Cybernetics Perspectives in Systems: Proceedings of 11th Computer Science On-line Conference 2022, Vol. 3 (Lecture Notes in Networks and Systems, 503) 3031090721, 9783031090721

Table of contents :
Preface
Organization
Program Committee
Program Committee Chairs
Program Committee Members
Technical Program Committee Members
Organizing Committee Chair
Conference Organizer (Production)
Conference Web site, Call for Papers
Contents
Method of Statistical Processing and Adaptive Estimation of Multispectral Aerospace Monitoring Data
Abstract
1 Introduction
1.1 Problem Setting
2 Model of Observation and Statistical Processing of Images
3 Algorithm of Adaptive Statistical Processing of Images
3.1 Step 1
3.2 Step 2
4 Conclusion
References
Providing Security for Critical Assets. Challenges and Prospects
Abstract
1 Introduction
2 Concepts and Terminology
3 Threat Model and Security Profile
4 Risk Management
5 Cost of CASS
6 Goals of Security Systems
6.1 The First Goal
6.2 The Second Objective
6.3 The Third Objective
7 Security Criteria
8 Conclusion
References
Electromagnetic Communication in Deep Space Between Mutually Moving Apparatus
Abstract
1 Introduction
2 One-Dimensional Electromagnetic Field with Radiator and Receivers
3 The Radiator Moves Relative to a Stationary Receiver
4 The Receiver Moves Relative to the Stationary Radiator
5 The Radiator Surpasses the Receiver
6 The Receiver Surpasses the Radiator
7 Discussion of Results
8 Conclusion
References
Modeling the Spread of a Message in a Population with Differential Receptivity
Abstract
1 Introduction
2 Model
3 Numerical Experiments
3.1 Experiment 1
3.2 Experiment 2
References
Comparative Analysis of Big Data Acquisition Technology from Landsat 8 and Sentinel-2 Satellites
Abstract
1 Introduction
2 Materials and Methods
2.1 Technology for Obtaining Remote Sensing Data from the Landsat- 8 Satellite
2.2 Digital Spectrum of Reflected Signals of RGB Channels. Sentinel-2A Satellites.
3 Result and Discussion
4 Conclusion
References
Implementation of the Automated Algorithm for Diagnosis of PCOS Based on Rotterdam 2003 Criteria
Abstract
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusions
References
Use of Remote Sensing Data in Intelligent Agrotechnology Control Systems
Abstract
1 Introduction
2 Monitoring the Condition of Agricultural Land and Organizational Management of Agricultural Technologies
2.1 Introductory Remarks
2.2 Making Decisions on Planting Dates
2.3 Making Decisions on Feed Harvest Dates
3 Management of Agricultural Technologies in Real Time
3.1 General Notes
3.2 Spatial Correction of Optimal Control Programs
4 Conclusion
References
Utilization of RTOS Solutions in IoT Modules Based on RISC Microcontrollers
Abstract
1 Introduction
2 RTOS Implementation in Microcontrollers
3 RTOS Systems
3.1 FreeRTOS
3.2 Zephyr RTOS
4 IoT Module as an Application with RTOS
5 Results and Discussion
5.1 Definition of Tests
5.2 Measurements Results
6 Conclusions
Acknowledgment
References
A Monitoring System Design for Smart Agriculture
Abstract
1 Introduction
2 State of the Art
3 Design of the Proposed Monitoring System for Smart Agriculture
4 Implementation of the Proposed Monitoring System for Smart Agriculture
5 Conclusion
References
Contribution Title Assessment of the Possibilities of Modern Microprocessor Technology for Integration with Modified Algorithms of Artificial Immune Systems in Complex Objects Control
Abstract
1 Introduction
1.1 Research Problem Statement
2 Materials and Research Methods
3 Assessment of the Capabilities of Modern Microprocessor Technology for Integration with Modified AIS Algorithms
4 Developing a Graphical Model Using the Ishikawa Method for Transforming AIS Models
4.1 Transformation of AIS Models to Solve Control Problems.
5 Conclusion
Acknowledgments
References
Student or Human Operator? Objective Results and Subjective Perceptions of the Process and Results of Offline and Online Learning
Abstract
1 Introduction
2 Research Methods
3 Results
4 Discussion
5 Conclusion
References
Implementation of Data Mining Using k-Nearest Neighbor Algorithm for Covid-19 Vaccine Sentiment Analysis on Twitter
Abstract
1 Introduction
2 Recent Studies
3 Result and Analysis
3.1 Research Framework
3.2 Data Collection
3.3 Labeling
3.4 Preprocessing
3.4.1 Cleaning
3.4.2 Case Folding
3.4.3 Tokenizing
3.4.4 Normalization
3.4.5 Stemming
3.4.6 Stopword
3.5 K-Nearest Neighbor Classification
3.5.1 Word Weighting (TF-IDF)
3.5.2 Distribution of Training Data and Testing Data
3.5.3 Sentiment Analysis with K-Nearest Neighbor
3.6 Evaluation
4 Conclusion
References
Towards Intelligent Vision Surveillance for Police Information Systems
Abstract
1 Introduction
1.1 Research Question and Contributions
2 Theoretical Background
2.1 Related Works
2.2 Selected Theoretical Techniques
3 The Proposed Framework for Intelligent Vision Surveillance
3.1 Data Acquisition Phase
3.2 Image Pre-processing Phase
3.3 Detection Phase
3.4 Evaluation Metrics
4 Experimental Evaluations and Results
4.1 Data Description and Experimental Setting
4.2 Experiment 1: Suspicious Behaviors Such as an Individual Loitering in One Place
4.3 Experiment 2: Benchmarking Popular Publicly Available Water Surface Video Dataset for Detection of Standing Too Long in a Place
5 Conclusion
Acknowledgment
References
Artificial Intelligence Systems Based on Artificial Neural Networks in Ecology
Abstract
1 Introduction
2 Non-algorithmizable Artificial Intelligence Systems (AIS)
3 Why ANNs Cannot be Brain Neural Networks Models in the Literal Sense
4 The New ANN-Based AIS New Properties
5 Discussion
6 Conclusions
References
Methodological Aspects of the Valuation of Digital Financial Assets
Abstract
1 Introduction
2 Materials and Methods
2.1 Research Methods
2.2 Experimental Research Base
2.3 Research Stages
3 Results
3.1 Object and Method of Valuation of DFA
3.2 Basic Principles of Valuation of DFA
4 Discussions
5 Conclusion
References
Capabilities of Artificial Neuron Networks for System Synthesis in Medicine
Abstract
1 Introduction
2 Object and Methods
3 Results
4 Discussion
5 Conclusions
References
Improving the Linkage of Internet Exchange Points Through Connected ISPs ASes
1 Introduction
2 Related Works
3 Data Sources and Methodologies
3.1 Current Status of Selected IXPs
3.2 Tracing with TraIXroute Tool
4 Results
4.1 Link Between IXPs Across Their ISPs
4.2 Peering
5 Conclusion
References
Ranking Requirements Using MoSCoW Methodology in Practice
Abstract
1 Introduction
2 Problem Description
2.1 Determination of the Reasons Preventing the Bank from Entering the Target Sales Figures
2.2 Identification of Problems in Setting up Communications with Clients of the Bank
2.3 Identification of Stakeholders for the Development of a Module for Supporting Processes of Interaction with Customers
2.4 Functions of the Bank for Communication with Customers
3 Prioritizing Requirements in Practice
3.1 Ranking Requirements by the MoSCoW Methodology
4 Conclusion
References
Novel Experimental Prototype for Determining Malicious Degree in Vulnerable Environment of Internet-of-Things
Abstract
1 Introduction
2 Related Work
3 Problem Description
4 Proposed Methodology
5 System Design
6 Results Discussion
7 Conclusion
References
Predicting Student Dropout in Massive Open Online Courses Using Deep Learning Models - A Systematic Review
Abstract
1 Introduction
2 Methodology
2.1 Source Identification
2.2 Articles Selection and Screening
2.3 Source Evaluation and Eligibility Criteria
2.4 Data Analyses and Synthesizing of Literature
3 Discussion of Results
3.1 Predicting Student Dropout Using Deep Learning Models in MOOCs
3.2 Attributes Relevant for Predicting Student Dropout
3.3 Research Challenges and Opportunities for Student Dropout Prediction in MOOCs During the Pandemic
4 Conclusion and Future Work
References
Methods of Evaluation of Substrate Radioactive Contamination Using Unmanned Radiation Monitoring Complex
Abstract
1 Introduction
2 The Main Part
3 Mathematic Model of Evaluation of Meteorological Parameters, Defining the Atmosphere Stable State Under Surface Layer Model
4 Mathematical Model of Radioactive Impurities Transfer in Environment
5 Radiation Situation Measurement System Using URMC
6 RMC Readings Calibration
7 Conclusion
References
Co-evolutionary Self-adjusting Optimization Algorithm Based on Patterns of Individual and Collective Behavior of Agents
Abstract
1 Introduction
1.1 Background
2 Methods
2.1 Proposed Methodology
2.2 Theoretical Basis
2.3 Co-evolutionary Self-adjusting Optimization Algorithm: Discussion
3 Experimental Results and Discussion
4 Conclusion
Acknowledgements
References
Use of Machine Learning to Investigate Factors Affecting Waste Generation and Processing Processes in Russia
Abstract
1 Introduction
2 Related Works
3 Methods
4 Problem Statement
5 Results
6 Discussion
7 Conclusion
References
Improved Genetic Local Search Heuristic for Solving Non-permutation Flow Shop Scheduling Problem
1 Introduction
1.1 Literature Review
2 Improvement Genetic Local Search Heuristic
2.1 Genetic Local Search
3 Computational Results
3.1 Test Problems
3.2 Evaluation of the Improved Genetic Local Search Heuristic
4 Conclusion
References
Software Package for Information Leakage Threats Relevance Assessment
Abstract
1 Introduction
2 Review and Analysis of the Literature
3 Methods and Models
4 Experimental Verification of a Software Package for Assessing the Relevance of Information Leakage Threats
5 Experiment
6 Discussion of the Results of the Experiment
7 Conclusion
References
Integration of a Digital Twin into Production Line Control
Abstract
1 Introduction
2 Methodology
2.1 Data Acquisition
2.2 Modelling
2.3 Validation
3 Results and Discussion
3.1 Experiment 1
3.2 Experiment 2
3.3 Experiment 3
4 Conclusion
Acknowledgement
References
An Ensemble Mode Decomposition Combined with SVR-RF Model for Prediction of Groundwater Level: The Case of Eastern Rwandan Aquifers
Abstract
1 Introduction
2 Related Work
3 Materials, Tools and Methodology
3.1 Case Study and Available Data
3.2 Methods
4 Results and Discussion
5 Conclusions
References
Computer Modeling of Manufacturers Behavior on the New Product Market
1 Introduction
2 Modeling the Demand for a New Product and Defining a Pricing Strategy
3 Modeling the Behavior of Competitors in the Market of a New Product
4 Results of Computer Realization
5 Conclusion
References
Comparison of K-means-Based Network Partition Algorithms with Different Initial Centers Seeding
Abstract
1 Introduction
2 Previous Algorithms
3 The Proposed Algorithms for Seeding K-means for Networks
3.1 Basic K-means Algorithm for Networks and Its Optimized Extension
3.2 K-means++ for Networks and Random Seeding
3.3 Community Detection Based Seeding
4 Testing and Results
4.1 Tested Networks
5 Conclusions
Acknowledgments
References
Digital Educational Resources in Teaching
Abstract
1 Introduction
2 Materials and Methods
3 Results and Discussions
4 Conclusion
Acknowledgements
References
Mathematical Modeling and Visualization of Large-Scale Flooding in the Flood Plain of Naryn River Using OpenFOAM
Abstract
1 Introduction
2 Mathematical Model
2.1 Determination of the Interphase Boundary
2.2 Generation of the Computational Grid
2.3 Mesh Generation Using SnappyHexMesh Utility
3 Initial Conditions
4 Boundary Conditions
4.1 Discretization Methods and Solution of Linear Equations System
5 Modeling the Flow During a Dam-Break Flooding in a Real Terrain
6 Conclusion
References
Evaluation of Acoustic Gunshot Localization Methods on Helicopters with Environmental Sound Simulations
Abstract
1 Introduction
2 Sound Characteristics of the Problem Field
2.1 Mission Requirements and Scenarios
2.2 Implementation Challenges
2.3 Helicopter Sound Characteristics
2.4 Gunshot Sound Characteristics
3 Simulation of Factors that Affect Gunshot Localization
3.1 The Simulation Environment
3.2 Outdoor Environmental Parameters: Temperature, Humidity and Pressure, T-H-P
4 Results
4.1 Number of Microphones, M
4.2 Source to Microphone Array Distance, D
4.3 Microphone Spacing, S
4.4 Angle of Incidence, θ
4.5 STFT Size
5 Conclusions
References
Coherent Demodulation of APSK and QAM Signals
Abstract
1 Introduction
2 APSK Signal
3 Demodulation Algorithm
4 Digital Demodulation Implementation
5 Demodulator Responses
6 Making Decision on the Received Symbol
7 Noise Immunity of the Digital Demodulation Algorithm
8 Conclusion
Acknowledgements
References
About the Set of Independent Paths in the Graph
Abstract
1 Introduction
2 Some Approaches to the Algorithmizing of the Problem
3 The Generation of Input Data Algorithms for the Testing
4 The Formal Description of Algorithms
4.1 Algorithm 1a
4.2 Algorithm 1B
4.3 Algorithm 2
4.4 Algorithm 3
5 Some Results of Computational Experiments and Conclusion
References
Binary Matrices with a Recurrent Element Derivation Rule and Their Properties and Applications
Abstract
1 Introduction
2 Methods
3 Properties of the Binary Matrices of a Pascal’s Triangle Type
4 Binary Matrices with Forbidden Positions
5 Discussions
6 Conclusion
Acknowledgments
References
A Deep Learning Approach to Diabetic Retinopathy Classification
1 Introduction
2 Related Works
3 Dataset
4 Methodology
4.1 Optimizer and Loss Function
5 Results and Discussion
6 Conclusion
References
Dependence of the Informativity of the Formed Patterns on the Quality of the Initial Data Sample
Abstract
1 Introduction
2 Materials and Methods
3 Results
4 Conclusions
Acknowledgements
References
Modeling of Critical Combinations of Events in Industrial Monitoring by Unmanned Aerial Vehicles
Abstract
1 Introduction
2 Statement of the Problem
3 Approach to Solving the Problem
4 Scenario Analysis of Accidents and Their Prevention
5 Conclusion
References
A Network Science Approach to Quantify the Extent of Co-occurrence of an Offense with Other Offenses in a Crime Event
Abstract
1 Introduction
2 Related Work
3 Methods
3.1 Data Set
3.2 Data Preprocessing
3.3 Clustering Algorithms
3.4 Eigenvector Centrality
4 Results
4.1 Co-occurrence Matrix
4.2 Co-occurrence Index
5 Conclusions and Future Work
Acknowledgement
References
Efficiency of Public Procurement in Russia
Abstract
1 Introduction
2 Methods
2.1 About the Contract System
2.2 Principles of Public Procurement
2.3 The Essence of the Concept of Public Procurement Efficiency
3 Results
4 Discussions
5 Conclusion
References
The Development of Electroencephalogram (EEG) in Neuromarketing Using Hedonic and Utilitarian Motivation
Abstract
1 Introduction
2 Literature Review
3 Research Methodology
3.1 Phase 1: Comprehensive Literature Review
3.2 Phase 2: Preliminary Testing
3.3 Phase 3: Data Collection
3.4 Phase 4: Model Development
3.5 Phase 5: Testing and Results
4 Result
5 Summary
Acknowledgment
References
Understanding Data Toward Going to Data Science
1 Introduction
2 A Review for the Problem
3 Methodology: Algorithm
4 A Discussion
4.1 For Establishing a Science
4.2 To Define the Frameworks
5 Conclusion
References
Detection of Locusta migratoria and Nomadacris septemfasciata (Orthoptera: Acrididae) Using MobileNet V2 Quantized Convolution Neural Network, Kazungula, Zambia
Abstract
1 Introduction
1.1 MobileNet V2 Quantised
2 Related Works
3 Methodology
3.1 Dataset
3.2 Data Pre-processing
3.3 Model Training
4 Results
5 Conclusion
Acknowledgments
References
Lung Cancer Segmentation Using Enriched Fuzzy Back-Propagation Neural Network
Abstract
1 Introduction
2 Abnormality and Normality Detection in Lungs Using SIFT and SVM Classifier
2.1 Feature Extraction Using SIFT
2.2 Abnormality Detection Using SVM
3 Lung Cancer Segmentation Using EFBPN
3.1 Adaptive Local Information Induced Fuzzy Clustering Method (ALIFCM)
3.2 Fuzzy Based Training Vector Quantization
3.3 Multi-directional Flipped Wavelet Transform (MDFWT) Based Training Vector Preparation
3.4 BPN Based Cancer Object Segmentation
4 Result and Analysis
4.1 Mean Square Error (MSE) Analysis for Lung Cancer Segmentation on DB-tCIA Database
4.2 Mean Square Error (MSE) Analysis for Lung Cancer Segmentation on DB-lOLA-11 Database
4.3 Peak Signal to Noise Ratio (PSNR) Analysis for Lung Cancer Segmentation on DB-tCIA Database
4.4 Peak Signal to Noise Ratio (PSNR) Analysis for Lung Cancer Segmentation on DB-lOLA-11 Database
5 Conclusion and Future Development
References
Adaptive Filters Detection of State Change in Pseudonomas Putida Cultivation
1 Introduction
2 Methods
2.1 Adaptive Filter
2.2 Adaptive Novelty Detection Methods
3 Results
4 Conclusion
References
A Review of Deep Learning Models for Detecting Cyberbullying on Social Media Networks
Abstract
1 Introduction
1.1 Contribution of the Study
2 Materials and Methods
2.1 Search Strategy
2.2 Study Selection
2.3 Eligibility Criteria and Quality Assessment
3 Results Analysis
3.1 Deep Learning-Based Cyberbullying Detection Models
3.2 Psychological Effects and Types of Cyberbullying in Social Networks
3.3 Datasets for Detecting Cyberbullying on Social Media Platforms
3.4 Identified Research Gaps in Detecting Cyberbullying in Social Networks Using Deep Learning Techniques
4 Conclusion and Future Work
References
Quantitative Analysis of Climatic Variability in Relation to Surface Loss with Landsat Data in Peruvian Snow-Capped Mountains 2010–2020
Abstract
1 Introduction
2 Literature Review
2.1 Background
2.1.1 Climate Variability
2.1.2 Glacier Retreat
2.2 Situation of Mountain Glaciers in Peru
2.2.1 Andean Region Climate
2.3 Regression Analysis
2.3.1 Multivariate Regression
2.3.2 Validation of Regressions Models
3 Materials and Methods
3.1 Study Area
3.2 Data Extraction Methodology
3.3 Data Analysis and Processing
3.4 Experimental Design
4 Results
4.1 Descriptive Statistics by Snowfall for the Variable Temperature
4.2 Descriptive Statistics by Snowfall for the Variable Precipitation
4.3 Descriptive Statistics by Snowfall for the Variable Area
4.4 Graphical Statistical Interpretation by Snowfalls
4.5 Multivariate Statistical Prediction Models for Snowfall
4.5.1 Regression Model of Huaytapallana Glacier
4.5.2 Regression Model of the Veronica Glacier
4.5.3 Regression Model of Coropuna Glacier
4.5.4 Regression Model of the Huascarán Glacier
5 Conclusions
References
Assessing the Bank Performance in the Process of Digital Innovation
Abstract
1 Introduction
2 The Banking Sector: The Role in the National Economy and Development Trends in the Context of the Digitalization
3 Trends of Digitalization in the Economy and Banking Sector of Russia
4 Modeling the Performance of Russian Banks in the Context of the Digitalization
5 Conclusion
References
Methodology for Analyzing the Scientific and Technical Complexes State Dynamics
Abstract
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
Acknowledgments
References
Testing EMC Properties of High-Speed PLC Adapters
1 Introduction
2 Testing EMC Properties of PLC Adapters
3 Measurement of Radiated Emissions and Test Setup of PLC Adapters
4 Measured Results and Their Interpretation
5 Immunity Test Against RF Electromagnetic Field
6 Tested Results and Their Interpretation
7 Conclusion
References
Hybrid Algorithms for Managing the Implementation of Convergent Research
Abstract
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
Acknowledgments
References
Analytical and Simulation Modeling in the System of Risk-Oriented Design and Usage Management of Complex Organization and Technical Objects
Abstract
1 Introduction
2 Methodology for the Construction and Use of a Simulation Model for Decision Quality Management in the Creation of COTO, Functioning Under Conditions of Uncertainty of a Probabilistic Nature
3 Conclusion
Acknowledgement
References
Author Index

Citation preview

Lecture Notes in Networks and Systems 503

Radek Silhavy   Editor

Cybernetics Perspectives in Systems Proceedings of 11th Computer Science On-line Conference 2022, Vol. 3

Lecture Notes in Networks and Systems Volume 503

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).

More information about this series at https://link.springer.com/bookseries/15179

Radek Silhavy Editor

Cybernetics Perspectives in Systems Proceedings of 11th Computer Science On-line Conference 2022, Vol. 3

123

Editor Radek Silhavy Faculty of Applied Informatics Tomas Bata University in Zlin Zlin, Czech Republic

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-09072-1 ISBN 978-3-031-09073-8 (eBook) https://doi.org/10.1007/978-3-031-09073-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed 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, expressed 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Cybernetics Perspectives in Systems section is presented in the proceeding. This proceeding is a Vol. 3 of the Computer Science On-line Conference 2022 proceedings. Papers in this part discuss theoretical and practices aspects and cybernetics, and control theory in systems or software. This volume constitutes the refereed proceedings of the Cybernetics Perspectives in Systems section of the 11th Computer Science On-line Conference 2022 (CSOC 2022), held online in April 2022. CSOC 2022 conference intends to provide an international forum to discuss the latest high-quality research results in all areas related to computer science. Computer Science On-line Conference is held online, and modern communication technology, which are broadly used, improves the traditional concept of scientific conferences. It brings equal opportunity to participate for all researchers around the world. I believe that you find the following proceedings exciting and valuable for your research work. April 2022

Radek Silhavy

v

Organization

Program Committee Program Committee Chairs Petr Silhavy Radek Silhavy Zdenka Prokopova Roman Senkerik Roman Prokop Viacheslav Zelentsov

Roman Tsarev

Stefano Cirillo

Tomas Bata University in Zlin, Faculty of Applied Informatics Tomas Bata University in Zlin, Faculty of Applied Informatics Tomas Bata University in Zlin, Faculty of Applied Informatics Tomas Bata University in Zlin, Faculty of Applied Informatics Tomas Bata University in Zlin, Faculty of Applied Informatics Doctor of Engineering Sciences, Chief Researcher of St. Petersburg Institute for Informatics and Automation of Russian Academy of Sciences (SPIIRAS) Department of Information Technology, International Academy of Science and Technologies, Moscow, Russia Department of Computer Science, University of Salerno, Fisciano (SA), Italy

Program Committee Members Boguslaw Cyganek

Krzysztof Okarma

Department of Computer Science, University of Science and Technology, Krakow, Poland Faculty of Electrical Engineering, West Pomeranian University of Technology, Szczecin, Poland

vii

viii

Monika Bakosova

Pavel Vaclavek

Miroslaw Ochodek Olga Brovkina

Elarbi Badidi

Luis Alberto Morales Rosales

Mariana Lobato Baes Abdessattar Chaâri

Gopal Sakarkar V. V. Krishna Maddinala Anand N. Khobragade (Scientist) Abdallah Handoura

Organization

Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology, Bratislava, Slovak Republic Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic Faculty of Computing, Poznan University of Technology, Poznan, Poland Global Change Research Centre Academy of Science of the Czech Republic, Brno, Czech Republic & Mendel University of Brno, Czech Republic College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates Head of the Master Program in Computer Science, Superior Technological Institute of Misantla, Mexico Research-Professor, Superior Technological of Libres, Mexico Laboratory of Sciences and Techniques of Automatic Control & Computer Engineering, University of Sfax, Tunisian Republic Shri. Ramdeobaba College of Engineering and Management, Republic of India GD Rungta College of Engineering & Technology, Republic of India Maharashtra Remote Sensing Applications Centre, Republic of India Computer and Communication Laboratory, Telecom Bretagne, France

Technical Program Committee Members Ivo Bukovsky Maciej Majewski Miroslaw Ochodek Bronislav Chramcov Eric Afful Dazie Michal Bliznak Donald Davendra Radim Farana Martin Kotyrba Erik Kral

Czech Republic Poland Poland Czech Republic Ghana Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic

Organization

David Malanik Michal Pluhacek Zdenka Prokopova Martin Sysel Roman Senkerik Petr Silhavy Radek Silhavy Jiri Vojtesek Eva Volna Janez Brest Ales Zamuda Roman Prokop Boguslaw Cyganek Krzysztof Okarma Monika Bakosova Pavel Vaclavek Olga Brovkina Elarbi Badidi

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Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Slovenia Slovenia Czech Republic Poland Poland Slovak Republic Czech Republic Czech Republic United Arab Emirates

Organizing Committee Chair Radek Silhavy

Tomas Bata University in Zlin, Faculty of Applied Informatics, email: [email protected]

Conference Organizer (Production) Silhavy s.r.o Web site: https://www.openpublish.eu Email: [email protected]

Conference Web site, Call for Papers https://www.openpublish.eu

Contents

Method of Statistical Processing and Adaptive Estimation of Multispectral Aerospace Monitoring Data . . . . . . . . . . . . . . . . . . . . . Valery V. Vasilevsky Providing Security for Critical Assets. Challenges and Prospects . . . . . . Vitaliy Nikolaevich Tsygichko

1 10

Electromagnetic Communication in Deep Space Between Mutually Moving Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gennady Tarabrin

21

Modeling the Spread of a Message in a Population with Differential Receptivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexander Petrov

35

Comparative Analysis of Big Data Acquisition Technology from Landsat 8 and Sentinel-2 Satellites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Svetlana Veretekhina, Krapivka Sergey, Tatiana Pronkina, Vladimir Khalyukin, Medvedeva Alla, Elena Khudyakova, and Marina Stepantsevich Implementation of the Automated Algorithm for Diagnosis of PCOS Based on Rotterdam 2003 Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alina Atalyan, Oleg Buchnev, Lyudmila Lazareva, Iana Nadeliaeva, Irina Danusevich, and Larisa Suturina

41

54

Use of Remote Sensing Data in Intelligent Agrotechnology Control Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ilya Mikhailenko and Valeriy Timoshin

60

Utilization of RTOS Solutions in IoT Modules Based n RISC Microcontrollers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Juraj Dudak, Gabriel Gaspar, Stefan Sedivy, and Roman Budjac

80

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Contents

A Monitoring System Design for Smart Agriculture . . . . . . . . . . . . . . . Zlate Bogoevski, Zdravko Todorov, Marija Gjosheva, Danijela Efnusheva, and Ana Cholakoska

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Contribution Title Assessment of the Possibilities of Modern Microprocessor Technology for Integration with Modified Algorithms of Artificial Immune Systems in Complex Objects Control . . . . . . . . . . 106 Galina Samigulina, Zarina Samigulina, and Dmitry Porubov Student or Human Operator? Objective Results and Subjective Perceptions of the Process and Results of Offline and Online Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Yu. I. Lobanova Implementation of Data Mining Using k-Nearest Neighbor Algorithm for Covid-19 Vaccine Sentiment Analysis on Twitter . . . . . . . . . . . . . . . 128 Irma Ibrahim, Yoel Imanuel, Alex Hasugian, and Wirasatya Aryyaguna Towards Intelligent Vision Surveillance for Police Information Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 Omobayo A. Esan and Isaac O. Osunmakinde Artificial Intelligence Systems Based on Artificial Neural Networks in Ecology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 G. V. Gazya, V. V. Eskov, T. V. Gavrilenko, and N. F. Stratan Methodological Aspects of the Valuation of Digital Financial Assets . . . 159 Vladimir Viktorovich Grigoriev, Alexey Fedorovich Glyzin, and Anna Antonovna Karpenko Capabilities of Artificial Neuron Networks for System Synthesis in Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 V. V. Eskov, E. V. Orlov, T. V. Gavrilenko, and E. A. Manina Improving the Linkage of Internet Exchange Points Through Connected ISPs ASes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 Yamba Dabone, Tounwendyam Frédéric Ouedraogo, and Pengwendé Justin Kouraogo Ranking Requirements Using MoSCoW Methodology in Practice . . . . . 188 Tatiana Kravchenko, Tatiana Bogdanova, and Timofey Shevgunov Novel Experimental Prototype for Determining Malicious Degree in Vulnerable Environment of Internet-of-Things . . . . . . . . . . . . . . . . . 200 G. N. Anil

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Predicting Student Dropout in Massive Open Online Courses Using Deep Learning Models - A Systematic Review . . . . . . . . . . . . . . . . . . . . 212 Elliot Mbunge, John Batani, Racheal Mafumbate, Caroline Gurajena, Stephen Fashoto, Talent Rugube, Boluwaji Akinnuwesi, and Andile Metfula Methods of Evaluation of Substrate Radioactive Contamination Using Unmanned Radiation Monitoring Complex . . . . . . . . . . . . . . . . . . . . . . 232 I. A. Rodionov and A. P. Elokhin Co-evolutionary Self-adjusting Optimization Algorithm Based on Patterns of Individual and Collective Behavior of Agents . . . . . . . . . 254 Sergey Rodzin, Vladimir Kureichik, and Lada Rodzina Use of Machine Learning to Investigate Factors Affecting Waste Generation and Processing Processes in Russia . . . . . . . . . . . . . . . . . . . 267 Yu V. Frolov, T. M. Bosenko, and M. D. Mironova Improved Genetic Local Search Heuristic for Solving Non-permutation Flow Shop Scheduling Problem . . . . . . . . . . . . . . . . . 279 Sabrine Chalghoumi and Talel Ladhari Software Package for Information Leakage Threats Relevance Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 V. Lakhno, V. Kozlovskyi, V. Klobukov, O. Kryvoruchko, V. Chubaievskyi, and D. Tyshchenko Integration of a Digital Twin into Production Line Control . . . . . . . . . . 302 Fedor Burčiar and Pavel Važan An Ensemble Mode Decomposition Combined with SVR-RF Model for Prediction of Groundwater Level: The Case of Eastern Rwandan Aquifers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 Omar H. Kombo, Santhi Kumaran, Emmanuel Ndashimye, and Alastair Bovim Computer Modeling of Manufacturers Behavior on the New Product Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Inna Trofimova and Ulyana Firyago Comparison of K-means-Based Network Partition Algorithms with Different Initial Centers Seeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Jiří Pospíchal and Iveta Dirgová Luptáková Digital Educational Resources in Teaching . . . . . . . . . . . . . . . . . . . . . . . 347 A. D. Nosova, T. T. Gazizov, L. V. Akhmetova, and A. N. Stas

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Mathematical Modeling and Visualization of Large-Scale Flooding in the Flood Plain of Naryn River Using OpenFOAM . . . . . . . . . . . . . . 357 A. Y. Kurbanaliev, B. R. Oichueva, Ch. M. Alieva, K. A. Bokoev, and S. S. Mamaev Evaluation of Acoustic Gunshot Localization Methods on Helicopters with Environmental Sound Simulations . . . . . . . . . . . . . . . . . . . . . . . . . 366 Murat Yılmaz and Banu Günel Kılıç Coherent Demodulation of APSK and QAM Signals . . . . . . . . . . . . . . . 386 Oleg Chernoyarov, Alexey Glushkov, Yury Kutoyants, Vladimir Litvinenko, and Alexandra Salnikova About the Set of Independent Paths in the Graph . . . . . . . . . . . . . . . . . 401 P. P. Starikov and Yu Yu Terentyeva Binary Matrices with a Recurrent Element Derivation Rule and Their Properties and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 O. V. Kuzmin and B. A. Starkov A Deep Learning Approach to Diabetic Retinopathy Classification . . . . 417 Anika Mehjabin Oishi, Md. Tawfiq-Uz-Zaman, Mohammad Billal Hossain Emon, and Sifat Momen Dependence of the Informativity of the Formed Patterns on the Quality of the Initial Data Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426 R. I. Kuzmich, A. A. Stupina, A. A. Zavalov, E. S. Dresvianskii, and M. V. Pokushko Modeling of Critical Combinations of Events in Industrial Monitoring by Unmanned Aerial Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 A. A. Kositzyn, A. S. Bogomolov, A. F. Rezchikov, V. A. Kushnikov, V. A. Ivashchenko, J. V. Lazhauninkas, R. B. Nurgaziev, L. A. Sleptsova, E. V. Berdnova, S. A. Korchagin, and D. V. Serdechnyy A Network Science Approach to Quantify the Extent of Co-occurrence of an Offense with Other Offenses in a Crime Event . . . . . . . . . . . . . . . 442 Yu Wu and Natarajan Meghanathan Efficiency of Public Procurement in Russia . . . . . . . . . . . . . . . . . . . . . . 452 Elena Nikolaevna Sochneva, Anna Andreevna Malakhova, Grigory Borisovich Dobretsov, Aleksey Gennadevich Rusakov, Dmitry Valeryevitch Zyablikov, Svetlana Pavlovna Dudina, Victoria Valerievna Faida, Igor Vasilevich Malimonov, and Dmitriy Ivanovich Kravtsov

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The Development of Electroencephalogram (EEG) in Neuromarketing Using Hedonic and Utilitarian Motivation . . . . . . . . . . . . . . . . . . . . . . . 469 Nurul Natasha Awinda Mohammad Nizam, Mohd Fahmi Mohamad Amran, Nurhafizah Moziyana Mohd Yusop, Siti Rohaidah Ahmad, and Norshahriah Abdul Wahab Understanding Data Toward Going to Data Science . . . . . . . . . . . . . . . 478 Mahyuddin K. M. Nasution Detection of Locusta migratoria and Nomadacris septemfasciata (Orthoptera: Acrididae) Using MobileNet V2 Quantized Convolution Neural Network, Kazungula, Zambia . . . . . . . . . . . . . . . . . . . . . . . . . . . 490 Brian Halubanza, Jackson Phiri, Mayumbo Nyirenda, Phillip O. Y. Nkunika, and Douglas Kunda Lung Cancer Segmentation Using Enriched Fuzzy Back-Propagation Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502 JannathlFirdouse Mohamed Kasim and Balasubramanian Murugan Adaptive Filters Detection of State Change in Pseudonomas Putida Cultivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519 Jakub Steinbach and Jan Vrba A Review of Deep Learning Models for Detecting Cyberbullying on Social Media Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 528 John Batani, Elliot Mbunge, Benhildah Muchemwa, Goabaone Gaobotse, Caroline Gurajena, Stephen Fashoto, Tatenda Kavu, and Kudakwashe Dandajena Quantitative Analysis of Climatic Variability in Relation to Surface Loss with Landsat Data in Peruvian Snow-Capped Mountains 2010–2020 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 Anthony Flores Kancha, Jair Torres Agüero, Juan J. Soria, Orlando Poma, and Milda Cruz Huaranga Assessing the Bank Performance in the Process of Digital Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 566 Vladislav Rutskiy, Ekaterina Kharitonova, Shadia Hamoud Alshahrani, Fahad Nasser Al-Khaldi, Hashem Almashaqbeh, and Roman Tsarev Methodology for Analyzing the Scientific and Technical Complexes State Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 576 A. D. Uliev, S. V. Pronichkin, A. V. Zubkov, and V. L. Rozaliev Testing EMC Properties of High-Speed PLC Adapters . . . . . . . . . . . . . 582 Martin Koppl, Roman Duriga, Jozef Hallon, Antonin Bohacik, Milos Orgon, and Stefan Pocarovsky

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Hybrid Algorithms for Managing the Implementation of Convergent Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593 A. R. Donskaya, S. V. Pronichkin, V. L. Rozaliev, and A. S. Kuznetsova Analytical and Simulation Modeling in the System of Risk-Oriented Design and Usage Management of Complex Organization and Technical Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 600 B. V. Sokolov, R. M. Vivchar, A. I. Ptushkin, and E. E. Shcherbakova Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611

Method of Statistical Processing and Adaptive Estimation of Multispectral Aerospace Monitoring Data Valery V. Vasilevsky(&) Moscow Aviation Institute, Federal State Budgetary Educational Institution, 4, Volokolamskoye Shosse, Moscow 125993, Russia [email protected]

Abstract. The paper addresses the ways of increasing the efficiency of aerospace monitoring and recognition of difficult-to-detect dynamic objects on the Earth's surface under conditions of prior uncertainty of the signal and jamming situation, based on the developed algorithms of statistical processing and adaptive estimation of multispectral image data. We propose a model of observation and multichannel processing of spectrozonal data based on a statistical nonlinear filter which implements an algorithm of adaptive nonparametric estimation in two processing stages. The solution of the complex stochastic problem of filtering and finding an unconditional mean risk, as well as determining the estimational functional of spectrozonal data can be reduced to a simpler deterministic problem of finding a partial minimum of a multivariable function. The first step of the processing and estimation algorithm consists in block measurements, adaptive filtering, and multi-kernel evaluation of the posterior observation data distribution density function with the data obtained in the corresponding spectral channels. Values of the smoothing parameter— transmission bandwidth—are selected using the adaptive method, taking into account the structure of outlying estimation of density and dispersion values. The second step of the processing and estimation algorithm consists in calculation of adaptive estimations of maximum likelihood (posterior estimations) of the observed image data conditions, synthesis of a single image, and in finding recognition confidence estimations based on the guarantee (quantile) criterion. An example is given, illustrating possible applications of the described approach in the model problem of aerospace monitoring and recognition of difficult-todetect terrain objects under conditions of prior uncertainty of the signal and background situation. #CSOC1120.

1 Introduction 1.1

Problem Setting

One of the significant aspects in creating and applying advanced aerospace systems for remote sensing of the Earth is the development of efficient methods and algorithms of automatic data processing that solve practical problems with the required confidence of detecting and recognising difficult-to-detect dynamic objects on the Earth's surface © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 1–9, 2022. https://doi.org/10.1007/978-3-031-09073-8_1

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V. V. Vasilevsky

characterised by the inhomogeneous structure of details, under conditions of prior uncertainty of the background and object situation. One of approaches to the solution of this task is related to the implementation of statistical multichannel processing of multispectral images (MSI) of aerospace monitoring received by photodetectors (PD) of the corresponding spectral range installed on aircraft [1]. Aerospace monitoring systems of this type allow to observe objects of various classes on the Earth's surface, characterised by prior uncertain energy and spectral images that depend on the observation conditions. At that, a relevant challenge in implementing this technology is the use of generative algorithms of adaptive processing of spectrozonal data (SD) of MSI frame flow, ensuring the required efficiency of detecting and recognising difficult-to-detect objects on the Earth's surface. The process of reconstructing the state of observed objects on the Earth's surface, referred to as image estimation and recognition, shall be carried out automatically in real time, without lagging relatively to the process of MSI frame reception by PD and the observation system that solves this task using a statistical method is a statistical filter. The task of multichannel processing of MSI under conditions of prior uncertainty of signal and jamming situation is one of the tasks of statistical filtering and estimation of the function of the distribution density of observed image data, ensuring the required reliability level of decision-making in image recognition. The input information is dynamic arrays of SD presented as multidimensional (dynamic) random fields. If the density of the observation data distribution probability depends on the informative features in a certain way, we can build an observation system (classifier) using estimations of these parameters based on the method of parametric estimation of SD measurements. However, obtaining object recognition confidence estimations based on parametric algorithms of multichannel SD processing is quite difficult due to the following factors [1, 2]: • prior uncertainty and insufficiency of data on the object and terrain (background) state vector, their geometry and radiometric properties; • difference of the obtained brightness distribution laws for the object and terrain (background) from the used model standard Gaussian functions. To compensate the influence of these factors, we can use a classifier based on a statistical nonlinear filter, implementing the method of non-parametric estimation of MSI data in spectral channels based on the probabilistic criterion of optimality. The main idea of the applied approach is to use a statistical nonlinear filter that implements the procedures of inertia accumulation and multichannel processing of SD measurement data, restoration of the function of posterior density of data distribution probability and inertia-less operation of optimum estimation calculation, as well as decision-making on the availability of the monitoring object based on the optimality guarantee criterion [2, 3, 5].

Method of Statistical Processing and Adaptive Estimation

3

2 Model of Observation and Statistical Processing of Images The aerospace monitoring model can be divided into difficult-to-detect objects on the Earth's surface of certain classes that need to be recognised and the observation system that includes a multichannel scheme for MSI processing and recognition of observed images of the Earth's surface [4]. The observation objects on the Earth's surface are considered stochastic images (systems) determined at the fixed moments of time t 2 ½t0 ; T  by an augmented state vector ½Z ðtÞT with such components Z ¼ ðx1 ; x2 ; x3 Þ as its spacial coordinates ðx1 ¼ x; x2 ¼ yÞ and structure topology ðx3 ¼ d; d ¼ 1; . . .; DÞ. Assume that the change of the observation object's state at t  t0 ¼ 0; t ¼ t þ kT; k ¼ 0; 1; . . . is described by the stochastic Ito equation in its difference form with the use of the Euler method: Zk þ 1 ¼ Zk þ Taðtk ; Zk Þ þ Bðtk ; Xk ÞDWk ; k ¼ 0; 1;:::

ð1Þ

Here, Z k is the approximate value of the Z ðtÞ observation object state vector at the moment t ¼ tk , its components being the spacial coordinates of the center and value of the image cluster brightness signal; DW k ¼ W ðtk Þ  W ðtk þ 1 Þ is the incremental vector of the Wiener process of image brightness signal measurement errors within the interval t 2 T of the integration time; aðtk ; Z k Þ; Bðtk ; Z k Þ are functions, the drift vector and the diffusion matrix, respectively, of implementations Z ðtÞ ¼ z of the Markovian process of variation of the observed image cluster's spacial state and brightness signals. The observation model presented in Fig. 1a contains s ¼ 1; . . .; S of spectral channels performing functions of MSI spectrozonal data (SD) processing, clustering and recognition of observed images based on the method of non-parametric statistical evaluation and statistical adaptive filtering.
 The MSI frame flow can be represented as a random field, a set U j ¼ uJ ; J 2 J of random brightness values uj , varying on a multidimensional lattice in discrete space
 and time J t ¼ J ¼ ðj1 ; j2 ; j3 Þ; jl ¼ 1; M l ; l ¼ 1; 2; 3; t 2 ½t0 ; T  . The recurrent procedure of unfolding and multichannel processing of the l-th image frame is presented as:   ut ¼ U utij ; i; j 2 G; i; j; l 2 J t ; t 2 ½0; T  ;

ð2Þ

where G ¼ fði; jÞ : i ¼ 1; M; j ¼ 1; Ng is a set of integers within the anomaly (cluster) area of  the  image frame at the time interval T of its processing; n o U utij ; l; j; l 2 J t are the functions of brightness measurement distribution utij ; i ¼ ms; j ¼ ns; s is the step of discretisation (unfolding) of an image frame by rows and columns, respectively, determined by the PD resolving power, m ¼ 0; M; M ¼   ½T i =s; n ¼ 0; N; N ¼ T j =s .

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V. V. Vasilevsky

At each moment of discrete time t ¼ t þ lT; l ¼ 1; . . .; K, based on the results of all SD brightness (block) measurements U T0 ¼ fU ðtÞ; 0  t  T g available by that time, it is required to obtain a n-dimensional vector of estimation of the observed image b ðtÞ as a functional of these measurements: (cluster) area state Z  Z^ ðtÞ ¼ w t; U0T ;

ð3Þ

 where w t; U T0 is the n-dimensional estimating functional of observed images Z ðtÞ in the MSI multichannel processing system. This expression sets the input-output relation of the synthesised statistical nonlinear filter of the observation system, which converts the input signal U ðtÞ; t 2 ½0; T  b ðtÞ estimating the observed image (cluster) and decision-making into the output signal Z on the presence of an observation object of a given class. The solution of a complex stochastic problem of finding an unconditional mean risk and determining the estimational functional w t; U T0 of MSI can be reduced to a simpler deterministic problem of finding a partial minimum of a multivariable function:   w t; U0T ¼ arg min J t;U0T ; w : w2Rn

ð4Þ

 R Q  where J t; U T0 ; w ¼ Rn ðt; z  wÞp t; zjU T0 dx is a function of conditional risk  expressed via posterior density p t; zjU T0 . For a quadratic function of losses, the function of conditional risk and an optimum estimation are determined as: Z   T ðz  wÞT CðtÞðz  wÞp t; zjU0T dz; J t; U0 ; w ¼ ð5Þ Rn

Z^ ðtÞ ¼

Z

   zp t; zjU0T dz ¼ M Z ðtÞjU0T ;

ð6Þ

Rn

where C ðtÞ [ 0 is a symmetric positively determined matrix of weight coefficients in the window of  MSI data analysis. p t; zjU T0 is the function of the posterior density of probability of the observation object state vector Z ðtÞ obtained by multichannel MSI processing. The estimations of the parameters of a probabilistic model of evaluating observed MSI images in spectral processing channels are adaptive, and the corresponding algorithm is an adaptive evaluation algorithm [2, 6].

Method of Statistical Processing and Adaptive Estimation

5

3 Algorithm of Adaptive Statistical Processing of Images The algorithm of adaptive statistical processing of MSI by the nonlinear statistical filter and decision-making on the recognition of observation objects on the Earth's surface of a given class includes computational procedures performed successively in two stages (steps) [3, 4]. 3.1

Step 1

Block measurements and adaptive filtering (smoothing) are performed on the observation data obtained in the corresponding spectral channels from a set of SD elements with unknown distribution density pðuÞ and sorted in order of ascending values: ut ¼ U0T ¼ fu1 ; ::: , un g 2 Rn : The kernel estimation b p ðuÞ of a Parzen probability density function for the observed SD image (cluster) is determined by ratios in the following form [7]: ^pðuÞ ¼

n n u  u  1X 1 X k K h ð u  uk Þ ¼ K ; n k¼1 nh k¼1 h

ð7Þ

K ðu  uk Þ is the estimation kernel, non-negative numerical sequence; h [ 0 is a smoothing parameter referred to as the transmission bandwidth with its value selected by the adaptive method, taking into account the structure of outlying density estimations and the dispersion values. The estimation kernel with the index h (weighed estimation kernel) is determined by the following expression: 1 u K h ð uÞ ¼ K : h h

ð8Þ

Calculation of a multi-kernel Parzen estimation of the function of the SD observation data distribution density is performed using the expression: K ð uÞ ¼

½m2  X 4i þ 1 i¼0

22i þ 1

i ð1Þi C2i Li ðuÞ;

ð9Þ

where Li ðuÞ are Legendre polynomials of the i-th degree calculated for approximation of the probability density estimations pðuÞ. 3.2

Step 2

 b ðtÞ ¼ w t; U T0 of the maximum likelihood Calculation of adaptive estimations Z (posterior estimations) of the observation objects state during SD processing and

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V. V. Vasilevsky

finding of estimates of recognition confidence based on the guarantee (quantile) criterion of optimality in the form of: ^na ¼ Q ^ ð aÞ ¼

n X

ZT Uk

k¼1

1  t  a K dt; h h

ð10Þ

t¼0

where np ¼ Qð pÞ ¼ inf fu 2 Rn : p  F ðuÞ; F ðuÞ : R ! ½0; 1g is the quantile function of the given level p ¼ a of probability determining the set of the MSI frame observation data, which, with the given guarantee, will take values lower than the value up of b ðaÞ. the threshold level b na ¼ Q The estimation of the mode, or the probability density maximum, using the Parzen method is consistent with each image object (cluster) being a center around which a kernel is built. Thus, the model of statistical multi-channel adaptive processing of MSI based on the statistical nonlinear filter (Ф) and a decision-making automaton (P) during the recognition of difficult-to-detect objects can be presented in the form of two sequentially connected blocks shown in Fig. 1, b.

a

b a - SD multichannel processing scheme; b - statistical filter (Ф) structure Fig. 1. Model of MSI multichannel processing.

The first block of the statistical filter is a relaxation circuit performing unfolding, b ðtÞ of measurement and conversion of an input signal U ðtÞ into an output signal Z

Method of Statistical Processing and Adaptive Estimation

7

estimation of variation (estimating functional) of a random brightness field for a set of highlighted images (clusters), determination of posterior probability density b p t; zjU T0 on-line with sending of the current MSI frame to SD processing channels, synthesis of a single image. The second block of the statistical filter is inertia-less and implements procedures for calculation of multi-kernel estimation K ðuÞ of the probability density function b p ð uÞ b ðaÞ of the level a. and estimation of the quantile function b na ¼ Q To test the developed calculation algorithm with a model problem of multichannel processing of MSI aerospace monitoring, a dedicated software has been developed which implements statistical multichannel SD processing, recognition of observation objects, finding of recognition decision confidence estimations—“correct recognition” Pobn , “false alarm” PLT , presented in Fig. 2.

a

b

a - multi-kernel estimation of the distribution density function; b - estimations of the observation object recognition confidence (“correct recognition” “false alarm” )

,

Fig. 2. Statistical multichannel processing of MSI.

In the considered model problem, a three-channel MSI processing system was used, where observed image (cluster) SD estimations were calculated and graphs for the sectors of the visible (optical) spectral range were plotted: close in blue, medium in green and far in orange, respectively. The practical application of the proposed method in a model problem of aerospace monitoring of difficult-to-detect objects is shown in Fig. 3, depicting the initial spectrozonal pattern of a section of the road with local inhomogeneities in the form of oil product spots (shown with the arrow) and the result of statistical multichannel processing and adaptive evaluation, synthesis of a single image under conditions of prior uncertainty of the signal and jamming situation. To build the presented dependencies, multiple modelling of background and object situation was applied with the subsequent running of the detection algorithm and the evaluation of statistics of correct and incorrect decisions.

8

V. V. Vasilevsky

The following ratios of the characteristics of linear sizes bob and the level of detail lk of observation objects bob ¼ 2  lk were set during statistical modelling of the multichannel processing system and synthesis of a single image:

a

b

a - spectrozonal image of the road section; b - recognition of local inhomogeneities (oil product spots) during the synthesis of a single image

Fig. 3. Recognition of difficult-to-detect terrain objects.

The estimations of the efficiency of recognition of difficult-to-detect terrain objects —the probabilities of automatic “correct detection” Pobn and the probabilities of a “false alarm” PLT corresponding to one decision on the object—were obtained at different values of parameter jqj of the signal-noise ratio in spectral channels and the correlation of background brightness fluctuations. At that, the probabilities of a “false alarm” that are significant for practical tasks shall fulfill the condition PLT  0:1, which guarantees on average no more than one false alarm within the terrain background area 10 times bigger than the object surface area: S  10  Sob . The required estimations of confidence of automatic recognition of difficult-todetect terrain objects are achieved with the signal-noise ratio values jqj  4 at the input of the MSI statistical multichannel processing scheme. Thus, the model of automatic decision-making during MSI recognition is characterised by a characteristic function with an adaptively changing value of the bandwidth of the statistical nonlinear filter during the evaluation of the posterior distribution density function. The procedure of synthesising a gray-scale single image allows to ensure statistical discriminability of objects and backgrounds with typical values of the SD background brightness correlation for the ranges of the optical spectrum reflective area, a significant (by 2…3 times) increase of the signal-noise ratio and observability of objects on the synthesised single image.

Method of Statistical Processing and Adaptive Estimation

9

However, a slight decrease of the multizone processing effect should be noted, which is due to the decorrelating influence of the emission recording receivers’ noise, discrepancies in mutual georeferencing of SD frames; inhomogeneity of Earth's surface brightness distribution and other factors. Conducted theoretical and experimental studies based on real images demonstrate a potential for significant increase of observability and probability of automatic detection of difficult-to-detect objects on a synthesised single image with a fixed probability of “false alarm”, improvement of the information system performance during the search and interpretation of observation objects.

4 Conclusion The paper addresses the ways of increasing the efficiency of aerospace monitoring during the detection and recognition of difficult-to-detect objects on the Earth's surface based on the application of statistical multichannel processing and non-parametric evaluation of MSI. A model is built for statistical multichannel processing of MSI during the detection and recognition of difficult-to-detect objects on the Earth's surface based on the application of the statistical nonlinear filter. The bandwidth of the statistical nonlinear filter is an adaptively variable parameter during the restoration of the posterior distribution density function and calculation of the observation object recognition confidence estimations. The use of the adaptive non-parametric evaluation algorithm and the guarantee criterion of optimality in the process of multichannel MSI processing demonstrates the possibility to improve the efficiency of automatic detection and recognition of difficultto-detect objects under conditions of prior uncertainty of the characteristics of background and object situation of aerospace monitoring.

References 1. Vasilevsky, V.V., Zhebel, A.N.: Method of Multizone Processing of Video Data in the Tasks of Aerospace Monitoring of Low-Observable Small-Sized Objects. Electronic Engineering. Series 2. Semiconductor Devices 1(248), 72–77 (2018) 2. Vasilevsky, V.V., Zhebel, A.N.: Multispectral processing of video data for aerospace monitoring of difficult-to-detect objects. electronic engineering. Series 2. Semiconductor Devices 4(251), 4–7 (2018) 3. Vasilevsky, V.V.: Adaptive Minimax Estimation of Video Data in Aerospace Monitoring Applications. Electronic Engineering. Series 2. Semiconductor Devices 1(256), 27–32 (2020) 4. Vasilevsky, V.V.: Spectral Technique for Complex Processing of Aerospace Monitoring Multispectral Video Data. Electronic Engineering. Series 2. Semiconductor Devices 4(259), 19–25 (2020) 5. Pugachev, V.S., Sinitsyn, I.N.: Theory of Stochastic Systems. Logos, Moscow (2000) 6. Fomin, V.N.: Recurrent Estimation and Adaptive Filtering. Nauka, Moscow (1984) 7. Fukunaga, K.: Introduction to Statistical Pattern Recognition. Transl. from English. Nauka. Main Office of Physical and Mathematical Literature, Moscow (1979)

Providing Security for Critical Assets. Challenges and Prospects Vitaliy Nikolaevich Tsygichko(&) The Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russia [email protected]

Abstract. This study addresses problems of the incompleteness and ambiguity of the conceptual and terminological framework in the area of critical asset security. Main terms and concepts are specified and explained. The most general theoretical problem statements in the field of providing security for critical assets are conceived. #CSOC1120. Keywords: State of critical assets
Threat model
Critical elements of an asset
Protection profile
Risk management
Efficiency of security systems

1 Introduction One of the principal objectives of a state is to ensure safe and reliable functioning of critical assets (CA) of the national infrastructure. Problems related to CA security are actively studied worldwide and in our country, however, many of these problems remain not fully solved [1–5].

2 Concepts and Terminology One of these problems is the absence of a unified conceptual and terminological framework in the field of CA security. The extreme complexity and multidimensionality of the problem of CA security, together with multiple available approaches to its solution, led to various interpretations of its terminology. A potential danger in the current state of affairs is not that the sets of used concepts considerably differ from area to area, but that these concepts have different content. Hence we may use a language with different starting points, thus losing the ability to adequately understand each other. The problem of creating a common conceptual framework is particularly relevant for the development and implementation of automated human-machine security systems for CA [1, 6, 7]. Most controversies are created by the central concept in the area—the concept of security. Most explanatory dictionaries define security as the absence of threat for some object. This definition from Wikipedia seems to be the most acceptable one:

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 10–20, 2022. https://doi.org/10.1007/978-3-031-09073-8_2

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11

“Security is a state of a complex system, where the impact of external and internal factors neither adversely affects the parameters of the system, nor results in impossibility of its further functioning and development”. In our case, however, this definition needs to be specified. One of the basic concepts in this research area is the concept of state, which is used in the above definition of security. In general, the state of an object is a set of parameter values, used for its description, recorded at a certain moment of time. From this perspective, the main concept in the area of CA security can be broadly defined as “the creation of conditions, under which the impact of external and internal factors neither adversely affects the state parameters of a CA, nor results in impossibility of its further functioning and development”. Providing security for any CA implies a certain set of countermeasures aimed at protecting the asset from threats. In the domain of security, threat is a basic concept, since any security system is build on this concept. However, there is no clear definition of this concept in the security theory and practices, which creates ambiguity in its interpretation. The term threat has a wide semantic range, i.e. its meaning changes depending on the context [4], and therefore different interpretations may arise. All known Russian language explanatory dictionaries, the Great Soviet Encyclopedia and the latest edition of the Great Encyclopedic Dictionary define threat as ‘an intention expressed in any form to inflict physical, material or other harm on society or personal interests in order to frighten or to force to perform certain actions’. Certainly, this definition of threat can also be used in the security industry, however, this connotation is only rarely used and can be defined as “a direct danger”. Any security system is designed and implemented to prevent a potential danger, even when there is no direct danger. It is this potential danger which is incorporated in the term threat. A threat is defined by the existence or emergence of a source of potential danger. For example, the threat of an earthquake is defined by its source, which is the level of seismic activity in the area. It can be defined as a permanent natural threat. The same can be said for industrial sources of threats. For example, almost any transportation vehicle is a potential threat, since merely its functioning can potentially endanger people; so it can be defined as a permanent industrial threat [7]. Sources of natural and man-made hazards are usually known, therefore in order to prevent and mitigate related permanent threats, appropriate security measures are taken. For example, in earthquake-prone areas, only seismically-proven buildings are allowed to be erected. For transportation vehicles, conditions and measures for their safe functioning are determined, etc. The main source of threat, which is difficult to predict, are terrorist and criminal activity in the information sphere of individuals, terrorist and criminal groups or communities, and in some cases states. This source generates terrorist and criminal threats. Most CA security systems (CASS) are designed to counteract sources of information danger, as well as terrorist and criminal threats. To acquire a deeper insight into the semantics of the term threat, it is important to elucidate its determination form, i.e. its type of cause-and-effect relationship with the source of potential danger and with the asset at which threats are potentially aimed.

