatial resolution of high resolution remote sensing satellites launched in China, the remote sensing data applied to the monitoring of geographical conditions have been enriched. The application of highresolution remote sensing images for change detection is one of the hot spots in remote sensing research and has important strategic significance and urgent scientific needs. In view of the characteristics of high resolution remote sensing images, it is of great significance to study the change detection method with high resolution remote sensing images. © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 176–184, 2019. https://doi.org/10.1007/978-981-13-7025-0_18

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With the rapid development of object-oriented information extraction technology, Lobo and Alpin et al. introduced object- oriented thinking into change detection in the form of “per-field” and “per-patch” [1]. In 2000, In view of the forest of northern Argentina, Willhauck used the image to adopt the object-oriented classification method of decision support, and the experiment shows that this method is better than the traditional visual interpretation method, and its automatic feature extraction effect is better too [2]. In 2001, Fan present that quantitative statistical analysis method can be proposed to select the appropriate threshold of difference and variation, and the change information of land use was extracted by using the image difference method as the main method and the comparison method after classification as the auxiliary method, and the results showed better [3]. In 2004, Frate et al. used the method of neural network to classify the image, then analyze and simulate the classification results of change detection of cities [4]. In 2004, Repaka selected IKONOS and QuickBird multi-spectral image of the Mississippi coast area, and the meantime used the image element based classification method and object-oriented classification method to extract the Complex network of small roads in residential areas and densely wooded areas, and established a model to compare these two kinds of methods, finally, the results show that the objectoriented classification method is relatively better [5]. In 2011, Zhu selected Aerial photograph image (0.15 m) from May 2006 and May 2007 to study the change detection of Gadwall North wetland ecosystem in the central California, and the results showed that the study of change detection for example geometric registration of changes and so on, caused Pseudo change information, and based on the MAD difference image method can get higher precision [6]. In 2013, Lao proposed an improved evaluation index and calculation model of the optimal segmentation scale, and the experimental results shows that the method is effectiveness and applicability [7]. In 2011, Xu applied the change vector analysis method to object-oriented change detection, and verified the effectiveness of this method in object level change detection [8]. Therefore it can be seen that there are some problems in the detection of multitemporal high-resolution remote sensing changes: (1) As spectral information is still widely used in the existing object-oriented classification, texture, spatial structure and other information are seldom used, there is still much room for improvement in classification accuracy. (2) Most of the existing studies classify the objects in the study area under an optimal segmentation scale, ignoring the objective fact that the optimal segmentation scales of different geological objects are different. In order to solve the above questions, adopted the hierarchical decision tree method for the high resolution remote sensing image change detection is implemented. First, multi-scale segmentation was carried out for the two images respectively, and the optimal segmentation scale was selected by the optimal segmentation scale determination method based on the classification accuracy. Secondly, construct the ratio index, and extract the feature information of the image object to construct the feature rule set. Then, the hierarchical decision tree can be used to classify, compare and analyze the results, so that the change detection could be realized. The technical route of this paper is shown in Fig. 1.

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Fig. 1. Technical route of object-oriented change detection

2 Study Area and Data In this study, the experiment data is the remote sensing images of the worldview-1 of 2012 and the Pleiades of 2013 in Lujiazhuang Wuhan and surrounding areas which has been image fusion and registration pretreatment. The spatial resolution is 0.5 m, including red, green, blue and NIR bands. The main ground objects in the study area are artificial, vegetation and water object. Figure 2 shows the experimental data of false color display.

2012

2013

Fig. 2. Experimental data

3 Optimal Segmentation Scale Selection Based on Classification Accuracy The Fractal Net Evolution Approach (FNEA) proposed by Baatz and Schape is a multiscale segmentation algorithm, which is widely used as the core algorithm of the objectoriented analysis software eCognition. The fundamental of FNEA is to use the heterogeneity between adjacent image objects as the basis to realize the image object

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merging from the bottom to the top, first, independent pixels are treated as separate objects, then the image element is merged into the image object step by step according to the local optimal mutual adaptation rule until there is no merged image object. The determination of optimal segmentation scale is especially important in multiscale segmentation. The optimal segmentation effect is to use a certain scale analysis to obtain the object with “maximum homogeneity within the class and maximum heterogeneity between classes”. However, improper scale will cause the phenomenon of “over-segmentation” or “insufficient segmentation” of images [9]. In order to obtain the optimal segmentation scale, this paper adopts to select the optimal segmentation scale method based on the classification accuracy. Combined with the law of segmentation, the ground objects in large area are suitable for large scale segmentation, while those in small area are suitable for small scale segmentation. In this paper, surface cover is mainly divided into vegetation, water and Manmade Objects. The optimal segmentation scale selection method based on classification accuracy is as follows: for the remote sensing image in 2012, the water increased by 5 in the range of scale parameters from 80 to 200, Manmade Objects increased by 5 in the range of scale parameters from 20 to 200, and vegetation increased by 5 in the range of scale parameters from 60 to 120. For the 2013 remote sensing image, the water increased by 5 in the range of scale parameters from 80 to 200, Manmade Objects increased by 5 in the range of scale parameters from 60 to 140, and vegetation increased by 5 in the range of scale parameters from 60 to 120. The segmentation scale was selected as the optimal segmentation scale when the classification accuracy of Manmade Objects, vegetation and water was optimal.

4 Feature Selection Using reasonable feature space to establish classification rules and rule sets can better classify ground objects. In this paper, using the traditional characteristics, including the spectral characteristics (object gray average values, standard deviation, brightness, ratio), the texture feature (angular second moment, angular second moment, entropy), geometric features (area, location, perimeter, smoothness), and index characteristics (normalized difference vegetation index features, normalized water index). Meanwhile, in view of the characteristics of water and vegetation, this paper uses the ratio between green band and NIR band to construct M index: M=

G NIR

ð1Þ

Where, G is green band and NIR is near infrared band. For vegetation, the reflection intensity of NIR band is significantly higher than that of green band, and M can enhance the characteristics of vegetation. As for water, due to the influence of aquatic plants, water has a strong reflection in the visible light band only in the green band, and M index can enhance the characteristics of water at the NIR band. Therefore, M index can enhance the classification effect of vegetation and water.

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5 Hierarchical Decision Tree Classification and Change Detection 5.1

Hierarchical Decision Tree Classification

Decision tree classification is a common method of object-oriented classification. Its growth is divided from top to bottom. First, test the attributes specified by the node according to certain criteria from the root node. If the node samples belong to the same category as leaf nodes and tag specified attribute, or according to a given instance attribute values corresponding to move down the branches, and take the root of the subtree of the new node to repeat the process. According to different ground objects, different segmentation scales can be adopted. In this paper, the hierarchical decision tree classification method is obtained by combining the scale characteristics of ground objects and the classification of decision trees. This method uses the optimal segmentation scale of each category to get the multi-scale object layer to establish the network hierarchy structure and carries on the decision tree classification under the optimal segmentation scale of all kinds of ground objects. For the image data in 2012, under the scale of 150, the NIR mean is less than 310, and is classified as water, while greater than or equal to 310 is classified as non-water. When the non-water is inherited to the scale 115, NDVI is divided into vegetation at 0.46 or greater, non-vegetation at 0.46 or less, vegetation at 0.8 or less, and non-vegetation at the rest. The water separated from scale 150, vegetation and nonvegetation separated from scale 115 were inherited into scale 100, and non-vegetation was divided into artificial ones. The 2012 hierarchical decision tree obtained in this paper is shown in Fig. 3.

Fig. 3. Hierarchical decision tree in 2012

For the image data in 2013, under scale 135, M value is greater than or equal to 1 and is divided into water, while less than 1 is non-water. When the non-water is inherited to scale 120, NDVI is divided into vegetation at 0.34 and non-vegetation at less than 0.34. For non-vegetation M index less than 0.44, vegetation is divided into

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vegetation, and the rest is classified as non-vegetation. The water separated from scale 135, vegetation and non-vegetation separated from scale 120 were inherited into scale 115 and the non-vegetation was changed into artificial vegetation. The 2012 hierarchical decision tree obtained in this paper is shown in Fig. 4.

Fig. 4. Hierarchical decision tree in 2013

5.2

Change Detection

The change detection method adopted in this paper is Post classification Comparison Method, and this method classifies the remote sensing images of different phases respectively, then realizes change detection by comparing the classification results of different phases. The types of results of change detection in this paper are the unchanged vegetation object, increase of vegetation, vegetation reduction, the unchanged manmade object, increase of artificial, artificial reduction, the unchanged water object, increase of water, water reduction and so on.

6 Experiment and Analyze This paper uses experimental data to verify and analyze the optimal segmentation scale selection, vegetation and water extraction based on M index, stratified decision tree classification and change detection respectively. 6.1

The Selection of Optimal Segmentation Scale

The optimal segmentation scale selection method based on classification accuracy was adopted to obtain the optimal segmentation scale of Manmade, vegetation and water object in 2012 and 2013 respectively, as shown in Tables 1 and 2. It can be seen from Tables 1 and 2 that the optimal segmentation scale of vegetation and water is larger than that of Manmade Objects. This conclusion conforms to the segmentation rule of multi-scale segmentation.

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Table 2. Optimal segmentation scale in 2013 Classes Manmade object Vegetation object Water object Scale 115 120 135

6.2

Vegetation and Water Extraction Based on M Index

The remote sensing image of 2013 was selected as the experimental data of this part. This paper was used to construct the M index categorize vegetation and water body, and with the NDVI extracting vegetation, water extracted with the use of NDWI classification results were analyzed, the vegetation classification precision comparing results as shown in Table 3, water classification precision comparing results as shown in Table 4. Tables 3 and 4 show that M has a high classification accuracy for the experimental data in this paper. Table 3. The results of comparison of vegetation classification accuracy Producer accuracy User accuracy Total accuracy M 0.94 1 0.94 NDVI 0.93 1 0.93

Table 4. Precision analysis of water classification Producer accuracy User accuracy Total accuracy M 0.93 1 0.93 NDWI 0.84 1 0.84

6.3

Hierarchical Decision Tree and Change Detection

The classification results using hierarchical decision tree classification are shown in Fig. 5. Combining Figs. 2 and 5, the qualitative analysis shows that the classification results obtained by the hierarchical decision tree classification used in this paper are consistent with the actual surface cover.

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2013

Fig. 5. Classification results in 2012 and 2013

The remote sensing image of 2013 was selected as the quantitative analysis experimental data in this part. The classification results of 2013 were accurately evaluated by using the obfuscation matrix, and the classification accuracy obtained was shown in Table 5. It can be seen from Table 5 that the hierarchical decision tree classification method achieves a high classification accuracy. Table 5. The classification accuracy of 2013 Water object Vegetation objects Manmade objects Producer accuracy 0.97 0.98 0.99 User accuracy 1 0.99 0.97 Kappa 0.97 0.97 0.99

Combining the classification results of 2012 and 2013, the change detection was carried out by using the post classification comparison method, and the change detection results were shown in Fig. 6, it can be seen that the changed area in Fig. 2 can be well detected. According to the statistical analysis of the change categories, the artificial area that comes from changes in vegetation in the 9 change categories was the largest, reaching 901093.5 m2. The water area which comes from artificial is the

Fig. 6. Change detection result

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smallest, at 8947.5 m2. From 2012 to 2013, the vegetation in this region was most affected by human activities, with the large area decrease of vegetation, large increase in artificial area and small change in water.

7 Conclusion In this study, the high resolution remote sensing image change detection method can be realized by using hierarchical detection tree, and the key technology in this method is studied, such as Optimal segmentation scale selection based on classification accuracy, Classification of vegetation and water based on M index, Hierarchical decision tree classification method and so on. The experiment shows that this method is effective and can provide technical support for the monitoring of geographical conditions.

References 1. Aplin, P., Atkinson, P.M., et al.: Per-field classification of land use using the forthcoming very fine spatial resolution satellite sensors: problems and potential solutions. In: Advances in Remote Sensing and GIS Analysis, pp. 219–239. Wiley, Chichester (1999) 2. Willhauck, G., Schneider, T., et al.: Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos, pp. 45–50. ISPRS, Amsterdam (2005) 3. Fan, H.: Case study on image differencing method for land use change detection using thematic data in Renhe District of Panzhihua. J. Remote. Sens. 01, 75–80 (2001) 4. Frate, F.D., Schiavon, G., Solimini, C.: Application of neural networks algorithms to Quick Bird imagery for classification and change detection of urban areas. In: Geoscience and Remote Sensing Symposium, pp. 1091–1094 (2004) 5. Repaka, S.R.: Comparing spectral-object-based approaches for extracting and classifying transportation features using high-resolution multi-spectral satellite imagery. Mississippi State University (2005) 6. Zhu, J.: The key technology of change detection using high resolution remotely sensed imagery. Zhejiang University, Hangzhou (2011) 7. Lao, X.: The research of object-based high resolution remote sensing land use change detection. Zhejiang University, Hangzhou (2013) 8. Xu, G.: Research on object-oriented change detection technology of remote sensing images. Information Engineering University, Zhengzhou (2011) 9. Wang, L.: Analyzing classification and segmentation parameters selection in high resolution remote sensing image based on object. Central South University, Changsha

Surface Features Classification of Airborne Lidar Data Based on TerraScan Maohua Liu1(&), Xiubo Sun2, Yue Shao1, and Yingchun You1 1

School of Civil Engineering, Shenyang Jianzhu University, Shenyang, China [email protected] 2 Liaoning Nonferrous Geological Exploration General Institute Co., Ltd., Shanghai, China

Abstract. This paper focuses on the classification of airborne lidar (LiDAR) data using TerraScan software. At first, the composition of the airborne lidar system and the organization and characteristics of the point cloud data are analyzed. Then, the basic principles of classification by TerraScanare analyzed based on the airborne lidar data in the urban. First, noise points such as blank and low points are removed, next, implement point cloud filtered according to the macro commands provided by TerraScan, and finally further classify and point cloud are thinned, included that classify ground points, vegetation points, building points, and model key points, this operation is generated mainly by program implementation; In order to ensure the accuracy of the classification, manual classification must be carried out. Consequently the classification results of TerraScan are summarized, involving the advantages and disadvantages of the classification, and the technical development requirements of classification using TerraScan are proposed. Keywords: Airborne lidar Point cloud classification


TerraScan
Point cloud filtering


1 Introduction With booming of Earth observation technology, it is possible to acquire high-precision geographic information using the new airborne laser radar system rapidly. The main content of post-processing of airborne lidar measurement data is filtering and classification. The filtering removes the features and vegetation foot points in the point cloud data, and extracts the Digital Elevation Model (DEM); It is necessary to classify the laser foot point data to distinguish artificial points and vegetation data foot points definitely data classification for carrying out ground object extraction and 3D reconstruction. John Secord uses aeronautical imagery to segment LiDAR point cloud data based on the similarity between LiDAR data points and points [1]. Cui et al. proposed a method of building extraction based on edge detection by Lidar data. This method firstly generates different scale DSM depth images from LiDAR point cloud data, and then extracts the edge of the building according to the edge detection operator [2]; Antonarakis et al. classify nine types of features on the three river meanders of the Garonne and Allier rivers in France through an object-oriented supervised taxonomy. © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 185–190, 2019. https://doi.org/10.1007/978-981-13-7025-0_19

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Six surface models for classifying target features were generated from LiDAR point cloud data during the experiment: vegetation height model, canopy percentage model, average intensity model, intensity difference model, probability distribution skewness and kurtosis model [3]; Ren Zizhen et al. proposed a LiDAR building extraction method based on contour shape analysis. The method firstly uses the LiDAR data to generate the DSM contour, then extracts the building contour according to the contour shape feature parameters, and finally extracts the building according to the topological relationship and the geometric characteristics of the building outline [4]. This paper focuses on the denoising of raw airborne radar data based on TerraScan, classification of ground, low vegetation, medium vegetation, high vegetation and buildings. The experimental data selected in this paper is point cloud data of a one city, with an area of about 1.5 km2, containing 7055604 points, and the data format is *.las. 1.1

Point Cloud Data Filtering

Firstly, the thinning and denoising of the point cloud data is carried out. Every 10 points is extracted and thinned. Denoising processing removes the point below the normal ground height by 0.5 m within 5 m from the reading point to achieve low-point separation by setting the parameters, meanwhile the distance of the points within 5 m radius and the high-order error is greater than 5 times standard deviation is removed [5]. The separation of the air points the denoising of the point cloud is realized. According to the characteristics of the research area, in the paper, TIN-based filtering method is carried out. The parameter including the iteration angle, the iteration distance and the maximum building size is repeat lysetted, showed in Fig. 1 are established. The filtering result is shown in Fig. 2. The yellow represents the ground point and the black represents the feature point.

Fig. 1. The parameter of the filter

Fig. 2. The result of the filter (Color figure online)

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2 Point Cloud Classification 2.1

Vegetation Point Classification

Vegetation points can be classified into three categories based on elevation values: low vegetation, medium height vegetation, and high vegetation. For example, low vegetation is set to a point below 0.5 m [6, 7]. The specific algorithm is a temporary triangle model established at the ground point, and then other points are compared with the elevation values of the triangle model. If it is less than 0.5 m, this point can be classified into the classification of low vegetation. According to the same algorithm, medium-height vegetation and high vegetation can be distinguished. The results obtained in the experiment according to the above method are not satisfactory, therefore, the points above 0.25 m above the ground point are divided into medium vegetation points. Similarly, the points in the middle vegetation point that are 2 meters above the ground are divided into high vegetation points [8, 9]. This method also classifies the building points into the vegetation, and provides a data foundation for the next building point classification. After the building classification is completed, the remaining is the vegetation data. The classification result is shown in Fig. 3.

Fig. 3. The result of classification of vegetation

2.2

The Classification of Buildings

The classification of buildings is based on the high of vegetation, thus the separation of buildings must rely on manual classification, The automatic classification results of buildings are not ideal. This problem is mainly overcome by manual classification. The manual classification is carried by means of the sectional view, and the angles need be cut in the original data to ensure the accuracy of the classification. As shown in Figs. 4 and 5.

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Fig. 5. The sectional view of the building

Fig. 4. The separation of buildings

3 Laser Point Cloud Data Classification and Accuracy Assessment After the automatic classification by TerraScan, it is necessary to manually process some position which exist shortcoming processed by macros. The macro processing is only automatic preliminary classification, and the process exist error. Therefore there is a step of manual classification. Due to the deviation of the parameter settings by macro during the classification, some point surround the house will not be right, even there may be some error points which do not belong to the building. This situation needs to be paid more attention to some in post processing. Therefore, the parameter setting by macros is very important and determined by several test (Figs. 6 and 7).

Fig. 6. The data before classification

Fig. 7. The data after classification

In this paper, the accuracy of TerraScan classification is determined according to the error classification rate of laser point cloud, which can obtain good filtering effect and preserve the integrity of terrain features. The original point-to-point spatial structure is related, however, in the actual operation of the classification method, this structural connection is not known, therefore the spatial connection between the points cannot be well used, resulting in some error in the process. If only the LiDAR point

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cloud data is classified into two types of feature data and ground data, the classification error can be divided into two species, the first type of error is classifying the ground point as the feature point incorrectly and the second type of error is in contrast. In general, it is considered to remove the feature points as much as possible, the parameters are set according to this standard, even at the expense of eliminating some topographic features, which causes too many second errors in the classification results. The results of this experiment classification are shown in Table 1. Table 1. The result of the classification

Reference data

The number of real ground points The number of real non-ground points Sum Proportion

The data after filtering Number of Number of nonground points ground points 89165 2152

Sum

Proportion

91317

12.94%

53827

560279

614106 87.06%

142992 20.27%

562431 79.73%

705423

The accuracy of the classification results is: the first error is 2.36%, the second error is 8.77%, and the total is 7.94%. It can be seen from the experimental results that first error is small, which means that there are fewer ground points that are mistakenly divided into ground points, and more points are mistakenly divided into ground points. The classification result is affected by the low local point, this condition leads to the increase of the second error. Especially when the classification elevation of low plants is less than 0.1 m, it is easy to increase the second error.

4 Conclusion (1) The classification method of this paper is based on the theory of polygons, This method assumes that the calculated value of the target point of the classification process is related to the points within a certain rang. It is more suitable for the distribution of actual points, because the spatial points are not independent and are related to the surrounding points, revealing the structural connection between the spatial points. Therefore the ideal results have been achieved. (2) In the process of vegetation classification, the conditions of distinguishing the small protrusions and the low vegetation are unreasonable. In the actual situation, the elevation of the low vegetation is less than 0.1 m, and the ground feature elevation is more than 0.1 m, thus this could increase the classification error; (3) In the process of building separation, the conditions for distinguishing building information from raised ground information are unreasonable. If the gradient of the ground information changes greatly, it is easy to judge it as building and eliminated, which requires a certain amount of manual intervention.

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References 1. Secord, J., Zakhor, A.: Tree detection in Urban regions using aerial lidar and image data. IEEE Geosci. Remote Sens. Lett. 4(2), 196–200 (2007) 2. Cui, J.-J., Sui, L.-C., Xu, Z.-H., et al.: Building extraction from LiDAR data based on edge detection. J. Surveying Mapp. Sci. Technol. 25(2), 98–100 (2008) 3. Antonarakis, A.S., Richards, K.S., Brasington, J.: Object-based land cover classification using airborne LiDAR. Remote Sens. Environ. 112(6), 2988–2998 (2008) 4. Ren, Z., Cen, M., Zhang, T., et al.: Building extraction from LIDAR data based on shape analysis of contours. J. Southwest Jiaotong Univ. 44(1), 83–88 (2009) 5. Zhang, K., Chen, S.-C., et al.: A progressive morphological filter for removing non ground measurements form airborne LiDAR data. IEEE Trans. Geosci. Remote Sens. 419(4), 872– 882 (2003) 6. Vosselman, G., Maas, H.G.: Adjustment and filtering of raw laser altimetry data. In: OEEPE Workshop on Airborne Laser scanning and Interferometric SAR for Detailed Digital Elevation Models, Stockholm, 1–3 March 2001, Official Publication OEEPE no. 40, 2001, pp. 62–72 (2001) 7. Sithole, G., Vosselman, G.: Experimental compasion of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS J. Photogrammetry Remote Sens. 59(1–2), 85–101 (2004) 8. Samadzadegana, F., Bigdelia, B., Hahnb, M.: Automatic road extraction from lidar data based on classifier fusion in urban Area. Int. Arch. Photogrammetry Remote Sens. Spat. Inf. Sci. 38(3), 81–87 (2009) 9. Shan, J., Sampath, A.: Urban DEM generation from raw lidar data: a libeling algorithm and its performance. Photogram. Eng. Remote Sens. 71(2), 217–226 (2005)

The Regionalization of Eco-Geological Environment System and Brief Function Evaluation of Luoyang City Liu Yang1,2(&), Jian-yu Zhang2, Chang-li Liu2, and Li-xin Pei2

2

1 China University of Mining and Technology, Beijing, No. Ding 11, Xueyuan Road, Haidian District, Beijing, China [email protected] Institute of Hydrogeology and Environmental Geology, CAGS, No. 268, Zhonghua North Street, Shijiazhuang, Hebei, China

Abstract. The construction of ecological civilization is a key way for sustainable development in the process of new urbanization in China. The functional regionalization and evaluation of urban eco-geological environment system is the primary premise and important foundation for the urban ecological civilization construction which provides geoscientific basis for scientific planning, construction of major projects and urban sustainable development. Taking Luoyang city as an example, this paper classifies and describes the ecogeological environment system of Luoyang from the perspectives of ecology and environmental geology in combined with the geological structure characteristics of Luoyang city, and carris out the regionalization and brief function evaluation of the eco-geological environment system. Finally, the countermeasures and suggestions for protecting different eco-geological environment systems in Luoyang City are put forward. Keywords: Eco-geological environment system
Regionalization characteristics
Function evaluation Protection countermeasures
Ecological civilization




1 Preface With the rapid economic growth, the tightening of resource constraints and serious environmental pollution, the degradation of ecosystems are severe, and the problems of unbalanced, uncoordinated and unsustainable economic development are increasingly prominent. The city is a large residential area formed by the accumulation of nonagricultural industries and populations. Urban development is not only promoted by human scientific and technological advancement, but also affected by natural and geological environment. The urban eco-geological environment is under tremendous pressure in the process of rapid urbanization since the 21st century. Therefore, the ecological civilization construction is the key way for sustainable development in the new urbanization process of China. The ecological civilization construction of new urbanization need to clarify the urban ecosystem function of city first, and it is necessary to make urban planning on the © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 191–200, 2019. https://doi.org/10.1007/978-981-13-7025-0_20

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basis of in-depth study of the geological environment. Therefore, the regionalization and function evaluation of urban eco-geological environment system is the foundation and important premise of urban scientific planning and sustainable development. Luoyang is one of the first pilot cities for the construction of water ecological civilization and one of the key cities in the Central Plains urban agglomeration. Based on the investigation of land and resources in China, this paper classifies and regionalizes the eco-geological environment system in Luoyang city, and evaluates the ecological and geological environment system of Luoyang city from the perspectives of the ecology and environmental geology. The thesis provides a scientific basis for urban planning, major projects implementation and ecological civilization construction in Luoyang, and also gives technical support for the sustainable development of the city.

2 Connotation and Classification of Eco-Geological Environment System 2.1

The Connotation of Eco-Geological Environment System

With science and technology, human beings can improve or transform the natural environment and make it more favorable for the development of social economy. On the contrary, if human activities, especially production and construction do not follow the natural principles, they can also destroy the natural environment and cause great losses to social economy and human life. The geological environment system, the natural environment system and the social economic system influence and restrict each other. The evolution mechanisms among them are complicated, therefore, these three systems are regarded as a unified dynamic system. Taking the geological environment of human being as the core, the relationship between human life system, natural ecological environment and social ecological environment is studied from an ecological perspective which is generally called ecogeological environment system. The eco-geological environment system regards the geological environment as an independent abiotic system and studies the relationship between the geological environment and the human living environment under the dual impact of the natural ecological environment and the social ecological environment [3]. 2.2

The Classification of the Eco-Geological Environment System in Luoyang City

There are several types of eco-geological environment system in Luoyang: The eco-geological environment system of forest-shrub-grassland: An ecogeological environment system dominated by shrub and grassland and composed of rock-soil mass and geological resources, mainly including the mixed forests and grass areas in the hills and gullies, seedling nurseries and various types of forests and grasslands, excluding the shrub-grassland ecosystem in wetland.

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The eco-geological environment system of the wetland: An eco-geological environment system composed of geological rock-soil mass, water resources and geological resources such as swamps, rivers, lakes, river shoals, reservoirs and swags. The eco-geological environment system of the industrial and mining land: An ecogeological environment system composed of vegetation and water within the land occupied by waste discharged or piled by mines (tailings), the disturbed geological environment, logistic (road) network, vegetation,, water bodies, land and various types of factory land and commercial service land within mine buildings and their surrounding areas. The eco-geological environment system of the arable land: An eco-geological environment system composed of rice paddies, arid land, orchards and towns, as well as rock-soil mass and geological resources within these areas, including sparsely distributed grassland eco-ecological system and wasteland eco-ecological system. The eco-geological environment system of the park: An eco-geological environment system composed of human and geological environment within cities and their affected areas, including the intra-city parks, zoos, botanical gardens, city-center gardens, and the green space for taking rests and beautifying environments, as well as the sparsely distributed shrub, grassland and woodland. The eco-geological environment system of the scenic areas: An eco-geological environment system composed of human and geological environment within cities and their affected areas, including land-soil mass, geological resources, sceneries (places of historic interest and scenic beauty, tourist attraction and revolutionary sites) and the buildings owned by their management institutions, including sparsely distributed shrubs, grasslands and forests. The eco-geological environment system of the residential lands: An eco-geological environment system composed of the residential land within Luoyang and its urban and rural areas. The eco-geological environment system of the waters and rivers: An eco-geological environment system composed of waters, river, tideland, ditch and hydraulic structure. The eco-geological environment system of the bare lands: An eco-geological environment system composed of mountains with less developed vegetation and their basic elements, including sparsely distributed grassland and wasteland eco-geological environment systems.

3 General Situation of Study Area and Eco-Geological Environment System Regionalization 3.1

General Situation of Study Area

Luoyang City is located at 112 º 15’–112 ° 38’ in the east and 34 º 32’–34 º 46’ in the north in the Luoyang Basin. It is adjacent to Qinyue in the east, Qinling in the west, Fu Niu in the south, and Taihang in the north. The terrain is high in the west and low in the east, and mountains and hills in the territory are staggered. The topography is complex and full of changes. Famous mountains, rivers, lakes, waterfalls, cave hot springs, virgin forests and other scenic spots are gathered here [4, 5].

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Luoyang is located in the transitional zone from the southern margin of the warm temperate zone to the north subtropical zone, with superior ecological environment, distinct seasons and pleasant climate. 3.2

Basic Principles of Regionalization

(1) The principle of adaptation to local conditions: The evaluation and regionalization of eco-geological environment functions must be based on the necessary attention to the eco-geological environment system and the existent eco-geological problems, and guarantee the full exploitation of major functions of existing ecogeological environment while taking the minor functions into account. (2) The principle of multi-functional coordination: The evaluation and regionalization of eco-geological environment functions must be linked to the national or local main functional regionalization, ecological function regionalization, and ecological construction planning and coordinate with the new urbanization construction, urban planning, and land planning, etc. 3.3

Regionalization of Eco-Geological Environment System in Luoyang

The regional eco-geological environment system in Luoyang city can be divided into the eco-geological environment systems of the forest-shrub-grassland, wetland, industrial and mining land, arable land, park land, residential land, scenic area, and waters and rivers. The areage and distribution of different eco-geological environment systems can refer to Table 1 and Fig. 1.

Table 1. Statistics of areas of all the eco-geological environment systems in Luoyang Serial No. 1 2 3 4 5 6 7 8 9 10 11 Total

Name of regionalization Forest-shrub-grassland eco-geological environment system Arable land eco-geological environment system Scenic area eco-geological environment system Residential land eco-geological environment system Waters and river eco-geological environment system Industrial and mining land eco-geological environment system Wetland eco-geological environment system Park eco-geological environment system Bare land eco-geological environment system Airport wetland eco-geological environment system Road wetland eco-geological environment system

Area (km2) 6247.75

Percentage

5968.26 1586.41 798.04 340.75 138.47

39.23% 10.43% 5.25% 2.24% 0.91%

15.72 12.68 6.96 2.00 96.57 15213.62

0.10% 0.08% 0.05% 0.01% 0.63% 100.00%

41.07%

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Fig. 1. Classification figure of eco-geological environment systems in Luoyang

4 Evaluation of Eco-Geological Environment Functions in Luoyang 4.1

Guiding Principles and Goals

We should apply the principles of ecology and environmental geology with an aim of coordinating the human-nature relationship, promoting ecological construction and new-type urbanization construction, and achieving the social and economic sustainability. We should conduct evaluation and regionalization of urban eco-geological environment functions on the basis of sufficient awareness of various urban ecogeological problems, eco-geological environment systems, and the spatial differentiation principles of the service function of the eco-geological environment system to better serve the construction of urban ecological civilization. 4.2

Evaluation Contents

Based on the stratigraphic lithology, geomorphology and hydrogeology ecological regulation ability (mainly water-source conservation, soil conservation, and flood diversion and storage aspects), product supply capacity (mainly agricultural and animal husbandry products aspects), resource supply capacity, human settlements, road traffic convenience and resource protection capacity of eco-geological environment system are mainly qualitatively evaluated from the viewpoint of the geography.

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Brief Evaluation of Eco-Geological Environment Functions in Luoyang

The characteristics of different eco-geological environment systems in different parts of Luoyang are described and the eco-geological environment functions are qualitatively evaluated. The details are as follows: (1) Forest-shrub-grassland eco-geological environment system: This system is mainly distributed in the northwest of Xin’an County, the south of Yanshi County, the southwest of Yiyang County, the west of Luoning County, the west and south of Song County, the south of Nuyang County, and most parts of Luanchuan County with an area of 6247.75 km2, accounting for 41% of the total area. If topography is examined, this area can be defined as bedrock middle-low mountains and bedrock hills. Specifically, the middle-low mountains are high and steep and have many deep canyons and sharp cliffs, with considerable variation of elevation. Within an elevation of 500–2000 m, lithology mostly features genesis and schist during Archean Eon and Proterozoic Eon, Carbonate series during Paleozoic Erathem, rock mass during Proterozoic Eon, and acid rock mass during Phase of Yanshan. The hilly area has an absolute height of less than 500 m and the relative height of less than 200 m. Bedrock is shallowly buried, and directly exposed on the top, but seriously weathered. Lithology features Permian sandstones, siltstone mixed with limestone, limestone of Ordovician system, and occasionally granite during Phase of Yanshan and sandstone during Cenozoic. With the developed fault structure, this area is susceptible to such geological disasters as collapse and landslides, and the geological conditions are less favorable for projects. With the topographic slope mostly within 15–25°, this area is characterized by developed gullies, strong water and gravity erosion, serious water and soil loss, small storage of groundwater resources. The topography of this area is high-terrain bedrock mountains and hilly areas, therefore, the conditions for grain and cropland growth are poor. This ecological area is vast, sparsely populated and rich in mineral resources (mainly in Luanchuan County). But the area has a high terrain and a big slope, serious water and soil erosion, scarce water resources, insufficient ability to offer human settlement security, a dire need of agricultural, husbandry and fishery products, and poor traffic security guarantee. Therefore, the area should focus on prevention and control of soil erosion, and vigorously increase the vegetation coverage and enhance biodiversity. (2) Arable land eco-geological environment system: This system is mainly distributed in the southeast of Xin’an County, most of Mengjin Yanshi and Yichuan County, the west and southeast of Yiyang County, the north of Song County, the east and southeastern suburb of Luoning County. The area is 5968.26 km2, accounting for 39.23% of the total area. Geomorphologically, most of Mengjing County, most of Yanshi County, most of Yichuan County, the north and southeastern suburb of Song County of the city belongs to piedmont alluvial-proluvial plain and valley plain. The topography is not undulate, and the terrain is flat. The ground lithology features clay, loam, sand loam, sandy soil in Quaternary alluvial layer, and fluvial-alluvial layer, and occasionally clay rock and mud stone during Neogene System. With the groundwater at the depth of over 50 m, the groundwater resources are rather abundant. The active faults are less noticeable, and the negative geological phenomena are not frequently seen. The geological conditions

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are favorable for projects. The arable land in the southeast of Xin’an County, the west of Yiyang County and the east of Luoning County belongs to the Loess Plateau Area in geomorpgrahy. With the ground lithology of aeolian deposit loess during the middle Pleistocene and upper Pleistocene, the lithology is loam and sub-sandy soil occasionally with calcic concretion layer. The groundwater has a depth of more than 100 m, and the groundwater resources are rather scarce. The blind faults are found in the area, and gullies are quite developed. Although there are small geological disasters like landslides and, debris flows and other geological disasters, the scale is not large, and the geological conditions are favorable for the engineering construction. Generally speaking, the ecological area has a relatively flat terrain, accessible transportation, satisfactory shelter for human settlements, and ability to provide agricultural, husbandry and fishery products, and moderate water and soil conservation performance. In the plain area, the comprehensive use of agricultural straws should be strengthened. Besides, the use of fertilizers and pesticides in agricultural production as well as the livestock and poultry breeding are likely to pollute surface water and ground water. The groundwater anti-pollution performance is relatively poor. Therefore, it is necessary to reinforce the protection of groundwater resources and ecological environment, and protect arable land by guaranteeing the minimum requirements for land use such as “three lines” system” and “three basic spaces” are met. (3) Scenic area eco-geological environment system: This system is mainly distributed in the north of Xin’an County, the east of Luoning County, the south of Luanchuan County, and the north and south of Song Count, with an area of 1586.41 km2, accounting for 10.43% of the total area. They are mainly distributed in Wangwu Mountain-Daimei Mountain World Geopark, Qingyao Mountain Scenic Area, Luoning Shenlingzhai National Geopark, Tianchi Mountain Scenic Area, Luchuan Laojun Mountain Provincial Geopark, Song County Baiyun Mountain Provincial Geopark, and Ruyang Dinosaur Fossils Provincial Geopark. As the bedrock middle-low mountain area, this ecological area has a high elevation of 500–1000 m. The lithology features genesis and schist during Archean Eon and Proterozoic Eon, Carbonate series during Paleozoic Erathem, rock mass during Proterozoic Eon, and acid rock mass during Phase of Yanshan. Inside the area, the fault structure is developed, which makes the area prone to such geological disasters as collapse and landslide. The geological conditions are less favorable for projects. With the topographic slope mostly between 15 and 25°, this area is characterized by developed gullies, strong water and gravity erosion, serious water and soil loss, deep buried groundwater, and poor grain and cropland growing conditions. Generally speaking, the ecological area has a high terrain and a big slope, serious water and soil loss, scarce water resources, insufficient ability to offer protection for human settlements, and in adequate agricultural, husbandry and fishery products. However, owing to the good performance of soil conservation wind-proof and sand fixation functions, the area serves as the best place for scenic spots and tourism. (4) Residential land eco-geological environment system: This system is mainly distributed in the Luoyang City and the counties and suburbs under its jurisdiction. The area is 798.04 km2, accounting for 5.25% of the total area. This ecological zone belongs to the valley plain and terrace area. The terrain is flat and not undulate. With the ground lithology of alluvial layer during Quaternary system, the lithology is clay,

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loam, sub-sandy soil and sand. The groundwater has an depth of 30–50 m, and the groundwater resources are abundant. There are no undesirable geological phenomena and the geological conditions are favorable for the construction of civil buildings. Generally speaking, this ecological area has a relatively flat terrain, accessible transportation, satisfactory protection for human settlements ability to provide agricultural, husbandry and fishery products, and good water and soil conservation as well as flood diversion and storage. (5) Waters and river eco-geological environment system: This system is mainly distributed in the City of Luoyang and those counties and suburbs under its jurisdiction. The area is 340.75 km2, 2.24% of the total area. This ecological area has a few rivers and reservoirs, including the Yellow River, Yi River, Luo River, Xiaolangdi Reservoir, Luhun Reservoir and Gu County Reservoir, so the local water resources are abundant. Generally speaking, this ecological area has convenient transportation, satisfactory ability to provide agricultural, husbandry and fishery products, and good ecological regulatory ability like water and soil conservation as well as flood diversion and storage. However, it is necessary to strengthen the protection of groundwater resources and ecological environment, and prevent pollution. (6) Industrial and mining land eco-geological environment system: This system, which consists of mining area and industrial area, is mainly distributed in Lengshui Township of Luanchuan County, Shishi Township of Xin’an County, Xunchun Township of Yiyang Conty, the south of Yanshi County, and Fudian Township of Ruyang County. The industrial area is mainly found in the suburb of Luoyang. The area is 138.47 km2, accounting for 0.91% of the total area. The mining area is mostly located in the bedrock mountain with high topography and noticeable topographic fluctuations between 500 and 1200 m above the sea level. The exposed bedrock is mainly the gray argillaceous strip-shaped limestone during Cambrian system, the sandpaper shale and limestone during Carboniferous system, and feldspar quartz sandstone and shale during Permian System. There are some local faults, so the stability of the earth’s crust is poor. The Vegetation in this area is seriously damaged and some geological disasters like ground subsistence occur from time to time. The industrial area is located in the river valley plain with flat ground and limited slope along the banks of Yihe River and Luohe River. Exposed on the ground is the alluvial player during Holocene Series. The lithology is mainly sub-sandy soil occasionally with thin clay soil. There are no fractures and unfavorable geological phenomena. The groundwater has a limited depth, and the water resources are abundant. Overall, the mining area of this ecological system is located in the middle and low mountains with large terrain, no developed traffic conditions, strong terrain cutting, insufficient water resources, water conservation, soil conservation and flood regulation, poor capacity for human settlement protection, and failure to supply agricultural, husbandry and fishery products. The area should be improved by the restoration of the vegetation damaged by mine development and the rational disposal of waste residues such as tailings to better improve and protect the ecological environment. The industrial area is located in the river valley plain with flat terrain and shallow groundwater. Different industrial activities can be conducted with good geological conditions, accessible transportation and strong ecological regulatory ability of water and soil conservation.

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(7) Wetland eco-geological environment system: This system is mainly distributed along two banks of the Yellow River in Jili District, Luoyang, with an area is 15.72 km2, 0.1% of the total area. The residential area and arable land can be occasionally found. Topographically, this area belongs to flood plain and core beach of the Yellow River. The terrain is not undulate flat, which features limited topographic relief and flat terrain. The surface lithology is mild clay, sub-sandy soil, sand and sandy pebble in alluvial layer during Holocene series. The groundwater has an limited depth (less than 5 m) and abundant water resources. There are no fractures or unfavorable geological phenomena. Generally speaking, the ecological area has strong ecological regulatory ability of water and soil conservation as well as flood diversion and storage, satisfactory protection for human settlements, and enough ability to provide agricultural, husbandry and fishery products, convenient transportation,. However, the groundwater antipollution performance is poor. Therefore, the solution is to protect groundwater resources and ecological environment. (8) Park eco-geological environment system: This system is mainly distributed in the city of Luoyang, including the theme parks, street parks, different peony parks and relics. The area is 12.68 km2, accounting for 0.08% of the total area. Topographically, this ecological area belongs to the river valley plain. The terrain is flat and not undulating and flat. The ground lithology features clay, loam, sub-sandy soil and sand in Quaternary alluvial layer. With an depth of 30–50 m, the groundwater is rich in resources The active faults are nowhere to be found, and no unfavorable geological phenomena occurred. In general, this ecological area has flat terrain, accessible transportation, satisfactory protection for human settlements and adequate supply of agricultural, husbandry and fishery products, strong good ecological regulatory ability of water and soil conservation as well as flood diversion and storage. However, the groundwater is susceptible to pollution. Therefore, the priority should be given to groundwater resources and ecological environment protection. (9) Airport, bare land road eco-geological environment system: The former two systems have an area of 2 km2 and 6.96 km2 respectively, accounting for 0.01% and 0.05% of the total area respectively. They’re not be evaluated as used as the main ecogeological environment systems. The road eco-geological environment system, which is distributed in a linear manner, mainly takes the form of key roads and railways. Since an independent region can’t be identified, no comments are made here.

5 Summary and Discussion Based on the regionalization of different eco-geological environment systems, this paper makes a brief function evaluation of eco-geological environment in combination with the geological and hydrogeologic conditions of Luoyang. The conclusions of the evaluation are as follows: (1) Forest-shrub-grassland eco-geological environment system function area is in an elevated position mostly at rocky mountains which are basically unaffected by human production activities. However, its own water conservation and soil conservation capacity are insufficient, and the road traffic security capacity is poor. Therefore,

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We should actively adjust the tree species structure, improve the forest environment, optimize the cultivation of existing vegetation, reduce soil erosion, and build a stable forest and grass eco-geological environment system. (2) Industrial and mining land eco-geological environment system function area is located in the middle and low mountains, which is greatly affected by human production activities, poor traffic conditions, strong terrain cutting, poor water resource conservation, soil conservation and flood storage capacity, and human settlement security, and the inadequacies in agricultural, animal husbandry and fishery products supply. Therefore, the recovery of vegetation caused by mines and the proper treatment of tailings and other waste residues should be strengthened to prevent the deterioration of ecological environment [1, 2]. (3) Arable land and residential land eco-geological environment system function area is flat and serves as the main area for human production activities with convenient transportation, better shelter for human settlements, and adequate supply of agricultural, animal husbandry and fishery products, medium water and soil conservation capacity. In order to further improve the ecological environment, it is necessary to strengthen the ability of groundwater pollution prevention and rational utilization of land. (4) Wetlands, scenic spots, parks, waters and river ecological environment systems functional areas enjoy better environment, less impacted human production activities, better protection for human settlements, adequate supply of agricultural, animal husbandry and fishery products, and improved water and soil conservation and flood regulation. In the area,it is necessary to strengthen the protection of groundwater resources and the reasonable disposal of municipal solid wastes, and take measures to protect vegetation and biodiversity in an effort to provide a better eco-geological environment for sustainable development. Acknowledgments. This paper is sponsored by environmental geological survey in 1:50000 of the northern town planning area in Zhongyuan Urban agglomeration (Project Code: DD20160244).

References 1. Hu, Y.L., Wang, P., Zhang, L.P., et al.: Application of remote sensing in mining area ecology environment evaluation. J. Resour. Dev. Market. 27, 584–586 (2011) 2. Ma, L.L., Tian, S.F., Wang, N.: Ecological environment evaluation of the mining area based on AHP and fuzzy mathematics. J. Remote Sens. Land Resour. 25, 165–170 (2013) 3. Chen, M.X.: Discussion on eco-geological environment system and comprehensive ecological environment geological survey. J. Hydrogeol. Eng. Geol. 3, 3–6, 12 (1999) 4. Yan, Zh.P., Jiao, H.J., Chen, G.Y., et al.: Environmental geological survey and evaluation report of main cities in Henan Province in China (Luoyang City). Zhengzhou, Henan Province (2009). (in Chinese) 5. Liu, C.L., Hou, H.B., Zhang, Y., et al.: Integrated report on Geological Survey of Urban key Urban agglomeration in Central China. Shijiazhuang, Hebei Province (2016). (in Chinese)

Cloud Detection in Landsat Imagery Using the Fractal Summation Method and Spatial Point-Pattern Analysis Ling Han1,2, Tingting Wu1(&), Zhiheng Liu1, and Qing Liu3 1

School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710064, China [email protected] 2 Shaanxi Key Laboratory of Land Consolidation and Rehabilitation, Chang’an University, Xi’an 710064, China 3 Track and Tunnel Branch Institute, Anhui Transport Consulting & Design Institute Co., Ltd., Anhui 233088, China

Abstract. Separation of clouds from snow and ice is fundamentally very challenging because these features display very similar spectral characteristics. To investigate how to improve the separation of these features using the fractal summation method and spatial point-pattern analysis, Landsat 8 Operational Land Imager (OLI) images containing both cloud and snow/ice were used as a data source. Selective principal component analysis (SPCA) was applied to those bands sensitive to information regarding cloud and snow, and the fractal summation method and spatial point-pattern analysis were then applied. This innovative cloud detection method was found to be an effective tool for reducing non-cloud false anomalies in images where snow and ice share similar spectra with clouds. Keywords: Remote sensing
Cloud detection Fractal summation method
Hotspot analysis


Snow/Ice


1 Introduction Cloud detection is useful in improving the accuracy of land cover classification when there are clouds present in the images. Cloud detection algorithms are relatively mature; however, the separation of cloud and snow/ice in bright areas remains a challenge. Cloud and snow is spectrally distinguishable although they have similar reflectance spectra in the visible-light range. The existence of cloud disturbs the digital number (DN) of a satellite image. Therefore, accurate cloud cover detection is vital for earth observation remote sensing data processing systems. A number of automated cloud detection methods have been developed based on the spectral value [1–3]. But cloud detection is still a challenge facing snow as a result of

L. Han and T. Wu—Contributed equally to this paper. © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 201–207, 2019. https://doi.org/10.1007/978-981-13-7025-0_21

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snow or brightly colored ground objects having similar spectral characteristics to clouds [4]. Furthermore, many researchers attempt to detect clouds using texture feature. Although the technique provides good cloud detection results for Landsat images, the results of this method are affected by the snow/ice because of the texture of snow and clouds are similar to each other [5–7]. This paper proposes a fractal summation model and spatial analysis of the point pattern method to separate clouds from snow/ice in bright areas. The aim of this method is to extend the techniques described previously by adding enhanced visual understanding to quantitative and automatic interpretation. Using this method, fractals are primarily used to detect “tone” anomalies, while singularities are mainly employed to detect “tone and shape,” and spatial analysis of the point pattern can detect features connected with “tone,” “pattern,” and other similar properties. Evidence shows that the symbiotic combination of different controlling factors, and theory and practice based on new statistical thinking, has potential advantages for the extraction of anomaly information over traditional cloud detection approaches.

2 Remotely Sensed Source Data The selected data is the Landsat 8 OLI image, it was launched on February 11, 2013 and normal operations started on May 30, 2013. L8 has a ground track repeat cycle of 16 days with an equatorial crossing time at 10:00 a.m. The Operational Land Imager (OLI) on L8 is a nine band push broom scanner with a swath width of 185 km and eight channels at 30 m and one panchromatic channel at 15 m spatial resolution.

3 The Cloud Extraction Based on the Pixel Values 3.1

Principal Component Analysis (PCA)

PCA is widely used in remote-sensing analysis, the purpose being to highlight spectral responses related to information pertaining to specific objects [8, 9]. As illustrated in Fig. 1, within the visible scope (band 3 of an OLI image), both snow and clouds have similar spectral curve shapes and are characterized by higher reflectivity, but their spectral curves subsequently separate at 1.6 µm (band 6) and 2.1 µm (band 7). The clouds maintain relatively higher reflectivity, while that of ice/snow reduces significantly. The reflectivity difference between snow and cloud is denoted as the highest difference among the three bands, which is beneficial for separating them [11]. PC1 can reflect the presence of cloud, it will lead to more false anomalies, while PC2 and PC3 show many spectral differences between clouds and snow, making them more useful for identifying clouds. Thus, it is reasonable to select PC2 and PC3 for the subsequent cloud detection analysis.

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Fig. 1. Reflection spectra of clouds and snow [10].

3.2

The Cloud Extraction Based on the Fractal Summation Model

The fractal model was originally developed for separating geochemically anomalous areas in relation to ore deposits [12]. However, this method is relevant not only to separate geochemical anomalies but also has found obvious capabilities for image classification [13]. A remotely sensed image is composed of an array of pixels, and each pixel is marked by a digital number (DN) value, thus this approach can be used to provide visual representation of the variance of an image based on pixel values and pixel value frequency distribution, or even the spatiotemporal and geometrical properties of image patterns [14]. The fractal model used in this study is known as the fractal summation method, which is not involved with the spatial attributes of the pixels. In accord with Feng et al. [15], the fractal summation model could be expressed in the following Equation: NðrÞ ¼ C  r Dn ;

ð1Þ

Where r is the characteristic linear measurement, here r stands for the imagery pixel- values from small to large; Dn (n = 1, 2, 3,4…) is the fractal dimension, each dimension corresponds to one scale-free (linear) segment; reflects the pixel number or summation of pixel-values that are equal to and greater to the corresponding r [16]. Taking logarithms of the above formula, we obtain Eq. (2): log NðrÞ ¼ D logðrÞ þ log C;

ð2Þ

A plot of log N(r) versus log (r) can produce several straight lines (at least two) with different slopes: D1, 2, 3… For a single straight line, by virtue of the liner least-square regression, the data set (N (ri), ri) (i = 1, 2, … N) can be fitted to a straight line, and its corresponding slope is noted as D. For two straight lines segments fitted by least squares with two slope D1 and D2, the dividing point is determined by the optimum

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least-square regression method as follows, that is the residual sum of squares (RSS). It is defined as follows: RSS ¼

i0 X

½lg Nðri Þ þ D1 lg ri  lg C1 2 þ

i¼1

N X

½lg Nðri Þ þ D2 lg ri  lg C2 2 ! Min:

ð3Þ

i¼i0 þ 1

Where Tn is the dividing point, i.e., the threshold of anomaly. In a similar fashion, slopes of several scale-invariant segments, as well as the thresholds (Tn, n = 1, 2, 3 …) between them, could be accurately determined. Details and the MATLAB implementation of this algorithm can be found in the references within Bo et al. [17].

4 The Spatial Analysis of the Point Pattern of Anomaly 4.1

Spatial Overlay

The PC2 and PC3 bands can highlight different objects information, first, both can detect the cloud-cover (including both thicker and thinner clouds). In this sense, through spatial overlay analysis can the authentic and false anomalies be separated. 4.2

Hotspot Analysis

Hotspot analysis can be used to illustrate the spatial cluster level of snow and cloud based on the Getis-Ord Gi* statistic using ArcGIS 10.2 software [18]. The larger the z Gi* score, the higher the attribute value in that region. Hence, a region that belongs to a high value area appears to have more spatial clustering. This intuitively shows the locations of high or low values of clustering and its degree. Details of this algorithm can be found in Yunus and Dou [19]. Findings of the hotspot analysis can be summarized into two main points. Firstly, two types of patches that represent cloud and snow/ice can be separated using the Z score. Generally snow/ice has a significantly lower Z score than cloud, even when the cloud-cover is thin. This confirms our assumption that cloud cover is integrated, while snow/ice is not. Secondly, according to the range of cloud Z scores, clouds can be further divided into thin, intermediate, and thick cover, with thick clouds having the largest Z score.

5 Results and Discussion This method achieves high-precision cloud detection in difficult areas. However, it is also important to determine whether this approach can be used more generally. To achieve this, we selected a multi-area, multi-temporal data set with snow and brightly colored ground objects on the underlying surface to test this method. Cloud detection in the presence of a normal underlying surface was found to be straightforward and consequently the results are not shown here. To highlight the effects of cloud and snow separation, a representative experimental result was chosen to demonstrate the

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versatility and feasibility of this algorithm. Feature areas were extracted from Landsat 8 images including clouds, snow, and brightly colored ground objects. Some typical results are shown in Fig. 2.

Fig. 2. Results of a multi-area and multi-temporal dataset, using imageries in different areas (cropping the regions of interest) (Only five selected results are shown here.).

The proposed method achieves high-precision cloud detection in areas where it is difficult to do so. The method proposed in this study uses pixel values, clustering characteristics to detect clouds. This method can be used to solve the problem of snow being misclassified as cloud, as frequently occurred using traditional detection methods. Furthermore, this method eliminates the salt and pepper noise generated by the brightly surface, greatly improving the accuracy of cloud detection.

6 Results and Discussion By introducing spatial distribution characteristics, we do not deny the value of texture analysis, but rather aim to arouse interest in spatial analysis, because spatial statistical analysis is also an important feature. Hence, a change in attitude from considering tone plus shape to tone plus spatial analysis is the primary purpose of our innovation. The statistical techniques of fractals and hotspots play an important role in cloud detection and minimize the costly contribution of visual interpretation. To validate this approach, we interpreted challenging regions, where the difficulty of interpretation was increased because cloud and snow share the same spectrum and ground objects, cloud, and desert are all bright. Hence, we reach the following conclusions: 1. PCA can separate objects that have different origins but are blended together. The principal components that are sensitive to cloud cover are highlighted by the different reflectivity of the snow. 2. In remote sensing, the fractal summation model performs better than the threshold method, but pixel values cannot play a role in distinguishing features because false anomalies share some spectral features with authentic anomalies. Hence, we must consider another diagnostic characteristic: spatial distribution.

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3. Hotspot analysis can be used to determine the locations where high or low values are spatially clustered. Misclassified snow and the underlying surface always show a more dispersed distribution. Consequently, most false anomalies can be removed by analyzing their clustering relationships. Acknowledgments. This work was financially supported by the project of open fund for key laboratory of land and resources degenerate and unused land remediation, under Grant [SXDJ2017-7], and the 1:50, 000 geological mapping in the loess covered region of the map sheets: Caobizhen (I48E008021), Liangting (I48E008022), Zhaoxian (I48E008023), Qianyang (I48E009021), Fengxiang (I48E009022), & Yaojiagou (I48E009023) in Shaanxi Province, China, under Grant [DD-20160060].

References 1. Roy, David P., et al.: Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sens. Environ. 114(1), 35–49 (2010) 2. Zhu, Z., Woodcock, C.E.: Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change. Remote Sens. Environ. 152, 217–234 (2014) 3. Kovalskyy, V., Roy, D.: A one year Landsat 8 conterminous United States study of cirrus and non-cirrus clouds. Remote Sens. 7, 564–578 (2015) 4. Lyapustin, A., Wang, Y., Frey, R.: An automatic cloud mask algorithm based on time series of MODIS measurements. J. Geophys. Res.-Atmos 13, 1–15 (2008) 5. Song, X., Liu, Z., Zhao, Y.: Cloud detection and analysis of MODIS image. In: Geoscience and Remote Sensing Symposium. IGARSS 2004. Proceedings. 2004 IEEE International, vol. 4, pp. 2764–2767. IEEE (2004) 6. Simpson, J.J., Gobat, J.I.: Improved cloud detection for daytime AVHRR scenes over land. Remote Sens. Environ. 55, 21–49 (1996) 7. Dybbroe, Adam, Karlsson, Karl-Göran, Thoss, Anke: NWCSAF AVHRR cloud detection and analysis using dynamic thresholds and radiative transfer modeling. Part II: tuning and validation. J. Appl. Meteorol. 44, 55–71 (2005) 8. Li, J., et al.: An Intercomparison of the spatiotemporal variability of satellite- and groundbased cloud datasets using spectral analysis techniques. J. Clim. 28(14), 150417113249008 (2015) 9. Pal, S.K., Majumdar, T.J., Bhattacharya, A.K.: ERS-2 SAR and IRS-1C LISS III data fusion: a PCA approach to improve remote sensing based geological interpretation. Isprs J. Photogrammetry Remote Sens. 61(5), 281–297 (2007) 10. Allen Jr., R.C., Durkee, P.A., Wash, C.H.: Snow/cloud discrimination with multispectral satellite measurements. J. Appl. Meteorol. 29(10), 994–1004 (2010) 11. Feng, S.Y., et al.: Method of cloud detection with hyperspectral remote sensing image based on the reflective characteristics. Chin. Optics 8(2), 198–204 (2015) 12. Cheng, Q.: The perimeter-area fractal model and its application to geology. Math. Geol. 27(1), 69–82 (1995) 13. Chen, G., Cheng, Q.: Singularity analysis based on wavelet transform of fractal measures for identifying geochemical anomaly in mineral exploration, Pergamon Press, Inc. (2016) 14. Brekke, C., Solberg, A.H.S.: Oil spill detection by satellite remote sensing. Remote Sens. Environ. 95(1), 1–13 (2005)

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15. Feng, S.Y., et al.: Method of cloud detection with hyperspectral remote sensing image based on the reflective characteristics. Chin. Optics 8(2), 198–204 (2015) 16. Blenkinsop, T.: Scaling laws for the distribution of gold, geothermal, and gas resources. Pure Appl. Geophys. 172(7), 2045–2056 (2015) 17. Zhao, B., et al.: Internal structural analysis of geochemical anomaly based on the content arrangement method: a case study of copper stream sediment survey in northwestern Zhejiang Province. Geophys. Geochem. Explor. 39(2), 297–305 (2015) 18. Mitchel, A.: The ESRI Guide to GIS analysis, vol. 2: Spartial measurements and statistics, Esri Guide to Gis Analysis (2005) 19. Yunus, A.P., Dou, J., Sravanthi, N.: Remote sensing of chlorophyll- a as a measure of red tide in Tokyo Bay using hotspot analysis. Remote Sens. Appl. Soc. Environ. 2, 11–25 (2015)

Extraction of Target Geological Hazard Areas in Loess Cover Areas Based on Mixed Total Sieving Algorithm Ling Han1,2, Tingting Wu1(&), Qing Liu3, Zhiheng Liu1, and Tingyu Zhang1 1

School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710064, China [email protected] 2 Shaanxi Key Laboratory of Land Consolidation and Rehabilitation, Chang’an University, Xi’an 710064, China 3 Track and Tunnel Branch Institute, Anhui Transport Consulting & Design Institute Co., Ltd., Anhui 233088, China

Abstract. Geological hazards occur frequently in the Loess Plateau, but because of the depth of the loess cover, especially when it is still covered with trees, it is difficult to interpret the potential areas of geological hazards. This paper mainly explores a new method for extracting potential geological hazards in the loess cover area. In order to ensure the accuracy of interpretation, different geomorphological units in the study area are partitioned. Then, in order to effectively study and express the geomorphological characteristics of the parameters or indicators of a certain significance for the extraction of topography factors, these terrain factors are often distributed from multiple models. In the training area, the threshold of representative terrain factor is extracted by mixing sieving algorithm. Results the potential area of geological hazard in loess cover area were obtained by overlay analysis. Keywords: Remote sensing
Geological hazards Mixed total sieving algorithm


Loess cover areas


1 Introduction China is a country with very serious geological disasters, such as landslides, collapses and debris flows [1]. The losses caused by geological disasters account for about 35% of the total disaster losses [2]. These disasters not only directly cause huge casualties and property losses, but also the disaster rehabilitation and remediation costs are as high as billions of yuan, and the other impact of disasters on society is incalculable. Loess region is one of the areas with a high occurrence of geological disasters in China. Because of the large thickness of loess accumulation, loose structure and serious soil and water loss, the loess plateau area in northwest China is characterized by gully, topographic fragmentation, landslide and collapse. Debris flow and other geological L. Han and T. Wu—Contributed equally to this paper. © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 208–214, 2019. https://doi.org/10.1007/978-981-13-7025-0_22

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disasters occur frequently, which seriously threaten the life and property safety of the people. Among them, Baoji loess area is vast, the topography is complex, landslide, collapse, debris flow has many characteristics, such as a large quantity, high density, large deformation modulus, etc. If relying solely on the traditional ground investigation, the period is long, the cost is high, and it is difficult to cover completely. The application of new remote sensing technology and a new method to geological hazard monitoring can be improved obviously. Work efficiency, plays a double effect with half the effort [3–6]. Many countries have done a lot of work on the application of geological hazard risk assessment, especially remote sensing in geological hazard research, and the better ones are Japan, the United States, the European Community and so on. Japan compiled a map of 1/50, 000 geological hazards in the whole country using remote sensing images. On the basis of a large number of landslides and debris flow remote sensing investigations, European Community countries systematically summarized the remote sensing techniques and methods and pointed out the different scales of identification. The spatial resolution of remote sensing images for landslide and debris flow with different brightness or contrast [7–12]. In just a few decades, scholars have made fruitful research on remote sensing technology applied in geological hazard research.