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Every source of potential danger generates a range of threats for each specific asset. For example, theft and misrepresentation of information in the public administration system can lead to its political or economic instability. In other words, a range of threats for an asset is conditioned by the nature of possible actions of a potential source of danger, by the characteristic features, structure and setup of critical elements of the potentially endangered asset. Although the relationship between a source of potential danger, an asset, and threats to that asset is strictly causal, there is always some uncertainty about the completeness of a list of threats to a particular asset. This uncertainty reflects incomplete knowledge about the capabilities and nature of actions of a potential danger source and applies primarily to terrorist and criminal sources of danger. Information, terrorist and criminal threats are variable in nature, as their specific sources come and go, and their targets, goals, forms and methods of criminal activity change. This makes providing security extremely difficult, since any security system can be effective only against specific threats and specific forms of their realisation. Therefore, to maintain the necessary level of security, it is necessary to monitor changes in the possibilities and actions of sources of danger, as well as to timely adjust the list of threats in accordance with the identified changes. Thus, threats for a specific asset exist objectively, if there is a potential source of danger; however, every danger may occur or may not occur with a certain probability, i.e. the realisation of a threat is of random nature. The assessment of probability of any particular threat is a complicated problem, often not solvable with objective methods, since the realisation of a threat is typically conditioned by unpredictable factors and is directly related to the CA’s degree of protection. The general dependence of the probability of a threat actualisation P on the degree of protection U is given in Fig. 1. This dependence reflects the obvious fact that the higher is the efficiency of counteractions against threats, the lower is the probability of threat realisation. For example, if the outside perimeter of an asset is effectively protected against penetration, a potential intruder will seek other options of performing a terrorist attack without intruding the protected boundary of the asset. In other words, a terrorist would seek a “weak spot” in the asset’s security system where it is possible to perform an attack with the greatest chance of success and with the lowest risk and cost. The same can be said about other types of threats, for example, the destruction of a building by an earthquake occurs due to the loss of stability of the weakest structure elements, etc. Taking this dependency into account is principally methodological, since it reflects the understanding of the degree of danger, the efficiency of its mitigation, the risk level for a given asset protection degree or the allocated costs for the implementation of countermeasures. The dependencies P(U) must be taken into account when designing the content and structure of a CASS. Finding dependencies is a difficult scientific and practical task, since there is usually no objective information needed for building them. These dependencies can be derived for every CA using a dedicated expert assessment procedure based on the analysis of various attack scenarios.

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Fig. 1. Dependence of threat actualisation on the CA’s degree of protection.

In practice, the uncertainty of place and time of realisation of a potential threat is resolved through the adoption of two postulates (often assumed implicitly): • a threat will be certainly realised at least once during the life cycle of an asset; • a threat will be realised at the weakest security point of an asset. According to these postulates, the security system must be built on the principle of equal and sufficient protection of all critical elements of a CA against all potential threats and ways of their possible realisation.

3 Threat Model and Security Profile These above concepts, related to the definition of threats, allow to create a principal scheme of their interaction in the form of a CA threat model. The procedure for designing a threat model can be presented as follows: • determining potential sources of threat, their ways of affecting a CA and possible ways of threat realisation; • determining critical elements of the CA, where a potential source of danger can breach security; • determining the list of possible threats from a potential source of danger for each critical element of the CA;

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• determining possible ways of realisation of each particular threat. A threat model for an asset is composed of a set of all currently known and possible threats together with ways of their potential realisation in the critical elements of an asset. A threat model is the starting point for designing a security system: every possible way of security breach has corresponding neutralisation countermeasures, which as a whole comprise the security profile of a CA. A security profile is an implementation-independent set of security requirements for each type of CA providing an adequate degree of protection. Global and domestic best practices adopt the allowable magnitude of the risk of a security breach as an indicator of sufficient protection of a critical asset. General risk theory [8] defines risk as an activity related to overcoming uncertainty in the situation of inevitable choice, in the process of which there may be an opportunity to qualitatively and quantitatively assess the probability of a presupposed result, failure and deviation from the goal.

4 Risk Management Risk management includes processes related to the identification and analysis of risks and to making decisions, such as maximising positive and minimising negative consequences of risk events [3–5]. As applied to our subject of research, the risk of a CA security breach is the probability of realisation of potential threats in the asset’s critical infrastructure elements (vulnerability assessment) within the current protection system. The risk of a CA security breach is a unified criterion for assessing the efficiency of all levels in the hierarchy of the security systems (SS). Risk management of CA security breach events is implemented by selecting and implementing methods, measures and arrangements (security profiles) which prevent and mitigate the negative impact of possible threats to CA based on the assessment of the efficiency of available SS, their technological, material and technical support. Measures and methods of risk assessment are selected on the basis of available information on potential threats and ways of their realisation [2]. Currently, there are three main approaches to assessing the risks of a CA security breach events: probabilistic, statistical and expert judgement methods [5]. An important parameter describing a risk event is the cost of risk, defined as potential damage for an asset if a threat is realised. The monetary value of the cost of risk is calculated for all threats and ways of their realisation. Most CA have regular methods of assessing the potential damage, incurred by potential security breach events. The categorisation of critical assets is performed using these methods [2]. Probabilistic methods are applied mostly in technical security systems, whose elements have known characteristics of reliability and functioning efficiency. They can also be adopted to assess risks of critical asset security systems [2], if objective information on the efficiency of security measures and protection profiles is available.

Providing Security for Critical Assets. Challenges and Prospects

15

The statistical approach is based on the available statistics of security breaches for every type of critical assets. This statistics allows to determine the probabilities of threat realisation during the life cycle of a CA [9]. Because of a scarcity or a total lack of objective information needed for both former methods, the method of expert assessment of security breach risks in CA is used [2]. In practice, experts usually design and analyse various risk event scenarios, select the most dangerous ones and do a quantitative or qualitative assessment of the security breach risk (vulnerability assessment) for an asset with regard to the existing and functioning security system. As a result of such procedure, all threats from the threat model for a given CA are ranked by priority (cardinal ranking scale) according to the potential damage for an asset in case a threat materialises. The choice of the adequate protection degree is one of the central problems in CA security. In principle, modern security technologies and methods allow to provide a full protection of CA against all potential threats, however, it is unattainable in practice because of several reasons. First, a system of 100% guaranteed security will be, as a rule, functionally and hardware-wise more complex that the protected CA itself, hence it will often be more expensive than the CA, as experience shows. Second, the functioning of such a comprehensive security system (SS) will negatively affect the functioning of most CA. Most commonly, it results in an essential deceleration of the CA functioning, decreasing its performance and efficiency. For example, the functioning of airports is disrupted if additional measures of passenger control are implemented. Third, to ensure an absolute (100% or close) security level for a critical asset, the security system must constantly control compliance with security requirements, monitor the technical state of a CA and all SS elements, the work performance of the staff and security services. The implementation of such total control system would require enormous costs and a large number of employees. Obviously, these economical, technical and organisational issues preclude the creation of an “ideal” security system. In practice, all CASS ensure only a certain security level according to the “cost vs efficiency” trade-off.

5 Cost of CASS The most important aspect in designing CASS is the determination of their allowable cost, which is directly related to the allowable risk of CA security breach events [2, 10]. A diagram of the dependence of the risk level P of a security breach in a CA’s critical element on its security expenditures S is presented in Fig. 2. The dependence shown in Fig. 2 can be explained as follows. At low expenditures invested in protection, elevated risk level persists. After some threshold in invested cost (1) has been achieved, further increase in expenditures yields a high gain. This trend ends after some saturation point (2), beyond which further increase in expenses yields only a small increase in the security efficiency. When choosing an adequate protection profile, one must consider that a full protection of an asset or its critical element is

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impossible. Whatever sophisticated and costly security measures are taken, there always remains some probability that their security may be breached. The goal is to find some inflexion point 2 (Fig. 2) for each critical element or for an asset as a whole, i.e. to determine minimum security expenditures corresponding to an acceptable risk level.

Fig. 2. Dependence of the risk magnitude P on expenditures S for the protection of an asset or its critical element.

Usually, when determining the allowable price of a CA security profile, potential damage to an asset and the probability of the materialisation of most dangerous threat are taken into account. Most CA do not have any regular methods for estimating allowable security expenditures, since it is extremely difficult or even impossible to assess the probability of the materialisation a potential threat. Analysis shows that expenditures for modern security systems are within the range from 1.5% to 5%, in some cases up to 10% of the maximum potential harm depending on the expert assessment of potential hazards. These values that limit allowable expenditures for security have been obtained by insurance companies and can be reasonably used in the field of CA protection. Expenditures for CA protection include the cost of equipment, its maintenance and the employment of security guards all year round [2, 9].

6 Goals of Security Systems The above conceptual framework allows to formulate the most general theoretical objectives of CA security.

Providing Security for Critical Assets. Challenges and Prospects

6.1

17

The First Goal

The first goal is to determine the efficiency of CA functioning with a given threat model and the existing CA protection profile. In fact, this is the problem of assessing CA vulnerability. As a result, risk magnitudes should be determined for the security breach from all components of the threat model and from the threat model as a whole, and all weak spots in a CA security system should be revealed. The obtained quantitative estimation of the security breach risks must become an objective foundation for the decision-making on the measures and procedures for increasing the asset protection. 6.2

The Second Objective

The second objective is to determine (with a predefined threat model) the CA protection profile, minimising its cost at an acceptable risk level of its security breach. This objective is achieved by creating a protection profile with an optimum cost for an asset at a fixed risk level of security breach and by creating a reliable set of requirements for its security. 6.3

The Third Objective

The third objective is to determine a security profile of a CA that ensures maximum security at the given expenditures for security systems. The achievement of the above listed objectives requires the following input data for every CA type: • threat model (a list of possible threats and ways of threat materialisation); • list of countermeasures against the established threat model and their efficiency indicators; • dependence of the functional efficiency of an asset on the degree of protection (according to each countermeasure); • legislative limitations on the efficiency of the asset functioning; • existing protection profile of a CA; • a limited budget for a CASS. This information has allowed to develop formal methods of solving the above CA security problems [3, 4]. A protection profile, in other words, a set of security requirements can be implemented at any CA using various technical and physical measures and organisational procedures. The question is whether these measures are reliable in ensuring compliance with security requirements for an asset. Substantially, this is a problem of assessing the efficiency of response to potential threats from a CASS. For most CA, assessing the efficiency of countermeasures against potential threats is a serious problem, which often cannot be solved using objective methods. First of all, this applies to the assessment of physical protection and administrative procedures where human performance is crucial. A human person is an integral part of a CASS, and they greatly influence its performance in accordance with security requirements [7].

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Compliance with security requirements is conditioned by the professional level of CASS staff, their reliability and sense of responsibility. Potential violations can be either accidental or deliberate. Unfortunately, it is almost impossible to completely exclude humans from SS and fully automate CA protection. Only by taking human performance into account, it is possible to reliably assess the CASS efficiency. In most cases in practice, the said problems are solved and a protection profile is selected by relying on experience, logical and heuristic conclusions from experts, rather than by calculations. It is generally accepted that compliance with these formulated requirements provides the required security level for CA. CASS objectives consist in the implementation of requirements for CA security, scheduled quality check and taking appropriate corrective actions if the requirements are not met or unexpected situations arise. The criterion for evaluating the level of CA protection is whether requirements are followed or not. What is the relation between this criterion and the general criterion, i.e. the risk of CA security breach?

7 Security Criteria Lets now consider in more detail the concept of criterion and its relation to the concept of state of an asset. Wikipedia defines CRITERION (from Greek) as an indicator, on the basis of which the quality of an economical asset or process is assessed, and the measure for such an assessment. In general, a criterion is the assessment of the state of an asset or process, which has a certain purpose. A commonly used formal definition is as follows: “a criterion is the objective function of the state of an object”. If there are several objective functions, i.e. there are several criteria, a known multi-criterion optimisation problem arises. In many cases, the targeted assessment of the state an asset can be presented by the values of one or several functional parameters, i.e. by its state indicator. For example, such indicator as the water level in a dam determines the security level of its functioning. In this case, the objective function consists in choosing an indicator value that is critical in relation to the state of an asset, and after reaching which the security system should act in order to prevent unallowable consequences of the event. In our example, to discharge water from a dam. To determined critical values of a CA state parameter, we need to assess the risk level of a security breach at different values of this parameter, i.e. to derive the dependence of the risk magnitude on the parameter value. If this dependence is known, the state parameter can serve either as a substitute of the general criterion or as its separate value. In our case, if we assess the risk of a CA security breach, the requirements serve as state parameters for the SS. However, it is not a common practice to perform a quantitative risk assessment. Compliance and non-compliance with security requirements in itself is considered as a SS state criteria. When standard sets of security requirements for CA security are designed, they are unable to account for all the particular features of specific CA, which poses a number of nontrivial questions to CASS projects, namely:

Providing Security for Critical Assets. Challenges and Prospects

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• • • • •

whether the standard threat model is comprehensive with respect to a given asset; whether the standard set of security requirements is adequate for a given asset; are there any redundant requirements? whether the requirements contradict each other? what is the risk of a CA security breach if one or more requirements are not followed; • what risk assessment procedures better correspond to the specific features of a given CA.

8 Conclusion The solution of these problems is one of the objectives for an automated CA security system. The main purpose of implementing an automated security system is to improve the efficiency of the existing CA security systems by automating security management processes, monitoring the CA security status, collecting, processing, storing and providing information in the required form in order to support decision-making on all issues of critical asset security. The suggested conceptual framework and methodological approaches to solving security problems can provide a theoretical basis for designing security systems with different purposes.

References 1. Baranov, A., Klementyev, S.: Provision and organisation of security of critical infrastructure in the USA. Int. Military Observer 8, 3–10 (2009) 2. Tsygichko, V.N.: Assessment of the efficiency of security systems for information security and national infrastructure objects. Modern problems and objectives in information security. Proc. the SIB-2014 Russian Research and Practice Conference, 2014, pp. 80–89. Moscow University of Finance and Law, Moscow (2014) 3. Tsygichko, V.N.: Risk management for CA security breach with incomplete information. Problems of risk assessment. AO Delovoj Ekspress Business Publishing House, Moscow 4 (12), 18–28 (2015) 4. Sholomnitsky, A.G.: Risk theory. Choice in the conditions of uncertainty and risk modeling. HSE State University Publishing House, Moscow (2005) 5. Tsygichko, V.N.: Strategic decision-making and risk-management in complex systems. In: Silhavy, R. (ed.) CSOC 2020. AISC, vol. 1226, pp. 415–432. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51974-2_40 6. Edt. Academician Mikhaylov, V.N.: Security of nuclear weapons in Russia. Sarov (1999) 7. Liberman, A.N.: Technology security: human performance. Saint Petersburg (2006) 8. White Paper. The Clinton Administration’s Policy on Critical Infrastructure Protection: Presidential decision Directive 63. May 22. 1998 9. Novitsky, Ye.G.: Risk management for information security in large diversified corporations. Institute of System Analysis of the Russian Academy of Sciences, Moscow (1999)

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10. Silhavy, R., Silhavy, P., Prokopova, Z. (eds.): CoMeSySo. AISC, vol. 1295. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63319-6 11. Bondarenko, A.: Basics of risk management system design in information security. Information Security 5 (2013) 12. Tsygichko, V.N.: Mathematical model of risk assessment in the area of cybersecurity of automated systems. Methods of cybernetics and information technology. Kolledzh, Saratov 2, 98–110 (2004)

Electromagnetic Communication in Deep Space Between Mutually Moving Apparatus Gennady Tarabrin(&) Department of Applied Mathematics, Volgograd State Technical University, Volgograd, Russia [email protected]

Abstract. This article can become a theoretical basis for the creation of devices for measuring the distance and velocity between a mutually moving apparatus in deep space and studying the physical properties of void. A flat one-dimensional field of electromagnetic waves described by Maxwell’s equations is considered. In the framework of solving Cauchy problems of hyperbolic equations, the period of harmonic waves at the receiver is investigated for different variants of the relativity of the motion of the radiator and receiver. It is established that when the radiator and receiver move relative to each other, the receiver receives waves of the wrong period, which creates a generator on the radiator. When the radiator and receiver approach, the wave period shortens, and when they move away from each other, the wave period lengthens. These phenomena are known to practical physics and are called blue and red shift. The same is observed when the radiator overtakes a moving receiver or, conversely, the receiver overtakes a moving radiator. It is concluded that the mathematical models of the detected physical effects are a manifestation of the physical properties of the medium in which electromagnetic waves propagate - a medium called a void. In the near future, humanity will go into deep space and reach velocities of movement in it comparable to the velocity of light. This will open up the possibility to conduct experiments with moving relative to each other apparatuses and obtain the results of direct observations of the physical properties of the void. Keywords: Maxwell’s equations type
The Doppler effect


The equations problems of hyperbolic

1 Introduction The Doppler effect has been known for a long time. However, the first mathematical models of this phenomenon, constructed purely theoretically as a result of solving particular problems of the Maxwell equations, were published relatively recently [1, 2]. This article is a continuation and development of these works. My many years of persistent attempt to find a mathematical model of the Doppler effect by other authors turned out to be in vain. Therefore, there will be no references to the works of other authors in this article of mine. This article is an English version of the article [3] in Russian. The widespread use of radio-electronic location devices dictates the need to create mathematical models of the electromagnetic field of the received waves, which occurs © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 21–34, 2022. https://doi.org/10.1007/978-3-031-09073-8_3

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in various situations of movement of the radiator and receiver relative to each other. This work is devoted to solving such problems. The ability to create purely theoretical mathematical models of the electromagnetic field between a mutually moving radiator and receiver turned out to be feasible as a result of applying a method called the method of characteristics for solving problems of partial differential equations. The most complete information about this method of solving problems in mathematical physics can be obtained from the monograph of R. Courant [4]. The effectiveness of the method of characteristics is explained by a special property of hyperbolic differential equations: if one of the two independent variables is given the meaning of a spatial quantity, and the other variable is given the meaning of the current time, then the characteristic lines on the plane of the independent variables become something that physicists call waves. Examples of solving various wave problems in the mechanics of a solid deformable body, acoustics, and the electromagnetic field can be found in the monograph [5].

2 One-Dimensional Electromagnetic Field with Radiator and Receivers We will use Maxwell’s equations here in the form written in [6]. Take an electromagnetic field that changes over time t in only one direction. We orient the Cartesian basis i; j; k and the Cartesian coordinates x; y; z associated with this basis so that the direction of change of the electromagnetic field coincides with the xaxis and so that we have E ¼ E ðx; tÞi - the electric field strength and H ¼ H ðx; tÞk - the magnetic field strength. Note that here, as it should be, such an electromagnetic field can be created by a point source of directed radiation of the laser type [7]. For such an electromagnetic field, the first Maxwell equation in vector form curlH ¼ ð1=cÞE;t ; where c - the velocity of propagation of electromagnetic waves, takes the form of a scalar partial differential equation. cH;x þ E;t ¼ 0:

ð1Þ

The vector potential U of the electromagnetic field under consideration is a function of two independent variables x; t and must be such as to transform the second Maxwell equation curlE ¼ ð1=cÞH;t into an identity. It is easy to see that the vector potential function U ¼ U ðx; tÞj determines the strength of the electric and magnetic fields by the relations:

Electromagnetic Communication in Deep Space

cE ¼ U;t ;

23

H ¼ U;x :

Differentiating the first of these equations by x, the second equation by t, and adding up the result of differentiation, we get the equation cE;x þ H;t ¼ 0:

ð2Þ

Equations (1) and (2) form a system of partial differential equations. Let’s write this system of equations in total differentials. Lines on the coordinate xy-plane, in the direction of which the system of equations of the form (1), (2) can be written in total differentials, are called characteristics of this system of equations. A system of differential equations of two independent variables with first-order partial derivatives that has two families of characteristics, according to the classification of equations of mathematical physics, is called a system of hyperbolic equations. Let the differentials of the independent variables dx; dt have such values that their ratio dx=dt is numerically equal to the cotangent of the slope of the characteristic to the x-axis. Then the total differentials dH ¼ H;x dx þ H;t dt;

dE ¼ E;x dx þ H;t dt

are the total differentials in the direction of the characteristic. These equations together with Eqs. (1) and (2) form an algebraic system of four equations with four unknowns E;x ; E;t ; H;x ; H;t . Let’s write it in matrix form 2

c 60 6 4 dx 0

0 1 dt 0

0 c 0 dx

3 2 3 32 0 1 H;x 6 7 6 7 07 76 H;t 7 ¼ 6 0 7: 4 5 4 5 dH 5 0 E;x dE dt E;t

ð3Þ

The system of Eqs. (1), (2), as a system of partial differential equations, has an infinite set of solutions. Therefore, the system of Eqs. (3) also has an infinite set of solutions. This algebraic system of equations is inhomogeneous. Therefore, the determinant of its main matrix and the determinants of all additional matrices are zero. By equating the determinant of the main matrix of the system of Eqs. (3) to zero, we obtain the differential equations of characteristics dx ¼ cdt:

ð4Þ

Here c ¼ const. Therefore, on the plane of variables x; t, differential Eqs. (4) define two families of lines of the form x ¼ x0  cðt  t0 Þ, where x0 ; t0 ¼ const. The characteristics of family x ¼ x0 þ cðt  t0 Þ are called positive direction characteristics, and the characteristics of family x ¼ x0  cðt  t0 Þ are called negative direction characteristics.

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By equating the determinant of any of the additional matrices of the system of Eqs. (3) to zero, we obtain the system of Eqs. (1), (2) in total differentials dE  dH ¼ 0:

ð5Þ

These equations have the meaning of relations of total differentials of functions on the characteristics of the positive (minus sign) and negative (plus sign) directions. Let’s solve the problem of wave propagation from a stationary radiator to stationary receivers to the left and right of the radiator. The radiator generates a periodically changing monochromatic electric field with a period T and amplitude A according to the law E ¼ A sinð2pt=T Þ:

ð6Þ

The stationary radiator is located in the plane x ¼ l (Fig. 1) and, starting from the

receiver

receiver

Fig. 1. Motionless radiator with motionless receivers

moment of time t ¼ 0, radiates electromagnetic waves with the same phases simultaneously in the positive and negative sides of the x-axis. Receivers are located in the planes x ¼ 0 and x ¼ 2l. On Fig. 1 we showed diagrams of the waves radiated by the radiator and received by the receivers and showed the characteristics of the positive and negative directions corresponding to the waves going from the radiator to the receivers. To the right of the plane x ¼ l, not lower than the characteristic x ¼ l þ ct, we take the point M and draw from it the characteristic of the negative direction to the intersection with the x-axis at point N . Let’s denote: E; H - values at point M and E ; H - values at point N . A curved integral over the differential Eq. (5) along the characteristic of the negative direction from point M to point N gives E  E þ H  H ¼ 0. At the points of the x-axis (i.e., at t ¼ 0) E ¼ 0; H ¼ 0. With this in mind, we have.

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25

E ¼ H: Since M is an arbitrary point, this relation will be performed at any point to the right of the radiator (to the right of the x ¼ l plane) at least (as we agreed) characteristics x ¼ l þ ct. Similarly, integrating Eq. (5) along the characteristic of the positive direction M þ N þ , we conclude that at any point to the left of the radiator (to the left of the x ¼ l plane) not lower than the characteristic x ¼ l  ct the relation E ¼ þH is performed. Earlier we said that the relations (5) take place along the characteristics. Therefore, these equalities mean the following: to the right of the radiator along the characteristics of the positive direction and to the left of the radiator along the characteristics of the negative direction value E; H remain unchanged, equal to their values at corresponding points of a straight line x ¼ l. Accordingly, the conclusion, on the characteristics of the positive direction to the right of the line x ¼ l (which corresponds to waves to the right of the radiator) and on the characteristics of the negative direction to the left of the line x ¼ l (which corresponds to waves to the left of the radiator), the law of operation of the generator (6), we have   ct  ðx  lÞ E ¼ H ¼ A sin 2p : cT We have solved the problem of hyperbolic equations with initial conditions given at the points of the line x ¼ l. Problems of hyperbolic equations with initial conditions on a line that does not coincide with the characteristic are called Cauchy problems. The results of wave reception are shown on the lines x ¼ 0 and x ¼ 2l (Fig. 1). Below we will solve Cauchy problems with initial conditions and take waves on lines of the form x ¼ l þ vt, where l; v ¼ const.

3 The Radiator Moves Relative to a Stationary Receiver The radiator moves in the positive direction of the x-axis from the point x ¼ 0 at a constant velocity vrad . At the beginning, the radiator approaches the receiver located in the x ¼ l plane, and then moves away from it. When the radiator passes through the receiver t ¼ s ¼ l=vrad , the period of waves received by the receiver changes abruptly, i.e., the Doppler effect is observed. On Fig. 2 the dotted line shows the path graph of the radiator x ¼ vrad t, the bold line shows the plane of the receiver location. The radiator generates oscillations in the electromagnetic field of the period T. The beginning of the first period T comes to the receiver (in the plane x ¼ l) along the characteristic x ¼ ct at time t ¼ t1 ¼ l=c (point M1 ðl; t1 Þ), and the end of this period comes to the receiver along the characteristic x ¼ vrad T þ cðt  T Þ at time t ¼ t2 . The

26

G. Tarabrin

point M2 ðl; t2 Þ belongs to this characteristic. Therefore, the equality l ¼ vrad T þ cðt2  T Þ is valid. From here we find t2 ¼ l=c þ ð1  vrad =cÞT. Obviously, T ¼ t2  t1 is the period of waves received by the receiver, which the radiator radiates as it approaches the receiver. Calculating, we obtain T ¼ ð1  lrad ÞT;

lrad ¼

vrad : c

ð7Þ

Here T \T.

Fig. 2. Radiator moves relative to stationary receiver

After the radiator passes through the receiver, when the radiator is removed from the receiver, the waves coming to the receiver are determined by the characteristics of the negative direction. The beginning of the period of such radiation can be taken at the point M3 ðl; sÞ. The perturbation from this point will propagate along the characteristic x ¼ l  cðt  sÞ. Then the end of this period in the generator will be at the point with the x ¼ l þ vrad T; t ¼ s þ T coordinates and will extend along the characteristic x ¼ l þ vrad T  c½t  ðs þ T Þ: The point M4 ðl; t4 Þ is the end of the period in question at the receiver. Therefore, T þ ¼ t4  s is the period of waves received by the receiver, which the radiator radiates, moving away from the receiver. The point M4 ðl; t4 Þ belongs to the characteristic x ¼ vrad ðs þ T Þ  c½t  ðs þ T Þ, so the equality l ¼ vrad ðs þ T Þ  c½t4  ðs þ T Þ is valid. Calculating t4  s, where s ¼ l=vrad , we obtain T þ ¼ ð1 þ lrad ÞT:

ð8Þ

Here T þ [ T. The jump in the period of received waves when the radiator passes through the receiver, characterized by the ratio T =T þ , is called the Doppler effect. Next, we will

Electromagnetic Communication in Deep Space

27

get other variants of the Doppler effect. Therefore, in this case, we agree to say that the radrec-Doppler effect is determined by the formula 

T Tþ

 ¼ radrec

1  lrad : 1 þ lrad

ð9Þ

4 The Receiver Moves Relative to the Stationary Radiator The receiver moves in the positive direction of the x-axis from point x ¼ 0 at a constant velocity vrec . First, the receiver approaches the radiator located in the x ¼ l plane, and then moves away from it. When the receiver passes through the radiator at t ¼ s ¼ l=vrec , the period of waves received by the receiver changes abruptly, i.e., the Doppler effect is observed. On Fig. 3 the bold line shows the graph of the path equation of the receiver x ¼ vrec t; the dotted line shows the plane of the radiator location. The radiator generates oscillations of the electromagnetic field of period T with the same phases simultaneously in the positive and negative sides of the x-axis. When approaching the radiator, the receiver receives waves corresponding to the characteristics of the negative direction. The radiation of the very first period is enclosed between characteristics x ¼ l  ct and x ¼ l  cðt  T Þ. Let M1 ðx1 ; t1 Þ; M2 ðx2 ; t2 Þ be the intersection points of these characteristics with the graph of the receiver path equation x ¼ vrec t. Then T ¼ t2  t1 is the period of waves received by the receiver. Systems of two equations x1 ¼ l  ct1 ; x1 ¼ vrec t1 and x2 ¼ l  cðt2  T Þ; x2 ¼ vrec t2 determine the coordinates of points M1 and M2 . Solving these systems, we calculate T ¼

T ; 1 þ lrec

lrec ¼

vrec : c

Here T \T.

Fig. 3. Receiver moves relative to stationary radiator

ð10Þ

28

G. Tarabrin

After the receiver passes through the radiator, in the process of removing the receiver from the radiator, the receiver receives waves corresponding to the characteristics of the positive direction. Let’s take the characteristics x ¼ l þ cðt  sÞ; x ¼ l þ c½t  ðs þ T Þ, which corresponds to the first period T of the radiator operation after the receiver passes through it. Let M4 ðx4 ; t4 Þ be the intersection point of the second of these characteristics with the graph of the receiver path equation x ¼ vrec t. Then T þ ¼ t4  s is the period of waves received by the receding receiver. The coordinates of the point M4 are determined by a system of two equations x4 ¼ l þ c½t4  ðs þ T Þ;

x4 ¼ vrec t4 :

Having solved it, we calculate Tþ ¼

T : 1  lrec

ð11Þ

Here T þ [ T. The Doppler effect T =T þ , which is detected by the receiver when it passes through a stationary radiator, we will call the recrad-Doppler effect. It is defined by the formula 

T Tþ

 ¼ recrad

1  lrec : 1 þ lrec

ð12Þ

5 The Radiator Surpasses the Receiver The radiator generates oscillations of the electromagnetic field of period T and moves in the positive direction of the x-axis at a constant velocity vrad . At the start of observation t ¼ 0, the radiator is in the x ¼ 0 plane, and the receiver is in front of it in the x ¼ l plane and also moves in the positive direction of the x-axis at a constant velocity vrec \vrad . At time t ¼ s ¼ l=ðvrad  vrec Þ, in the x ¼ n ¼ vrad s plane, the radiator passes through the receiver, which at this moment registers a jump-like change in the period of the waves it receives, which is called the Doppler effect. Subsequently, the radiator moves in front of the receiver, moving away from it.

Electromagnetic Communication in Deep Space

29

Fig. 4. Radiator catches up with receiver and moves away from it

When the radiator catches up with the receiver, i.e., at t\s, the receiver receives the waves that the radiator radiates in front of it. These waves correspond to the characteristics of the positive direction (Fig. 4). Let’s take the characteristics that limit the perturbations of the first period T. These will be the characteristics x ¼ ct and x ¼ vrad T þ cðt  T Þ. Let’s denote: M1 ðx1 ; t1 Þ; M2 ðx2 ; t2 Þ are the intersection points of these characteristics with the receiver path graph x ¼ l þ vrec t. Then T ¼ t2  t1 is the period of waves received by the receiver. By solving the systems of equations x1 ¼ ct1 ;

x1 ¼ l þ vrec t1 and x2 ¼ vrad T þ cðt2  T Þ;

x2 ¼ l þ vrec t2 ;

we find t1 ¼ l=ðc  vrec Þ;

t2 ¼ ½l þ ðc  vrad ÞT =ðc  vrec Þ

and calculate T ¼

1  lrad T: 1  lrec

ð13Þ

Here T \T, since lrec \lrad . When the radiator moves away from the receiver, i.e., at t [ s, the receiver receives the waves that the radiator radiates behind it. These waves correspond to the characteristics of the negative direction (Fig. 4). Let’s take the characteristics that limit the area of disturbances created by the radiator from the generator operation during the first period after overtaking the receiver. These will be the characteristics x ¼ n  cðt  sÞ and x ¼ n þ vrad T  c½t  ðs þ T Þ, where s ¼ l=ðvrad  vrec Þ; n ¼ vrad s are the coordinates of the intersection point of lines x ¼ vrad t and x ¼ l þ vrec t, i.e., the coordinates of the point M3 ðn; sÞ corresponding to the moment when the radiator passes through the receiver.

30

G. Tarabrin

Let M4 ðx4 ; t4 Þ be the intersection point of the graph of the receiver path equation x ¼ vrad t with the characteristic x ¼ n þ vrad T  c½t  ðs þ T Þ. Then the system of equations x4 ¼ n þ vrad T  c½t4  ðs þ T Þ; x4 ¼ vrad t4 determines the coordinates of the point M4 ðx4 ; t4 Þ. Excluding x4 , we get. ðc þ vrec Þt4 ¼ ðc þ vrad ÞT þ n þ cs  l: Obviously, T þ ¼ t4  s is the period of waves received by the receiver when the radiator moves away from the receiver. Calculating this period and taking into account that n ¼ vrad s; l ¼ ðvrad  vrec Þs, we find Tþ ¼

1 þ lrad T: 1 þ lrec

ð14Þ

Here T þ [ T, since lrec \lrad : The Doppler effect T =T þ detected by the receiver when the radiator overtakes the receiver, we will call the rad-after-rec-Doppler effect. It is defined by the formula 

T Tþ

 ¼ radafterrec

ð1  lrad Þð1 þ lrec Þ : ð1  lrec Þð1 þ lrad Þ

ð15Þ

6 The Receiver Surpasses the Radiator The receiver moves in the positive direction of the x-axis at a constant velocity vrec . First, the receiver approaches the radiator, and then moves away from it. At the start of observation t ¼ 0 the radiator is located at a distance l from the receiver. The radiator also moves in the positive direction of the x-axis at a constant velocity vrad \rrec and generates electromagnetic oscillations of a constant period T. At time t ¼ s ¼ l=ðvrec  vrad Þ in the x ¼ n ¼ vrec s plane, the receiver passes through the radiator and registers a jump-like change in the period of the waves it receives, which is called the Doppler effect. When the receiver catches up with the radiator, i.e., at t\s, the receiver receives the waves that the radiator radiates behind it. These waves correspond to the characteristics of the negative direction (Fig. 5). We will take the characteristics x ¼ l  ct and x ¼ l þ vrad T  cðt  T Þ corresponding to the first period T of the generated oscillations. Let M1 ðx1 ; t1 Þ; M2 ðx2 ; t2 Þ be the intersection points of these characteristics with the receiver path graph x ¼ vrec t. Then T ¼ t2  t1 is the period of waves received by the receiver. Solving the systems of equations

Electromagnetic Communication in Deep Space

x1 ¼ vrec t1 ;

x1 ¼ l  ct1 and x2 ¼ vrec t2 ;

31

x2 ¼ l þ vrad T  cðt2  T Þ;

we find t1 ¼ l=ðc þ vrec Þ;

t2 ¼ l=ðc þ vrec Þ þ T ðc þ vrad Þ=ðc þ vrec Þ

and calculate T ¼

1 þ lrad T: 1 þ lrec

ð16Þ

Here T \T, since lrec [ lrad .

Fig. 5. Receiver catches up with radiator and moves away from it

When the receiver moves away from the radiator, i.e., at t [ s ¼ l=ðvrec  vrad Þ, the receiver receives the waves that the radiator radiates in front of it. These waves correspond to the characteristics of the positive direction (Fig. 5). Let’s take the characteristics that limit the perturbation region created by the radiator during the first period ðs; s þ T Þ of the generator operation immediately after the receiver passes in the x ¼ n ¼ vrec s plane. These will be characteristics x ¼ n þ cðt  sÞ;

x ¼ n þ vrad T þ c½t  ðs þ T Þ:

Let M4 ðx4 ; t4 Þ be the intersection point of the second characteristic with the graph of the receiver path equation x ¼ vrec t. Then T þ ¼ t4  s is the period of waves received by the receiver when the receiver is removed from the radiator. The system of equations

32

G. Tarabrin

x4 ¼ vrec t4 ;

x4 ¼ n þ vrad T þ c½t4  ðs þ T Þ

determines the coordinates of the point M4 ðx4 ; t4 Þ. Excluding x4 in this system, we get ðc  vrec Þt4 ¼ ðc  vrad ÞT þ cs  n. Given that here n ¼ vrec s, we find Tþ ¼

1  lrec T: 1  lrad

ð17Þ

Here T þ [ T, since lrec \lrad . The Doppler effect T =T þ detected by the receiver when the receiver begins to move away from the radiator, overtaking it, we will call the rec-after-rad-Doppler effect. It is defined by the formula 

T Tþ

 ¼ recafterrad

ð1 þ lrad Þð1  lrec Þ : ð1 þ lrec Þð1  lrad Þ

ð18Þ

7 Discussion of Results All the formulas obtained are the result of an exact solution of one-dimensional wave problems in mathematical physics for second-order partial differential equations classified as problems with initial conditions of hyperbolic equations (also called Cauchy problems). All problems are special problems of the Maxwell equation and, without causing doubts, are adequate to reliable events of the real physical world. No new physical concepts and hypotheses are introduced in all problems at all stages of their solution. It is established that when the radiator and receiver move relative to each other, the receiver receives waves of the wrong period, which creates a generator on the radiator. When the radiator and receiver approach, the wave period is shortened (7), (10), (13), (16), and when they are removed from each other, the wave period lengthens (8), (11), (14), (17). These phenomena are known to practical physics and are called blue and red shifts. The formulas allow us to see that at a constant velocity of mutual motion, there is a qualitative difference in the periods of received waves when the radiator is moving (7), (8), and the receiver is stationary, and, conversely, when the receiver is moving (10), (11), and the radiator is stationary. In all problems of mutual motion of the radiator and receiver, the Doppler effect formulas are obtained (9), (12), (15), (18). What is remarkable is that the Doppler effect does not change because the radiator is moving and the receiver is stationary, or, conversely, the receiver is moving and the radiator is stationary. This is easily detected by comparing formulas (9) and (12). It is more difficult to see this when both the radiator and receiver are moving simultaneously, first approaching and then moving away from each other. But here, if the roles of rad and rec are changed in formulas (15) and (18), the value of the Doppler effect will not change. For example, we take lrad ¼ 0:5; lrec ¼ 0:25 in the formula (15). After calculating the Doppler effect, we

Electromagnetic Communication in Deep Space

33

get 5=9. Now in formula (18) we take lrec ¼ 0:5; lrad ¼ 0:25. By calculating the Doppler effect using this formula, we get the same result 5=9. The property of the Doppler effect to be indifferent in what is moving relative to what (the radiator relative to the receiver or vice versa), is a manifestation of the socalled principle of relativity of motion. In deep space on a spacecraft, if there is no acceleration of the ship’s movement, it is impossible to solve the question for astronauts whether the ship is moving or at rest, visually, by ear or by touch. As a result, when a space object approaches the spacecraft, it is impossible for the astronauts on the spacecraft to decide whether this space object is catching up with them or moving towards them. In this case, it is still possible for astronauts to solve location problems using electromagnetic waves, acting on the principle of relativity of motion, i.e., using the Doppler effect. The properties of electromagnetic waves to shorten or lengthen when radiated by a moving radiator or when received by a moving receiver, as well as all Doppler effects are a manifestation of the properties of the medium in which these waves propagate. This environment is called void. In fact, this environment is not such. The founder of electromagnetism, Michael Faraday, knew this. And James Maxwell put this knowledge into the mathematical model of electromagnetism, strictly following the results of Faraday’s experimental research. The complexity of studying the physical properties of void is due to the fact that it is a matter that is not perceived by any of the five human senses. All the formulas derived in this work allow us to obtain quantitative indicators of the yet unidentified properties of void.

8 Conclusion In the near future, humanity will go into deep space and reach velocities comparable to the light velocity. This will make it possible to conduct experiments with vehicles moving relative to each other and get the results of direct observations, rather than indirect ones, in which the necessary conditions are met, but sufficient conditions remain questionable, which leaves room for doubt. Let’s say, for example, you want to measure the light velocity in deep space. Earth sends two ships into space for this purpose, one after the other – the second ship catches up with the first. Both ships have receivers and monochromatic generators. Astronauts know the thrust of their main engines, know the mass of their ships. In space, there are no aerodynamic drag forces (in General, there are no resistant forces). Knowing the duration of the engine pulses, astronauts can easily calculate the velocity of the ship’s removal from the Earth. If the Earth is taken as the origin, then these velocities are the velocities of the radiator and receiver, as is accepted above. Astronauts have the ability to measure the period of received waves. Comparing them with the period of the generator, they use the formulas obtained above to calculate the light velocity. This will be a direct observation of the light velocity in the void, in the absence of the Earth’s electromagnetic field, in the absence of the Earth’s gravitational field, etc. Although the human spacewalk will not happen soon, preparations for such experiments should begin now. These experiments are extremely important because

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they will lead humanity to discover the physical properties of void, which, we can assume, is what became the basis for the formation of everything that we detect with our senses in the world around us or detect indirectly with the help of various equipment. As, for example, the genius Michael Faraday discovered the electromagnetic field.

References 1. Tarabrin, G.T.: Doppler effects in cauchy problems of maxwell’s equations. electromagnetic waves and electronic systems. Elektromagnitnye volny i elektronnye sistemy. 15(11), 37–43 (2010). Russian 2. Tarabrin, G.: Radiated and reflected doppler effects. J. Electromag. Waves Appl. (2016). https://doi.org/10.1080/09205071.2015.119069 3. Tarabrin, G.T.: Electromagnetic waves between mutually moving radiator and receiver. electromagnetic waves and electronic systems. Elektromagnitnye volny i elektronnye sistemy. 26(2), 24–35 (2021). Russian 4. Courant, R.: Partial Differential Equations. Interscience Publishers, New York-London (1962) 5. Tarabrin, G.T.: Non-chrestomathy Problems of Mathematical Physics. Palmarium Academic Publishing. Saarbrucken (2014). Russian 6. Landau, L.D., Lifshic, E.M.: Theory of Field. Theoretical Physics, vol.2. Nauka. Moskva (1964). Russian 7. Crawford, F.: Waves. Berkeley Physics Course, vol. 3. McGraw-Hill Book Company (1965)

Modeling the Spread of a Message in a Population with Differential Receptivity Alexander Petrov(&) Keldysh Institute of Applied Mathematics RAS, 4, Miusskaya sq, Moscow 125047, Russia [email protected]

Abstract. Mathematical models of information dissemination in a population often use the simplifying assumption that all individuals are equally receptive to information. Simply put, if an individual has learned some rumor, then with a probability of, say, 20%, he internalizes the rumor and will relay it to other people. The present work introduces a model in which individuals differ in their receptivity. It is easy to imagine, for example, the indicated probability is 20% for half of the population, 10% for a third of the population, and 15% for the rest. This provision adjusts the model. Thus, the process in question is the spread of a message in a population with differential receptivity. This issue is addressed to clarify whether and to what extent the above simplifying assumption affects the accuracy of the model. We conducted a series of numerical experiments with a mathematical model assuming that the population is composed of three groups with different values of receptivity and a model containing a simplifying assumption for a population with the same parameters. It was found that the simplifying assumption that receptivity is the same for all individuals distorts the description of the dynamic processes of the model. #CSOC1120.

1 Introduction The importance of information processes, primarily information confrontation in society, has been increasing rapidly on a large scale. Mathematical modeling in this area counts its history from the rumor models proposed by Daley and Kendall [1] and Mackie and M. Thompson [2], respectively, in 1964 and 1973. There is extensive literature on rumor modeling today. Most of it follows the Daley-Kendall and Mackie-Thompson approaches. For example, a model with several categories of spreaders was considered in [3], and a model considering the relay of a rumor after an incubation period was introduced in [4]. The rumor model included demographic events in [5], and a mathematical generalization was carried out in [6, 7]. Models of this class do not consider the mechanisms of information dissemination other than interpersonal communication. Another approach [8, 9] assumed that the message spreads both through interpersonal communication and the broadcasting of mass and social media. This approach placed more emphasis on sociological and psychological aspects when building models. For example, in [10], factors such as incomplete coverage of society by mass media, the relaying of a message by an

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 35–40, 2022. https://doi.org/10.1007/978-3-031-09073-8_4

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individual only after repeated receipt of it, and the forgetting of information (or losing interest in it) were introduced into consideration. Among other approaches, we note the neurological model [11] (see also [12]), which focused on the process of an individual's decision-making considering which party of information warfare to support. A significant number of works focused on information influence and the spread of information and game-theoretic models [13–16]. Related empirical papers that used the Internet as a source of data collection are numerous, but they focused not on studying the dynamics of the spread of messages, but on studying the topology of the online social nets and the linguistic analysis of online posts and search queries (see, e.g., [17–20]). All these mentioned approaches assume that individuals are identical in terms of the perception of the message. Roughly speaking, if an individual has learned some rumor, then with a probability of, say, 20%, he internalizes the rumor and will relay it to other people. The present work introduces a model in which individuals differ in their receptivity. It is easy to imagine, for example, that the indicated probability equals 20% for half of the population, 10% for a third of the population, and 15% for the rest. This provision adjusts the model. Thus, the process in question is the spread of message in a population with differential receptivity.

2 Model Consider the spread of a message in a population of N individuals. In relation to this message, each individual belongs to one of two categories: spreader or ignorant. Spreaders are those who have received and internalized the message and disseminate it through interpersonal communication. Ignorants are those who have not learned the message or have lost interest in it. An individual can switch from spreader to ignorant and back an unlimited number of times. The number of individuals converting from spreaders to ignorants per unit of time is proportional to the number of spreaders. The number of individuals turning from ignorants to spreaders per unit of time due to interpersonal communication is proportional to the frequency of meetings between spreaders and ignorants. This frequency is assumed to be proportional to the product of the numbers of individuals in these categories. In addition, ignorants are converting to spreaders due to media broadcasting; the number of corresponding transitions is proportional to the number of ignorants. The spread of the message in the population is often described in similar ways, see, for example, [9, 10]. The novelty of this paper is that we differentiate individuals according to their receptivity to the message. Namely, we assume that the population consists of k groups, each of which is characterized by its own value of receptivity. In this case, the model has the form xj ðt þ 1Þ  xj ðtÞ ¼ rj b þ c

Pk

xi ð t Þ Pi¼1 k i¼1 ni

!

  nj  xj ðtÞ  axj ðtÞ; j ¼ 1; :::; k:

ð1Þ

Here the time is assumed to be discrete: t ¼ 0; 1; 2; . . .; xi ðtÞ is the number of spreaders in the i-th group at the moment t; the number of individuals in i-th group, so

Modeling the Spread of a Message in a Population with Differential Receptivity

37

that ni  xi ðtÞ is the number of ignorants in it, and n1 þ . . . þ nk ¼ N. Parameters a, b, and c describe the intensities, respectively, of the transition of spreaders to ignorant, mass media broadcasting, and interpersonal communication. The parameters r 1 ; . . .; r k describe the receptivity of different groups to the message. As to the initial conditions, we assume that no one is familiar with the message at t ¼ 0, i.e., all individuals are ignorant: x1 ð0Þ ¼ ::: ¼ xk ð0Þ ¼ 0

ð2Þ

The next section deals with numerical experiments with model (1), (2) in the case of three groups.

3 Numerical Experiments In the case of three groups, model (1), (2) takes the form   x1 ðtÞ þ x2 ðtÞ þ x3 ðtÞ x1 ðt þ 1Þ  x1 ðtÞ ¼ r1 b þ c ½n1  x1 ðtÞ  ax1 ðtÞ; n1 þ n2 þ n3   x1 ðtÞ þ x2 ðtÞ þ x3 ðtÞ x2 ðt þ 1Þ  x2 ðtÞ ¼ r2 b þ c ½n2  x2 ðtÞ  ax2 ðtÞ; n1 þ n2 þ n3   x1 ðtÞ þ x2 ðtÞ þ x3 ðtÞ x3 ðt þ 1Þ  x3 ðtÞ ¼ r3 b þ c ½n3  x3 ðtÞ  ax3 ðtÞ: n1 þ n2 þ n3 x1 ð0Þ ¼ x2 ð0Þ ¼ x3 ð0Þ ¼ 0

3.1

ð3Þ ð4Þ ð5Þ ð6Þ

Experiment 1

Let the parameters be a ¼ 0:2; b ¼ 0:03; c ¼ 4; r1 ¼ 0:1; r2 ¼ 0:4; r3 ¼ 0:6; n1 ¼ 5000000; n2 ¼ 3000000; n3 ¼ 2000000: Here the groups are numbered in ascending order of receptivity (r 1 \r 2 \r 3 ), the largest part of the population is the least receptive, while the most receptive group is the smallest (n1 \n2 \n3 ). The solution graph appears in Fig. 1.

38

A. Petrov

Fig. 1. Experiment 1: the numerical solution.

Suppose we accept the simplistic assumption that all individuals are equally receptive to the message. The average receptivity for Experiment 1 data is r ¼ ðr1 n1 þ r2 n2 þ r3 n3 Þ=ðn1 þ n2 þ n3 Þ ¼ 0:29: Consider the spread of the message in a population with this receptivity. 3.2

Experiment 2

Take the size of the population and constants a, b, and c the same as in Experiment 1: n ¼ 10000000, a ¼ 0:2; b ¼ 0:03; c ¼ 4. The average receptivity is calculated above r ¼ 0:29. The dynamics of the number of spreaders x(t) are governed by the equation   xðtÞ x ð t þ 1Þ  x ð t Þ ¼ r b þ c ½n  xðtÞ  axðtÞ; xð0Þ ¼ 0 n

ð7Þ

(This equation is obtained from (1) by setting k = 1). The solution appears in Fig. 2 along with the total number of spreaders from Experiment 1.

Modeling the Spread of a Message in a Population with Differential Receptivity

39

Fig. 2. The numerical solution for Experiment 2 and the total number of spreaders from Experiment 1 (dotted).

The difference between the functions from the “big” model (3)–(6) and simplified model (7) differ significantly; the final number of spreaders also differs. More numerical experiments have been conducted, varying the parameters of the model. Usually, an obvious difference remains between the total number of spreaders in the model with differential receptivity and the simplified model. The conclusion is that models that do not consider differentiated receptivity tend to describe both the dynamics and the final number of spreaders inaccurately.

References 1. Daley, D.J., Kendall, D.G.: Stochastic rumors. J. Inst. Math. Appl. 1, 42–55 (1964) 2. Maki, D.P., Thompson, M.: Mathematical Models and Applications. Prentice-Hall, Englewood Cliffs (1973) 3. Isea, R., Mayo-García, R.: Mathematical analysis of the spreading of a rumor among different subgroups of spreaders. Pure Appl.Math. Lett. 2015, 50–54 (2015) 4. Liang’an, H., Huang, P., Guo, C.: Analyzing the dynamics of a rumor transmission model with incubation. Discret. Dyn. Nat. Soc. 2012(328151), 21 (2012) 5. Kawachi, K.: Deterministic models for rumor transmission. Nonlinear Anal. Real World Appl. 9(5), 1989–2028 (2008) 6. Dickinson, R.E., Pearce, C.E.M.: Rumors, epidemics, and processes of mass action: synthesis and analysis. Math. Comput. Model. 38(11–13), 1157–1167 (2003) 7. Pearce, C.E.: The exact solution of the general stochastic rumour. Math. Comput. Modelling Int. J. 31(10–12), 289–298 (2000) 8. Samarskii, A.A., Mikhailov, A.P.: Principles of Mathematical Modelling: Ideas, Methods, Examples. Taylor and Francis Group (2001) 9. Mikhailov, A.P., Marevtseva, N.A.: Models of information struggle. Math. Models Comput. Simul. 4(3), 251–259 (2012)

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10. Mikhailov, A.P., Petrov, A.P., Marevtseva, N.A., Tretiakova, I.V.: Development of a model of information dissemination in society. Math. Models Comput. Simul. 6(5), 535–541 (2014). https://doi.org/10.1134/S2070048214050093 11. Petrov, A., Proncheva, O.: Modeling propaganda battle: decision-making, homophily, and echo chambers. In: Ustalov, D., Filchenkov, A., Pivovarova, L., Žižka, J. (eds.) AINL 2018. CCIS, vol. 930, pp. 197–209. Springer, Cham (2018). https://doi.org/10.1007/978-3-03001204-5_19 12. Proncheva, O.: A model of propaganda battle with individuals’ opinions on topics saliency. In: 13th International Conference Management of Large-Scale System Development, pp. 1– 4. MLSD, Moscow, Russia (2020). https://doi.org/10.1109/MLSD49919.2020.9247796 13. Kozitsin, I.V., Marchenko, A.M., Goiko, V.L., Palkin, R.V.: Symmetric convex mechanism of opinion formation predicts directions of users’ opinions trajectories. In: Twelfth International Conference Management of Large-Scale System Development, pp. 1–5. MLSD, Moscow, Russia (2019). https://doi.org/10.1109/MLSD.2019.8911064 14. Kozitsin, I.V., et al.: Modeling political preferences of russian users exemplified by the social network vkontakte. Math. Models Comput. Simul. 12, 185–194 (2020). https://doi. org/10.1134/S2070048220020088 15. Chkhartishvili, A.G., Gubanov, D.A., Novikov, D.A.: Social Networks: Models of Information Influence, Control and Confrontation. Springer, Cham (2019). https://doi.org/ 10.1007/978-3-030-05429-8 16. Gubanov, D., Petrov, I.: Multidimensional model of opinion polarization in social networks. In: Twelfth International Conference Management of Large-Scale System Development. IEEE (2019). https://doi.org/10.1109/MLSD.2019.8910967 17. Akhtyamova, L., Alexandrov, M., Cardiff, J., Koshulko, O.: Opinion mining on small and noisy samples of health-related texts. In: Shakhovska, N., Medykovskyy, M.O. (eds.) CSIT 2018. AISC, vol. 871, pp. 379–390. Springer, Cham (2019). https://doi.org/10.1007/978-3030-01069-0_27 18. Akhtyamova, L., Cardiff, J.: LM-based word embeddings improve biomedical named entity recognition: a detailed analysis. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds.) IWBBIO 2020. LNCS, vol. 12108, pp. 624–635. Springer, Cham (2020). https:// doi.org/10.1007/978-3-030-45385-5_56 19. Boldyreva, A., Sobolevskiy, O., Alexandrov, M., Danilova, V.: Creating collections of descriptors of events and processes based on internet queries. In: Sidorov, G., Herrera-, O. (eds.) MICAI 2016. LNCS (LNAI), vol. 10061, pp. 303–314. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62434-1_26 20. Boldyreva, A. Alexandrov, M., Koshulko, O., Sobolevskiy, O.: Queries to Internet as a tool for analysis of the regional police work and forecast of the crimes in regions. In: 14-th Mexican Intern. Conf. on Artif. Intell., vol. 10061, chapter 25, pp. 290–302. Springer, Cham, LNAI (2016)

Comparative Analysis of Big Data Acquisition Technology from Landsat 8 and Sentinel-2 Satellites Svetlana Veretekhina1(&) , Krapivka Sergey1, Tatiana Pronkina2 Vladimir Khalyukin1 , Medvedeva Alla1 , Elena Khudyakova3 and Marina Stepantsevich3

, ,

1

Russian State Social University, 4, Building 1, Wilhelm Pieck Street, Moscow 129226, Russia [email protected] 2 Institute of Digital Economy, Yugra State University, Khanty-Mansiysk, Russia 3 Russian State Agrarian University Moscow Agricultural Academy named after K. A. Timiryazev, 49, Timiryazevskaya Street, Moscow 127550, Russia

Abstract. In a scientific study, the authors conduct an experiment to obtain spatial data from the Landsat-8 and Sentinel-2 satellites. An analytical review and a system analysis of the data is carried out. A step-by-step instruction of the BIG DTA collection technology has been developed and described. The review of ISO Geographic information standards is carried out. The technology of obtaining remote sensing data of the earth is described. An example of obtaining spatial images is given. An example of encoding a spatial image is given. Information about Landsat 8 OLI/2TIRS C2L images (individual and unique image number, date of shooting, rows) is described. The table “International classification of BIG DATA processing levels” is presented. The technology of converting multi-time composites into digital form is described. The RGB channel is used for the conversion. The collection, systematization and processing of satellite big data is described. The characteristics are described – turnover, variety, accuracy. The main task of remote sensing data processing is determined. An example of processing photos or images with the necessary radio-metric and geometric characteristics is given. The technology of digital color representation is described. It is proved that artificial intelligence algorithms effectively work out the reverse reflected signal represented by a digit. Portals for obtaining spatial data for free are defined. The article describes the summation of information about the state of the natural environment, human economic activity in a remote area. The scope of application of the technology for obtaining BIG DATA from the Landsat-8 and Sentinel-2 satellites is determined. To develop a technology for monitoring terrestrial objects based on ultra-highresolution satellite images, the authors of the scientific experiment carried out scientific research. In conclusion, the authors express their gratitude to domestic and foreign scientists, including the Russian State Social University, Moscow. Keywords: System analysis
Landsat-8 and Sentinel-2 satellites
International classification of BIG DATA processing levels
Portals for free spatial data acquisition © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 41–53, 2022. https://doi.org/10.1007/978-3-031-09073-8_5

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1 Introduction Remote sensing data of the Earth (DDZ) occupy the leading positions of modern spatial data. Actually, the possession of BIG DATA of space information. Spatial data from the Landsat8 and Sentinel-2A satellites for scientific research and business are provided free of charge. The processes of obtaining and processing spatial data are standardized. The standards are combined into a common ISO series “Geographic information”. According to the introduced terminology, geographical information is understood as information about objects and phenomena contained in an explicit or implicit form, an indication of their location relative to the Earth. One of the more popular standards of the series is ISO Geographic information. This standard is adopted in most countries as a national standard for the content of spatial information metadata, as well as in international organizations. The principles of computer representation of geographical information are set by the following standards [1–4]. The main task of using these standards is to determine such data as latitude, longitude, projections, points, lines and polygons. With the help of the obtained values, it becomes possible to model the real picture of the world. The traditional discrete data model in GIS is contrasted with a continuous one. Based on this, we can distinguish standards in the field of “coatings” [5–7]. When working with sensing data, such a concept as “location” is very important. In order to be able to analyze the obtained images, it is necessary to determine where the information was collected in the plane of the Earth, since the surface of our planet is diverse. Based on this, the following ISO standards of this direction were introduced [8–11]. When presenting information received from satellites during Earth surface sensing, it is necessary to adopt a series of standards related to web services. This aspect is necessary for transmitting data to display devices of the received data [12–15]. ISO standards have a huge number of characteristics that can be useful for the field of Earth sensing. However, not all of them fall into the series considered in this scientific work. The list of standards that are not included in the series are related to the representation of geographical coordinates, the representation of dates and times. It should be noted that in addition to ISO standards, the OGC Open Geospatial Consortium is engaged in developments in the field of sensing [16].

2 Materials and Methods 2.1

Technology for Obtaining Remote Sensing Data from the Landsat- 8 Satellite

The main research materials are the approbation of obtaining BIG DATA from satellites. The practical method of the conducted scientific research is the experiment of obtaining spatial data. Let's clarify: as far as it is possible to get spatial data for free. To conduct the experiment, we will use the data capture technology. Step 1. To get images from the Landsat 8 satellite, a portal is used https:// earthexplorer.usgs.gov/ EarthExplorer from the United States Geological Survey (US Geological Survey). This resource is used for searching and ordering satellite images, aerial photographs. Pre-registration is required. After registration, you can download Big Data. Step 1. The main page of the service-click “Login” (Fig. 1).

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Fig. 1. Screenshot of the main page of the site https://earthexplorer.usgs.gov/

Step 2. Create an account - “Create a New Account”, register in the system. The user identification procedure is carried out. Information about the purposes of using the Earth remote sensing data is entered into the user's a priori data. We determine the status “free access to data is important”. The “Submit” section provides the data. On the Earth Explorer service, there is a search bar on the left. Select a location (specify the GIS coordinates of the studied earth’s surface: latitude/longitude).

Fig. 2. Coordinates of the Klyazma reservoir, Moscow region.