2 Data and Methods 2.1

Data

The research area is located in the northern area of Baoji city, Shaanxi Province, with an area of 2451 km2. A pan-sharpened multi-spectral ZiYuan-3 surveying satellite (ZY-3) image (spatial resolution: 2.1 m), was used in this study. Topographic characteristics computed from 5 m resolution digital elevation model (DEM) data. indly assure that the Contact Volume Editor is given the name and email address of the contact author for your paper. 2.2

Methods

2.2.1

Processing the Study Area According to Different Geological Backgrounds According to the geological background, the study area can be divided into loess hilly areas, loess tableland areas, river valley areas, bedrock areas, thick loess cover areas as shown in Fig. 1. 2.2.2 Extraction of Topography Factors Topography factor is a significant parameter or index for the effective study and expression of geomorphological features. Common topography include slope, slope direction, elevation, slope variability and so on. What they describe is the information of a differential point unit, the magnitude of which is generally affected only by the elevation of the point in which it is located and the elevation information in the small neighborhood. Combined with the different geological background units divided in the

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Fig. 1. Different geological background of the study area.

previous section, this work comprehensively considered and experimented on the processing effect of different influence factors, selected the most favorable factors for extracting geological hazard information, and finally extracted the elevation and gradient of slope units. Slope direction and topography four influencing factors have been obtained, which provide more scientific data for geological hazard risk assessment. 2.2.3

Determining the Threshold Value of Topography Factors Based on the Mixed Sieving Method A global distribution corresponds to a primary geochemical process, and a normal distribution mixes with each other in time and space to form a multi-modal distribution, simply put, if a variable is the result of a number of small independent random factors, Then it can be considered that the variable satisfies the normal distribution. Then some form, state or mode of output of the same feature or the same feature is found to follow the normal distribution because it has more uniform internal results. Loess or other rocks of a certain mechanical strength have their own distribution patterns in space, and that loess and regions which are easy to form geological hazards must be able to correspond to an independent normal distribution. The technical means are as follows: (1) The study area is divided and the training area of the specific geomorphologic unit is selected; (2) Extraction of terrain and slope factors for the effective study and expression of geomorphological features of the parameters or indicators of a certain significance; (3) In the training area, representative terrain factors were mixed and screened respectively to record the total threshold of the normal distribution with the highest frequency in the disaster site. The above terrain factors were often distributed from multiple modes. Therefore, a hybrid population screening model is proposed to select the threshold values that can represent the conditions of terrain factors that are prone to geological hazards, thus greatly reducing the target area for interpretation.

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3 Results Taking the bedrock area as an example, it is found that the slope range between 25° to the max can form an independent normal distribution. The proportion of normal landforms is larger, the proportion of places with disasters is smaller, while the bedrock area is smaller, and the slope is steep, so it is likely to be a high disaster area. Therefore, in order not to omit potential disaster areas, 20° to max is artificially taken here for disaster extraction; the relief of terrain is more reasonable than 30 m but combined with the geomorphological characteristics of the area, 30 m is more likely to correspond to the alpine regions. Disasters above 30 m have little to do with production and life. They are deep mountains and old forests, while 5 m directly affect life and production, so take 5 m-max. Generally speaking, landslides occur mostly in the south. The division of this potential area focuses on the south, southeast and southwest (Fig. 2). 0.7

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Fig. 2. The mixed total sieving algorithm results of slope in bedrock areas.

According to the digital number values frequency, the calculated results are as follows: the average values of population I and II are respectively 4.494 and 8.0395. The weight of population I and II were 52.024 and 47.976, respectively (Fig. 3). 2.5

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Fig. 3. The mixed total sieving algorithm results of slope aspects in bedrock areas.

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According to the gray level frequency, the results obtained by using the mixed sieving principle are as follows: the average values of population I and II are respectively 7.198 and 9.252. The weights of population I and II were 34.674 and 65.326 respectively. In different geological background areas, topographic slope, topographic direction, and topographic fluctuation are different, and the threshold values of three kinds of geological hazards influence factors are also different, as follows (Table 1). Table 1. Quantitative results of mixed total sieving algorithm. Heading level Thick loess cover areas Loess tableland areas Bedrock areas River valley areas Loess hilly areas

Slope (°) 3–34 3–29 20–45 20–48 13–35

Slope aspects (°) Topographic fluctuation (m) 67.5–292.5 0–48 67.5–292.5 0–97 67.5–292.5 0–91 67.5–292.5 5–86 67.5–275.5 15–96

The target areas of geological disasters are obtained by superposition analysis of different topography factors. As shown in Fig. 5, the yellow area in the map is the potential area of geological disaster which is finally obtained through the mixed screening model. The area is 759 km2 accounting for 30.96% of the total area of the study area. The surrounding area has greatly reduced the scope of the geological survey, reduced the cost of the geological survey, and improved the accuracy of land adjustment (Fig. 4).

Fig. 4. Target geological hazard areas in loess cover areas based on mixed total sieving algorithm.

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Figure 5 shows the geological hazard interpretation mark of the loess area obtained by field investigation as the disaster site interpreted in the potential area. In the study area, 184 geological hazards were interpreted: 131 landslides, 17 collapses, 34 debris flows, including 86 small-scale landslides. 45 Large-scale landslides, 27 small debris flows, 9 Large-scale debris flows. The main disaster sites in this area are landslide points, which account for 71.19% of the total number of disaster points.

Fig. 5. Interpretation of geological hazards in the study area.

4 Conclusion This paper proposed a new method for Remote Sensing Extraction of geological hazards in Loess covered areas. It is proposed to explore more effective methods of remote sensing investigation of landslides and debris flows, use remote sensing information sources to interpret geological disaster points, and analyze multiple ways in a comprehensive way. It will supplement, expand and innovate the existing theory of remote sensing interpretation of geological disasters, and provide basic geological hazard data for geological research in Loess covered areas. Acknowledgments. This work was financially supported by the project of open fund for key laboratory of land and resources degenerate and unused land remediation, under Grant [SXDJ2017-7], and the 1:50, 000 geological mapping in the loess covered region of the map sheets: Caobizhen (I48E008021), Liangting (I48E008022), Zhaoxian (I48E008023), Qianyang (I48E009021), Fengxiang (I48E009022), & Yaojiagou (I48E009023) in Shaanxi Province, China, under Grant [DD-20160060].

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References 1. Petley, D.: Global patterns of loss of life from landslides. Geology 40(10), 927–930 (2012) 2. Hong, Y., Adler, R., Huffman, G.: Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophys. Res. Lett. 33(22) (2006) 3. Derbyshire, E.: Geological hazards in loess terrain, with particular reference to the loess regions of China. Earth Sci. Rev. 54(1–3), 231–260 (2001) 4. Wang, Z.-R., Wei-Jiang, W., Zhou, Z.-Q.: Landslide induced by over-irrigation in loess platform areas in Gansu Province. Chin. J. Geol. Hazard Control 15(3), 43–46 (2004) 5. Xiangyi, L.: The hazards of loess landslides in the southern tableland of Jingyang county, Shaanxi and their relationship with the channel water into fields. J. Eng. Geol. 3(1), 56–64 (1995) 6. Gabet, E.J., Dunne, T.: Landslides on coastal sage-scrub and grassland hillslopes in a severe El Nino winter: The effects of vegetation conversion on sediment delivery. Geol. Soc. Am. Bull. 114(8), 983–990 (2002) 7. Yi, L.U.: Application and prospect of remote sensing techniques in geological hazard survey. Energy Energy Conserv. (2011) 8. Dai, F.C., Lee, C.F., Ngai, Y.Y.: Landslide risk assessment and management: an overview. Eng. Geol. 64(1), 65–87 (2002) 9. Ohlmacher, G.C., Davis, J.C.: Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng. Geol. 69(3), 331–343 (2003) 10. Chung, C.J.F., Fabbri, A.G.: Validation of spatial prediction models for landslide hazard mapping. Nat. Hazards 30(3), 451–472 (2003) 11. Carrara, A., Guzzetti, F., Cardinali, M., et al.: Use of GIS technology in the prediction and monitoring of landslide hazard. Nat. Hazards 20(2–3), 117–135 (1999) 12. Yin, Y., Wang, F., Sun, P.: Landslide hazards triggered by the 2008 Wenchuan earthquake, Sichuan, China. Landslides 6(2), 139–152 (2009)

Research on Heat and Humidity Transfer Performance Evaluation of Spraying Mine Exhaust Air Heat Exchanger Lingling Bao(&), Yang Zhao, Xiu Su, Ziyong Wang, and Yajing Rong College of Energy and Environmental Engineering, Hebei University of Engineering, Guangmingnan Street 199, Handan 056038, China [email protected]

Abstract. The indexes for evaluating the thermal performance of the spray chamber at home and abroad are introduced and analyzed. Due to the high relative humidity of the exhaust air in the mine, the air-water heat and humidity exchange process is usually carried out along the saturation line, and it is found that the general heat exchange efficiency is basically 1 through testing of different heat and humidity treatment processes, therefore, this indicator has been unable to accurately evaluate the performance of the heat and humidity exchange unit. An index for evaluating the heat and humidity exchange performance of the heat and humidity exchange unit is proposed, defining the heating efficiency and cooling efficiency by used water as the treatment medium in the heat and humidity exchange unit, taking the cooling and dehumidification process of counter-flow air-water heat and mass transfer as an example, the formulas for theoretical calculation include overall heat exchange efficiency, heat transfer efficiency and heating efficiency are derived, furthermore, three dimensionless efficiencies are obtained, and their relationship with the dimensionless mass transfer unit number and water-air ratio is analyzed. It provides a theoretical basis for thermal calculation and performance analysis of counterflow heat and humidity exchange equipment. Keywords: Mine return air
Energy recovery
Heat and mass transfer Efficiency
Number of mass transfer unit (NTUm)




1 Introduction When moist air directly contacts with water, if the temperature and humidity differences exist, the heat and mass transfer will happen. Due to the higher heat transfer efficiency compared with type-wall heat exchange technology, this method is widely used in heating, ventilation and air-conditioning (HVAC) system, e.g., air washers, cooling towers and direct evaporative cooler. In deep mining, the mine exhaust air (MEA) contains a large quantity of lowtemperature heat which is released to the atmosphere directly in most cases. The MEA temperature will be mostly dependent on the geological conditions and depth of the mine. © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 215–228, 2019. https://doi.org/10.1007/978-981-13-7025-0_23

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The temperature and relative humidity (RH) of MEA was 18 °C and 100% respectively in winter, someone of deep mine in Canada [1]. In general, for the temperature range of MEA is about 15 °C–25 °C, and the RH is near 100%, the air flow rate is about 50–500 m3/s, so that MEA is a proper heat source for the Water Source Heat Pump (WSHP) system [2, 3]. The integrated system of a MEA heat recovery system plus a WSHP system will meet part or all of heating demand in winter and cooling demand in summer, and the energy saving of MEA is apparent compared with the boiler plus water chiller system [4]. The key device of the integrated system is the MEA heat exchanger where the water from WSHP releases heat to MEA in summer mode and extracts heat from MEA in winter mode when the water sprayed from the nozzles is in contact with MEA [5]. Obviously, energy conservation and the feasibility of the integrated system depend highly on the heat and mass transfer efficiency of MRA heat exchanger [1, 4]. Usually, the performances of cooling towers are described in terms of effectivenessnumber of thermal units (NTUe) [6, 7] and latent effectiveness-number of transfer units for mass transfer (NTUm) [8–10]. In addition, the NTUe method for sensible heat exchanger is modified and used for studying the performance of indirect evaporative coolers [11]. The performances of spray chamber are described in terms of the efficiency of overall heat transfer, contact coefficient and heat transfer efficiency [4, 12, 13]. The saturating efficiency is used to evaluate the cooling efficiency of the direct evaporative cooler [14]. Due to two main features of the MEA heat exchanger compared with cooling tower, spray chamber and direct evaporative cooler, the performance indicators for the three devices are not applicable for the performance evaluation of MEA heat exchanger. First, the heat and mass transfer processes of air in “enthalpy-humidity” psychometric diagram of humid air are the cooling and dehumidification in winter mode and heating and humidification in summer mode. Second, the object of the MRA heat exchanger is water which is different from air washer and direct evaporative cooler. In addition, the site experimental results in Shuguang Mine indicate that due to the MEA is near saturated state, the general efficiency of heat exchange approximately equals to 100%, which results in an invalid evaluation for heat and mass transfer efficiency [5]. The performance indicators for heat and mass transfer devices are playing a crucial role in theory, experimental and simulation researches. In this paper, based on the heat and mass transfer theory, two performance efficiencies have been defined for the heat and mass transfer devices where the air is near saturation and the water is the object, i.e. MEA heat exchanger. Taking the cooling and dehumidified process of air in the MEA heat exchanger as an example, the theoretical formulas including the new performance efficiency, the efficiency of overall heat transfer and heat transfer efficiency have been derived. The influences of NTUm and water to air ratio on the efficiencies have been obtained and analyzed.

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2 Methodology The principle of heat and mass transfer on the MEA heat exchanger is the same to that in the vertical spray chamber. However, due to the characteristics of the MEA, some performance indicators are invalid to evaluate the performances of the MEA heat exchanger directly, i.e. the general heat exchange efficiency. A new performance indicator class should be present for MEA heat exchanger which can evaluate the heat and mass transfer efficiency. In this paper, the performance indicator includes the efficiency of overall heat transfer and heat transfer efficiency. In order to make it easier to understand the derivation process of theoretical formulas of these efficiencies shown in Sect. 3, the definitions of the efficiency of overall heat transfer and heat transfer efficiency should be introduced first. Taking cooling and dehumidification of moist air in a vertical heat and mass transfer device as an example, the ideal process in the enthalpy diagram of moist air is shown in Fig. 1, the definitions of the efficiency of overall heat transfer and heat transfer efficiency are shown in Eqs. (1) to (2) [12, 13]. As shown in Fig. 1, the initial and final states of moist air are point 1 and 2, point 1ʹ and 2ʹ are the projections of the two points along the isenthalpic line, respectively. Point 5 and 4 are the initial and final states of the saturated air on the surface of water. Assumed that the RH = 100% is a straight line, the efficiency of overall heat transfer can be defined in formula (1). g¼

10 20 þ 45 ðts1  ts2 Þ þ ðtf 2  tf 1 Þ ts2  tf 2 ¼1 ¼ 0 ts1  tf 1 ts1  tf 1 15

ð1Þ

10 20 i1  i2 ¼ 1 0 5 i1  if 1

ð2Þ

n¼

Fig. 1. The cooling and dehumidified process of moist air in psychrometric chart

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The g indicates the ratio of actual overall-heat transfer capacity (the sum of heat rejected by air and heat absorbed by water) to ideal overall-heat transfer capacity. The n indicates actual heat capacity released by air to the ideal maximum. g takes air-water as objects, and n takes air as objects. However, in the MEA heat exchanger, water is the heating and cooling object which will be heated by the MEA in winter and cooled by MEA in summer. Thus it is necessary to present two heat transfer efficiencies for water in the MEA heat exchanger. In a MEA heat exchanger (or spray chamber), if at least one of temperature difference and humidity ratio difference exists, in which between the moist air and the saturated air on the surface of water, heat and mass will exchange between them. In other words, when the wet bulb temperature of the moist air does not equal the water temperature (i.e., enthalpy difference exists), heat will exchange between them. Assumed the air flow rate and the contact time are infinite, when temperature of water is higher than that of moist air, it will be cooled and its temperature will drop till it equals to the wet bulb temperature of moist air; conversely, it will be heated and its temperature will increase till it equals to the wet bulb temperature of moist air. Thus, the wet bulb temperature of air is the final temperature of water under ideal conditions. Based on this, the water heated efficiency and water cooled efficiency are proposed and defined. When the inlet temperature of water is lower than the wet bulb temperature of inlet air, the water will be heated, and the efficiency of heated water can be used to evaluate the heated effect of water which is defined in Eq. (3). The efficiency of heated water equals to the ratio of true temperature increase to maximum possible temperature increase for water. On the contrary, when the inlet temperature of water is higher than the wet bulb temperature of inlet air, the water will be cooled, and the efficiency of cooled water can be used to evaluate the cooled effect for water which is defined in Eq. (4). The efficiency of cooled water equals to the ratio of true temperature reduction to maximum possible temperature reduction for water [15]. gwh ¼

45 tf 2  tf 1 ¼ 10 5 ts1  tf 1

ð3Þ

tf 1  tf 2 tf 1  ts1

ð4Þ

gwc ¼

In addition to evaluating the perfection of heat-humidity exchange in the spray chamber, the heat-humidity exchange efficiency of the above-mentioned spray chamber is another important function for performing thermal calculation of the spray chamber. There are usually two methods for thermal calculation of water spray chambers. One is based on the method of heat and mass exchange coefficient, and the other is based on the method of heat and humidity exchange efficiency. However, the heat-humid exchange coefficient and heat-humid exchange efficiency are expressed as empirical, semi-empirical formulas or line graphs of related factors based on the experimental data [16]. Due to the complexity of the movement between the air and the water droplets in the spray chamber, it is currently impossible to theoretically accurately solve the

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thermal calculation problem of the spray chamber. Yin [17] is used as the research object of the downstream water spray chamber, on the basis of assumption or iteration, the theoretical formula of g and n is derived, and the relation between the two is established, a thermal calculation method of the water spray chamber which does not involve any experimental results is proposed, but the method is not suitable for the reverse flow chamber.

3 Theoretical Calculation Formula In the mine exhaust air heat exchanger, the treating process of air is usually the cooling and dehumidification process (heat pump unit heating condition operation) and the heating humidification process (heat pump unit refrigeration condition operation), the design of the air-conditioning spray chamber is usually based on the cooling and dehumidification process, the theoretical formula between g, n and gwh in the air cooling and dehumidification process of the counter-flow type water spray chamber is discussed. Figure 2 is a schematic view of a single row counter current vertical spray chamber. The water is sprayed vertically downwards, and the air is flowing vertically upwards. Assuming the cross-sectional area of the heat and humidity exchange unit is A, and the height is H, and the nozzle is 0 point and the vertical direction is the positive direction, and establishing Z coordinate.

Fig. 2. Single row counter-flow vertical spray chamber

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Within the micro-element of dz, the amount of heat and humidity exchange between air and water is: (I) Humidity exchange The difference in humidity ratio is the driving force for the exchange of humidity between air and water, which is between saturated air boundary layer and surrounding air on water droplet surface. In air, the weight of water reduced by water vapor condensation is equal to the weight of water. According to the principle of mass balance, within the micro-element of dz, the amount of humidity exchange is: dW ¼ GdðdÞ ¼ dam Aðd  df Þdz

ð5Þ

In the formula, G is mass flow of dry air, kg/s; W is mass flow of water, kg/s; d is humidity ratio of air, kg/kgdry air; d is mass exchange coefficient based humidity ratio difference, kgdry air/(m2
s); am is overall mass transfer surface area of water droplets per unit volume of water spray chamber, m2/m3; df is humidity ratio in the boundary layer of saturated air on the surface of water droplets, kg/kgdry air. (II) Sensible heat exchange The difference between temperature of air and temperature of the saturated air layer on surface of water droplet is the driving force for sensible heat exchange. Air to water heat transfer is affected by temperature difference. According to the principle of heat balance, within the micro-element of dz, sensible heat exchange is: Gcp;a dt ¼ ks ah Aðt  tf Þdz

ð6Þ

In the formula, t is temperature of air, °C; tf is temperature of water, °C; cp,a is constant pressure specific heat capacity of air, kJ/(kg
K); cp;f is constant pressure specific heat capacity of water, kJ/(kg
K); ks is Sensible heat exchange coefficient, kW/(m2
K); ah is overall heat transfer surface area of water droplets per unit volume of water spray chamber, m2/m3. Supposing am ¼ ah ¼ a, and in the micro-element of dz, overall heat exchange between air and water is: dqt ¼ G½cdðdÞ þ cp;a dt ¼ ½cðd  df Þ þ

ks ðt  tf ÞdaAdz d

ð7Þ

Supposing Le ¼ ckp;as d ¼1, then formula (7) can be simplified to: dqt ¼ Gdi ¼ daAði  if Þdz

ð8Þ

In the formula, i is enthalpy of air, kJ/kg; making D ¼ i  if , then dD = di  dif , the enthalpy difference at the water inlet is D1 ¼ i2  if1 , the enthalpy difference at the water outlet is D2 ¼ i1  if2 .

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Due to the existence of a certain proportional relationship between enthalpy of air and the corresponding wet bulb temperature in the temperature range of the air conditioning, therefore, there is a certain proportional relationship between the enthalpy of di the saturated air on the water surface and the water temperature [18, 19], that is m ¼ dtf . f Because m is related to wet bulb temperature and atmospheric pressure, m can be obtained by formula or enthalpy wet diagram. In the mine exhaust air waste heat recovery system, the temperature of spray water (outlet temperature of heat pump unit) is generally 5 °C to 15 °C (the operating condition of heat pump for cooling), or 25 °C to 35 °C (the operating condition of heat pump for heating). m does not change much within a certain range, so the average value of the corresponding temperature interval can be taken. Water temperature is decreasing along the direction of air flow, dqt ¼ cp;f Wdtf ¼ 

cp;f W dif m

ð9Þ

Available from formula (6) and (9): dif ¼ 

m dqt dqt ; di ¼  cp;f W G

dD = di  dif ¼ 

1 m þ dqt G cp;f W

ð10Þ ð11Þ

Available formula (9) into formula (11): daAdz ¼ ð

1 m 1 dD þ Þ G cp;f W D

ð12Þ

Integrating the formula (12) Z 0

H

Z daAdz ¼

D1

D2

ð

1 m 1 dD þ Þ G cp;f W D

ð13Þ

Supposing the volume of the spray chamber is V ¼ AH, then formula (13) is simplified to: daV ¼ ð

1 m 1 D1 þ Þ ln G cp;f W D2

D1 1 m Þ ¼ exp½daVð  G cp;f W D2

ð14Þ ð15Þ

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Because D1 ¼ i2  if 1 , D2 ¼ i1  if 2 , Di ¼ i1  i2 , Dif ¼ if 2  if 1 , so: D2 ¼D1 þ ðDi  Dif Þ

ð16Þ

qt ¼ Gði1  i2 Þ ¼ cp;f Wðtf 2  tf 1 Þ

ð17aÞ

Heat balance equation:

qt ¼ GDi ¼

cp;f W Dif m

ð17bÞ

Available formula (17a, 17b) into formula (16): D2 ¼D1 þ Dið1 

Gm Þ cp;f W

ð18Þ

Formula (18) is divided by D1 on both sides, after simplification, it can be obtained: 1  cGm D1 p;f W ¼ 1 Di exp½daVðG  cp;fmW Þ  1

ð19Þ

The definition of heat transfer efficiency n (2) is: n¼

i1  i2 Di D1 ¼ ð þ 1Þ1 ¼ i1  iw1 D1 þ Di Di

ð20Þ

Available formula (19) into formula (20): n¼ð

1  cGm p;f W exp½daVðG1  cp;fmW Þ  1

þ 1Þ1

ð21Þ

Formula (21) is the formula for theoretical calculation on the heat transfer efficiency of the spray chamber. The formulas for theoretical calculation on overall heat exchange efficiency, general heat exchange efficiency, and heating efficiency are as follows. In the working range of heat and humidity exchange unit of mine exhaust air heat recovery, assuming that the ratio between enthalpy of the exhaust air and the wet bulb temperature is a fixed value, then formula (1) and (3) can be simplified as: g¼1 gwh ¼

i2  iw2 i1  iw1

iw2  iw1 i1  tw1

ð22Þ ð23Þ

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According to formulas (17a, 17b), (20) and (23), the relationship between n and gwh can be obtained: gwh ¼ n

Diw Gm ¼n cp;f W Di

ð24Þ

Thus, the theoretical calculation formula of gwh is: gwh ¼

1  cGm Gm p;f W ð þ 1Þ1 cp;f W exp½daVðG1  cp;fmW Þ  1

ð25Þ

Available from formula (20), (22) and (24): ð26Þ

g ¼ n þ gwh Consequently, the theoretical calculation formula of g is: 1  cGm Gm p;f W Þð g ¼ ð1 þ þ 1Þ1 cp;f W exp½daVðG1  cp;fmW Þ  1

ð27Þ

W Setting the number of mass transfer units NTUm ¼ daV G , water-air ratio b ¼ G , then, by substituting into formulas (21), (23), and (27), the relationship between the three efficiencies and the dimensionless numbers NTUm, b can be obtained.

n¼ð

1  cp;fm b exp½NTUm ð1  cp;fm bÞ  1

þ 1Þ1

W ¼ f ðNTUm ; b; mÞ ¼ f1 ðD; ; aH; qv; tf Þ G gwh ¼

m

ð

1  cp;fm b

cp;f b exp½NTUm ð1  cp;fm bÞ  1

þ 1Þ1

W ¼ f2 ðD; ; aH; qv; tf Þ G g¼ ð1 þ

m

ð29Þ

1  cp;fm b

Þð þ 1Þ1 cp;f b exp½NTUm ð1  cp;fm bÞ  1

W ¼ f3 ðD; ; aH; qv; tf Þ G

ð28Þ

ð30Þ

Through the theoretical deduction, the dimensionless formula for the theoretical calculation of n, gwh and g in counter-flow spray chamber is obtained, and the theoretically relevant formula between them also is obtained. This has more theoretical help for thermal calculation, performance analysis and simulation analysis of counter-flow

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spray chamber and other similar systems. Formulas (28) through (30) show that m and cp;f can approximate as constants over a range of temperatures, therefore, both n, gwh and g are the function of dimensionless numbers (NTUm, b), that is the function of water droplet diameter D, convective mass transfer coefficient d, structural form and size aH, mass velocity of exhaust air qv, water-air ratio W G.

4 Results Analysis For cooling and dehumidification processes, the water temperature is usually between 5 °C and 15 °C, thus taking the average value of m is 3.157, specific heat capacity of water is 4.182 kJ/(kg
K). Figure 3 shows the trend of n with NTUm and b, it can be seen from Fig. 3 that as the NTUm and b increase, n gradually increases, from the trend of change, with the increase of NTUm and b, the trend of increasing n is slowed down. In addition, when NTUm is greater than 2 and b is less than 1, the effect of changes in NTUm and b on n is slow, when NTUm is constant, n increases as b increases, but the tendency to increase is slower and slower. Increasing b is beneficial to advance n, but in actual engineering, excessive b will cause waste of water resources and mechanical energy consumption, so should choose the appropriate b. Besides, the increase of NTUm is affected by diameter of water droplets, the size of heat and humidity exchange unit, the air mass flow rate, on the other hand, when NTUm exceeds a certain value (this value is related to the selection of b), the influence of the change on n is not obvious, in order to avoid waste of resources, NTUm can not increase without limit, NTUm also be chosen properly. Therefore, when designing the air-water heat and humidity exchange unit, the water temperature after the heat and mass exchange can reach the inlet water temperature required by the heat pump unit, and b should be increased as much as possible so that the heat pump unit can obtain more heat or cold.

Fig. 3. The effect of NTUm on n in different b cases

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It can be seen from Fig. 4 that in the case of b constant, gwh increases with the increase of NTUm; when NTUm is less than 2, the influence of the increase of NTUm on gwh is more obvious; the changes in b have a significant impact on gwh , and the smaller b, the increase of NTUm would take greater influence on gwh . gwh ¼ 0:58 when NTUm ¼ 2:5; b ¼ 1; gwh ¼ 0:74 when NTUm ¼ 2:5; b ¼ 0:7. The variation trend of gwh in the water spray chamber with NTUm and b is different from the heat transfer efficiency n. It is reflected that when NTUm is certain, as gwh decreases, gwh gradually increases, the main reason is that due to the balance of heat exchange between the air and water sides, a smaller b will cause the water temperature to rise sharply, which will increase gwh , and a smaller b leads to a small reduction in enthalpy of air, so that n is lower, it will also make the heat pump unit less heatgenerating. In summary, it is found that the effect of b on gwh and n is reversed.

Fig. 4. The effect of NTUm on gwh in different b cases

Fig. 5. The effect of NTUm on g in different b cases

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Both n and gwh that can be seen from Figs. 3 and 4 are less than 1, which is consistent with the theoretical meaning of the definitions (2) and (3). Figure 5 reveals the effect of NTUm and b on g. The wet bulb temperature of the outlet air during the cooling and dehumidification process of counter-flow spray chamber is likely to be lower than the outlet temperature of water, according to the definition (1) of g, g is probably greater than 1, which is consistent with the results shown in Fig. 5. g is equal to the sum of n and gwh in formula (26), so the impact of NTUm and b on g is the superposition of the two influential factors on n and gwh , which is consistent with the meaning of g, “combined heat and humidity exchange results of air and water”. As can be seen from Fig. 5, NTUm is usually less than 2.5, b is in the range of ½0:5; 1 in the ordinary water spray chamber [20], at which time g increases as NTUm increases, and g increases as b decreases, and in the case which both NTUm and b are small, the influence of NTUm and b on g is more significant. When NTUm  2:5, g increases first and then decreases with the increase of b, and b the maximum value of g falls within the b 2 ½0:5; 1 interval, this is mainly due to the superposition effect of n and gwh . In addition, it can be seen from Fig. 5 that there are some intersections between the curves, such as points ðNTUm; bÞ ¼ ð1:2; 0:4Þ1 , ð2:3; 0:2Þ2 , and the reduction of the intersection from b1 to b2 has no meaning for the improvement of g, the smaller b makes the NTUm at the corresponding intersection smaller, which also verifies the conclusion that NTUm and b have a certain relationship.

5 Conclusion Due to small thermal resistance and high heat transfer efficiency, air-water direct contact heat and humidity exchange method is widely used in energy, chemical and other fields, such as spray chamber, cooling tower, evaporative cooler, etc. The determination and evaluation of air-water heat and humidity exchange efficiency has theoretical significance, and the evaluation of the heat and humidity exchange performance can be used to evaluate the performance of air-water heat and humidity exchange near the saturation line. As the temperature is about 15 °C–25 °C and the air flow rate is relatively higher and more stable, mine exhaust air (MEA) is a high quality waste heat source to the Water Source Heat Pump (WSHP) system. The key device of the integrated system which consists of a MEA heat recovery system and a WSHP system is the MEA heat exchanger. In MEA heat exchanger, water from WSHP was sprayed from the nozzles and was in contact with MEA. In summer, it released heat to MEA mode, and in winter, extracted heat from MEA. So the heat transfer efficiency of MEA heat exchanger would influence the application of MRA heat recovery system. As the critical reasons of no packing of MEA heat exchanger and nearly saturated MEA, the heat transfer efficiencies for cooling tower, indirect evaporative cooler and spray chamber are not applicable for MEA heat exchanger. First, based on the heat and mass transfer theory, two performance efficiencies, i.e. the efficiency of heated water and the efficiency of cooled water, have been defined for the heat and mass transfer devices where the air is near saturation and the water is the object, i.e. MEA heat exchanger. Then, a new performance indicator class is present for MEA heat exchanger which consists of the

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efficiency of heated water (and the efficiency of cooled water), the efficiency of overall heat transfer and heat transfer efficiency. Moreover, taking the cooling and dehumidified process of air in the MEA heat exchanger as an example, the theoretical formulas that the efficiency of heated water, the efficiency of overall heat transfer and heat transfer efficiency have been derived. Finally, the influences of NTUm and water to air ratio on the efficiencies have been obtained and analyzed.

References 1. Sbarba, H.D., Fytas, K., Pareszczsk, J.: Economics of exhaust air heat recovery systems for mine ventilation. Int. J. Min. Reclam. Environ. 26(3), 185–198 (2012) 2. Lv, X., Zhao, J.: Application of water source heat pump in coal mine. Build. Energy Environ. 2, 64–67 (2011). (In Chinese) 3. Peterson, W.O., Walker, J.N., Duncan, G.A., et al.: Composition of coal mine air in relationship to greenhouse environment control. Trans. ASAE 18(1), 140–144 (1975) 4. Bao, L.L., Wang, J.G., Zhang, Q.Q., Shi, Z.Z.: Economic analysis of the mine return air heat recovery system. In: International Conference on Energy and Power Engineering (EPE 2014), Hong Kong, 26–27th April, pp. 76–81 (2014) 5. Bao, L., Wang, J., Wang, J.: A waste heat utilization technology of deep coal mine. In: APEC Conference on Low-Carbon Town and Physical Energy Storage, Changsha, Hunan, China, 25–26th May 2013 6. ASHRAE: HVAC 1 toolkit: a toolkit for primary HVAC system energy calculation. American Society of Heating. Refrig. Air Cond. Eng. (1999) 7. Jin, G.Y., Cai, W.J., Lu, L., Lee, E.L., Chiang, A.: A simplified modeling of mechanical cooling tower for control and optimization of HVAC systems. Energy Convers. Manag. 48, 355–365 (2007) 8. Zhang, L., Niu, J.L.: Effectiveness correlations for heat and humidity transfer processes in an enthalpy exchanger with membrane cores. Heat Transf. 124, 922–929 (2002) 9. Kadylak, D., Cave, P., Mérida, W.: Effectiveness correlations for heat and mass transfer in membrane humidifiers. Int. J. Heat Mass Transf. 52, 1504–1509 (2009) 10. Sphaier, L.A., Worek, W.M.: Parametric analysis of heat and mass transfer regenerators using a generalized effectiveness-NTU method. Int. J. Heat Mass Transf. 52, 2265–2272 (2009) 11. Hasan, A.: Going below the wet-bulb temperature by indirect evaporative cooling: analysis using a modified e-NTU method. Appl. Energy 89(1), 237–245 (2012) 12. Huang, X., Wang, T.: Air Conditioning Engineering. Machinery Industry Press, Beijing (2006). (in Chinese) 13. Yu, L.: Heat and mass transfer in air washer. J. China Text. Univ. 1, 26–34 (1987). (in Chinese) 14. Fouda, A., Melikyan, Z.: A simplified model for analysis of heat and mass transfer in a direct evaporative cooler. Appl. Therm. Eng. 31, 932–936 (2011) 15. Zhao, Y.: Study on the influence mechanism of crosswind on heat and mass transfer of natural ventilation countercurrent wet cooling tower. Shandong University, Jinan (2009). (in Chinese) 16. Mcquistion, E.C., Parker, J.D.: Heating Ventilation and Air Conditioning Analysis and Design, p. 441. Wiley, Hoboken (1977) 17. Yin, P.: An approach to the thermodynamic calculation of air washer. J. Refrig. 4, 49–55 (1987). (in Chinese)

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18. Zhang, Y., Zhu, Y., Jiang, Y.: Theoretical analysis and modeling of overall heat transfer of air handling unit by using spraying water. J. Tsinghua Univ. (Sci. Technol.) 39(10), 35–38 (1999). (in Chinese) 19. Muangnoi, T., Asvapoositkul, W., Wongwises, S.: An exergy analysis on the performance of a counter flow wet cooling tower. Appl. Therm. Eng. 27(56), 910–917 (2006) 20. Yaozhen, S.: Model calculation and analysis of heat and mass transfer of air and water in a direct contact forward flow. Trans. Chin. Soc. Agric. Eng. 22(1), 6–10 (2006). (in Chinese)

Land Cover Change Analysis in Wuhan, China Using Google Earth Engine Platform and Ancillary Knowledge Yahya Ali Khan(&), Yuwei Wang, and Zongyao Sha School of Remote Sensing and Engineering, Wuhan University, Wuhan, China [email protected]

Abstract. The land type cover information has significantly important role to understand land type change and earth activities. Despite the availability of numerous high-resolution satellite data, very minimal number of land type change researches are available up till now due to the computational limitations. This study provides land type change mapping based on high-resolution (30 m) using high computing cloud base platform (google earth engine). The supervised classification training technique is being used in our study. We gathered the land cover type of two year (2016–2017) and addressed the transformations among them during these years. The study is based on four land cover types specifically water, urban land, forest and crop land. This method is provided to overcome the limitation of high computing platform and lack of the availability of highresolution land cover change data. Keywords: LCC: land cover change
GEE: google earth engine NDVI: normal difference vegetation index




1 Introduction Exploring land cover types and monitoring the change among them is very important to manage the environmental and urban planning issues. Remote sensing techniques are considered as one of the most effective tools to learn about the LCC dynamics [1]. The satellite imagery is considered as the basic source of information, when dealing with LCC. Numerous datasets are available with different properties and various resolution scales. These datasets provide a great opportunity to the researcher for extracting useful information [2, 6]. The most widely used datasets for LCC are mostly the medium spatial resolution datasets [7]. However, the shortage of high resolution dataset is reported. High resolution land cover data (30 m) is needed to be explored, for monitoring the LCC critically and effectively. The currently available data limits our ability to explore land cover changes [3]. The high spatial resolution such as 30 m is a plus point for LCC analysis, as the researcher have to face the limitation for temporal resolution of datasets [7]. Many researches have been conducted, which used image segmentation to examine the land cover change. As a result, a number of learning and training algorithms are being developed to address the land cover type issues and other scientific problems [4]. © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 229–239, 2019. https://doi.org/10.1007/978-981-13-7025-0_24

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The commonly used technique is derivation of a land type matrix by applying training methods over a largescale data belonging to different time periods [5]. High computing platform is needed to perform different operations on datasets in order to gather valuable information. Google earth engine is a new cloud base high computing platform, which allows the researchers not only to access the increasing number of datasets, but also to perform various computations on them for free. The free availability of these type of platforms has gain a huge interest of researchers. As a result, various researches are carried out for LCC analysis using 250 m and 500 m datasets [3]. Many researches elaborated the importance of the scale, while conduction research for LCC. The ready to use LCC data with low spatial resolution is limited due to ‘hard’ classification, which raises a question on the accuracy of LCC of these low spatial resolution datasets [7]. Different researches has been carried out to deal with the high resolution LCC problem. One of the studies, introduced a binary approach for training classification, which emerged the result of various classifiers into single binary class. The following study used the combination of NDVI, NWDI and night light data to perform LCC analysis [3]. Another study proposed to use different datasets to identify various type to analyze the LCC. Here, we propose a LCC analysis method which is different from other models in two aspects. First, we used the same dataset for all types of classification and secondly, we improved the classification results on the basis of prior knowledge. The contribution of this study is vital. Which is to enable the researchers to perform effective classification by relating it to the ancillary knowledge. In this way, the researcher can validate and improve the effectiveness of the classification results. To deal with the limitations of ‘hard’ classification and low spatial resolution, this paper aims to explore LCC using high spatial resolution dataset (30 m) and Training supervised classification approach to address the current need of LCC. The paper includes the LCC of Wuhan, China for two years (2016–2017). The google earth engine supervised classification method was used to identify four types of classes of land which includes water, urban, forest and crop lands. This study aims to detect the changes with high accuracy, which is only possible with high spatial resolution datasets. Due to that Landsat 8 TOA Reflectance (30 m) is used for the analysis. In our research, we used pixel base classification technique to differentiate the land types and analyze the change. The total area acquired by various land types during both years is reported and critically analyzed later in the paper. The detection of change from one land type to another during the whole-time period, remains the main pillars of the study and are briefly discussed in the paper.

2 Study Area Wuhan is located at central china and its said to be the most populous city of central china. It is the capital city of Hubei province. As shown in Fig. 1, The urban center of Wuhan is located at 30°N and 114°E. The Study area is located at the middle reaches intersection of yangtze river and han river. Wuhan city is regarded as a transportation hub for travel to nine provinces, which means that it is surrounded by many seaways, expressways and railways. Wuhan has area of 8,494.41 km2 having population of more

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than 10,607,700 people. Wuhan has seven urban districts and six suburban or rural districts. It has four seasons. The summer is humid and hot, having average temperature variation from 22 °C to 30 °C and the average temperature in winter variate from 10 °C to 0 °C. The recorded lowest and highest temperature in Wuhan is −18 °C and 33 °C respectively.

3 Satellite Imagery Datasets Landsat 8 Reflectance TOA (12 band 30 m) was selected to conduct our research. The area bound was set as the boundary of Wuhan, China. Two image collections were created, specifically for the years 2016 and 2017. The filter date functionality was applied to get the desired data. These collections consist of the images from January to April each year. The image collection for 2016 contained 10 objects, while the image collection for contained 17 objects. Because the collection may contain cloudy image which would have a negative impact on the results, the cloud sorting algorithm was applied on image collections to get the least cloud images. For reducing these collections to a single image, the mode image of each collection was computed. By following the above-mentioned steps, the two final images for 2016 and 2017 were computed. (see Fig. 1). We imported SRTM Digital Elevation Model data to compare it with our classification results. The image was from year 2000 and 30 m resolution.

Fig. 1. GEE code editor

4 Supervised Classification The GEE code editor was used to do the land classification (not the explorer). The training data polygons were selected on the basis of visualization of image from each year. Using high resolution images (30 m), four land type classifications were done including water, urban, forest and crop land setting pixel values 1, 2, 3 and 4 respectively. Blue, green and red bands were used for computations. For each land type classification, it was made sure that the training data polygons were sufficient to perform effective classification. Specifically, 12 polygons for water, 6 for urban land, 9 for forest and 12 for crop land were designated as the sample data points for classification. The pixel based classification was adopted using above mentioned data polygons. For results of 2016 see Fig. 2.

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Fig. 2. Classification 2016 (Color figure online)

5 Computational Model The computation of already gathered final classified images consists of following procedures. Land classification images from 2016 and 2017 were used to perform further operations (see Fig. 3). The images were having four possible values for the pixels, which were 1 for the water, 2 for the urban area, 3 for the forest and 4 for the crop lands. To examine the change in the LCC types, the images for 2016 was multiplied by 10 and the sum of this specific image and 2017 image was calculated. The result image was having 16 possible pixel values 11, 12, 13, 14, 21, 22, 23, 24, 31, 32, 33, 34, 41, 42, 43 and 44, Each identifying specific information. The classification can be denoted as C and formulated by: C ¼ w þ u þ f þ c

ð1Þ

Where w is the water classified pixels, u is built up areas, f is forest pixels and c is cropland. The LCC can be denoted by UC, which is: UC ¼ 10 ðC16 Þ þ C17

ð2Þ

The C16 is the classification result for the year 2016 and C17 is the classification result for the year 2017.

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Table 1. Pixel value information for results Pixel value Land change information 12 Water changed to urban land 13 Water changed to forest 14 Water changes to crop land 21 Urban changed to water 23 Urban changed to forest 24 Urban changes to cropland 31 Forest changed to water 32 Forest changed to urban 34 Forest changed to crop land 41 Crop land changed to water 42 Crop land changed to urban 43 Crop land changed to forest

For the pixel values 11, 22, 33 and 44, the land type in specific area remained constant. Particularly, 11 means that the area was water during 2016 and remained same during 2017, same for the case of urban area (22), forest (33) and crop land (44). The remaining 12 values indicates the land category change during these years. The information for these changes are provided in Table 1.

Fig. 3. Structural model

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6 Results The Classification results can be compiled by looking at the LCC Fig. 4 for 2016 and Fig. 5 for 2017. Most of the land was classified as crop land followed by urban land during 2016. The pattern remained same during the year of 2017. However, the change in the area of each LCC type was reported. The water land doesn’t experience much change during both years. The significant change was seen in crop land, which tended to be increased during 2017 as compared to 2016 by 22%. This increase in crop land affected the forest and the urban land.

Fig. 4. LCC 2016

Fig. 5. LCC 2017

The forest and urban land decreased by 20% and 26% respectively in 2017 comparing with 2016 results. The accuracy of the classifier remains an important factor during critical analysis of these results (see Table 2). The total accuracy of the classifier was 0.964 and for each class accuracy, the information is available in the table. The accuracy of water type was excellent. However, the performance of the classifier for crop and urban land seemed to be bit lower as compared to water land. The forest land accuracy was reported as higher than that of urban and crop land. For the water land out of 13619 pixels, 13600 pixels were classified as water. The total count for urban land pixel was 40247 out of which 38786 pixels were reported as urban land. forest land contained 12441 pixels, 11685 were classified as forests. Out of 48917 pixels, 47032 pixels were reported as crop land.

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Table 2. Accuracy matrix Water Urban Forest Crop Water 13600 16 0 3 Urban 20 38768 73 1386 Forest 14 96 11685 646 Crop 1 1297 587 47032

To do accuracy analysis we also calculated the kappa value. Give that Kappa ¼ ðobserved accuracy  expected accuracyÞ=ð1  expected accuracyÞ Let OW, OU, OF and OC be the number of accurately classified pixels for water, urban, forest and cropland. The observed accuracy is given by: OA ¼ ðOW þ OU þ OF þ OCÞ = Total pixelÞ ¼ 0:96 Let the product of count of classified pixels and actual type be EW, EU, EF and EC. The expected accuracy is given as: EA ¼ ðEW=TotalÞ þ ðEU=TotalÞ þ ðEF=TotalÞ þ ðEC=TotalÞ ¼ 0:33 Computing the value of observed accuracy and expected accuracy, the kappa value for the presented model is kappa = 0.94.

Fig. 6. LCC change results

As it can be seen in Fig. 6, most of the pixel value count for 11, 22, 33 and 44 are greater than the other pixel values, which means these are the constants. The LCC didn’t changed for these pixels. However, it is noted that the count for pixel values 24 and 34 is higher, which is an evidence that the increase in the crop land affected urban

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land and forest land. Moreover, a noticeable count of value 42 elaborates that some crop lands were transformed to urban land. The concept can be grabbed that the places for urban lands and crop land were displaced during the period of study.

7 Post Classification Correction As our classification was based on the training data, there was a high chance of errors in the data. We used SRTM Digital elevation data for the comparison and correctness. It was found that the slope of water area was less than 5 and the slope for forest was greater than 60. When these slope results were integrated with NDVI values, it was intimated that the water bodies were having NDVI value less than 0.1 while forest land was having NDVI value greater than 0.7 (Fig. 7). When we tested the same hypotheses on the data for 2016, the correctness rate was around 15%. The pixels for 2016, which were urban before and become water after corrections are show in Fig. 8. The correctness was performed on the accuracy of water vs urban and forest vs cropland, using the prior knowledge from DEM data.

Fig. 7. Correction model

The post correction can be denoted as PC: PC ¼ C þ WC þ FC

ð3Þ

WC ¼ CNDVI \ 0:1

ð4Þ

FC ¼ CNDVI [ 0:7

ð5Þ

Where WC is water correction and FC is forest correction.

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Fig. 8. Correction results water

8 Discussion The high resolution and frequently updated datasets are essential for exploring land surface activates. Before, the high resolution LCC mapping remained always challenging despite the free availability of huge amount of satellite imagery data. This was due to lack of the high computing platforms. The GEE provided the researcher with a great opportunity of free and efficient cloud computing platform. The availability of more than 40 years multiscale data is a plus. With the availability of GEE, high resolution LCC monitoring is no more a challenge. As it can be done efficiently with very low cost using GEE. The resources on GEE is used by various scientists for water mapping [8], population mapping [9], crop land monitoring [10] and forest monitoring [11]. In this study, we monitored the LCC during years 2016 and 2017 using high resolution satellite data (30 m). We also computed the change in LCC and analyzed it. The four different types of land cover and their dynamic were mentioned in this study. As a result, we found a significant change between the forest and crop land. Moreover, the area of urban land area and crop land area remained almost same which the geometric variations with in the region. The study also presented the accuracy of supervised classification using GEE, which was 0.964 during the training time. There are many advantages of GEE including the efficient computation power and acceptable processing time [3, 12]. The GEE has eliminated the effort of downloading data and filtering data by providing the integration of remote sensing data and processing algorithms [13]. The GEE provides parallel computing environment with the effective memory management techniques [15]. The random forest is an efficient algorithm to perform the classification in highly variated regions and can be used with some additional operations for noisy data [14].

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Numerous advance researches are being conducted by different researchers on LCC big data based approaches. A solution for ghost city problem was proposed by researchers, which states that the analysis of ghost cities with night light data is not worthy, it can be efficiently done by the combination of OLS and Landsat data [16]. Tsai introduced a method to do a reliable analysis of LCC dealing with the cloudy data using GEE. The advanced functions of GEE (e.g.: total tree count) were used to conduct this research [17]. The impact of urban land use change on the urban heat island was explored recently, which remains an important issue. It was reported that world temperature is increased by 2.72° during last two decades [18]. Recently, a method is introduced for urban development and construction, which suggest to use both horizontal and vertical processes [19]. In fact, still there is a gap in methods available for LCC, which is needed to be filled by researchers.

9 Conclusion In this paper, we presented a method of LCC classification using high spatial resolution data (30 m). The supervised classification was done using random forest algorithm. The study duration was from 2016 to 2017. Accuracy of the classification was also presented and analyzed in this paper. As a result of this study, we found four different LCC and monitored the change among them during these two years. Moreover, we provided the details of LCC transformation from one land type to another. There is always a need of updated LCC information as this information plays a vital role in various types of urban planning. We consider our study important because it aim to overcome the lack of availability of high spatial resolution LCC data. The accuracy of high spatial resolution is much better than that of low spatial resolution data. GEE is a useful addition to the cloud computing tools. It provides a platform to researchers where they can process, evaluate and analyze the data under one umbrella. It provides the code editor and API of JavaScript and python, using which the researcher can implement their own algorithm to get the desired information. With the availability of high resolution datasets on GEE, quality research is expected in future taking advantage of the effective computation power of GEE. Acknowledgement. The National Natural Science Foundation of China (Nos. 41371371 and 41871296), Open Fund of Key Laboratory of Geographic Information Science (Ministry of Education) and East China Normal University (No. KLGIS2017A05).

References 1. Chen, J., Liao, A.P., Cao, X.: Global land cover mapping at 30 m resolution: a POK-based operational approach. ISPRS J. Photogramm. Remote. Sens. 103, 7–27 (2015) 2. Castelluccio, M., Poggi, G., Sansone, C., Verdoliva, L.: Land use classification in remote sensing images by convolutional neural networks. http://arxiv.org/abs/1508.00092 3. Midekisa, A., et al.: Mapping land cover change over continental Africa using landsat and google earth engine cloud computing. PLoS ONE 12(9), e0184926 (2017). https://doi.org/ 10.1371/journal.pone.0184926

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Overview of Speed Sensorless Control of Permanent Magnet Synchronous Motors Yuhang Zhang(&), Wangyu Qin, Dawei Zheng, Chongxia Zhou, and Jianhui Liu Department of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei, China [email protected]

Abstract. The control system of speed sensorless for permanent magnet synchronous motor (PMSM) is an important issue for its advantages such as high precision in control, quite convenience in installation and strong reliability in performance. This paper introduces the development history and present situation of the speed sensorless control technology of PMSM. Then, it is analyzed and compared on the control strategy of speed sensor technology in both medium-to-high speed range and zero-to-low speed range. Finally, the research which emphasizes and development trend of speed sensorless control technology is pointed out. Keywords: Permanent magnet synchronous motor (PMSM) Speed sensorless
Control strategy




1 Introduction The PMSM has the advantages of high torque inertia ratio, high energy density and high efficiency. In recent years, it has been widely used in aerospace, electric vehicle and industrial control fields. The speed signal plays an important role in the highperformance control system of PMSM, in addition to being the closed-loop feedback signal of the speed, it is also the basis of coordinate transformation. Conventional control systems often utilize mechanical sensors such as photoelectric encoder and resolver to obtain speed signals. These mechanical sensors have many problems in practical applications, such as high cost, complex installation and be susceptible to the environment [1]. These problems reduce the reliability and robustness of the system and limit the application of PMSM in some special occasions. Speed sensorless control technology has emerged in order to overcome the drawbacks of the above mechanical sensors. In the case that the rotor and the base of the motor are not equipped with electromagnetic or photoelectric sensors, the speed sensorless control technology utilizes the relevant electrical signals in the motor to obtain the amount (voltage, current) by means of direct calculation, parameter identification, state estimation, etc. [2]. To realize the closed-loop control of the motor and solve various defects effectively caused by the mechanical sensor, the rotational speed can be estimated by utilizing physical quantities we detected and mathematical models of the motor. Speed sensorless control © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 240–251, 2019. https://doi.org/10.1007/978-981-13-7025-0_25

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technology is of great significance to improve system reliability and environmental adaptability, which has become an important issue in the field of motor control technology [3].

2 Development and Present Situation With the rapid development of control theory, digital signal processing and computer technology, the sensorless control technology of PMSM has received widespread attention. The research of speed sensorless control methods began in the 1970s, Abbondan proposed a slip frequency estimation method for asynchronous motors, which first time carried out research on speed sensorless control methods [4]. Ishida and other scholars utilized rotor tooth harmonics to detect the rotational speed. Due to the limitation of the processor’s operating speed, satisfactory results were obtained only in the high speed range [5]. In 1983, Joeten applied speed sensorless control technology to induction motor vector control, who pointed out the direction for subsequent research [6], then Jones and other scholars made use of the state observer to estimate the rotor magnetic pole position of PMSM, which realized the speed sensorless control of PMSM for the first time [7]. Many universities and research institutions abroad have studied the speed sensorless control technology of PMSM. For example, Lorenz of the University of Wisconsin first proposed the concept of high frequency signal injection method in 1993, whose technology can extended to low speed or even zero speed range [8]. Sul of Seoul National University in South Korea began researching the sensorless control technology in 1995, which realized the pulsed high frequency signal injection method was proposed for asynchronous motors, it applied to the speed sensorless control of the surface-mount permanent magnet synchronous motor (SPMSM) [9]. In recent years, Holtz of Wuppertal University in Germany, A. Consoli of Italy, M.F. Rahman of Australia have also invested in the research work of speed sensorless control technology of PMSM from different angles, they obtained many research results [10]. At the same time, speed sensorless control technology of PMSM has also received attention from domestic research institutions. Scholars of Beihang University have analyzed the traditional high frequency pulsating injection method and improved traditional method by adding a virtual high frequency rotating coordinate. A position detection method of PMSM based on virtual pulse high frequency injection is proposed [11]. For sensorless system of PMSM during running, the conventional sliding mode observer system exists high frequency chattering problem, scholars of Xi’an University of Science and Technology replaced switching sign function with sigmoid function, a new sliding mode observer is constructed to improve controlling method of original sliding mode observer [12]. In order to solve the problem of high cost, mounting volume increases, low reliability, being susceptible to interference and difficult to apply in a complex application environment caused by the traditional mechanical, a new method based on the Kalman Filter Algorithm control to estimate the rotor position and speed was put forward by scholars of University of Shanghai for Science and Technology [13]. In addition, Zhejiang University, Harbin Institute of Technology and

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related research institutes have also made important progress in speed sensorless control technology of PMSM [14–16].

3 Classification and Introduction of Control Strategy Speed sensorless control of PMSM can be divided into two categories according to their applicability. One type is the control methods suitable for the medium-to-high speed range (above 10% of rated speed), which are based on the mathematical model of the motor. The other type is the control methods applicable to the zero-to-low speed range (0–10% of rated speed). These methods are based on the salient pole effect of the motor. As shown in Table 1: Table 1. PMSM speed sensorless control strategy classification Speed range

Method

Example

Medium-to-high Open-loop algorithm

Zero-to-low

Direct calculation method Integration of back electromotive force method Closed-loop algorithm Model reference adaptive system method Sliding mode observer method Extended kalman filter method High frequency signal injection method Rotating high frequency voltage injection method Rotating high frequency current injection method Fluctuating high frequency voltage injection method Low frequency signal injection method Fluctuating high frequency voltage injection method Estimation based artificial intelligence method Neural network method Fuzzy control method

The control methods suitable for the medium-to-high speed range can be divided into the open-loop algorithm and the closed-loop algorithm. The open-loop algorithm mainly includes direct calculation method, integration of back electromotive force method, etc. the closed-loop one mainly includes model reference adaptive system method, sliding mode observer method, extended kalman filter method, etc. These methods are convenient to implement in the medium-to-high speed range with high reliability, but the back electromotive force is little at a low speed, the signal is interfered easily which reduces the accuracy of control. The control methods applicable to the zero-to-low speed range include high frequency signal injection method, low frequency signal injection method, estimation based artificial intelligence method, etc. This type of methods can be applied to low speed or even zero speed control without motor parameters. In order to achieve better control effects in the zero-to-low speed range, additional detection signals needs to be applied. The filtering effect is sensitive to motor parameters, frequency and load, which increases the difficulty of implementation.