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The coordinates of the Klyazma reservoir, Moscow region, were selected for the experiments. We specify the time range for receiving images, for example, from February 11, 2013. then we order images-the “Data Sets” button. The Landsat 8 satellite provides the Landsat archive, specify the checkboxes next to Landsat 8 OLI/TIRS C2L2, then click the “Results” button. The results appear in the format shown in Fig. 3.

Fig. 3. Landsat 8 OLI/TIRS C2L2

Fig. 4. The fragment “List of images”.

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Information about Landsat 8 OLI/2TIRS C2L images: 1. ID-an individual and unique photo number. For example, one of our found plots according to the example below is called LC08_L2SP_085238_20210625_ 20210630_02_T2. We can determine that this is Landsat 8, because LK08, the year from 18 to 21 characters: 2021. Day, from 22 to 25 character: this is June 25; 085238-085-column (path); 238-row (moat); 2. Date featured - date shot; 3. Path-column; 4. Row. Description of Fig. 4: 1. 2. 3. 4.

“Show footprint” - shows the boundaries of the snapshot; “Show Browse Overlay” (show overview overlay) – puts a snapshot on the map; “Compare Browse” (compare the review); “Show Metadata and Browse” (show and view metadata)- displayed data about the snapshot (available functions-download, pro-view and download, view metadata in FGDC format).

FGDC (Federal Geographic Data Committee) is the Federal Committee on Geographical Data. It is a US government commission that promotes the development, use and dissemination of geo-spatial data on a national basis. Example of a fragment of the encoding of a snapshot:



U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center 20060510 Landsat 8 OLI/TIRS Collection 2 Remote-Sensing Image

U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center Sioux Falls, South Dakota, USA

https://earthexplorer.usgs.gov



“Download options” – you can download the archive with all the files presented below at once (894.36 MB) or separately (weight from 14.82 kb to 99 mb). Here are the files in the TXT, XML, TIF format (Fig. 5).

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Fig. 5. Available formats for downloading

Step 3. Collection, systematization and processing of satellite big data. The most important task of processing remote sensing data is to obtain photos or images with the necessary radiometric and geometric characteristics. Table 1 describes the standard levels of processing images of the Earth's surface. Starting from level 1A, each subsequent level is based on the previous one, but with the addition of some transformations. The data that we receive during remote sensing of the earth for the current period is characterized not only by a significant increase in the size of data, but also by an increase in the speed of their receipt. Only twelve satellites generate more than 2 terabytes of information per day or half a petabyte per year, and two of them guarantee almost half of this size. There is certainly no doubt that this is big data here. In 2001 the Meta Group company Doug Laney published the work “3D Data Management: Controlling Data Volume, Velocity and Variety” [17, 18], where he identified the main characteristics that big data, so called “3V”, should meet: Volume (English volume, the amount of physical volume); Speed (English velocity, both the speed of data growth, and the need for high-speed processing and obtaining final results); Variety (English variety, the ability to process structured and semi-structured data at the same time).

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Table 1. International classification of BIG DATA processing levels.

Standard Description level 0 Absolutely “ raw “ data from shooting cameras, without any transformations, compressed information. It is the base level that gives the core to subsequent processing levels 1A It includes only radiometric correction of distortions caused by the difference in the sensitivity of individual sensors of the shooting system. Absolute radiometric calibration coefficients are provided 1B It includes radiometric correction of the processing level 1A, as well as geometric correlation of systematic errors of the sensors of the scanning system, including panoramic distortions, distortions caused by the rotation and rotation of the Earth, fluctuations in the height of the satellite's orbit. Absolute radiometric calibration was applied. Additionally, the coefficients of the rational polynomial (RPC) can be provided, approximating the geometry of the image 2A The images are converted to a standard geographical projection without using ground reference points. The image is projected onto the middle plane, or a global digital terrain model (DEM) is used with a step on the terrain of 1 km 2B The level 2A images are reduced to a standard cartographic projection using ground reference points. The image is projected onto the middle plane, or a global digital terrain model (DEM) is used. RPC coefficients can be provided 3A Unlike Level 2B, level 3A images are projected into a given cartographic projection by orthotransformation, using a snapshot model, reference ground points and a terrain model, the resulting images are orthocorrected with a certain accuracy. Images are usually cut into standard map sheets 2B Level 3B implies combining level images. 3A into single seamless raster mosaics covering large areas

Image format Not defined

Metadata format Not defined

RAW TIFF

CEOS XML

RAW TIFF

CEOS XML ASCII

GeoTIFF XML, ASCII

GeoTIFF XML, ASCII

GeoTIFF XML, ASCII

GeoTIFF XML, ASCII

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Digital Spectrum of Reflected Signals of RGB Channels. Sentinel-2A Satellites.

The variety of processing of the reflected satellite signal is contained in the digital spectrum of the linked RGB channels. Digital RGB decomposition of the color palette provides an opportunity to form a multi-time composite, with the help of which human vision is able to perceive and visualize the reflected signal, thereby visually and conveniently analyzing the state of terrestrial objects. Absolutely any of the colors visible to the human eye, we perceive three types of receptors that are responsible for color vision. The retina of our eye detects three radiations - the long-wave part of the visible spectrum (Red-red), the medium-wave (Green-green) and the short-wave part (Blueblue). Color selection according to the RGB system (Fig. 6). All the collected information from spacecraft is stored in the archives for further use. Archiving – compressing files into an archive. Cataloging is the creation of metadata catalogs in which you can find interesting information on certain parameters.

Fig. 6. Color palette RGB/ RGB Color Palette/

Let's apply a color palette to convert a multi-time composite into RGB images (Fig. 7 Image from the Sentinel-2A satellite. Atmosphere Fig. 8. A picture from the Sentinel-2A satellite. Earth.)

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Color RGB 18, 50, 50

32,49, 57

64,93, 97

70,131, 83

74,117, 2 85,141, 140 82,226, 227

RGB 97, 201, 198 107, 232, 228

120, 249, 245

127, 197, 195

133, 188, 185

Color 139, 251, 102, 185, 55 254

Fig. 7. A picture from the Sentinel-2A satellite. Atmosphere.

Figures 7 and 8 show how a color multi-time composite can be converted into digital data. The RGB channel is used for conversion (Fig. 6). Digital data is processed by artificial intelligence algorithms. Volume - the amount of data created every second. Turnover - the speed of data creation and movement (a good example of real-time verification is the detection of fraud with credit cards). Diversity - a large number of different types of data (financial reports, sensor readings, video and sound, social media posts, photos). Accuracy - the degree of ordering of the data. So, we have a huge amount of data of different formats and quality. Why should they change our world? The fact is that we now have the technology to collect and analyze information around us, regardless of its volume.

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Color RGB Color

61, 73, 71 63, 63, 44 29, 68, 57 43, 77, 86 68, 76, 79 89, 60, 56 88, 123, 91

RGB

110, 134, 129, 106, 132, 110, 43, 123, 32 68, 116, 110 92 189 172

151, 131, 117

94, 129, 109

Fig. 8. A picture from the Sentinel-2A satellite. Earth.

3 Result and Discussion The use of BIG DATA remote sensing allows you to get up-to-date, complete and reliable information about the state of the natural environment and about economic activity in a remote area. To develop a technology for monitoring urban areas using ultra-high-resolution satellite images, the authors of the scientific experiment carried out a number of scientific studies: – the analysis of obtaining geospatial information was carried out, the requirements for geospatial information were determined (ISO Geographic information series of standards, section Introduction); – ultra-high-resolution satellite images were studied, de-encryption was carried out (Figs. 1, 2, 3, 4, 5, 6, 7 and 8); – the accuracy of sighting at the points of ultra-high-resolution satellite images is investigated, depending on the shooting parameters and the type of contour (Table 1 International classification of BIG DATA processing levels); – a step-by-step technology of registration on portals for obtaining data from the Landsat8 satellite is described (the portal is used https://earthexplorer.usgs.gov/ EarthExplorer from the United States Geological Survey - US Geological Survey);

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– the procedure for obtaining multi-time composites and converting the color gamut to the RGB channel is described (the procedure for digital color representation Fig. 7, Fig. 8); – the authors have proved that artificial intelligence algorithms process the reverse reflected signal, which is converted into a digit; – data formats according to the international classification are defined (image format and metadata format, columns 3 and 4 (Table 1 International classification of BIG DATA processing levels). Conclusions: as a result of the system analysis, the authors ‘ actual research methods have been identified, which can be applied to the following areas: – monitoring the restoration of disturbed natural landscapes as a result of their industrial use; changing the territories occupied by cities, settlements, industrial zones, their condition; – environmental monitoring, identification of zones of environmental violations (contamination of soil, atmosphere, reservoirs); – monitoring of individual objects of urban infrastructure (roads, bridges, industrial facilities); – identification of production and consumption waste disposal facilities; – urban planning, construction, transport, housing and communal services; cadastral works. Having analyzed the composition of the task of remote sensing of objects of populated areas, it is possible to identify the following main areas of application of remote sensing information and briefly formulate their features: – environmental monitoring of the spread of pollution in all three main natural areas (atmosphere, land surface, water environment), the development of erosion and other processes of degradation of the natural environment; – detection of the fact and targeted localization of large industrial and other sources of environmental pollution; – monitoring of the largest clusters of industrial enterprises; – monitoring of emergency situations, including assessment of the scale and nature of destruction; – forecasting of earthquakes and other destructive natural phenomena; – notification of tsunamis, floods, mudslides, chemical and other contamination of the area, forest fires, large oil spills, etc.; – creation and updating of a wide range of general geographic and thematic cartographic materials (topographic maps, digital maps, GIS for various purposes); – information support of land management activities, laying of transport highways, construction of industrial facilities and urban construction, compilation of cadastres.

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4 Conclusion It is necessary to note the works of Russian and foreign authors. They proposed various methods of soil assessment. Previous studies of the authors professor S. V. Veretekhina et al. the scientific journals “Ecoloji” and “Eurasian Journal of Biological Sciences” showed the need to use new technologies for quality assessment [19, 20]. Thanks to the efforts of scientists, a knowledge base was formed, methods of land assessment were shown, new opportunities of information portals were described. The author of technologies for using satellite data for monitoring the state of forest stands is the scientist Myshlyakov S. G. [16]. It should be concluded that remote sensing information obtained in the interests of solving the above tasks must meet a number of requirements for its parameters, the main of which are the following: – spatial resolution (terrain resolution), radiometric resolution (characterizes the number of brightness gradations in satellite images or the sensitivity of remote sensing devices), – the number of spectral channels or spectral resolution, – the frequency of the survey (breaks between repetitions of observations of the same areas), – the span of the capture strips. Based on the performed scientific research, a technology for obtaining spatial data for free is proposed and developed. A step-by-step technology for obtaining data from Landsat-8, Sentinel-2 are described. The authors express their gratitude to their Russian and foreign colleagues for their scientific research in the fields of ecology, computer engineering, system analysis, and information technologies.

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

ISO 19107:2019 Geographic information — Spatial schema ISO 9108:2014 Geographic information — Temporalschema ISO 19111:2007 Geographic information — Spatial referencing by coordinates ISO 19112:2019 Geographic information — Spatial referencing by geographic identifiers ISO 19123:2018 (Part 2) Geographic information — Schema for coverage geometry and functions ISO/TS 19129:2019 Geographic information — The data structure of images, coordinate grid and coverage (Imagery, gridded and coverage data framework) ISO 19130:2018 Geographic information — Image Sensor Models for Geolocation-Part 1: Basics (Sensor and data models for imagery and gridded data) ISO 19116:2019 Geographic information — Positioning services ISO 19132:2016 Geographic information — Location-based services (LBS) - Reference model ISO 19133:2016 Geographic information — Location-based services (LBS) - Tracking and navigation ISO 19141:2017 Geographic information — Schema for moving features ISO 19119:2016 Geographic information — Services

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13. ISO 19128 :2021 Geographic information — Map server interface 14. ISO 19136-1:2020 Geographic information — Geography Markup Language- Geography Markup Language (GML)/ basics 15. ISO 19142:2010 Geographic information — Web Feature Service 16. Myshliakov, S.G.: Capabilities of Sentinel-1 radar images for solving agricultural problems Sovzond company. http://geomatica.ru/clauses/13-2/. Accessed 12 Jan 2022 17. 3D Data Management: Controlling Data Volume, Velocity, and Variety, Technical report, META Group. http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Manage ment-Controlling-Data-Volume-Velocity-and-Variety.pdf. Accessed 12 Jan 2022 18. House of Innovation and cooperation in the field of geolocation technologies. Your global resource for geospatial information and standards. https://www.ogc.org/. Accessed 12 Jan 2022 19. Veretekhina, S.V., et al.: Informational system for monitoring the state of the natural environment according to the Russian satellite. Ekoloji 27(106), 461–469 (2018) 20. Veretekhina, S.V., Pankov, V., Krapivka, S.V.: Comparative characteristics of russian and bulgarian soil control methods. EurAsian J. BioSci. 14(1), 1359–1366 (2020)

Implementation of the Automated Algorithm for Diagnosis of PCOS Based on Rotterdam 2003 Criteria Alina Atalyan1(&), Oleg Buchnev2, Lyudmila Lazareva1, Iana Nadeliaeva1, Irina Danusevich1, and Larisa Suturina1 1

2

Scientific Center for Family Health and Human Reproduction, Irkutsk, Russian Federation [email protected] Irkutsk National Research Technical University, Irkutsk, Russian Federation

Abstract. Background: nowadays there are many clinical decision support systems, but there is no system to diagnose PCOS. The purpose of the article is to describe the information system, developed on PCOS diagnostic algorithm based on the 2003 Rotterdam Consensus. Methods: we have worked with a database containing 1,492observations of premenopausal women aged 18 to 45 years, 153 of them are diagnosed with PCOS. The information system and PCOS diagnostic algorithm are used Django framework. We have applied SQLite3 to manage the database and a sensitivity-specificity test to check the algorithm. Results: the result of the work is PCOS diagnostic algorithm applied as a part of the special information system. The accuracy, sensitivity and specificity of the algorithm have been evaluated with high marks. Conclusions: as it is rather complicated for a doctor to diagnose PCOS in the daily practice, it makes necessary to work out an automated clinical decision support system. The given system can support clinical decisions, and also it provides longitudinal monitoring, stores patients’ data and PCOS diagnostics. #CSOC1120. Keywords: Automated diagnostic system Clinical decision-support system


Polycystic ovary syndrome


1 Introduction Decision support systems (DSS), based on the methods of mathematics, computer science and information technology, are successfully used to solve the problems of medical diagnostics in many areas of medicine. As a rule, DSS use mathematical modeling and computer modeling methods available as special computer programs and soft-ware. Clinical decision support is always connected with numerous specific factors impacting mutually and signaling various diseases. Simultaneously, some objective and subjective reasons influence a doctor’s data processing. So, it is obvious, that automated decision support systems in medicine can improve the quality of medical care. Nowadays, the dissemination of clinical decision support systems in the medical field is slow due to several reasons: lacking comprehensibility, low user acceptance, specific problem settings and unique application environment [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 54–59, 2022. https://doi.org/10.1007/978-3-031-09073-8_6

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Despite this, CDSS are inevitable to develop quickly and spread widely: modern medical information systems include specialized medical databases, search engines, medical data processing systems [2]. CDSS use clinical laboratory datasets that help a doctor take into consideration complex factors in diagnoses. Artificial intelligence progress provides CDSS. Currently, in all scientific fields including medicine, artificial intelligence methods are getting more and more popular: neural networks, machine learning, etc. But, as a fact, if there is a definite, formalized algorithm to diagnose a particular disease, it is better to use it. Today, in medicine there are many special software complexes for an automated diagnostic. In order to diagnose PCOS – a widespread female neuroendocrine disease, an automated diagnosis system worked on ultrasound images has been developed [3]. This system analyzes diagnostic images via ultrasound automatically. But to diagnose PCOS, besides ultrasound research, we need clinical data and a laboratory indicator analysis. Therefore, we decided to develop an automated system to analyze all symptoms and make a PCOS diagnosis. The purpose of this research is to develop a medical information system with a PCOS diagnostic algorithm based on the on the 2003 Rotterdam Consensus [4]. Polycystic ovary syndrome (PCOS) is a common reproductive and endocrine disorder found in 6–10% of premenopausal women [5]. This disease has a wide range of clinical manifestations, including: hyperandrogenemia, hyperinsulinemia, luteinizing hormone increased secretion (LH), menstrual disorder, hirsutism and infertility. Due to the Rotterdam criteria, PCOS [4] is defined in the age groups of 18–45 years old as oligo- or anovulation, clinical and/or biochemical signs of hyperandrogenism and polycystic ovaries [6–9]. To diagnose PCOS we need two of three criteria having excluded related disorders.

2 Materials and Methods We use the basic data obtained from the electronic data collection system REDCap [10]. This system was used to collect and process data research of PCOS epidemiology and phenotype in the Eastern Siberia (ESPEP) held in the Irkutsk region and the Republic of Buryatia (Russia) during 2016–2019 [11]. The database has 1,492 cases of premenopausal women aged 18 till 45, including 153 PCOS patients. We used these data to check the given algorithm. The tests showed high results: the mistakes in diagnostics by the algorithm were caused only by the mistakes in the initial data. We have already mentioned that, there are only three criteria for PCOS diagnosis: clinical data, ultrasound study data, and laboratory data. Accordingly, besides general information about age, ethnicity and research inclusion criteria, we used clinical and laboratory tests, divided into three groups: 1. Clinical data included vital function scores, menstrual cycle details, menstrual cycle periods and disorders. 2. Ultrasound data on an ovarian volume, a follicle count, and a cystic diameter.

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3. Laboratory data (testosterone values, IFA, SHBG, DHEAS). As the algorithm is developed to diagnose the women in Eastern Siberia, it is adapted to the regional upper limits of the norm (UNL) for TT, FAI and DHEAS due to a patient’s race. There are four PCOS subphenotypes defined due to the combination of clinical and biochemical PCOS signs: Phenotype A – clinical and/or biochemical hyperandrogenia (HA) and menstrual dysfunction (MD), as well as polycystic ovarian morphology (PCOS); B – HA and MD; C – HA и PCOM; and D – MD and PCOM. We excluded the related disorders such as uncompensated thyroid dysfunction, hyperprolactinemia and 21-hydroxylase deficient non-classic congenital adrenal hyperplasia (NC-CAH) when diagnosing PCOS, taking into account prolactin levels, 17-OH progesterone (17-OHP) and TSH levels in all the patients with hyperandrogenism, oligoamenorrhea and/or PCOM. In case a patient has only one clinical, ultrasound or laboratory PCOS sign, the algorithm defines this condition as a “grey” zone. The recommendation is to provide such patients with monitoring, as this shows some disturbances in the body.

3 Results The PCOS diagnostic algorithm is applied as a part of a specially developed information system to monitor PCOS patients. The system is used Django framework; the system database is controlled with SQLite3. The information system operates as a web application, consists of web pages operating under Django server control. All program pages are divided into two parts: external and internal. The first part is external, opened to all users; the second one is internal, it requires the authorization in the system because of patients’ personal and medical information. The open part has a page with general PCOS notes: a definition, diagnostic criteria, prevalence, etc. The second page shows symptoms for self-diagnostics. Further, we intent to develop a self-diagnostic algorithm in open access for the women with health disorders. The third page gives contacts of the Irkutsk Scientific Center for Family Health and Human Reproduction. The fourth page is an authorization form with two typing boxes for a user to enter a login and a password. After a successful authorization a user enters the program. The internal part has an interface to work with patients’ database, which reflects patients, doctor's appointments, clinical data, ultrasound results and laboratory data. The internal pages are designed in a style of REDCap familiar to doctors. The program has a form of a patient's card, which registers all patients’ visits, clinical, ultrasound and laboratory data. Keeping all data on one form makes it easier for a doctor to track a change in a patient's conditions. Also, a doctor can have a hint making a decision about PCOS: here we can use PCOS diagnostic algorithm. The diagnostic algorithm determines PCOS formal signs, a phenotype, exclusion, a “grey” zone and a text description. The diagnostic result is in the database. You can find the current version of the program, available in the article publication period, at: https://github.com/BuchnevOS/PCOS_dignostics.git. To run the program, you download and install it on your computer. The program is planned to have an option to change the regional critical values of testosterone and ELISA, now it uses only the thresholds defined for Eastern Siberia. It is going to be integrated with REDCap.

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In order to test PCOS diagnostic algorithm we used sensitivity and specificity tests. These tests confirm hypotheses in clinical medicine. The likelihood ratio allows you to best test the results of clinical tests when making a diagnosis [12–14]. To demonstrate the effectiveness of the given algorithm, we compared the results of the algorithm with an expert decision on PCOS diagnosis made by three experienced obstetricians and gynecologists on a sample of 1,492 patients. We tested the results of our algorithm by calculating sensitivity and specificity for three classes [15]: 0 – PCOS was not diagnosed, 1 – PCOS was diagnosed, NA – not included in the diagnosis or insufficient data for diagnosis. Table 1 gives the test results. Table 1. Results of multi-class classification test. Predicted class 0 Predicted class 1 Predicted class NA Actual class 0 494 3 66 Actual class 1 0 153 0 Actual class NA 0 0 776

Predictive classes are the algorithm result. Actual classes are an expert evaluation made by the experienced obstetricians and gynecologists. The table shows three cases of the algorithm errors in PCOS diagnostic. It is the fault of unaccounted conditions in the algorithm; they are not related to PCOS diagnostic criteria, such as an increased ovarian volume due to cancerous tumor and premature ovarian insufficiency (PNIA). Design specificity for class 0 - 0.998, sensitivity - 1,000. Design specificity for class 1 1000, sensitivity - 0.877. Design specificity for class NA - 0.907, sensitivity - 1,000. The accuracy of the algorithm is 0.954. Thus, the given algorithm diagnoses the disease quite well.

4 Discussion Nowadays, artificial intelligence methods are widely used to solve the problems of medical diagnosis. Numerous current publications are devoted to neural networks or machine learning for medical diagnoses. Information systems have been developed to manage databases and clinical decision support. We have developed one of such an information system. It follows the uniquely defined algorithm to diagnose PCOS. The information system operates as a web application which, besides PCOS diagnosis, allows you to store clinical, ultrasound and laboratory data of patients. It can also give the result of PCOS diagnosis due to stored data, PCOS phenotype, “grey” zone, exclusions, etc. The described system is easy to install and easy to use. Via it a doctor can reduce the likelihood of mistakes in PCOS diagnosis. Also, the information system makes it much easier to track disease dynamics for each patient.

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5 Conclusions As PCOS is complicated for a doctor to diagnose in the daily practice, it makes it necessary to develop information systems with clinical decision support. We have developed and tested the information system with PCOS diagnostic algorithm due to the Rotterdam Consensus. The algorithm uses regional thresholds for Eastern Siberia, and shows high efficacy on 1,492 patients. The calculation of sensitivity, specificity and accuracy shows the given algorithm as an effective tool of diagnosing the disease analyzing the available data. The developed information system supports clinical decision, as well as provides longitudinal monitoring and saves patients’ data and PCOS diagnosis results.

References 1. Richter, J., Vogel, S.: Illustration of clinical decision support system development complexity. Stud. Health Technol. Inform, 261–264 (2020). https://doi.org/10.3233/ SHTI200544 2. Bright, T.J., et al.: Effect of clinical decision-support systems: a systematic review. Ann. Intern. Med. 157(1), 29–43 (2012). https://doi.org/10.7326/0003-4819-157-1-20120703000450 3. Deng, Y., Wang, Y., Shen, Y.: An automated diagnostic system of polycystic ovary syndrome based on object growing. Artif. Intell. Med. 51(3), 199–209 (2011). https://doi. org/10.1016/j.artmed.2010.10.002 4. Teede, H.J., et al.: International PCOS Network; Andersen, M., et al.: Recommendations from the International Evidence-Based Guideline for the Assessment and Management of Polycystic Ovary Syndrome. Hum. Reprod. 33(9), 1602–1618 (2018). https://doi.org/10. 1093/humrep/dey256 5. Bozdag, G., Mumusoglu, S., Zengin, D., Karabulut, E., Yildiz, B.O.: The prevalence and phenotypic features of polycystic ovary syndrome: a systematic review and meta-analysis. Hum. Reprod. 31(12), 2841–2855 (2016). https://doi.org/10.1093/humrep/dew218 6. Belenkaya, L., Lazareva, L., Walker, W., Lizneva, D., Suturina, L.: Criteria, phenotypes and prevalence of polycystic ovary syndrome. Minerva Ginecol. 71(3), 211–223. https://doi.org/ 10.23736/S0026-4784.19.04404-6 7. Lazareva, L., Sharifulin, E., Belenkaya, L., Suturina, L.: Polycystic ovary syndrome in women of reproductive age: phenotypic variety and diagnostic approaches. Review of Literature. Doctor.Ru. 19(6), 50–56 (2020). https://doi.org/10.31550/1727-2378-2020-19-650-56 8. Rodgers, R.J., et al.: Is polycystic ovary syndrome a 20th century phenomenon? Med. Hypotheses 124, 31–34 (2019). https://doi.org/10.1016/j.mehy.2019.01.019 9. Zore, T., Lizneva, D., Brakta, S., Walker, W., Suturina, L., Azziz, R.: Minimal difference in phenotype between adolescents and young adults with polycystic ovary syndrome. Fertil. Steril. 111(2), 389–396 (2019). https://doi.org/10.1016/j.fertnstert.2018.10.020 10. Harris, P.A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., Conde, J.G.: Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 42(2), 377–381 (2009). https://doi.org/10.1016/j.jbi.2008.08.010

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11. Atalyan, A.V., Kolesnikova, L.I., Kolesnikov, S.I., Grjibovski, A.M., Suturina, L.V.: Research electronic data capture (redcap) for building and managing databases for populationbased biomedical studies. Hum. Ecol., 52–59 (2019). https://doi.org/10.33396/ 1728-0869-2019-2-52-59 12. Parikh, R., Parikh, S., Arun, E., Thomas, R.: Likelihood ratios: clinical application in day-today practice. Indian J. Ophthalmol. 57(3), 217 (2009). https://doi.org/10.4103/0301-4738. 49397 13. Trevethan, R.: Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice. Front. Public Health 5, 307 (2017). https://doi.org/10.3389/ fpubh.2017.00307 14. Maxim, L.D., Niebo, R., Utell, M.J.: Screening tests: a review with examples. Inhal. Toxicol. 26(13), 811–828 (2014). https://doi.org/10.3109/08958378.2014.955932 15. Tharwat, A.: Classification assessment methods. Appl. Comput. Inform. 17(1), 168–192 (2021). https://doi.org/10.1016/j.aci.2018.08.003

Use of Remote Sensing Data in Intelligent Agrotechnology Control Systems Ilya Mikhailenko1(&) and Valeriy Timoshin2 1

2

Agrophysical Research Institute, 22, ap. 71, PulkoskoeShosse, St. Petersburg 196158, Russia [email protected] Agrophysical Research Institute, 3, build. 5, ap. 21, Krjijanovskiy Street, St. Petersburg 193231, Russia

Abstract. An overview of new approaches to the intellectualization of the use of Earth remote sensing (ERS) is presented. The paper shows that such approaches can be implemented only when solving control problems in precision farming systems. Two groups of tasks are considered - organizational management, in which control decisions are made by farm management, and technological control tasks, implemented by robotic machines. When solving both types of problems, remote sensing data are used as a means of system-wide feedback. This feedback is implemented in the form of algorithms for evaluating non-quantitative indicators and parameters of the state of crops and the soil environment. To implement such algorithms, mathematical models of the estimated parameters themselves and models of their connection with remote sensing data are needed. In this case, the models of the parameters of the state of crops and the soil environment are the basis for the construction of control algorithms in real time. The purpose of this work is to present the above approaches as far as the volume of one article allows.#CSOC1120. Keywords: Intellectualization
Management
State parameters
Crop sowing
Soil environment
Precision farming systems
Assessment
Nonquantitative and quantitative states

1 Introduction In a large review work, an analysis of the literature was carried out in the period from 2000 to 2019, focused on the use of remote sensing technologies in agriculture, from field preparation, planting and application during the growing season to harvest [1]. This review is carried out with the aim of promoting scientific understanding of the potential of remote sensing technologies. It is shown here that most of the previous research in the field of remote sensing has focused on monitoring soil moisture and seasonal diseases of crops. As a result of the analysis, it was found that remote sensing technologies can be used to support specific management decisions at various stages of production, helping to optimize crop production while addressing issues of environmental quality, profitability and sustainability.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 60–79, 2022. https://doi.org/10.1007/978-3-031-09073-8_7

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However, the authors did not provide detailed information about the methods used in previous studies, and did not give recommendations on the use of remote sensing data for making management decisions. The authors focused their attention on the types and platforms of remote sensing sensors used in various aspects of industrial agriculture, and the accuracy of remote sensing data in relation to ground measurements. The authors conclude that remote sensing technologies have evolved over the years, and the modern agricultural sector has many options in terms of platforms (satellite, manned aircraft, unmanned aerial vehicle), and sensors (e.g. visible, multispectral, hyperspectral, thermal) to collect various agricultural data. With such sensors and platforms in place, it is important for the agricultural community to better understand the capabilities and limitations of each technology in order to provide valuable information from the data while minimizing costs and overcoming the technical difficulties associated with collecting and using the data. Using remote sensing data, it is possible to determine and quantify the state of agricultural systems, helping to make management decisions that can increase the profit of farms. The purpose of this work is to highlight the scientific and methodological foundations of new little-studied areas of intellectualization of the use of remote sensing data in the management of agricultural technologies in modern precision farming systems.

2 Monitoring the Condition of Agricultural Land and Organizational Management of Agricultural Technologies 2.1

Introductory Remarks

Agricultural land monitoring systems are a means of promptly obtaining and storing operational information sufficient for analyzing the situation and making managerial decisions at the organizational level of agricultural production management. At the same time, large areas of agricultural monitoring objects require high-performance and inexpensive monitoring tools. Such means are remote sensing means based on various technical platforms, such as spacecraft, aviation unmanned aerial vehicles, surface sensing means, mounted directly on agricultural vehicles. The following are the most common operational monitoring tasks that use remote sensing data: • Recognition of species and areas of weeds, diseases and pests; • Recognition of types and varieties of crops, assessment of crop areas; • Assessment of the state and forecasting of crop biomass, incl. allocation of the marketable part of crops; • Assessment of soil moisture and water content; • Evaluation of the content of nutrients N, P, K and acidity pH of soils; • Evaluation of the humus content in soils; • Evaluation and forecasting of mass and quality indicators of forage crops and the development of recommendations for the optimal timing of the procurement of forage;

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• Evaluation and forecasting of the state of the soil cover and the development of recommendations for the optimal sowing time; • Identification and forecasting of latent frosts on the crop surface. According to the method of solution, all the above monitoring tasks can be divided into two main groups: 1 - the tasks of recognizing and detecting various objects or phenomena, 2 - the tasks of obtaining quantitative estimates of the state of crops and the soil environment, on the basis of which management makes control decisions. We have already discussed the methodology for solving problems of the first group in the previous section, and here we will focus on the tasks of the second group. At the same time, we highlight the most important tasks, such as choosing the optimal dates for sowing crops and harvesting. 2.2

Making Decisions on Planting Dates

Making decisions about the date of sowing crops is one of the primary decisions taken by farm management. In this case, the decision to carry out spring sowing of crops is always made on the basis of information about the parameters of the state of the soil, among which the most significant are its temperature and humidity, or the moisture content of the soil in the root layer. At this time of the year, the surface of the soil cover is open and Earth remote sensing (ERS) data generated by satellite aircraft can be used to estimate temperature and humidity. Most of the recent studies in the field of ERS spacecraft have been devoted to the development of methods for remote determination of the soil surface temperature from the measurements of multichannel radiometers AVHRR (satellites of the NOAA, MetOp series) and MODIS (satellites EOS-Aqua, Terra). Currently, one of the main tools for calculating vertical moisture and heat fluxes from vegetated surface areas are the models of vertical heat and moisture transfer in the soil - vegetation - atmosphere (SVAT) system developed with varying degrees of detail. By means of programs developed on the basis of these models, thematic processing of data from AVHRR/NOAA and MODIS/Terra radiometers is carried out, and the user can obtain estimates of the soil surface temperature Tsg without resorting to independent processing of spectral information. Moreover, the relative standard deviation of such an estimate is within 10–25% [2–5]. Determination of soil moisture or its moisture content is also one of the services provided by satellite monitoring systems. Of greatest interest is the US meteorological observation system, using the MetOp satellite, supplemented by the AmeriFlux groundbased observational network. It implements synchronous satellite and ground measurements of soil moisture. Moisture measurement is carried out both in relative units (%) and in volumetric units (m3/m3). Depending on the type of terrain and dryness of the terrain, the RMS of the estimate of humidity is in the range of 10–50% [1, 4, 5]. Due to the high level of errors, direct use of satellite estimates of soil temperature and moisture to make decisions about sowing dates will lead to high risks of crop losses. Therefore, it is important to additionally estimate these parameters using ground-based measurements.

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To generate optimal estimates taking into account ground measurements, a dynamic model is used for the specified soil conditions [5] "

#_ w_

2

#

 ¼

a11 a21

a22 a23



  #ðtÞ c11 þ wðtÞ c21

c12 c22

c13 c23

c14 c23

f1 ðtÞ

3

6 7  n ðt Þ  1 6 f2 ðtÞ 7 ; 6 7þ 4 f3 ðtÞ 5 n2 ð t Þ

ð1Þ

f4 ðtÞ t 2 ð0; T Þ; #ð0Þ ¼ #0 ; wð0Þ ¼ w0 ; where: # - average soil surface temperature, °C; w- average moisture content of the upper soil layer,%; f1 - temperature of the surface air layer, °C; f2 - the level of radiation of the environment, W m−2; f3 - intensity of precipitation, mm; f4 - wind speed in the surface air layer, m s−1; n1 n2 - random modeling errors with zero mean values and covariance d1, d2, d1d2; a11 – a22, c11 – c23 - model parameters estimated from experimental data. Model (1) is a source of a priori information on the parameters of the soil state, which must be supplemented by a source of a posteriori information, which is the satellite ERS information on these parameters of the soil state, which we will present in the following form: y1 ðtÞ ¼ #ðtÞ þ e1 ðtÞ; y2 ðtÞ ¼ wðtÞ þ e2 ðtÞ;

ð2Þ

where: y1, y2 - remote sensing data on soil temperature and moisture; e1, e2 - random errors of satellite estimates with zero means and covariance s1, s1s2, s22. To construct an algorithm for estimating and predicting the temperature and moisture state of the soil, models (1), (2) are represented in the canonical vector-matrix form Z ¼ AZ ðtÞ þ CF ðtÞ þ X ðtÞ; Y ðtÞ ¼ Z ðtÞ þ EðtÞ;

ð3Þ

here: ZT = [z1z2], z1 = J, z2 = wis the vector of the heat-moisture state of the soil, cov [N] = D, cov [E] = S. Taking into account the adopted designations, the algorithm for estimating soil temperature and moisture according to remote sensing data is as follows [5, 6]

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  Z^ ¼ AZ^ ðtÞ þ CF ðtÞ þ RðtÞS1 Y ðtÞ  Z^ R ¼ D þ RðtÞAT þ ARðtÞ  RðtÞS1 RðtÞ; Z ð0Þ ¼ M ½Z0 ; Rð0Þ ¼ cov½Z0 ;

ð4Þ

here: R is the covariance matrix of the prior estimation errors. Taking estimates (4) as the starting point, it is possible to build forecasts of the heat-moisture state of the soil for a given time interval T relative to the current time t Z^ ðTjtÞ ¼ AZ^ ðTjtÞ þ CF ðT Þ;

ð5Þ

where: F(T) - forecasts of meteorological conditions. It has not yet been shown how accurate ground-based measurements of temperature and humidity are used. Considering that accurate ground-based measurements of soil temperature and moisture are possible only at a small number of points, it is from this information that it is advisable to estimate the parameters of the model (1), which makes it possible to provide an accurate assessment and forecast of the heat and moisture state of soils over the entire area of the observed agricultural fields. These points are test sites located next to the main field, the area of each of them is 20–30 m2, and their total number is 10–12. Test sites are equipped with stationary temperature and moisture meters of the surface layer of the soil. Based on these measurements, the parameters a11-a22, c11-c23 of model (1) or matrices A, C) of model (3) are estimated. The decision to carry out spring sowing should be made simultaneously according to two parameters, temperature and moisture of the surface layer of the soil. The simplest possible option is to choose the threshold values of these parameters, the simultaneous achievement of which will serve as an indicator of such a decision. This is exactly what agronomists do today. However, due to the correlation between these parameters and the influence of many other unaccounted for factors, the choice of the above threshold values is not obvious. To make decisions on the timing of sowing, it is advisable to use the germination rate of crops, which functionally depends on the combination of temperature and moisture of the surface layer of the soil, is an important technological indicator and can be measured by means of remote sensing. Such measurements can be used to refine the parameters of the germination indicator model, denoted as v, v ¼ PT Z^ ðTjtÞ;

ð6Þ

and the decision on the timing of sowing is made as follows PT Z^ ðTjtÞ  dðsowing at time T Þ;

ð7Þ

PT Z^ ðTjtÞ / dðdo not sow at time T Þ;

ð8Þ

where d is the specified threshold value of the germination rate (70–75%), P is the vector of parameters of the model of the seed germination rate.

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Figure 1 shows graphs for constructing assessments of soil temperature and moisture according to remote sensing data, as well as the indicator of seed germination. Decision-making on this indicator is illustrated in Fig. 2, where the desired date for the threshold value of 70% is highlighted by a thickened column.

Fig. 1. Graph of the process of constructing optimal estimates of soil temperature, moisture and of seed germination according to satellite remote sensing data.

Fig. 2. Illustration of making a predictive decision on the date of sowing.

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Making Decisions on Feed Harvest Dates

Among the management decisions made by the management of farms, the most important and responsible are the decisions on the dates of harvesting crops. These solutions are especially relevant in the tasks of managing the processes of harvesting forage from perennial grasses, which are repeatedly taken during the growing season. The harvesting date affects the age of the stand and the ratio between the quantitative and qualitative indicators of the forage mass. Therefore, the choice of the criterion for the optimality of the decision made should reflect the balance between the quantitative and qualitative indicators of the herbage. The quality indicators of the finished feed affect its digestibility (assimilation) by animals. This indicator is most widespread in dairy farming; it is estimated as a percentage and can be determined by the following regression relationship [5, 7, 8] pðk; pÞ ¼ b0 þ b1 k þ b2 p þ b3 kp;

ð9Þ

where: p, k - percentage of protein and fiber in dry matter of grass; b0–b3 - model parameters. When substantiating the criterion of optimality for making decisions on the preparation of feed, it must be borne in mind that the purpose of the entire system of feed preparation is; “obtaining a predetermined yield of grass with predetermined quality parameters.” Taking into account the previously adopted designations, the formalized version of the criterion using the feed digestibility indicator has the following form I ðT Þ ¼ g1 ðmðT Þ  m Þ2 þ g2 ðpðk; pjT Þ  p  ðk; pÞÞ2

ð10Þ

where: m(T), m* - predicted and specified value of the biomass of grass stand (yield), kg m−2; pðk; p^uTÞ; p
ðk; pÞ-predicted and set value of the index of digestibility of biomass of herbage, which is a function of the% content in dry matter of fiber and protein; g1, g2 - weight factors, by means of which the required balance is established between the mass and quality components of the criterion and its dimensionless character. Criterion (10) has a pronounced minimum in terms of the harvesting date, which corresponds to the balance of its components, i.e., reflects a compromise between the quantity and quality of the harvested biomass. Then the decisive rule about the timing of the procurement of feed, taking into account the balance of the components of the criterion, is as follows: T = T* (cut), T* = argmin I(T). The choice of the optimal mowing date is carried out on the basis of the available information characterizing the state of the grass stand at once over the entire area of the field, in relation to which the control decision is made. Currently, such information is contained in ERS data. All that is required is to reliably interpret these data and give them a dynamic form that allows for fairly reliable short-term forecasts. To obtain

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estimates of the parameters of grass stand included in criterion (10), mathematical models are needed that reflect the dynamics of biomass parameters and its qualitative indicators, as well as models that reflect the relationship between the parameters of the state of biomass and the parameters of optical reflection in remote sensing systems. To solve this problem, two structural links are used: a block of biomass structure and a block of its qualitative indicators. Moreover, each of the blocks has the following canonical vector-matrix form [5, 7, 8]. a block of biomass structure in expanded form, including: • dynamic model of mass indicators of herbage 

x_ 1m x_ 2m





a11 ¼ a21  þ 

a12 a22

b11

b12

b21

b22 

  m

  m

x1m ð0Þ ¼ x2m ð0Þ



  xðtÞ1m c11 þ xðtÞ2m c21

u1 ð t Þ



ð11Þ

; t 2 ðTi ; Ti þ 1 Þ;

u2 ð t Þ d11 0

2 3 f 1 ðt Þ  c13 6 7 m4 f2 ðtÞ 5 c23 f 3 ðt Þ

c12 c22

d12 0

d13 0



2

3 w 1 ð 0Þ 4 w 2 ð 0Þ 5 ; m w ð 0Þ 3

block output model 

    1 1 x1m ðtÞ y1m ðtÞ ¼ 100 0 ; y2m ðtÞ x2m ðtÞ y1m ðtÞ

ð12Þ

model in symbolic vector-matrix form X_ m ¼ Am Xm þ Cm F ðtÞ þ Bm U ðtÞ;

ð13Þ

Xm ð0Þ ¼ Dm1 W ð0Þ; Ym ðtÞ ¼ Hm ðtÞXm ðtÞ

ð14Þ

block of qualitative indicators of biomass in expanded form, including: • dynamic model of qualitative indicators of grass stand biomass 2

3 2 x_ 1k a11 6 7 6 4 x_ 2k 5 ¼ 4 a21 x_ 3k

2

a31

b11 6 þ 4 b21 b31

a12 a22

32 3 2 xðtÞ1k a13 c11 76 7 6 a23 5 4 xðtÞ2k 5 þ 4 c21 a33 k xðtÞ3k c31

c12 c22

a32 c32 3 b12   7 u1 ð t Þ b22 5 ; t 2 ðTi ; Ti þ 1 Þ; u2 ð t Þ b32 k

32 3 c13 f1 ðtÞ 76 7 c23 5 4 f2 ðtÞ 5 c33 k f3 ðtÞ

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3 2 x1k ð0Þ d11 4 x2k ð0Þ 5 ¼ 4 d21 x3k ð0Þ d31 2

32 3 d13 w 1 ð 0Þ d23 5 4 w2 ð0Þ 5; d33 k w3 ð0Þ

d12 d22 d32

ð15Þ

• model of output parameters 3 2 100 y1k ðtÞ y1m ðtÞ 4 y2k ðtÞ 5 ¼ 6 4 0 y3k ðtÞ 0 2

0 100 y1m ðtÞ

0

3 0 2 x1k ðtÞ 3 0 7 54 x2k ðtÞ 5; 100 x3k ðtÞ y ðtÞ

ð16Þ

1m

• a model of communication between blocks of mass and quality indicators 3 2 k1 x1k ðtÞ 4 x2k ðtÞ 5 ¼ 4 k2 x3k ðtÞ k3 2

32 3 0 x1m ðtÞ 0 54 x2m ðtÞ 5; 0 x3m ðtÞ

ð17Þ

block of quality indicators in symbolic vector-matrix form X_ k ¼ AXk þ Ck F ðtÞ þ Bk U ðtÞ;

ð18Þ

Xk ð0Þ ¼ Dk1 W ð0Þ; Yk ðtÞ ¼ Hk ðtÞXk ðtÞ;

ð19Þ

Xk ðtÞ ¼ KHm ðtÞ:

ð20Þ

In the block of biomass structure, the states are:x1m, x2m - dry and wet aboveground mass of plants, kg m−2. In the block of qualitative indicators, the states are x1k, x2k and x3k - the mass of fiber, readily soluble carbohydrates and crude protein in the dry mass of plants, hdw ha−1. External disturbances in both blocks are f1 - average daily air temperature, °C; f2 - average daily radiation level, W (m2
h)−1; f3 - average daily precipitation, mm; f4 - soil moisture content, %. The perturbations of the initial conditions in both blocks of the model are:w1 - content of available nitrogen in the soil, g kg−1; w2 is the content of available potassium, g kg−1; w3 is the content of available phosphorus, g kg−1. The directly observed values in the biomass structure block were taken as y1m - total aboveground plant biomass, kg m−2; y2m is the percentage of dry matter in the total biomass, and in the block of qualitative indicators y1k, y2k and y3k is the percentage of fiber, readily soluble carbohydrates and crude protein in dry matter, respectively; control factors in models (4), (8) are: u1 – content of available nitrogen in soil, g m−2; u2 - soil moisture content, mm; a11,m-a22,m are the dynamic parameters of the biomass structure model, c11,m-c23,m are the parameters of the transfer of external disturbances in the biomass structure model, b11,m-b22,m are the parameters of the

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control transfer in the biomass structure model, d11,m-d13,m - parameters of the initial conditions model in the biomass structure model; Xm is the vector of state parameters of the biomass structure model, Ym is the vector of observed values in the biomass structure block; Am, Cm, Bm, Dm - respectively, the dynamic matrix, matrix of transmission of disturbances, controls and initial conditions in the vector-matrix form of the biomass structure model; a11,k-a33,k are the dynamic parameters of the model of the block of qualitative indicators of biomass, c11,k-c33,k are the parameters of transfer of external disturbances in the model of the block of qualitative indicators of biomass, b11, k-b22,k are the parameters of the transfer of controls in the model of the block qualitative indicators of biomass, d11,k-d33,k - parameters of the model of the initial conditions in the model of the block of qualitative indicators of biomass; Xk - vector of state parameters of the model of the block of qualitative indicators of biomass, Yk - vector of observed values in the block of the block of qualitative indicators of biomass; Ak, Ck, Bk, Dk - respectively, the dynamic matrix, the matrix of transmission of disturbances, controls and initial conditions in the vector-matrix form of the model of the block of qualitative indicators of biomass; k1, k2, k3 - parameters of the relationship between mass and quality parameters of biomass; F, U, W, are vectors of external disturbances, controls and disturbances of initial conditions common for both blocks of the model. Taking into account the variables of the above models, the decision criterion (10) will have the following form I ðT Þ ¼ ½ðg1 ðx1m ðT Þ þ x2m ðT ÞÞ  m 2 þ g2 ½pðy1k ; y3k jT Þ  p ðy1k ; y3k Þ2 :

ð21Þ

Now we need to show how the parameters of the biomass state are estimated from the remote sensing data of sowing perennial grasses. From the point of view of modern information theory, such a procedure is characterized as the task of restoring information about the state of an object from indirect observations [6, 7]. Here, based on the law of light reflection from an inhomogeneous rough surface, which can be represented by the sowing surface, the following model can be used [9] z1 ¼ p1 ep2 ðx1m þ x2m Þ þ x1 ;

ð22Þ

z2 ¼ p3 ep4 x2m þ x2 ;

ð23Þ

where: z1, z2 are the reflection parameters of the remote sensing system in the near infrared and visible ranges, p1-p4are the model parameters, are random modeling errors with zero mean values and variances d1, d2. The exponential form of the ERS models (22), (23) is unstable to the procedure for identifying parameters; therefore, it is advisable to expand exponential functions in power series of the following form [7, 10–13] z1 ¼ p11 þ p12 x1m þ p13 x2m þ p14 x21m þ p15 x21m þ p16 x31m þ p17 x32m þ x1 ;

ð24Þ

z2 ¼ p21 þ p22 x1m þ p23 x2m þ p24 x21m þ p25 x21m þ p26 x31m þ p27 x32m þ x2 ;

ð25Þ

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introducing vectors: Z T ¼ ½ z1 

W T ðXm Þ ¼

x1m

x2m

z2 ; x22m

x21m

X T ¼ ½ x1

x31m

 x32m ;

x2 ;

and the parameters matrix 

p12 p22

p P ¼ 11 p21 T

p13 p23

p14 p24

p15 p25

p16 p26

p17 p27

 ;

the model can be converted to vector-matrix form Z ¼ PT W ðXm Þ þ X:

ð26Þ

Thus, an analytical presentation of the criterion and the decision rule for making decisions based on it, as well as mathematical models, allowing one to evaluate and predict its value, have been obtained. The presence of a model of the dynamics of herbage biomass (13) and a model of optical measurements of remote sensing data (26) makes it possible to construct optimal estimates of the biomass structure using the method of optimal filtration [5, 6]   ^m    @W T X ^ _X m ¼ AX ^m ðtÞ þ Cm F ðtÞ þ Bm U ðtÞ þ F ðtÞ ^m S1 Z ðtÞ  W X ^ @ Xm     ^m ^m @W T P; X @W P; X T 1 R_ ¼ RðtÞAm þ Am RðtÞ  RðtÞ S RðtÞ; ^m ^m @X @X _

_

ð27Þ

_

X M ð0Þ ¼ X 0M ; Pð0Þ ¼ cov½X 0M ; S ¼ cov½N; _

_

X k ðtÞ ¼ KX m ðtÞ: ^m is the optimal average over the field area estimate of the vector of parameters where: X of the state of herbage biomass, R is the covariance matrix of the prior estimation   ^0m is the covariance matrix of the initial conditions in the model errors, Pð0Þ ¼ cov X of the dynamics of the state of herbage biomass; S ¼ cov½- matrix of covariance of ^k is the optimal field-average estimate of the optical parameters measurement errors, X vector of qualitative parameters of herbage.

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Estimates of vectors of mass and quality parameters of grass stand, obtained from remote sensing data at time t, serve as initial values for predicting these parameters based on dynamic models (13), (18). According to these forecasts, forecasts of the decision-making criterion are formed by the date of harvesting of the grass stand for the time T I ðtjT Þ ¼ ½ðg1 ðx1m ðtjT Þ þ x2m ðtjT ÞÞ  m 2 þ g2 ½pðtjT Þ  p ðtÞ2 : Figure 3 shows the process of estimating the parameters of the state of biomass according to remote sensing data. Here, as in the identification of mathematical models, the value of estimation errors does not exceed the 10% level. In Fig. 4 shows a graph of the predicted values of the criterion (20), where the date of the decision to mow is the 14th day from the beginning of the first intercutting period of the growing season of perennial grasses. At the same time, the set values of the biomass yield is 150 hdw ha−1, and the set value of the digestibility index is 89%. The predicted values for deciding on the mowing date are: biomass yield - 148 hdw ha−1, digestibility index 85%. The weighting factors of the decision making criterion were: g1 = 0.07; g2 = 3.0.

Fig. 3. The process of assessing the state of biomass of herbage.

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Fig. 4. Prediction of the criterion for making a decision on the date of harvesting of grass biomass.

3 Management of Agricultural Technologies in Real Time 3.1

General Notes

The task of managing agrotechnology in real time is to reproduce optimal control programs operating during one growing season, taking into account the change in the phenophases of the culture [5]. Such optimal control programs are sequences of technological operations formed for the average long-term values of climatic conditions. If the real climatic conditions always corresponded to the average long-term values, and the parameters of the used mathematical models, as well as the time of the onset of phenophases, remained unchanged, then the optimal control programs would also remain unchanged in real time. But in fact, climatic conditions always do not correspond to the average long-term values, due to the parameters of all used mathematical models are always disturbed, and in addition, the state of the soil environment, and with it the state of crops, is always inhomogeneous over the area of the field. Realtime control is designed to compensate for all of the above disturbances and uncertainties. In this type of control, we will single out only those components that use remote sensing data as the main means of feedback, without which real-time control is impossible. More strictly, its tasks are: • Clarification of the optimal control programs upon detection of the fact of a significant deviation of climatic conditions from the calculated values, according to which the current version of the optimal control program was synthesized; • Setting the time for the implementation of technological operations on the fact of the real onset of phenological phases; • Spatial correction of the parameters of technological operations, due to the heterogeneity of the parameters of the state of crops and soil environment.

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73

Spatial Correction of Optimal Control Programs

Figure 9 shows example of an optimal program of technological operations performed at the time moments of the onset of the identified phenophases in the development of spring wheat sowing.

Fig. 5. Example of an optimal program for managing the state of spring wheat crops.

The program presented in Fig. 5 contains the sizes of technological operations averaged over the field area. Spatial correction of optimal control programs is carried out taking into account the real state of crops and soil environment in individual elementary areas and can be presented in the following form [5]   ^i ðt; x; yÞ  V ^i ðt; x; yÞ; Ui ðt; x; yÞ ¼ Vi ðtÞ þ BTi Xi ðtÞ  X

ð28Þ

where: V*(t) is the vector of technological influences (operations) averaged over the field area, representing the optimal control program; Xi* (t) is the optimal program of the vector of parameters of the seeding state corresponding to the optimal control ^i ðt; x; yÞ - evaluation of the vector of parameters program, averaged over the field area; X ^i ðt; x; yÞ - evaluation of of the state of sowing at the time of technological operations, V vectors of parameters of the state of the soil environment at the time of carrying out technological operations, Bi - matrices of correcting regulators of technological operations; x, y - spatial coordinates of the elementary area where technological impacts are implemented, i - phenophase index.

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As can be seen from expression (28), local control in a separate elementary section (area of 2.0–3.0 m2) consists of three components. The first, the same over the entire area, is the optimal program of technological operations, which is the elements of plant nutrition and moisture content in the soil; the second component is the correction of the program for assessing the parameters of the real state of sowing; the third component is an assessment of the real content of nutrients and moisture content of the soil. To implement such a three-component control, the parameters of the matrices of the correcting regulators Bi are required. To construct matrices of these parameters, it is necessary to repeatedly solve the problem of programmed control with different values of the vectors of the parameters of the state of crops at the phenophases, on which programmed control is carried out. According to the variations of these vectors and variations of the parameters of technological operations, the parameters of the matrices Biare estimated. In addition, for the implementation of local management, it is necessary to construct estimates of the parameters of the state of crops in elementary plots in order to compare them with the optimal program for the development of crops. The algorithm for constructing such estimates based on remote sensing data has the following form [5] ^_ i ðt; x; yÞ ¼ Ai X ^i ðt; x; yÞ þ Bi V ^i ðt; x; yÞ þ Ci F ðtÞ X   ^i     @W T X ^i ðt; x; yÞ ; þ Ri ðt; x; yÞPi Kzi1 Zi ðt; x; yÞ  W X ^i @X _Ri ðt; x; yÞ ¼ Ri ðt; x; yÞATi þ Ai Ri ðt; x; yÞ     ^i ^ @W T X 1 @W Xi  Ri ðt; x; yÞPi Kzi ATi Ri ðt; x; yÞ; ^ ^ @ Xi @ Xi

ð29Þ

where: Ai, Bi, Ci, Pi - matrices of parameters of mathematical models of the parameters of the state of crops and the ERS model at the i-th phenophase; WT(Xi) - remote sensing model operator of the form (27); Kzi - covariance matrix of the ERS model errors; Ri - covariance matrix of estimation errors. Due to the fact that the parameters of the state of the soil environment are inaccessible for optical sounding, the algorithm for their estimation becomes much more complicated. For its implementation, the fact is used that the chemical parameters of the sowing biomass are available to optical sensing, which in turn are directly related to the chemical parameters of the soil environment. For this, a dynamic model of the chemical parameters of inoculation is introduced into the estimation algorithm [5, 13, 14] X_ h ðt; x; yÞ ¼ Ahx Xh ðt; x; yÞ þ Bhx dNv ðt; x; yÞ þ Mhx V ðt; x; yÞ þ chx f2 ðt; x; yÞ;

ð30Þ

where: Xh(t, x, y) is the vector of parameters of the chemical state of the sowing biomass (the content of nutrients in plants), dNv(t, x, y)is the doses of foliar application of nitrogen; Ahx, Bhx, Mhx matrices of model parameters. To estimate the parameters of the chemical state of the sowing biomass at time t, the same algorithm of the form (29) is used

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^h ðt; x; yÞ þ Bhx dNv ðt; x; yÞ þ Mhx V ^ ðt; x; yÞ ^_ h ðt; x; yÞ ¼ Ahx X X   ^h ðt; x; yÞ   @W T X 1 ^h ðt; x; yÞ ; þ chx f2 ðt; x; yÞ þ Rh Ph Kzh Zh ðt; x; yÞ  X ^ @ Xh ð31Þ     ^h ^h @W T X @W X T 1 T R_ h ðt; x; yÞ ¼ Rh ðtÞAhx þ Ahx Rh ðtÞ  Rh ðtÞPh Khx Ph Rh ðtÞ; ^h ^h @X @X ^h ð0Þ ¼ Xh ð0Þ; Rh ð0Þ ¼ Khx : X where: Kzh is the covariance matrix of the optical measurements of the parameters of the chemical state of the crop biomass, the parameters of which are estimated simultaneously with the parameters of the model (5), Rh is the covariance matrix of the estimation errors. The parameters of the chemical state of the soil are estimated according to the estimates obtained in the algorithms (29), (31) and the dynamic model of soil parameters ^_ ðt; x; yÞ ¼ Ahp V ^ ðt; x; yÞ þ Bhp Dðt; x; yÞ V ^m ðt; x; yÞ  Nhp X ^h ðt; x; yÞ þ Chp F ðt; x; yÞ  Mhp X

ð32Þ

^ ðt; x; yÞ ¼ V ð0Þ; V ^ ðt; x; yÞ - estimates of the vector of parameters of the chemical state of the soil, where: V ^ ^h ðt; x; yÞ Xm ðt; x; yÞ - estimates of the vector of mass indices of the sowing biomass, X estimates of the vector of chemical parameters of the sowing biomass, D(t) - the vector of doses of nutrients to the soil and irrigation rates Ahp, Bhp, Chp, Mhp are the matrix of model parameters.

Fig. 6. Fragment of the distribution of nitrogen doses over the field area.

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Figures 6, 7, 8, 9 and 10 show a fragment of the spatial control of the state of spring wheat at the interfacial period “milk ripeness - full ripening of grain” in accordance with the law (28). Here, technological impacts are carried out on elementary areas with spatial coordinates (x, y) and an area of 2–3 m2. Naturally, for the implementation of such control, fundamentally new technological machines with separate working bodies serving elementary sections are required [5].

Fig. 7. Fragment of the distribution of potassium doses over the field area.

Fig. 8. Fragment of the distribution of phosphorus doses over the field area.

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Fig. 9. Fragment of the distribution of magnesium doses over the field area.

Fig. 10. Fragment of the distribution of irrigation norms over the field area.

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4 Conclusion Intellectualization of the use of Earth remote sensing data of agricultural land is possible only when solving control problems in precision farming systems. These tasks include organizational management, where management decisions are made by the management of farms, and technological management carried out by robotic machines. In both types of tasks, remote sensing data are a means of system-wide feedback on the state of the control object, which is an agricultural field. This system-wide feedback is implemented in the form of procedures for evaluating non-quantitative indicators and parameters of the state of crops and the soil environment according to remote sensing data and selective ground measurements. To implement the estimation procedures, it is necessary to have mathematical models of the estimated state parameters and models of the connection of remote sensing data with these parameters. The presence of such models is a prerequisite both for assessment procedures and for solving the problems of managing the state of agricultural crops themselves. In this regard, it can be argued that further progress in the intellectualization of the use of remote sensing data will be mainly determined by the development of mathematical models of technological processes and objects in controlled precision farming systems.

References 1. Sami, K., Kushal, K.C., Fulton, J.P., Shearer, S., Ozkan, E.: Remote sensing in agriculture— accomplishments, limitations, and opportunities. Remote Sens. 12(22), 3783 (2020). https:// doi.org/10.3390/rs12223783 2. Becker, F., Z.-L., Li.: Temperature-independent spectral indices in thermal infrared bands. Remote Sensing Environ. 32(3), 17–33 (1990). https://doi.org/10.1016/0034-4257(90) 90095-4 3. Chevallier, F., Chedin, A., Cheruy, N., Mocrette, J.J.: TIGR-Iike atmospheric profile database for accurate radiative flux computation. Q. J. R. Meteorol. Soc. 126, 777–785 (2000). https://doi.org/10.1002/qj.49712656319 4. Muzylev, E.L., Uspenskiy, A.B., Volkova, E.V., Startseva, Z.P.: The use of satellite information in the modeling of vertical heat and moisture transfer for river watersheds. Exploration Earth Space 4, 35–44 (2005). https://doi.org/10.21046/2070-7401-2019-16-344-60 5. Mikhailenko, I.M.: Theoretical Foundations and Technical Implementation of Agricultural Technology Management. Polytechnic University, St. Petersburg (2017) 6. Kazakov, I.E.: Methods for Optimizing Stochastic Systems. Nauka, Moscow (1987) 7. Mikhaylenko, I.M., Timoshin, V.N., Danilova, T.N.: Mathematical modeling of the soilplant-atmosphere system using the example of perennial grasses. Rep. Russian Acad. Agric Sci. 4, 61–64 (2009). https://doi.org/ https://doi.org/10.3103/S106836740904020X 8. Mikhailenko, I.M., Timoshin, V.N.: Making decisions on the date of harvesting feed based on Earth remote sensing data and adjustable mathematical models. Modern problems of remote sensing of the Earth from space. 15(1), 164–175 (2018). https://doi.org/10.21046/ 2070-7401-2018-15-1-23-04 9. Rachkulik, V.I., Sitnikova, M.V.: Reflective properties and state of vegetation cover. Gidrometeoizdat, Leningrad (1981)

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10. Mikhailenko, I.M., Timoshin, V.N.: Estimation of the parameters of the biomass state of spring wheat sowing. Bull. Russian Agricultural Sci. 1, 2–6. (2021). https://doi.org/10. 21046/2070-7401-2021-18-4-102-114 11. Mikhailenko, I.M., Timoshin, V.N.: Estimation of parameters of the state of crops and soil environment to remote sensing data. In: 19- th International Scientific Conference Engineering for Rural Development, pp. 153–164 (2020). https://doi.org/10.22616/ ERDev2019.18.N472 12. Mikhailenko, I.M., Timoshin, V.N.: Estimation of the parameters of the state of agrocenoses according to the data of remote sensing of the Earth. Modern problems of remote sensing of the Earth from space. 18(4), 102–114 (2021). https://doi.org/10.21046/2070-7401-2021-184-102-114 13. Mikhailenko, I.M., Timoshin, V.N.: Development of a methodology for assessing the parameters of the state of crops and soil environment for crops according to remote sensing of the Earth. IOP Conf. Series: Earth and Environmental Science. 548, 052027 (2020). https://doi.org/10.1088/1755-1315/548/5/052027 14. Mikhailenko, I.M., Timoshin, V.N.: Estimation of the chemical state of the soil environment according to remote sensing of the Earth. Modern problems of remote sensing of the Earth from space 4, 125–134 (2018). https://doi.org/10.21046/2070-7401-2018-15-7-102-113 15. Jouven, M., Carrère, P., Baumont, R.: Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures. 1. Model description. Grass Forage Sci. 61(2), 112–124 (2006). https://doi.org/10.1111/j.1365-2494.2006.00517.x 16. Kochubey, S.M., Shadchina, T.M., Kobets, N.I.: Spectral properties of plants as a basis for remote diagnostic methods. Naukovadumka, Kiyev (1990) 17. Mulla, D.: Twenty-five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng 114, 358–371 (2012). https://doi.org/ https://doi. org/10.1016/j.biosistemseng. 2012.08.009 18. Quemada, M., Gabriel, J., Zarco-Tejada, P.: Airborne hyperspectral images and ground-level optical sensors as assessment tools for maize nitrogen fertilization. Rem. Sens 6, 2940–2962 (2014). https://doi.org/10.3390/rs6042940 19. Oliver, M., Bishop, T., Marchant, B.: An overview of precision agriculture. In Precision Agriculture for Sustainability and Environmental Protection. Eds. Rout (2013). https://doi. org/10.4324/9780203128329 20. Sanderson, M.A., Rotz, C.A., Fultz, S.W., Rauburn, E.B.: Estimating forage mass with a commercial capacitance meter, rising plate meter, and pasture ruler. Agron. J. 93, 1281–1286 (2001). https://doi.org/10.2134/agronj2001.1281 21. Timofeyev, Y.U.M., Martynov, A.A.: On the determination of the temperature and emissivity of the surface of the earth from space. Expl. Earth Space 4, 12–17 (1996). https:// doi.org/10.21046/2070-7401-2018-15-3-236-242 22. Zlinszky, A., Heilmeier, H., Balzter, H., Czúcz, B., Pfeifer, N.: Remote Sensing and GIS for Habitat Quality Monitoring. New Approaches and Future Research. Remote Sens. 7(6), 7987–7994 (2015). https://doi.org/10.3390/rs70607987 23. Zarco-Tejada, P.J., Guillén-Climent, M.L., Hernández-Clemente, R., Catalina, A., González, M.R., Martín, P.: Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agricultural and Forest Meteorology, 171–172, 281–294 (2013). https://doi.org/10.1016/j.agrformet.2012.12. 013

Utilization of RTOS Solutions in IoT Modules Based on RISC Microcontrollers Juraj Dudak1

, Gabriel Gaspar1(&) , Stefan Sedivy2 and Roman Budjac1

,

1 Research Centre, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia {juraj.dudak,gabriel.gaspar,roman.budjac}@uniza.sk 2 TNtech, s.r.o., Lucna 1014/9, 014 01 Bytca, Slovakia [email protected]

Abstract. The IoT modules are currently considered part of smart home, remote control applications or data collection solutions. Mostly they are built on solutions that use low-power but sufficient microcontrollers with ARM architecture. A significant part of the market is made up of STM32 microcontrollers, from Cortex M0 to M4 cores. Several RTOS solutions are available on the market, i.e., solutions that provide, when writing code for a microcontroller, functionality that is familiar from real operating systems - the implementation of parallel processing through the creation of tasks and the cyclic switching of their context. In this paper, solutions for the use of RTOS systems on STM32 and ESP32/8266 hardware platforms are presented. The implementation of these RTOS systems, focuses on efficiency, i.e., the number of supported functions with respect to the overhead of the library used. From the tests performed, it is clear which RTOS is adequate for application regarding performance or power consumption and saving resources. Keywords: IoT


Microcontroller
RTOS
FreeRTOS
Zephyr

1 Introduction Hardware modules as part of smart home, remote control applications or data collection solutions are currently widespread. A significant part of these modules are built on solutions that use microcontrollers designed specifically for this group of solutions. These are low-power but sufficient microcontrollers with ARM architecture. A significant part of the market is made up of STM32 microcontrollers, from Cortex M0 to M4 cores. Another aspect is the development of programs (firmware) for these microcontrollers. Architecture-specific libraries are used for firmware development. When writing code that is executed in the main loop of the program, over time we encounter the limitations that this way of programming has. These are mainly limitations in the need for parallel event processing, or ensuring that the behavior of the program is such that it is non-blocking other functions. There are several RTOS solutions on the market, i.e., solutions that provide, when writing code for a microcontroller, functionality that is familiar from real operating systems - the implementation of parallel processing © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 80–93, 2022. https://doi.org/10.1007/978-3-031-09073-8_8

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through the creation of tasks and the cyclic switching of their context. Individual solutions differ in the memory footprint, library size and the number of supported functions. In this paper, solutions for the use of RTOS systems on STM32 and ESP32/8266 hardware platforms are presented. In the implementation of these RTOS systems, the focus is on efficiency, i.e., the number of supported functions with respect to the overhead of the library used. The issue of sensor data processing in embedded solutions is an application problem. It is therefore only the method of reading the required data or the design of a program structure with which this reading is simplified to the actions “read value” or “write setting”. The problem of implementing a set of sensors on the ESP32 microcontroller is addressed in [6], where the development of applications for ESP32 microcontrollers is described. Here is given an overview of the possibilities of developing applications on this platform in the field of measurement and data processing. In the IoT area, the use of ESP32 microcontrollers is very common. A hardware module that includes IoT support must have implemented functionality or communication protocols used in this area. This is in particular the MQTT protocol. The paper [5] describes the implementation of the Mongoose RTOS in a hardware module built on the ESP8366 microcontroller as an IoT node. In addition to the actual implementation of the functionality, the article also presents the processing and visualization of the measured data using the mentioned MQTT technology. Using RTOS systems when implementing hardware modules, especially if it is a module that contains multiple function blocks, avoids later problems when these function blocks are blocked. A practical example of implementing an RTOS in a Nano Satellite Control for Responding to Space Environmental Conditions module is described in [2]. The authors used the STM32F446 microcontroller and mbedOS. The paper concludes with a comparison according to which the processing is 3.7x faster using RTOS and the required FLASH memory is 16% more (49.9 kB non-RTOS vs 58.1 kB RTOS) and RAM is 146% more (3 kB non-RTOS vs 7.4 kB RTOS) with RTOS. A similar problem was addressed in [1], where the authors used the STM32F4 microcontroller and FreeRTOS. The result was the STUDSAT-2 satellite module. A detailed analysis of the use of RTOS for embedded devices is in [3], where the authors develop a state and function model of the RTOS system including the techniques used such as tasks, task scheduling and task synchronization. In [4], an RTOS simulator solution for real-time software development is presented. In this paper, several task model cases are presented when it is appropriate to use RTOS along with simulation outputs - the use of different RTOS parameters.

2 RTOS Implementation in Microcontrollers Each generation of microcontrollers brings wider capabilities and in time, when internet connectivity with touchscreens is a necessity, it makes more and more sense to use an RTOS when implementing an application. Some of the advantages over the classical approach (program in the loop) are:

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• RTOS is deterministic and guarantees response to events within defined time intervals, • preemptive multitasking design suited for applications changing in each release. This design ensures that the responses of each event are independent of each other. As a result, adding new features does not disrupt existing ones, • integrated communication drivers. Often the RTOS includes communication for TCP/IP and USB with drivers for these peripherals, • debugging tools suitable for detecting memory overflows and inadequate memory usage, delayed responses to events, or higher CPU resource usage, • efficient use of CPU resources. Normal looping program runs have to do a lot of querying to learn if an interrupt has occurred. As a result, most of the controller's time is spent idle. With interrupt control, this polling can be largely eliminated in an RTOS, allowing the CPU to perform useful tasks. In this paper we will describe work with STM32F411CE, ESP32 and ESP8266 microcontrollers. We will compare the task processing speed, ease of implementation and overhead when using RTOS. Table 1 shows a comparison of the performance parameters of the mentioned microcontrollers: Table 1. Comparison of basic parameters of microcontrollers Core frequency RAM FLASH * External QSPI

STM32F411CE up to 100 MHz 128 kB 512 kB FLASH memory

ESP32 up to 240 MHz 520 kB * 4–16MB

ESP8266 80 or 160 MHz 80 kB * 4–16MB

3 RTOS Systems A Real-time Operating System (RTOS) is an Operating System (OS) used to serve applications that process data on the fly as it arrives. Thus, a real-time operating system is time-bounded and has well-defined, fixed-time tasks. The processing of tasks must be done at precisely defined times or the system will fail. These kinds of systems mostly operate on the principle of event-driven control or time-sharing. Event-driven systems change between tasks depending on their set priorities whereas time-shared based on time interruptions. The term task can be thought of as any running program under an operating system. If an axis can execute multiple tasks at the same time, it is called multitasking. An ordinary processor can only execute one such task at a time. This effect suppresses the multitasking axis by quickly alternating between tasks. The systems use scheduling to process tasks. Normally, tasks have three states: 1. Running (currently running on the CPU), 2. ready (ready to run), 3. blocked (waiting for an event).

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As such, choosing an RTOS is a must if we want to use multiple devices or processes and timing is more important than average performance. So, the basic rule is that if we have multiple processes that need to run on a certain timing schedule, we should use an RTOS. Some of the factors in making the decision are: • • • • • • • •

the type of processor on which the RTOS is to run (support), the size of the RTOS (smaller devices have less memory), the amount of latency (delay) during interrupts, the speed of changing the runtime context, peripheral support, development environment and debugging tools, RTOS licensing, documentation.

The basic building constructs in any RTOS are: tasks, queues, semaphores, and mutexes. Tasks To create a task in FreeRTOS, the recommended structure is shown in Listing 1. A task is defined as a function with no return value. This task is started by calling another function: xTaskCreate, which defines a pointer to the task to be started, the name of the task, the input parameters for the task to be called, the priority of the task, and the stack size for the task.

void vTaskFunction( void *pvParameters ) { for( ;; ){ // task code } vTaskDelete(NULL); } Listing 1. Task creation

Each task can be assigned a priority from 0 up to the set maximum amount in the configuration (configMAX_PRIORITIES - 1). An idle task has a predefined priority of 0. The FreeRTOS scheduler ensures that tasks in the ready or running state always receive CPU time over tasks with lower priorities. If a task is placed in the running state, it always has the highest priority among other tasks [8]. Since the memory size needs to be precisely defined for each task used in the RTOS, it may happen that the thread will not have enough memory allocated. Several parameters affect the determination of the correct stack size, but there is no exact answer. For example, the size is affected by the depth of function calls and their memory requirements. Local function variables are also counted in this memory. The most common way of determining the size of the stack is by trial and error. FreeRTOS offers two options to detect stack overflow. The first is to use the uxTaskGetStackHighWaterMark function, which returns the number of unused words in the thread's stack. The second way to detect stack overflow within FreeRTOS is to set a constant

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called CHECK_FOR_STACK_OVERFLOW to 1. What this setting achieves is that every time a thread exits FreeRTOS checks the value of the current stack pointer. If it is higher than the top of the thread's stack, it is very likely to have overflowed the stack. In this case, the vApplicationStackOverflowHook function is automatically called. Queues Queues are used in multithreaded programming when we need to exchange data between multiple threads that have different response times. The producer generates a piece of data and puts it into a queue. The consumer takes data from the queue stack one element at a time, always inserting the earliest element first. To create a queue, there is a function xQueueCreate, where the maximum queue size and the size of a single item in the queue must be defined. Each item in the queue must have the same size. The principle of how a queue works is in Listing 2: unsigned long ulVar = 10UL; void vATask( void *pvParameters ) { QueueHandle_t xQueue1, xQueue2; struct AMessage *pxMessage; xQueue1 = xQueueCreate( 10, sizeof( unsigned long ) ); xQueue2 = xQueueCreate( 10, sizeof( struct AMessage * ) ); if( xQueue1 != 0 ) { if(xQueueSendToBack(xQueue1,(void*)&ulVar,(TickType_t)10) !=pdPASS) { /* failure handling */ } } if( xQueue2 != 0 ) { pxMessage = &xMessage; xQueueSendToBack(xQueue2, (void *)&pxMessage, (TickType_t)0); } /* task code... */ } Listing 2. Queue principle

Semaphore In concurrent programming, Semaphore is used to control access for multiple execution streams that require access to a common resource. The simplest form of a semaphore is a boolean value that serves as a condition for access to a resource.

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In FreeRTOS, semaphores are implemented as a single-element queue. If the queue is empty, the first thread that tries to use the queue puts a flag in it and continues its execution. Other threads will not be able to add their flags to the queue unless the thread that succeeded removes its flag. Tasks and interrupts thus don't take in concern what's in the queue, they are only interested in whether it is full or empty. An example of the use of a binary semaphore is for example in a task that has to serve a peripheral device. The goal is to run the task only if the peripheral device is available and can be used. We achieve this by using a binary semaphore by blocking the task while it tries to acquire the semaphore. We write an Interrupt Service Routine (ISR) for the peripheral that releases the semaphore only when the peripheral device needs service. The task always just acquires the semaphore, but never releases it, only the ISR routine does the releasing. Mutex Mutual Exclusion (Mutex) is similar to a binary semaphore, except that mutexes are used more to protect shared resources, while semaphores are mostly used for thread synchronization. Mutexes, like semaphores, allow defining the time of blocking tasks. If a higher priority task attempts to acquire a mutex that is held by a lower priority task, the priority of the task currently holding the mutex is raised to the priority of the blocked higher priority task. Mutexes should not be used within ISR because they involve priority inheritance, which only makes sense within tasks. 3.1

FreeRTOS

FreeRTOS is the officially selected and recommended RTOS for microcontrollers from the ST manufacturer. The run context change rate in the FreeRTOS implementation is factory set to 1 ms. After this time, the Tick function is called periodically. This function controls the context change between tasks. The selected preset is a good compromise between task execution speed and context change overhead. After the period has elapsed, the vTaskSwitchContext function is called to switch the context. After this function is called, the kernel looks for tasks in Ready mode and if any task has a higher priority set than the one currently running, it changes the context to allow such task to run [10]. 3.2

Zephyr RTOS

Zephyr RTOS is one of the newest and most widely used alternatives to FreeRTOS. It was also chosen due to its suitability for commercial applications, security and opensource software. One of the specific features that Zephyr offers is a larger number of scheduling algorithms. The specific options are Cooperative and Preemptive scheduling, Earliest Deadline First (EDF), Meta IRQ scheduling and Timeslicing [11]. A detailed comparison of the characteristics of each category among the RTOS is shown in Table 2.

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J. Dudak et al. Table 2. Comparison of FreeRTOS and Zephyr OS properties [12].

OS Supported hardware Cortex M4 (STM32) Xtensa (ESP) Scheduling Based on priorities Round-Robin Rate monotonic (RMS) Semaphore/mutex management Support of peripherals I2C, SPI, UART USB, CAN, CANopen 6LoWPAN Bluetooth, Ethernet, WiFi TLS NFC, RFID Memory footprint RAM ROM

FreeRTOS

Zephyr OS

yes yes (unofficial fork)

yes (mainly) yes

yes yes yes yes

yes yes, cooperative yes yes

yes depending on manufacturer no yes

yes yes

yes no

yes yes

236 B scheduler + 64 B/task 5 - 10 kB

very small/configurable, ability to disable peripherals 2 - 3 kB

yes yes

Regarding job scheduling capabilities, both RTOS systems are priority-based with the Round-Robin algorithm. In addition, the Zephyr RTOS also includes a co-operative scheduling system. A thread can be defined as co-operative based on priority (must have a negative value). In this case, the thread runs until it completes its task and terminates its run. Both RTOSes allow so-called Rate Monotonic Scheduling (RMS). RMS is an algorithm using priority assignment. Static priorities are assigned according to the cycle length of the job, so a shorter cycle time means a higher priority. In terms of safety, FreeRTOS is currently better off as it has all the important safety certifications.

4 IoT Module as an Application with RTOS For a practical comparison of the performance parameters was suggested an application built on two RTOS systems and three hardware platforms. RTOS used: FreeRTOS, Zephyr RTOS. Hardware platforms used: STM32F411, ESP8266 and ESP32.

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Fig. 1. Main menu of the application

Hardware module description: The module consists of a temperature sensor, a relative humidity sensor, a display unit and a communication unit allowing to connect the module to the Internet and send the measured data. Module technical specification [7]: • CPU - by version STM32F411, ESP8266, ESP32. • Temperature and relative humidity sensor: DHT11. Communicates via digital I/O pin using proprietary protocol. • Display unit: LCD display with ILI9341 driver, resolution 240x320 pixels. It utilizes the SPI interface. • Touch layer display: XPT2046. Utilizes SPI interface. • Wifi module (ESP32 and ESP8266) Figure 1 shows a preview of the working window of the application, where you can select the action to be performed by the module. The application is divided into five tasks, the definitions of which are as follows: Two tasks are focused on measuring sensor data (temperature or humidity), two on displaying the measured data, and one on processing the touch by the user. The decision to use two tasks separately for measuring and displaying the value for a given quantity stems from the assumption that the user can choose which of the quantities he wants to display on the screen, so in the case of multiple specific sensors we do not want to measure a value unnecessarily when it does not need to be measured. This saves CPU time and application overhead. In Fig. 2, we can see that there is no thread running at the start of the application after the initial setup function is executed. Since the touch controller interrupt pin is used in the application, we don’t need to poll the touch status in the loop. As soon as the user touches the display, the system switches to ISR mode, in which it determines the touch coordinates and pushes them to the queue. By adding a value to the queue that the TaskChangeMenu task is waiting for, the task unblocks and retrieves the value from the queue. After processing the data, the task renders the selected menu to the display and unblocks the corresponding task for

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measurement by calling the vTaskResume function; if a task is also ready to measure another value, it cancels it by calling the vTaskSuspend function. The task will thus remain blocked until it is allowed to run again using vTaskResume [7].

Fig. 2. The principle of demo application in RTOS

Then, the measurement task gets started. After the measurement, the task sends the measured value to the queue to which the task is bound for the value dump. This task unblocks when a measured value is added to the queue and waits until the queue is empty. The measurement and display tasks alternate until the user changes the request using the touchscreen. At that point, if there was selected a different quantity to measure, the current measurement task is switched to inactive and the measurement task for the selected quantity starts running.

5 Results and Discussion In this section, we will compare the efficiency and suitability of using RTOS systems on the selected microcontrollers. When comparing RTOSs, the Intra Process Communication (IPC) is mainly the important factor. The IPC parameter is an indicator of whether the system can meet the requirements in a timely manner. 5.1

Definition of Tests

We use task switching speed, semaphore acquire and release time, message queue scrolling and take rate, ISR processing speed, and memory used by tasks as performance and memory footprint criteria. We will use two tasks as a methodology to measure the task switching speed. Task A has a higher priority than task B. Task A goes to sleep as soon as it wakes up and a context change to task B occurs, which in turn wakes up task A. The time between the switching of tasks A and B is measured. We can neglect the time to execute the task itself in the test, since its only task is to sleep and wake up.

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To measure semaphore acquiring and releasing, we will use a single task that repeatedly acquires and releases a semaphore. Initially, the semaphore is initialized as acquired. The two times for acquiring and releasing the semaphore are measured together. Ideally, this task should not affect the timings of the other tasks. We also use a single task to measure the speed of scrolling and taking a message from the queue. The task will repeatedly scroll and take messages. The queue is initialized as empty. The times for scrolling and taking a message are measured together as a single value. The wake-up rate of a task from the ISR can be performed by two tasks. Task A has a higher priority than task B. As soon as task A wakes up, it goes to sleep, which causes task B to start executing for an indefinite period of time. As soon as an interrupt occurs, its processing wakes up task A. The time required to start task A from the ISR handler is measured. In Fig. 3, the letter K represents the context change time. We can find out the memory used by tasks by using the free stack dump functions in each of the selected OSes. However, when determining the memory footprint, it is also useful to measure the memory fetch and commit time. A fixed memory size is used in the test. In this benchmark, only one task is used, which acquires a fixed size portion of memory and then releases it.

Fig. 3. Principle of task switching speed test

5.2

Measurements Results

Since the system timer (SysTick) was set to 1 ms in the application, we achieved a resolution of 1ms in the measurements. Therefore, the tests were adapted to run for example 1000 times and we measured the time before all tests start and end. This gives us the time in milliseconds at the end, which we can divide by the number of tests (1000) to get the average time per operation in microseconds. In the first performance test, where the speed of task switching was measured, it was enough to pause and wake up the task 1000 times. The time in microseconds that we obtained is the average total time involving putting the task to sleep and waking it up. The real value of the time spent in context switching will be about half of the measured time.

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Fig. 4. Measured time of task switching in microseconds [7]

In Fig. 4., we can see that the ESP8266 microcontroller performed the best at the FreeRTOS context switching speed. This can be explained by the utilization of a different platform. Unlike the other microcontrollers, for the ESP8266 a platform from espressif called RTOS sdk was used. The graph also shows that Zephyr is generally worse than FreeRTOS in task switching.

Fig. 5. Measured time of acquire and release semaphore in microseconds [7]

The semaphore acquire and release test is evaluated in Fig. 5. From the measured data, we can conclude that both RTOS systems have comparable semaphore handling times. Thus, this should not be a deciding factor in the selection of a particular RTOS. A queue with depth 1 was created to test receiving and sending a message to the queue. The message to be pushed was of type boolean, so that the queue would have the least number of resources. The result is on Fig. 6. - time of sending and receiving messages from the queue.

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Fig. 6. Measured time for sending and receiving messages from the queue in microseconds [7]

The smallest possible memory size was used when measuring the speed of memory read and release. After memory allocation, the address was stored in a variable, which was then used for memory freeing. In Fig. 7. can be seen that Zephyr is significantly worse off in terms of memory handling. The difference may be mainly due to the way each OS allocates memory. The heap_4 strategy in FreeRTOS tries to avoid fragmentation by using a predefined memory region. The algorithm used in this strategy is called first fit. It means that the pointer keeps a list of all free memory blocks and accepts the memory block allocation requests of the incoming process.

Fig. 7. Measured time of memory acquire and memory release in microseconds [7]

After the request, the pointer starts looking for the largest first free memory block for the process and allocates it to the process [9]. The advantage with this algorithm is just the speed of memory allocation, which may explain the measured difference. Zephyr uses a recursive function to search for free memory blocks when allocating memory. Both memory search methods are not deterministic. Another interesting indicator when comparing systems can be energy consumption. A wattmeter with a resolution of 0.05 W was used for the measurement. A program was loaded on all the microcontrollers which endlessly alternated between two tasks.

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No external peripherals were powered during the test, only the microcontroller itself. The results of the test are shown in Fig. 8. The plotted values are the maximum of the measured values. From Fig. 8, we can deduce that the Zephyr system's strength is its power consumption. On all the microcontrollers, a significant jump in consumption can be seen relative to FreeRTOS. The higher power consumption on the ESP32 and ESP8266 microcontrollers may be due to the Wifi/bluetooth network communication features enabled, which Zephyr disables by default.

Fig. 8. Measured consumption in watts [7]

6 Conclusions The aim of the present research was to examine current possibilities of commonly available RTOS systems for microcontrollers. From the findings we performed, it is clear that if performance is an essential factor in the application under development, we should rather reach for FreeRTOS. If, on the other hand, we are more interested in power consumption and saving resources, Zephyr is definitely a better alternative. In Table 2, we summarized in detail all the features of FreeRTOS and Zephyr and explained the differences between them in terms of schedule capabilities and security. Advantages and disadvantages of the systems are also summarized in Table 2. The findings of this article have a number of practical implications. The areas of benefit of the work are automation and the IoT world where performance and fault tolerance of the systems are needed. This article highlights the advantages and disadvantages of Zephyr and FreeRTOS RTOS systems based on which a suitable RTOS can be selected for a particular application. Research we presented indicates that both RTOS systems have their strengths and weaknesses. The evidence from this study suggests that the type of RTOS chosen should depend on the application area and the requirements. From the tests performed, it is clear that if performance is an essential factor in the application under development, we should rather reach for FreeRTOS. On the other hand, if we are more interested in power consumption and saving resources, Zephyr is definitely a better choice.

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Acknowledgment. This paper was supported with a project of basic research: Expanding the base of theoretical hypotheses and initiating assumptions to ensure scientific progress in methods of monitoring hydrometeors in the lower troposphere”, which is funded by the R&D Incentives contract. This paper was supported under the project of Operational Programme Integrated Infrastructure: Independent research and development of technological kits based on wearable electronics products, as tools for raising hygienic standards in a society exposed to the virus causing the COVID-19 disease, ITMS2014+ code 313011ASK8. The project is co-funding by European Regional Development Fund.

References 1. Rajulu, B., Dasiga, S., Iyer, N.R.: Open source RTOS implementation for on-board computer (OBC) in STUDSAT-2. IEEE Aerospace Conference 2014, 1–13 (2014). https:// doi.org/10.1109/AERO.2014.6836377 2. Putra, A.C.A.Y., Wijanto, H., Edwar: Design and Implementation RTOS (Real Time Operating System) as a Nano Satellite Control for Responding to Space Environmental Conditions. In: 2021 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob), 2021, pp. 178–182. https://doi.org/10.1109/APWiMob51111.2021.9435247 3. He, Z., Mok, A., Peng, C.: Timed RTOS modeling for embedded system design. In: 11th IEEE Real Time and Embedded Technology and Applications Symposium, 2005, pp. 448– 457 (2015) https://doi.org/10.1109/RTAS.2005.52 4. Razaghi, P., Gerstlauer, A.: Host-compiled multicore RTOS simulator for embedded realtime software development. In: 2011 Design, Automation & Test in Europe, 2011, pp. 1–6. https://doi.org/10.1109/DATE.2011.5763046 5. Kodali, R.K., Yadavilli, S.: Mongoose RTOS based IoT Implementation of Surveillance System. In: 2018 International Conference on Communication, Computing and Internet of Things (IC3IoT), 2018, pp. 155–158. https://doi.org/10.1109/IC3IoT.2018.8668137 6. Babiuch, M., Foltýnek, P., Smutný, P.: Using the ESP32 microcontroller for data processing. In: 2019 20th International Carpathian Control Conference (ICCC), 2019, pp. 1–6. https:// doi.org/10.1109/CarpathianCC.2019.8765944 7. Schmidt, Erik: Embedded aplikácie s podporou RTOS na STM32, EPS8266, ESP32 [Diploma thesis]. – Slovenská technická univerzita. Materiálovotechnologická Fakulta 8. Carmine Noviello. Mastering STM32. New York, NY: Leanpub, 2016, pp. 602–666 (2016) 9. Brent, R.P.: Efficient implementation of the first-fit strategy for dynamic storage allocation. ACM Trans. Program. Lang. Syst. 11(3), 388–403 (1989). https://doi.org/10.1145/65979. 65981 10. Barry, R.: Mastering the FreeRTOSTM Real Time Kernel. Pre-release 161204 Edition. Real Time Engineers Ltd. (2016). https://www.freertos.org/ 11. Zephyr Project. Zephyr Project Documentation (2021). https://docs.zephyrproject.org/ 12. micro-ROS. Comparison between RTOSes (2021). https://micro.ros.org/docs/concepts/rtos/ comparison/

A Monitoring System Design for Smart Agriculture Zlate Bogoevski, Zdravko Todorov, Marija Gjosheva, Danijela Efnusheva(&), and Ana Cholakoska Computer Science and Engineering Department, Faculty of Electrical Engineering and Information Technologies, SS. Cyril and Methodius University in Skopje, Skopje, Republic of North Macedonia {todorovz,danijela,acholak}@feit.ukim.edu.mk

Abstract. Agriculture is the main food source necessary for the existence of the human population. Many of the technological advances were made for improving the process of harvesting crops. Other technological advances found their purpose in agriculture, such as Internet of Things. Therefore, it is very important to build Internet of Things in the agriculture. In this work we will show the steps of making a device based on Internet of things by using sensors for the needs of the agricultural process. Monitoring of agricultural data parameters will be visually and accurately enabled through a SIM card that will receive information from the sensors, making it easier for the farmers to act on events that require attention. Keywords: Smart agriculture
Monitoring system
Internet of Things (IoT)
Data tracking

1 Introduction Technology advancement has a great impact on the process of automatizing tasks that can replace the human dependancy. Assuming that agriculture is a profession that is human dependent, many new emerging technologies such as ubiquitous computing, cloud computing and Internet of Things can be applied in the domain of agriculture to make agriculture smarter. Smart agriculture represents a system of solutions for farmers, starting from the idea of modernizing the existing general working conditions, such as planting, harvesting, irrigation, spraying with pesticides, to monitoring all plantations or farms. In general, implementing the latest technologies, has a great impact in improving the living standards of the people. Today, these technologies are widely available, to such an extent that they can be implemented in the agriculture. The application of technology facilitates agricultural production, helps in activities performed in fruit harvesting, vegetable planting, and also in the cultivation of planting areas. The Internet of Things finds many applications in agriculture [1]. For example, there is an IoT application used in drip irrigation, in which various sensors collect data on temperature, humidity, soil, water content. The obtained data is then further analyzed in order to determine the optimal amount of water for the plant [2]. Additionally, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 94–105, 2022. https://doi.org/10.1007/978-3-031-09073-8_9

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water is pumped using energy from renewable sources such as solar panels [3]. Another interesting application is the use of RFID chips in livestock in order to provide tracking and thus to protect them from theft [4]. In addition to that, this approach allows the animal to be found if they had escaped while grazing. In fact, the RFID tag readers are placed in certain places in order to load the position of the livestock and then to display it on a map. On the other hand, by using a heat sensor in forestry, a fire can be prevented [5]. Further, to prevent illegal logging, barcodes can be stamped in the trees so they can be traced to the final consumer [6]. There are IoT applications such as weather forecast, which helps farmers to monitor and predict the weather conditions. This approach can be applied in monitoring systems for crops or livestock. Generally, the supervision is set up so that the decision makers can monitor crops and possible plant infection and then respond in a timely manner to prevent possible damage [7]. This is achieved with a sensor network, in which nodes are interconnected and the information is passed to a central system for further analysis. For example, GPS devices are used to ensure the safe transport of the product to the desired location by tracking transport and storage [8]. When it comes to smart agriculture it can be said that it is mostly used to mark the application of IoT solutions in agriculture. Therefore, by using IoT sensors for data collection, farmers can make informed decisions and improve almost all aspects of their work - from livestock to plants. For example, by using smart crop sensors to monitor the condition of crops, farmers can accurately define how much pesticides and fertilizers to use to achieve optimal yields. This paper illustrates an approach of how farmers nowadays, can improve their everyday work. Actually, a prototype of a device designed to help in the agricultural process is proposed in this paper. The rest of this paper is organized as follows: Sect. 2 gives an overview of different state of the art solutions. Section 3 describes the proposed monitoring system for smart agriculture, including its components (various sensors, SIM module, solar panel and STM32 microcontroller). Section 4 presents the implemented system design and also discusses the monitoring results in real time. Section 5 concludes the paper, outlining the benefits of the proposed monitoring system for smart agriculture.

2 State of the Art The adoption of IoT solutions for agriculture is constantly growing. Namely, COVID19 had a positive impact on the IoT application in the agriculture market [9]. Assuming that this market is still evolving, there is still a huge opportunity for businesses who want to get involved. Building IoT products for agriculture in the next years can make one stand out as an early adopter and as such can widely open the way to success. Undoubtedly, the most popular smart accessories for agriculture are meteorological stations, which combine various smart sensors that are located across the field in order to collect various data from the environment and send it to the “cloud” systems. The

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provided measurements can be used to map the climatic conditions, and therefore can help in the selection of suitable agricultural plantations. Some examples of such agricultural monitoring devices are allMETEO, Smart Elements and Pycno [10]. Farmers usually perform manual intervention to control the greenhouse effect. Accordingly, the use of IoT sensors for that application would allow them to obtain accurate real-time information about the conditions of the greenhouse, such as: lighting, temperature, soil condition and humidity. Therefore, these data could be used to automatically adjust the conditions in the greenhouse to meet the required parameters. For example, Farmapp and Growlink are IoT-based products that offer this kind of application in agriculture [11]. Additionally, GreenIQ [12] is also an interesting product that uses smart sensors for agriculture. It is a smart sprinkler controller that enables remote control of irrigation and lighting systems. Another type of IoT products used in agriculture as an element of precision agriculture are crop management devices. Similarly, like the meteorological stations, they also need to be placed in the field in order to collect crop-specific data, such as: temperature, rainfall, leaf potential and overall crop health. Therefore, crop growth and any anomalies could be monitored to effectively prevent any diseases or infestations that may harm the crop. Arable and Semios [13] can serve as good products for that purpose. Just like crop monitoring, there are IoT solutions that include sensors that can be attached to farm animals in order to monitor their health and performance through the day. Livestock monitoring helps in collecting data on health, well-being and physical location. Therefore, such sensors can identify sick animals so that farmers could separate them from the herd and avoid further contamination. For example, SCR from Allflex [14] and Cowlar [15] use sensors to provide insight into the temperature, health, activity and nutrition of each individual cow, as well as collective information about the herd. On the other hand, the use of drones to monitor livestock in real time helps farmers to reduce staff costs. This works similarly to IoT pet care devices. More complex IoT products used in agriculture are the so-called agricultural productivity management systems. They typically include a number of IoT devices and sensors, installed on the premises, as well as the ability to analyze the collected data and issue various reports. This approach offers wide possibilities for remote monitoring of a farm. Such product solutions are represented by FarmLogs and Cropio [16]. One of the most promising agro-technical advances is the use of agricultural drones in smart farming. Also known as UAVs (unmanned aerial vehicle), drones are better equipped than airplanes and satellites for collecting agricultural data. In addition to surveillance capabilities, drones can perform numerous tasks that usually require human intervention, including: planting crops, fighting pests and infections, spraying in agriculture, monitoring crops, and so on. DroneSeed, for example, is building drones purposed to plant trees in devastated areas [17]. The use of such drones is six times more efficient than a human work. Furthermore, the Sense Fly eBee SQ agricultural drone [18] can perform multispectral image analysis to assess crop health with reasonable price.

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3 Design of the Proposed Monitoring System for Smart Agriculture The global challenge of finding new ways to meet projected food needs by 2050 has brought a wave of novel technological advances in agriculture and the food industry. There is a common agreement in the agricultural society that for the food needs of an additional 2 billion people by 2050 [19], solutions related to the innovative use of information and communication technologies (ICT) should be found. A commonly used term for marking technological advances in agriculture is “Agriculture 4.0”, which is sometimes considered as the fourth agricultural revolution [20]. Similar terms that are often used in that sense are: “Smart Agriculture” and “Digital Agriculture”. According to the European Association of Mechanization (CEMA), the term “Agriculture 4.0” is analogous to the term “Industry 4.0”, which is used to describe the current trend in automation and data exchange in production technologies. Assuming the enormous increase of food resources requirements [19], in this paper we propose a prototype of a device that would help farmers in the process of vegetables and fruits production. The proposed device is implemented in accordance with the requirements of the people who come from the agriculture area. For that purpose, twenty farmers were consulted and a decision was made which sensors and components will be used to potentially solve some of their problems with the device. As a result of this research, the following components were selected: STM32 microcontroller, soil moisture sensor (capacitive), soil moisture sensor (resistive), light detection sensor, rain detection sensor, temperature and humidity sensor, solar panel and SIM module with antenna. The proposed architecture of the monitoring system for Smart Agriculture is shown in Fig. 1.

Fig. 1. Architecture of the proposed monitoring system with its hardware components.

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The proposed device is based on the STM32 microcontroller, which includes a microprocessor, computer memory, timers and input/output ports. Unlike personal computers, microcontrollers use processors with speeds of several megahertz, which is quite enough for their needs. Also, they consume very low power and when they are idle they have the opportunity not to spend any energy at all. The STM32 microcontroller can be programmed using the STM32CubeIDE software environment, which is an open-source and free tool suite for commercial programming and development [21]. The (capacitive) soil moisture sensor measures the capacitance between the two probes of the sensor. Actually, this sensor measures the soil moisture level by capacitive sensing. It is made up of corrosion resistant material which gives it an excellent endurance and service life [22]. The (resistive) soil moisture sensor includes two parts [23]. One is the LM393 low power dual voltage comparator and the other one is the humidity sensor. The humidity sensor operates by measuring the resistance between the two probes of the fork that is inserted into the soil. The more soil moisture, the lower the resistance. As the soil dries, the resistance increases. Figure 2. visually illustrates this dependence. This sensor is used to detect soil moisture and thus to assess if there is a moisture around the sensor, so it can be decided whether the plants in the garden need irrigation [23].

Fig. 2. Dependence between the soil moisture and resistance.

The light detector sensor consists of a light-dependent resistor, also called a lightsensitive resistor, which is often used to indicate the presence or absence of light, or to measure the intensity of light [24]. The light-detector can detect the sunlight intensity. However, in the proposed system this sensor is used to monitor the weather condition, by showing if there is only sun, or there are some clouds as well.

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The rain detection sensor is used to detect rain and also to measure the intensity of precipitation [25]. This module includes a rain panel and a control panel. It also has an LED power indicator and adjustable sensitivity through a potentiometer. The module is based on the LM393 low power dual voltage comparator. It also includes a printed circuit board (control panel) that “collects” raindrops. As raindrops collect on the plate, they create parallel resistance paths that are measured by an operational amplifier. The lower the resistance (or more water), the lower the output voltage. In contrast, the less water, the higher the output voltage of the analog pin. A completely dry board, for example, will cause the module to emit five volts. Temperature and humidity sensor (DHT11) is a basic digital temperature and air humidity sensor [26]. It uses a capacitive humidity sensor and a thermistor to measure the air temperature. To detect humidity, DHT11 measures the electrical resistance between the two electrodes. The electrodes are on the surface of the component that is the substrate for moisture retention. The substrate absorbs water and ions and this way the conductivity between the two electrodes is increased. As can be seen in Fig. 3. The change in resistance is proportional to the relative humidity. In fact, the resistance between the electrodes decreases with higher relative humidity and increases with lower relative humidity. To measure air temperature, the device uses a temperature sensor – NTC component or a thermistor. Thermistors are variable resistors that change resistance based on the temperature. The resistance of the thermistor decreases when the temperature rises.

Fig. 3. The relation between the resistance of a thermistor and air temperature.

The solar panel [27] absorbs sunlight from the sun and becomes the powering source of the device. A battery is used to provide data transfers if there is no light. The solar panel allows the battery to be charged by the sunlight. The battery is large enough to fulfill the device power requirements.

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Sim800L [28] is a GSM/GPRS module that is used for delivering sensor data from the device to the end user’s mobile phone, in SMS format. With this module one can send a message, make a call or transfer data through GPRS. The SIM800L module has a set of serial interface at TTL level and a set of power supply interface. In addition, this module has a special place for antenna, which is required for connecting the SIM card to a mobile network. Here a PCB Dipole GSM antenna is connected to the module. The SIM module supports Quad-band 850/900/1800/1900 MHz, and can be used to transmit voice messages, SMS and data with low power. From the aspect of consumption, the module enables low energy consumption and total cost savings. It is characterized with dimensions of 15.8  17.8  2.4 mm. The proposed monitoring device for Smart Agriculture combines the components mentioned before. The sensors have been tested and the results they give are matched to the results the end user is expected to see. It was decided that for the end user the value displayed in Volts is not important, but something that would give him some information, all in order to know how he can and should continue in his work. As a result, the proposed device should help tackle farmers’ problems by giving a picture of what exactly is happening in real time in the field where the device is placed. The farmer, meaning the user, can monitor, and see the recommendations from the device sensors, whenever he wants, at any time of the day. He will only need to use a single smartphone. Smartphones are one of the most widely used devices by people that work in agriculture. Today, whether one is connected through a wireless network or using an Internet data through a mobile operator (GSM networks), a special-purposed devices can be connected to different websites, applications and other information services needed for Smart Agriculture, like: ThingSpeak, Amazon Web Services, Microsoft Azure etc. These are typically third-party software services for smartphones and tablets that can perform certain functions, online or offline. Apart from monitoring the data through web/mobile applications, the same monitoring can be established through SMS messages that would arrive on a mobile phone, as suggested in this paper. This approach is much simpler and very applicable to farmers that are not very familiar with modern ICT technologies.

4 Implementation of the Proposed Monitoring System for Smart Agriculture The first step of the device testing is to connect it. Therefore, the hardware components are properly connected so that each of them can get a value for the appropriate parameter, hopefully, in an acceptable range. Additionally, the SIM module is also connected, which is the key component for connecting the given device to the mobile network. Figure 4 shows the fully connected monitoring system for Smart Agriculture.

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Fig. 4. Hardware implementation of the proposed monitoring system for Smart agriculture.

According to Fig. 4 it can be noticed that all the components of the monitoring system are connected to the STM32 microcontroller, and as well all the sensors are configured in the STM32CubeIDE platform. The information that is loaded from the sensors is processed and then sent in a form of a status/recommendation message to the mobile phone of the end user. In that process, it is necessary to describe the format of the messages, as well as the way the end user of the mobile phone receives them. For that purpose, when configuring the device, the controller is programmed with the telephone number where the SMS messages are to be sent. In fact, the user's phone does not have to be a smartphone, as this device would work with any mobile phone that is connected to a network. Figure 5 presents the sensor results. It shows some recommendations, extracted from the data from certain sensors. These are recommendations that the farmer as a user would receive on a mobile phone (as SMS) and then would take appropriate actions.

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Fig. 5. Sensor results shown in STM32CubeIDE environment.

Figure 6 below shows how the SMS message looks when an end farmer user receives it on his mobile phone in Macedonian language (appropriate translation in English is also given).

Fig. 6. Example of an SMS message that is received by an end farmer (in Macedonian and English language)

Figure 7 shows the real use of the smart farming device in plantations owned by an agricultural family, as part of their own family business. In practice, the device was placed both outdoors and under foil. In both cases the device proved to be an effective device in an attempt to solve some plantation problems as well as to monitor the overall production of the planted crops.

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Fig. 7. Real use of the proposed monitoring device for Smart Agriculture in practice.

Different types of crops require different soil types, different quantities of nutrients and different water capacity. The water needs for the plant depends on the season and also on the environment in which it grows. With the right choice of crops for the specific humus (soil) and weather conditions, there can be an increase in the yield, so the water can be saved for irrigation needs. This could provide rational use of water as an important resource on Earth. Some of the benefits of the proposed monitoring device for Smart Agriculture are listed below:

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Production may increase; Water can be used rationally; The quality of the production can be increased; Plant yields can be increased; Crop evaluation can become much more accurate and precise

The use of a device intended for Smart Agriculture is applicable wherever it is planted or wherever a crop is grown, regardless of whether it is fruit or vegetables. N. Macedonia is a country that has enough fertile land and a lot of water, which is quite enough for growing any type of crops. With these two important resources (water and fertile land), we can say that N. Macedonia is an “agricultural land” that should take advantage of these natural benefits and give more emphasis on food production.

5 Conclusion The modernization of agriculture has progressively advanced over the last three decades, with the development of the IT sector in it being one of the main drivers of this process. The technologies used in today's agricultural industry, which employs more than 40% of the world's population, are estimated to 7.8 billion dollars [19]. In this paper, a device for monitoring agricultural plantations is proposed. Using this device in an agriculture field, one can discover real-time information about the soil moisture, temperature, humidity, whether it rains or not or whether it is day or night. All this information, as part of the monitoring, is displayed to the end user on a mobile device. The received data is analyzed according to the sensor results and the algorithms that are proposed to provide the user with recommendations on what is happening in the field where a particular crop grows and what is the next action he should take. This is a big and important step in our society, because the implementation of such or similar devices can contribute to many more people engaged in agriculture, because in this way they would have relief, if the entire production can be monitored via mobile phone. This can only be the first step towards a new expansion of this device, where in the near future as our recommendation is to connect it to a database of weather forecast and thus give more extensive advices to the end user. Another recommendation would be to add a few more sensors (ex. for plant health) to get an even more detailed picture of the crops, and so on until it is optimally completed.

References 1. Marcu, J.M., Suciu, G.: IoT based system for smart agriculture. In: Electronics, Computers and Artificial Intelligence, ECAI, Romania. IEEE Xplore (2019) 2. Wardana, I.N.K., Crisnapati, P.N., Aryanto, K.A.A., Krisnawijya, N.N.K., Suranata, I.W.A.: IoT-based drip irrigation monitoring and controlling system using NodeMCU and Raspberry Pi. In: Proceedings of the International Conference on Science and Technology ICST, pp. 557–560. Atlantis Press (2018) 3. Shaw, R.N., Mendis, N., Mekhilef, S., Ghosh, A. (eds.): AI and IOT in Renewable Energy. SIC, Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-1011-0

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4. Anu, M., Deepika, M.I., Gladance, L.M.: Animal identification and data management using RFID technology. In: International Conference on Innovation Information in Computing Technologies, ICIICT, India. IEEE (2018) 5. Horowitz, B.T.: IoT makes fire detection systems smarter. IEEE Spectrum (2020) 6. Timpe, D.: Alternatives to log stamping for wood identification in forestry? FSCN Report R05-61. Mid-Sweden Univ., Sundsvall, Sweden (2005) 7. Math, R.K.M., Dharwadkar, N.V.: IoT based low-cost weather station and monitoring system for precision agriculture in India. In: International Conference on IoT in Social, Mobile, Analytics and Cloud, I-SMAC, India. IEEE (2018) 8. Nižetić, S., Šolić, P., González-de-Artaza, D.L., Patronod, L.: Internet of Things (IoT): opportunities, issues and challenges towards a smart and sustainable future. J. Clean. Prod. 274, 122877 (2020) 9. GSMA: COVID-19: Accelerating the use of digital agriculture. Technical paper (2021) 10. Sowmiya, M., Prabavathi, S.: Smart agriculture using Iot and cloud computing. Int. J. Recent Technol. Eng. 7(6), 251–255 (2019) 11. Rajagopal, S., Thangaraj, S.R., Mansingh, J.P., Prabadevi, B.: 5 technological impacts and challenges of advanced technologies in agriculture. In: Chatterjee, J.M., Kumar, A., Rathore, P.S., Jain, V. (eds.) Internet of Things and Machine Learning in Agriculture: Technological Impacts and Challenges, pp. 83–106. De Gruyter (2021) 12. GreenIQ. https://easternpeak.com/works/iot/. Accessed 21 Nov 2021 13. Syamu, K., Singh, B.P., Ravi, T.: A survey on precision agriculture using effective crop monitoring with enhanced farming. Int. J. Adv. Res. Ideas Innov. Technol. 5(1), 168–172 (2019) 14. Allflex Lifestock Intelligance. https://www.allflex.global/. Accessed 21 Nov 2021 15. Cowlar. https://www.cowlar.com/. Accessed 21 Nov 2021 16. Navya, B.S.: IoT in agriculture. Int. J. Adv. Res. Sci. Commun. Technol. 6(1), 7–10 (2021) 17. DroneSeed. https://droneseed.com/. Accessed 21 Nov 2021 18. Spalevic, Z., Ilic, M., Savija, V.: The use of drones in agriculture-ICT policy, legal and economical aspects. EКOHOMИКA 64(4), 93–107 (2018) 19. Food and Agriculture Organization of the United Nations: The future of food and agriculture: trends and challenges. FAO, Italy (2017) 20. Liu, Y., Ma, X., Shu, L., Hancke, G.P., Abu-Mahfouz, A.M.: From industry 4.0 to agriculture 4.0: current status, enabling technologies, and research challenges. IEEE Trans. Ind. Inform. 17(6), 4322–4334 (2021) 21. STMicroelectronics: STM32CubeIDE user guide. Technical paper (2021) 22. DFRobot: Capacitive Soil Moisture Sensor v1.2, Data sheet (2017) 23. Soil Moisture Sensor Module. https://components101.com/modules/soil-moisture-sensormodule. Accessed 21 Nov 2021 24. RS Components: Light dependent resistors, Data sheet (1997) 25. Rain Sensor Module. https://www.electroduino.com/rain-sensor-module-how-its-works/. Accessed 21 Nov 2021 26. OSEPP Electronics: DHT11 Humidity & Temperature Sensor. Technical paper (2021) 27. Ranabhat, K., Patrikeev, L., Revina, A.A., Andrianov, K., Lapshinsky, V., Sofronova, E.: Istrazivanja i Projektovanja za Privredu 14(4), 481–491 (2016) 28. SIMCom: SIM800L, Data sheet (2013)

Contribution Title Assessment of the Possibilities of Modern Microprocessor Technology for Integration with Modified Algorithms of Artificial Immune Systems in Complex Objects Control Galina Samigulina1 , Zarina Samigulina2(&) and Dmitry Porubov1,2 1

,

Institute of Information and Computing Technologies, Almaty, Kazakhstan 2 Springer Kazakh-British Technical University, Almaty, Kazakhstan [email protected]

Abstract. At present, a large-scale digital transformation of industrial enterprises is underway. Modern IT technologies are applied at all stages of the production life cycle through the development of digital infrastructure. Such technical capabilities make it possible to use the latest advances in artificial intelligence for the development of effective systems of monitoring, equipment diagnostics, technical failures predicting and complex objects control systems. The article presents an assessment of modern microprocessor technology from leading manufacturers, as well as software for integration with artificial intelligence methods. There was developed a graphic model based on the Ishikawa diagram for the effective transformation of models and algorithms of artificial immune systems in the development of intelligent control systems based on modern microprocessor technology. #CSOC1120. Keywords: Artificial intelligence
Models and algorithms of artificial immune systems
Microprocessor technology
Industrial production

1 Introduction Nowadays, the introduction of new technologies in industrial production, such as working with big data, scheduled maintenance, simulation modeling, virtual and augmented reality, the creation of “digital twins” of the enterprise, the Internet of things, automation and robotization form the basis of the fourth industrial revolution “Industry 4.0”. The modern level of development of computer technology and the latest achievements in the field of artificial intelligence (AI) have led to the possibility of creating highly efficient intelligent systems for complex objects control operating under conditions of uncertainty of parameters. The introduction of AI into industrial production makes it possible to solve the problems of collecting, processing information, monitoring, diagnosing and controlling in real time. Major manufacturers of microprocessor technology such as Honeywell, Siemens, Schneider Electric, Mitsubishi © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 106–120, 2022. https://doi.org/10.1007/978-3-031-09073-8_10

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Electric, Yokogawa, etc. are actively engaged in the development of intelligent controllers and distributed control systems based on them to improve the efficiency of enterprises. In the Republic of Kazakhstan (RK), projects for the implementation of the Industry 4.0 program began to actively develop, starting in 2017. For the effective implementation of this program at the enterprises of the Republic of Kazakhstan, there is currently a shortage of domestic technology developers and qualified personnel. In this connection, research on the development of new intelligent technologies that can be applied on the basis of existing automation platforms and with the use of modern microprocessor technology for implementation in real industrial production is relevant [1]. Neural networks (NN), genetic algorithms (GA), swarm intelligence (RI), artificial immune systems (Artificial Immune Systems, AIS), etc. have become widespread in the development of intelligent control systems based on AI. Developments with the help of AIS for complex objects control are of particular interest. 1.1

Research Problem Statement

It is necessary to assess the capabilities of modern microprocessor technology and analyze software products from leading manufacturers of industrial equipment for integration with artificial intelligence methods. Based on the analysis carried out, to develop a graphical model using the Ishikawa method to select the key factors affecting the effective transformation of models of artificial immune systems in order to be introduced into real industrial production.

2 Materials and Research Methods Complex high-tech industrial complexes generate a huge stream of production data in real time, the processing of which and timely forecasting of the equipment condition is impossible without the introduction of artificial intelligence methods. Neural networks are successfully used to solve problems of complex objects control. For example, in [2], there is considered a tunable PID controller with an adaptive neural network for studying frequency load control in a system with distributed generation of electrical and thermal energy for the following objects: wind turbine generators, battery energy storage systems, diesel engine generators, fuel cells, and etc. The PID controller parameters are configured in two ways. In the first case, using the optimization method based on simulating the behavior of a grasshopper (Grasshopper Optimization Algorithm, GOA), in the second case, using the neural network. The use of neural networks showed the best results in terms of data processing speed. Research [3] is devoted to the development of a fault detection algorithm based on the genetic algorithm and Markov decision-making process. In [4], there is considered the use of swarm intelligence for solving the problem of optimizing the parallel processing process during turning operations. Using the Particle Swarm Optimization (PSO) algorithm, optimal processing parameters are found, which leads to a decrease in the cost of operations and computation time.

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Research on the creation of control systems based on the approach of artificial immune systems and their modified algorithms is promising [5]. AIS is widely used for solving problems of optimization, forecasting, development of equipment diagnostics systems, adjusting the parameters of the controller, creating intelligent control systems for complex objects [6]. In [7], an AIS application for controlling the speed of a servo motor is presented. The PID controller parameters were tuned using AIS to minimize the Integral Time Absolute Error (ITAE). A comparative analysis with the Ziegler Nichols heuristic method for adjusting the controller parameters has been carried out. The AIS approach showed the best result. In [8], parametric identification based on modified clonal selection algorithms is considered. A new combined method of AIS based on the Clonal Selection Algorithm (CSA) and a neural network is presented for short-term load prediction. To select informative features, a new method is introduced based on fuzzy sets and fuzzy clustering. The overall performance improvement of the CSA algorithm comes from three modifications to enhance the CSA search. The simulation results are presented based on the daily peak of electrical load consumption. Therefore, the development of domestic technologies based on modified AIS algorithms (for example, RF-AIS [5], in which a random forest is used as an algorithm for identifying informative features, and the solution of the pattern recognition problem is based on the approach of artificial immune systems) and modern microprocessor technology for implementation in real industrial production is an urgent task and contributes to the implementation of the “Industry 4.0” program.

3 Assessment of the Capabilities of Modern Microprocessor Technology for Integration with Modified AIS Algorithms The world’s largest manufacturers of programmable logic controllers (PLCs) are: Honeywell (USA), Rockwell Automation Allen Bradley (USA), Emerson (USA), Siemens (Germany), Schneider Electric (France), Mitsubishi Electric (Japan), Yokogawa (Japan), Omron (Japan), RealLab (Russia), Advantech (China), etc. These companies are engaged in the development of intelligent controllers or software based on artificial intelligence methods to create a new generation of automated control systems. For example, Schneider Electric and Microsoft launched a large-scale project in 2019 with Inria, Sigfox, Elaia, Energize, and France Digital to help AI startups (Accenta, BeeBryte, Craft AI) to move Europe towards efficient renewable sources [11]. On the territory of the Republic of Kazakhstan, the equipment of these manufacturers is widely used at the largest industrial enterprises, such as TengizChevroil, Karachaganak Petroleum Operating, Kazchrome, Kazakhmys Corporation, Sokolovsko-Sarbai mining and processing production association, Aluminum of Kazakhstan JSC, Kazakhmys Energy LLP, KazAzot LLP, etc. The leading developments based on artificial intelligence of world manufacturers are presented in Table 1.