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Control Methods for Medium-to-High Speed Range Speed

3.1.1 Open-Loop Algorithm Based on Mathematical Model of PMSM a. Direct calculation method The direct calculation method is a simple estimation method. By calculating the voltage and current of the stator, the phase of the stator flux, the rotor flux and the back electromotive force vector, it is convenient to obtain the rotor position signal, then the estimation value of the speed is available. The calculation formula of rotor position angle [17] is given as follow: h ¼ arc tanðA=BÞ

ð1Þ

A ¼ ua  Ria  Ld pia þ xib ðLd  Lq Þ

ð2Þ

B ¼ ub þ Rib þ Ld pib þ xi@ ðLd  Lq Þ

ð3Þ

where

The amount used to calculate the rotor position and speed can be obtained by actual measurement. The calculation process is simple and immediate which does not rely on complicated convergence control algorithm. The dynamic response of the methods is faster. The accuracy depends mainly on the accuracy of the parameters in the mathematical model, which is greatly affected by parameter changes or noise interference. Since there is no feedback link, the error cannot be corrected in time, which is hard to guarantee the normal operation of the motor in bad environments. b. Integration of back electromotive force method When the motor is running stably, the stator and rotor flux linkages maintain synchronous rotation. The angular difference between them is the load torque angle. By calculating the stator flux phase angle, the rotor position is obtained. The stator flux linkage can be received from the voltage equation by the integration of back electromotive force [18]: Z wrds ¼ ðvrds  Rs irqs Þdt ð4Þ Z wrqs

¼

ðvrqs  Rs irqs Þds

ð5Þ

 .  hws ¼ arctan wrqs wrds

ð6Þ

stator flux phase angle:

The existence of pure integral link will bring a series of problems such as zero drift and phase shift, what’s more, this method has a large dependence on motor parameters. The accuracy decreases when the motor parameters change due to temperature changes and magnetic saturation effects [19]. Comparing to direct calculation method, this method is an essentially open-loop one, which is susceptible to external interference

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and parameter changes. In order to meet the requirements of some high-performance control occasions, this method usually combined with the techniques of error correction and parameter identification to improve the accuracy of control. 3.1.2 Closed-Loop Algorithm Based on Mathematical Model of PMSM a. Model reference adaptive system (MRAS) method The main idea of the MRAS method is as follows: the equation of the parameter to be estimated is used as the adjustable model, the equation without the unknown parameter is utilized as the reference model, both models have the same physical meaning for the output. In order to achieve the purpose of controlling the output tracking reference model of the object, two models work at the same time and take the difference of their output to adjust the parameters of the adjustable model according to the appropriate adaptive rate. MRAS method is a parameter identification method based on stability theory design, which guarantees the asymptotic convergence of parameter estimation. What’s more, the accuracy of this position estimation method is related to the selection of the reference model itself. The vector control with rotor flux linkage orientation is adopted as its basic control strategy [20]. A MRAS based on fuzzy control is proposed to estimate the rotor position and rotational speed. The simulation results show that the MRAS based on fuzzy control can estimate the rotor position and speed of the motor under cyclic pulsating load conditions, which has better robustness. The offline identification technology is introduced in the MRAS parameter identification method [21]. The offline identification result is used as the initial value of the online identification algorithm. It establishes the parameter online identification model based on the PMSM vector control system to improve the identification result in high convergence speed effectively. The estimation accuracy of MRAS is directly related to the mathematical model of motor, the adaptive law and the selection of filter coefficients. In particular, it is still a research focus to choose a reasonable adaptive law to improve the convergence speed and ensure the robustness of the system, which needs to be studied in depth. b. Sliding mode observer (SMO) method The SMO method replaces the control loop in the general state observer with the form of a sliding mode variable structure, so that the state of the original system is stabilized on the previously set sliding mode plane finally through the variable structure changing switch [22]. The basic principle of this method is to establish SMO according to the mathematical model of PMSM, in order to design the sliding surface by observing the estimation error between observed current and actual one. By measuring current estimation error, the back electromotive force is reconstructed and utilized to estimate the speed. The SMO method focuses on the selection of the sliding surface and the sliding mode gain. It is not only to ensure the convergence and convergence speed of the algorithm, but also to avoid excessive pulsation caused by the excessive operation of the motor [23]. The sliding mode variable structure control is essentially discontinuous switching control. Once entering the sliding mode, the switching time and space lag will lead to the buffeting of the SMO, the estimated current will oscillate around the actual value, which will affect the estimated accuracy. For the chattering problem in the

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estimation of SMO, a variable sinusoidal saturation function with boundary layer is utilized to replace the traditional switching function [24], which proposes a softswitching sliding mode observer for sensorless control of PMSM. The experimental results show that the sinusoidal soft-switching SMO can observe the rotor speed and position accurately, which eliminates the chattering of the estimated results obviously. Aiming at the problem of speed tracking control of PMSM, a sliding mode control strategy based on inversion terminal is proposed [25]. It improves the response speed of the speed control system and the ability to suppress external disturbance. c. Extended Kalman Filter (EKF) method The kalman filter is a method of optimal predictive estimation in the sense of minimum variance proposed by American scholar R.E. KALMAN in the 1960s, which can weaken the effects of random interference and measurement noise effectively [26]. EKF is a generalized form of kalman filter in nonlinear systems which calculation process includes three parts: prediction link, correction link and kalman gain. EKF can realize real-time online estimation of PMSM speed. The basic principle is to linearize the motor equation in the d-q mathematical model, estimate the optimal speed of the rotor through the observer, then utilize the recursion formula to calculate the real-time speed and iterate the motor speed continuously [27]. The output of EKF is expert in tracking system state quantities. Different from other observers, it is random and nonlinear. Not only does it have optimization and selfadaptation capabilities, but it performs better on suppresses measurement and disturbance noise. The method minimizes the observation error of the state variable and selects the optimal feedback gain matrix to ensure the stability and convergence speed of the system, so it is widely applied to AC motor control. A method is proposed to improve particle swarm algorithm to optimum EKF noise matrix and achieve sensorless control of PMSM [28], which solves the problem of optimum noise matrix obtained difficultly in state estimation of EKF. By combining genetic algorithm (GA) and particle swarm algorithm, the optimized EKF system noise matrix by IPSO is applied in sensorless direct torque control (DTC) system of PMSM. The simulation results show that this method can improve filtering performance and enhance the control property of the motor sensorless DTC system obviously. 3.2

Control Methods for Zero-to-Low Speed Range Speed

3.2.1 High Frequency Signal Injection Method a. Rotating high frequency voltage injection method The main idea of the rotating high frequency voltage injection method is to obtain the rotational speed signal by the asymmetry of the motor’s cross-axis inductance. The basic principle is to superimpose a three-phase balanced high frequency voltage excitation on the fundamental excitation. The current vector induced by the high frequency voltage vector includes two components: a positive sequence component and a negative one. Because of the salient pole effect of the motor, the high frequency carrier current is modulated by the salient pole of the motor, so the rotor position information is contained in the negative sequence component of the same frequency carrier current signal. To extract the desired negative sequence component, the current vector signal is converted into a reference coordinate system synchronized with the carrier excitation

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voltage vector, then the positive sequence high frequency signal becomes a direct current signal. In order to achieve the negative sequence component containing the rotor pole information, the high-pass filter can be utilized to filter out the fundamental component of the high frequency signal and the carrier signal, which can estimate the motor speed [29]. This method is not affected by parameters obviously, but the signal processing process is more complicated. Multiple filters are added in the signal processing stage, which causes additional delay in the speed estimation and reduces the dynamic performance of the system. Tracking failure is more likely to occur when the load is abrupt or the speed command changes greatly. b. Rotating high frequency current injection method The basic principle of the rotating high frequency current injection method [30] is to superimpose a three-phase balanced high frequency current excitation on the fundamental excitation current. According to the simplified mathematical model of the PMSM under the rotating high frequency excitation, a high frequency voltage vector can be obtained in which the rotor position information is included in the phase of the negative sequence high frequency voltage component. In order to obtain the negative sequence high frequency voltage component containing the rotor position information accurately, it is necessary to improve the conventional Proportional Integral (PI) regulator, so that it can simultaneously adjust the fundamental frequency current component and the high frequency current component at any injection current frequency. Compared with the rotating high frequency voltage injection method, the biggest advantage of the rotating high frequency current injection method is that a current signal with a smaller amplitude, which contains information of the rotor position, can be injected to obtain a voltage signal with a larger amplitude. c. Fluctuating high frequency voltage injection method The fluctuating high frequency voltage injection method differs from the rotating high frequency voltage injection method, the latter selects two sinusoidal signals of equal amplitude, equal frequency and phase difference of 90 , which injected from the stationary coordinate axis @  b to generate a rotating voltage vector in space, the formal is to select one of the axes from the estimated synchronous rotating coordinate system to generate a pulse voltage vector in space. Fan etc. compared both methods of their principles in details [31]. The basic principle of the fluctuating high frequency voltage injection method is to filter the current response on the q-axis through a band-pass filter to obtain a high frequency current component, By multiplying the high frequency current component by the high frequency sinusoidal signal, the input signal of the speed estimator can be obtained by passing through the low-pass filter, then the speed regulator output value is measured by the PI regulator [32]. This method has strong robustness, which is not sensitive to motor parameter changes and measurement errors. It can estimate the rotor position and speed of the motor in both the zero speed and low speed regions accurately, while the regulator parameters are more sensitive and the signal processing process is more complicated.

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3.2.2 Low Frequency Signal Injection Method Low frequency signal injection method mainly refers to low frequency current signal injection method. which basic principle [33] is to establish the relationship between the estimated and actual two-phase rotating coordinate system, then inject a current signal of a specific frequency into the motor to estimate the d-axis. If the coordinate system is not positioned correctly, the injected current will generate an additional high frequency torque of the cross-axis, which causes the high frequency shake of the motor to generate the back electromotive force. Finally, by adjusting the back EMF to zero, the estimated coordinate system can be positioned in the actual coordinate system accurately, then the estimate of the motor speed can be obtained. Satisfactory observations can be obtained at low speeds and smooth output torque can be obtained by this method. The principle is similar to the high frequency signal injection method, additional excitation is required by both of them. The difference is that the latter utilizes the simplified high frequency voltage equation of the motor, while the low frequency injection method utilizes the mechanical motion equation of the motor. Their application is also different, low frequency injection method is suitable for the hidden-pole permanent magnet synchronous motor. The salient pole effect of the motor will affect the correctness of the observation result. Compared with the high frequency injection method of observing the rotational speed by using the salient pole effect of the motor, the injection current is related to the magnitude of p2/J, which is usually between several to hundred Hz [34]. The observation accuracy of the rotor angle is related to the moment of inertia J of the motor. If the rotating inertia of the motor is too large, the effect of observation will be worse. 3.2.3 Estimation Based Artificial Intelligence Method Since the rotational speed can be regarded as a function of the stator voltage and current, the artificial intelligence network has powerful ability to approximate any nonlinear function, so the method of estimating the motor speed based on artificial intelligence theory (such as neural network [35], fuzzy control, etc.) has received more and more attention. a. Neural network method The method of speed identification based on neural network is a theoretical estimation method of artificial intelligence. By specifying the network structure and learning the input and output of the motor system, the system performance indicators meet the requirements, so that the relationship hidden in the system of the input and output can be summarized [36, 37]. This method can replace the complex observer with the neural network without the motor parameters, which obtain the adaptive law by using the error reverse transform to realize the motor speed estimation. Some scholars present a neural network inverse based decoupling control strategy for PMSM [38]. The strategy can achieve good decoupling control performance, and obtain good dynamic and static performance. Based on the motor system reversible proof, the linearized decoupling control characteristics are analyzed. Aiming at the disadvantage of the inverse decoupling control strategy which is too dependent on the mathematical model, the neural network inverse based decoupling control strategy is proposed, and an additional speed and current controller based on sliding mode variable structure are designed. A 5.2 kW PMSM is used as the controlled object for

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simulation and experimental study, and the result show that the neural network inverse based decoupling control strategy can achieve good decoupling control performance, and can obtain good dynamic and static performance. b. Fuzzy control method Fuzzy control is one kind of computer numerical control based on fuzzy set theory, fuzzy logic reasoning and fuzzy linguistic variables. It can be used on those situations where mathematical models cannot be established or difficult to establish. Fuzzy control method has the function of simplifying the complexity of the control system, which can reduce the chattering phenomenon of the control system [39]. Aiming at the problem of poor adaptive capacity of electric vehicle PMSM in complex environment and the problem of slow response time, overshoot and large steady-state error in traditional PI control algorithm, a new type of fuzzy autodisturbance rejection torque control method was put forward [40]. According to the control principle of auto-disturbance rejection control and fuzzy, designed the torque axis fuzzy auto-disturbance rejection controller and linked to the advantages of fuzzy control, improved to realize the on-line self-tuning of the auto-disturbance rejection control parameters and improved the adaptive capability of the control system. In contrast with the traditional PI control algorithm, the control algorithm reduces about the 70% torque response time and about the 50% steady-state error by simulation and experimental verification. Estimation based artificial intelligence method has little dependence on the motor parameters and the system stability is satisfactory. However, this technology is not mature enough. Nowadays, such methods are mostly combined with other algorithms, and most of them are still in the stage of theoretical research. The road to industrialization is still relatively long.

4 Conclusion Speed sensorless control strategy of PMSM is a promising development direction of PMSM control theory. For the medium-to-high speed range, the advantages and disadvantages of various observer-based control methods are obvious. It is difficult to achieve the desired control effect with one single control method. The focus of current research is how to combine the various control methods to achieve the desired control effect. In the zero-to-low speed range, the carrier signal injection method and estimation based artificial intelligence method have absolute advantages. The future research focuses on improving the recognition accuracy of salient poles and choosing more effective methods in signal processing on the premise of guaranteeing system performance [41]. In addition, since the sensorless technology cannot get the initial position of the rotor from the electrical characteristics when the motor is stationary, the motor without the position sensor can only operate normally after the motor starts to a certain speed. Therefore, the starting problem also has become the focus of current speed sensorless control technology for PMSM. Each speed sensorless control strategy for PMSM has its own advantages and disadvantages. Some have better accuracy at the medium-to-high speed range, while some can only achieve excellent control effects at the zero-to-low speed range. If both

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methods are combined reasonably, the full speed range compound control method can be designed [42]. At present, there is still no speed sensor control method to achieve high-performance operation in the full speed range of the system. The combination of medium-to-high speed and zero-to-low speed control methods is the future development trend of the speed sensorless control technology for PMSM.

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Experimental Study on Lateral Compaction Characteristics of Filled Gangue Under Limited Roof Condition Xin-wang Li1,2, Xin-yuan Zhao1,2, Li Li3, Jian-gong Liu2, Li-chao Cheng1,2(&), and Yi-ling Qin1,2

2

1 College of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, China [email protected] Coal Resources Development and Construction Application Technology Research Center of Universities in Hebei Province, Hebei University of Engineering, Handan 056038, China 3 College of Civil Engineering, Hebei University of Engineering, Handan 056038, China

Abstract. Based on the principle of similar simulation, through laterally pushing on the gangue bulk, the influence of discharge step distance, pushing force, water content and loess content on the compaction characteristics of the filled gangue under the limited roof was analyzed. Experimental results show that the increase of discharge step distance is not conducive to the consolidation of gangue; Pushing force increases contributing to the consolidation of gangue, pushing force 1.25 KN is the inflection point for the increasing of rest angle and the slowing of volume change rate; The water content has a significant effect on the consolidation characteristics of the gangue under lateral pushing. Loess content has the most significant effect on consolidation of gangue bulk under the lateral pushing. Keywords: Filling mining
Gangue bulk
Lateral pushing force
Rest angle
Volume change rate

Gangue filling is a green mining technology that uses a large number of abandoned gangues to fill the goaf to control surface subsidence, reflecting the concept of green mining [1–3]. Coal gangue as a filling aggregate, Its compaction characteristics are related to the effect of gangue filling [4–6]. For the compaction characteristics of gangues, Hu [7] carried out the gangue compression test using large vessel, large particle size, large load and simulated coal body conditions, and summarized the shape and characteristics of the stress-strain relationship curve and the axial pressure side pressure relationship curve during compression; Zhang [8] carried out compaction tests on loose gangue, and obtained the relationship between strain, compaction and stress during compaction; Ma [9] summarized the variation of axial strain and elastic modulus during the compaction process of loose coal gangue, and analyzed the deformation L. Cheng—Ph.D, associate professor, mainly engaged in research backfill mining theory and technology. © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 252–264, 2019. https://doi.org/10.1007/978-981-13-7025-0_26

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mechanism of compaction process. Jiang [10] explored the relationship between compaction of coal gangue and chopping of blocks by special compaction test. Qian [11] carried out compression mechanics tests on continuous grade gangue, and obtained the relationship between the size of primary gangue and the compressibility of gangue. Many experts and scholars also carried out related analysis and research [12–15]. Most of these results have studied some of the confined compaction bearing characteristics of filled gangue under vertical pressure, and little research has been done on the lateral compaction characteristics of filled gangue under the limited roof. During the filling and mining process, due to the joint support of the filling bracket and the surrounding rock, the roof will not instantaneously sink and fall within a certain distance behind the working surface, but in the short term, the pushing space of the filling gangue under the limited roof condition is formed [16, 17]. In the pushing space of the filling gangue, the tamping mechanism on the filling bracket laterally presses the filling gangue, so that the loose filling material is piled up and pressed, and is connected with the roof to increase the compactness of the filling body and improve the filling quality [18–20]. For loose gangues, after the tamping mechanism is retracted, the loose body after the compaction will naturally fall down, forming a certain rest angle, and the volume will change before the lateral pushing is applied. When loose gangue is mixed with other adhesive materials, the filling quality will be significantly improved under lateral pushing. At the actual filling site, the lateral compaction characteristics of the filled gangue under a limited roof are often affected by many factors, such as the discharge step distance, pushing force, water content and so on. Based on the principle of similar simulation, this paper independently designed a set of lateral pushing simulation test bench for filling gangue. The effects of factors such as discharge step distance, pushing force, water content and loess content on the lateral compaction characteristics of gangue bulk under the condition of limited roof are studied. It has important theoretical research value and guiding significance for improving the quality and economic benefit of gangue filling in the mined area of mine.

1 Test Bench Design 1.1

Test Prototype and Similarity Ratio

The simulated background is a filling working surface of a mine in Shanxi. The filling working face is near-horizontal coal seam mining with an average mining height of 3 m and a roof sandstone with high hardness and few faults. The width of the working face filling hydraulic support is 1.5 m, and the width of the retaining plate and the pushing plate on the tamping mechanism are both 1.5 m and the height is 0.75 m. The pushing plate adopts the upper and lower tamping modes for the filling body. The filled gangue has a bulk density of 25 KN/m3 and a particle size range of 0–50 mm.

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According to the similarity theory, and based on the actual situation of the site and the conditions of the experimental model, the simulation similarities are established as follows: CL ¼

LH ¼ 15 LM

ð1Þ

Where, CL is the geometric similarity ratio, LH is the prototype size, m; LM is the model size, m. Cc ¼

cH ¼ 1:4 cM

ð2Þ

Where, Cc is the bulk density similarity ratio. cH is the actual filling material bulk density, KN/m3; cM is the test material bulk density, KN/m3. Cr ¼ CL  Cc ¼ 21

ð3Þ

Where, Cr is the stress similarity ratio, CL is the geometric similarity ratio; Cc is the bulk density similarity ratio. 1.2

Test Bench Structure Design

In order to simulate the compaction process of the filling work surface, the test bench was designed and manufactured. The test bench is made of high-strength thick steel plate and split jack, which can simulate different test heights, different filling steps, and different width of goafs. Three high-strength thick steel plates are stacked one on another, and the upper part is covered with high-strength glass plates, Three highstrength thick steel plates and the high-strength moving blocks are tightly enclosed into a rectangular semi-closed space, and the space laterally can withstand a maximum pushing force of 30 kN. Split jack simulates the tamping mechanism of the filling bracket, It is the pushing device of the whole test bench. The front of the jack cylinder is covered with high-strength steel plate, which can push the test material laterally. The structure of the test bench is shown in Fig. 1. According to the 1:15 geometric similarity ratio, the size of the goaf model used in this test is 20 cm  20 cm  20 cm (length  width  height). The model size of the push plate is 20 cm  17 cm (width  height), and simulate the full-section horizontally and one-time push, and the maximum lateral pushing force is 10 kN. The top plate model of the goaf is replaced by a 5 cm thick high-strength glass plate. The entire test bench is shown in Figs. 1 and 2.

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Fig. 1. Test bench structure design

Fig. 2. Simulation test bench physical map

2 Test Material Selection The primary gangue after coarse crushing was randomly selected and respectively sieved by four kinds of sieves of 10 mm, 20 mm, 30 mm and 40 mm to obtain the mass proportion of different particle size ranges of the primary gangue. Then, according to the similar theory, the primary gangue is finely crushed, sieved with a mesh having a mesh diameter of 0.63 mm, 1.25 mm, and 2.5 mm, and the test materials are prepared according to the proportion of the respective primary gangue particle size ranges. The test material was tested to have a bulk density of 18 kN/m3 after compression. The quality of the test material in different particle size ranges is shown in Table 1.

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Primary gangue particle size ranges/mm 0–10 10–20 20–30 30–40 40–50

The test material particle size ranges/mm 0–0.63 0.63–1.25 1.25–2.50

Quality ratio/% 15.60 18.15 33.67

2.50–3.50

32.58

3 Test Plan and Process 3.1

Test Plan

In order to study the influence of the discharge step distance, lateral thrust, water content and loess content on the lateral compaction characteristics of gangue bulk, the test plan is shown in Tables 2, 3, 4 and 5. Table 2. Design of influencing factors for discharging step distance Scheme number A1 A2 A3 A4 A5 A6 A7 A8

Discharge step distance/cm 2 4 6 8 2 4 6 8

Pushing force/kN 5 5 5 5 7.5 7.5 7.5 7.5

Water content/% 0 0 0 0 0 0 0 0

Loess content/% 0 0 0 0 0 0 0 0

Table 3. Design of influencing factors for pushing force Scheme number B1 B2 B3 B4 B5 B6 B7

Discharge step distance/cm 4 4 4 4 4 4 6

Pushing force/kN 0 1.25 2.5 5 7.5 10 0

Water content/% 0 0 0 0 0 0 0

Loess content/% 0 0 0 0 0 0 0 (continued)

Experimental Study on Lateral Compaction Characteristics Table 3. (continued) Scheme number B8 B9 B10 B11 B12

Discharge step distance/cm 6 6 6 6 6

Pushing force/kN 1.25 2.5 5 7.5 10

Water content/% 0 0 0 0 0

Loess content/% 0 0 0 0 0

Table 4. Design of influencing factors for water content Scheme number C1 C2 C3 C4 C5 C6 C7 C8 C9 C10

Discharge step distance/cm 4 4 4 4 4 4 4 4 4 4

Pushing force/kN 5 5 5 5 5 7.5 7.5 7.5 7.5 7.5

Water content/% 1 2 3 4 5 1 2 3 4 5

Loess content/% 0 0 0 0 0 0 0 0 0 0

Table 5. Design of influencing factors for loess content Scheme number D0 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12

Discharge step distance/cm 4 4 4 4 4 4 4 4 4 4 4 4 4

Pushing force/kN 2.5 2.5 2.5 2.5 2.5 5 5 5 5 7.5 7.5 7.5 7.5

Water content/% 5 5 5 5 5 5 5 5 5 5 5 5 5

Loess content/% 0 2 4 6 8 2 4 6 8 2 4 6 8

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3.2

Test Process

In the actual operation process, except for controlling various factor variables, the other test processes are basically the same, and will not be described here. The same test process is generally the following: Mix the test materials ! pour into the test bench ! measure the initial rest angle and initial volume of the gangue pile ! push the gangue pile laterally ! remove the pushing plate ! measure the rest angle and volume after pushing. The test process of the effect of water content of 3% on the lateral compaction characteristics of gangue is taken as an example, as shown in Fig. 3. First, add 3% tap water to the gangue material and mix well (shown in Fig. 3(a)). The test materials after stirring and mixing are shown in Fig. 3(b). Then use a square shovel to slowly pour the gangue bulk at a height of 17 cm from the bottom surface of the goaf model and 4 cm from the inner steel plate. When the gangue bulk height is 17 cm, stop dumping (Fig. 3(c)), and measure the rest angle and initial volume of the gangue bulk (Fig. 3(d)). Then the jack pushes the push plate to push the gangue bulk, observe the hydraulic watch reading, when the pressure is 5 kN and 7.5 kN, the duration is 5 s, then withdraw the push plate, observe the consolidation state of the gangue bulk at this time (Fig. 3(e)), Finally, the rest angles and the volume of the gangue in the case of two lateral pushing force are respectively measured and recorded (Fig. 3(f)). According to this similar operation, the rest angle and the volume of the compaction at the lateral thrust of 5 kN and 7.5 kN were measured and recorded respectively when the water content of the gangue was 1%, 2%, 4%, and 5%.

Fig. 3. Test procedure of water content influencing factor

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In order to simulate different discharge step distance, the filling materials were slowly poured into the inner steel plates at 2 cm, 4 cm, 6 cm and 8 cm respectively. In order to study the influence of lateral thrust, the lateral pushing force is loaded to 0 kN, 1.25 kN, 2.5 kN, 5 kN, 7.5 kN and 10 kN respectively, and the simulated discharge step distance is 4 cm and 6 cm respectively; In order to study the effect of loess content, 0%, 2%, 4%, 6%, 8% of loess was mixed into the gangue respectively with 5% water content, the discharge step distance was 4 cm, and the lateral pushing force was 2.5 kN, 5 kN, 7.5 kN. In the test, the two deformation parameters of rest angle and volume change rate were used to quantify the influence of various factors on the compression consolidation characteristics of gangue. The larger the rest angle of gangue bulk, the larger the volume change rate, indicating that the compression consolidation characteristics of gangue bulk are more significant. Wherein, the rest angle is the angle between the slope of the material accumulation body and the bottom edge, and the volume change rate is defined as the ratio of the volume changed after the pressing to the initial volume, namely: A¼

Vi  Vp Vi

Where, A is the volume change rate, Vi is the initial volume of the gangue just placed on the test bench, cm3; Vp is the volume after each lateral pushing, cm3.

4 Test Results and Analysis 4.1

Influence of Discharge Step Distance on Lateral Compaction Characteristics of Gangue

Change the discharge step distance of the gangue by the test scheme A1–A8 of Table 2, and in the case of the lateral thrust of 5 kN and 7.5 kN, The data of the rest angle and the volume change rate of gangue bulk are calculated respectively as shown in Figs. 4 and 5.

45

rest angle/ º

44 43 42 41 pushing force 5kN

40 39

pushing force 7.5kN

38 37

2

4

6

8

discharge step distance/cm

Fig. 4. The rest angle of gangue bulk in different discharge step distance

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volume change rate

0.2 0.15 0.1 pushing force 5kN 0.05

pushing force 7.5kN

0

2

4

6

8

discharge step distance/cm

Fig. 5. Volume change rate of gangue bulk in different discharge step distance

It can be seen from Figs. 4 and 5: (1) In the case of lateral pushing force of 5 kN and 7.5 kN, the rest angle of the gangue bulk increases with the increase of the discharge step distance, but the angle increases little, only increasing by 3°–4°. (2) When the lateral pushing force is 5 kN and 7.5 kN, the volume change rate of the gangue bulk decreases with the increase of the discharge step distance. When the discharge step distance reaches 6 cm, the volume change rate begins to decrease slowly. When the discharge step distance is 8 cm, the volume change rate is the smallest, which is 0.118 and 0.125 respectively. (3) The increase of the discharge step distance has an adverse effect on the lateral pressing consolidation of the gangue. 4.2

Influence of Lateral Pushing Force on Lateral Compaction Characteristics of Gangue

Through the test schemes B1–B12 of Table 3, different lateral thrusts are applied to the gangue bulk with the discharge steps distance of 4 cm and 6 cm respectively, and the data of the rest angle and volume change rate of the gangue bulk are calculated, and the measured data are plotted as shown in Figs. 6 and 7. 45 44 rest angle / º

43 42 41 40

discharge step distance 4cm

39

discharge step distance 6cm

38 37

0

1.25

2.5

5

7.5

10

pushing force/kN

Fig. 6. The rest angle of gangue bulk under different pushing forces

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0.15

volume change rate

0.12 0.09 0.06 discharge step distance 4cm 0.03 0

discharge step distance 6cm

0

1.25

2.5

5

7.5

10

pushing force/kN

Fig. 7. Volume change rate of gangue bulk under different pushing forces

It can be seen from Figs. 6 and 7: (1) The rest angle of the gangue bulk increases with the increase of the lateral pushing force, but the angle increases little. The lateral pushing force of 1.25 kN is the inflection point of the slow increase of the rest angle of the gangue. (2) With the increase of lateral thrust, the volume change rate of gangue bulk increases rapidly and then slows down. The lateral pushing force of 1.25 kN is the inflection point of the volume change rate from a rapid increase to a slow increase, at which time the volume change rate is 0.08. (3) The increase of lateral thrust is conducive to the consolidation of the gangue bulk. 4.3

Influence of Water Content on Lateral Compaction Characteristics of Gangue

Through the experimental schemes B4, B5 and C1–C10, the effect of the water content of gangue on the consolidation characteristics of gangue bulk was studied. The experimental results are shown in Figs. 8 and 9.