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When integrating the approaches of artificial intelligence and microprocessor control systems, a number of difficulties arise. For example, the need for a large amount of memory and computing resources, while the memory of the microprocessor is limited. In this regard, the development of new, modified algorithms adapted to the requirements of the production environment is relevant. These algorithms should be capable of functioning in time close to real. Table 1. Intelligent controllers from leading manufacturers. Manufacturer Siemens (Germany) https://new.siemens. com

Schneider Electric (France) http://www.se.com

Omron (Japan) https://industrial. omron.ru

Omron (Japan) https://industrial. omron.ru

AI integration solution Neuroprocessor module TM NPU for working with the SIMATIC S7– 1500 CPU module or ET200MP peripheral station

Specifications The SIMATIC S71500TM NPU module is equipped with the Intel MovidiusTM AIpowered MyriadTMX Vision processing unit for efficient neural network processing Zelio Logic smart They are connected to relays the Modbus network using the Modbus slave. Possibility of remote monitoring and control due to the modem communication interface Sysmac AI controller Based on NX7 and with machine learning NY5 controller, includes AI Predictive function Maintenance library

Fuzzy logic controller The C200H can C200H-FZ001 manage 10 I/O modules and have up to 128 rules. Fuzzy logic processing is divided into three stages: conditional processing, postprocessing, and defuzzification

Application area Industrial automation. Allows to increase the processing speed of incomplete, partially distorted and noisy data based on neural networks Designed to create simple control systems from 10 to 40 I/O

Industrial automation. Allows to reduce the risk of equipment damage, identify problems and generate recommendations for their elimination Complex systems control that were previously controlled by specialists Fuzzy Support Software is used to transfer information to the database and monitor the operation of the fuzzy control system (continued)

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Manufacturer STMicroelectronics (Netherlands, Amsterdam) https://www.st.com/ https://www. compel.ru Honeywell (USA) https://www. honeywellanalytics. com/

AI integration solution Specifications Application area Microcontroller The construction of a Microprocessor systems STM32G4 for ANN neural network is carried out on the basis of the X-CUBE-AI program System 57 controller

System 57 allows the installation of fire alarm and gas detection boards in a single rack. The 5701 single-channel control card or the 5704 fourchannel card are available STARDOM is a Yokogawa Electric STARDOM line of Corporation (Japan) intelligent controllers: Networked-based FCN, FCJ and FCN- Control System (NCS) http://www. RTU yokogawa.com

System 57 high precision intelligent controller accepts inputs from combustible and toxic gas sensors as well as flame, smoke, temperature and manual alert points STARDOM standalone controllers are designed for industrial automation systems and are suitable for installation in various locations

For example, Mitsubishi Electric (Japan) has developed its own algorithm based on deep neural networks (Deep Learning, DL), which can significantly reduce the amount of calculations while maintaining a high level of output accuracy [9]. The work [10] considers the possibility of implementing a neural network on a microcontroller with limited computing capabilities. As a result of the experiment, it can be seen that the neural network takes a lot of time to predict due to floating point calculations. In this regard, it is necessary to modify the algorithms so that only integer operations are used. Promising software products of leading manufacturers based on artificial intelligence for solving industrial automation problems are presented in Table 2.

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Table 2. Intelligent software and technologies from leading manufacturers Manufacturer Honeywell (USA) https://www. honeywellanalytics. com/ Mitsubishi Electric (Japan) https://www. mitsubishielectric. com/

Name Honeywell Forge

Maisart® AI technology

Siemens (Germany) https:// siemens.com

MindSphere platform

Aspen Technology, Inc. (USA) https://www. aspentech.com/

Aspen AIoT Hub™

Technical characteristics Secure data collection and processing with a library of artificial intelligence models Algorithm of the software: – assessment of the boundaries of the elements of motion; – definition of standard schemes of work; – detection of nonstandard behavior MindSphere allows to connect machines, installations and any equipment to the cloud. Open standards and interfaces allow receiving data from third-party devices [11] The AIoT Hub provides a cloud-ready infrastructure that supports V12 AI applications such as Aspen AI Model Builder ™ and Aspen Event Analytics ™

Application area Oil and gas production in cooperation with ADNOC (Abu Dhabi National Oil Company) Analysis of assembly work in factories based on the three-dimensional positioning of both hands of the worker (measured by sensors)

Provides access to “big data” and tools for their processing

Integrated data management, peripheral and cloud infrastructure, a production-grade artificial intelligence environment for building industrial applications

In addition to software products from manufacturers of industrial automation systems, modern platforms for engineering calculations, such as MATLAB, Maple, PTC Mathcad Prime, Rapid Miner, Weka, etc., provide ample opportunities for developing applications based on artificial intelligence. Table 3 presents software packages and their solutions for industrial data processing and integration with microprocessor technology.

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Manufacturer MATLAB (USA) https://www. mathworks. com

Name MATLAB Support Package for Raspberry Pi Hardware

MATLAB (USA) https://www. mathworks. com

Deep Learning HDL Toolbox™

Rapid Miner (USA) https://www. rapidminer. com MATLAB (USA) https://www. mathworks. com

RapidMiner Automated Model Ops

MATLAB Support Package for Raspberry Pi Hardware

Technical characteristics The package allows to interactively communicate with the Raspberry Pi board, receive data from sensors and image processing devices connected to the Raspberry Pi, and process them in MATLAB [13] The package creates bitstreams to run a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. [fourteen] A tool for automated model development based on an intuitive interface

Application area Design and deploy standalone applications to execute algorithms, including deep learning running on Raspberry Pi boards

The package allows to interactively communicate with the Raspberry Pi board, receive data from sensors and image processing devices connected to the Raspberry Pi, and process them in MATLAB [13]

Design and deploy standalone applications to execute algorithms, including deep learning running on Raspberry Pi boards

Provides features and tools for prototyping and implementing deep learning networks on FPGAs and SoCs Big data processing, data mining, forecasting

Thus, the modern software and hardware level of development of industrial automation systems, as well as new technologies in the field of artificial intelligence, contribute to the development of modified algorithms for artificial immune systems adapted to the requirements of microprocessor technology to create intelligent systems for complex objects control based on them. Further research used data mining software Rapid Miner and Weka. Data collection from a real complex object of Unit 700 for gases purification from acidic components was carried out on the basis of the Esperion PKS (Honeywell) distributed control system of the TengizChevroil enterprise (Table 1, 3).

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4 Developing a Graphical Model Using the Ishikawa Method for Transforming AIS Models The intellectualization of industrial production based on the approach of artificial immune systems is a complex interdisciplinary task. Real production imposes its own limitations on the use of bioinspired AI algorithms [6]. However, artificial immune systems are easy to implement, possess memory, adaptability and self-learning abilities in an unfamiliar situation. The advantages are low requirements for computing resources, as well as the ability to optimize using parallel computing. A convenient tool for studying cause-and-effect relationships and identifying factors influencing the solution of the problem is the development of a graphical model based on the Ishikawa diagram (Fig. 1). For the first time this method was proposed for the Japanese industry and subsequently implemented at the Toyota. This approach is aimed at visualization, organization of knowledge in order to improve production processes [16, 17]. Figure 1 shows the Ishikawa diagram [16] developed by the authors on the basis of the Microsoft Visio software product for efficient transformation of AIS models for the purpose of implementation in industrial production. The primary arrow in the center indicates a problem that needs to be addressed. Arrows pointing to the right represent negative factors, while arrows pointing to the left represent options for solving it. The structure of the graphical model is presented as follows and is based on the “6M” principle: TransformationAIS ¼ fA; B; C; D; E; Fg

ð1Þ

where A«Mother Nature» - category “Environment”, the influence of external factors on the achievement of the goal; B«Men» - the category “People”, shows the influence of the human factor on the solution of the problem; C«Machine» - category “Machines”, reflects industrial equipment, units, technological installations at the enterprise; D«Material» - category “Materials” physical and technical characteristics of materials in production (pressure, temperature, humidity, etc.); E«Measurements» - category “Measurements”, methods of measurement and data collection to solve the problem; F«Methods» - the category “Methods” represents ways of performing production operations, algorithms and methods to achieve the goal. The construction of the Ishikawa diagram allows to systematize all the factors affecting the object of study, establish cause-and-effect relationships, rank them by categories, and also exclude insignificant ones. 4.1

Transformation of AIS Models to Solve Control Problems.

The research was carried out on real production data of TengizChevroil enterprise. Installation 300 for cleaning gases from acidic components was considered as a complex object [18]. The technical characteristics of the computing device on which

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the simulation was carried out are as follows: Intel Core i7-7700HQ, 2.8 GHz, NVIDIA GeForce GTX 1060, Windows 10. The Rapid Miner Studio platform is used as a software product for data mining. A fragment of the database (DB) of daily measurements from sensors (L-level, P-pressure, F-flow, T - temperature) is shown on Fig. 2 [19]. The database consists of 1,500 data instances. Experts identified 3 classes: Class 1. «Good» – normal operation of the equipment; Class 2. «Problem» – operation of equipment with minor deviations, diagnostics of malfunctions is required; Class 3. «Alarm» – emergency operation of equipment. С

Outdated hardware (PLC, process units)

PLC with AI integration

В Lack of qualified personnel

Personnel training (trainings, seminars, exchange of experience)

Interdisciplinary research

А

Assessment of risks associated with hardware and software failure

Specialists in theoretical Influence of immunology weather conditions and aggressiveness of the environment AI and IT specialists

Application of new technologies to ensure communications

Monitoring the range of operating indicators of sensors, for complex object control

Unstable internet

Lack of communication with enterprises

D

Development of network technologies and organization of dedicated interfaces for transferring data to a central storage

Poor product quality

SCADA systems with the ability to connect to data analysis programs

Filtering data

E

Equipment that generates only local data

Increase in throughput of communication channels and data protection

Using distributed control systems

OPC Data Acquisition Technology (OPC Toolbox ™)

Remote locations of technological objects Remote locations of technological objects

Creation of digital twins of the enterprise

Experts - consultants from production

Equipment failures and breakdowns Equipment durability

Servers and data loggers

Connecting sensors from different manufacturers

Feature engineering

Ontological approach to knowledge representation

Invalid data

Development of modified AIS algorithms

Missing data Algorithms optimization Large amount of difficult to formalize production data

Parallel computing

Comparative analysis with other AI algorithms

Outdated archiving systems Providing real-time data processing

Large datasets unsuitable for processing

Transformation of AIS models for the development of IMS based on modern microprocessor technology

Difficulty in interpreting data

Low predictive ability of algorithms

Complexity of Algorithms Implementation

High requirements for computing resources (device memory, processor power)

Evaluating the effectiveness of algorithms

F

Fig. 1. The developed graphic model based on the Ishikawa diagram for intelligent systems of complex objects control using AIS

Next, based on the developed Ishikawa diagram, we will consider an example of applying modified AIS algorithms for complex objects control. According to category F “Method” (Fig. 1), an important step is data preparation (construction of features) (Fig. 3).

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Fig. 2. Fragment of the database of a complex object parameters

Fig. 3. Visualization of a fragment of the daily measurements database of the Unit 300 using the example of pressure sensor readings in the Rapid Miner Studio application package

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Then the problem of identifying informative features is solved based on the following algorithms: Decision Trees (DT) [20], Random Forest (RF) [21], Gradient Boosted Trees (GBT) [22]. The ranking of the parameters of the Unit 300 database by the degree of significance is shown on Fig. 4. The indicator that has the highest value of the “weight” parameter is informative, the indicators with the lowest value are subject to reduction.

Fig. 4. Ranking parameters by degree of significance

Based on the results of identifying informative features, a database of optimal parameters is compiled. After the reduction of uninformative features based on the DT algorithm, the following parameters are selected: 11LT31013.PV, 11FIC31011.OP, 11TE301020.PV, etc. Parameters with a low “weight” value are not used for the creation of an optimal database. Based on the results of solving the problem of identifying informative features, 3 optimal databases were obtained: DB_DT (consists of 1000 data instances); DB_RF (700 data instances); DB_GBT (1100 data instances). Then the AIS-based pattern recognition problem is solved. Consider the pattern recognition problem [23]. Let the number of classes be defined as follows: K ¼ f1; . . .; k g

ð2Þ

For the considered example, the classes are presented in the form: K ¼ fGood; Problem; Alarmg

ð3Þ

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“Pattern” is described as an n-dimensional column vector: Im ¼ ½Im1 ; Im2 . . .; Imn T

ð4Þ

where Im1; Im2;...; Imn – real numbers. Pattern recognition in general is described as follows: f ðImÞ ! f1; . . .; kg

ð5Þ

For this example f ðImÞ ! fGood; Problem; Alarmg. “Standards” are presented in the form: Et ¼ fEt1 ; Et2 ; . . .; Etm g

ð6Þ

The “Standards” class with which the “Image” is compared is described as follows: f ðEt1 Þ ¼ k 1 ; . . .; f ðEtm Þ ¼ km

ð7Þ

In this example, the classes of “Standards” are presented as: f ðEt1 Þ ¼ Good; f ðEt2 Þ ¼ Problem; f ðEt3 Þ ¼ Alarm

ð8Þ

Solving the problem of pattern recognition using algorithms of artificial immune systems is reduced to finding a class of an arbitrary n-dimensional vector C, f ðC Þ ¼ class. The following algorithms are considered as an example: AIRS1 [24], AIRS2, Parallel AIRS [25]. The results of modeling using the software product for data mining WEKA (Waikato Environment for Knowledge Analysis) [26] are presented in Table 4. Table 4. Pattern recognition results based on modified AIS Algorithm for informative features selection

AIS prediction model AIRS1 AIRS2 CCI MAE RMSE CCI MAE Decision Tree 81% 0,126 0,355 78% 0,146 Random Forest 87% 0,086 0,294 87% 0,086 Gradient Boosted Tree 97% 0,02 0,141 95% 0,033 Correctly Classified Instances (CCI) Mean Absolute Error (MAE) Root Mean Squared Error (RMSE)

RMSE 0,383 0,294 0,182

Parallel AIRS CCI MAE RMSE 79% 0,14 0,374 81% 0,126 0,355 99% 0,006 0,081

Table 4 discusses the following estimates of the performance of modified AIS algorithms [27]: – CCI Score - Shows the percentage of test instances that were correctly classified.

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– MAE is less sensitive to outliers than the root mean square error and is calculated using the formula: MAE ¼

j p1  a 1 j þ . . . þ j pn  a n j n

ð9Þ

where a1 ; . . .; an - Actual Target Values, ATV; p1 ; . . .; pn - Predicted Target Values, PTV; RMSE is determined as follows: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðp1  a1 Þ2 þ . . . þ ðpn  an Þ2 RMSE ¼ n

ð10Þ

The best results were shown by the modified GBT-Parallel AIRS algorithm, the simulation time of which is 0.02 s. and efficiency ratings have the following meanings: CCI = 99%; MAE = 0,006; RMSE = 0,081.

5 Conclusion The development of modern modified algorithms of artificial immune systems for the intellectualization of control systems is a promising area of research that allows solving the following problems: – – – – – –

multidimensional data processing and simple interpretation of the results obtained; forecasting under conditions of uncertainty of parameters; pattern recognition on the class boundary; reducing the requirements for computing power and memory resources; implementation based on existing available engineering platforms; adaptation for a specific production.

Thus, industrial artificial intelligence opens broad prospects for creating a new generation of automated control systems. Acknowledgments. These studies were carried out with the financial support of the SC MES RK of the Republic of Kazakhstan (GF no. AP09258508).

References 1. Mohammed, T.A., Qasim, M.N., Bayat, O.: Hybrid solution of challenges future problems in the new generation of the artificial intelligence industry used operations research industrial processes. In: International Conference on Data Science, E-Learning and Information Systems, pp. 213–2018 (2021)

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2. Debnath, M.K., Agrawal, R., Tripathy, S.R., Choudhury, S.: Artificial neural network tuned PID controller for LFC investigation including distributed generation. Int. J. Numer. Model. Electron. Netw. Devices Fields 33, e2740 (2020) 3. Escandon, I., Cerrada, M.: Active fault diagnosis based on consistencies to a class of hybrid systems by using genetic algorithms and Markov decision process. In: IEEE ANDESCON, pp. 1–8 (2020) 4. Xie, S., Wang, G.: Optimization of parallel turnings using particle swarm intelligence. In: 10th International Conference on Advanced Computational Intelligence (ICACI), pp. 230– 234 (2018) 5. Samigulina, G.A., Samigulina, Z.I.: Modified immune network algorithm based on the Random Forest approach for the complex objects control. Artif. Intell. Rev. 52(4), 2457– 2473 (2018). https://doi.org/10.1007/s10462-018-9621-7 6. Samigulina, G.A., Samigulina, Z.I.: Development of Smart-Technologies for Prediction and Control of Complex Objects Based on Modified Algorithms of Artificial Immune Systems: Monograph. Science Book Publishing House, Yelm (2020) 7. Saleh, H., Saad, Z.: Artificial immune system based PID tuning for DC servo speed control. Int. J. Comput. Appl. 155(2), 23–26 (2016) 8. Fefelov, A.A., Lytvynenko, V.I., Taif, M.A., Lurie, I.A.: Parametric identification of the Ssystem by the modified clonal selection algorithm. Control Syst. Comput. 5(271), 43–53 (2017) 9. Mitsubishi electric. https://www.mitsubishielectric.com/. Accessed 30 Nov 2021 10. Rueil-Malmaison: Start-ups from Schneider Electric and Microsoft’s joint accelerator are transforming the energy sector in Europe with artificial intelligence. Press release Schneider Electric, 1–3 (2020) 11. Skripov, S.A.: On the use of microcontrollers for the implementation of artificial neural networks. Young Scientist Inf. Technol. 46(284) (2019) 12. Bekasov, D.: Opportunities of Siemens for digital transformation of industrial production. Control Eng. Russia IIOT 60, 60–65 (2018) 13. MathWorks Simulink Team. Raspberry Pi Hardware Resource Manager. MATLAB Central File Exchange (2021) 14. MathWorks. MATLAB Deep Learning HDL Toolbox User’s Guide (R2021a). The MathWorks, Inc. 278 (2021) 15. MathWorks Maker Team. Arduino_Engineering_Kit_Project_Files_Rev_2. MATLAB Central File Exchange (2021) 16. Botezatu, C., Condrea, I., Oroian, B., Hriţuc, A., Eţcu, M., Slătineanu, L.: Use of the Ishikawa diagram in the investigation of some industrial processes. In: 10th International Conference on Advanced Manufacturing Technologies (2019). IOP Conf. Series: Materials Science and Engineering 682, pp. 1–8 17. Hidayah, E.N., Veronica, G., Cahyonugroho, O.H.: Identification and factors of failure risk in refill drinking water quality by using Ishikawa diagram. IOP Conf. Ser. Mater. Sci. Eng. 1125(1), 1–8 (2021) 18. Technological regulation for cleaning process of hydrocarbon gases on the equipment 300. TP – KTL-2.3-300-11, 32–34 (2013) 19. Samigulina, G.A., Samigulina, Z.I.: Development of Smart technology for complex objects prediction and control on the basis of a distributed control system and an artificial immune systems approach. Adv. Sci. Technol. Eng. Syst. J. 4(3), 75–87 (2019) 20. Albarka, U.M., Chen, Z., Liu, Y.: A hybrid intrusion detection with decision tree for feature selection. Inf. Secur. Int. J. 49, 1–20 (2021)

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21. Tandra, S., Manashty, A.: Probabilistic feature selection for interpretable random forest model. In: Arai, K. (ed.) FICC 2021. AISC, vol. 1364, pp. 707–718. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73103-8_50 22. Wijaya, A., Kharis, Prastuti, W.: Gradient boosted tree based feature selection and Parkinson’s disease classification. In: 5th International Conference on Science and Technology (ICST), pp. 1–5 (2019) 23. Blum, V.S., Zabolotsky, V.P.: Immune system and immune computing. St. Petersburg Institute of Informatics and Automation RAS, 1–16 24. Jenhani, I., Elouedi, Z.: Re-visiting the artificial immune recognition system: a survey and an improved version. Artif. Intell. Rev. 42(4), 821–833 (2012). https://doi.org/10.1007/s10462012-9360-0 25. Abdelkhalek, R., Elouedi, Z.: A belief classification approach based on artificial immune recognition system. In: Lesot, M.-J., et al. (eds.) IPMU 2020. CCIS, vol. 1238, pp. 327–340. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50143-3_25 26. Waikato University. http://www.cs.waikato.ac.nz/ml/weka/. Accessed 20 Oct 2021 27. Frank, E., Hall, M.A., Witten, I.H.: The WEKA Workbench. Data Mining Practical Machine Learning Tools and Techniques. Morgan Kaufman, Burlington (2016)

Student or Human Operator? Objective Results and Subjective Perceptions of the Process and Results of Offline and Online Learning Yu. I. Lobanova(&) St. Petersburg State University of Architecture and Civil Engineering (SPbGASU), 4, 2-ya Krasnoarmeyskaya Street, Saint-Petersburg 190005, Russia [email protected]

Abstract. The article analyzes the different levels of students perceptions and attitudes and training areas to different learning formats: online and offline. The paper is the research a continuation conducted by the author since the pandemic beginning, reflecting the learners’ attitudes dynamics towards learning different aspects and distance learning in general. The study objects were a technical university different training areas student, who have studying technical and humanities disciplines experience in different formats. The study subject was both objective indicators of different formats (grades comparison received during offline and online learning, and health status). The process subjective characteristics evaluation and learning results in different formats were also compared: satisfaction with the obtained educational results, the monotony, emotions state manifestation experienced when learning in different formats. In other words, the study compares objective and subjective characteristics of different formats: learning achievements and health preservation, satisfaction with the educational process and experienced emotional results. The empirical part is built on the empirical data analysis obtained through the questionnaire designed by the author. The hypotheses regarding the more positive attitude of students towards offline learning and the differences’ presence in attitude towards formats depending on which disciplines are emitted - technical or humanities are tested. The study results showed that the learners’ marks in the transition from offline to online format often increase or remain at the same level. However, subjectively, learners assess the learning process in the online format rather negatively, regardless of whether technical or humanities subjects are studied: the learning process pleasure decreases, monotony increases and satisfaction with the learning outcomes decreases. And this is despite the deteriorating health noted by the respondent’s majority. The conclusion is made about the learning process formalization in the transition to online, the transferring and changing personal meanings’ difficulty, the personal knowledge formation, which are the learning main criteria. It is noted that an important factor in the negative assessment of online learning are the differences in instrumental and technological readiness of participants in the educational process in the use of information and communication digital technologies. Lack of consideration of the impact of the design of the used learning tools on the process and the result of training also negatively affect the evaluation of online © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 121–127, 2022. https://doi.org/10.1007/978-3-031-09073-8_11

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Yu. I. Lobanova learning. It is pointed out the need to find a “formula” for blended learning and the learning technologies development - ICT and digital - up to the immersive reality creation, which will make the departure from offline to online learning the basis for effective learning, bringing satisfaction to all participants in the educational process. #CSOC1120. Keywords: Online education
Information and communication technology Digital technologies
Human operator
Moodle




1 Introduction The coronavirus pandemic has forced the educational process participants to work under the natural pedagogical experiment conditions…Self-isolation and quarantine regimes introduced in different countries have made the distant future of digital learning real and (accordingly) available for empirical study. The studies a large number on the educational process new realities have appeared [1, 4–10]. In June 2020, the article author carried out student behaviour a comparative analysis at the transition beginning from offline to online learning. There was a slight decrease in all students’ attendance (when working online compared to offline), differences in the using microphones and chatting activity in communication with the teacher by senior and junior students. It was shown that less technically advanced older learners enjoyed not only the new learning format but also the opportunities it offered. The younger students, despite the technology itself their better understanding, behaved more reticently and even passively during the classes [8]. A further study [9] explored the online learning individual advantages and disadvantages: • the online learning main advantage turned out to be saving time for packing and travelling (saving time and psychophysiological resources). According to students’ feedback, they found lectures-presentations with attached audio files in Moodle especially comfortable and convenient. • the distance learning main disadvantages were pronounced hypodynamic and a heavy load on the visual apparatus. In the ongoing pandemic context, it has become evident that modern digital and information communication technologies, e-learning itself are a step into the future, including the educational process future, but this future needs to be seriously prepared for. The transition to online learning transforms an ordinary student (or even a teacher) into a so-called “human operator”. But, if a real “human operator” for work in a manmachine system is carefully prepared (using psychological selection, specialized professional training), then the same students are included in these systems all without exception and any special training. Therefore, corresponding formats use rather imposes special requirements on those who organize and carry out such training formats - that is on training organizers and teachers (instructors). The study results help them.

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However, both of them (especially in the Russian reality conditions) also have used different formats with insufficient experience - before the pandemic most educational institutions worked exclusively offline. And now it is relevant for all educators to solve the problem - to conduct training in parallel and search for optimal platforms, programs, learning methods and means. The research results presented in this paper are intended to help address these challenges. The study objects were the technical universities different courses training different areas students, who had a learning experience in all formats in the both technical and humanities cycle disciplines study. Most of them were trained using Moodle platform and Teams program. A total of 71 people have interviewed: 21 boys and 50 girls. The average age was 20, 3 years old. The three institute’s students were interviewed: automobile and road, engineering and environmental systems and architecture. The study subject was the learning different formats objective and subjective evaluations comparison supported by (among others) different information and communication technology (ICT) and digital technologies. Hypotheses tested in the study: 1. Trainees will view the transition from offline to online learning negatively, both objectively and subjectively. 2. Students of technical universities than humanities disciplines will perceive studying technical disciplines in an online format more negatively.

2 Research Methods The empirical part is built on the empirical data analysis obtained with a questionnaire help designed by the author (taking into account the results obtained in earlier studies). The questionnaire involves the rating analysis given by the trainees to the two learning formats (offline and online) on characteristics a range, (mainly a 9-point scale was used), among which were: • the monotony state severity; • the pleasure expression in learning; • satisfaction with learning outcomes. Objective indicators were also assessed: Scores were recorded on a regular 5-point scale. Changes in health levels were recorded using three ratings: improved, worsened, no change.

3 Results The individual formats evaluation analysis according to specific characteristics is shown in Table 1. The data in Table 1 indicate that the marks obtained in online learning either did not change as compared to offline learning or were higher (Table 2).

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Table 1. Change in marks (grade point average) when moving from offline to online learning. Online (compared to offline), % No change 54.9 Deteriorated 3.9 Improved 35.2

Table 2. Health (change patterns) in the transition from offline to online learning. Online No change 35.2 Deteriorated 43.7 Improved 21.1

Based on the data from the table, many trainees noted a deterioration in their health when learning in an online format. Process and learning outcomes subjective evaluations an analysis in the transition from offline to online learning is presented in Tables 3 and 4. Table 3. Satisfaction expression dynamics with learning outcomes in the transition from offline to online learning. Online No change 31 Deteriorated 53.5 Improved 15.5

Based on the data from the table, the satisfaction with the learning outcomes when transitioning from offline to online learning often decreases among the trainees. Table 4. Monotony state expression dynamics during the transition from offline to online learning. Monotony state severity Hasn’t changed strengthened It’s starting to show less

From offline to online 22.5 53.5 18.3

It is clear from the data in the table that the state of monotony became more evident in the trainees when they switched to online learning. At the second stage, all the received questionnaires were divided into two groups according to the combination of 4 answers (satisfaction with the learning process,

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monotony state expression, marks received, satisfaction with learning results) depending on whether the dynamics of marks coincided during the transition from offline to online learning in technical and humanities disciplines. The formats assessments are identical (did not differ for different cycles disciplines) only for 32.4 respondents (for this reason they refused to fill in the table for humanities disciplines separately). They differ no more than by 1 point for another 36 or 50.7 per cent. In other words, we can say that the trainees’ majority (83.1) did not notice any difference in studying technical and humanities disciplines online. Differences in the technical disciplines and humanities evaluation, respectively, were found only in 16.9 per cent of questionnaires. It should be noted that more than the students a third assessed the transition to online learning rather as a reduction in the quality of training (both objectively and subjectively) - see Table 5 - slightly less than the respondents a third did not notice the changes in the learning conditions and technologies, about the students a fifth, perceived the transition rather positively. Table 5. Students’ evaluation of the transition to online learning. The change assessment in the learning format (when studying technical and humanities disciplines) The relevant type responses percentage (based on respondents 100 per cent)

++

==

−−

18.3

29.5

35.2

From 16.9% of students who noted differences in the technical and humanities disciplines study of when changing the format study, most were positive about the transition to online learning in humanities disciplines and negative about technical disciplines, but some, on the contrary, were negative about moving humanities disciplines online but positive about technical disciplines (Table 6). Table 6. The grades dynamics comparison when transferring different cycle disciplines to online (when there is grading sign a mismatch for technical disciplines and humanities disciplines). The change assessment in the learning format (when studying technical and humanities disciplines) The relevant type responses percentage (based on respondents 100 per cent)

−+

??

+−

8

5.6

2.8

4 Discussion Despite the preservation and marks even increase, the online learning format is perceived by the students rather negatively than positively. At the same time, there is no significant difference in the humanities and technical disciplines study, although the

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surveyed students are studying in a technical university, and the motivation to study different disciplines may be different and accordingly affect the learning conditions and results assessment. From the personal constructs theory position the learning process is in many respects already existing one’s new meanings and reconstruction a construction [11– 14], that learning is achieving personal knowledge a process, and that is especially important - that in the learning process the learner needs to exchange personal meanings [2, 3], then it seems very likely that in online learning the meanings construction and exchange turn out to be difficult or impossible. Learning becomes more formal, and therefore, despite the increase in grades, satisfaction with the process (as well as the pleasant feeling from the learning process) decreases.

5 Conclusion The first hypothesis was confirmed: online learning is generally perceived more negatively by the learners than offline. The problem may be in the certain stereotypes’ formation: the respondents have been studying in offline format for many years and in the imperfect organization of online learning itself and information and communication and digital technologies used in conducting classes: most trainees are familiar only with the platform Moodle and Teams program. And in the already mentioned difficulty of exchanging personal meanings, focusing only on external, formal meanings. The second hypothesis - about the differences in learning format perception in technical and humanities disciplines - did not prove to be true. Online learning negative aspects at the moment are strong enough and are so pronounced in the learning process that the learning motivation absence or presence does not compensate them at all. Someday artificial intelligence will be able to fully paraphrase, to present information with the same meanings, but change words, language, meaning, thereby achieving understanding… For now, it is too early to exclude from the learning process a person in live direct contact with the student. The pure online format is not ideal at the moment and it does not make sense to move away from offline classes completely. In the future, it is necessary to compare the three learning formats estimates (including mixed) to derive a formula for the ideal one that combines the best characteristics of each. Focused interviews with trainees indicate that an important factor in the negative evaluation of online learning are the differences in the instrumental and technological preparedness of the participants in the educational process. With the forced active use of information and communication digital technologies, representatives of generation Y and even more so of generation X, who possess encyclopedic knowledge and easily work in direct contact with students, not only have difficulties in mastering new technologies, but also obviously underestimate their importance: the modern digital generation proved to be quite critical with respect to the quality of presented images and presentation design. Digital pedagogy clearly lacks “fusion” with engineering psychology - a human operator perceives and works with signs and images differently. The future is behind active research in this area.

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Another way in which digital pedagogy can be evolved is the development of information and communication digital technologies - up to the creation of an immersive reality in the conditions of the educational process. The training of participants in the educational process in the future as a “human operator” is a crucial issue. Both variants are possible only when experts, teachers and IT specialists are united in one team.

References 1. Vinogradova, V.V.: Problems and prospects of modern education in the development of digital technology. In: Proceedings of the V International Scientific-Practical Proceedings “Pedagogical Parallels”, pp. 114–120. Russia (2018) 2. Cherubini, L.: Exploring prospective teachers’ critical thinking: case-based pedagogy and the standards of professional practice. Teach. Teach. Educ. 25(2), 228–234 (2009) 3. Dillon, P.: Creativity, integrative and pedagogy of connection. School Educ. Lifelong Learn. Thinking Skills Creativity 1(2), 69–83 (2006) 4. Dzhangarov, A.I., Hanmurzaev, H.E., Potapova, N.V.: Digital education in the coronavirus era. J. Phys. Conf. Ser. 1691, 012133 (2020) 5. Harri-Augstein, S., Thomas, L.: Self-organized learning and the relativity of knowing: towards a conversational methodology. In: Stringer, P., Bannister, D. (eds.) Constructs of Sociality and Individuality. Academic Press, London (1979) 6. Kvashko, L.P., Aleksandrova, L.G., Shesternina, V.V., Erdakova, L.D., Kvashko, V.V.: Distance learning during self-isolation: comparative analysis. J. Phys. Conf. Ser. 1691, 012013 (2020) 7. Yarychev, N., Mentsiev, A.: New methods and ways of organizing the educational process in the context of digitalization. J. Phys. Conf. Ser. 1691, 012128 (2020) 8. Lobanova, Y.: Distant learning experience reflection during the pandemic of Covid-19 (On the example of teaching in the technical university). J. Phys. Conf. Ser. 1691, 012152 (2020) 9. Lobanova, Y.I.: Distance learning advantages and disadvantages: teaching experience analysis at the university with the basis on different informational-communicative technologies. In: Silhavy, R. (ed.) CSOC 2021. LNNS, vol. 229, pp. 499–506. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77445-5_46 10. Makarenya, T.A., Stash, S.V., Nikashina, P.O.: Modern educational technologies in the context of distance learning. J. Phys. Conf. Ser. 1691, 012117 (2020) 11. Orishev, A.B., Mamedov, A.A., Kotusov, D.V., Grigoriev, S.L., Makarova, E.V.: Digital education: Vkontakte social network as a means of organizing the educational process. J. Phys. Conf. Ser. 1691, 12092 (2020) 12. Polanyi, M.: Personal Knowledge. Progress, Moscow (1985) 13. Thomas, L.: Learning and meaning. In: Fransella, F. (ed.) Personal Construct Psychology 1977. Academic Press, London (1978) 14. Thomas, L.: Nothing more theoretical than good practice: teaching for self-organized learning. In: Issues and Approaches in Personal Construct Theory. In: Bannister, D. (ed.) Academic Press, London (1985)

Implementation of Data Mining Using k-Nearest Neighbor Algorithm for Covid-19 Vaccine Sentiment Analysis on Twitter Irma Ibrahim(&), Yoel Imanuel, Alex Hasugian, and Wirasatya Aryyaguna Department of Computer Science, Bina Nusantara University, Jakarta, Indonesia [email protected], {yoel.imanuel,alex.hasugian, wirasatya.arryaguna}@binus.ac.id

Abstract. The purpose of this research was to determine public sentiment towards the preparation and implementation of the COVID-19 vaccine in Indonesia through the social media platform Twitter that contained ‘Tweets’ with positive or negative sentiments. The research method was divided into data retrieval on Twitter related to the COVID-19 vaccine in Indonesia, gathering the preprocessing data, and followed by classification based on the K-Nearest Neighbor algorithm. The results of this research determined positive and negative sentiments from the data. From the 800 data points tested, there were 529 positive sentiments and 271 negative sentiments. The accuracy value of the KNearest Neighbor algorithm in this research was 78% with k = 3. Keywords: Data mining
Keyword
K-Nearest Neighbor
Sentiment analysis

1 Introduction This research was based on the presence of COVID-19 or coronavirus which was first discovered in Wuhan, China in 2019. The first case of this virus in Indonesia was confirmed by the government on March 2, 2020 in the city of Depok. Over time, cases of this virus continued to grow and became a pandemic. To deal with COVID-19, the government brought in COVID-19 vaccines to prevent the virus from spreading further. The first vaccines to arrive were 3 million doses of Sinovac in December 2020 [1]. As the vaccination program progressed, a lot of news and information was inaccurate or incomplete which caused people to have doubt about getting the vaccine. This also hindered the government’s vaccination program to achieve herd immunity. People’s doubts and fears about the COVID-19 vaccine are the background of the problem in this study. Thus, the authors wanted to ascertain what the public sentiment was regarding the importation and use of the COVID-19 vaccine in Indonesia. The hypothesis of this study was that public perception of the COVID-19 vaccine tended to

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 128–135, 2022. https://doi.org/10.1007/978-3-031-09073-8_12

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be positive. This study analyzed public sentiment regarding the COVID-19 vaccine in Indonesia. With the realization of this research, it could help the government in determining public sentiment. Thus, the government can also correct or eradicate confusing news and information regarding the COVID-19 vaccine. This also helps the community to be convinced of the efficacy of vaccination.

2 Recent Studies Several studies related to news on social media related to COVID-19 have been carried out, including research conducted by Kwon, Grady, Feliciano and Fodeh. They examined news reports on Twitter related to the possibility and likely implementations of social distancing as part of the protocol to mitigate the effects COVID-19, from January to March, 2020, in three major American cities. This study found that the development of social distancing content shown on Twitter reflected the actual events as they transpired and could prove useful to flag potential outbreak centers [1]. A furrther study was carried out in Italy by Andreadis et al., who devised a framework to for the collection, analysis, and visualization of Twitter posts. The framework was specifically deigned to monitor the spread of the virus in Italy, which was gravely affected by COVID-19. The data was presented in the form of an interactive map which could display and filter the analyzed posts, utilizing the outcomes of localization techniques [2]. Many other similar studies showed that tweets on Twitter and other social media are important in monitoring and analyzing communications related to the COVID-19 pandemic to find out public opinion along with its positive and negative sentiments [3–5]. In studies related to sentiment analysis there are several methods used. In a study conducted by Ullah et al., both modes of data were analyzed combined and separately with both machine learning and deep learning algorithms for finding sentiments from Twitter based airline data using several features such as TF–IDF, Bag of words, Ngrams, and emoticon lexicons [6]. Another study conducted by Loukas Ilias and Ioanna Roussaki analyzed Twitter conversations using the Natural Language Processing (NLP) deep learning technique [7]. Many other methods were used in similar research related to sentiment analysis on social media [8–10].

3 Result and Analysis 3.1

Research Framework

The research framework of this study is shown in the following figure (Fig. 1):

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Labeling

Preprocessing 1. Cleaning 2. Case Folding 3. Tokenizing 4. Normalization 5. Stemming 6. Stopword

Classification of K-NN 1. Weighting of TF-IDF 2. Distribution of training data and testing data 3. Analysis using K-NN & Testing

Evaluation

Fig. 1. Research framework

3.2

Data Collection

The data used for this research was Tweets (messages posted on the social media platform, Twitter) related to the COVID-19 vaccine in Indonesia. Twitter has a policy regarding the retrieval of Tweet data in which data collection must be carried out within a week of the event you want to analyze. The Tweet data taken in this study was related to the procurement or injection of the COVID-19 vaccine in Indonesia. The keywords used to collect this data were Tweets containing the word “vaccinated”. The data retrieval process used one of the libraries in Python, namely ‘Tweepy’ which served to access the Twitter API (application programming interface). To be able to connect the Tweepy library with Twitter, you do not only need to do coding, but you also have to get an API key first. After waiting one day the authors got the API Key. Data retrieval could be conducted by coding Python and including the API key and Access Token that were obtained so that the Tweepy library in Python could be connected to Twitter. The Tweet data that the authors attained includes the usernames, time of the Tweets, and the content of the Tweet itself in the form of text. The results of data retrieval from Tweepy were saved in.CSV format. The scraping results consisted of 1000 Tweets. The results of this scraping contained Tweets that are not relevant to research purposes, such as Tweets containing news and duplication. Therefore, the authors performed manual data cleaning for Tweets that contained news or duplicates. After cleaning the data, the remaining data amounted to 800 Tweets.

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Labeling

The labeling was conducted manually by providing a code with the letter ‘P’ for Tweets that (according to the authors) were positive for the COVID-19 vaccine in Indonesia and ‘N’ for Tweets that were negative for the COVID-19 vaccine. Any Tweets that were neutral or ambiguous or out of context from the research were deleted. Following the labelling process, 800 Tweets were considered to fit the remit of this research. In labeling, several keywords in the Indonesian language were the factors that determined whether the authors gave a positive or negative label to the Tweet. Keywords used for positive labels were “Mau divaksin” (I want to be vaccinated), “Ayo semua vaksin” (Let’s all get vaccinated), “Jangan takut vaksin” (Don’t be afraid to get vaccinated), “Akhirnya divaksin” (Finally [I am] vaccinated), and “Setuju vaksin” ([I] agree [with] vaccines). Keywords used for negative labels are “Tidak mau divaksin” ([I do] not want to be vaccinated), “Untuk apa vaksin?” (What are vaccines for?), “Takut vaksin” ([I am] scared of vaccines), and “Banyak yang meninggal setelah vaksin” (Many died after vaccination). 3.4

Preprocessing

This stage aimed to make the data more easily processed. This stage also helped to run the data classification process with the K-Nearest Neighbor algorithm. 3.4.1 Cleaning Data cleaning was the stage in which unnecessary characters and words that could result in non-optimal calculations in the classification stage using source code were stricken from the Tweets. Characters that were omitted in data cleaning were non-text such as symbols, images, videos, links, emoji, and incomplete URLs. 3.4.2 Case Folding The preprocessing stage of case folding was to make the shape of the data more uniform. Tweet data could take various forms. This of course affected the classification results. Since uniformity at the case folding stage was very necessary, the Tweet data was standardized to lowercase for optimal classification. 3.4.3 Tokenizing The tokenizing preprocessing stage was the data beheading stage. Data in the form of sentences was cut into words. 3.4.4 Normalization The normalization stage was carried out with the aim of changing non-standard words into standard ones according to the Indonesian dictionary. This was very useful in the classification process, and was done manually in the source code by entering nonstandard words and standard words. After the data was successfully normalized, the data was returned to tokenized form so that it could be processed easily in the next stemming stage.

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3.4.5 Stemming The preprocessing stemming stage is the stage of changing words that had passed the normalization stage into basic words (root words). This stage also included the removal of affixes from words. 3.4.6 Stopword This stage was carried out by removing words that were less important or words that often appeared that could affect the results of the classification, such as connecting words. Python already provided a library that could be used, called ‘Literature’. This library provided Stopwords for Indonesian. However, because the data was in the form of Tweets, the authors added a few more words to combine with the existing Stopwords. The results obtained from the Stopword stage was the final preprocessing data that would be used for the next stage. The results can be seen in Fig. 2.

Fig. 2. Sample result of stopword

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K-Nearest Neighbor Classification

3.5.1 Word Weighting (TF-IDF) The initial stage of classifying sentiment using K-Nearest Neighbor was to assign a weighted value to each dataset to be tested. The authors gave weights using the source code so that all data could get values quickly and accurately. 3.5.2 Distribution of Training Data and Testing Data At this stage, the K-Nearest Neighbor Algorithm was implemented. Before implementing it, it was necessary to call the existing library in Python (Fig. 3).

Fig. 3. Python library K-nearest neighbor

The percentage distribution for training data was 90% and the testing data was 10% (Fig. 4).

Fig. 4. Source Code for data training and data testing distribution

3.5.3 Sentiment Analysis with K-Nearest Neighbor The trained dataset was classified using K-Nearest Neighbor. This process began by entering training data into K-Nearest Neighbor. Then K-Nearest Neighbor automatically predicted the dataset (Fig. 5).

Fig. 5. Source code for entering training data into K-nearest neighbor & data prediction

At this stage, the authors entered the value of K to be able to test the training data and testing data. The authors conducted several tests to get the greatest accuracy value and it was obtained at 78% with K = 5. The purpose of getting the accuracy value was to assess the accuracy of the system made by the authors in predicting datasets that had been labelled or annotated (Table 1).

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= = = = = = = = = =

2 3 4 5 6 7 8 9 10 11

55.001% 72.5% 73.75% 78.75% 75% 72.5% 73.75% 73.75% 73.75% 75%

After the accuracy values were available, there were several other calculations, namely precision, recall, and F-1 Score. These four calculations were included in the Confusion Matrix calculation (Fig. 6).

Fig. 6.

3.6

Source code and results of calculation of accuracy, precision, recall, and F-1 score

Evaluation

From the data evaluation, 66% of the data contained positive sentiments and 34% contained negative sentiments towards the COVID-19 vaccine in Indonesia and these were generated with a system accuracy rate of 78%.

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4 Conclusion The use of K-Nearest Neighbor in this study the data showed that 66% had a positive sentiment and 34% had negative sentiment toward the COVID-19 vaccine with a data accuracy rate of 78%. This system could be further developed so that it could be presented in the form of a dashboard that would be more easily used. This sentiment analysis modeling could also be used for other topics to be developed further. Also, we could try to use other methods to have higher accuracy in further studies.

References 1. Kwon, J., Grady, C., Feliciano, J.T., Fodeh, S.J.: Defining facets of social distancing during the COVID-19 pandemic: Twitter analysis. J. Biomed. Informatics 111 (2020) 2. Andreadis, S. et al.: A social media analytics platform visualizing the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets. Online Social Networks and Media 23 (2021) 3. Obembe, D., Kolade, O., Obembe, F., Owoseni, A., Mafimisebibembe, O.: COVID-19 and the tourism industry: an early stage sentiment analysis of the impact of social media and stakeholder communication. Int. J. Info. Manag. Data Insights 1 (2021) 4. Lyu, H. et al.: Social media study of public opinions on potential COVID-19 vaccines: informing dissent, disparities, and dissemination. Intell. Med. (2021) 5. Ridhwan, K., Hargreaves, C.: Leveraging twitter data to understand public sentiment for the COVID-19 outbreak in Singapore. Int. J. Info. Manag. Data Insights 1 (2021) 6. Ullah, M.A., Marium, S.M., Begum, S.A., Dipallah, N.S.: An algorithm and method for sentiment analysis using the text and emoticon. ICT Express 6, 357–360 (2020) 7. Ilias, L., Roussaki, I.: Detecting malicious activity in Twitter using deep learning techniques. Appl. Soft Comput. 107 (2021) 8. Ansari, M.Z., Aziz, M.B., Siddiqui, M.O., Mehra, H., Singh, K.P.: Analysis of political sentiment orientations on Twitter. Procedia Comput. Sci. 167, 1821–1828 (2020) 9. Kaur, S., Kaul, P., Zadeh, P.: Monitoring the dynamics of emotions during COVID-19 using Twitter data. Procedia Comput. Sci. 177, 423–430 (2020) 10. Zervoudakis, S., Marakakis, E., Kondylakis, H., Goumas, S.: OpinionMine: a Bayesianbased framework for opinion mining using Twitter data. Mach. Learn. Appl. 3 (2021)

Towards Intelligent Vision Surveillance for Police Information Systems Omobayo A. Esan1(&) and Isaac O. Osunmakinde2 1

School of Computing, College of Science, Engineering and Technology, University of South Africa, Pretoria, South Africa [email protected] 2 Computer Science Department, College of Science, Engineering and Technology, Norfolk State University, Norfolk, VA, USA [email protected]

Abstract. Traditional surveillance systems detect suspicious activities with the assistance of human operators watching screens showing video streams of activities captured from different cameras, which often leads to fatigue and failure to identify the suspicious activities. This is an area of considerable interest for security agents like police carrying out surveillance operations. Besides the current literature effort, this research investigates how suspicious loitering in a location can be detected before the crime occurs. Since human behavior is dynamic, this research develops an intelligent vision framework based on an integrated convolutional neural network (CNN) adaptive to specific locations at a time rather than building a generalized model prone to errors. We demonstrate the efficiency of the proposed system with preliminary results obtained on real-life image frames captured from multiple cameras. This model outperforms the conventional approaches in terms of the detection of suspicious locations with an average F1-score of 0.9666, a false positive rate of 0.0922, and an accuracy of 94.49%. The deployment of this new model can help to augment the work of police information systems. Keywords: Image processing
Surveillance systems
Deep learning Convolutional neural network
Crime
Police information system




1 Introduction With the increasing demand for public security and safety, a vast number of surveillance cameras have been installed in many places such as shopping centers, schools, hospitals, banks, etc., to prevent mishaps and guarantee public safety [1]. These cameras detect suspicious activities with the assistance of human operators watching screens showing video streams of activities captured from different cameras, which often leads to fatigue and failure to identify the suspicious activities [2], which is an area of considerable interest for security agents like the police to carry out surveillance operations. In the context of crowd scene videos, suspicious behavioral patterns are difficult to detect due to the human dynamic nature which forms the part of the information that © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 136–148, 2022. https://doi.org/10.1007/978-3-031-09073-8_13

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described the environment [3]. The different undetected behavioral patterns in crowded scenes often result in a crime. However, the current surveillance system lacks the technological infrastructure to perform suspicious detection functions before it manifests to crime. To ensure the safety and security of the people, many organizations around the world have invested a huge amount in surveillance security technology [4]. Recent studies have investigated the amount invested by the government worldwide from 2015 to 2019 in purchasing surveillance cameras, see Fig. 1.

Fig. 1. Statistics of the amount invested in surveillance systems around the world (adapted from [4]).

Figure 1 shows the statistics of the amount invested in surveillance systems around the world from 2010 to 2019, the report revealed that of the total $8,5 billion was spent in 2010, approximately $11 billion was spent in 2011, in 2012 the total of $12.8 billion was invested on the surveillance system, $13.5 billion was spent in 2013, the total of $14 billion was invested worldwide on surveillance in 2014 and approximately $15.5 billion was invested in 2016 and it is estimated that the world market for surveillance video cameras will increase significantly to $35 billion by 2021 [4]. However, with money spent every year on the surveillance system, the existing technology is still not at the stage where it can be used to prevent crime before it manifests. To address these crime issues scientifically, research works on crime detection models have been investigated such as Support Vector Machine (SVM) [5–7], knearest neighbor [8, 9], etc., but none have given conclusive solutions due to shortfall of revealing adequate crime information required. Besides the current literature effort, this research investigates how suspicious loitering in a location can be detected before the crime occurs. Since human behavior is dynamic, this research develops an intelligent framework based on an integrated convolutional neural network (CNN) adaptive to specific locations at a time rather than building a generalized model prone to errors. In doing so, the main research question is raised below.

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Research Question and Contributions

Based on the above background, this research poses the question: How can a red flag of individual loitering in a location be detected from a set of camera frames before the crime occurs? The computer vision model, which addresses the research question first utilizes a deep learning model to obtain loitering suspicious behavioral patterns in image frames before security personnel makes a conclusive decision. The contributions of this paper are as follows: • Deployment of an intelligent vision framework based on integrated convolutional neural network (CNN) adaptive to specific locations at a time rather than building a generalized model prone to errors. • Maximizing the correct true positive rate and minimizing the false positive rate (false alarm) for the efficacy of police information systems detecting suspicious loitering behaviors before the crime occurs. The deployment of this approach with application to a crowded environment is an emerging area. The remainder of this paper is arranged in the following order: Sect. 2 provides a review of the existing state-of-the-art model and the theoretical background of the proposed model. Section 3 presents the detailed explanation of the computer vision model on median filtering and CNN; Sect. 4 discusses various experiments and evaluations of the model. The concluding remarks are shared in Sect. 5.

2 Theoretical Background 2.1

Related Works

An online real-time anomalous detection in videos was introduced in [10] on the Spatio-temporal composition of video volumes modeled using a probabilistic framework. The result of the experiment on the dataset used gives an accuracy of 78.5% and the approach is robust to spatial and temporal changes, however, the accuracy decreases significantly when tested under the influence of noise. The research in [11] presents the holistic features for real-time crowd behavior detection using two different anomaly detections approaches which are the Gaussian mixture model (GMM) and support vector machine (SVM). These approaches were tested on the violent-flows anomalous dataset. The experimental result shows that SVM gives an accuracy of 85.53% while the GMM gives 65.8% respectively. However, these approaches achieve noticeably computation performance on a smaller dataset and fail with a large dataset. Different traditional techniques for suspicious behavioral detection in image frames have been investigated in the literature. However, none has given conclusive solutions to address the shortfall of revealing suspicious behavioral patterns as loitering that could lead to crime, as an early warning in crowded scenes for

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the security personnel. This research develops a deep learning model to address the misinterpretation and late detection of loitering suspicious behavioral patterns in surveillance security systems. The selected theoretical backgrounds are explained in Sect. 2.2. 2.2

Selected Theoretical Techniques

Median Filtering Median filtering is a nonlinear filter-processing technology that is based on statistics as in [12]. The noise value of the image or the sequence is replaced by the median value of the neighbors (mask) as in (1). gðx; yÞ ¼ medff ð x  i; y  jÞ; i; j 2 W

ð1Þ

where f ðx; yÞ, gðx; yÞ are the original image, and the output image W is the twodimensional mask which can be linear, square, circular, etc. One of the advantages of median filtering is that it is an efficient filter to remove unwanted noise from the image; it is also simple to implement. However, to extract and detect suspicious behavioral patterns within the image frame, a convolutional neural network (CNN) is utilized, as explained in the next section. Convolutional Neural Network A CNN is a type of artificial neural network that uses a convolutional layer to filter inputs for obtaining useful information for the network [13]. A CNN commonly comprises many kinds of repeating layers and activation functions which are further discussed in detail in the subsequent sections. Convolutional Layer This layer uses convolutional filters often called kernels, with a defined size, which cover the entire input data to perform a convolution operation. The filter slides over the input matrix with a stride, and this process teaches how to detect patterns from the previous layers as in (2). ðf k Þi;j ¼ ðW k  XÞi;j þ bk

ð2Þ

where ðf k Þi;j is the convolved image, X represents the input image, W k is the weight, and bk is the bias. Rectified Linear Unit ReLU (Rectified linear unit) applies a non-saturating activation function to remove negative values from an activation map by setting them to zero as in (3). f ðxÞ ¼ maxð0; xÞ

ð3Þ

The ReLU increases the non-linear properties of the decision function and the overall network without affecting the receptive field of the convolutional layer.

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Pooling Layer The purpose of the pooling layer in CNN is for subsampling. The pooling reduces the dimensionality and performs the invariance of rotational and translational transformation as in (4). y½i ¼

Xk k¼1

x½i þ r:k w½k

ð4Þ

where x½i is the 2D input image, w½k is the filter of length k, r is the stride with which the image input is sampled, and y½i is the output of the convolution image. The subsequent section gives a detailed explanation of the methodology used for the implementation of the new technique for the detection of suspicious behavioral patterns in a crowded environment.

3 The Proposed Framework for Intelligent Vision Surveillance This section explores the proposed system model for the detection of loitering suspicious behavioral patterns in a crowded scene. The system model has three stages, the data acquisition stage, image pre-processing stage, and detection stage, see Fig. 2. 3.1

Data Acquisition Phase

In this research, the tested image frames are a combination of those captured from the multiple smart surveillance cameras mounted at various hotspot locations in the campus environment, as well as those available on public online repositories [14]. The acquired image frames were directed to image pre-processing for further processing. 3.2

Image Pre-processing Phase

In computer vision, image pre-processing has become a regular operation in image processing for computational efficiency. See Fig. 2. The processes that are involved in the image pre-processing of this research are explained in subsequent sections. Image Resizing In this research, the original image captured from the camera is 576  576. To reduce the computational complexity of the image data, this image is resized by 256  256 using a bilinear interpolation algorithm [15]. This is then passed to median filtering for further processing.

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Fig. 2. Intelligent vision surveillance framework

Noise Removal and Background Subtraction The output of the resized image frames is fed into the median filtering stage as shown on the middle layer see Fig. 2. Here the filtered image is fed into background subtraction where the current image background Iðx; y; tÞ at the time (t) is subtracted from the previous image frame Iðx; y; t  1Þ at a time (t-1) using the frame differencing technique, as in (5). Foreground ¼ jI ð x; y; tÞ  Iðx; y; t  1Þj [ Thr

ð5Þ

where Iðx; y; tÞ is the current image background at the time (t), Iðx; y; t  1Þ is the previous image frame at a time ðt  1Þ, and Thr is the threshold value which ranges from 0–255. This operation removes the background and maintains the foreground. The output of the foreground image is passed to the next stage for further processing. 3.3

Detection Phase

The output of the closing morphological image is used as the input of the CNN model. The CNN takes the original image of size 256  256 with 1  1 kernel size and 1 filter produced 256  256  1 output. The output is passed as input to the convolution layer 1, where a convolution operation is performed on the image with the 3  3 kernel

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size and filters of 24 to obtain 128  128  24 as the output. The pooling layer 1 takes the output of the convolutional layer 1 as input and uses a 2  2 kernel size with a filter of 32 to obtain 64  64  32 as a feature map. The kernel size of the convolutional layer 2 is the same as that of convolutional layer 1 with a filter size of 48, and this results in 32  32  48 as a feature map. The output of convolutional layer 2 is fed as input to the pooling layer 2 with 4  4 kernel size and 32 filters resulting in 16  16  32 as a feature map. The ReLU activation function is applied to increase the nonlinear properties of the decision function in the neural network, as in (4). Thereafter the Softmax function is implemented to classify the behavioral patterns in the image as either normal or suspicious. 3.4

Evaluation Metrics

There are numerous techniques required to evaluate new techniques. Hold-Out Cross-validation The hold-out cross-validation technique is one of the scoring evaluation schemes used to measure the performance of the new model [16]. This technique involves splitting the dataset into training and testing. The new model is trained on the training dataset and the test dataset is used to see how well the model performs on the unseen data. This is adopted in this research. Confusion Matrix A confusion matrix is a table that contains the number of instances that are normal and detect suspicious patterns from the new technique [17]. The performance of the technique is computed with the cross-validation which shows the occurrence values as rows and columns (matrix) see Table 1. Table 1. Confusion matrix Detected/Predicted Actual Normal Suspicious Normal TP FP Suspicious FN TN

From Table 1, the performance of the new technique is computed as in Eqs. (6)– (10) respectively. Recall ¼

TP TP þ FN

Precision ¼

ð6Þ

TP TP þ FP

False Positve RateðFPRÞ ¼

FP TN þ FP

ð7Þ ð8Þ

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F1 ¼ 2 

Precision  Recall Precision þ Recall

Accuracyðacc:Þ ¼

TP þ TN TP þ TN þ FP þ FN

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ð9Þ ð10Þ

where TP (True Positive) stands for the number of normal behavioral patterns that are correctly detected as normal; TN (True Negative) expresses the number of suspicious patterns that are correctly detected as suspicious; FP (False Positive) means the number of normal behavioral instances that are incorrectly detected as normal; FN (False Negative) represents the number of suspicious patterns that are incorrectly detected. All the metrics in Eqs. (6)–(10) are used in this experiment to evaluate the performance of the proposed technique for the detection of loitering suspicious behavior.

4 Experimental Evaluations and Results 4.1

Data Description and Experimental Setting

The implementation software used in this research is MATLAB R2017. The image frames used in the implementation are captured from campus crime hotspots. The image frames contain a mixture of both normal and suspicious activities resulting in 12700 image frames each obtained from pedestrian walkways and car parking lots. The normal image frames were manually labeled (resulting in the total number of 9990 image frames each) and trained with the new model. The suspicious image frames include a total number of 2800 image frames from standing too long in a place (loitering). This is used to validate the performance accuracy of the new technique. 4.2

Experiment 1: Suspicious Behaviors Such as an Individual Loitering in One Place

Here, the objective of this experiment is to use the proposed model to detect suspicious behaviors such as standing too long in one place. The qualitative observations of the new model see Fig. 3.