90

rest angle/ º

80 70 60 50

pushing force 5kN

40

pushing force 7.5kN

30

0

1

2

3

4

5

water content/%

Fig. 8. The rest angle of gangue bulk with different water content

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volume change rate

0.25 0.2 0.15 pushing force 5kN

0.1

pushing force 7.5kN

0.05 0

0

1

2

3

4

5

water content/%

Fig. 9. Volume change rate of gangue bulk with different water content

It can be seen from Figs. 8 and 9: (1) When the water content is less than 2%, the rest angle of the gangue bulk increases slowly, and the maximum angle is about 50°. When the water content reaches 3%, under the lateral pushing force of 5 kN and 7.5 kN, the rest angle of the gangue bulk is close to 90° and begins to consolidate into a relatively regular rectangular body, as shown in Fig. 3(e). (2) The volume change rate of the gangue bulk increases rapidly with the increase of water content and then increases rapidly. The water content of 3% is the inflection point of the slope of the volume change rate curve from small to large. (3) The water content has a significant influence on the lateral pressing consolidation characteristics of the gangue bulk. When the water content reaches 3%, the gangue bulk begins to form and solidify. 4.4

Influence of Loess Content on Lateral Compaction Characteristics of Gangue

In order to study the effect of loess content on the lateral compression consolidation characteristics of gangue bulk, the test results were obtained by tests C5, C10 and D0– D12 as shown in Figs. 10 and 11. It can be seen from Figs. 10 and 11: (1) After adding different amounts of loess to the gangue bulk with water content of 5%, when the lateral pushing force is greater than 2.5 kN, the rest angle is 90°, and the gangue has been consolidated into a compact rectangular body. (2) As the amount of loess increases, the volume change rate also increases. After the content of loess exceeds 4%, the volume change rate increases slowly, indicating that the excessive amount of loess is relatively weaker on the consolidation of gangue. (3) The amount of loess is obviously affected by the consolidation and forming characteristics of gangue bulk, but the amount of loess should not be too much.

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91 90

rest angle/ º

89 88 pushing force 2.5kN

87

pushing force 5kN

86

pushing force 7.5kN

85 84

0

2

4

6

8

loess content/%

Fig. 10. The rest angle of gangue bulk with different loess content

volume change rate

0.4 0.35 0.3 0.25

pushing force 2.5kN pushing force 5kN

0.2 0.15

pushing force 7.5kN 0

2

4

6

8

loess content/%

Fig. 11. Volume change rate of gangue bulk with different loess content

5 Conclusions (1) Under the lateral pushing force of 5 kN and 7.5 kN, with the increase of the discharge step distance, the rest angle of the gangue bulk increases slightly, while the volume change rate decreases gradually, indicating that increasing the discharge step distance is not conducive to the lateral pushing consolidation of the gangue. (2) The increase of the lateral pushing force contributes to the lateral pressing and consolidation of the gangue bulk. The lateral pushing force of 1.25 kN is the inflection point at which the rest angle increases and the slope of the volume change rate becomes gentle. (3) The water content has a significant influence on the consolidation characteristics of gangue. At 5 kN and 7.5 kN lateral pushing force, when the water content reaches 3%, the gangue bulk begins to form and solidify. (4) The effect of loess content on the consolidation and forming characteristics of guague bulk is most significant. After the addition of loess exceeds 4%, the effect on the consolidation of gangue is relatively weakened.

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Acknowledgments. This work was financially supported by Fund Project: Hebei Province Key Research and Development plan project (18273815D); Hebei Province Higher Education Science and Technology Research Project (QN2017031).

References 1. Qian, M., Miao, X., Xu, J.: Mining coordinating with resource and environment. J. China Coal Soc. 32(1), 1–7 (2007) 2. Zhang, Q., Zhang, J.X., Huang, Y.L., et al.: Back filling technology and strata behaviors in fully mechanized coal mining working face. Int. J. Min. Sci. Technol. 22(2), 151–157 (2012) 3. United Nations: Report of the world commission on environment and development: our common future [R/OL], 1–3 (1987). http://www.un-documents.net/wced-ocf.htm 4. Zhu, C., Zhou, Z., Li, Q., et al.: Experimental study on the compression properties of gangue. J. Hunan Univ. Sci. Technol. (Nat. Sci. Ed.) 30(4), 1–6 (2015) 5. Liu, Z., Zhang, J., Zhou, N.: Random gravel model and particle flow based numerical biaxial test of solid backfill materials. Int. J. Min. Sci. Technol. 23(4), 463–467 (2013) 6. Zhang, J., Zhang, Q., Sun, Q., et al.: Surface subsidence control theory and application to backfill coal mining technology. Environ. Earth Sci. 74(2), 1439–1448 (2015) 7. Hu, B., Guo, A.: Testing study on coal waste back filling material compression simulation. J. China Coal Soc. 34(8), 1076–1080 (2009) 8. Zhang, J.: Mobile control of gangue directly filled with fully mechanized rock strata and its application. China University of Mining and Technology, Xuzhou (2008) 9. Ma, Z., Pu, H., Zhang, F., et al.: Research on compaction characters of coal gangue. J. Min. Saf. Eng. 1, 95–96 (2003) 10. Jiang, Z., Ji, L., Zuo, R.: Research on mechanism of crushing-compression of coal waste. J. China Univ. Min. Technol. 30(2), 139–142 (2001) 11. Qian, Z., Xu, D., Guo, G., et al.: Research on continued gradation compression experiment of primary coal rejects. Coal Eng. 6, 100–106 (2012) 12. Zha, J., Wu, B., Guo, G.: Experimental investigation on gradation characteristics and compression property of filling refuse. Express Inf. Min. Ind. 12(12), 40–42 (2008) 13. Tu, Q., Zhang, X., Liu, P., et al.: Different particle size gradation gangue dispersion experimental study on deformation compression. Coal Eng. 11, 68–70 (2009) 14. Su, C., Gu, M., Tang, X., et al.: Experiment study of compaction characteristics of crushed stones from coal seam roof. Chin. J. Rock Mech. Eng. 31(1), 18–26 (2012) 15. Yan, H., Zhang, J., Zhang, S., et al.: Macro-micro research on compaction properties of granular backfilling materials. J. China Coal Soc. 02(42), 413–420 (2017) 16. Li, M., Zhang, J., Jiang, H., et al.: A thin plate on elastic foundation model of overlying strata for dense solid backfill mining. J. China Coal Soc. 39(12), 2369–2373 (2014) 17. Wang, J., Yang, S., Yang, B., et al.: Simulation experiment of overlying strata movement features of longwall with gangue backfill mining. J. China Coal Soc. 37(8), 1256–1262 (2012) 18. Liu, Q., Niu, J., Shi, T.: Design on automatic coal refuse backfill and tamping control system of fully-mechanized coal mining. Coal Sci. Technol. 43(11), 111–115 (2015) 19. Zhang, Q., Zhang, J., Ju, F., et al.: Backfill body’s compression ratio design and control theory research in solid backfill coal mining. J. China Coal Soc. 39(1), 64–71 (2014) 20. Li, M., Zhang, J., Huang, Y., et al.: Research on compression ratio design based on compaction properties of solid backfill materials. J. Min. Saf. Eng. 34(6), 1110–1115 (2017)

Using Improved Genetic Algorithm to Solve the Equations Yifan Zhang(&) and Dekang Zhao Department of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei, China [email protected]

Abstract. The origins of traditional genetic algorithms are based on the selection of better biological individuals by selection of species. In this paper, the improved genetic algorithm is adopted to solve the problem of equations, and the optimized punch-wheel algorithm is used to reduce the redundancy and duplication of code, instead of the traditional bubbling sorting and array sorting. Through the calculation of the mathematical model, the genetic algorithm can better solve the problem of solving the equation, the reader can better understand the process of solving the equation. Function model solving based on genetic algorithm proves that genetic algorithm opens up new ideas for solving equations, which can make people better understand the process of solving equations and divergent the thinking of solving equations. Keywords: Genetic algorithm


Equations
Punch-wheel algorithm

1 Introduction The initial application of genetic algorithm is to predict the development direction of the evolution and reproduction of biological population. Through the calculation of genetic algorithm, the environment transformation of individual or population of species is carried out and the evolution direction of species is controlled. In this paper, the improved roller gambling algorithm is applied to genetic algorithm so that the improved genetic algorithm can clearly solve the problem of equations. The experiment have quoted many scholars and experts to solve similar problems and conducted indepth research [1]. For the solution of monotone linear equation, we refer to the scattering model of the nonlinear schrodinger equation proposed in the literature [2]. The existence, uniqueness and regularity of the diffusion equations are given by referring to the gradient term [3, 5]. The genetic algorithm is optimized by referring to the inverse scattering problem method for the eigenvalue problem of the xue ding ‘e e equation [4]. At the same time, we also refer to the existence of solving diffusion equation based on Riemannian manifolds in literature [6], and propose a MPRP type non-derivative algorithm for solving symmetric nonlinear equations [7], and propose lm method in literature [1]. By referring to the above methods the paper using the optimized roller gambling algorithm [8], different from the original comparison method, the interval of the weight is stored in the array, the value within the region is © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 265–271, 2019. https://doi.org/10.1007/978-981-13-7025-0_27

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obtained randomly, and the value is divided into the original equation. After several rounds of substitution, the optimized value will come out.

2 Improved Genetic Algorithm 2.1

Coding

The genetic algorithm USES the real number coding of the double-stranded DNA model [13]. Each individual in the population corresponds to a solution in the search space. Step 1 initialization: randomly generate the initial morphology of the population, determine the parameters of execution, and the initial state can be expressed as ið0Þ ¼ ði1 ; i2 ; i3 . . .::iN Þ N is the population size, try a defined population, i(0) mains the initial population as a whole. N as the size, but should pay attention to the size of the initial population, the greater the number of initial population, the greater the amount of calculation [2]. The inappropriate size N will cause unnecessary calculation. The initial population size is smaller, the calculation error will come out. And that will need to set more generations. Step 2 evolution: first, assess the fitness of individuals and select N individuals from the population as parents. At the same time, the generation of the population should be set. If the current generation is one, the fitness is directly substituted into the calculation of fitness. Fitness represents the expression ability of an individual in the expected aspect. The evolution of solution set is in the direction of higher fitness. With the generation of the population, the population left after the calculation and selection is improved [4]. Step 3 search space: according to the individual fitness distribution and coding characteristics of t + 1 generation population, the search space is continuously adjusted and the individuals falling outside the new search space are repaired. With the increase of population generation, the individuals with high fitness will appear. In the process of artificial selection, it is hoped to select more individuals with high fitness to form the next generation through genetic algorithm screening. Step 4 termination criteria: if the termination criteria are satisfied, output I(t) as the approximate optimal solution to the problem; otherwise, let t = t + 1 turn to step 2. 2.2

Tournament Selection

Assumed in N samples randomly selected from the value of m function, that is, the size of the championship is m. Genetic algorithms using tournament selection, the basic idea is in m individuals randomly selected two groups of the same number of individuals, prioritize, each group of individuals by the fitness selection fitness of the best in the two groups of two individuals as a parent with two cross crossover rate PC, and then from the rest of the m − 2 individuals, randomly selected two groups of the same

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number of individuals, prioritize, each group of individuals by the fitness selection fitness of the best in the two groups of two individuals as a parent with two cross crossover rate PC and so on, until the parent select Until the required number is satisfied [8], the adoption of tournament selection is conducive to eliminating the influence of the optimal individual on the worst individual, avoiding the premature phenomenon of the algorithm, and not requiring the function F(x1, x2… Xn) make any changes [6]. Betting wheel selection method:

The fitness value of the obtained individual information is stored in the array, and each index of the array is marked and segmented. Among the randomly selected Numbers, the items with high fitness take up a large proportion of the array. So individuals with high fitness are more likely to be selected at random. 2.3

Variation Operation

The usual variation operation is to change the decimal number in the solution set into a binary character, and based on the binary character [9], change the corresponding position 0 into 1 or 1 into 0, and then get the number after the variation, which is the final number. For example: 6 ¼ ðbinaryÞ110; 220ðvariationÞ ¼ 111 Where, c1 and c2 are normal Numbers; R1, r2 are random Numbers on [0, 1]; B is the parameter that controls the change of the size of element xi. Solution of the optimization relies mainly on the selection and crossover operation to complete, and the mutation operators avoid algorithm into the search space is the

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main purpose of a local area, so as to guarantee the global convergence of the algorithm. Assuming that PM is a mutation rates, for each individual (excluding the best individual), generate a random number r 2 (0, 1), if r < PM is the individual need to the mutation operation. 2.4

Local Search

Algorithm USES the optimal individual reserve strategy, the basic idea is the best individual fitness in current population does not participate in the crossover operation and mutation operation, but use it to replace the contemporary populations after genetic operation such as crossover and mutation have the worst of the fitness of individuals [10]. In order to speed up the algorithm, local search was selected. Individuals with the highest fitness were selected for replication, and mutation and crossover were conducted directly, while those with low fitness were ignored [9], it said: how long does it take to use a local search. The purpose of using local search is to improve the function of the xl and speed up the improvement algorithm The folding rate. 2.5

Termination Criteria

The termination condition of genetic algorithm is that the individuals with the highest fitness in the population do not change, and the difference between the lowest and highest individuals in the population is almost zero. The highest fitness in the population is F(bad) the lowest F(best). FðbadÞ ¼ FðbestÞ The above formula is that the value of the lowest fitness in the population is equal to the value of the highest fitness, which is the best condition for the termination algorithm.

3 Improved Algorithm Steps Step 1 Initialize the iter = 0. Input for problems of all kinds of data and control parameters: the population size N, biggest ITERMAX genetic algebra, crossover rate Pc and mutation rate Pm, local search cycle Kls, the current genetic algebra iter, the tournament size m. Step 2 adopts decimal floating point number coding, randomly selects the initial population satisfying the constraint conditions, and calculates the fitness of each individual. Step 3 tournament selection according to individual fitness. Step 4: according to the rate update of Particle Swarm optimization algorithm, the improved mutation operation is used to carry out the mutation of individuals in the population. Step 5 each Kls generation makes use of local search to update the population. Step 6 make iter = iter + 1.

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Step 7 if the termination criteria are not satisfied, go to step 3. Otherwise, end the algorithm with the current optimal solution as the solution to the problem [11, 12]. The program flow chart is as follows [13]:

4 Numerical Simulation In order to verify the validity and reliability of the proposed algorithm, two specific examples are selected for the following numerical simulation. Case 1: ð1Þ x þ 2y  10 ð2Þ x þ y ¼ z ð3Þ x 2 ½0; 10; y 2 ½0; 5 CalculateZðmaxÞ Define the initial population: variable ðx; yÞ ½F1ð1; 1Þ; F2ð2; 2Þ; F3ð3; 3Þ; F4ð5; 1Þ; F5ð6; 1Þ; F6ð7; 1Þ; F7ð8; 1Þ Calculate each fitness: (F = Z) F1 ¼ 2; F2 ¼ 4; F3 ¼ 6; F4 ¼ 6; F5 ¼ 7; F6 ¼ 8; F7 ¼ 9

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The above result is the proportion of each fitness in the bet wheel algorithm

Set the mutation rate to 0. Local search is used to select several populations with high fitness. Here are the result of the first iteration: F1 ¼ 8; F2 ¼ 8; F3 ¼ 9; F4 ¼ 9; F5 ¼ 9; F6 ¼ 9; F7 ¼ 9 Check out the minimum is equal to the maximum, return to the next iteration. When the minimum value of the population is equal to the maximum value, the condition is the algorithm termination condition. The final optimization results can be determined: F = 10, X = 8, Y = 1.

5 Conclusion In this paper, through the selection of the genetic algorithm, the operation of the punchwheel algorithm is improved, and each interval is divided into an array, and local search is added to improve the efficiency. Algorithm in improving termination criterion and the termination criterion, enrich the termination conditions of the algorithm. The use of improved floating-point genetic algorithm to solve the nonlinear equations problem: in the process of nonlinear equations of the problem into a constrained optimization problem; Secondly, the constraint optimization problem is solved by improving the strategy. Finally, the local search information is used to obtain the optimal solution with high precision.

References 1. He, Y., Ma, C., Fan, B.: A new l-m method for solving nonlinear equations. J. Fujian Normal Univ. (Nat. Sci. Ed.) (02) (2014) 2. Li, C.: MPRP derivative free algorithm for symmetric nonlinear equations. J. Southwest Univ. (Nat. Sci. Ed.) 36(01), 67–71 (2014) 3. Ru, Q.: Existence and nonexistence of solutions for a class of nonlinear reaction-diffusion equations on Riemannian manifolds. Appl. Math. (04) (2013) 4. Jiang, L., Xu, N.: Scattering of nonlinear Schrodinger equations. Appl. Math. (02) (2013) 5. Pai, X., Wang, Z.: Application of genetic algorithm with gradient information in solving nonlinear equations. J. China Petrol. Univ. (Nat. Sci. Ed.) (03) (2009) 6. Zeng, Y.: Application of improved genetic algorithm in solving nonlinear equations. J. East China Jiaotong Univ. (04) (2004) 7. Hu, N., Pan, Q.: Genetic algorithm for solving multiple equations. J. Jingzhou Normal Univ. (02) (2002) 8. Tsoulos, I.G.: Modifications of real code genetic algorithm for global optimization. Appl. Math. Comput. 203(2), 598–607 (2008)

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9. Kaelo, P., Ali, M.M.: Integrated crossover rules in real coded genetic algorithms. Eur. J. Oper. Res. (1) (2005) 10. McCall, J.: Genetic algorithms for modelling and optimisation. J. Comput. Appl. Math. 184 (1), 205–222 (2005) 11. Nyarko, E.K., Scitovski, R.: Solving the parameter identification problem of mathematical models using genetic algorithms. Appl. Math. Comput. 153(3), 651–658 (2004) 12. Lin, C.T., Lee, C.S.G.: Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems. J. Women’s Health (1996) 13. Holland, J.H.: Adaptation in natural and artificial system. J. Women’s Health (1975)

Vector Control of Three Phase Permanent Magnet Synchronous Motor Based on Neural Network Sliding Mode Speed Controller Jingli Miao, Wangyu Qin(&), and Dawei Zheng School of Information and Electrical Engineering, HeBei University of Engineering, Handan, Hebei, China [email protected]

Abstract. In order to improve the speed regulation performance of the threephase permanent magnet synchronous motor drive system, based on the mathematical model of the surface-mount permanent magnet synchronous motor in the d, q rotating coordinate system, a neural sliding mode speed controller is proposed. Firstly, the sliding mode controller is established by the approach rate method, and the stability analysis is carried out. Then based on this, combined with the radial basis function neural network to derive the control rate of the system. The method can effectively reduce chattering and improve the control performance of the system while maintaining the robustness of the sliding mode controller. The simulation results show that the system can track the reference speed quickly and has strong robustness to load disturbance. Keywords: Permanent magnet synchronous motor RBF neural network
Vector control




Sliding mode control




1 Introduction The three-phase permanent magnet synchronous motor (PMSM) is a strongly coupled, complex nonlinear system. Advanced PMSM control algorithms can be designed by establishing appropriate mathematical models [1]. The vector control of motor is a reference DC motor armature current and excitation current are perpendicular to each other, uncoupled and can be controlled independently [2]. Based on the theory of coordinate transformation, by controlling the magnitude and direction of the stator current in the synchronous rotating coordinate system, the decoupling of the orthogonal axis and the quadrature axis can be achieved, thus the decoupling control of the magnetic field and torque can be realized, and the control performance of the AC motor can be similar to that of the DC motor. However, its magnetic field orientation accuracy is susceptible to changes in motor parameters, PI control with a fixed gain of the motor speed also reduces control performance due to changes in load torque. In recent years, in order to overcome the influence of parameter time-varying and load disturbance on the motor drive system, some nonlinear control methods are applied to the control of permanent magnet synchronous motor, such as sliding mode variable structure control [3, 4], auto disturbance rejection control [5] and backstepping © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 272–279, 2019. https://doi.org/10.1007/978-981-13-7025-0_28

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control [6], etc., among them, sliding mode variable structure control is valued because of its simple physical implementation, good robustness and fast control [7]. However, the traditional sliding mode control adopts the discontinuous switching control law, and the system will generate high frequency chattering, which may cause system instability [8]. The neural network has strong learning ability, can fully approximate arbitrarily complex nonlinear relations and has strong robustness [9]. This paper improves the traditional surface-mount three-phase PMSM sliding mode variable structure vector control, firstly, combined with the mathematical model of the permanent magnet synchronous motor rotating coordinate system, the sliding mode speed controller is designed by exponential approach rate method; secondly, a Radial Basis Function (RBF) neural network is introduced in the sliding mode speed controller, which utilizes the learning and data processing capabilities of the neural network to enable the system to maintain robustness against perturbations and load disturbances [10]. Chattering of weak systems improves system steady-state accuracy. The simulation results verify the correctness and effectiveness of the design. In this paper for the design of the controller, the d-q coordinate system and the a-b coordinate system are analyzed in this paper. The voltage equation of the motor in d-q rotor synchronous rotating coordinate system is:


ud ¼ Rid þ uq ¼ Riq þ

d dt wd d dt wq

 xc wq : þ xe wd

ð1Þ

The stator flux linkage equation is:


wd ¼ Ld id þ wf : wq ¼ Lq iq

ð2Þ

Ud and Uq are d and q axis components of stator voltage; Id and Iq are d and q axis components of stator current respectively; R is a stator resistance; wd and wq are the d and q axis components of the stator flux respectively; xe is the electric angular velocity; Ld and Lq are inductance components of d and q axis respectively; wf is the flux linkage of permanent magnet. Under the synchronous rotating coordinate of d-q rotor, the torque equation of the motor is:     3 Te ¼ pn iq id Ld  Lq þ wf : 2

ð3Þ

Pn is the number of pole-pairs. The mechanical motion equation of the motor is: Te  TL ¼ J

dX : dt

TL is load torque, J is moment of inertia, and X is rotor speed.

ð4Þ

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Transform the abc natural coordinate system into the coordinate transformation of the a-b stationary coordinate system, which is called Clark transformation, constant amplitude conversion of voltage is used in this paper: 

xa xb



" 2 1 ¼ 3 0

 12 pffiffi 3 2

#2 3 xa  12 pffiffi 4 x 5: b  23 xc

ð5Þ

The transformation of the a-b stationary coordinate to the d-q synchronous rotating coordinate system is called Park transformation, the transformation of the d-q synchronous rotating coordinate system to the a-b stationary coordinate system is called inverse Park transform:  

ud uq ua ub



 ¼



 ¼

cos hr  sin hr cos hr sin hr

sin hr cos hr  sin hr cos hr

 

 ua : ub

ð6Þ

 ud : uq

ð7Þ

h is the angle between the rotor flux and the a axis.

2 Design of Sliding Mode Controller 2.1

Sliding Mode Controller

For the surface-mount PMSM, the rotor magnetic field orientation control method with id = 0 can obtain better control effect. At this time, the mathematical model of the motor can be transformed into: 8   < diq ¼ 1 Riq  pn wf xm þ uq Ls

dt  : 3Pn wf 1 m : dx dt ¼ J TL þ 2 iq

ð8Þ

Ls is the stator inductance. Define the state variables of the PMSM system:


x1 ¼ xref  xm : x2 ¼ x_ ¼ xm

ð9Þ

xref is the reference speed of the motor, xm is the true value of the speed. Deriving x1x 2: 8

 < x_ ¼ x_ ¼ 1 T  3pn wf i 1 m L q J 2 : 3p w : x_ ¼ x € m ¼  2Jn f iq 2

ð10Þ

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3p w Define: u ¼ _iq , D ¼ 2Jn f , Sliding surface function: s = cx1 + x2, Then there are:



x_ 1 x_ 2





0 1 ¼ 0 0



   x1 0 þ u: x2 D

s_ ¼ c_x1 þ x_ 2 ¼ cx2 þ x_ 2 ¼ cx2  Du:

ð11Þ ð12Þ

using the method of exponential approach law: s_ ¼ e sgnðsÞ  qs

ð13Þ

the expression of the controller is: u¼

1 ½cx2 þ e sgnðsÞ þ qs: D

ð14Þ

The reference current of the q axis is: 1 iq ¼ D

2.2

Zt ½cx2 þ e sgnðsÞ þ qsds:

ð15Þ

0

Stability Analysis

For Sliding mode approach law (13), the system state s can reach the equilibrium point s = 0 under its action. According to formula (13), the relationship can be obtained: s_s ¼ s½esgnðsÞqs

ð16Þ

It must be less than 0. According to the existence and reachability conditions of the sliding mode approach law of the continuous system, the approach law is satisfied s_s\0 and exists and is reachable [11]. Therefore, the system state s can reach the equilibrium point s = 0 under the action of the approaching law.

3 Design of Neural Network The traditional sliding mode control has high frequency oscillation phenomenon caused by the switching phase due to the switching phase. The high frequency chattering may cause the unmodeled dynamic characteristics of the system, making the system unstable. Secondly, the sliding mode variable structure control is susceptible to measurement noise, and a large control signal is needed to overcome the uncertainty of the parameters and the influence of the disturbance. In order to reduce the chattering and further weaken the steady-state error of the system, the radial basis function neural

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network is used to adjust the control output id of the sliding mode structure to make the output current more accurate and stable. The object of RBF network approximation is: G p ðsÞ ¼

D : s2

ð17Þ

Design the RBF function in the controller’s s function, the network input of RBF is the error of speed and current. Radial basis function is Gauss’s function: ! X  cj 2 hj ¼ exp  ; j ¼ 1; 2; . . .; m 2b2j

ð18Þ

The initial weight of the network is [0 0 0 0], the initial weight  of the Gauss 0:1 0:1 0:1 0:1 . The learning function parameter is B = [3 3 3 3], cj ¼ 0:1 0:1 0:1 0:1 parameters of the network are a = 0.05, η = 0.3. The simulation model of the design controller is as follows (Fig. 1):

Fig. 1. Simulation model of the neural slip mode speed controller

4 Simulation Result In order to verify the correctness of the method used in this paper, the simulation model of the surface-mounted three-phase PMSM vector control system was established by Matlab/Simulink, as shown in Fig. 3. The parameters of the motor used in the simulation are set to: the pole pair Pn = 4; the stator inductance Ls = 8.5 mH; the stator resistance R = 2.875X; the flux linkage wf = 0.175 Wb; the moment of inertia J = 0.003 kg•m2; the damping coefficient B = 0.008 N•m•s. The simulation condition is set to: DC side voltage Udc = 311 V; PWM switching frequency is set to fpwm = 10 kHz; cycle is set to Ts = 10 ls; variable step size ode23tb algorithm is adopted; relative error (Relative Tolerance) is set to 0.0001; simulation time is set to 0.4 s. The sliding mode controller parameters are: c = 60, e = 200, q = 300, reference speed during simulation Nref = 1000 r/min, the initial load torque TL = 0 N•m, and the load torque becomes TL = 10 N•m at t = 0.2 s. The simulation model is (Fig. 2):

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Fig. 2. System simulation model

The simulation results are shown as follows:

(1)Speed response (2)Electromagnetic torque response (3)Current response Fig. 3. Simulation results of speed, electromagnetic torque and current under neural sliding mode control

It can be seen from Fig. 3 that when the motor rises from zero speed to the reference speed of 1000 r/min, although the motor speed has some overshoot at the beginning, it still has a fast dynamic response speed. And when t = 0.2 s, the load torque TL = 10 N•m is suddenly applied, and the motor speed returns to the given reference speed value within 0.02 s. In order to compare the control effects of the controller, Fig. 4 shows the system response when using a conventional sliding mode controller that does not introduce a neural network. The motor parameters used in the simulation are unchanged.

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(1)Speed response (2)Electromagnetic torque response (3)Current response Fig. 4. Simulation results of speed, electromagnetic torque and current under traditional sliding mode control

It can be found from Fig. 4 that when the traditional sliding mode control is used, the overshoot of the motor speed is large when the motor starts, and the maximum speed exceeds 1200 r/min. After the load torque of 0.2 s is suddenly increased, the speed is obviously decreased. When using the sliding neuron controller, the overshoot of the speed is small, the maximum speed is lower than 1200 r/min, and the whole curve is relatively stable. The electromagnetic torque response time under the traditional sliding mode control is longer, the chattering is larger, and the electromagnetic torque response under the neural sliding mode controller is less chattering. It can be seen that when the load is disturbed, the neural sliding mode controller can make the motor speed quickly track the reference value of the speed reference, and the torque chattering is small, and excellent speed regulation performance is obtained.

5 Conclusion In this paper, the vector control of surface-mount PMSM is combined with the RBF neural sliding mode controller, and a high-performance PMSM vector control system speed control method is proposed. The stability analysis is carried out. Under the condition of load disturbance, the tracking control of the motor speed is realized, and the chattering of the torque is reduced. The simulation results show that the method can not only achieve fast and precise control of the speed, but also has good adaptability to load disturbance.

References 1. Qiao, M., Zhang, X., Ren, X.: Research of the mathematical model and sudden symmetrical short circuit of the multi-phase permanent-magnet motor. In: Proceedings of the International Conference on Power System Technology, Powercon 2002, vol. 2, pp. 769–773. IEEE (2002) 2. Xie, X.G., Chen, J.: Vector control with id = 0 simulation of PMSM based on Matlab/Simulink. J. New Industrialization 6(05), 47–54 (2016)

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3. Tong, K.W., Xing, Z., Yu, Z., et al.: Sliding mode variable structure control of permanent magnet synchronous machine based on a novel reaching law. Proc. CSEE 21(28), 102–106 (2008) 4. Mao, L.L., Kai, Z., Wang, X.D.: Variable exponent reaching law sliding mode control of permanent magnet synchronous motor. Electr. Mach. Control 20(4), 106–111 (2016) 5. Zuo, Y., Zhang, J., Liu, C., et al.: Integrated design for permanent magnet synchronous motor servo systems based on active disturbance rejection control. Trans. China Electrotechnical Soc. 31(11), 51–58 (2016) 6. Fang, Y.M., Li, Z., Wu, Y.Y., et al.: Backstepping control of PMSM position systems based on terminal-sliding-mode load observer. Electr. Mach. Control 18(9), 105–111 (2014) 7. Jing, L., Hong-Wen, L.I., Deng, Y.T.: PMSM sliding-mode control based on novel reaching law and disturbance observer. Chin. J. Eng. 25(10), 2645–2660 (2017) 8. Hou, L.M., Wang, H.Z., Yong, L.I., et al.: Robust sliding mode control of PMSM based on cascaded sliding mode observers. Control Decis. 31(11), 2071–2076 (2016) 9. Qiao, J.F., Han, H.G.: Optimal structure design for RBFNN structure. Acta Automatica Sin. 6, 865–872 (2010) 10. El-Sousy, F.F.M.: Robust wavelet-neural-network sliding-mode control system for permanent magnet synchronous motor drive. Electr. Power Appl. IET 5(1), 113–132 (2011) 11. Zhang, Y., Guang-Fu, M.A., Guo, Y.N., et al.: A multi power reaching law of sliding mode control design and analysis. Acta Automatica Sin. 42(3), 466–472 (2016)

Design of Security Alarm System Based on LoRa Technology Yafei Chen(&), Peng Gao, and Zhihua Li School of Information and Electrical Engineering, Hebei University of Engineering, Handan 056038, Hebei, China [email protected]

Abstract. At present, in order to overcome the shortcomings of traditional wired cabling and deployment, including complex construction and high cost, most of the security alarm systems employ ZigBee, Wi-Fi and other wireless technologies, whose communication distance, however, are severely limited. This paper proposes a new type of wireless security alarm system, which uses LoRa radio frequency chip of SX1278 series and adopts a star-shaped network topology to achieve the acquisition and long-distance transmission of data from alarm detectors. It meets the demand of low-cost, wide-coverage for alarm network deployment. Through testing, this wireless security alarm system has the advantages of long transmission distance, low cost, high reliability, and convenient networking. It offers a significant reference for Internet of Things applications, which requires a wide wireless network coverage. Keywords: Internet of Things Low Power WAN
LoRa


Security & protection system


1 Introduction The traditional security alarm system is generally wired transmission. Due to the need for wiring, not only the construction difficulty is increased, but also the installation cost is increased. However, wireless security detectors such as smoke detectors and infrared intrusion detectors based on ZigBee and Wi-Fi technology have problems such as short communication distance, poor network stability, and high power consumption, which make it difficult to deploy a large range of security systems. As a new wireless communication technology in the Low Power Wide Area Network (LPWAN) Internet of Things, LoRa (Long Range) utilizes advanced spreadspectrum modulation technology and adopts forward error correction coding technology to add redundancy to the transmitted information to effectively resist multipath fading. Therefore, LoRa technology is very suitable for communication requirements of long-range, low-power, anti-interference security alarm systems. This paper proposes a regional security alarm system based on LoRa technology, which has low cost, low energy consumption, strong expandability and large network capacity compared with wired transmission. It can timely, accurately and efficiently alarm and respond to the alarm. © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 280–288, 2019. https://doi.org/10.1007/978-981-13-7025-0_29

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2 System Overview The LoRa-based security alarm system consists of a sensing layer, a network layer, and an application layer. As shown in Fig. 1, the sensing layer is located at the bottom of the entire system and is composed of multiple alarm sensing devices. The sensing device can collect the data of each front-end detector accessed in real time, and send the data to the LoRa gateway node through the LoRa wireless module. The gateway is a link between the connection sensing device and the Ethernet, and can realize the conversion between the LoRa network protocol and the Ethernet communication protocol. The application layer mainly includes a web server for storing and displaying the working status of each alarm sensing device.

gateway

sensing device

Internet

Internet server

gateway

sensing layer

network layer

application layer

Fig. 1. System composition topology

2.1

Wireless Technology Choice

At present, there are two main types of IoT wireless communication technologies, one is short-range communication technology, such as ZigBee, Wi-Fi, Bluetooth, etc. The other is LPWAN communication technology, LoRa, which operates in unlicensed bands, and NB-IoT (Narrow Band Internet of Things), which operates in licensed bands, are two leading technologies [1] (Table 1). LoRa is an emerging low-power long-range communication technology operating in the Sub-GHz ISM unlicensed band, featuring low power consumption, low speed, and wide coverage. LoRa uses spread spectrum communication technology to linearly transmit the transmitted digital signal to the entire linear spectrum interval [2]. According to the Shannon formula, the maximum information transmission rate C is determined by the bandwidth B and the SNR (signal-to-noise ratio). When the

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capacity

ZigBee 65000

frequency bandwidth coverage cost topology

2.4 GHz 250 Kbps 75 m low star/mesh

Wi-Fi depending on the IP address 2.4/5 GHz 54 Mbps 100 m medium star/peer to peer

NB-IoT 10 k/cell

LoRa 100 million/Hub

800/900/1800 MHz 234.7 kbps 15 km medium star

433/868/915 MHz 50 Kbps 15 km low star/peer to peer

channel capacity is constant, increasing the channel bandwidth can reduce the SNR requirement. Spread spectrum technology reduces the signal-to-noise ratio requirement of the receiver by expanding the signal bandwidth. Even if the signal power is close to noise, even if the signal power is smaller than the noise, the receiver can correctly extract the signal, significantly improving the receiving sensitivity, thereby improving the communication performance [3]. NB-IoT is a narrowband IoT communication technology based on cellular network. It is one of the important components of LPWAN. It is characterized by low power consumption, low speed and wide coverage. The modulation mechanism and protocol of NB-IoT are complex, and the module cost is relatively high. In addition, it works in the licensed frequency band, and the infrastructure depends on the telecom operators, and the infrastructure construction and operation costs are high. It is necessary to pay a certain service fee to the network operator, Compared with LoRa working in the license-free ISM band, it does not have an advantage in operating costs [1]. In summary, by comparing the advantages and disadvantages of different wireless networking methods, the system finally selects LoRa wireless technology with ultralow power consumption and wide coverage as a communication scheme. 2.2

Network Topology Design

LoRa uses a star network architecture. A single-hop star network topology is adopted between the node device and the gateway. Each node has no link between each other, which further reduces the power consumption of the node and improves the reliability of the network. The star network architecture can be used in the security alarm system to meet the actual needs. In theory, a single LoRa master node can support about 300,000 slave nodes, but when the gateway node accesses more slave devices, it will cause network congestion, making the gateway node overburdened [4]. Therefore, in the actual application, the security monitoring area can be partitioned and managed, and different regional devices set different frequencies to reduce inter-node interference and network communication delay. For scenarios with low latency requirements, host polling or time division multiplexing networking can be used. For the case where the real-time requirement is high, the slave can actively report to the host. This design adopts this method. When the number of nodes is large, the base station of the LoRaWAN standard protocol can be

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used to meet the requirements for network capacity, quality of service, and transmission reliability [5]. Different ways can be selected according to different occasions to improve networking efficiency.

3 System Hardware and Software Design 3.1

Sensing Device Hardware

As shown in Fig. 2. The sensing device can collect the detection sensor data, and the data is sent to the gateway node through the LoRa radio module; at the same time, the LoRa radio module can receive and respond to the control command issued by the gateway node to complete the action of the sound and light alarm. The device can be connected to fire, infrared intrusion, glass breakage, door magnetism, vibration detectors, etc. The micro control unit uses STM8L151 series microcontroller, built-in 16 Kb FLASH, 2 Kb RAM, which can meet the needs of system design. The SX1278 RF module communicates with the MCU through the SPI bus. The high-precision real-time clock chip DS3231 communicates with the MCU through the IIC bus.

Interface Circuit

Microcontroller (STM8L151K4T6)

Fig. 2. LoRa sensing device hardware structure

3.2

LoRa Gateway Hardware

The LoRa gateway is a bridge between the sensing device and the server. It mainly collects the detector data uploaded by each sensing device, and encapsulates the data and transmits it to the server through the IP network, and simultaneously completes the functions of the upper layer control command. The gateway can transmit the data of the LoRa wireless network to the server via Ethernet or Wi-Fi or mobile network according to user requirements, and complete the upload of the alarm information. The hardware structure is shown in Fig. 3. Considering the heavy communication task of LoRa gateway, this system uses STM32F407 series high-performance microcontroller with network MAC module as the main controller, and the Ethernet interface chip model is DP83848. The Wi-Fi module and the GPRS module communicate with the microcontroller through the UART serial port. Network connection and data communication can be completed by sending corresponding AT commands to the module, which reduces the difficulty of network programming.

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Fig. 3. LoRa Gateway hardware structure

3.3

Sensor Device Programming

The program design principle of the alarm sensing device is to periodically collect the detector data of the zone layout, after the data is encoded by CRC16, it is sent to the gateway through the LoRa network, and waits for the gateway to answer, completing the data collection and reporting. When the gateway does not receive the response, it will delay and send again. If it fails to send three times, it will generate a communication failure warning. At the same time, the sensing device receives the control command sent from the LoRa gateway, first performs CRC check on the data, and if the check is correct, performs related actions, such as generating an audible and visual alarm. The alarm sensing device software workflow is shown in Fig. 4. START

System init

System Interrupt

Read sensor data & send date

Read LoRa module data

Receive ACK

Receive data & CRC correct

Delay 10s and resend data

Execute & ACK

Not receive ACK 3 times

Alert warning

Fig. 4. Sensor device program flow chart

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LoRa Gateway Programming

The LoRa Gateway is the link between the sensing device and the Internet, enabling the conversion of the LoRa protocol to different types of network protocols (Ethernet or Wi-Fi or mobile networks). The gateway software adopts the FreeRTOS embedded operating system, which can perform multi-tasking operations and bring convenience

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to system parallel multi-tasking programming. The LoRa gateway communicates with the server using the MQTT protocol, a “lightweight” transport protocol based on the publish/subscribe messaging model for low-power applications and IoT applications with limited network bandwidth. The sender and receiver of the MQTT protocol need not directly establish contact, and only need to forward the message through the intermediate agent, that is, there is no coupling between the sending and receiving parties, which improves the message transmission efficiency. The data frame is in JSON format, and this lightweight data interaction format greatly reduces network transmission consumption. After receiving the data frame sent by the sensing device, the LoRa gateway first performs CRC check. If the check is correct, the data is encapsulated into a JSON format data frame and sent to the server through the MQTT protocol to complete the transmission of the alarm data. Similarly, the command sent by the alarm management platform to the sensing device through the LoRa gateway first performs JSON analysis, and then the data is CRC-encoded and then forwarded to the LoRa sensing device.

4 Testing and Analysis In order to test the communication effect of LoRa wireless technology in the practical application of security alarm system, we tested LoRa communication distance and packet loss rate in the empty streets and buildings in the city. Verify that the designed security alarm system is reliable based on LoRa technology. 4.1

Communication Distance Test

This test sets all nodes to send and receive frequencies at 470 MHz. The transmit power is set to 20 dBm (100 mW), the antenna gain is 1 dBi, three types of bandwidth are selected: 62.5, 150, 250 kHz, and three types of spreading factors are selected: 7, 9, and 12. Each parameter combination sends 100 data frames, each containing 50 Bytes of data to simulate the transmission of alarm data. Select a different straight line distance in an empty street for the Packet Loss Rate (PLR) test. As can be seen from Table 2, the larger the spreading factor setting, the higher the corresponding receiving sensitivity and the farther the communication distance. The narrower the bandwidth setting, the more concentrated the radio wave energy transmission in the air, the farther the corresponding communication distance. In this test result, the nodes are in an open environment within about 2 km apart, and no packet loss is detected. The distance is greater than 3 km and packet loss begins to occur. When the actual test distance is greater than 5 km, communication can only be completed using a higher spreading factor or lower bandwidth. Combined with the actual application, the radius of the general residential area and the factory area is less than 2 km, so SF = 7, BW = 125 kHz, and the transmission power of 20 dBm can meet the actual communication needs.

km SNR 0 1 3 0 2 4 1 2 3

SNR 0 4 3

2.439 RSSI −114 −117 −116 −118 −117 −117 −119 −122 −121

1F RSSI −124 −113 −118

SF/BW 7/250 7/125 7/62.5 9/250 9/125 9/62.5 12/250 12/125 12/62.5

SF/BW 7/125 9/125 12/125

PLR 0% 0% 0%

PLR 0% 0% 0% 0% 0% 0% 0% 0% 0%

km SNR −5 0 2 −4 1 2 −3 2 2 PLR 5% 0% 0% 0% 0% 0% 0% 0% 0%

2F RSSI SNR PLR −113 2 0% −121 −5 0% −116 1 0%

3.376 RSSI −124 −119 −120 −121 −119 −120 −122 −120 −123

km SNR −9 −5 −6 −9 −6 −1 −8 −5 −3 PLR 8% 5% 1% 5% 7% 0% 0% 0% 0%

4.294 km RSSI SNR PLR 100% −129 −7 94% −128 −3 68% −132 −12 32% −130 −6 8% −134 −7 6% −133 −10 2% −136 −9 12% −131 −4 27%

3F RSSI −124 −127 −127 SNR −6 −7 −6

PLR 7% 0% 0%

4.701 km RSSI SNR PLR 100% 100% 100% −131 −8 8% −129 −8 4% −130 −7 2% −136 −10 8% −136 −9 2% −134 -8 0%

5.395 km RSSI SNR PLR 100% 100% 100% 100% −135 −12 89% −139 −7 58% −139 −16 49% −136 −10 32% −134 −8 17%

4F 5F 6F RSSI SNR PLR RSSI SNR PLR RSSI SNR PLR −123 −6 21% −125 −8 84% 100% −129 −10 0% −129 −7 4% −134 −12 89% −130 −7 0% −132 −10 1% −143 −16 9%

Table 3. Test results in the building

3.877 RSSI −128 −127 −125 −130 −130 −126 −132 −130 −129

Table 2. Empty street test results

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In-Building Test

The communication test was carried out with low transmission power inside the building, and the transmission power was set to 2 dBm (about 1.6 mW), the bandwidth was selected to be 125 kHz, and the spreading factor was selected to be 7, 9, and 12. The length of the east and west of the building is about 120 m, and the transmitting node is placed at the east end of the first floor. The receiving nodes test the packet loss rate on the west side of the corresponding floor (Table 3). It can be concluded from the test data that LoRa communication can communicate well from one floor to three floors with low transmission power. Exceeding this floor range can increase the transmission power or increase the spreading factor. If necessary, the relay node can be added to ensure good communication. Through testing, LoRa technology can realize long-distance communication, so that the coverage of security alarm system can be extended to campus, community, factory area, etc. When the node is deployed in an open environment and less than 2 km, there is no error and packet loss in communication. Deploying in a complex building requires actual testing, so that the signal strength and signal-to-noise ratio can achieve good communication effects. If necessary, repeater nodes can be added to meet the actual needs of the network. 4.3

System Test

The system is tested by actual deployment on our campus. The test equipment includes one LoRa base station and seven alarm sensing devices. The sensor nodes are deployed in a five-story school building, and the gateway is deployed in the third floor. Two of the nodes are deployed on campus walls that are 350 m and 800 m away from the gateway. The sensing device and gateway transmit power is set to 20 dBm, the spreading factor is set to 7, and the bandwidth is set to 125 kHz. Through real-time monitoring of the detected data, after 240 h of uninterrupted trial operation, the system can transmit the detector data collected by the sensing device to the alarm display platform in time, and there is no error and packet loss in LoRa communication. During the test period, a large number of simulated fire and smoke alarms and human intrusion alarms, the system can obtain the location of the alarm in a timely and accurate manner, and the display platform can issue alarm prompts in time.

5 Conclusion The security alarm system based on LoRa technology designed in this paper uses LPWAN technology to realize the collection and wireless transmission of security detector data in the security field. The test results show that the system has the advantages of long communication distance, flexible network layout, high reliability, low cost, etc. It can meet the needs of security alarm field well and has a good application prospect.

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References 1. Sinha, R.S., Wei, Y., Hwang, S.H.: A survey on LPWA technology: LoRa and NB-IoT. Ict Express 3.1 (2017) 2. Zheng, N., Yang, X., Wu, S.: A survey of low-power wide-area network technology. Inf. Commun. Technol. (2017) (in Chinese) 3. Wang, Y., Wen, X., Lu, Z., et al.: Emerging technology for the internet of Things—LoRa. Inf. Commun. Technol. (2017). (in Chinese) 4. Zhao, T., Chen, L., Yuan, L., et al.: Design and implementation of smart meter reading system based on LoRa. Comput. Measur. Control (2016). (in Chinese) 5. Sornin, N., Luis, M., Eirich, T., et al.: LoRaWANTM Specification V1.0.[S].LoRaTM Alliance (2015)

Study on Relationship Between Filling Rate and Ground Settlement in Strip Mining Ming Li1, Zhao-jiang Zhang1,2(&), and Yu-lin Li1 1

2

College of Mining and Geomatics, Hebei University of Engineering, Handan 056038, China [email protected] Collaborative Innovation Center of the Comprehensive Development and Utilization of Coal Resources, Handan 056038, China

Abstract. The study of filling rate is of great significance to liberate the “three lower” coal pressure and improve the mine productivity in filling strip mining. Taking the 7/8/9 working face of Tao-yi Coal Mine as the research object in Handan, this paper studies the determination of the most suitable filling rate under the existing geological and mining conditions by means of numerical simulation and field measurement. The results show that,when the mining width is 1:1, the basic roof force and the basic roof subsidence decrease gradually with the increase of filling rate; the influence range of surface subsidence decreases gradually; after considering the cost and the effect of controlling surface subsidence, 90% was selected as the most suitable filling rate of the working face; the result of simulation is as the same as surface subsidence. Therefore, the surface subsidence of strip filling mining decreases with the increase of filling rate, and the filling rate of 90% should be selected as the reference value of strip filling mining under similar geological conditions. Keywords: Filling mining
Filling rate
Numerical simulation Subsidence value
Sustainable development




1 Introduction Strip mining divide the coal seam into more regular strip shape, leaving a strip while mining [1], and make the strip left enough to support the overlying strata load, so as to effectively reduce the surface subsidence [2]. According to different filling materials, strip filling mining can be divided into gangue filling, cemented filling, paste filling and ultra-high water filling. There are many successful examples of strip filling mining in China [3]: In the study of strip filling mining in Tiao-shuihe Phosphate Mine, numerical simulation method is used to simulate different backfill width [4]; in the study of backfill spacing in Caocun Coal Mine, the interval of strip backfill suitable for geological conditions of the mine is determined [5], etc. China has done some research on the selection of parameters in strip filling mining, and obtained the types of overburden rock, filling rate, filling strength, filling width, the width of blank strip and so on [6].

© Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 289–301, 2019. https://doi.org/10.1007/978-981-13-7025-0_30

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However, there are few studies on the filling rate in strip filling mining, most of which focus on the strength of filling body, the width of coal pillar and the width of retained pillar [7]. The determination of the suitable filling rate can maximize the production efficiency of the mine and effectively reduce the surface subsidence, so as to achieve better sustainable development. Therefore, it is of great significance to study the rule of surface settlement under different filling rates in strip mining. The determination of suitable filling rate under the condition of backfill strip mining is a problem worthy of further study. Based on the mining practice of 7/8/9 working face in Taoyi Coal Mine in Handan, this paper analyzes and compares the suitable filling rate by means of numerical simulation and field monitoring, which can provide reference for other similar projects.

2 Engineering Background Tao-yi Coal Mine in Handan is located in 15 km northwest of Handan City, Hebei Province. It is one of the backbone mines of Handan Mining Group. The No. 9 working face is the first mining face for filling test, which is located in the southern flank of the seventh mining area and within the protective pillar of the stopping village. The area of this district is about 900,000 m2 and the recoverable reserves are 358,000 tons. According to the actual situation, 3 strip working faces were designed and the working face length is not less than 50 m. The test should be in the east of the fourth village and the western part of the coal yard. There are houses, gullies and terraces on the ground. The face is along the 2# seam direction and is pushed forward. The work surface of 9 is 330 m long and 218 to 330 m width. The experimental coal seam is 2# coal seam, belonging to the Lower Permian Shanxi Formation, the structure is complex. The thickness of coal seam is generally 1.8–3.8 m, and the average thickness of coal is about 3.2 m. On the filling face, the roadway coal seam is thicker, with a thickness of 3.0–3.8 m, and the lower roadway coal seam is about 2.4 m–3.0 m; the lower belt roadway and the track roadway are thinner, and the coal seam thickness is unstable; in the middle and lower part of the coal seam, there is a layer of siltstone with a thickness of about 0.1 m, about 1 m from the floor. The 2# coal seam color is black, streaks are gray black and gray, massive, layered structure, like metallic luster. The coal seam is a single inclined structural coal seam with a dip of about N110 E and about 10–13°; the coal seam is directly roofed by coarse siltstone, gray-black, mainly quartz, containing pyrite nodules, with a thickness of about 5.63 m; the floor is diorite, gray-white, about 4.1 m thick. The top and bottom slate histogram of coal seam is shown in Fig. 1.

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Fig. 1. The roof and floor histogram of coal seam

3 Numerical Simulation 3.1

Conceptual Design

According to the relevant data, considering the profit, filling cost and surface control effect of the difference of coal resources are synthesized [8], and in order to better study the relationship between filling rate and surface subsidence in strip mining, the ratio of mining width to mining retention is selected as 1:1, and the filling rate is 0% (strip caving mining), 80%, 90% and 95% respectively. The coal seam inclination in each model is simulated by near horizontal coal seam (Table 1). Table 1. Numerical simulation scheme for filling strip mining Project 1 2 3 4

Filling rate (%) Mining width/m Residual width/m 0 50 50 80 50 50 90 50 50 95 50 50

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Initial Condition

In order to make the simulation results more comparable and closer to reality, the initial stress, boundary conditions, loads, physical parameters and other initial conditions of each simulation scheme are consistent. Detailed values of rock mechanics parameters are shown in Table 2. Table 2. Mechanical parameters of rock stratum Rock stratum name Sandstone Medium grained Sandstone Fine siltstone Sandstone 2# Coal Seam Igneous rock Backfill

3.3

Density /Kg*m−3 2650 2750

Shear /GPa 5.5 5.5

Bulk /GPa 11 10

Tension /MPa 3.04 2.9

Consion Friction angle Depth /MPa /° /m 7.12 39 1.85 5.75 37 5.35

2650 2650 1700 2850 1800

5.9 5.5 2.35 9 2.2

12 11 5 20 4.5

3.14 3.04 1.75 9 0.3

7.12 6.12 0.95 19 0.85

40 39 30 422 30

0.75 5.6 3.7 4.12 3.33

Model Establishment

Before simulating and calculating the excavation of coal seam, the stress of the model should be balanced initially, and the rock strata should be in the state of in-situ stress to the greatest extent [9]. (1) The mesh of numerical calculation model before mining, the model size is 1100 m * 700 m * 360 m original rock stress distribution diagram as shown in the Fig. 2;

Fig. 2. The graph of original rock

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(2) Determination of boundary conditions: According to the actual conditions of coal mining and the general principles of determining the boundary conditions simulated by FLAC3D, the boundary conditions of this model are determined as follows: (1) Horizontal constraints are applied to the four vertical planes before and after the model, and the horizontal displacements of the four vertical planes are all 0 (u = 0); The bottom boundary is fixed by vertical and horizontal constraints, i.e. the horizontal and vertical displacements are both 0 (u = v = 0); the top of the model is a free boundary, i.e. no constraints are applied. (3) Determination of initial stress: there is an original stress state before the excavation of underground mining engineering, FLAC3D software can also simulate this condition: By setting the initial conditions to complete the simulation of this part of the state. The magnitude of the initial stress will affect the accuracy of the whole numerical simulation. (4) Selection of the model: In this paper, the Mohr-Coulomb model is selected to establish the model. The Mohr-Coulomb criterion is expressed by the three principal stresses ð1, ð2 and ð3. 8 e e e > < r1 ¼ a1 Dn1 þ a2 ðDn2 þ Dn3 Þ r2 ¼ a1 Dne2 þ a2 ðDne1 þ Dne3 Þ > : r3 ¼ a1 Dne3 þ a2 ðDne1 þ Dne2 Þ

ð1Þ

Where, alpha 1 and alpha 2 are material constants expressed by bulk modulus K and shear modulus G respectively (Fig. 3). The expression of Moore Kulun yield criterion is shown in the following formula: pffiffiffiffiffiffi fs ¼ r1  r3 N/ þ 2c N/ ð2Þ Where, N/ is the tensile strength of rock mass material N/ ¼ 1  sin /; U is the internal friction angle of rock mass material; C is the cohesion of rock mass.

Fig. 3. Mohr Kulun constitutive model curve display

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Analysis of Numerical Simulation Results

By numerical simulation, the stress field and displacement field distribution of the overlying strata in goaf are shown in Figs. 4 and 5. In Fig. 4, if use the strip caving mining (filling rate is 0), vertical stress distribution near the goaf by strip distribution, far away from the goaf is saddle distribution; roof and floor pressure by coal pillar, coal pillar stress concentration. Figure 5 shows that when the strip is released, the roof will collapse directly and the old roof will be bent.

Fig. 4. Distribution of stress field in mining strata (strip caving mining method)

Fig. 5. Vertical displacement distribution of mining strata (strip caving mining method)

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Force Analysis of Numerical Simulation Base

After strip mining with different filling rates, the basic roof stress above the goaf and subsidence curve are simulated as follows:

Fig. 6. Basic stress curve

Fig. 7. Basic roof subsidence curve

In Fig. 6, the stress concentration above the pillar bearing strata in the filling strip mining is smaller than that above the pillar bearing strata in the strip caving mining (the filling rate is 0). The reason is that the pressure on the roof and floor is divided by the filling body after filling the goaf. The fluctuation value is smaller than that of the strip caving method, and the failure range of the overlying strata is smaller because the pressure of the roof and floor is uniformly distributed after filling. The bending

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disturbance is too large, and the deflection of the overlying strata of the direct roof is too small due to the influence of filling, which makes it separate from the upper strata [11]. With the increase of the filling rate of the strip filling mining, the stress of the basic roof decreases continuously. In Fig. 7, the subsidence of the basic roof decreases and the fluctuation amplitude decreases significantly compared with that of the strip caving method; the position of the wave trough in the graph corresponds to the central roof of the goaf in actual production, and the wave crest represents the value of the central subsidence of the coal pillar in actual production. The roof subsidence value decreases with the increase of filling rate and the surface subsidence decreases with the increase of filling rate. 3.6

Numerical Simulation of Surface Subsidence Analysis

The purpose of filling strip mining is to reduce surface subsidence and protect surface buildings. When the mining width is 1:1 (50 m in this paper), the extremum of surface deformation of different mining schemes is shown in Table 3. In order to better analyze the characteristics of filling strip mining, the surface subsidence cloud map with 90% filling rate (Fig. 11) is selected for analysis. The surface subsidence curves at different filling rates are shown in Fig. 11. Table 3. Surface deformation values of different mining schemes Number

1 2 3 4

Subsidence value /mm 489 173 145 107

Horizontal movement /mm 150 70 56 45

Inclination deformation /(mm/m−1) 1.60 0.81 0.67 0.46

Horizontal deformation /(mm/m−1) 1.18 0.88 0.57 0.43

Curvature (10−3/m−1) 0.0151 0.0097 0.0077 0.0051

Fig. 8. Surface subsidence curves of strip mining with different filling ratios

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Table 3 and Fig. 8 is shown that: (1) when the filling rate is 0%, the surface subsidence value is 489 mm; when the filling rate is 95%, the maximum subsidence value is 107 mm; when the filling rate is increased from 80% to 95%, the surface subsidence rate is reduced by 382 mm when compared with the traditional strip caving method; (2) When the filling rate is increased from 80% to 95%, the parameters such as surface subsidence and horizontal movement decreased significantly [10]; (3) With the increase of filling rate, the trend of surface subsidence curve is basically the same, but the fluctuation degree decreases with the increase of filling rate.

Fig. 9. Ground subsidence cloud map with strip caving method (0%)

Fig. 10. Strip filling mining (90%) surface subsidence cloud map

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In the picture above, the influence area of subsidence basin by filling strip mining is smaller than that by full caving method, and the subsidence value by filling strip mining is significantly smaller than that by full caving method. According to the above diagram and Table 3, it can be concluded that: (1) the subsidence trend area of the surface and the basic roof is consistent; (2) considering the coal production, filling cost, filling efficiency, surface subsidence value and surface control effect, 90% filling rate is the most suitable filling rate for the project (Figs. 9 and 10).

4 Field Observation of Ground Subsidence 4.1

Monitoring Points Arrangement

Combined with the actual situation, considering the three working faces, four observation lines, named Q, L, J and B, laid on six datum points on the surface and 80 observation stations are laid on the working face. The L-line is arranged along the strike direction of the working face, and the Q, L and B observation lines are arranged along the coal seam inclination roughly. The distance between the measuring points is 20 m. The relative position between the observation line and the working face is shown in Fig. 11.

Fig. 11. Relative position of observation line and working face

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Measured Results

The observation stations should be observed regularly according to the progress of mining. Finally, according to the observed results, the surface subsidence contour map drawn is shown in Fig. 12.

Fig. 12. Surface settlement contour (part)

Surface subsidence contour line as shown in the above diagram. From the analysis of the map can be drawn: (1) subsidence basin area is small, indicating that the surface has not been fully mined; (2) subsidence basin is not symmetrically distributed along the working face, indicating that charging 7, charging 8 working face mining on the charge 9 working face has disturbance; (3) along the working face strike the maximum subsidence is located at L1 point. The subsidence value is 147 mm; the J1 point subsidence value is the largest, reaching 178 mm. According to the classification of damage grades of surface buildings in relevant codes, it can be considered that the damage grade of this project is grade I damage [12, 13]; (4) The field measurement results are basically consistent with the numerical simulation results. 4.3

Comparative Analysis

As shown in Fig. 12, the main observation lines are the L-line along the strike direction and the Q-line along the dip direction, and the observation points on the J-line and

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L-line are few. Therefore, the observation and simulation results on the L-line and Q-line are analyzed comprehensively. The following Fig. 13 shows:

Fig. 13. Comparison of measured and simulated subsidence curves

It can be seen from the diagram that: (1) when the filling rate reaches 90%, the filling effect meets the requirements; the field measurement results show that the L-line measured results are in good agreement with the simulation results, indicating that the simulation parameters are properly selected; (2) the Q-line measured results are quite different from the simulation results, mainly because the observation line is fully considered in the actual layout of the observation line. There is a simulation error between the extracted value of the simulation results and the actual observation value of the observation point. (3) when the filling rate is 0, 80%, 90%, 95%, the surface subsidence value is 107–489 mm, and the measured subsidence coefficient is 0.048. (4) The field measurement results show that the filling strip mining can reduce the subsidence. The subsidence rate has been reduced by 65%. Field measurement shows that there is no wave-shaped subsidence basin on the surface, indicating that the surface movement and deformation have been effectively controlled to meet the actual needs.