Fig. 3. Qualitative observations of individual loitering

Figure 3(a) consists of the original noisy image with a suspicious behavioral pattern frame. Figure 3(b) presents the output of noise removal using median filtering. Figure 3(c) is the output of background subtractions in which the foreground regions

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are extracted from the image. Figure 3(d) presents the detection region in red of the suspicious behaviors such as standing for unusually long in one place, extracted from corresponding frames. To further verify the efficiency of the proposed model for the detection of suspicious behavior such as standing too long in one place, the new technique is compared with other existing models (k-means segmentation, SVM) by conducting a quantitative experiment with a cross-validation technique of 90% for training and 10% testing. The result of the implementation, see Fig. 4(a)–(c).

(a) k-means segmentation

(b) Support Vector Machine

(c) Proposed model Fig. 4. Confusion matrix for suddenly breaking into a run using k-means segmentation SVM and proposed model

Figures 4(a)–(c), the summary of results of k-means, SVM, and proposed model with other performance metrics such as recall, precision, FPR, F1-score, and accuracies see Table 2. Table 2. Summary of performance metrics on conventional techniques Model k-means segmentation SVM Proposed model

Recall 0.7835 0.8865 0.9502

Precision 0.5802 0.6786 0.9632

FPR 0.5158 0.4306 0.1140

F1-score 0.6667 0.7687 0.9566

Accuracy 0.6267 0.7299 0.9346

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From Table 2, one can see how each technique detects loitering suspicious behavioral patterns in terms of performance. The proposed model has higher precision and recall values compared with other models. In addition, one can observe that the proposed model has an F1-score value higher than other models; these higher values indicate that the proposed model is the better model for the loitering suspicious detection dataset. Furthermore, the accuracy of the new model is 0.9346. This accuracy is significantly different compared with other models for the detection of loitering suspicious activities. This high detection accuracy in the new technique has occurred per the cooperative nature of the new model which combined the merits of both median filtering and the CNN model described in Sect. 2. From these results, one can see that the new model can be used effectively in the development of a security detection system to send quick signal awareness through security personnel phones on the likely location where crime could occur. 4.3

Experiment 2: Benchmarking Popular Publicly Available Water Surface Video Dataset for Detection of Standing Too Long in a Place

This section presents the experiment for the detection of suspicious behavioral patterns such as an individual standing unduly long in one place, on a publicly available water surface dataset with 10000 datasets on the proposed technique. The qualitative result, see Sect. 4.3.

Fig. 5. Qualitative observations of standing unduly long in one place

Figure 5(a) consists of the original noisy image with a suspicious behavioral pattern frame. Figure 5(b) presents the output of noise removal using median filtering. Figure 5(c) is the output of background subtractions in which the foreground regions are extracted from the image. Figure 5(d) presents the detection region in red of the suspicious behaviors such as standing for unusually long in one place, extracted from corresponding frames. The corresponding confusion matrix is presented in Fig. 6.

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Fig. 6. Confusion matrix

The implementation results were computed and generated from Fig. 6 and presented in Table 3. Table 3. Implementations results Precision True positive rate (recall) False positive rate (FPR) F1-Score Accuracy

0.9800 0.9657 0.0000 0.9728 0.97811

From Table 3, one can observe that the new model has a precision of 0.98, a recall of 0.9657, an F1-score of 0.9728, and an accuracy of 0.97811. However, with these results, one can see the efficiency of the proposed technique. This serves as evidence that the new method can be used to develop a preventive security detection system in a crowded environment to detect any suspicious behavioral patterns that could lead to crime.

5 Conclusion The early detection of suspicious behavioral pattern systems can be detected by recognizing the psychological changes of suspicious patterns in crowded environments most especially in a noisy image frame to curb potential security threats. Nevertheless, detecting suspicious patterns in a crowded environment has proven to be a difficult problem due to the shortfall of revealing adequate suspicious behavioral patterns most especially in a noisy image frame. The classical techniques and most current practices on campuses rely heavily on surveillance cameras, often leading to misinterpretation of events. The new model is designed based on computer vision of a convolutional neural network model based on deep learning, to address the delays of revealing suspicious behavioral patterns for police information systems. The experimental results show that the new model was conducted on real-life datasets, and benchmarked with conventional detection methods see Table 3, indicating that the proposed model was able to

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accurately detect suspicious behavioral patterns and outperform other popular detection models. However, there are still some deficiencies in the study, including finding the links between crime and unobserved crime information pairs. Therefore, continuous improvement is needed in the follow-up research. Acknowledgment. The authors acknowledge the financial support and resources made available from the University of South Africa, South Africa, and Norfolk State University, USA.

References 1. Sai, D.K., Sahithi, K., Sameera, D.: Detection of abnormality in CCTV footage: computer vision. Eur. J. Mol. Clin. Med. 7(4), 1148–1154 (2020) 2. Hamdi, S., Bouindour, S., Snoussi, H., Wang, T., Abid, M.: End-to-end deep one-class learning for anomaly detection in UAV video stream. J. Imaging 7(90), 1–15 (2021). 10.3390 3. Thomaz, L.A., Jardim, E., da Silva, A.F., da Silva, E.A.B., Netto, S.L., Krim, H.: Anomaly detection in moving-camera video sequences using principal subspace analysis. J. Latex Class Files 14(8), 1–9 (2015) 4. Cropley, J.: Global professional video surveillance equipment market set for third year of near double-digit growth. In: 2019 IHS Market Video Surveillance Intelligence Service (2019) 5. Cui, X., Liu, Q., Gao, M., Metaxas, D.N.: Abnormal detection using interaction ENergy potentials. In: Proceedings of the 25th IEEE International Conference on Computer Vision and Pattern Recognition (2012) 6. Hu, J., Zhu, E., Wang, S., Liu, X., Guo, X., Yin, J.: An efficient and robust unsupervised anomaly detection method using ensemble random projection in surveillance. Sensors 19 (4145), 1–20 (2019) 7. Shojaee, S., Mustapha, A., Sidi, F., Jabar, M.A.: A study on classification learning algorithms to predict crime status. Int. J. Digit. Technol. Appl. (JDCTA) 7(9), 361–369 (2013) 8. Hun, X., Hu, S., Zhang, X., Zhang, H., Luo, L.: Anomaly detection based on local nearest neighbor distance descriptor in crowded scenes. Sci. World J. (1–12) (2014) 9. Bharati, A., Rak, S.: Crime prediction and analysis using machine learning. Int. Res. J. Eng. Technol. (IRJET) 5(9), 1038–1042 (2018) 10. Roshtkhari, M.J., Levine, M.D.: An online real-time learning in videos using spatio-temporal composition. Comput. Vis. Image Underst. 117(10), 1436–1452 (2013) 11. Marsden, M., McGuines, K., Little, S., Connor, N.E.O.: Holistic features for real-time crowd behavior anomaly detection. In: Conference on Image Processing, pp. 918–922 (2016) 12. Sreejith, S., Nayak, J.: Study of hybrid median filter for the removal of various noises in a digital image. In: First International Conference on Advances in Physical Sciences and Materials, vol. 1796, no. 2020, pp. 1–7 (2020). https://doi.org/10.1088/1742-6596/1706/1/ 012079 13. Shri, S.J., Jothilakshmi, S.: Anomaly detection in video events using deep learning. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 8(9), 1313–1316 (2019) 14. Dou, J., Qin, Q., Tu, Z.: Background subtraction based on circulant matrix. SIViP 11(3), 407–414 (2016). https://doi.org/10.1007/s11760-016-0975-5 15. Khalaf, O.I., Romero, C.A.T., Pazhani, A.A.J., Vinuja, G.: VLSI implementation of a highperformance non-linear image scaling algorithm. J. Healthc. Eng. 2021, 1–10 (2021)

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16. Abdelhafiz, D., Yang, C., Ammar, R., Nabavi, S.: Deep convolutional neural networks for mammography: advances, challenges and applications. In: 7th IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS 2017), vol. 20, pp. 76–103 (2017). https://doi.org/10.1186/s12859-019-2823-4 17. Chadha, G.S., Islam, I., Schwung, A., Ding, S.X.: Deep convolutional clustering-based time series anomaly detection. Sensors 21(5488), 1–20 (2021)

Artificial Intelligence Systems Based on Artificial Neural Networks in Ecology G. V. Gazya1(&), V. V. Eskov2, T. V. Gavrilenko2, and N. F. Stratan2 1

FGU The FNTS Scientific Research Institute for System Research of the Russian Academy of Sciences, The Separate Division FNTS NIISR RAS in Surgut, 34, Basic St., Surgut 628426, Russia [email protected] 2 Surgut State University, 1 Lenina Ave., Surgut 628408, Russia

Abstract. Due to the discovery of the Eskov-Zinchenko effect throughout biomedicine and biocybernetics, any prospects lack for algorithmic artificial intelligence systems further use becomes clear. The authors prove special opportunities for using artificial neural networks in artificial intelligence systems. By the industrial ecology problem example, the neural networks’ capabilities for finding order parameters (system synthesis) are revealed. Such problems solution within the already existing neural networks framework is impossible, as the authors use two special modes (chaos and multiple reverberations - retuning) of artificial neural networks. #CSOC1120.

1 Introduction Most artificial intelligence systems (AIS) are based on algorithmic computing systems. In this case, it is possible to use digital computers and obtain new information about objects. However, any computer uses models and methods of dynamic systems theory or various stochastic methods. In this case, it is possible to create an algorithm based on deterministic and stochastic science (DSS) theory [1] and the problem will be algorithmizable. Over the past 20 years, the Eskov-Zinchenko effect (EZE) has been proven in the biosystems any parameters any samples statistical stability lack form. This means that we can no longer use DSS models and methods in any biosystems description and study [1–7]. Also, the models have a one-time, unique character. This was first pointed out by the information theory W. Weaver founder one [1]. However, for 50 years after his publication, no one in the world has tried to prove Weaver’s hypothesis (biosystems - systems of the third type (STT) - not DSS an object) and even less has no one tried to prove a special Complexity (and Uncertainty) for STT. All these years, DSS models and methods continued to dominate all biocybernetics in the biomedical systems’ study [1–7]. Since these DSS methods underlie any algorithms (computer-based) that are used to study biosystems (including those with the AIS systems use, it becomes obvious that there are no prospects when using the AIS to further study and model any biosystems (STT). This is what Weaver [1] tried to say, but nobody paid attention to his work. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 149–158, 2022. https://doi.org/10.1007/978-3-031-09073-8_14

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Further neglect of EZE (proof of statistical instability of any samples [8–15]) cannot provide all science (DSS) development in its application aspect for STT study [9–13]. We need other models and methods and another science just for studying STT Complexity [1]. It should not be based on the DSS and the methods of algorithmic processes. In this regard, we now emphasize non-algorithmizable AIS, for example, based on artificial neural networks (ANN) [14–21].

2 Non-algorithmizable Artificial Intelligence Systems (AIS) It should be noted at once that there are quite a few problems in science that cannot be solved within the existing models and methods of traditional DSS. They include pattern recognition problems that have been tried for decades within the traditional science framework, i.e., on the various algorithms’ basis. As a result, back in the 20th century, it was proved that pattern recognition is impossible within the DSS framework (using algorithms). It is impossible to create an algorithm for a portrait, voice, any sound and smell recognition. Therefore, in the 20th century second half, artificial neural networks (ANNs) began to be actively used for these purposes. As a result, many such tasks have been solved with the ANNs help. It is believed that an ANN operation is similar to that of a human brain neural network (BNN). However, many real properties of ANNs are now still not used in ANN operation [14–18]. At the same time, very outstanding results have been achieved not only in pattern recognition problems but also in solving systems of differential equations and many other algorithmizable problems. In this sense, ANNs are a broader tool (in capabilities terms) and universal methods both in science and in various everyday (social) decisionmaking systems. Nowadays, ANNs are AISs a very broad class that receive, process information and eventually make decisions in science, technology, and life support systems various fields. However, there is still a very important problem that has no solution in modern mathematics. It is the new information created by the man himself, by brain neuro nets (new theories, methods, models development), which have no solution in DSS for fundamental reasons. We are talking about the problem of system synthesis, i.e. finding parameters of order, jokers, channels. All this has no general solution in modern mathematics. Moreover, it cannot have any solution at all just for biosystems (STT), for which W. Weaver proposed a new (third, after DSS) science of biosystems [1]. The system synthesis problems solution is now implemented on a statistics’ basis. However, it is impossible to find the main diagnostic attributes (order parameters) for unstable biosystems (STT) within the DSS framework [22–29] precisely because of EZE (any STT sample is unique). For each sample of any parameter xi (t) and the whole biosystem state vector x = x(t) = (x1, x2,…, xm) T we will find order parameters (OPs) and channels as artefacts. All this is of historical significance for STT, since STT samples are not statistically repeatable. System synthesis (SS) problems cannot be solved within known algorithms

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(DSS) due to the uniqueness of samples (EZE) and the impossibility to predict the future for STT [30–39]. DSS has not yet learned how to study and model statistically unstable (unique) biosystems. Therefore, new methods and new models are needed to solve such statistically uncertain systems (to study and model them). This applies to ANNs that are non-algorithmizable, but they cannot yet find OPs, channels and jokers [11, 14, 16–20, 30, 32–34]. In general, this paragraph outcome is the following statements: ANN as an example of AIS on the non-algorithmizable computing systems basis; STT (biosystems) cannot be the algorithmic computing systems object (due to EZE, any samples xi(t) uniqueness; finally, there is a serious problem in the ANN prospects in solving system synthesis problems (finding OP) [11–16, 30–34].

3 Why ANNs Cannot be Brain Neural Networks Models in the Literal Sense It should be reminded that the first artificial neural networks used in their work brain real neural networks properties (BNN) a very limited set. In particular, threshold properties of a neuron, communication properties (the neural network itself as dispersed elements a set), and several other properties are used in ANN. However, the STT main properties are not used by any modern ANN. These properties follow from EZE [9–20, 30, 32–34], when any sample statistical stability lack is proved [7–11]. Indeed, initially, EZE was proved in biomechanics in the tremorograms statistical instability form (TSI) and tepigrams (TPG) as involuntary and involuntary movements [2–6, 8, 13, 14, 17]. EZE was then proved in muscle work [13, 22] and in cardiac work [9, 12, 19, 23, 35–39]. The entire cardiovascular system operates in the statistical chaos mode (EZE is global for all biosystems). As a result, we have proved that the brain, too, operates in the chaotic mode [13–16, 27, 30–35]. To illustrate the above, let us present a typical matrix (see Table 1) of electroencephalograms (EEG) paired comparisons, which were obtained in a row from the same subject in a calm state (sitting). As a result, such (similar) matrices thousands were calculated for TSI, TPG, electromyograms (EMG), cardio intervals (CI) and heart performance other parameters, for electroneurograms (ENG), human blood glucose concentration, etc. In all cases the pattern is the same; any sample xi(t) will be unique [2–12, 29–39]. Let us present a matrix of pairwise comparisons of 15 EEG samples (one subject, at rest.) in the form of Table 1. Here the number of k pairs of EEG samples that have one (common) general population is small (k1 = 33!). For each such pair, the Wilcoxon criterion pij
0.05, i.e., these two samples statistically coincide (may have a common general population).

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Table 1. The same healthy person electroencephalograms (EEG) samples paired comparison matrix (repeats N = 15) during relaxation in T6-Ref, (Wilcoxon test, the criterion of differences p < 0.05, number of coincidences ke = 33). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

0.00 0.32 0.05 0.10 0.64 0.01 0.55 0.00 0.28 0.31 0.00 090 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.58

0.32 0.00 0.75 0.00 0.03 0.67 0.19 0.00 0.01 0.30 0.02 0.10 0.00 0.00

0.05 0.00 0.75 0.00 0.07 0.83 0.00 0.00 0.00 0.06 0.03 0.04 0.00 0.00

0.10 0.00 0.00 0.00 0.00 0.00 0.41 0.38 0.66 0.03 0.00 0.21 0.00 0.00

0.64 0.00 0.03 0.07 0.00 0.21 0.86 0.00 0.21 0.52 0.00 0.66 0.00 0.00

0.01 0.00 0.67 0.83 0.00 0.21 0.02 0.00 0.00 0.01 0.19 0.00 0.00 0.00

0.55 0.00 0.19 0.00 0.41 0.86 0.02 0.08 0.93 0.15 0.00 0.97 0.00 0.00

0.00 0.00 0.00 0.00 0.38 0.00 0.00 0.08 0.06 0.00 0.00 0.07 0.00 0.01

0.28 0.00 0.01 0.00 0.66 0.21 0.00 0.93 0.06 0.00 0.00 0.36 0.00 0.00

0.31 0.00 0.30 0.06 0.03 0.52 0.01 0.15 0.00 0.00 0.00 0.05 0.00 0.00

0.00 0.00 0.02 0.03 0.00 0.00 0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.90 0.00 0.10 0.04 0.21 0.66 0.00 0.97 0.07 0.36 0.05 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.58 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 -

We emphasize that this k will 1always be less than 35% for EEG (k1 < 35%) of all 105 comparison pairs in such Table 1. These are special values since for TSI k2 < 5%, for TPG, EMG and CI their k < 15%, etc. All this proves the lack of statistical stability of sampling any parameters xi (t) of human body functions. It can be said that the human brain (its BNN) is the most statistically stable, as all periphery shows k lower values [1–7, 21–39]. This proves the globality of EZE, but at the same time shows the loss of connection between the past state of the STT (biosystem) and it’s future. If there is no future prediction within DSS, then we cannot use DSS all models and methods. Any sample will be unique (it is just an artefact) and in the DSS all theories do not work (they have a one-time, unique nature) [1–7, 30–39]. In the end, we come to the W. Weaver hypothesis proof [1], in which he spoke of biosystems as not objects of modern science (DSS). There are no algorithms in DSS that can predict the future state of STTs. Any information about STT within DSS is historical (it is the past some kind of!). As a result, we come to the rationale that biosystems can’t create predictive algorithms. The future for STT has no predictive character. This means that it is impossible to use any AIS to describe and predict STT, i.e. STT is not an object of modern science (DSS). However, the non-algorithmizable ANNs use can provide a biosystem future state study and prediction. This requires ANNs two new properties an understanding, which no one has used so far (due to EZE ignorance). Indeed, Table 1 proves (and hundreds of similar ones that we have already [2–11, 13–28] calculated) that EEG samples are statistically unstable. The human brain, its BNN, continuously and chaotically generates EEG samples that are statistically almost

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inconsistent (see Table 1, EZE). Consequently, chaos in EEG description is brain neural networks a basic property. EZE is a brain neural networks basic property, and this property must be introduced into the ANN operation, i.e., into the AIS [14, 16–23, 30–34]. The brain second property is the BNN continuous (and chaotic) reverberation. If brain biopotentials xi(t) (EEG) will be equal to zero, i.e. xi(t) = const, and x2 = dxi/ dt = 0, then such a brain will be a dead brain (these are some of the fundamental criteria of brain death). Then we have to introduce these two basic properties into the ANN: chaos and continuous (repeated) reverberations (EEG) [14, 16–23, 30–34].

4 The New ANN-Based AIS New Properties If we introduce two new (fundamental) properties (chaos and reverberations) into the ANN, we obtain the AIS as a special property, which provides a solution to the system synthesis problem. In this case, we can find order parameters - OP (main diagnostic features xi(t)). In this case, the states initial phase space dimensionality (PSD) immediately decreases, i.e. we move from m to n, where n < m. Moreover, in this case, it is possible to build channels - the main models of STT on the OP basis [14–18]. Let us consider a specific example from the industrial ecology field. In Table 2 we present the seven spectral signal density (SSD) parameters (signal - CI) male samples pairwise comparison for four (different) groups results. Here the 1st group is men under 35 without exposure to weak industrial electromagnetic fields (WIEMF), the 2nd group is men over 35 without exposure to WIEMF, the 3rd group is men under 35 with exposure to WIEMF, the 4th group is men over 35 with exposure to WIEMF. Table 2. The results of the examined groups 1–4 heart rate variability parameters significance permissible level ranks mean values pairwise comparison results using the nonparametric U test of Mann - Whitney. Indicator 1–2 1–3 1–4 2–3 2–4 3–4 2 VLF, ms 0.540 0.028* 0.784 0.048* 0.923 0.041* LF, ms2 0.395 0.016* 0.043* 0.002* 0.395 0.001* HF, ms2 0.105 0.109 0.007* 0.003* 0.212 0.001* Total, ms2 0.584 0.026* 0.252 0.010* 0.673 0.001* LFnorm, % 0.355 0.564 0.060 0.988 0.501 0.322 HFnorm, % 0.337 0.549 0.060 0.953 0.501 0.318 LF/HF 0.348 0.438 0.053 0.918 0.460 0.371 1 - men under 35 without EMF sources; 2 - men after 35 without exposure to EMF sources; 3 - men under 35 exposed to EMF sources; 4 - men after 35 exposed to EMF sources; P - significance level reached (with critical level < 0.05); * - groups statistically belonging to different general populations.

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As a result, in Table 2 we found statistical coincidences a preponderance in all 7 SPS parameters pairwise comparison (for all 4 groups, i.e. we have only 6 different parameters). For the 42 different comparison pairs in Table 2, only 13 pairs showed Mann-Whitney criterion U < 0.05. In other cases, U > 0.05, i.e. such two samples xi(t) can have a common general population (they statistically coincide). In the new science framework (Chaos-Self-Organization Theory (CST)) [2–7, 11– 14] we are now talking about type 1 uncertainty (statistics do not work). Type 1 uncertainty is characterized by the statistical differences’ absence, but biosystems are different and they are indifferent ecological and physical conditions (statistics show any difference absence (out of 42 pairs, only 13 pairs differ) [14–20, 23]. Pairs 1–2 and 2–4 do not differ at all in all seven SSD parameters. We used the ANN in two new modes: we randomly set the initial weights w ioof the features xi (t) from the interval wio 2 (0.1) and repeatedly tuned the ANN in these modes (with new values of wio) at each tuning iteration. Finally, we partitioned all samples and eliminated type 1 uncertainty, which is presented in Table 2. Indeed, in Table 2, pairs 1–2 and 2–4 were not statistically different at all. However, these are different pairs in terms of age (1–2) and different pairs in physical impact terms (2–4). In Table 3, we present the ANN setting averaging the 50 repeated iterations results (reverberation mode). At each tuning, we selected w randomly from the interval with 2 (0.1). As a result, we obtained for each sample the mean values of the feature weights wI (50 values in each sample), and these samples were statistically processed. We present the ANN results in Table 3. Obviously, for the pair 1–2 here from all 7 features xi(t) two order parameters are allocated. The parameter HF (the CI spectrum high-frequency component density) has an average weight = 0.695 ± 0.255. The parameter LF (the CI spectrum low-frequency component) ranks second, its = 0.662 ± 0.286. We obtained a similar result for pair 2–4 (see Table 3), where = 0.747 ± 0.268 and = 0.650 ± 0.243. However, a third OP, Total (the long CI whole SSD integrative density), also appeared here. Table 3. Results of statistical processing of feature weights wi after 50 sampling iterations xi(t) for comparison groups 1–2 and 2–4. Comparable groups

wi

VLF

LF

HF

Total

LFnorm

HFnorm

LF/HF

1–2

M±r

0.601 ± 0.259

0.662 – 0.286

0.695 – 0.255

0.570 ± 0.256

0.392 ± 0.222

0.419 ± 0.243

0.566 ± 0.255

2–4

M±r

0.564 ± 0.243

0.650 – 0.243

0.747 – 0.260

0.653 – 0.281

0.465 ± 0.249

0.470 ± 0.216

0.454 ± 0.266

Overall, the ANNs in the two new modes not only split all samples and eliminated type 1 uncertainty, but also computed OPs, the main diagnostic features. At the same time, the dimensionality of PSD dropped from m = 7 to n = 2 (for pair 1–2). All this is system synthesis and no algorithmic AIS (and any other theory) can do it now. It cannot be done by a man himself either, i.e. we can now say that our AIS -based ANN (in two special modes) can do what neither DSS nor a great scientist (genius) can do exactly.

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5 Discussion The use of artificial neural networks in various non-algorithmizable tasks opens new perspectives in the development of artificial intelligence. ANNs solve problems that traditional science (based on digital and analogue computers) cannot solve. However, all currently working ANNs do not fully use all modes of operation of real neural networks of the human brain (BNN). Moreover, all modern science cannot generally solve the system synthesis problems, i.e. to find order parameters, channels and jokers. The situation became even more complicated after the Eskov-Zinchenko effect (EZE) discovery. In this EZE they proved the uniqueness of any sample of any parameter xi(t) of human body functions [2–11, 35–41]. This means that the DSS further all methods and models are ad hoc (historical) in nature. It means that all biocybernetics, biology, medicine, psychology and other sciences of living systems have studied artefacts. Knowledge of the past for STT does not guarantee us a biosystem future state prediction. It is obvious that further DSS to STTs (biosystems) application is useless and a new science should be created [2–14, 29–39]. In connection with the discovery of EZE, it also becomes apparent that all artificial intelligence systems (AIS) based on DSS have no prospects since they describe the STT biosystems past. In this STT science disappointing scenario (and biocybernetics as part of the whole DSS), certain perspectives emerge for the ANN use in the AISs operation. It turned out that ANN-based AISs can not only resolve type 1 uncertainties (they are not resolvable in DSSs) but also find order parameters [11–16], i.e., the main diagnostic features. Such problems can now only be solved by the human brain in heuristic mode. Usually, such problems are solved by geniuses, but they are unique.

6 Conclusions Very often in the study of biosystems (in biocybernetics) type 1 uncertainties arise. In this case, statistics do not show differences between samples, and new methods and algorithms based on ANN can do this. The now widely used ANNs cannot solve the problems of system synthesis. They can only reveal type 1 uncertainty. However, this is already an action that algorithmic MEDIAI cannot perform under any conditions because of the Eskov-Zinchenko effect (lack of statistical stability of any samples of any parameters of the human body). The use of ANN in chaos and multiple reverberation modes provides finding of order parameters - the main diagnostic features. This is based on ranking the weights wi after many iterations (ANN settings with chaotic wio).

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Methodological Aspects of the Valuation of Digital Financial Assets Vladimir Viktorovich Grigoriev(&), Alexey Fedorovich Glyzin, and Anna Antonovna Karpenko Financial University Under the Government of the Russian Federation, Moscow, Russia [email protected]

Abstract. The relevance of the problem under study is due to the need to form a methodology for the valuation of digital financial assets and insufficient development of the theoretical and scientific and methodological aspects of the valuation of these assets. In this regard, this article aims to identify or disclose identification of methodological problems in the valuation of digital financial assets. The purpose of the article is to formulate methodological provisions for the valuation of digital financial assets in the context of the development of a general methodology for the valuation of property. The leading approaches to the study of this problem are general methodological, philosophical and systemic, which allow us to identify the specific content of the object and method of this scientific discipline: the valuation of digital financial assets, as well as to define the methodology for their valuation. The article presents the definitions of the object and method and methodology for the valuation of digital financial assets as a scientific discipline, briefly describes their content, formulates the principles of the valuation of digital financial assets. The methodological provisions formulated in the article are useful for the development of practical methods for the valuation of digital assets, as well as for their practical assessment. The materials of the article may be of interest to investors, scientists, university professors and specialists in their theoretical and practical activities with digital financial assets. Keywords: Digital asset valuation
Methodology
Cryptocurrency
Tokens
Valuation

1 Introduction At the present stage of the initial development of the digital economy, a methodology for the valuation of digital financial assets is being formed, which has its own object (subject) and method. However, within the scope of the CFA valuation, later, with their development, a number of methodologies will be formed related to the assessment of specific types of CFA objects: digital payment asset, digital invested asset, digital insurance asset, digital corporate financial asset, etc. (Fedotova and Grigoriev 2017).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 159–170, 2022. https://doi.org/10.1007/978-3-031-09073-8_15

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Valuation objects and associated valuation have their own research subjects and significant specific features in methods (including valuation methods) (Tapscott and Tapscott 2020). Moreover, the research methods are completely independent. Using a systematic approach, individual spheres of DFA functioning can be represented as subsystems of the general system of DFA functioning. (Fedotova et al. 2020). Thus, along with the object and the method of valuation of CFA in the future, independent spheres of the valuation of certain types of CFA will be formed with their own specific objects and methods (Grigoriev 2021). And proceeding from the dialectical law of the cognitive process - from simple to complex - we can conclude that the general methodology for the valuation of CFA can be relatively fully formed only after the appropriate development of certain areas of value estimation associated with the assessment of certain types of CFA (Viña and Casey 2017). In this regard, in this article we make an attempt to formulate the object the method and principles of the valuation of the CFA, as well as to show their specific features of these assets.

2 Materials and Methods 2.1

Research Methods

In the course of the research, the following methods were used: theoretical (analysis; synthesis; concretization; generalization; method of analogies; modeling); empirical (studying the experience of digital financial assets, regulatory documentation); experimental (ascertaining, formative, control experiments). 2.2

Experimental Research Base

The experimental base of the study was the Financial University under the Government. 2.3

Research Stages

The study of the problem was carried out in three stages: at the first stage, a theoretical analysis of the existing methodological approaches in the philosophical, financial, socio-economic and evaluative scientific literature, dissertations on the problem, as well as the theory and methods of evaluative research was carried out; the problem, purpose, and research methods are highlighted, a plan of experimental research is drawn up. At the second stage, the concepts of the object, subject and valuation methodology were formulated, its functions were identified and substantiated, the content of the valuation object as a scientific discipline was analyzed, principles for the valuation of digital financial assets were developed, the conclusions obtained during the experimental work were analyzed, verified and refined. At the third stage, the experimental work was completed, the theoretical and practical conclusions were refined, the results obtained were generalized and systematized.

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3 Results 3.1

Object and Method of Valuation of DFA

Thus, along with the object and the method of valuation of DFA in the future, independent spheres of the valuation of certain types of DFA will be formed with their own specific objects and methods. And proceeding from the dialectical law of the cognitive process - from simple to complex - we can conclude that the general methodology for the valuation of DFA can be relatively fully formed only after the appropriate development of certain areas of value estimation associated with the assessment of certain types of DFA. In this regard, in this article we make an attempt to formulate the object, the method and principles of the valuation of the DFA, as well as to show their specific features of these assets. The term “method” comes from the Greek “methods”, which means “the way to something.“ In this case, a method is understood as an orderly activity to achieve a specific goal. The method of a particular scientific discipline is a tool for the theoretical study of an object (subject). The formation of a scientific method is based on the properties, features, laws of the object (subject) under study. The method of a scientific discipline can be imagined from two sides: objective and subjective. The objective side of the scientific method is reflected by the known laws and patterns, the subjective side of the method - the methods of research (evaluation) of objects (objects) that have arisen on the basis of laws and patterns. In our case, these are separate methods for valuation of DFA. Thus, the scientific method of valuation of DFA is a set of concepts and principles of this scientific discipline, their interrelationships, as well as economic, mathematical and statistical tools, is used for the valuation of DFA. However, the concept of the scientific method of the value of DFA includes not only the description of individual principles, concepts, methods, techniques for the valuation of DFA or their combination, but also knowledge about their origin, application, development, etc. Thus, the method of valuation of DFA as a method of scientific discipline - this is both the toolkit for the valuation of the DFA and the methodological knowledge about it. It is a valuation method in the broadest sense of the word. And in the narrow sense of the word, it is a separate methodological technique or method for assessing the DFA. In contrast to the method of valuation of DFA as a method of scientific discipline, the concept of the methodology for valuation of DFA is broader. It includes both the scientific method itself with its problems of knowledge about the origin, structure, interrelationships with other sub-sectors of the sphere of valuation of the DFA, development, ways of its optimal use and efficiency, as well as laws, patterns, concepts and principles of other sciences and scientific disciplines: philosophical, economic, mathematical, statistical and others, designed to explain the existing economy, including the digital one, and to increase the efficiency of the DFA valuation method. The scientific method of valuation of DFA is mainly used to gain new knowledge in this innovative financial field. But this is not the only function of the DFA valuation method. For a deeper consideration of the value of this method, it is necessary to

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highlight the cognitive, methodological and practical functions of the method of valuation of the DFA. The cognitive function of the DFA valuation method consists in the knowledge of financial, monetary, economic processes and phenomena, the development of theoretical questions, questions of the formation of patterns, concepts in the field of value. estimates of the DFA. The scientific concepts, patterns and provisions obtained in the course of theoretical research serve as the basis for the formation of a scientific method for the valuation of DFA, which provides reliable scientific knowledge in the field of DFA assessment, which, in turn, determines the receipt of a qualitative assessment of DFA. The cognitive function of the DFA valuation method consists in the knowledge of financial, monetary, economic processes and phenomena, the development of theoretical questions, questions of the formation of patterns, concepts in the field of value. estimates of the DFA. The scientific concepts, patterns and provisions obtained in the course of theoretical research serve as the basis for the formation of a scientific method for the valuation of DFA, which provides reliable scientific knowledge in the field of DFA assessment, which, in turn, determines the receipt of a qualitative assessment of DFA. The methodological function of the DFA valuation is that part of the theoretical knowledge of a given scientific discipline in the form of laws, regularities, concepts and provisions, forms the methods of other scientific disciplines, for example, digital economy or digital management, etc. The practical function of the valuation of the DFA is that the valuation of the DFA is the basis for making decisions in the field of the digital economy (Alkire and Foster 2011). Any scientific discipline, including the cost estimate of the DFA, is represented by the unity of theory, that is, by the totality of knowledge about the subject of research, expressed in a system of concepts, patterns and other forms of theoretical thinking, on the one hand, and on the method, on the other hand…. Moreover, in the process of development of the theory of valuation of DFA, the scientific method is enriched and improved, and, conversely, the development of the method of scientific discipline is impossible without the development of the theory. There is a constant mutual enrichment of theory and method. Hence, the more developed the theory of a scientific discipline, the more developed the method. This circumstance can be illustrated by the example of mathematical science. Here the whole science has turned into a universal method of studying reality (Tolstolesova 2012). The DFA valuation method, like any complex phenomenon, has its own structure. It is due to the structural heterogeneity of the object value assessment of the DFA, as well as the varied nature of the content of the method itself of this scientific discipline. It should be noted that the scientific method of valuation of the DFA answers the question of how to implement it, and the object scientific discipline characterizes what is being investigated. Moreover, the scientific method is determined by the object scientific discipline. It is necessary to distinguish the subject of scientific research from the object of research. The object of the research is what the scientific cognitive activity of the subject is directed to, the subject of the research - the sides, properties, and

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interrelationships of the research object considered for a specific purpose. For example, many scientific disciplines study one object - an investment crypto company, which is characterized by a system of indicators reflecting the volume of investments in innovative startups, as well as various facets of the investment process: the state and dynamics of financial, labor and material resources. Studying one object of research, different scientific disciplines have different subjects: the valuation of the CF examines the process of assessing the value of an investment crypto company, the scientific discipline production management - the process of organizing the management of an investment crypto company, law examines the legal relations that develop in the process of investment activities of a crypto company, etc. (Allgood and Walstad 2016). Analyzing the object of valuation of the DFA, one can single out, for example, its following subjects. 1. General problems of valuation of DFA: goals, role, principles, functions of valuation of DFA (Chekalkina 2013). 2. Characteristics of the processes of valuation of the DFA: the structure of the valuation process, a description of the methodological, mathematical, software, organizational, informational and technical support for the valuation of the DFA, the problems of the functioning of the DFA and ways of solving these problems (Allen et al. 2016). 3. Modeling the object of assessment. In this case, we are talking about an objective display of the assessed digital asset, its composition and structure, as well as its internal and external connections. DFA modeling is carried out on the basis of the analysis and synthesis of the object assessment, the creation (if possible) of a simulation economics-mathematical model of it, the development of methods for assessing the parameters of this model, and the choice of the optimal assessment result. Modeling can be carried out not only for the object assessment, but also for the assessment process of the DFA. 4. Improvement and creation of new methods, methods and procedures for the valuation of DFA. 5. Investigation of quality problems in the valuation of DFA. These are the problems of verifying the results of the assessment. The principles for the valuation of DFAs provide a uniform methodological framework for the application of a variety of methods for valuing DFAs. 3.2

Basic Principles of Valuation of DFA

Due to the fact that the valuation of DFA is closely related to the general process of property valuation, moreover, it is a part of this process, it is natural to assume that the basic principles of property valuation are the basic principles of valuation of DFA. These are valuation principles that fall into four groups. Group 1. Principles of user assessment results (Atkinson and Messy 2011).

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1. The principle of utility. It states that the DFA has value if it can be useful to potential investors. A financial asset can be useful if it can be used to obtain benefits: tangible (for example, making a profit) or intangible (for example, realizing a sense of pride in owning this asset, or realizing other of it, - the investor, psychological and other needs). The usefulness of a DFA is its ability to satisfy the user’s needs with an asset at a given location and for a specified period of time. 2. Substitution principle. This valuation principle suggests that a reasonable buyer will not pay more for a DFA than the lowest price asked in the market for another DFA with the same degree of utility, which means that it is unreasonable to pay more for the DFA in question than it costs to create a new similar DFA in acceptable terms. The maximum cost of the considered assessed DFA is determined by the lowest cost at which another DFA with the same utility can be purchased. 3. Waiting principle. In most cases, the usefulness of a DFA is related to the expectation of future profits from the use of DFA. For income-generating DFAs, their value is often determined by the mass of the expected profit that can be obtained from the use of this financial asset, as well as the amount of cash proceeds from its resale. Expectations - in this case, this is the expectation of future profit or other benefits that can be obtained in the future from the use of the estimated DFA. Since funds, when they are in circulation, bring a percentage of income “at the same time, inflationary processes take place, the ruble (including the digital ruble) received in the future has a lower value than today’s ruble.” In this case, we are talking about the value of money over time. To determine the present value of the future profits expected from the use of the DFA, it is necessary to adjust the projected profit taking into account the adjustment for its value over time (Lusardi et al. 2017). Group 2. Valuation principles associated with the subject matter: 1. Contribution principle The principle of contribution means that the addition to the cost of the DFA, which provides an increase in its value in amounts exceeding the actual cost of this addition. However, the contribution can be negative if the actual costs incurred exceed the amount of the contribution. The contribution is the amount by which the value of the DFA or the profit received from it increases or decreases due to the presence or absence of any improvement or addition to the existing properties or functions of the estimated DFA (Huston 2010). 2. Optimality principle This principle can apply to any element of the DFA. Many types of digital financial assets are complex innovative systems made up of many interconnected elements. For example, a payment distribution ledger (blockchain) is a system consisting of a trust protocol, electronic wallets, a transaction evidentiary system, technical means, and a multitude of users. And each of these constituent elements of the blockchain can have its own individual specific optimal characteristics. In this regard, the principle of optimality can be formulated as follows: the cost of a DFA will be maximum with the optimal characteristics of each element of the DFA system (Königsheim et al. 2017).

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3. The principle of economic division and combination of property rights to DFA The system of property rights in the Russian Federation makes it possible to separate and combine separately property rights to property, including the DFA. In this case, each individual property right can be presented as a component of one package. The principle of economic division and combination of property rights states: property rights to DFA should be divided and connected in such a way as to increase the value of the financial asset in question. The division of property rights into DFA can be carried out as follows: – physical separation of a financial asset (for example, a payment blockchain can be divided into parts and the dedicated parts can be used for others, including business purposes); – division by time of ownership of the DFA (for example, various types of DFA lease, - short-term, long-term lease, lifetime ownership, future property rights to DFA; – division according to the rights to use the DFA (for example, the limited right to use the DFA); – division by types of property rights (for example, joint lease of DFA, partnership, trust management, contract with agreed conditions); – division according to the creditor’s rights to take possession of the DFA (for example, pledges, mortgages, participation in capital). The main criterion for dividing the package of property rights into DFA is the realization of the interests of investors who invest in the asset and the owners of DFA. Economic division and combination of property rights to a DFA is always carried out when there is a difference of interests in this DFA. This division and combination of property rights to the DFA, as a rule, leads to an increase in its value. Group 3. Principles related to the external market environment (Kramer 2016). This group of principles for assessing DFA includes the following principles: principles of dependence, conformity, supply and demand, competition and the principle of change. The principle of dependence is formulated as follows: the cost of a DFA depends on many factors. And the larger the scale and the more complex the DFA, the more factors its cost depends on. This dependence is determined not only by the number of factors affecting the cost of DFA, but also by the nature of the links (the nature of these dependencies), which are measured by the cost of time, money, or other units of measurement of these links. For example, the speed of payment systems created on the basis of blockchain technology significantly depends on such factors as the reliability of the transaction proof, which, in turn, depends on the complexity of this procedure. And this complexity, and hence reliability, and hence the quality of the payment system, and hence the cost of this payment system will be higher. We see this in the Bitcoin payment system, which currently has the most complex and most reliable transaction proof algorithm. Including in this regard, the capitalization of bitcoin is the highest on the crypto market (Antonopoulos 2020). The principle of dependence is also expressed in the fact that the DFA itself affects the value of the surrounding assets (and not only other types of DFA, but also other

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types of digital assets). This is especially noticeable in existing innovation ecosystems, such as, for example, the ecosystem of Sberbank, where the cost of related nonfinancial products depends on the main, financial component of Sberbank. One of the most important scientific problems in assessing DFA is the study of connections and dependencies between the cost of DFA and the factors influencing it. The results of these studies are needed for a more reliable and accurate valuation of DFA (Eskindarova and Fedotova 2018). Correspondence principle reads as follows: Any DFA, in order to have a high value, must correspond to market perceptions of this asset. Compliance is the extent to which the DFA itself, its architecture, functions and properties are consistent with the digital environment and the needs of the respective financial market. The cost of DFA is significantly influenced by a change in the ratio of supply and demand of a product in the market: if demand for DFA exceeds supply in the market, then prices for DFA grow and vice versa, if supply exceeds demand, prices fall (Frijns et al. 2014). Prices become stable if demand for an asset matches supply. Of course, in view of the imperfection of the digital market in which the purchase and sale of DFAs is carried out, in addition to the ratio of supply and demand of assets, such factors as the art of trading, the number of bidders, financing schemes for this transaction and other factors that are difficult calculate when determining the cost of the DFA. Moreover, the influence of these factors is enhanced if the transaction in fact needs to be carried out in a short time. It should also be noted that demand has a greater impact on the cost of DFA than supply, since it is more volatile. This market situation is especially typical for the digital market of the Russian Federation, where DFA prices are mainly dependent on the capabilities of investors (De Beckker et al. 2019). Competition principle is known that the main property of capital movement: it moves where there is a large profit. In this regard, where excess or monopoly profits are extracted, new market entities are trying to get. Competition is the competition of entrepreneurs in making a profit (Hizgilov and Silber 2020). Competition is intensifying in all spheres of the economy where profits are growing. Increased competition leads to increased supply, lower prices and lower profits, if demand does not increase. This situation is currently typical for the digital market in all countries, including the Russian Federation. The principle of change states: the cost of a digital asset is constantly changing under the influence of changes in internal and external factors that affect the cost of a digital asset. The situation in the digital market is constantly changing. Some DFAs are being created (as a rule, more progressive ones), others are outdated and disappear from the market. Political, economic, social, technological and other spheres of activity are changing, which affect the cost of the DFA. The life cycle of a DFA, like any other asset, usually goes through the stages of birth, growth, stabilization and decline. And on what stage of the life cycle the digital asset is in, its value depends: if it is in the growth stage, then its value will be higher, if it is in decline, then the value will be lower (Bachmann and Hens 2015). For this reason, the evaluators evaluate the DFA for the competitive date. At the same time, in the Russian Federation, regulatory assets establish that the results of asset valuation are “valid” for 6 months from the date of valuation.

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The principle of the best and most efficient use of the DFA is the main principle for assessing the asset’s market value of the asset, since it determines the choice of factors that affect the value of the assessed DFA, and therefore predetermines its value. The principle of the best and most efficient use of the DFA is the reasonable and possible use of this asset, which provides it with the highest value at the valuation date, that is, its use, selected from reasonable and possible alternative options and leading to the highest cost of the DFA. This valuation principle unites all the other valuation principles discussed above. This principle is implemented as a result of analyzing various options for using the DFA and choosing the optimal option from them, on the assumption that with a given use of the object of assessment, we will get the optimal one, i.e. market value. At the same time, the following are analyzed (Fernandes et al. 2014): 1. the ability of the digital market to accept this use case for the estimated DFA and what is the ratio of supply and demand for such assets; 2. the legal basis for the creation and functioning of the DFA and the corresponding restrictions introduced by the regulator; 3. The technical characteristics of the assessed DFA, for example, for a distribution register of payments is the reliability of the transaction, its speed, cryptographic characteristics and other characteristics that determine a particular use case and the corresponding cost of the DFA. 4. the financial feasibility of using one or another version of the DFA - its future cash flows, scalability and efficiency. that satisfies potential buyers in the digital market. At the same time, special attention is paid to the sources of income from the use of one or another variant of using the DFA, their values and time of receipt, as well as material and non-material costs for the creation and operation of assets. The essence of the principle of the best and most effective use of DFA is to form and compare various options for using DFA and choose the optimal one based on the value of the DFA.

4 Discussions We reviewed the basic general principles of valuation of DFA that are used in all other assets. However, we propose to single out the so-called specific principles for the valuation of DFAs, which are determined by the special specific properties of the specific DFAs being assessed. This classification of principles for the valuation of DFA is due to the functional (based on the assessment functions) and subject (based on the subject of assessment, i.e. one or another type of DFA) necessity. At the same time, the functional necessity of classifying the principles for assessing DFA is expressed in the use of general principles for assessing their value in the process of cost appraisal of DFA, taking into account the specifics of the object of assessment: one or another type of DFA. The substantive need for the classification of valuation principles is expressed in the use of the principles of creating specific digital financial systems in the process of

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valuation of the DFA, since certain types of DFA represent complex innovative economic systems. For example, a cryptocurrency is a set of interrelated elements: cryptographic digital records, a distributed ledger, various technical means and materials that ensure the functioning of this system. In the valuation of assets, there is a requirement that each particular type of object of valuation requires a special specific valuation method, since its valuation requires: a) specific information and data; b) special methods of processing this information; c) analysis of specific factors affecting the value of this particular type of asset being assessed; d) specific methods for assessing that particular type of subject matter.

5 Conclusion At the present stage of the initial development of the digital economy, a methodology for the valuation of digital financial assets is being formed, which has its own object (subject) and method. However, within the scope of the DFA valuation, later, with their development, a number of methodologies will be formed related to the valuation of specific types of DFA objects: digital payment asset, digital invested asset, digital insurance asset, digital corporate financial asset, etc. The concept of the methodology for the valuation of the DFA covers the research of the object scientific discipline, its structure and functioning schemes, the place of the valuation of the DFA in the digital economy, the theoretical foundations and tasks facing the valuation of the DFA. Valuation objects and associated valuation have their own research subjects and significant specific features in methods (including valuation methods). Moreover, the research methods are completely independent. Using a systematic approach, individual spheres of DFA functioning can be represented as subsystems of the general system of DFA functioning. The basis of the methodology for the valuation of DFA is, first of all, the main provisions of the economics of finance, in which the problems of property valuation are investigated. The methodological foundations of valuation also include parts of scientific disciplines that consider the theoretical laws of the development of social production, sectoral economics, systems theory, statistics, etc. All methodological laws, principles, concepts and provisions drawn from other sciences, when used in valuation, DFAs are adapted, adapt to the study of processes and phenomena in the field of DFA valuation. In our case under consideration, the valuation of DFA, this requirement is especially relevant, since each separate type of DFA is a special innovative product that requires its own assessment methods. Improving the methodology for the valuation of DFA requires a clearer formulation of its content, and the content of the object and the method of scientific discipline, theory and knowledge of other scientific disciplines used in this area of knowledge. At the same time, the methodological knowledge of the cost estimate of the DFA does not

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remain unambiguously fixed. They develop and are enriched with new results of scientific research. Principles for the valuation of digital financial assets are a set of basic requirements for the valuation of digital financial assets and are objectively determined by the general trend in the development of the digital economy and the valuation methodology.

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Capabilities of Artificial Neuron Networks for System Synthesis in Medicine V. V. Eskov1(&), E. V. Orlov2, T. V. Gavrilenko1, and E. A. Manina1 1

Surgut State University, 1, Lenina pr., Surgut 628400, Russia [email protected] 2 Samara State Medical University, 89, Chapaevskaya Street, Samara 443099, Russia

Abstract. Eskov-Zinchenko effect was proved in biology and medicine. The effect appears when measuring any biosystem’s parameters results that every sample is unique and stochastic approach becomes not useful. It is uncertainty of the second type. But there is uncertainty of the first type when samples are equal but biosystems demonstrate real distinguishes. For such case we propose the artificial neuron networks in special chaotic regimes such provide the identification of order parameters and solution of global system synthesis for solution of first type uncertainty. So artificial networks demonstrated new possibility for informatics. #CSOC1120. Keywords: System synthesis
Cardiovascular system
Uncertainty of the first type
Chaos
Eskov-Zinchenko effect

1 Introduction The study of the influence of special environmental factors of the North of the Russian Federation on the parameters of the functions of the human body (living in the North) is an important problem of biomedicine and human ecology. At the same time, it has now been proven that the living of the visiting population in the North fundamentally leads to a decrease in the quality of life. This is confirmed by the reduction in the retirement period for everyone who has worked in the North of Russia for more than 20 years. It is believed that such a period significantly changes the quality of human life in the North. At the same time, the state introduces various monetary supplements (and coefficients) for the inhabitants of the North. Obviously, this is an official sign of the harmful effects of the ecological factors of the North on the human body. At the same time, the problem of the indigenous inhabitants of the North remains outside the brackets [1–7]. Today, the problem of the influence of factors of the North of the Russian Federation on the state of the functions of the human body living in these special conditions remains poorly studied. There are no detailed studies to identify differences in the state of the body of the indigenous inhabitants of the North (in our country, these are Khanty) and the visiting population. At the same time, great problems arise in cybernetics and medicine due to the statistical instability of samples of the parameters of the human body (the Eskov-Zinchenko effect) [1–7]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 171–179, 2022. https://doi.org/10.1007/978-3-031-09073-8_16

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In this regard, this study aims to close this research gap. Using the example of the state of the cardiovascular system of a person in the North of the Russian Federation, we studied six parameters of cardiovascular system (parameters of the spectral density of the signal - power spectral density, using the example of the parameters of cardiointervals (RRs) as the main signal about the state of the cardiovascular system [8– 15]. possibilities of artificial neural networks in medicine.

2 Object and Methods Three age groups of Khanty women were examined (average age of the younger group = 27 years, average age = 43 years and average age of the older group = 58 years) and three similar age groups of women living in the North region for more than 10 years in the North of Russia (Yugra). to study the cardiovascular system used patented device “elox-01”. The surveys were carried out sitting in a quiet state for at least 5 min, cardiointervals samples were recorded. As a result, any sample contained at least 300 cardiointervals. These samples were processed using a computer program using the fast Fourier transform for cardiointervals [8–15]. These six parameters are: x1 - VLF - spectral density of very low frequency signals; x2 - LF - spectral density of low-frequency signals; x3 - HF - spectral density of highfrequency signals; x4 - LF (p) - normalized LF; x5 - HF (p) - normalized HF; x6 LF/HF - the ratio of these two spectral densities (all in units - conventional units). As a result, all samples of these six parameters for all six age groups were statistically processed, and the mean (median) values (Me) made up the samples (38values Me in each sample for each specified parameter). A pairwise comparison was made (for each age group: 1–1, 2–2, 3–3) of such samples (out of 38values Me) using the MannWhitney test [8–15]. After determining the statistical differences between these samples, we used artificial neural networks in two new modes: chaotic setting of the initial weights Wi0 (from the interval Wi0 2 (0, 1)) and multiple repeated settings (reverberation) of these neural networks with these Wi0 (at each iteration of the settings). As a result, we obtained samples of the final values of the weights Wi diagnostic signs xi and these samples were statistically processed (up to mean values and confidence intervals).

3 Results First of all, note that all samples of all six parameters xi the state vectors x (t) for the power spectral density, x = x (t) = (x1, x2,…, x6)T were checked for their assignment to the normal distribution. It was found that only 1.5–2% of samples of all xi(t) can show a Gaussian distribution. Therefore, we performed all further calculations within the framework of nonparametric distributions. The Mann-Whitney test was used.

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Primary samples were processed before calculating Me (and percentiles), and all 38 medians for each group formed their own sample Me, which was further subjected to statistical processing. In particular, a pairwise comparison of such samples was carried out Me for the same age groups (1–1, 2–2, 3–3). This is presented in Table 1. Table 1. Results of pairwise comparison of mean values of ranks (permissible level of significance of heart rate variability parameters of heart rate, groups of women of the indigenous and non-indigenous population of Yugra by age). Spectral power density parameters were studied using the Mann-Whitney test. Noted criteria are significant at Parameters p - level. 1 with 1 VLF 0.42 LF 0.05 HF 0.01 LF (p) 0.60 HF (p) 0.60 LF/HF 0.51

the level of p < 0.05 p - level. 2 with 2 p - level. 3 s 3 0.16 0.28 0.40 0.94 0.92 0.86 0.51 1.00 0.51 0.99 0.36 0.92

In this Table 1 we have the Mann-Whitney criterion pi,j for each pair of comparison of samples for all six power spectral density parameters (for cardiointervals). We emphasize that if pi,j  0.05, then such a compared pair can have one (common) general population. It is easy to see that for all such different 18 comparison pairs (for three different age groups 1–1, 2–2, 3–3) we have an extremely small number of pairs, where pi,j < 0.05. Almost all of these pairs (there are 17 of them) show pi,j  0.05, i.e. they are statistically the same, they can have a common general population. Only the parameter HF for pair 1–1 showed pi,j = 0.01, i.e. samples vary. We define this situation as the first type of uncertainty. Obviously, statistics do not work here anymore (they make no difference). At the same time, there is also an uncertainty of the second type [16–27]. It forms the basis of the new theory of chaos-self-organization [28–32]. Uncertainty of the first type within the framework of traditional statistics cannot be solved in any way [1–7]. It requires the creation of new theories and new methods for studying systems of the third type, which W. Weaver spoke about back in 1948 [33]. To study them, we are now creating a new theory of chaos-self-organization [25–32]. Within the framework of this new theory of chaos-self-organization, we have proved the Eskov-Zinchenko effect for the neural networks of the human brain [10, 14]. It turned out that these neural networks generate samples of electroencephalograms in chaos (no statistical repetitions) and continuous reverberations [1–7, 12–14, 23, 24, 34, 35]. It is these two modes that we used in the work of artificial neural networks [1–7].

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Until now, all existing artificial neural networks did not operate in these two special modes. Therefore, we used them to assess the differences in age groups. Note that both the same age groups are almost completely statistically the same, and the comparison of different age groups (for the Khanty and for newcomers) separately also gives coincidences. Table 2, we present the results of pairwise comparison of the power spectral density samples for the indigenous (Khanty) and visiting female population of Yugra. Obviously, here (as in Table 1), statistical coincidence of samples of almost all parameters of the power spectral density prevails. For example, the 1st column (comparison of the 1st and 2nd age groups, Khanty) showed complete statistical coincidence (all pi,j  0.05). Table 2. Results of the pairwise comparison of the mean values of ranks (the permissible level of significance of heart rate variability parameters of women’s indigenous and non-Yugra population) spectral parameters using the Mann - Whitney. Selected criteria are significant at p < 0.05 Parameters p - level 1 to 2 p - level, 1 s 3 p - level, 2 s 3 Indigenous Non-indigenous Indigenous Non-indigenous Indigenous Non-indigenous VLF LF HF LF (p) HF (p) LF/HF

0.45 0.05 0.62 0.35 0.35 0.27

0.13 0.00 0.00 0.80 0.80 0.82

0.01 0.00 0.00 0.80 0.78 0.94

0.47 0.00 0.00 0.40 0.40 0.39

0.07 0.15 0.01 0.19 0.19 0.17

0.60 0.05 0.01 0.52 0.52 0.53

Overall, 75% of all pairs (from 36 different) showed statistical coincidence of the samples. This is a very high percentage of uncertainty of the first type when the samples are statistically the same. To resolve all these uncertainties (see Tables 1 and 2), we use artificial neural networks in two special modes. As a result, all the samples were divided and we were able to rank the significance of all these diagnostic features (parameters of the power spectral density for cardiointervals). In the figure, we present one typical example of such a result. The Fig. 1 shows the results of statistical processing of data on Wi for comparing power spectral density of 1 and 2 groups of the indigenous population - (A) and 1–2 groups of the visiting population of the North of Russia (Yugra) - (B).

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Fig. 1. Results of 50 trainings of the neural network in solving binary classification problems (average values of the weights of the signs xi(t)) in the diagnosis of differences in the spectral parameters of heart rate variability in women of groups 1 and 2 of the indigenous (A) and non-indigenous (B) population of Yugra.

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Fifty repeated adjustments of the artificial neural networks were performed with a chaotic assignment of Wi0 from the intervals Wi0 2 (0.1). It follows from this figure that for a comparison pair of 1–2 Khanty women, we have an order parameter in the form of LF (for it, the average weight = 1). Second place is HF ( = 0.54) and in third place LF/HF = 0.52. These are the results of the artificial neural networks operation. For the 1–2 age pair of visiting women (Fig. 1 - B), we have a different order parameter. Here in the first place = 0.97, and in second place = 0.84 and LF is already in third place = 0.75. There has been a complete change in the significance of these three diagnostic features, and this proves the difference in the parameters of the ATP for the Khanty and visiting women. Table 1, we could not identify these differences, and the figure demonstrated this (after using the artificial neural networks in chaos and reverberation modes).

4 Discussion A detailed study of the spectral density parameters for cardiointervals in different age groups showed a very high degree of statistical coincidence of the samples. This applies to both comparisons of the same age groups (1–1, 2–2, 3–3) in the Table 1 and different age groups (see Table 2) for Khanty women and visiting women (Table 2). Both of these tables prove the presence of statistical coincidences in the power spectral density samples of almost all age groups (for almost all six power spectral density parameters). It is obvious that further use of statistics here is no longer expedient and new methods and new models are needed in studying the influence of environmental factors in the North of Russia on the state of the cardiovascular system (in particular, on the power spectral density) [1–7]. There is an uncertainty of the first type, which in modern cybernetics cannot be allowed. As such new methods and models, we use artificial neural networks in two special modes. These new modes were prompted by the study of real neural networks of the human brain. It turned out that they operate in the mode of continuous statistical chaos (samples are continuously changing) and this occurs during continuous reverberations (electroencephalogram) [1–7, 10, 14, 34, 35, 37]. If the parameters of the brain (its electroencephalogram) show zero, then it will be a dead brain. Therefore, we introduced the chaos of the initial weights into the work of the artificial neural networks Wi0 of diagnostic features xi(t) and forced the artificial neural networks to re-adjust (reverberate) many times. In this case, we established that all pairs of samples are different. Moreover, after many iterations (we had n = 50) we get samples Wi and we can find (statistically) their mean . After these special modes of operation of the artificial neural networks, we get the ranking of the features xi(t). It turned out that one can find the main diagnostic features (order parameters), which are > 0,5. The average weights of these features for each comparison pair (for all six power spectral density parameters) have different values for different comparison pairs. This proves the difference between the age groups of Khanty women and visiting women. As a result, we not only got rid of the first type of uncertainty (see Tables 1 and 2), but also found the order parameters. This

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constitutes the basis of systems synthesis, which now has no general solution in computer science [10–19].