5 Conclusion (1) Long-term monitoring of surface movement and deformation in strip filling mining in Taoyi Coal Mine shows that the surface has not been fully mined, the maximum subsidence point is J1, which is roughly located in the center of No. 09 working face, the maximum subsidence is 178 mm, the maximum inclination point is L28, the maximum inclination deformation value is 1.778 mm/m, and the maximum curvature value is 0. 107 * mm/m2, the surface subsidence basin decreased obviously.

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(2) Surface subsidence tends to weaken with the increase of filling rate, but considering the factors of cost and surface subsidence, it is not the higher filling rate, the better, but the existence of the most appropriate filling rate should be considered. (3) Based on the principle of equivalent mining height, the strata and surface movement law of strip mining with different filling rate under the same width and condition are verified by numerical analysis [14]. The results show that the filling mining has changed the mechanical properties of the “three zones” in the traditional caving mining method. The stress concentration in the goaf and above the coal pillar is not obvious. The vertical subsidence of each stratum is obviously reduced, and the degree of surface damage is reduced [15]. (4) When the mining width is 1:1, the filling rate of 90% is the most suitable, which can effectively control the surface subsidence in a safe range and realize sustainable development. This project can provide reference for other similar projects.

References 1. Liu, W.P.: Study and application of subsidence law for strip mining in deep thick coal seam. China University of Mining and Technology (2015) 2. Li, B.: Experimental study on the bearing behavior of open-type filling and consolidation with ultra-high water materials. China University of Mining and Technology (2016) 3. Zhang, H., Zhao, Y.X.: Research status and development trend of strip mining. Coal Min. 03, 5–7 + 4 (2000) 4. Chen, X.W., X,F., He, Z.L.: Study on strip filling mining parameters of the Tiao-shui River Phosphate Mine. Min. Res. Develop. 35(02), 5–8 (2015) 5. Dong, E.Y.: Study on backfill arrangement parameters of paste strip filling mining. Taiyuan University of Technology (2015) 6. Xu, J.L., You, Q., Zhu, W.B.: Theoretical study on controlling mining subsidence by strip filling. J. Coal Mine 02, 119–122 (2007) 7. Dai, J.Z.: Basic research on strip filling mining technology in goaf. Taiyuan University of Technology (2013) 8. Zhang, Z.J., Zhang, T., Zhang A.B., et al.: Study on surface subsidence law of super-high water material filling strip mining. Coal Eng. 48(07), 97–99 + 103 (2016) 9. Peng, W.B.: FLAC 3D Practical Course, pp. 5–10. Machinery Industry Press, Nanjing (2008) 10. Zhang, T.: Study on the law of strata and surface movement during backfilling mining under ultra-high water material railway. He-bei University of Engineering (2015) 11. Cao, Z.Z., Ding, Q.L., et al.: Analysis and research on overburden rock movement and surface subsidence in solid filling mining under different filling ratio. Coal Eng. 11(07), 92– 95 (2014) 12. Hu, B.N.: Guidelines for the Preservation and Mining of Coal Pillars in Buildings, Water Bodies, Railways and Main Mines and Lanes, pp. 196–197. Coal Industry Press, Beijing (2017) 13. He, G.Q.: Mining Subsidence Science, pp. 88–100. China University of Mining and Technology press, Xuzhou (2010) 14. Jiang, F.X.: Mine Pressure and Strata Control, pp. 67–70. Coal Industry Press, Beijing (2004) 15. Sun, C.D.: Super High Water Material Filling Mining Technology, pp. 225–231. Science Press, Beijing (2017)

Division of “Three Zones” of Gas in U Type Ventilation Goaf Under Different Seam Inclination Angle Yong-chen Yang, Shao-fang Cao(&), and Hong-yuan Mao School of Resource, Hebei University of Engineering, Handan 056038, China [email protected], [email protected] Abstract. Using the experimental platform of gas flow field in goaf, a similar simulation study is carried out to study the influence of coal seam inclination angle on gas flow field distribution in goaf and the law of gas distribution variation in goaf. The gas seepage law in goaf is simulated when the inclination angle of coal seam is 13° or 37° or 68° respectively. The distribution law of gas flow field in goaf under three inclination angles is compared and analyzed, and the specific position of spontaneous combustion in goaf is further determined, which is the mining of coal seam with different inclination angle. It can be used for reference in preventing gas explosion in goaf and preventing spontaneous combustion of coal left in goaf. Keywords: Coal seam inclination


Goaf gas
Spontaneous combustion

1 Introduction China is rich in coal resources and widely distributed. The conditions of coal seam occurrence are different in different areas. The treatment of gas in goaf in coal mining has been puzzling the safe production of coal mine. Chinese scholars have done a lot of research on gas in goaf, especially for the near horizontal coal seam, and have made a lot of research results. However, there is a lack of research on gas accumulation and its formation mechanism in the upper corner of large dip coal seam, and gas explosion is easy to occur in the upper corner of large dip coal seam, which seriously affects the safe production of the working face. Therefore, the study of gas flow field in goaf with inclined angle of coal seam is studied [1–3]. The distribution influence law and gas distribution law are of great significance in determining the specific location of spontaneous combustion in goaf [4–8], preventing gas explosion in goaf and spontaneous combustion of coal left in goaf, and ensuring normal and safe production of working face.

Yang Yongchen (1960), Professor of Lingshou, Hebei Province and tutor of Master’s degree, mainly engaged in coal mining and safety research. © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 302–307, 2019. https://doi.org/10.1007/978-981-13-7025-0_31

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2 Experimental Simulation 2.1

Making Physical Model of Goaf

According to the actual situation of a mine stope, the model of gas flow field experimental platform in goaf is made by similar simulation experiment [2]. The length of the model is the length of the face, the length of the wide goaf is the length of the goaf, and the height is the mining height. The length sizes are 127 cm/76 cm/10 cm respectively, and two diameter 6 cm holes are opened at both ends of the model length, respectively, to simulate the upper and lower air entry roadways and the return air lanes of the working face respectively. By changing the physical model of gas flow field in goaf to simulate different dip angle of coal seam, the section of goaf is shown in Fig. 2. Under the condition of 13° and 37° and 68° different inclination angle, the goaf tile is obtained. The law of gas seepage in goaf under different inclination angle is obtained by analyzing the law of gas seepage [9, 10]. 2.2

Principle of Experiment

By using the fluidity and diffusivity of flue gas, this experiment simulates the layout of fan in the return air roadway in the section. Under the action of the fan, the working face will produce negative pressure field, and the smoke will produce the moving law of gradient distribution under the action of pressure field. The flue gas will eventually form a certain concentration gradient in the goaf of coal seam. According to the principle of similar materials, this experiment developed a gas flow field experimental bench in the goaf of coal seam, and used the self-made smoke generator to produce enough smoke. Under the action of the fan, the flue gas was entered into the test bed, and the distribution of the gas migration and distribution was recorded by sketch method. According to the experimental results, the distribution of the gas flow field in the goaf of the coal seam under different inclination angles was inferred. Experimental schematic diagrams, such as Fig. 1.

1. Smoker 2. Carrier 3. Smoking covers. Valve 5. Air duct Road 6. Cooler 7. Goaf flow field 8. Fan

Fig. 1. Experimental schematic diagram

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Experimental Process

First of all, the smoke generator is connected with the self-made experimental platform of gas flow field in goaf through rubber ring, the purpose is to make the gas leakage between the two have good sealing, to prevent smoke leakage to improve the reliability of experimental data, and to turn on the smoke generator so that the fan can run on electricity. Check whether there is any smoke leakage between the devices, after the detection is completed, then close the smoke transmitter, let the fan continue to run, the gas from the gas flow field experimental platform in the goaf will be completely extracted to prepare for the formal start of the test. Secondly, by means of trigonometric function determination, the vertical height from the return wind lane to the air entry lane at different inclination angles of 13°*37°*68° is calculated respectively as shown in Fig. 2.

Fig. 2. Face goaf section

Finally, the smoke generator and the fan are turned on. Under the action of the fan, a negative pressure force field is formed inside the test bed, so that the smoke can move freely under the pressure gradient, and the fan will continue to operate. After the smoke movement in the gas flow field of goaf is stable, the migration and distribution law of smoke in the goaf is observed, the smoke flow track is recorded by video recording and sketch method, and the data are sorted out and calculated. The gas flow field in goaf is divided. After changing the inclined angle of coal seam, that is, changing the vertical height of the return wind lane to the air entry lane, repeating the above experimental steps, observing the migration and distribution law of the smoke in the goaf again, arranging and calculating the data, The regional distribution and variation of gas flow field in goaf of different inclination coal seam are obtained. By comparing and analyzing the figures obtained three times, the conclusion is drawn.

3 Result Analysis The right angle coordinate system is established on the experimental platform of gas flow field in goaf, in which the origin of the air entry lane represents the length of the face, the unit mm y axis is the vertical distance between the goaf and the working face, and the unit mm is the drawing method. The distribution curves of gas flow field in goaf under different dip angles are described. The experimental results are compared and analyzed, and the percolation law of gas in goaf under different inclination angles of coal seam is obtained.

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In this paper, the migration and distribution of tracer flue gas are drawn on the experimental platform of gas flow field in goaf of self-made coal seam under the condition of coal seam inclination of 13° by sketch method. As shown in Fig. 3.

Fig. 3. The migration and distribution of gas tracer gas in goaf of coal seam when the inclination angle of coal seam is 13°

In this paper, the migration and distribution of tracer flue gas are drawn on the experimental platform of gas flow field in goaf of self-made coal seam under the condition of coal seam inclination of 37° by sketch method. As shown in Fig. 4.

Fig. 4. The migration and distribution of gas tracer gas in goaf of coal seam when the inclination angle of coal seam is 37°

The migration and distribution law of tracer smoke was drawn on the experimental platform of gas flow field in goaf of self-made coal seam with seam inclination of 68° by sketch method. As shown in Fig. 5.

Fig. 5. Tracing the distribution of gas flow in the goaf of coal seam when the coal seam dip is 68°.

Through the analysis of the above data, it can be found that the area of strong seepage zone increases from 0*6.5 mm to 0.16.7 mm at the end of air entry roadway

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with the increase of coal seam inclination angle from 13° to 0.16.7 mm. When the dip angle of coal seam increases to 60°, the area of strong seepage area is 0*26.5 mm, and when the coal seam inclination angle is 13°, the area of strong seepage zone is 0*26.5 mm. The range of weak percolation zone is 6.5v 44.6 mm, when the coal seam inclination angle is 37°, the weak percolation zone is 16.7 V 49.4 mm, when the seam dip angle is 68°, the weak percolation area is 26.5 mm, and in the middle part of the face, the weak seepage area is with coal. When the dip angle increases from 13° to 37°, the area of weak seepage zone decreases gradually when the dip angle of coal seam increases from 37° to 68°, and the range of strong seepage zone at the end of return air lane does not change, but the range of weak seepage zone changes to a lesser extent. The overall range does not change much. Through comparison and analysis, it can be found that with the increase of coal seam inclination, the range of strong seepage and weak percolation areas in the air inlet is increased, which is mainly due to the large amount of gas emission in the working face, because the density of gas is smaller than the air, and the gas will move upward as the dip angle of the coal seam increases, and the effect of gas floatation is produced. Due to a certain height difference between the working face and the working face, there is a certain pressure difference between the two. With the increase of the dip angle of the coal seam, the height difference between the working face and the goaf becomes larger, the pressure difference increases, and the depth of the pressure gradient moves to the deep goaf. The scope of the weak seepage zone in the middle of the face increases first with the increase of the inclined angle of the coal seam and then decreases, which is mainly due to the fact that with the advance of the working face, the roof caving goaf in the rear goaf of the face is gradually compacted and the porosity decreases. And with the change of coal seam inclination, the rest angle of coal gangue in goaf also has great change, so the scope of weak seepage zone in the middle of the face increases first and then decreases with the increase of coal seam inclination.

4 Conclusion In this experiment, the effect of coal seam inclination angle on gas flow field distribution in goaf of inclined coal seam is preliminarily studied by using the self-made test bench of gas flow field in goaf of coal seam. The conclusion can be drawn as follows. (1) With the increase of coal seam inclination, the range of strong seepage zone and weak seepage zone at the end of air entry roadway increases and moves to the deep direction of goaf. (2) With the increasing dip of coal seam, the range of strong seepage area in the middle part of working face increases, and the range of weak seepage area decreases with increasing dip of coal seam; (3) Through the above conclusions, we can further determine the specific range of spontaneous combustion in the upper and lower roadways in goaf, and compare the gradient range of gas concentration in goaf synthetically, we can know that the most easily satisfying the three conditions of gas explosion lies in the seepage zone. The specific location is within the weak seepage area of the goaf in the upper

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and lower roadways, which further satisfies the mechanism of gas accumulation in the upper corner of the working face, and provides guidance significance for the gas explosion in the goaf of the coal seam.

References 1. Yang, Y., Li, Q., Li, L., Mi, W., Sun, Y.: U-shaped ventilation, simulation and experimental study on gas migration and distribution in goaf. Coal Technol. 35(06), 132–134 (2016) 2. Yang, Z., Yang, Y., Zhao, H.: Experiment on the effect of ventilation on the distribution of gas flow field in goaf. Coal Chem. Ind. 38(09), 45–47 (2015) 3. Yang, Y., Zhao, H.: Division of gas explosion area in goaf of coal mine. Coal Mine Saf. 45(05), 167–169 (2014) 4. Hua, M.: Study on evolution and gas migration of mining fissure field and its engineering application. China University of Mining and Technology, Beijing (2013) 5. Dong, G.: Study on gas distribution in goaf of mining face by adjacent strata. Anhui University of Technology (2013) 6. Chang, Y.: Study on the Law of Gas Combustion (explosion) induced by Coal spontaneous Combustion in Goaf and its Prevention and Control. China University of Mining and Technology (2013) 7. Yu, T.: Study on prevention mechanism and technology of gas and coal spontaneous combustion composite disaster in goaf. University of Science and Technology of China (2014) 8. Wu, Y., Wu, J., Wang, J., Zhou, C.: Gas distribution in goaf in fully mechanized caving with double U type ventilation system. Acta Sinica Sinica (21) 36(10), 1704–1708 (2011) 9. Che, Q.: Study on three dimensional Multi-field Coupling of Gas in Goaf. China University of Mining and Technology, Beijing (2010) 10. Jin, L., Yao, W., Zhang, J.: CFD simulation of gas seepage in goaf. Acta Sinica Sinica 35(09), 1476–1480 (2010)

High-Precision Dynamic Deformation Monitoring Model of GPS/Pseudolites Integrated System Xi Zhang, Zhao-jiang Zhang(&), and Yu-lin Li College of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, China [email protected]

Abstract. Aiming at the characteristics of small deformation range and strong correlation of multipath errors in space, a high-precision dynamic deformation monitoring model of GPS/pseudolites integrated system is proposed by introducing time forgetting factor. The model can effectively eliminate the influence of multipath of Pseudolite on combined positioning and improve the positioning accuracy and reliability in theory. The findings of the research have led the author to the conclusion that high-precision integration of GPS/pseudolites dynamic deformation monitoring model can weaken the influence of pseudolites multipath on integrated positioning more efficiently than conventional model, which increases the positioning precision largely. Keywords: Integration of GPS/pseudolites positioning technology Multipath error
Deformation monitoring




1 Introduction Due to the unique structure of the GPS constellation and the occlusion of the satellite signal by the objects around the receiver, the positioning accuracy in the elevation direction is not as good as the plane direction, and the accuracy in the N direction is lower than the E direction in the mid-latitude region [1]. In the severe area of the surface signal occlusion (mountain and Urban canyons, etc.), dilution of precision is large or available satellites number is less than four [2, 3] that high-precision positioning cannot be performed. In order to improve the geometry of GPS satellites, Pseudolite technology provides an efficient way to solve this problem: Pseudolite-laid on the ground by positioning signal transmitter, which is similar to the GPS signal. The GPS/Pseudolite integrated positioning technology can increase the number of visible satellites, effectively improve the geometric structure and the positioning accuracy, especially in the elevation direction [2, 4, 5]. Integration of GPS/pseudolites positioning is affected by errors such as nonlinear error, satellite position deviation, multipath error, atmospheric delay error and satellite clock error. Especially for pseudolite multipath errors, the literature [6–8] has more effective modeling of pseudolite multipath errors, but the model is only applicable to static baseline solutions. Three suggestions for solving pseudolite multipath errors [9] © Springer Nature Singapore Pte Ltd. 2019 Y. Xie et al. (Eds.): GSES 2018, CCIS 980, pp. 308–322, 2019. https://doi.org/10.1007/978-981-13-7025-0_32

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are given in: (1) GPS antenna with choke is used in both the base station and the rover; (2) more reasonable weights are given to pseudolite observations; (3) When the visible GPS satellites is larger than four, the pseudolite multipath is taken as a parameter, and the least-squares estimation is performed together with the coordinate values. Where (1) and (2) can weaken the influence of pseudolite multipath effect to a certain extent, but mainly as hardware support or assistance, (3) for more than four GPS visible satellites, theoretically obtain reliable coordinate values and multipath error value, but it cannot be applied to less than four GPS visible satellites, and the multipath error is estimated as a parameter, the redundant observation value is reduced, and the reliability of the baseline solution is reduced. In view of the limited displacement of the deformation monitoring body, the multipath effect has a strong spatial correlation, especially the long-period term [10]. A high-precision dynamic for deformation monitoring model of GPS/pseudolite combination is proposed by using time forgetting factor to estimate and remove the influence of pseudolite multipath error on positioning effectively. Then redundant observations are increased to improve the reliability and accuracy of positioning.

2 Pseudolite Enhanced GPS System For specific engineering requirements, it is necessary to improve the positioning accuracy in a certain direction; pseudolite can increase the number of visible satellites, effectively improve the geometric structure and positioning accuracy in the specified direction. The single point position dilution of precision (DOP) can be used as a quantitative indicator of the geometric strength of the satellite constellation positioning. Different representations of the precision factor can be calculated by the direction cosine matrix, and the positioning error corresponding to GPS can be approximated to some extent as follows [8]. mp = DOP
mr :

ð1Þ

In this formula, mp is the position error, mr is the measurement error. In T-time, the pseudo-range observations for the i-th pseudolite and j-th GPS satellite are as follows [11]:
PL  PLi i i i RPL ðtÞ  dtr ðtÞ þ dqPL r ðtÞ ¼qr ðtÞ þ c
dt t ðtÞ PLi PLi i þ dqPL pos ðtÞ þ dqmPr ðtÞ þ er ðtÞ ði ¼ 1; 2; . . .; mÞ;


 Rrj ðtÞ ¼ qrj ðtÞ þ c dt j ðtÞ  dtr ðtÞ þ dqtj ðtÞ þ dqij ðtÞ þ erj ðtÞ ðj ¼ 1; 2; . . .; nÞ;

ð2Þ ð3Þ

j i Where, RPL r ðtÞ is pseudo-range observations of pseudolites, Rr ðtÞ is pseudo-range i observations of GPS, qPL r ðtÞ is the geometric distance between pseudolites and receiver j r, qr ðtÞ is the geometric distance between GPS satellites and receiver r, c is the speed of light in vacuum, dtPLi ðtÞ is pseudolite clock difference, dt j ðtÞ is GPS satellite clock

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i difference, dtr ðtÞ is the receiver clock error, dqPL t ðtÞ is pseudolite tropospheric delay j errors, dqt ðtÞ is GPS tropospheric delay errors, dqij ðtÞ is GPS ionospheric delay, PLi PLi i dqPL pos ðtÞ is pseudolite position deviation, dqmPr ðtÞ is pseudolite multipath error, er ðtÞ j is pseudolite observation noise, er ðtÞ is GPS observation noise, m and n are the number of visible satellites.

i qPL r ðtÞ ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðXrPLi ðtÞ  XÞ2 þ ðYrPLi ðtÞ  YÞ2 þ ðZrPLi ðtÞ  ZÞ2 ði ¼ 1; 2; . . .; mÞ: ð4Þ

qrj ðtÞ ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðXrj ðtÞ  XÞ2 þ ðYrj ðtÞ  YÞ2 þ ðZrj ðtÞ  ZÞ2 ðj ¼ 1; 2; . . .; nÞ:

ð5Þ

Where ðXrPLi ðtÞ; YrPLi ðtÞ; ZrPLi ðtÞÞ is the coordinate of the i-th pseudolite at time t, ðXrj ðtÞ; Yrj ðtÞ; Zrj ðtÞÞ is the coordinate of satellite j at time t, and (X, Y, Z) is the station coordinates. Then: at time t, the pseudo-range expression of the pseudolite enhanced GPS system is shown as follows: 8 > > > > > > > > > > > > > > > > >
t ðtÞ þ dqpos ðtÞ þ dqmPr ðtÞ þ er ðtÞ r ðtÞ ¼ qr ðtÞ þ c
ðdt > > > > PL2 PL2 PL2 PLi PL2 2 2 > ðtÞ  dtr ðtÞÞ þ dqPL RPL > t ðtÞ þ dqpos ðtÞ þ dqmPr ðtÞ þ er ðtÞ r ðtÞ ¼ qr ðtÞ þ c
ðdt > > > > .. > > > . > > > : PLm PLm PLm PLm m m i ðtÞ þ c
ð dt ðtÞ  dt ðtÞ Þ þ dqPL ðtÞ þ dqPL Rr ðtÞ ¼ qPL r t r pos ðtÞ þ dqmPr ðtÞ þ er ðtÞ

ðj ¼ 1; 2; . . .; k; i ¼ 1; 2; . . .; m; k þ m ¼ nÞ:

ð6Þ The same type of symbolic meaning (2) and (3). When the number of visible satellites n  4, regardless of the influence of nonlinear error, linearize the Eq. (6) by the predicted satellite position, pseudolite coordinates and initial station coordinates.

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R1r ðtÞ  q10 ðtÞ  Er1 ðtÞ

3

2

a1X ðtÞ

a1Y ðtÞ

a1Z ðtÞ

7 6 2 7 6 aX ðtÞ a2Y ðtÞ a2Z ðtÞ 7 6 7 6 . .. .. 7 6 . . . 7 6 . 7 6 7 6 ak ðtÞ k k aY ðtÞ aZ ðtÞ 7 6 X 7 ¼6 PL1 1 1 7 6 aX ðtÞ aPL aPL Y ðtÞ Z ðtÞ 7 6 7 6 PL2 2 2 7 6 aX ðtÞ aPL aPL Y ðtÞ Z ðtÞ 7 6 7 6 . .. .. 7 6 .. . . 5 4 PLm PLm PLm PLm PLm PLm aX ðtÞ aY ðtÞ aZ ðtÞ R ðtÞ  q0 ðtÞ  Er ðtÞ 3 2 dx 7 6 6 dy 7 7 6 6 dz 7 ðk þ m ¼ nÞ; 5 4 c
dtr ðtÞ

6 6 R2r ðtÞ  q20 ðtÞ  Er2 ðtÞ 6 6 .. 6 . 6 6 k k 6 Rr ðtÞ  q0 ðtÞ  Erk ðtÞ 6 6 PL PL1 1 6 R 1 ðtÞ  qPL 0 ðtÞ  Er ðtÞ 6 6 PL2 PL2 2 6 R ðtÞ  qPL 0 ðtÞ  Er ðtÞ 6 6 .. 6 . 4

1

3

7 1 7 7 .. 7 7 . 7 7 1 7 7 7
1 7 7 7 1 7 7 .. 7 . 7 5 1

Erj ðtÞ ¼ c
dt j ðtÞ þ dqtj ðtÞ þ dqij ðtÞ ðj ¼ 1; 2; . . .; kÞ: PLi PLi i EmPLi ðtÞ ¼c
dtPLi ðtÞ þ dqPL t ðtÞ þ dqpos ðtÞ þ dqmPr ðtÞ

ði ¼ 1; 2; . . .; mÞ; aXj ðtÞ; aYj ðtÞ; aZj ðtÞ ðj ¼ 1; 2; . . .; kÞ

311

ð7Þ

ð8Þ ð9Þ

Where q0j ðtÞ (j = 1, 2, … , k) is the approximate geometric distances between the i receiver and the GPS satellite. qPL 0 ðtÞ (i = 1, 2, … , m) is the approximate geometric distances between the receiver and the pseudolite. aXj ðtÞ; aYj ðtÞ; aZj ðtÞ (j = 1, 2, … , k) are the unit vector components of the distance between the j-th PLi PLi i satellites and the station in the X, Y, Z direction, respectively. aPL X ðtÞ; aY ðtÞ; aZ ðtÞ (i = 1, 2, … , m) are the unit vector components of the distance between the i-th pseudolites and the station in the X, Y, and Z directions, respectively. ½ dx dy dz T is the receiver position correction value. Equation (7) can be abbreviated as follows: m V m (t)¼Am 1 ðtÞ
dX  L ðtÞ:

dX¼½ dx

dy

dz

c
dtr ðtÞ T :

ð10Þ ð11Þ

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2

a1X ðtÞ

6 2 6 aX ðtÞ 6 6 . 6 . 6 . 6 6 ak ðtÞ 6 m A1 ðtÞ ¼ 6 PLX 1 6 aX ðtÞ 6 6 PL2 6 aX ðtÞ 6 6 . 6 .. 4 m aPL X ðtÞ

a1Y ðtÞ

a1Z ðtÞ

a2Y ðtÞ .. .

a2Z ðtÞ .. .

1

3

2

R1 ðtÞ  q10 ðtÞ  E 1 ðtÞ

3

7 7 6 6 R2 ðtÞ  q20 ðtÞ  E 2 ðtÞ 7 1 7 7 7 6 7 6 .. .. 7 7 7 6 . . 7 7 6 7 7 6 k k k 7 7 6 k k R ðtÞ  q0 ðtÞ  E ðtÞ aY ðtÞ aZ ðtÞ 1 7 m 7 6 ; L ðtÞ¼ 7: 7 6 PL1 PL1 PL1 PL1 PL1 7 7 6 aY ðtÞ aZ ðtÞ 1 7 6 R ðtÞ  q0 ðtÞ  E ðtÞ 7 7 7 6 PL2 2 2 2 ðtÞ 7 6 RPL2 ðtÞ  qPL aPL aPL 1 7 0 ðtÞ  E Y ðtÞ Z ðtÞ 7 7 6 7 6 .. 7 .. .. .. 7 7 6 . 5 . . . 5 4 PLm PLm PLm PLm PLm aY ðtÞ aZ ðtÞ 1 R ðtÞ  q0 ðtÞ  E ðtÞ ð12Þ

In-type, superscript m represents pseudolite enhanced GPS positioning technology, At t time, the covariance matrix of unknown parameters is shown as follows: 2 Qm 1 ðtÞ

¼

T ðAm 1 tÞ




1 Am 1 ðtÞÞ

qm1 11

6 m1 6 q21 ¼6 6 qm1 4 31 qm1 41

qm1 12

qm1 13

qm1 22

qm1 23

qm1 32

qm1 33

qm1 42

qm1 43

qm1 14

3

7 qm1 24 7 7: 7 qm1 34 5 qm1 44

ð13Þ

Using geographic coordinate system according to variance and covariance propagation law is expressed as follows: 2

qm1 11

0 6 m1 Qm 1 ðtÞ ¼ R
4 q21

qm1 31

qm1 12

qm1 13

3

2

0

qm1 11

qm1 22

7 T 6 m10 qm1 23 5
R ¼ 4 q21

qm1 32

qm1 33

0

qm1 31

qm1 12 qm1 22 qm1 32

0

qm1 13

0

3

0

0 7 qm1 23 5:

0

0

qm1 33

ð14Þ

Then at time t, the dilution of precision of the pseudolite enhanced GPS system can be expressed as follows: Absolute positioning geometric dilution of precision: GDOP ðtÞ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi m1 m1 m1 m1 q11 þ q22 þ q33 þ q44 . pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi m1 m1 Spatial position dilution of precision:PDOP ðtÞ ¼ qm1 11 þ q22 þ q33 , other directional dilution of precision are similar in expression.

3 Pseudolite Technology and Its Main Error Sources Pseudolite positioning technology is an effective enhancement system for GPS, and ground copying for GPS. The related theory is the same or similar to the GPS positioning theory.

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At time t, the carrier phase observation equation of receiver r for i pseudolite is [4, 7]: PLi PLi PLi i k
/PL ðtÞ r ðtÞ ¼qr ðtÞ þ k
Nr þ c
ðdt PLi PLi PLi i dtr ðtÞÞ þ dqPL t ðtÞ þ dqpos ðtÞ þ dqmP/ ðtÞ þ e/ ðtÞ: ði ¼ 1; 2; . . .; mÞ

ð15Þ

PLi i Where k is the wavelength, /PL r ðtÞ is the carrier phase observation, qr ðtÞ is geometric distance between receiver r and pseudolite i, c is the speed of light, dtPLi ðtÞ is pseudolite clock, dtr ðtÞ is receiver clock, NrPLi is integer ambiguity of carrier phase, PLi PLi i dqPL t ðtÞ is tropospheric error, dqpos ðtÞ is pseudolite position error, dqmP/ ðtÞ is multipath i error of carrier phase, ePL / ðtÞ is measurement noise of carrier phase. In Eq. (15): since the observation angle of the pseudolite is usually low (