5 Conclusions A detailed study of the samples of six parameters of the power spectral density for cardiointervals showed that almost all samples cannot show a normal distribution (only 1.5–2% give a parametric distribution). Therefore, to study the power spectral density, it is advisable to use a nonparametric distribution. Pairwise comparison of power spectral density samples for all six groups (both for the same ages, i.e., 1–1, 2–2, 3–3, and for different ages: 1–2, 2–3, etc.) showed extremely low values of statistical differences. Many pairs of samples show statistical coincidences. We interpret this as the first type of uncertainty. Uncertainty of the 1st type can be disclosed only within the framework of a new science (theory of chaos-self-organization). Here it is necessary to calculate either the parameters of the pseudo-attractors, or use artificial neural networks. In this report, we used these artificial neuron networks, which, in two special modes, not only separates the power spectral density samples, but also provides their ranking. In this case, we solve the problem of system synthesis - we find the main diagnostic features (order parameters).

References 1. Gazya, G.V., Eskov, V.M.: Uncertainty of the first type in industrial ecology. Earth Environ. Sci. Conf. Ser. 839, 042072 (2021). https://doi.org/10.1088/1755-1315/839/4/042072 2. Kozlova, V.V., Galkin, V.A., Filatov, M.A.: Diagnostics of brain neural network states from the perspective of chaos. J. Phys. Conf. Ser. 1889(5), 052016 (2021). https://doi.org/10. 1088/1742-6596/1889/5/052016 3. Grigorenko, V.V., Nazina, N.B., Filatov, M.A., Chempalova, L.S., Tretyakov, S.A.: New information technologies in the estimation of the third type systems. J. Phys. Conf. Ser. 1889, 032003 (2021). https://doi.org/10.1088/1742-6596/1889/3/032003 4. Gazya, G.V., Eskov, V.V., Filatov, M.A.: The state of the cardiovascular system under the action of industrial electromagnetic fields. Int. J. Biol. Biomed. Eng. 15, 249–253 (2021). https://doi.org/10.46300/91011.2021.15.30 5. Filatova, O.E., Bashkatova, Yu.V., Shakirova, L.S., Filatov, M.A.: Neural network technologies in system synthesis. IOP Conf. Ser. Mater. Sci. Eng. 1047, 012099 (2021). https://doi.org/10.1088/1757-899X/1047/1/012099 6. Eskov, V.V.: Modeling of biosystems from the stand point of “complexity” by W. Weaver and “fuzziness” by L. A. Zadeh. J. Phys. Conf. Ser. 1889(5), 052020 (2021). https://doi.org/ 10.1088/1742-6596/1889/5/052020 7. Eskov, V.M., Filatov, M.A., Grigorenko, V.V., Pavlyk, A.V.: New information technologies in the analysis of electroencephalograms. J. Phys. Conf. Ser. 1679, 032081 (2020). https:// doi.org/10.1088/1742-6596/1679/3/032081 8. Zilov, V.G., Eskov, V.M., Khadartsev, A.A., Eskov, V.V.: Experimental verification of the bernstein effect “repetition without repetition.” Bull. Exp. Biol. Med. 163(1), 1–5 (2017). https://doi.org/10.1007/s10517-017-3723-0

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Improving the Linkage of Internet Exchange Points Through Connected ISPs ASes Yamba Dabone1(B) , Tounwendyam Fr´ed´eric Ouedraogo2 , and Pengwend´e Justin Kouraogo1 1

Universit´e Joseph KI-ZERBO, UFR-SEA, L.A.M.I, Ouagadougou, Burkina Faso [email protected] 2 Universit´e Norbert ZONGO, UFR-ST, L@MIA, Koudougou, Burkina Faso

Abstract. The role played by Internet Exchange Points (IXP) in autonomous systems (ASes) interconnection makes them indispensable for Internet ecosystem evolution. Their implementation is therefore necessary in all regions of the world. So we are interested in the improvement of exchanges between Internet Service Provider (ISP) ASes through IXPs interconnection via ISPs customers ASes. For such a study we have chosen 11 African Internet exchange points from which we have collected details such as number of participants and peak. Thus, we made a possible linkage of these exchange points using ASes of their ISPs. All this in order to facilitate peering between ISP customers, which will improve data exchange and encourage connectivity to Internet exchange points.

Keywords: Internet Service Provider eXchange point · Interconnection

1

· Autonomous system · Internet

Introduction

The improvement of Internet ecosystem is due to various technologies, including Internet exchange points, which are a crucial part of this ecosystem [4]. According to Internet Society, an Internet exchange point is a physical and generally neutral location where different networks meet to exchange local traffic via a switch. Because of their importance, IXPs are deployed in all regions of the world. There are a total of 1,115 Internet Exchange Points spread across the 5 regions of the world, of which 720 are active or 65% (Fig. 1) [13]. Africa is one of regions that has experienced slow and delayed implementation of IXPs. IXPs established have enormous difficulties in functioning. Indeed, Africa has only 82 IXPs, of which only 58 are active with a total of 1,717 participants [13]. To overcome one of difficulties, which is low traffic at IXPs, we propose a African IXPs interconnection through of ISP customers ASes. It should be noted that links between IXPs are still weak. So with this solution we will be able to facilitate communication between ASes by boosting the peering between ISPs. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 180–187, 2022. https://doi.org/10.1007/978-3-031-09073-8_17

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Our paper is organized as follows. Section 2 presents related studies that have been developed so far. Section 3 describes all methodologies and data sources that have been useful for this research. Section 4 gives data analysis. We summarize our conclusions in Sect. 5.

Fig. 1. IXPS state by region

2

Related Works

Several studies have already been conducted on improving the architecture and operation of IXPs. According to the study: Improving the Discovery of IXP Peering Links through Passive BGP Measurements, Vasileios Giotsas and Shi Zhou [10] find that 1,062 of the links which they discovered are included in topological information obtained from CAIDA Arch and DIMES. Their methodology used can help to: – Reduce the cost of active measurement of methodologies dedicated to IXP peering discovery – help to perform better targeted surveys – provide a new source of IXP data for peering policies. An AS is identified by a unique 32 bits number (ASN) and can be assigned one or more IP addresses. In addition majority of missing links are invisible p2p links that are primarily located in the edge of ASes graph. The discovery of p2p links in IXPs is the key to obtaining complete AS connectivity maps. Juan C. C. R. and Rade S. [6] studied the traffic exchanged in IXPs, as well as interaction between different factors that affect the operation of IXPs. IXPs occupy between 15% and 20% of inter-domain traffic on Internet. Each AS has

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a number of globally measurable parameters that can characterize its position in the Internet ecosystem, such as the size of the IP address space, neighbouring ASes, number of bittorrent clients. For Luis A. D. K. et al. [8], one of the main challenges in managing IXP networks is the management of so-called ”elephant” flows and connecting to IXPs reduces the cost. Current challenges for IXPs include monitoring ARP traffic, establishing VPNs, and meeting service level agreements (SLAs). It is important to discover the path of elephant flows. IXP business operations are divided into two distinct groups : private and community. Most IXPs are primarily composed of Layer 2 networks or MP LS routers. All these informations have been a guide in our study that will improve the African IXPs functioning.

3

Data Sources and Methodologies

Our methodology used combines multiple sources of IXPs-related information with determination of IXP’s ISPs-client ASes and the possible links between them. In order to do this, we selected 11 IXPs in 10 African countries. We sought to determine paths that lead from one IXP to another across one or more IXP clients while choosing the path that has fewer ISPs to cross. We also used traIXroute tool for tracing a few selected IXPs as well as CAIDA’s AS Rank data [3] and also IXPs sites data [1,2,5,7,11,14,15,17]. Since the tracing was based on ASes, for some IXPs we traced their clients to access them. Interconnection between IXPs will facilitate more traffic. 3.1

Current Status of Selected IXPs

In this section we illustrate number of participants, peak of 11 IXPs selected for our study. All these informations are illustrated in Table 1 [13]. Through this table we can evoke the low number of participants and peak on the majority of IXPs. 3.2

Tracing with TraIXroute Tool

In our methodical approach, for the detection of IXPs, we will trace them. So, we used traIXroute tool that allows us to trace some of IXPs selected for our study (Fig. 2, 3, 4, 5, 6 and Fig. 7).

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Table 1. IXPS participants and peak ASN

IXP name

Participants Peak (Gbps)

37186 NAPAfrica Johannesburg 477

1600

37195 NAPAfrica Cape Town

298

233

36932 IXP-N Lagos

61

230

37704 KIXP Nairobi

53

45,4

30997 GIXA

21

4,8

328010 BFIX

12

11,10

9

2,97

328014 RINEX

6

0,31

327818 Benin-IX

37774 TGIX

6

0,97

37780 GABIX

10

0,64

36946 CIVIX

6

0,42

TraIXroute is a tool that detects IXPs based on data from PeeringDB (PDB) and Packet Clearing House (PCH). Specifically, it uses exact IP addresses of BGP routers connected to IXP subnets; IXP member ASes; IXP prefixes; and IP address mappings to ASes; and combines several pieces of information to detect IXPs with greater confidence than simply relying on IXP prefixes [16]. – Burkina Faso Internet exchange point (BFIX) (Fig. 2)

Fig. 2. BFIX

– Ghana internet exchange point (GAIX) (Fig. 3)

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Fig. 3. GAIX

– Nigeria internet exchange point (IXPN) (Fig. 4)

Fig. 4. IXPN

– Kenya internet exchange point (KIXP) (Fig. 5)

Fig. 5. KIXP

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– NapAfrica Johannesburg (Fig. 6)

Fig. 6. NAPAfrica Johannesburg

– NAPAfrica Cape Town (Fig. 7)

Fig. 7. NAPAfrica Cape Town

4

Results

In this section we show results of our study on facilitation of exchange between IXPs through ISPs ASes. We are more interested in the possible connections between IXPs and the increase in peering at IXPs. 4.1

Link Between IXPs Across Their ISPs

By going through ISPs we can create a junction between IXPs (Fig. 8) [12]. This junction will boost the exchanges between the ISPs. Thus, it will also increase local traffic, which is even one of the main goals of an IXP.

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Fig. 8. Links between IXPs

4.2

Peering

According to Internet Society, peering is when ISPs agree to freely exchange traffic with each other for mutual benefit. When performed over IXPs, it improves connectivity and reduces costs [9]. There are two types of peering: – private peering is an exchange between networks using a private infrastructure. This exchange will have a high cost which is not profitable for ISPs – public peering is an exchange of data between ASes using a single connection. It is mostly done over an IXP to keep traffic local and to further reduce the cost. The lack of connectivity between IXPs makes peering between ISPs difficult. This also makes the cost of data exchange high. Linking Internet exchange points will be an asset for ISPs. Thus, it will further encourage other ISPs to connect to the IXPs.

5

Conclusion

Three conclusions emerge from our results. First, traffic is improved because it remains more local. Second, the reduction of traffic cost and finally, the encouragement of IXPs connectivity. Local and low-cost exchange is one of the main objectives of IXPs thanks to the interconnection between IXPs. Improved traffic and reduced cost will likely encourage other ISPs to connect to IXP, increasing the number of participants. Connection between IXPs is therefore an important element in the better functioning of ISP customer traffic.

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References 1. Accueil. http://www.tgix.tg//. Accessed 26 Oct 2021 2. Admin. KIXP. Technology Service Providers of Kenya, 30 September 2015. https:// www.tespok.co.ke/?page id=11648. Accessed 26 Oct 2021 3. AS Rank: A ranking of the largest Autonomous Systems (AS) in the Internet. https://asrank.caida.org/asns. Accessed 28 Sept 2021 4. Augustin, B., Krishnamurthy, B., Willinger, W.: IXPs: mapped? In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference - IMC 2009, pp. 1–2. ACM Press (2009). ISBN 978-1-60558-771-4. https://doi. org/10.1145/1644893.1644934. http://portal.acm.org/citation.cfm?doid=1644893. 1644934 5. Bfixl. https://bfix.bf/. Accessed 26 Oct 2021 6. Restrepo, J.C.C., Stanojevic, R.: IXP traffic: a macroscopic view. In: Proceedings of the 7th Latin American Networking Conference on - LANC 2012, pp. 2–5. ACM Press (2012). ISBN 978-1-4503-1750-4. https://doi.org/10.1145/2382016.2382018. http://dl.acm.org/citation.cfm?doid=2382016.2382018. Accessed 18 Oct 2020 7. Civix. https://www.civix.ci/. Accessed 26 Oct 2021 8. Knob, L.A.D., Esteves, R.P., Granville, L.Z., Tarouco, L.M.R.: SDEFIX— identifying elephant flows in SDN-based IXP networks. In: NOMS 2016– 2016 IEEE/IFIP Network Operations and Management Symposium, pp. 2–6. IEEE, April 2016. ISBN 978-1-5090-0223-8. https://doi.org/10.1109/NOMS.2016. 7502792. http://ieeexplore.ieee.org/document/7502792/. Accessed 17 Oct 2020 9. Explicatif: Qu’est-ce le peering sur internet? Internet Society. https://www. internetsociety.org/fr/resources/doc/2020/explicatif-quest-ce-le-peering-surinternet/. Accessed 31 Apr 2021 10. Giotsas, V., Zhou, S.: Improving the discovery of IXP peering links through passive BGP measurements. In: 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 1–4. IEEE, April 2013. ISBN 978-1-4799-0056-5 978-1-4799-0055-8 978-1-4799-0054-1. https:// doi.org/10.1109/INFCOMW.2013.6562878. http://ieeexplore.ieee.org/document/ 6562878/. Accessed 20 Nov 2020 11. GIXA. http://www.gixa.org.gh/. Accessed 26 Nov 2021 12. Hurricane Electric BGP Toolkit. https://bgp.he.net/. Accessed 27 Oct 2021 13. Internet Exchange Directory—PCH. https://www.pch.net/ixp/dir. Accessed 25 Sept 2021 14. Internet Exchange Point of Nigeria—Just another WordPress site. https://ixp.net. ng/. Accessed 26 Oct 2021 15. NAPAfrica. https://www.napafrica.net/. Accessed 26 Oct 2021 16. Nomikos, G., Dimitropoulos, X.: traIXroute: Detecting IXPs in traceroute paths, pp. 1–3, 11 November 2016. http://arxiv.org/abs/1611.03895. Accessed 23 Sept 2020 17. RINEX - Rwanda Internet Exchange. https://www.rinex.org.rw/. Accessed 25 Oct 2021

Ranking Requirements Using MoSCoW Methodology in Practice Tatiana Kravchenko1 , Tatiana Bogdanova1 and Timofey Shevgunov2(&) 1

2

,

National Research University Higher School of Economics, Myasnitskaya Ulitsa 20, 101000 Moscow, Russia {tkravchenko,tanbog}@hse.ru Moscow Aviation Institute (National Research University), Volokolamskoe shosse 4, 125993 Moscow, Russia [email protected]

Abstract. Requirement prioritization is performed in order to analyze business requirements and to define the required capabilities leading to potential solutions that will fulfill stakeholder needs. During the analysis, the needs and informal concerns are transformed into formal solution requirements describing the behavior of solution components in details. The developed models can describe the current state of the organization and are used for validating the solution scope among mangers and stakeholders. This facilitates identification of open opportunities for improvement or assists stakeholders in understanding the current state. Several techniques have been applied for requirements prioritization in a case study of the conventional commercial bank where the main problems of the communication management process have been formulated and illustrated by the fishbone diagram. The MoSCoW technique has been applied to identification of four requirement groups, whose impact on the results principally differ within the scope of the identified problems. The obtained list of prioritized requirements should be used on next project stages since it will be exploited by the managers during their planning future jobs on the solution implementation. The paper results are aimed at helping the stakeholders develop a common point of view on the strategic goals of the project. Keywords: Requirements MoSCoW technique


Requirement prioritization
Fishbone diagram


1 Introduction Both increasing level of competition and current economic difficulties are forcing banking business to move from price-based competition towards a customer-centric one. More and more banking institutions are implementing a CRM (Customer Relationship Management Systems) concept on both operational and analytical levels. However, the initial complexity and inconsistency of banking systems cause a number of difficulties during the implementation of any new module into the existing ITinfrastructure of the organization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 188–199, 2022. https://doi.org/10.1007/978-3-031-09073-8_18

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These difficulties are imposed on usual constraints associated with any project, such as budgetary, temporal, or qualitative ones. In this regard, it is not enough to identify key requirements to the future solution while planning activities on implementing new modules into IT-infrastructure. The fulfillment of all the identified requirements is likely to violate one or several project constraints. Therefore, a process of requirements prioritization is one of the key stages of requirements analysis process. Prioritization of requirements ensures that analysis and implementation efforts focus on the most critical business requirements [1–8]. The following work provides the results of requirements prioritization in a case study of the conventional commercial bank [9]. This bank was previously considered as a case-study for data warehouse development in [10] where expert decision-support system was involved [11]. However, the task of requirement prioritization is not specific for financial service sector. Similar issues can arise in electric power industry [12, 13], high-precision navigation systems deployment [14, 15] and maintenance [16], project management in aircraft and space industry [17, 18], education management [19], in high-tech prototyping and marketing [20, 21]. The paper objective is to put into practice requirements prioritization techniques, which are suggested in “A Guide to the Business Analysis Body of Knowledge® (BABOK® Guide)”. The object of the study is process of customer communication management established in the bank. The subject being analyzed is the list of requirements set by the various divisions of the bank to the developing communication support system.

2 Problem Description 2.1

Determination of the Reasons Preventing the Bank from Entering the Target Sales Figures

The leading business activity for the bank being considered was consumer lending in partnership with large retail chains and small regional companies. Recently, the new strategic development course has been chosen, which goal is to increase the loan portfolio by means of credit products diversification. Since the level of competition in the market of general-purpose loans is high, it has been decided to enter the market by means of cross-selling. This sales method, which implies loan offer to existing customers, has been chosen for its relative cheapness and ease of implementation. Nevertheless, some time after the new strategic development course has been approved, it becomes clear, that the increase of the amount of general-purpose loans to existing customers cannot be fulfilled. In order to determine possible underlying sources of the problem, the root cause analysis is performed by graphing a fishbone (also known as Ishikawa) diagram (Fig. 1). This tool helps to focus on the cause of the problem versus the solution and organizes ideas for further analysis. The diagram serves as a map depicting possible cause-and-effect relationships [3, 7, 8].

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Fig. 1. The cause-and-effect diagram

2.2

Identification of Problems in Setting up Communications with Clients of the Bank

Because of the fact that the cross-selling mechanism is based on continuous communications with the bank’s customers, the main sources of the problem are discovered in areas of planning and implementing communications with customers, which in turn stem from the technical imperfection of the communication management system available in the bank. As part of the cross-selling process established in the bank, the following mechanism of planning and implementing communications was used. Clients could be offered a new credit product via email-messages, SMS, and phone calls. Because of the intensive growth of the bank's client base, as well as the increase in the share of general-purpose loans in the overall loan portfolio of the organization, the problems of the existing solution for setting up communications with clients that hampered the further development of the cross-selling direction became obvious. These problems are following: • the inconsistency of the systems used to plan and implement communications (each of the three types of communications was supported by separate and unrelated systems). At the same time, there was a need to make a centralized decision to conduct all types of communications in order to consistently develop relations with the Bank’s clients and to harmoniously increase the client base without distortions to a particular client segment;

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• the processes of marketing communications management were automated on a low level. For instance, employees of departments responsible for different stages of the process often had to manually create files, format them and put them in the necessary directories; • the marketing communication management processes were not flexible enough to comply with rapidly changing risk and communication policies of the bank. Lack of flexibility led to the situations, when the communication strategy chosen for the certain customer at the beginning of month did not correspond with the real credit offer to the customer at the moment of contact. All the stated problems along with the new strategic development course chosen by the bank top management established a new business need of the organization. The need is to obtain a new tool that supports centralized, automated, and flexible communications management process. It has been decided to develop a unified information system for managing client communications, which will completely replace some outdated modules and will also eliminate restrictions, which exist in other modules. 2.3

Identification of Stakeholders for the Development of a Module for Supporting Processes of Interaction with Customers

The stakeholders of the project of communication process support system development are shown in Table 1. The stakeholders’ roles and responsibilities in the process of communication formation and execution are listed (in parentheses in the field “Stakeholder” the abbreviations for the names of divisions are shown to be used in prospect).

Table 1. Stakeholder list. Stakeholder

Role in the process

Customer Service Department (CS)

The division executes the major part of the direct contacts with customers. It is responsible for both SMS and e-mail delivery, incoming line support, and outbound call-down. The division is an owner of the Customer Service process The division forms the list of contacts for further call-down on sales topics. It is responsible for the upper-level communication strategy and for the customer relationships development. The division is an owner of the cross sales process

Cross Sales Management Department (XS)

Level of interest High

Level of influence High

High

High

(continued)

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Stakeholder

Role in the process

Card Portfolio Management Department (CP) Remote Sales Management Department (RS)

The division is responsible for the communication strategy development with the card owners of the bank The division is responsible for the development of the internet and mobile bank. It uses SMS and e-mail mailing technologies to attract customers to the new remote sales channels The division is responsible for setting up and sending out validation messages in cases when the customer performs one of the key actions: product activation, transaction execution, initiating the change of personal data, establishing a personal cabinet in the Internet bank, etc The division is an owner of the Arrears Collection process

Information Security and Anti-Fraud Department (AF)

Collection Department (CL)

2.4

Level of interest High

Level of influence Medium

Medium

Medium

High

High

Low

Low

Functions of the Bank for Communication with Customers

The communication activities that are established in the bank can be divided to several groups by their functions. The primary function of communication with customers is to offer then a new loan product, thus, the process of contact with the client is directly related to the achievement of the organization’s strategic goal, which is to increase the share of general-purpose loans in the total loan portfolio of the bank. The second function of the communication process with customers is related to maintaining stronger relationship with the clients, who already have an active product of the bank. Thus, the communication process is aimed to positively influence the level of customer loyalty, which, in its turn, positively affects the company’s future earnings. Communications considering customers awareness of information security and countering fraud can also be called as actions aimed to increase customer loyalty level. Activities aimed to inform the clients of their arrears are considered in this case increasing the level of communications consistency. For instance, while forming a new message to the client, it is necessary to make sure that its content will not contradict with the current status of the client and will not mislead the customer. Indirectly, this function of communication with the client can also be attributed to the group aimed at increasing customer loyalty. The stakeholder requirements for the future solution are presented in the format of business initiatives, in which each of the stakeholder groups tried to formulate the problems facing the division while performing communication activities and the requirements for their solution. The problems facing the stakeholder groups were identified during brainstorming of the divisions’ employees [2, p. 227].

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3 Prioritizing Requirements in Practice In view of the limited project in time, resource and financial limitations of the project, the list of stakeholder requirements should be systematized, and the relative importance for each requirement should be determined. These priorities will be used in order to determine requirements should be implemented first during the development of the communication process support system. During the process of prioritizing the requirements of stakeholders, it has been decided to assess the received requirements in terms of the urgency of their implementation. Since the initiative to develop a new communication module comes from the business sector, which is responsible for increasing the share of cross-sales in the bank’s portfolio, it is necessary to make sure that the requirements being implemented are primarily aimed at increasing the amount of general-purpose loans and solve the fundamental problems formulated during the process of identifying the business need. In this case, the highest priority of the requirement will mean that this requirement must be implemented at the earliest possible stage of the project. The main problems identified in a communication management process of the bank (Fig. 1) boil down to the technical limitations of the existing communication support system, which provoke difficulties in the process of forming communications with customers, complicate the process of implementing planned communications, and, finally, may reduce the level of customer loyalty due to excessively frequent, contradictory and irrelevant communications. It is advisable to assign the highest priority to the requirements, the implementation of which contributes to the elimination of technical imperfections in the communication support system and makes the existing processes of forming and implementing communications with customers more efficient. Requirements, the implementation of which involves adding new functionality to the system or contributing to the emergence of new types of communication with customers, can be postponed until the next round of system development. 3.1

Ranking Requirements by the MoSCoW Methodology

In order to assess the value of the requirements in terms of their compliance with the main business goals set for the future solution, the requirements have been ranked using the MoSCoW (Must, Should, Could, Would) analysis technique [2, p. 368; 7; 8], where: • Must – requirements that must be satisfied in the final solution for the solution to be considered a success, directly affect the loyalty level of the bank’s clients. • Should – high-priority items that should be included in the solution if it is possible. • Could – the requirement, which is considered desirable but not necessary. • Won’t or Would (W) – the requirement that stakeholders have agreed will not be implemented in a given release, but may be considered for the future. The fulfillment of such requirements does not directly affect the solution of the priority problems identified in the analysis, and the value of implementing them more likely

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refers to the expansion of the possibilities of communication with customers, rather than addressing current problems in the processes of forming and implementing communications. The results of applying MoSCoW technique to the stakeholder requirements are shown in Table 2. There is a priority matrix with the following columns. Stakeholder requirements are shortly formulated in the column “Requirement description”. Consequences of the requirement implementation are stated in the column “Justification of the requirement assessment”, along with the consistency level of the implementation results for each requirement with the strategic goals of the project. Table 2. MoSCoW priority matrix. Code

Requirement description

XS1

To automate the process of downloading the client database to the gateway directory for sending SMS

XS2

To add an email channel as a full-fledged communication channel with the client

XS3

To increase the speed of sending e-mail messages to 1 million per hour with the ability to adjust the speed of sending

Justification of the requirement assessment Implementation of this requirement will save human resources of both XS and CS units, however it will not directly lead to the simplification of communication planning and implementation processes, nor will it contribute to increasing the level of customer loyalty The implementation of the requirement will remove the existing restriction on the number of target audience for the communication, which will facilitate the implementation of communication, as well as it will attract new loyal customers The implementation of the requirement will help optimize the use of CS department human resources, will increase the flexibility of the process of interaction with customers, and will increase the level of customer loyalty

M

S

C

W x

x

x

(continued)

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Table 2. (continued) Code

Requirement description

XS4

To automatically update the stop-list with the information about clients’ refusals

XS5

To provide the opportunity to generate a report on the status of e-mail-messages delivery

CS1

To connect communication module with the CRM system

CS2

To provide the opportunity to centrally form the list of invalid contacts

CS3

To provide the opportunity to control the intensity of the client's participation in communications

CS4

To track information on budget spent on communications in online mode

Justification of the requirement assessment The implementation of the requirement will lead to an increase in the consistency level of communications, which positively affects customer loyalty level and is helpful for reducing costs The implementation of the requirement will increase the efficiency of the processes of forming and implementing communications with customers The implementation of the requirement helps to increase the level of customer loyalty, since it will reduce the number of uncoordinated and inconsistent communications with the clients The implementation of the requirement will improve the coherence of communications, reduce costs, and simplify the processes of communications formation and implementation The implementation of the requirement will improve the coherence of communications, reduce costs, and simplify the processes of communications formation and implementation The implementation of the requirement will reduce costs, and simplify the processes of communications formation and implementation

M

S

C

W

x

x

x

x

x

x

(continued)

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Code

Requirement description

CL1

To connect the communication module with the customer debt database

CL2

To implement the unified communication reference book

CL3

To log multi-channel communication actions

CP1

To integrate the communication module being developed with both CRM (Customer Relationship management) and RTM (Real Time Marketing) systems

CP2

To connect the communication module with the credit cards database

Justification of the requirement assessment The implementation of the requirement will reduce the response time to the client's status change (which increases the level of customer loyalty), and will reduce the level of inconsistency of data in different modules of the communication management support system The implementation of the requirement will increase the level of coordination between various communications of different divisions, and also will reduce the costs of the processes of communication formation and implementation Implementation of the requirement will save human resources of the CL unit, however, it will not affect the effectiveness of the processes of communication formation and implementation in a positive way The implementation of the requirement will increase the level of customer loyalty. However, it presumes the creation of a new communication channel and will not resolve the problems identified in the existing processes The implementation of the requirement will help promptly inform clients of the status of the card reissue. This will positively affect the level of customer loyalty

M

S

C

W

x

x

x

x

x

(continued)

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Table 2. (continued) Code

Requirement description

RS1

To integrate the CRM system outbound module with the bank’s website form by means of the new communication module

AF1

To automatically update the client data in cases when changes have been made in the verification system

AF2

To reduce the response time to card activation requests up to 5 min To automate the process of downloading the client database to the gateway directory for sending SMS

AF3

Justification of the requirement assessment The implementation of the requirement will save human resources of the RS division. Also, it will positively affect the level of customer loyalty The implementation of the requirement will help increase the efficiency of the communication formation process, as well as it will expand the volume of a valid client base and will increase the level of client loyalty on account of client data relevance increase The implementation of the requirement will increase the level of customer loyalty The implementation of the requirement will increase the speed of response to suspicious activities, which will influence the increase in the level of customer loyalty in the existing communication process

M

S

C

W

x

x

x

x

Thus, based on the results of MoSCoW prioritization technique, the primary priority group has been identified, that includes requirements, the implementation of which contributes to the efficiency of existing communications management processes at a fundamental level. It allows synchronize data in various modules of the communication support system with customers as well as increases the level of correctness of data on the bank’s customers. The requirements that are desirable to perform includes facilitating partial data synchronization in some modules of the communication support system and increasing the efficiency of the processes of communication formation and implementation. The requirements that are desirable but not necessary to be fulfilled will increase the level of customer loyalty with regard to communication with them in the context of existing types of interactions, but technological problems of the formation and implementation of communication are practically not solved by them. Such requirements are to be implemented in case when enough resources and time remain.

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Requirements, the implementation of which can be postponed until next time, are mainly focused on increasing the level of customer loyalty by adding new types of interaction with clients. Implementation of these requirements is more likely to be useful in the future, when the current problems of communications management processes will be resolved.

4 Conclusion The following work provides the results of requirements prioritization in a case study of the conventional commercial bank. Several techniques and recommendations stated in BABOK® Guide have been applied. First, all the inputs stated for the task of requirements prioritization have been taken into consideration. The identified business need of the organization have been described using the tool supporting centralized, automated, and flexible communications management process. Then, the main problems of the communication management process have been formulated based on the description of different communication types persisting in the organization. Underlying sources of the problem have been illustrated on a Fishbone diagram [2, p. 336]. The list of stakeholders consisting of the divisions involved into the communication management process has been made. Finally, the list of stakeholder requirements has been prepared. The basis for requirements prioritization has been chosen. It is the consistency level between the results of requirement implementation and the problems present in the current communication management process. The MoSCoW technique has been applied in order to identify four groups of the requirements, which differ from each other by the impact the results of their implementation have on the solution of the identified problems. The list of requirements with identified priorities allows to more clearly identify the objectives of the project, including for project sponsors. In this case, the goals of the project are to eliminate technical imperfections of the current system of communication with customers, as well as to facilitate the process of forming and implementing various types of communications. The list of prioritized requirements may be useful for the project manager while planning works on the solution implementation. The results of the work should also help the stakeholders develop a common point of view on the strategic goals of the project. Keeping to the list of prioritized requirements will help the organization improve a communication management support system in short time and with considering primary goals of the project.

References 1. A Guide to the Business Analysis Body of Knowledge® (BABOK® Guide). International Institute of Business Analysis. 2nd ed. (2009) 2. A Guide to the Business Analysis Body of Knowledge® (BABOK® Guide). International Institute of Business Analysis, 3rd ed. (2015)

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3. Tague, N.R.: The quality toolbox, Second Edition. Books & Standards (2005) 4. Lehtola, L., Kauppinen, M., Kujala, S.: Requirements prioritization challenges in practice. In: Bomarius, F., Iida, H. (eds.) PROFES 2004. LNCS, vol. 3009, pp. 497–508. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24659-6_36 5. Berander, P., Andrews, A.: Requirements prioritization. In: Aurum A., Wohlin C. (eds) Engineering and managing software requirements, pp 69–94. Springer, Berlin (2005) 6. Karlsson, J., Ryan, K.: A cost-value approach for prioritizing requirements. IEEE Softw. 14 (5), 67–74 (1997) 7. Agile Extension to the BABOK Guide. Agile Alliance (2017) 8. Blais, S.P.: Business analysis: best practices for success. Wiley (2011) 9. Bidzhoyan, D.S., Bogdanova, T.K., Neklyudov, D.: Credit risk stress testing in a cluster of Russian commercial banks. Bus. Inform. 13(3), 35–51 (2019) 10. Kravchenko, T., Shevgunov, T.: A brief IT-project risk assessment procedure for business data warehouse development. In: Silhavy, R. (ed.) CSOC 2021. LNNS, vol. 228, pp. 230– 240. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77448-6_22 11. Kravchenko, T., Shevgunov, T., Petrakov, A.: On the development of an expert decision support system based on the ELECTRE Methods. In: Silhavy, R. (ed.) CSOC 2020. AISC, vol. 1226, pp. 552–561. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-519742_51 12. In’kov, Y.M., Rozenberg, E.N., Maron, A.I.: Simulation of the process of implementation of an intelligent electric power metering system. Russian Electr. Eng. 91, 65–68 (2020) 13. Maron, A.I., Kravchenko, T.K., Shevgunov, T.Y..: Estimation of resources required for restoring a system of computer complexes with elements of different significance. Bus. Inform. 13(2), 18–28 (2019) 14. Guschina, O.: Refining time delay estimate of complex signal using polynomial interpolation in time domain. In: 2021 Systems of Signals Generating and Processing in the Field of on Board Communications, pp. 1–6 (2021) 15. Garvanov, I., Kabakchiev, H., Behar, V., Garvanova, M., Iyinbor, R.: On the modeling of innovative navigation systems. In: Shishkov, B. (ed.) BMSD 2019. LNBIP, vol. 356, pp. 299–306. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24854-3_23 16. Efimov, E., Neudobnov, N.: Artificial neural network based angle-of-arrival estimator. In: 2021 Systems of Signals Generating and Processing in the Field of on Board Communications, pp. 1–5 (2021) 17. Kalugina, G.A., Ryapukhin, A.V.: Methods of digital marketing positioning in the global civil passenger aircraft market. Bus. Inform. 15(4), 36–49 (2021) 18. Tsybulevsky, S.E., Murakaev, I.M., Studnikov, P.E., Ryapukhin, A.V.: Approaches to the clustering methodology in the rocket and space industry as a factor in the formation of a universal production model for the economic development in the space industry. INCAS Bulletin 11, 213–220 (2019) 19. Ostanina, E.: Influence of the technical equipment on the educational process. Revista de Tecnología de Información y Comunicación en Educación 19(1), 145–155 (2021) 20. Zaripov, R.N., Murakaev, I.M., Novikov, S.V., Ryapukhin, A.V.: Corporate structure for innovative enterprises. Russ. Eng. Res. 40(2), 137–139 (2020). https://doi.org/10.3103/ S1068798X20020239 21. Ryapukhin, A.V., Kabakov, V.V., Zaripov, R.N.: Risk management of multimodular multiagent system for creating science-intensive high-tech products. Espacios 40(34): 19 (2019)

Novel Experimental Prototype for Determining Malicious Degree in Vulnerable Environment of Internet-of-Things G. N. Anil(&) Department of Computer Science and Engineering, BMSIT&M (Autonomous), Bengaluru, India [email protected]

Abstract. Security has been always an increasing concern in an Internet-ofThings (IoT) owing to multiple unaddressed issues within the connected devices itself. Existing review of literature showcase that encryption-based techniques are on rise for device security; however, its loopholes are yet not addressed. Hence, this paper presents a discussion of computational framework which can carry out extraction of malicious behaviour of a node automatically on the basis of newly formulated rules. The study contributes towards developing a practical prototype for evaluating the malicious behaviour of an IoT nodes with a completely new and simplified design methodologies based on novel rulesets. The experimental outcome of the model carried out in standard test environment shows that proposed system offers faster attack identification with lower cost in contrast to frequently used security protocols in IoT. Keywords: Internet-of-Things protocol
Attack


Encryption
Malicious behaviour
Security

1 Introduction Internet-of-Things (IoT) comprises of interconnected devices which is capable of sensing information followed by processing and transmitting the information over a larger network [1]. The term ‘thing’ refers to various form of smart appliances which are connected to each other via ‘internet’ [2]. At present, various forms of modernized application exists as well as futuristic applications of an IoT is under the roof of research [3]. However, there is an increasing concern about security aspect in IoT [4, 5]. The IoT device usually doesn’t secure configuration as well as design aspect and hence are highly attack prone to various threats [6]. At present, there are various security-based approaches evolved to address this vulnerability issues [7–9]; yet, no full-proof security approaches in an IoT has been proven against dynamic attacks. Out of all the attack, the most lethal form of attack is basically a node capture attack, where the complete IoT node hardware is controlled by a malicious node [10] In such condition, it is quite a difficult task to assess the difference between regular node and attacker node as the communication behaviour of both the nodes could have similar patterns of exhibits of data transmission. At present, there are also various studies © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 200–211, 2022. https://doi.org/10.1007/978-3-031-09073-8_19

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which is based on malicious behaviour detection [11, 12]; however, such studies are found not to consider real-time behaviour of an IoT device from hardware perspective as well as real-world application perspective. Apart from this, it is quite challenging to understand the existing security scheme from practical world implementation perspective. Therefore, the proposed system presents a novel solution to address this problem. A unique malicious behaviour detection scheme has been presented in this work. The organization of this paper is as follows: Sect. 2 discusses about the existing research work followed by problem identification in Sect. 3. Section 4 discusses about proposed methodology followed by elaborated discussion of algorithm implementation in Sect. 5. Comparative analysis of accomplished result is discussed under Sect. 6 followed by conclusion in Sect. 7.

2 Related Work This section discusses about the existing schemes that are used for securing communication within an IoT devices. The recent work carried out by Verma et al. [13] have presented a security improvement scheme by introducing a unique scanning mechanism over IoT device. Optimization of secure routing system has been investigated by Shin et al. [14] considering mobility management for distributed nodes. Samaila et al. [15] have presented a framework which is capable of using existing encryption standards for securing the IoT device at the time of configuration with network. Significance and impact of various forms of attack in IoT has been studied by Lounis et al. [16] while Li et al. [17] have presented a trust and reputation-based security system in IoT when integrated with cloud environment. Apart from this, there are certain security schemes that are frequently exercised. The first scheme to highlight is Datagram Transport Layer Security (DTLS) which is capable of handling untrusty layers of transport with end-to-end security. DTLS scheme has been implemented in different ways in existing system viz. Simplifying handshaking using Software-Defined Network (Ma et al. [18], Park et al. [19], DTLS integrated with encryption (Banerjee et al. [20]), adoption of legacy protocol with DTLS scheme (Lee et al. [21]). Another frequently used security scheme is Elgamal encryption which uses discrete logarithms in order to generate secret keys (Guruprakash and Koppu [22], Mohan et al. [23], Jayashree et al. [24]. Advanced Encryption Standard (AES) is considered as another robust security protocol frequently used in IoT and they are reported to be highly resilient from any intrusive events (Tsai et al. [25], Sovyn et al. [26], Kane et al. [27], Dhanuskodi et al. [28], Noor et al. [29], Kim et al. [30], Yu et al. [31], Bui et al. [32]). Existing system has also witnessed usage of Elliptical Curve Cryptography for securing communication in IoT owing to smaller key size and faster key generation process (Liu et al. [33], Mehrabi et al. [34], Ding et al. [35], He et al. [36], Almajed et al. [37], Yeh et al. [38], Liu et al. [39]). Therefore, there are various security scheme available for IoT device with reported beneficial performance as well as pitfalls too. The next section highlights the identified research problem after reviewing the above mentioned studies.

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3 Problem Description The identified research problems are as follows: i) majority of the existing security techniques are designed using conventional encryption algorithms in different ways, ii) the studies presenting model description lacks consideration of practical world application as well as there is no invocation of attackers reported, iii) not much studies has been carried out towards claiming cost-effective security solution exhibiting the reliable applicability of it, iv) there are lesser extent of available benchmarking approaches towards securing large number of different forms of IoT devices, v) not much emphasis is offered to impact of ruleset towards secure design configuration in practical environment. Therefore, the proposed system identifies this set of problems and chooses to address them in its presented solution in next section.

4 Proposed Methodology The core idea of the proposed system is to introduce a smart security framework that is capable of identifying the threat on practical IoT devices using existing available applications as well as protocols. The idea is to evolve up with a cost effective security measure to identify the discrete behaviour of threat. Following is the block diagram of proposed scheme:

Fig. 1. Proposed block diagram

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Figure 1 highlights that proposed scheme introduces a mechanism to identify threats on the basis of Node Utilization Report which will carry a distinct information about the interaction of the nodes as well as network communication behaviour. The study also contributes towards set of rule generation using smart appliances and standard application which can easily find the degree of compliance as well as violations. The unique part of this study implementation is that its is capable of resisting the threat without any form of dependency to know about specification of this threat prior execution. The next section elaborates about the system design.

5 System Design The core target of the proposed system is to analyze the significant malicious behaviour of suspected IoT nodes in vulnerable environment. The proposed system presents a Node Utility Report (NUR) framework in order to analyze the behaviour of the suspicious node. The NUR is capable of representing dual means of IoT node behaviour viz. Node Interaction Behaviour and Link Interaction Behaviour; further this is followed up by identification process at the end. The proposed system make use of mobile application, which is universal and supported on various ranges of phone in order to establish a communication link between compatible devices and hardware in IoT. The system design of proposed model is as follows: • NUR Modelling: Existing system make use of manufacturing usage description in order to explore the malicious behaviour of any connected node; however, they cannot be used for understanding complex behaviour of an IoT nodes. Hence, NUR model is used for defining the rules of behaviour for an IoT nodes. Figure 2 highlights the basic structure of NUR model which consists of set of rules, name of node, and identity. The block for set of rules is further used in single direction of communication in IoT for framing up access rules for network with respect to ports, essential attributes of packet, and decision. Similarly, for IoT nodes with two-way communication is followed by constructing latent rules of network which basically consists of decision to be formulated and attributes of latent packet. Finally, a set of action is formed for the interacting IoT nodes which generates rules for decision, action, as well as specific form of trigger.

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Fig. 2. Structure of NUR model

• NBR Modelling: This is the next important model in proposed system called as Node Behavioral Report (NBR). The block of trigger in NUR modelling is used by IoT nodes to represent specific behaviour, especially in case of automation. At present, there already a present of smart mobile application which can successfully perform controlling of such devices. Hence, presence of this process can be used for extracting interaction of different device with each other followed by successful formulation of new rules. There are availability of API which is capable of instructing the IoT node to access/process information within them by its authorization. Table 1 highlights the simplified rules of IoT nodes interaction using any form of third parties where the key elements are identity of an event (Event_ID), information of a Device (Info_Device), and type of capability (Type_Capability) Table 1. Sample rule for IoT node interaction Event_ID Info_Device Type_Capability init_Trigger D123 i_Feature Create_action D456 i_Command

Hence, obtaining the rules from existing available API as well as smart application of IoT is not a bigger task.

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• AIS Modelling: Attack Inference System (AIS) is the unique part of proposed modelling which is used to evaluate the reports generated by the smart application in IoT. AIS further allocates a specific index for each terms as well as phrases in the generated report in order to establish a possible chain of connection among each report. Figure 3 highlights one such process of AIS, which is shown in the form of a tree.

Fig. 3. Tree structure of AIS modelling

The above tree structure showcase the possible connection in the presence of light in its turn on condition which when identified about its condition, the system performs actions. The proposed system make use of AllenNLP [40] in order to perform deeper processing of AIS while it make use of Word2Vec [41] in order to find equivalent terms for establishing relationship. Hence, this approach can be used for generating all forms of internal as well as external behaviour of both IoT nodes and its associated communication; where further violations of the ruleset can be used as a threshold to positively identify the malicious behaviour within the network and IoT nodes. • Identification of Intrusion: Prior to highlights the procedure of confirming the intrusion, it is necessary to give a brief about the protocol as well as other latent feature of this model. The proposed system make use of standard DNS in order to perform querying of the domain name of server in IoT device. One beneficial point of using this is – normal resolution of DNS will be restricted to only restricted number of domain names whereas it is feasible for malicious node to randomly resolve as many number of domain names as they want. From the perspective of the latent feature, the proposed study consider number of data packets, duration interval of data packets, and sequence number of data packet.. The beneficial point for selecting duration interval of data packets is that normal IoT nodes always has a limitation to cater up increasing traffic while the malicious node carry out increasing number of link establishment in less time. This sudden increment in number of data packet contributes towards identification of presence of attacker node in ruleset.

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Similar characteristic also suits well for duration interval of data packets where normal IoT nodes will take longer duration while malicious node will perform data transmission over shorter duration. Figure 4 highlights the identification process for intrusion.

Fig. 4. Identification of intrusion

The third parameter of sequence number of data packet also contributes towards identification process. In this case, all the data packers captured by router are clustered followed by obtaining information about time of data packet, connection flag, size of packet, protocol, port, and direction. Further the proposed system mathematically represented as follows: probðiÞ ! f ðhÞ

ð1Þ

Equation (1) represents probability prob computed for all the data packet with specific symbols. This operation is carried out by using a function f(x) that performs gating operation for recurrent neural network [42]. By further appending a threshold T, if the value of prob < T than it will reflect as presence of attacker. The next section briefs about the outcome obtained from this implementation.

6 Results Discussion This section discusses about the result obtained after implementing the proposed system with respect to experimental environment and result accomplished. • Experimental Environment: The proposed experiment is carried out using SmartThing API [43] which is capable of extracting information associated with configured application, connected device information, location information, etc. The study also deploys OAuth2 [44] which is basically a security protocol for

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processing the permission of user when connected to real-time devices. Further applying open-source toolkit of utility in Java, behaviour of each devices can be monitored. Network protocol analyzer can be further used for analyzing the data packets. The experiment also make use of Drools which performs analysis of the ruleset for identifying compliance and violation of IoT Nodes. • Result Accomplished: The analysis of the proposed study is carried out considering two standard performance parameter i.e. attack identification time and cost involved in operation. For an effective analysis, the comparison is carried out from existing standard security approaches used i.e. Datagram Transport Layer Security (DTLS), Elgamal Encryption, Advanced Encryption Standard, and Elliptical Curve Cryptography. The outcomes are analyzed over 8 smart IoT devices, whose identity has been masked programmatically in order to evaluate the success rate of identification of proposed system using SmartThings. For this purpose, the experiment has been carried out using 10 different misbehaving applets in order to introduce attack event in the test scenario. The overall outcomes are shown and discussed as following

Fig. 5. Comparative analysis of attack identification

Figure 5 highlights the comparative analysis of attack identification to showcase that proposed system offers faster detection of misbehaving applets. Elgamal encryption is quite a heavier system owing to usage of managing and controlling digital signatures causing increased identification time. This problem is not there for Elliptical curve cryptography; however the process of extracting the best key from finite field is quite a time consuming process especially when exposed to large number of unidentified attackers in IoT. DTLS is found to be better compared to above two scheme; however, its multi-step handshaking mechanism is quite a massive step in order to secure the communication channel. On the other hand, advanced encryption standard has better hardware acceleration for IoT devices used and hence its response time is

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satisfactory. However, it make use of geometric computational pattern for its key generation, which could further complicate if observation time is increased

Fig. 6. Comparative analysis of cost involved in operation

Due to similar reasons stated for Fig. 5, the proposed system witnessed reduced cost of operation in compared to existing system as shown in Fig. 6. Cost of operation is basically number of resource being utilized to initiate a set of action for establishing link, perform secure data communication, and validate the data. Further, a probability is applied for cost computation which is calculated as favorable cost divided by total cost of operation.

7 Conclusion The contribution of this paper are as follows: i) a simplified process of analyzing malicious node behaviour supportive of practical world scenario has been presented, ii) without using any form of complex security protocol, the approach is quite simpler and efficient to be deployed on real-test environment, iii) it is capable of identifying any form of security threats in IoT unlike existing approaches which has narrowed scope of identification, iv) the ruleset constructed are quite simpler and can be easily customized, and v) usage of semantics for construction of ruleset further offers more novelty. Our future work will be further in direction of optimizing the study outcome.

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Predicting Student Dropout in Massive Open Online Courses Using Deep Learning Models - A Systematic Review Elliot Mbunge1(&), John Batani2, Racheal Mafumbate3, Caroline Gurajena1, Stephen Fashoto1, Talent Rugube4, Boluwaji Akinnuwesi1, and Andile Metfula1 1

2

Department of Computer Science, Faculty of Science and Engineering, University of Eswatini, Kwaluseni campus, P. Bag 4 Matsapha, Manzini, Eswatini [email protected] Faculty of Engineering and Technology, Botho University, P. Bag A7156, Maseru 100, Lesotho 3 Department of Educational Foundations and Management, Faculty of Education, University of Eswatini, Kwaluseni campus, P. Bag 4 Matsapha, Manzini, Eswatini 4 Institute of Distance Education at the University of Eswatini, Kwaluseni campus, P. Bag 4 Matsapha, Manzini, Eswatini

Abstract. Predicting student dropout is becoming imperative in online learning platforms. Before COVID-19, predicting student dropout was systematically done manually. Therefore, there is a need to analyse students’ behaviour, cognitive learning styles, and other metacognitive patterns of learning in real-time from available data repositories to reduce student dropout and subsequently develop robust strategies for instructional design and remedial interventions to enhance student success and retention. In this study, we present a comprehensive review of deep learning models applied to predict student dropout in online learning platforms. In addition, challenges and opportunities associated with online learning are presented in this study. The study revealed that convolutional neural networks, recurrent neural networks, long short-term memory and bidirectional long short-term memory have been predominantly used to predict student dropout using predictors such as course assessments, socio-economic, access to online resources, personal skills and course attributes. However, the study revealed that the psychological state of students was not taken into consideration by many authors, yet it impacts students’ learning outcomes and assists policymakers in providing remedial interventions. Therefore, future work can delve deeper into the integration of psychological attributes such as stress, anxiety, attitude towards studying, student interests and counselling sessions to predict student dropout during disasters and health emergencies. Keywords: Student dropout Prediction


Deep learning
Massive open online courses


© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 212–231, 2022. https://doi.org/10.1007/978-3-031-09073-8_20

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1 Introduction The outbreak of coronavirus disease 2019 (COVID-19) presents unprecedented challenges in education sectors, globally, posing threats to progress made towards inclusive education in the previous decades. On the 30th of January 2020, the World Health Organization (WHO) declared COVID-19, a public health emergency of international concern [1]. Several measures and restrictions such as stay-at-home, social distancing and self-isolation have been implemented to contain the pandemic [2]. These measures and restrictions significantly affect many educational institutions and consequently lead to the temporary closure of schools, colleges, and universities. To ensure that teaching and learning continues, several educational institutions drastically adopted online platforms, video conferencing platforms, massive open online courses (MOOCs) and learning management systems (LMS). These educational technologies have been prominently utilized by educators and to access online modules, interactive videos, learning materials and access tests, discussion forums and holding consultation hours via video conferencing. However, such drastic transformation presents new challenges and uncertainties to parents, students, instructors, and educational institutions. As teaching and learning continue to be conducted online on an untested and unprecedented scale replacing face-to-face classes, students with fragile socio-economic backgrounds, mental health issues and disabilities [3] are at risk of underperforming and dropout. This is exacerbated by various factors such as lack of engagement, participation, poor internet access, digital divide, poor access to online learning resources [4] and computing devices among others. Several works including [5] and [6] state that student dropout is a major issue in higher education in many countries. Students’ final decision to drop out can be predicted by cognitions of withdrawal, including thoughts of quitting, search intentions, performance and dropout intentions. Hence, student dropout is based on a longer-lasting decision-making process. Contribution of the Study Student dropout was systematically done manually before COVID-19 but this comes with lots of challenges. Therefore, there is a need for analysing students’ academic performance, behaviour, cognitive learning styles, and other metacognitive patterns of learning in real-time from available data repositories to reduce student dropout and subsequently develop robust strategies for instructional design [5], and future development of remedial actions [7]. The progression of the accumulated educational data has stimulated the emergence of several research communities to predict students at risk of dropout and provide indicators for optimized policy formulations. Numerous deep learning models have been applied to predict students’ academic performance, however, their application in predicting students at risk of dropping out in online courses during COVID-19 is still in its infancy. In support, [8] highlighted that deploying deep learning techniques to predict students at-risk of dropout during COVID-19 is rather a new area of research. In addition, [9] stated that online learning platforms still face challenges of high dropout rate and prediction accuracy. Students’ dropout rate also becomes a prominent problem if compared to traditional learning. Therefore, this study aimed to provide a comprehensive analysis of deep learning models applied to predict student dropout in online courses to provide timely

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intervention, provide suitable pedagogical support and help stakeholders to promote necessary pedagogical guidelines for students’ retention. The study sought to address the following research objectives: • To analyze the significance of deep learning models for predicting students’ dropout in online learning with the view to overcome the shortcomings of using traditional methods. • To analyze the performance of deep learning models and identify attributes relevant to predict students’ dropout in online learning • To identify research challenges and opportunities for student dropout prediction in online learning platforms in the context of the pandemic. The remaining part of the paper is structured as follows: Sect. 2 presents the methodology adopted to carry out the review. Section 3 discuss the results and findings of the study. Lastly, Sect. 4 presents the conclusion and future work.

2 Methodology The study adopted the Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) proposed by [10] to systematically identify literature on the application of deep learning techniques to predict students’ dropout in online courses. The PRISMA model illustrates the steps required to conduct a systematic literature review. It is predominantly used in healthcare studies [11], therefore, some elements are not applicable in education research [12]. This review followed PRISMA steps namely, source identification, articles screening, eligibility and synthesizing of literature from included studies. 2.1

Source Identification

We identified electronic databases such as Web of Science, Google Scholar, Science Direct, IEEE Xplore digital library, Springer Link, ACM Digital Library and Ebscohost. These electronic databases were chosen as they are relevant to the subject area we intended to study and academically relevant. In conducting a systematic review, a wellplanned search strategy is paramount to guide the literature search and to ensure that all relevant studies are included in the search results [13]. We constructed the search keywords to identify published papers that applied deep learning techniques used to predict students’ dropout in MOOCs (Massive open online courses) from 2018–2021 (December). Therefore, the following keywords were utilized in each electronic database “deep learning” AND “students’ dropout” AND “online learning” OR “MOOCs” OR “Massive Open Online Courses.” We further performed citation chain for each retrieved article to make sure that all relevant articles are considered for screening. Table 1 shows the papers selected from the abovementioned electronic databases.

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Table 1. Initial number of papers from various electronic databases Electronic database Google scholar Science direct IEEE Xplore Springer link ACM digital library Ebscohost

2.2

Number of papers 842 187 115 165 75 29

Articles Selection and Screening

The second phase of the PRISMA model is the screening stage, where only relevant articles are selected based on the inclusion criteria. Papers that directly discusses deep learning techniques applied to predict students at risk of dropout in online learning platforms were included. The criteria used for selecting articles are as follows: First, we excluded opinion pieces, non-peer‐reviewed articles, incomplete articles, and studies in other languages with no English translation. We excluded studies that predict students at risk of dropout using data from the traditional face-to-face classroom. In addition, we also removed articles that applied data analytics, data mining and classical machine learning techniques. We also discarded studies that are not well-presented, contains unclear methodology, dataset, and performance metrics. We also included papers from reputable journals and publishers. 2.3

Source Evaluation and Eligibility Criteria

All authors double-screened all articles for quality assessment and eligibility. At the initial phase, we selected studies by reading the titles and abstracts relevant to the topic. After that, irrelevant studies were removed from the list of selected studies. We then created a list of eligible articles that initially passed the initial phase. In the second eligibility phase, we removed all duplicates. To further check the eligibility of literature, we read the full text and to determine whether the contribution of the study is relevant to the objectives of this study. This assist in further eliminating studies that were not related to the application of deep learning models to predict students’ performance in online learning environments. 2.4

Data Analyses and Synthesizing of Literature

Initially, from the search in the databases, we identified a total of 1413 articles. We removed duplicates, especially articles extracted from Google Scholar and left with 763. After the triage, we further read the titles and abstracts, excluding the repeated ones and those with no relation to the application of deep learning models or techniques to predict student dropout, as well as those without performance metrics such as precision, accuracy, F1-score, recall and receiver operating characteristic curve or area under the curve (AUC). This left us with 209 studies and we further read full articles

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and excluded 85 articles in the eligibility phase. We further analysed the remaining 26 articles and removed 4 articles that were published in non-reputable journals. Finally, 22 journal articles were selected according to the above criteria guided with the PRISMA model, as shown in Fig. 1.

Fig. 1. PRISMA model

The journal or publishers of the selected articles are shown in Table 2. To analyse and synthesize of literature, we considered the deep learning predictive model, performance metrics, variables used and the study limitations. Table 2 shows the publishers where selected articles were published. Table 2. Number of articles published in major journals Journal/Publisher Science direct ACM digital library IEEE Xplore Springer link

Number of papers 3 7 9 3

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From the selected articles shown in Table 3, we identified the period of publication of these articles. The analysis reveals that most of the selected articles were published between 2018 and 2021, most of them in the year 2020 (41%), as shown in Fig. 2.

Fig. 2. Frequency of articles per publication year

Table 3. Summarized description of each study selected for the systematic review Ref Model

Performance

[14] CONV-LSTM AUC: 85% model Precision: 91% Recall: 88% F1-Score: 89% [15] Deep learning AUC: range from 92.8% to 98.1%

[16] Deep learning model

Accuracy: 72.4% AUC: 7.1%

[14] CNN- Long short-term memory

Accuracy: 87.6% Precision: 87.4% Recall: 86.5% F1-Score: 86.9%

[17] Convolutional Accuracy: 92.17% neural network Precision: 76% Recall: 86.36% F1-Score: 80.85% [18] Convolutional Accuracy: 87.64% neural network Precision: 89.38% Recall: 95.79% F1-score: 92.47%

Database

Variables

Limitations

Science direct

Courses, enrolment records and activity logs

The study did not include the interaction patterns of students

ACM digital library

Discussion forums, The model used single multiple choice quizzes, MOOC course data course material clickstream Student’s activity logs The dataset used was small

ACM digital library Science direct

IEEE Xplore

IEEE Xplore

Students’ behavioural characteristics such as study time spent, discussion forums and videos Demographic data, high school results and course activity logs Clickstream log data

The shortage of training the model on largescale datasets limits its use in different MOOC environments The dataset used was limited to 6078 students and a single MOOC course The model temporal dropout prediction and early dropout prediction once sufficiently large amounts of data are obtained

(continued)

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E. Mbunge et al. Table 3. (continued)

Ref Model

Database

Variables

Limitations

[19] Recurrent Accuracy: 89.2% neural network

Performance

IEEE Xplore

[20] Deep learning (DP-CIL) model

Accuracy: 84.71% Recall: 86.53% F1-score: 81.94%

IEEE Xplore

The imbalanced nature of the dataset used in the single MOOC for the experiment. Also, the learner behaviour or interaction with a given content was not included in the study The model did not include withdrawal behaviour from a more nuanced perspective

[21] Hybrid Deep Neural NetworkCNN, SE-Ne and GRU [7] FWTS-CNN model

Accuracy: 94.58% Recall: 97.26% F1-score:96.59% Precision: 95.93%

IEEE Xplore

Clickstream logs of forums, quizzes, assignments, videos, audios, wiki, downloadable files, lecture content pages, announcement, calendar, grade book Course information, video information and interactive information between students and videos Courses information and activity logs

Accuracy:87.1% Precision: 86.3% Recall: 86.5% F1-score: 86.4%

[22] GRU-RNN

Accuracy of 85%

IEEE Activity logs of a XPLORE student accessing objects, discussing, navigating courses, closing pages, trying to solve problems, watching videos, and browsing wikis IEEE Biographical Xplore information, assignment grades, clickstream features from the log that contains all interaction events generated from user behaviours IEEE Activity logs on video, Xplore wiki, discussion, navigate and page close

[23] E- Long short- AUC of 97.02% term memory

[24] CNN-based model

Precision: 84.52% Recall: 84.93% F1-Score: 84.21% AUC: 87.85%

Springer link

The model did not include students’ psychological data

Some of the students in the dataset used in this study did not have a learning period of five weeks, resulting in insufficient valid data that can be put into the experiment The model was only used in students’ endpoints in one course at the end of the semester

The study did not consider behavioural events, further capture heterogeneous information data and integrate different types of behavioural data The model did not Activity records of include assignment students, such as viewing lectures, course submission and videos, reading course performance information, course wikis, participating in forum posts and discussion forums, accessing other course interaction information modules, and closing the web page

(continued)

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Table 3. (continued) Ref Model

Database

Variables

[25] Recurrent Precision: 77% neural network Recall: 76% F1 score: 75% Accuracy: 77%

Performance

Springer link

Clickstream log data from XuetangX

[26] Long shortterm memory

ACM digital library

[27] Deep neural network

Precision: 91.53% Recall: 98.8% F1score: 95.03% AUC: 91.55% Precision: 99.52% Recall: 99.09%

[16] Fully connected deep neural networks

Accuracy: 72.4% Precision: 74.7% Recall: 66.7% AUC: 77.1%

[28] Random Accuracy: 92.73% vector function-link neural network [7] Recurrent AUC: 88.1% neural network

ACM digital library ACM digital library

ACM digital library ACM digital library

[29] Recurrent neural networks

Accuracy: 55.1%

Springer link

[30] CNN and LSTM

AUC: 96.17%

IEEE Xplore

[31] Gated Recall over 90% recurrent units on both datasets (GRUs) and autoencoders

Science direct

Limitations

The dataset used requires further data pre-processing and sophisticated feature engineering techniques Enrolment records and The model needs to be website log files improved due to the high-dimensional and sparse problems No limitations were Clickstream log data such as course attributes noted and study duration Pre-admission data, The model did not assessment scores and consider sociodemographic data economical and psychological data and achieved average performance Course attributes data The model needs to and enrolment data include more deep from China XueTangX learning features and MOOC improve performance Clickstream log data The model did not consider socioeconomical and psychological data Clickstream data such The model did not as assessment scores include hyperand quizzes from parameter tuning to find MOOC the best number of units and layers in the GRU network The model was trained Course data extracted from clickstream log, with a limited dataset KDDCup15 and and it excluded XuetangX psychological data and some important course attributes The model primarily Two datasets on focuses on the common MOOCs – XuetangX scenario of single and KDDCup15 with MOOCs. However, the variables such as epandemic brought some learning activities and time spent were used to new important variables in predicting student test the model dropout

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3 Discussion of Results The analysis of this study is presented in the following ways: (i) deep learning models applied to predict student dropout, (ii) attributes relevant for predicting student dropout, and (iii) challenges and opportunities for student dropout prediction in online learning platforms in the context of the pandemic. 3.1

Predicting Student Dropout Using Deep Learning Models in MOOCs

Deep learning extends classical machine learning by adding more “depth” (complexity) into the model as well as transforming the data using various functions that hierarchically allow data representation, through several levels of abstraction. Deep learning uses techniques to construct a model with multiple layers to learn representations from raw data. This representation learning consists of multiple layers, where each layer transforms the representation to a more abstract form for the next layer [32]. Deep learning models can solve more complex problems efficiently with better classification accuracy as compared to machine learning models [33] because of automatic feature extraction from raw data and massive parallelization. Among other deep learning models, convolutional neural networks, long short-term memory (LSTM), recurrent neural networks, Bidirectional-LSTM and Gated Recurrent Unit- RNN have been predominantly used to predict student dropout in online learning platforms. Convolutional Neural Networks Convolutional neural networks refer to a class of deep neural network algorithms used for computer vision use cases, such as object classification and detection [34]. However, researchers are also using CNN for automatic feature extraction [14,9]. A CNN comprises three layers – convolutional, pooling and fully connected layers [34]. A fully connected (FC) layer is one in which neurons are connected to all the neurons of the previous and subsequent layers [35]. A convolutional layer takes a tensor as input, applies a certain number of kernels, adds a bias, applies Rectified Linear Unit ( ReLU) activation function (for nonlinearity), and produces an output matrix [36]. Kernels are also called filters, and they are square matrices with nk  nk dimensions [36]. Figure 3 is a block diagram for the architecture of CNN. Convolutions use filters to perform convolution operations by scanning the input with respect to its dimensions, using filters and padding.

input

convolution

pooling

convolution

Fig. 3. CNN architecture

pooling

FC

FC

Softmax

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The output matrix size of a convolutional layer is computed as: n¼

n þ 2p  f þ1 s

ð1Þ

where n is the size of the input feature map, p = padding size, f = filter dimension or filter width and s = stride. The ReLU activation function is usually introduced at the convolutional layer to introduce nonlinearity [36]. Using the ReLU activation function, [14] applied CNN to the student dropout prediction, where the output of the convolutional layer is calculated as: X ij ¼ ReLUð

X ðX l1  W lji þ blj ÞÞ i

ð2Þ

i
F j

where F j is the matrix input feature map of time units of the ith layer, X l1 is the feature i map for the output of the convolutional layer l  1. Which is the input of the lth layer. W lji is the matrix used to generate the ith feature map in the lth layer from the feature map generated in the l-1th layer, blj is the bias of the generated jth feature map in the lth layer, and X ij represents the feature map for each output of the convolutional layer [14]. A pooling layer reduces the size of a feature map and dimensions. The common pooling layers include max pooling and average pooling [35]. Both the max and average pooling layers divide a feature map into multiple regions. For each region, the max-pooling layer takes the maximum value to represent the entire region, whereas the average pooling layer takes the average value to represent the whole region. Some researchers applied CNN in student dropout prediction, albeit in combination with other models. For instance, [14] applied a hybrid model including CNN and LSTM and obtained a precision of 91%, recall of 88%, F1-score of 89% and AUC of 85%. Moreover, [5] applied CNN as a hybrid model with Bi-LSTM and static attention and obtained an accuracy of 87.6%, precision of 87.4%, recall of 86.5% and F1-score 86.9%. [17] used CNN to predict student dropout using demographic data, high school results and course activity logs. The model achieved an accuracy of 92.17%, precision of 76%, recall: 86.36% and an F1-score of 80.85%. However, some authors including [5, 7, 14, 21, 24] and [30] used a hybrid model which combines CNN with another deep learning model(s) to predict student dropout. Long Short-Term Memory The Long Short-Term Memory is a deep neural network model proposed to tackle the gradient disappearance problem common in recurrent neural networks owing to long input sequences [5]. The model comprises input and output layers and input, output and forget gates [37]. It works by adding or discarding part of the information by using the ‘gate’ control mechanism before combining the current input, historical memory and historical state to update the memory cell state [5]. The input gate is responsible for controlling the input information of the neural unit at the present moment, the output gate controls the output information of the neural unit at the present moment, while the forget gate controls the historical information stored by the neural unit at the previous moment. The ‘gate’ is a structure used to selectively filter information and comprises

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the sigmoid activation function, as shown in Fig. 4. The sigmoid is a binary activation function, where 0 and 1 represent complete rounding and complete passing, respectively [5].

Fig. 4. The structure of the LSTM [5].

Figure 4 shows the LSTM structure, where xt represents the feature data input at the present t moment, ht symbolizes the state value of the memory cell at the present t moment [5]. The following equations are used to compute LSTM: it ¼ rðwi
½ht1 ; xt  þ biÞ

ð3Þ

ft ¼ rðwf
½ht1 ; xt  þ bf Þ

ð4Þ

Ot ¼ rðw0
½ht1 ; xt  þ b0Þ

ð5Þ

gt ¼ rðwc
½ht1 ; xt  þ bcÞ

ð6Þ

Ct ¼ fc
Ct1 þ it :gt

ð7Þ

ht ¼ Ot
tanh ðCt Þ

ð8Þ

where r(
) represents the sigmoid activation function, wi, wf, wo, bi, bf, and bo represent the weights and biases of the input gate (it), forget gate (ft) and output gate(ot), respectively, from which the output (ht) at the current moment (t) and cell state (Ct) at the current moment (t) is calculated. This model’s popularity in predicting student dropout is increasing as some researchers combine it with CNN to effectively improve dropout prediction accuracy [5, 14]. Some of the researchers who have applied LSTM to student dropout prediction include [14] and [26,30] applied a hybrid model that combines CNN and LSTM to predict student dropout using course data extracted from clickstream log, KDDCup15 and XuetangX and achieved an AUC of 96.17%.

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Bidirectional Long Short-Term Memory (Bi-LSTM) A Bi-LSTM is a sequence processing deep learning model that comprises two LSTMs for input taking, albeit in different directions (forward and backward directions). Using two LSTMs to take input from both directions increases the amount of information to the deep neural network; thus, improving the context available; for instance, in natural language processing, it helps to know what words immediately precede and succeed a word. For instance, Long et al. [38] used Bi-LSTM for sentiment analysis to enhance the sentiment categorization model. However, this model has also been used in predicting student dropout. For example, Fu et al. [5] used Bi-LSTM to extract hidden long memory features of time series from datasets and encode the data in a time series way. Bi-LSTM consists of two recurrent neural networks (RNNs) that take input in both positive and negative (forward and backwards) directions [39]. Figure 5 shows the architecture of the Bi-LSTM model.

Fig. 5. Bi-LSTM architecture [39]

Fu et al. [5] applied Bi-LSTM and CNN and obtained an accuracy of 87.6%, precision of 87.4%, recall of 86.5% and F1-score 86.9%. Recurrent Neural Networks (RNN) RNN is a deep learning model that deals with sequential data, that is, data in which the order is important, such as voice/audio processing or a series of words in a sentence [36, 40]. They allow previous outputs to be used as inputs while hiding the state [41]. RNN applies the same operation on every element of the sequence, as shown in Fig. 6; hence, the word recurrent. RNN can be used, for instance, to predict the next word in a sentence [36].

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Fig. 6. RNN architecture [41]

The output y\t [ and activation function a\t [ for timestamp t are expressed as follows: a\t [ ¼ g1ðW aa a\t1 [ þ W ax x\t [ þ ba

ð9Þ

y\t [ ¼ g2ðW ya a\t [ þ by Þ

ð10Þ

Where W aa ; W ax ; W ya , ba and by are temporarily shared coefficients and g1 and g2 are activation functions. [19] applied RNN to predict student dropout using clickstream logs of forums, quizzes, assignments, videos, audios, wiki, downloadable files, lecture content pages, announcement, calendar and grade book and achieved an accuracy of 89.2%. Also, [25] predicted student dropout using RNN using clickstream log data from XuetangX and achieved a precision of 77%, recall of 76%, F1-score of 75% and accuracy of 77%. Some authors such as [42] and [29] also predicted student dropout using RNN. Gated Recurrent Unit- RNN (GRU-RNN) Unlike recurrent neural networks, gated recurrent units support the gating of the hidden state; implying a dedicated mechanism of when to update or reset the hidden state [43]. As a variant of the long short-term memory, the gated recurrent unit combines the forget and input gates into a single gate, making it simpler than the standard long shortterm memory [44], as shown in Fig. 7.

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Fig. 7. The GRU unit architecture [44]

GRU has registered success in numerous applications, including temporal and sequential data [45]. In GRU, the learning stops when the error converges to a minimal value [44]. The critical structure in the GRU-RNN model is the GRU unit that receives data in sequential order [44]. Below are the formulae used to calculate the output of the different components: Update gate: zt ¼ rðW hz ht1 þ W xz xt þ bz Þ

ð11Þ

r t ¼ rðW hr ht1 þ W xr xt þ br Þ

ð12Þ

Reset gate:

Output layer: ht ¼ zt
ht1 þ ð1  zt Þ
h

t

yt ¼ rðnetty Þ E¼ t

XT t¼1

Et ¼

XT 1 2 ðydt  yt Þ t¼1 2

ð13Þ ð14Þ ð15Þ

where h ¼ tanhðr t W hh ht1 þ W xh xt þ bh Þ; zt ; r t and ht are the outputs of the update, reset and memory gates, respectively, at time t; xt and ht1 denote the input and output vectors of the memory module at time t and t  1, respectively; W hz and W hr are weight matrices at time t  1, bz , br and bh are biases for the update and reset gates t andh , respectively. Qiu et al [24] applied GRU-RNN to the student dropout problem and obtained over 80% prediction accuracy. Prenkaj et.al [31] applied GRU-RNN to predict student dropout using two datasets from XuetangX MOOC and KDDCup15

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and the model achieved recall over 90% on both datasets. Also, [46] applied GRURNN to predict student dropout using demographic data, assignment grades, clickstream features from the log that contains all interaction events generated from user behaviours and the model achieved an accuracy of 85%. 3.2

Attributes Relevant for Predicting Student Dropout

After synthesizing literature, this study revealed that different studies applied various attributes to predict student dropout in online courses. These influential factors are categorized into course assessments (scores), socio-economic, access to online resources, personal skills, course attributes and psychological attributes, as shown in Fig. 8. Personal skills such as academic skills to use the available online platforms, students’ abilities and prior experience with online courses are imperative in predicting student dropout in smart learning platforms such as MOOCs [14]. More importantly, personal characteristics such as cognitive learning styles, learning behaviours [24] and learning skills and prior professional experiences [20], as well as psychological attributes are also the most distinctive dropout factors among students in online courses, as shown in Fig. 8.

Internal assessments Socio-economic data

Personal skills Attributes relevant for predicting student dropout in online courses

Access to online resources

Couse attributes Psychological data

Fig. 8. Attributes relevant for predicting student dropout

The findings of the study revealed that socio-economic information such as family economic data such as expenditure, student’s income data, family characteristics, occupation, and size [47] as well as demographic data such as gender, age, disability, race or ethnicity, parents’ education [8] and religion are important predictors of predicting student dropout. Moreover, several studies including [24, 26] and [29] shows that clickstream logs forums, quizzes, assignments, videos, audios, wiki, downloadable files, course attributes such as course design, content complexity are important predictors of student dropout in online learning platforms. Also, study duration, assessment score [7] and submission are found to be relevant in predicting student dropout. Moreso, academic emotions, especially anxiety, academic procrastination [6] and students’ commitment to online courses contribute significantly to their academic performance.

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Research Challenges and Opportunities for Student Dropout Prediction in MOOCs During the Pandemic

The adoption of online learning during the pandemic brought unprecedented opportunities and challenges to boost research works, principles, policies, prototypes, theories, ethics, improve online course design, pedagogical methods, and instructional design. Studying online through online learning platforms has its own advantages such as self-paced learning, interactivity, and flexibility. For example, intelligent MOOCs can monitor students’ learning progress and their learning cognitive styles which subsequently assist instructors and management to design learning content tailored as per students’ needs. This presents significant opportunities to review the process of digital transformation in learning institutions, designing more scalable and personalized online learning models, designing of online learning model that will reduce the workload on the instructors, and redesigning the learning process and policies. Despite the opportunities posed by the pandemic, online learning presents new challenges and uncertainties to parents, students, instructors, and higher institutions of learning. Teaching and learning during the COVID-19 pandemic have been a difficult process coupled with many challenges which consequently lead to student dropout. Some students from fragile socioeconomic backgrounds experience challenges such as poor learning environments and insufficient financial support. Furthermore, the study established that huge costs of technology infrastructure, lack of digital skills, digital divide [48], poor network connectivity, high internet cost, and unavailability of computing devices significantly affect students’ participation in online courses. In some countries, learning blended learning strategies have been initiated, requiring learners to have access to radio, television and the internet, vulnerable families are faced additional economic burdens further widening the inequality gap [49]. Moreso, students with mental health issues, and those that abuse substances abuse require counselling services that might be difficult to access remotely. Students with poor time management, lack of assertiveness and training may require proper guidance as well as robust and resilience support systems and policies. The drastic change of teaching methods to embrace innovative pedagogical methods such as online course design, change of assessment methods, course delivery method affects students’ cognitive learning styles and outcomes. This is orchestrated by the lack of training of students and instructors on how to conduct online classes and designing course materials that incorporate learning principles in technology-mediated environments [50, 51]. Emerging challenges such as teenage pregnancies and pandemic-related anxiety negatively affect student academic performance and consequently risk dropping out [52]. In addition, [53] alluded that the complexity and nature of the course, the flexibility of the learning environment, timing and lack of experiences as well as learning skills are some of the major contributing factors to student dropout in online learning platforms [54].

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4 Conclusion and Future Work Predicting student dropout on time provides tremendous benefits to management, instructors, students, and policymakers. The study reveals that deep learning techniques play a significant role to predict student dropout, monitoring students’ cognitive learning styles to facilitate pedagogical content knowledge, instructional designs and remedial interventions promptly. Deep learning models such as convolutional neural networks, recurrent neural networks [55], long short-term memory have been predominantly used to predict student dropout to enhance student’s success and retention. These models use various performance predictors such as course assessments, socioeconomic, access to online resources, personal skills, course attributes and psychological attributes. However, the study revealed that the psychological state of students was not taken into consideration by many authors, yet it impacts students’ learning outcomes during the pandemic. Online learning is not immune to challenges despite the opportunities it offers to instructors, students and policymakers. The study revealed that online learning faces emerging challenges such as poor learning environments and insufficient financial support. In addition, huge costs of technology infrastructure, lack of digital skills, digital divide, poor network connectivity, high internet cost and unavailability of computing devices. To alleviate these challenges, there is a need to develop educational policies, prototypes, and theories to improve online course design, pedagogical methods, and instructional design that addresses students’ needs during the pandemics like COVID-19. Future work will delve deeper into the integration of psychological attributes such as stress, anxiety, student interests and counselling sessions to predict student dropout as teaching and learning progresses.

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Methods of Evaluation of Substrate Radioactive Contamination Using Unmanned Radiation Monitoring Complex I. A. Rodionov and A. P. Elokhin(&) National Research Nuclear University MEPhI, Moscow, Russia [email protected]

Abstract. The study examines methods of evaluation of substrate radioactive contamination based on two scenarios. In the first one the contamination analysis is performed as per geophysical model of the atmosphere surface layer, while radioactive impurity transfer, that causes the substrate contamination, is calculated as per turbulent diffusion model. The second scenario includes analysis of the substrate radioactive contamination of random nature due to anthropogenic reasons, and it is performed using unmanned radiation monitoring complex (URMC). The latter allows to significantly reduce a human direct involvement in territory radiation monitoring. The study is focused on unmanned aerial vehicles flight program (height, route, etc.), radiation monitoring complex structure, URMC mathematical support, mathematical models for evaluation of the atmosphere meteorological parameters behind the model of surface layer and radioactive impurity propagation in atmosphere. #CSOC1120. Keywords: Radiation monitoring
Radioactive contamination
Environment
Substrate
Unmanned aerial vehicle
Unmanned radiation monitoring complex

1 Introduction Practice of traditional forecasting methods application, if there are fixed (project) sources of radiation hazard (stack effluents, spray ponds emissions, etc.), has generally proved its efficiency [1]. However, the experience gathered from emergencies at various nuclear facilities (Mayak Production Association, Chernobyl Nuclear Power Plant, Fukushima Nuclear Power Plant, etc.) has showed the necessity of development (modernization) and/or update of the existing systems with a new - remote (noncontact) method of the environment radiation control [2]. For instance, the Fukushima nuclear disaster exposed a certain shortage of traditional methods of radiation recording using automated radiation monitoring system. At the moment of radioactive release [3] the persons, responsible for decision on evacuation and hazard assessment, did not have the complete information - both qualitative and on geographical distribution of impurity of radionuclides [4–6], since control stations were damaged (23 of 24) by seismic sea wave [3]. In such situations, when there is a necessity to evaluate the level of radiation contamination of a site or facility, there are two ways of proceeding: traditional © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 232–253, 2022. https://doi.org/10.1007/978-3-031-09073-8_21

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sampling method, with direct involvement of human, performing the location radiation assessment, and prospective - non-contact, using, for instance, unmanned radiation monitoring complex (hereinafter referred to as URMC), usage of which would reduce the risk of irradiation of personnel, performing search and exploration works, with radiation doses exceeding the threshold limit values. When using the traditional way, there are some difficulties - existence of places or areas with large radiation doses for humans, that means the impossibility of staying there for a period of time sufficient for measurements performing. This circumstance, specifically the lack of the detailed landscape and radiation anomalies (“hot spots”) location map, significantly complicates the planning of radiation accidents remedial action procedures [7]. However, in case of unsanctioned release into atmosphere at nuclear facilities, i.e. if the release source location geometry is known, the scenario is applied, under which the evaluation of radioactive contamination of substrate and air is performed using air-mass transport geophysical models, among which the simplest one is the atmosphere surface layer model of Leihtman D.L. [8]. Radioactive impurity spatial distribution is calculated by using the turbulent diffusion equation with meteorological parameters, determined when solving the equations, describing the specified geophysical models. These meteorological parameters define the basic nature of radioactive impurities transfer in environment.

2 The Main Part During nuclear plant design in the specified region the series of researches of this region characteristics are preliminary performed to minimize the consequences in case of any emergencies. These studies include meteorological researches. These studies are performed for several years in the selected region with met masts with meteo-sensors installation along its perimeter for observation and recording the speed of wind and its variation nature, precipitation, pressure, ambient air and substrate temperature, etc. [9– 11]. Results of these studies are summarized in table (see Table 1), for analysis of which the air-mass transport geophysical model is used, allowing to select the most characteristic weather conditions inherent for this region. By taking the most characteristics parameters (wind speed, temperature) for this region and time of these parameters observing from the table, it is possible to significantly simplify its analysis and, using the atmosphere surface layer model, obtain the parameters, defining the state of air environment, that is called the atmosphere stability state. Under the atmosphere surface layer model, these parameters are surface layer scale L (Monin–Obukhov scale) and airflow dynamic velocity v .

24.83

17.64

11.23

19.48

24.73

16.88

9.95

19.52

Summer 8:00 p.m Autumn 8:00 p.m Winter 8:00 p.m Spring 8:00 p.m *The parameter 274.51

221.15

221.46

Jordan [9] Wind direction at height of 10 m (degr.) 242.77

3.03

2.28

2.32

3.73

Wind speed at height of 10 m (m/s)

19.39

12.21

18.58

24.75

Temperature at height of 58 m (°C)

is calculated using quadratic extrapolation method: y = ax2 + bx + c.

of NPP area in Temperature at height of 10 m (°C)

Averaged meteorological data Season Time Temperature at height of 1.5 m (°C)

276.31

219.48

234.72

Wind direction at height of 58 m (degr.) 267.67

Table 1. Averaged meteorological data of NPP area in Jordan [9].

4.79

3.09

3.99

6.25

Wind speed at height of 58 m (m/s)

942

946

944

Average pressure value (kPa) 939

19.53

9.69

16.73

* Temperature at height of 0 m (°C) 24.71

234 I. A. Rodionov and A. P. Elokhin

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3 Mathematic Model of Evaluation of Meteorological Parameters, Defining the Atmosphere Stable State Under Surface Layer Model The atmosphere surface layer model is based on measurements of wind speed u(z), potential temperature h(z) (hðzÞ ¼ T ðzÞð1000=PðzÞÞ0:29 , where T(z)―measured temperature K, P(z)―atmospheric pressure mbar) and, basically, airflow humidity, measured at two levels, differentiated in height z (see the Table). Also, the ground level temperature T0 should be additionally measured, considering that wind speed at ground level is always zero. Difference of wind speed Du = u(z2) − u(z1) and temperature Dh = h(z2) − h(z1) at a priori set parameter L, under the atmosphere surface layer model, is used for determination of dynamic speed v , using the following relations pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 0 00 [8–11]: v ¼ v
Du=Dun ; v ¼ v ðgL=T0 ÞðDh=Dhn Þ, where v = 0,4―Karman constant; g―free-fall acceleration; un, hn, Dun, Dhn―dimensionless wind speed, temperature and their difference respectively, depending on dimensionless height zn = z/L, 0 00 i.e. on surface layer parameter L. Since v and v present the same value, it is evident that at some L* their difference or relative error e will be close to zero, if equations for un(zn), hn(zn) are known:  lim

L!L

Du Dun

lim e ¼

L!L

2

n

   Dh   Tg0 L Dh  ¼ 0; n

ðDu=Dun Þ2  jðgL=T0 ÞðDh=Dhn Þj ðDu=Dun Þ2

\!endarray [ o 100 = ¼ 0

ð1Þ

When these conditions are met (when solving this task the minimum e is simply determined in some cases), this means that the required parameters: surface layer scale L* and dynamic speed v are determined. This allows to find the equations for dimensionless values of airflow traveling speed un(zn), temperature hn(zn) and its other characteristics, determined under the atmosphere surface layer model, defining the intermediate parameter y as function of zn from Ferrari equation (Korn G., Korn T. Mathematical handbook: For scientists and engineers, M.: Nauka. 1968, p. 720) [8]:  Zn ¼ 2=y  2y3 3  4=3;

ð2Þ

analytic solution of which is as follows [9, 10]. rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 8 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffii h pffiffiffiffiffiffiffiffi > 2 AþB >  A þ B þ ð A þ B Þ4  ðA þ2 BÞ þ 3 > 2 > < ; Zn   4=3; 2 rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi y¼ q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi h i > pffiffiffiffiffiffiffiffi 2 > > > A þ B þ ðA þ BÞ4 A þ2 B ðA þ2 BÞ þ 3 : ; Zn   4=3; 2

ð3Þ

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where vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u u u u 2 4 2 3 3 tð2 þ 1:5 Zn Þ tð2 þ 1:5 Zn Þ ð2 þ 1:5 Zn Þ ð2 þ 1:5 Zn Þ4 þ 64 þ  64 þ A¼ ;B ¼ : 2 4 2 4

Values un(zn), hn(zn) and other dimensionless characteristics in the atmosphere surface layer model are defined by the following equation system [8]: un ¼ 2=y þ 2arctð yÞ þ ln

j1  yj þ c1 ; 1þy

ð4Þ

kn ¼ 1  y4 ;

ð5Þ

bn ¼ y 2 ;

ð6Þ

ZZn hn ¼ Z0 n

dzn ; aT k n

ð7Þ

where c1―constant, defined at boundary condition at roughness level z0, i.e. u(z)|z = z0 = 0; kn(zn)―dimensionless turbulence diffusivity coefficient; bn(zn)―turbulent fluctuations energy, significant in impurity atmospheric cross dispersion; aT(zn) = kT/ k―ratio of turbulence factors for heat and momentum [8]. With the determined above mentioned un, hn, bn, kn their dimensional values are defined with the equations: uðzÞ ¼ un v =v; k ðzÞ ¼ kn vLv ; bðzÞ ¼ v2 c1=2 bn ¼ 4:6625 v2 bn

ð8Þ

Solution of task (1)–(8) was performed in studies [9–11] for the corresponding meteorological conditions of regions for countries, in which NPP construction was planned. Solution results for stable and unstable states of atmosphere are presented in Fig. 1, 2, 3, 4, 5, 6, 7 and 8. Solution of the specified task (1)–(8) starts with selection of the atmosphere surface layer parameter L, for determination of which some value is set for Lmax * 100 m and varied, for instance, Li = DLi, i = 1,2,3,…,N; DL = Lmax/N, N * 100 until the difference or relative error, specified in formula (1), is minimum. The determined value of L*, under which e is minimum, defines the required parameter L: L* = DLi*. Defining the scale L and recalculating zn at specified z1 and z2, i.e., thus 0 recalculating Dhn; Dun, the dynamic speed v ¼ vDu=Dun is determined. Under the atmosphere surface layer model the value of L can have different signs (L > 0, L < 0), corresponding to its stable or unstable state respectively, therefore generally the current value of parameter Li is defined the following way: Li = DL(2N + 1 − i). With this determination Li > 0 for i < 2N + 1 and Li < 0 if i > 2N + 1. Area i = 2N + 1 vastly differs on diagrams from the required area and is not considered (see Fig. 1).

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The presented results of calculation clearly demonstrate the nature of dependence of meteorological parameters u(z); k(z); b(z), determined under the atmosphere surface layer model and defining the impurity transfer in air environment, from atmosphere stability state.

Fig. 1. Dependence of relative error ei(i) at stable state of atmosphere Autumn, 20.00. Li = 39 m, m* = 0.376 m/s.

Fig. 2. Dependence of relative error ei(i) at unstable state of atmosphere. Spring, 20.00. Li = −5 m, m* = 0.763 m/s.

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Fig. 3. Dependence of wind speed U(y) as a function of height at stable state of atmosphere. Autumn, 20.00. Li = 39 m, m* = 0.376 m/s.

Fig. 4. Dependence of wind speed U(y) as a function of height at unstable state of atmosphere. Spring, 20.00. Li = −5 m, m* = 0.763 m/s.

Methods of Evaluation of Substrate Radioactive Contamination

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Fig. 5. Dependence of turbulence diffusivity coefficient k(y) as a function of height at stable state of atmosphere. Autumn, 20.00. Li = 39 m, m* = 0.376 m/s.

Fig. 6. Dependence of turbulence diffusivity coefficient k(y) as a function of height at unstable state of atmosphere. Spring, 20.00. Li = −5 m, m* = 0.763 m/s.

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Fig. 7. Dependence of turbulent fluctuations energy b(y) as a function of height at stable state of atmosphere. Autumn, 20.00. Li = 39 m.

Fig. 8. Dependence of turbulent fluctuations energy b(y) as a function of height at unstable state of atmosphere. Spring, 20.00. Li = −5 m.

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4 Mathematical Model of Radioactive Impurities Transfer in Environment Under the stationary model of turbulent diffusion (at zero airflow cross speed) the volumetric activity is defined by solution of equation, in which the impurity cross dispersion is performed as per Gauss’s law [8]:  .  Sðx; zÞ qðx; y; zÞ ¼ pffiffiffiffiffiffi exp y2 2 r2y ; 2pry ð xÞ

ð9Þ

where ry ð xÞ - root-mean-square deviation; function S(x, z) is defined with equation: Zþ 1 Sðx; zÞ ¼

Zþ 1 qðx; y; zÞdy ¼ 2

1

qðx; y; zÞdy:

ð10Þ

0

Thus, for volumetric concentration of gas-aerosol impurity the following equation is used: u where uð x; zÞ ¼



@S @S @ @S w ¼ k ðzÞ  rS þ u; @x @z @z @z

ð11Þ

 f ð x; y; zÞdy ¼ Mdð xÞd z  hef ; f = Md(x)d(y)d(z-hef)―source of

þR1 1

gas-aerosol impurity, contaminating the environment; M―radioactive impurity release rate (Bq/s) from the source; hef―effective release height, that is a sum of geometrical height of source H0 and increment Dh, caused by radioactive impurity jet rise in atmosphere due to its initial speed and temperature, if the latter is higher than environment temperature (hef = H0 + Dh); r―radioactive gas-aerosol impurity relaxation constant, that is an impurity washout constant r0 (s−1), therefore r = r0; w―gravitational impurity precipitation rate. Under the examined transfer model the value r2y ð xÞ  is presented as: r2y ð xÞ ¼ bx2 =u2 1 þ axb=ku (Under other models, describing impurity transfer in atmosphere, this dependence can be presented in different way (see, for instance, “Rostechnadzor Order” No. 458 dated 11.11.2015 “Recommended Methods of Parameters Calculation which are Necessary to Prepare and Establish Permissible Limits of Radioactive Substances Discharges into Atmosphere”)), where u; k; b―values of the above mentioned parameters, averaged with respect to surface layer with a height Hsurf  100 n, a = 0.015. 1 u¼ Hetc

ZHetc 0

1 uðzÞdz; k ¼ Hetc

ZHetc 0

1 kðzÞdz; b ¼ Hetc

ZHetc bðzÞdz: 0

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Boundary condition are defined with equations: Sðx; zÞjx¼0 ¼ 0;

ð12Þ

Sðx; zÞjx!1 ¼ 0;

ð13Þ

Sðx; zÞjz!1 ¼ 0;

ð14Þ

 @S k  ¼ ðb  wÞSjz¼z0 @z z¼z0

ð15Þ

where: b―rate of dry deposition of gas-aerosol impurity on substrate; z0―substrate roughness level. Thus, with the functional dependencies of airflow transfer parameters, determined under the atmosphere surface layer model, in the turbulent diffusion mathematical model the radioactive impurity transfer can also be examined, considering on a first approximation that it has the same disperse nature. Analytical solution of task (11)–(15) with constants is described with Eq. (16), while volumetric activity of gas-aerosol radioactive impurity, propagating in atmosphere, is directly calculated as per formula (9).  

M r0 x w2 x w z  hef Sðx; zÞ ¼ exp  þ þ 2 u 4ku 2k   9 8 2 2 exp  ½z þ hef u=4kx þ  ½zhef u=4kx > > > > ffiffiffiffiffiffiffi p  > > > > > > pkux > > >

> > >   2 = < ð2bwÞ ð2bwÞðz þ hef Þ 2bw kx  exp  þ ; u 2k ku 2k > > > > " # > > > >  qffiffiffiffiffiffiffiffiffiffi > >  > > z þ hef Þ ðp > > ffiffiffiffiffiffiffiffi > > kx u þ erfc 2bw ; : 2k 2 kx=u

ð16Þ

The observed solution is of the most interest for analysis of axial and cross distributeons of volumetric activity of radioactive impurity in atmosphere at small height from substrate of * 0.5–1.5 m at various distances from emission source. By selecting such height you can be sure that the calculated concentration of radioactive impurity will be on the substrate after some period of time, thus creating the surface activity. The nature of axial and cross distributions at propagation of radioactive impurity depending on type of atmosphere stability state is presented in Figs. 9, 10 [11].

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Fig. 9. Axial distribution of volumetric activity at unstable (1) and stable (2) atmosphere states

Axial distribution of impurity shows that depending on atmosphere stability state the maximum of propagation curves is shifted along axis (see Fig. 9). Under the same release the maximum of volumetric activity at unstable state is closer to the source, while its value in maximum carry over in wind direction at atmosphere stability state. With increase of distance x from the source the picture significantly changes: at unstable state the volumetric activity rapidly drops due to cross (Gaussian) dispersion (see Fig. 10(a), (b)), therefore its amplitude value at bigger distances from the source will be significantly lower than at stable state.

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Fig. 10. Cross distribution of volumetric activity at unstable (1) and stable (2) states of atmosphere at distance of 2500 m (a) and 4000 m (b) at z = 1.5 m.

Specifics of impurity transfer at both atmospheric conditions also form the radioactive contamination of substrate, which isolines distribution nature is presented in Fig. 11 and Fig. 12 [11].

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Fig. 11. Isolines of radioactive pollution of the under-line surface at a steady condition of the atmosphere.

Fig. 12. Isolines of radioactive pollution of the underline surface at an unstable condition of the atmosphere.

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The presented distributions show that, depending on type of stability state of atmosphere, the nature of radioactive contamination of substrate significantly changes, posing certain requirements for the first scenario, under which the sequence of actions, directed to analysis of radioactive contamination of substrate, at unsanctioned radioactive impurities release into atmosphere, is examined: 1. Determination of meteorological parameters of atmosphere; 2. Performing calculations under existing models of impurities transfer in environment and their software. At the same time it should be borne in mind that calculation methods proved their worth for analysis of radioactive contami-nation at distances from a source, characterized with constant or slightly varying roughness level, since change of latter results in significant disturbance of airflow speed in lower layers of atmosphere [12]. But since situation with slightly varying roughness level at long distances is unlikely, the calculation data at long distances result in higher error, that can be reduced by introduction of correcting coefficient k, defined by ratio of kп = D′meas/D′calc (measured dose rate D ′meas to its calculated value D′calc), thus correcting the calculation results by multiplying them by this coefficient. For these purposes it is ad-visable to use unmanned radiation monito-ring complex (URMC), since such vehicle can evaluate dose rate both from contaminated substrate and in any point in the air, which coordinates are set.

5 Radiation Situation Measurement System Using URMC Main URMC parts are carrier (quadcopter, helicopter, etc.), unmanned aerial vehicle (UAV), containing radiation-monitoring equipment (c-radiation dose rate detector, cspectrometer). Also the equipment for auxiliary functions can be installed at UAV: determination of location coordinates - sensors (GPS and/or GLONASS), height detector or air pressure detector for determination of flight altitude, video camera, etc. In terms of UAV operation the most important is its design feature - flight time limitation due to fuel storage or electric battery operating capabilities (for quadcopters), which significantly differs for various UAV, as well as their cost, and varies, for instance, from 25 min to 5 h (INDELA-SKY). With payload increase the flight time reduces due to fuel shortage. Thus, with these limitations there is a task to select the optimum flight path, and solution of this task will allow to perform a comprehensive analysis of vast areas with minimum time and fuel spent [13]. At the time, there are many experiments have been performed with URMC of various type. The main focus of researchers was on its equipment used to solve various tasks. For instance, to evaluate the level of contamination of an area with radioactive impurity the sensitive radiometer or dose rate sensor can be sufficient. If we are talking about composition of radionuclides, contaminating the substrate, the equipment, measuring the c-radiation spectrum, through energy of which the required characteristics are determined, should be installed at UAV. The task is complicated depending on the goal set: simply to evaluate the radioactive contamination of substrate of the required area or to give the comprehensive information on this area applicability for its further use in commercial activity. Solution of the latter task requires application of URMC, equipped with various radiation monitoring equipment and devices for real-time data transmission (through radio channel) into analytic center for its further processing [9].

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In study [14] the scintillation crystal of bismuth germanate Bi4Ge3O12 (BGO), which c-radiation resolution with energy of 662 keV (137Cz) is *9.0―9.8%, was used for observation of natural anomaly, caused by high content of natural radionuclides. This scintillant is more efficient than NaI(Tl) due to lack of hygroscopicity and high density (7.13 g/cm3), therefore the main contribution to absorption of X-ray photons with energy in the range of 50–200 keV in BGO is made by photoeffect (contribution of Compton effect and Rayleigh scattering at Ec < 100 keV is small compared to photoeffect). Among other domestic-made (Institute of Solid State Physics RAS) efficient scintillants there is a scintillant based on LaBr3:Ce crystal, which 137Cz cradiation energy resolution is *3%, density is 1.44 times higher than NaI(Tl) density, de-excitation time is 12.5 times lower than NaI(Tl) de-excitation time and is 16 ns, while light yield is 1.7 higher than NaI(Tl) light yield. Disadvantages include hygroscopicity and radio activity because natural lantanium includes isotope 138La57 (T½ = 1.35
1011 years, Ec1 = 1.4354 meV, with quantum yield m1 = 67.9% and Ec2 = 0.7879 meV, m2 = 32.1%), which c-radiation contribution can add a certain error at low-background measurements of the observed environments or materials (Fig. 13).

Fig. 13. INDELA-SKY UAV, INDELA KB (Belarus) (1), INDELAOGD-20HIR system (2), consisting of four integrated modules (thermal camera, colour camera, laser ranging device, inertial module), is a basic operational load for INDELA-I.N. SKY UAV for solving traditional tasks of exploration, observance and monitoring.

Compared to presented characteristics of crystals of above mentioned cspectrometers, radiation characteristics of xenon c-spectrometer with inert xenon gas as a medium, that fills the ionization chamber at pressure of 30–40 atm, look significantly better. Indeed, the xenon c-spectrometer - xenon c-detector (XGD) during operation does not require low-temperature cooling, unlike germanium semiconductor detectors, has sufficiently high 137Cz c-radiation energy resolution (1.7–2%) due to high atomic number of xenon Z = 54 and, as mentioned, high density of its gas in operation chamber [15]. This detector can operate in temperature range of

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−20―+180 °C; has a high radiation resistance for neutron flux of *1012 n/cm2. Efficiency of c-radiation recording can be significantly improved by means of chamber volume increase, but, on the other side, it can be considered as a disadvantage. It should be noted that XGD capability to record spectral content of c-radiation of radioactive impurity, propagating in atmosphere or contaminating substrate, along with dose rate measuring, provide full capability to evaluate radionuclides composition, contaminating some environment, assess radiation exposure of personnel and population and obtain other radiation characteristics, defining ecological and radiation safety of environment. After getting an insight into scenarios of evaluation of radioactive contamination of environment as a result of radiation emergency at nuclear facility, mathematical model for its radioactive contamination evaluation and calculation methods, as well as its radiation control special means, which are unmanned radio-controlled vehicles equipped with spectrometric and radiation monitoring equipment, intended for remote (without direct contact of human with radioactive materials) radiation control, let’s examine the topic of UAV routes optimization with “hot” spots indication. It should be noted that the issues, related to URMC route optimization, are currently not studied enough. For that purpose we will examine the second scenario, mentioned earlier. The scenario implies that radioactive contamination of substrate can be random in this case, thus not allowing to apply the above mentioned mathematical methods for evaluation of such radioactive spots, that, for instance, are presented in Fig. 14 and numbered.

Fig. 14. Illustration of URMC route under the task of route optimization considering fuel saving.

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In this case the evaluation of radioactive contamination of substrate using URMC should be performed in two stages. At the first stage, for obtaining the overall picture the vehicle should be risen as high as possible, but without c-detectors sensitivity loss, and it should record the coordinates of these “spots”. At the second stage the all possible routes should be mapped for URMC between the “spots”, but only with straight lines, connecting the “spots” in such a PN j way, that any path, consisting of many sections, presents a finite length line: Lj ¼ j¼1 li;j , where li,j―straight sections of the path, sum of which will define their overall length Lj; Nj―integer number, defining number of basic sections. Such way of the task solving is presented in Fig. 14 with two (blue and red) options of path selection: L1 = l0.1 + l1.2 + l2.3 + l3.4 + l4.5 + l5.6 + l6.0; L2 = l0.1 + l1.6 + l6.5 + l5.4 + l4.3 + l3.2 + l2.0. Since grid is set, the distances li,j of these routes are easy to find. Then, with the known average flight speed of UAV Vav it is easy to determine the time, that will be spent for a flight for each of the paths Tj = Lj/Vav, and with specified fuel consumption per hour of flight (K, kg/h) the total weight of fuel spent for a path Lj is determined as: Mj = K
Tj, where j = 1, 2,…M, M  1. By selecting the lowest of the determined values of Mj, we define the optimum route, for which the flight task should be set. It is clear that with increase of number of radiation spots on substrate the second scenario of its scanning is the most practical one, since the number of connections between the spots increases. At the same time the average or optimum scanning height hD, on which the detailed measurements of radiation characteristics are possible in case of radioactive contamination of substrate, in this case can be 60 m as per recommendations from studies [2, 9]. Thus, by selecting the minimum value of Mj, the optimum solution of transport task can be determined, allowing to minimize financial expenses on fuel purchasing and time, required for making decisions during mitigation of radiation emergency consequences. Other method of route selection, presented in Fig. 15, was proposed in study [7] in Japan. The “snake”-type path was selected as a route (grey line in Fig. 15). Clearly, this route is also preliminary, since it allows to cover sufficiently large area at scanning its radioactive contamination, but with selection of this route such RMC parameters as its sensitivity, scanning radius and optimum flight height were not considered. Therefore the proposed method can not be considered as optimum. In studies [7, 14, 16] the value of 9–12 m was often used as flight height, but this is not always true, since there can be obstacles above this value on URMC route. It should be noted that study [14] includes measurements on a height of 40 m, but, as authors noted, readings of RMC, made based on BGO, were close to background readings in this case. The latter, most likely, reflects low value of substrate activity. In such cases it is necessary to simply reduce the substrate scanning flight height. At substrate scanning the first question that arises is related to reasoning of scanning height and error due to scanning radius limitations. In studies [2, 9, 17], where URMC is presented as a complex, containing xenon c-spectrometer and c-detector of UDMG type, for solving these tasks the following recommendations are offered: optimum flight height hD is 40–60 m; URMC scanning radius is related to height hD with a ratio of R = 3hD and requirement of uniform distribution of surface contamination over area S ¼ pR2 . The latter can be met with error of 13%, that is lower than dosimetry error (15–20%). Considering these recommendations the proposed “snake”-type route can be modernized in such a way, for a distance l between two closest straight routes to be not more than 2R (see Fig. 16).

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Fig. 15. “Snake”-type route of URMC. Japan. Radiation control in the area of Fukushima_1 NPP.

Fig. 16. “Snake”-type route at random and anthropogenic radioactive contamination of substrate.

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6 RMC Readings Calibration As shown in [7, 14], RMC readings have a significant drawback - lack of dependence on scanning height, thus restricting the possibility of the specified measurements applying for direct evaluations of surface activity of radioactive contamination. This problem solution was proposed in studies [2, 9, 17] and based on rather simple principle of comparison of the measured dose rate, generated by radioactive contamination of substrate, and its calculated value, presented as analytical formula, containing the scanning height hD and surface activity v(x0, y0), that depends on observence point, as a constant value, where x0, y0―observence point coordinates. Requirement of uniform distribution of activity surfaces in a circle with diameter of 2R presents a possibility to obtain the simple formula for radionuclides concentration, that on dedicated area is defined as: 0 Dtot ðx0 ;y0 Þ
pi 8 9 vðx0 ; y0 ; Ei Þ ¼ < R1 = 1 R pffiffiffiffiffiffiffiffiffiffi exp½lðEi ÞU

exp½lðEi ÞU

ai 2 þ1 UðEi ;pi Þ dU dU þ exp l ð E Þh ð b 1 Þ m ½

i ef i 1bi U U pffiffiffiffiffiffiffiffi :hef ; h m2 þ 1 ef

where: UðEi ; pi Þ ¼ 2p
1:458
105

N X

0

cðEi ÞEi vðEi ÞbðEi ÞwðEi Þpi ; Dtot ðx0 ; y0 ; hD Þ:

i¼1

c―radiation dose rate, recorded by c―detector at height hD; pi―radionuclide weight, defined by equation: , N X pi ¼ ½aðEi ÞAðEi ÞDðEi Þ=vi

½aðEi ÞAðEi ÞDðEi Þ=vi

i¼1

a(Ei)―coefficient, characterizing the energy dependence of xenon spectrometer, defined experimentally as per monolines of c-radiation A(Ei); D(Ei)―amplitude and peak width at half height, measured at its half-height, of processed spectrum; m(Ei) ―quantum yield of energy of c-radiation of radionuclide; c(Ei), l(Ei)―coefficients of absorption and linear attenuation of c-radiation; b(Ei)―energy sensitivity of c-detector, defined experimentally; m―non-obligatory integer number; ai, bi―dimensionless parameters of Berger formula, that depend on gamma-radiation energy [18]; w(Ei) ―correction coefficient.

7 Conclusion The study presents the analysis of methods of radiation control of substrate in case of its radiation contamination. Analysis is performed under two scenarios, one of which is based on conditions of radioactive contamination, caused by radiation emergency at nuclear facility, and it allows to use methods and equipment of radiation control system of this facility, including automated radiation monitoring system equipment, its

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meteorological equipment (atmosphere meteorological parameters measuring devices, met mast, etc.) and software (physical and mathematical), allowing to obtain the comprehensive information on weather stability classes during initiation and progression of radiation emergency, ultimately allowing to obtain the complete picture of radioactive contaminations isolines calculation on location map and evaluation of radiation exposure of personnel and population. The latter provides the nuclear facility administration with information for making decisions to mitigate and minimize the radiation emergency consequences at facility. Under another scenario of radiation emergency, caused by random anthropogenic reasons for its initiation and development, the task solving was examined in two stages with usage of UAV equipped with radiation monitoring and spectrometric devices. At the first stage the radioactive spots coordinates were defined using GLONASS and/or GPS systems, and at the second stage their analysis was performed using URMC and mathematical (software) means as an application to these systems. The study shows that under the second scenario the optimization of route is required during selection of flight route, resulting in necessity of linear programming problems instruments use during optimization of fuel used by UAV.

References 1. Sosnovsky, R.I., Levin, I.M., Raou, D.F.: Efficiency of hybrid monitoring of atmospheric radiation pollution. At. Energ. 71(3), 244–249 (1991) 2. Elokhin, A.P., Zhilina, M.V., Parkhoma, P.A.: The method of the Contactless Estimation of Radioactive Pollution of the underlying Surface in the Trace of Radioactive Emission. Izvestiya Vuzov. North Caucasian region. Technical science. Special issue, pp.137–145 (2010) 3. Stohl, A., et al.: Atmos. Chem. Phys. 12, 2313–2343 (2012) 4. Omoto, A.: Nuclear Instruments and Methods in Physics Research Section a: Accelerators, Spectrometers, Detectors and Associated Equipment (2013) 5. Nuclear Accident Independent Investigation Commission. The Official Report of the Fukushima Nuclear Accident Independent Investigation Commission. NAIIC, Tokyo (2012) 6. Povinec, P.P., Hirose, K., Aoyama, M.: Fukushima Accident. Elsevier, Boston (2013) 7. Yuki, S., et al.: Remote detection of radioactive hotspot using a Compton camera mounted on a moving multi-copter drone above a contaminated area in Fukushima. J. Nuclear Sci. Technol. 57(6), 734–744 (2020) 8. Leihtman, D.L.: Physics of the Boundary Layer of the Atmosphere, p. 340. Hydromet Publishing House, Leningrad (1970) 9. Elokhin, A.P.: Methods and Means of Radiation Monitoring Systems of the Environment: Monograph, p. 520. NRNU MEPhI, Moscow (2014) 10. Alalem, E.A., Elokhin, A.P., Ksenofontov, A.I., Fedorov, P.I.: Meteorological Characteristics for the NPP site in Jordan. Glob. Nuclear Safety 3(24), 19–34 (2017) 11. Elokhin, A.P., Alalem, E.A., Ksenofontov, A.I.: Meteorological Condition of the Bushehr NPP area. Glob. Nuclear Safety, Iran 4(25), 23–47 (2017) 12. Elokhin, A.P., Zhilina, M.V., Kholodov, E.A.: Influence of changes in underlying surface roughness on the formation of the track of radioactive pollution of the surface. Meteorol. Hydrol. 5, 69–79 (2008)

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13. Linear and non-linear programming. Under the general. Lyashenko, M.N. (ed.) Higher School, Kiev, p. 372 (1975) 14. Šáleka, O., Matolína, M., Grycb, L.: Mapping of radiation anomalies using UAV miniairborne gamma-ray spectrometry. J. Environ. Radioactivity 182, 101–107 (2018) 15. Ulin, S.E., et al.: Compressed Xenon c-ray spectrometers for the detection and identification of radioactive and fissile materials. Probl. Electromech. 114, 43–50 (2010) 16. Kaliberda, I.V., Bryukhan, F.F.: Remote measurements of radiation contamination of territories using an unmanned dosimetric complex. Vestnik MGSU 4, 186–194 (2012) 17. Elokhin, A.P., Zhilina, M.V., Parkhoma, P.A.: Peculiarities of Scanning of the Underlying Surfact with an Pilotless Dosimeter complex. Atomic Energy 107(2), 103–112 18. Mashkovich, V.P., Kudryavtseva, A.V.: Protection Against Ionizing Radiation, p. 496. Directory. Energoatomizdat, Moscow (1995)

Co-evolutionary Self-adjusting Optimization Algorithm Based on Patterns of Individual and Collective Behavior of Agents Sergey Rodzin, Vladimir Kureichik, and Lada Rodzina(&) Southern Federal University, Taganrog 344090, Russia [email protected], [email protected]

Abstract. The article describes a co-evolutionary self-tuning algorithm for solving global optimization problems. The algorithm simulates the selfish behavior of herbivores attacked by a herd of predators. Search agents are controlled by a set of attractive search operators based on patterns of individual and collective behavior of agents, as well as mechanisms of population selection in the “prey-predator” system. Agents move in the space of solutions to the optimization problem using a set of operators imitating various types of behavior, including selfishness. The proposed co-evolutionary self-adjusting algorithm allows not only simulating multiple types of selfish behavior, unlike most competing algorithms. The algorithm includes computational mechanisms to maintain a balance between the rate of convergence of the algorithm and the diversification of the solution search space. The algorithm's performance is analyzed using a series of experiments for the problems of finding the global minimum in a set of 5 known test functions. The authors have compared the results with seven competing bioheuristics for indicators such as the average best-to-date solution, the median best-to-date solution, and the standard deviation of the current best solution. The accuracy of the proposed algorithm turned out to be higher than that of competing algorithms. Nonparametric proof of the statistical significance of the results obtained using the Wilcoxon signed-rank test allows us to assert that the results of the co-evolutionary self-tuning algorithm are statistically significant. Keywords: Co-evolutionary algorithm
Global optimum
Agent
Behavior pattern
Premature convergence
Variety of solutions
Test function
Wilcoxon test

1 Introduction 1.1

Background

Optimization is a pressing issue in the following areas: pattern recognition, robotics, computer networking, information security, engineering design, data mining, finance, and the digital economy. Many different approaches have been proposed to solve a wide range of actual problems of search engine optimization because of the intensification of research aimed at developing optimization tools. Bioheuristics is one of the most popular approaches. These are algorithms inspired by nature. An example is the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 254–266, 2022. https://doi.org/10.1007/978-3-031-09073-8_22

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intelligent collective behavior of many animals living in social groups (herd, colony, swarm, flock). Researchers are studying and adapting collaborative behavior models as frameworks for solving complex optimization problems. These are well-known algorithms for optimizing a swarm of particles (PSO), ant (ACO) and bee (ABC) colonies, bats (BA), crows (CSA), cuckoos (CS), fireflies (FA), flower pollination (FPA), gray wolves (GWO), krill school (KHA), light-flying moths (MFO), spider colonies (SSO), group of whales (WOA), bacterial chemotaxis (BFO), school of fish (FFS), jumping frogs (SFLA), etc. [one]. However, they have certain disadvantages, such as premature convergence, difficulties in overcoming local optima when searching for a global optimum. The reasons for this kind of problem can be different. For example, in performing bioheuristics, the population of solutions quickly loses its diversity, or, conversely, a slow convergence is observed [2]. In this case, the addition of attractive operators and procedures can help balance the convergence rate of the algorithm and the diversification of the solution search space. Many animals were living in a collective exhibit trait of cooperative behavior. However, this is not necessarily true for every individual living in society. In contrast to the hypothesis that social behavior is based on mutual benefit for the entire collective, the idea from the selfish herd theory proposed by W. Hamilton [3] seems to be an alternative. According to this theory, the actions of an individual within the collective (herd) are distinguished by a certain degree of selfishness, especially in a situation where the herd is in danger of being attacked by predators. The selfish herd theory states that both the individual and the pack can benefit from the decisions made by the individual in the herd. The article proposes a co-evolutionary self-adjusting optimization algorithm that simulates the behavior of an herb of herbivores attacked by a school of predators, according to the theory of W. Hamilton. The algorithm is compared with competing methods.

2 Methods 2.1

Proposed Methodology

Hamilton suggested that selfish behavior is that everyone in the herd tends to reduce its chances of being caught by a predator. Therefore, dominant and more assertive animals tend to get central positions in the aggregation, with a low probability of becoming a victim of predators. Individuals located on the periphery of the herd are in greater danger of being attacked by defenders than individuals situated in the center. Subordinate and weaker animals are forced into riskier positions. Predators attack the nearest prey, which, as a rule, is located on the periphery of the aggregation. Several factors can affect the chosen directions of movement, including the initial spatial position of the herd, the population density in the herd, the strategy of the predator's attack, and vigilance. Particularly in a group escape situation, the safest position is in front of the herd. In this case, the most likely targets for the predator are the slower members of the herd.

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There are many examples of selfish herd behavior in nature. One of the most widely studied examples is herds of wildebeests and zebras at predation risk [4]. 2.2

Theoretical Basis

The model assumes that the space for finding solutions is an open plan with a herd of animals living there. The algorithm simulates two different search agents: a flock of herbivores and a pack of predators that hunt for prey. Both types of search agents are individually driven by other heuristic operators based on behavioral aspects observed in the prey-predator relationship. Let us consider the main steps of the algorithm. Population Initialization. The first step of the iterative algorithm is a random initialization of the population H from N herbivores and carnivores: H = {h1, h2,…, hN}. Each ith individual is represented as an n-dimensional vector hi = [hi,1, hi,2,…, hi,n], which is a possible solution to the optimization problem. Individuals of the population are initialized using a random uniform distribution within a pair of predetermined decision space boundaries:
  rand ð0; 1Þ
xhigh  xlow h0i;j ¼ xlow j j j

ð1Þ

where i = 1..N, j = 1..xjlow и xjhigh – lower and upper bounds of the solution space, rand(0, 1) is a random number from the interval [0, 1]. The whole population of animals H divided into two groups: P = {p1, p2,…, pNp} is herbivorous herds and R = {r1, r2,…, rNr} is pack predators (Np – number of herbivores, Nr – number of predators, Np + Nr = N). In nature, the number of animals in a herd usually exceeds the number of animals in a pack of predators. In the algorithm Np randomly selected in the range 70–90% from N: Np = floor(N⋅rand(0.7, 0.9)). Vitality Assessment. Everyone in an herbivore or a herd of predators, depending on its ability to survive, has a chance to survive an attack or succeed in prey, respectively. In the algorithm, everyone hi is assigned the value of the survivability function SFhi, which estimates its chances of survival relative to its current position in the decision space. This estimate depends on the distance relative to the safest and most risky place, which are currently known to all members of the population in the context of the global optimization problem, are represented by the current best and worst solutions found for a given iteration of the algorithm. The vitality score for everyone is calculated as follows: SF hi ¼ ðf ðhi Þ  f best Þ=ðf best  f worst Þ

ð2Þ

where f(hi) is the value of the fitness function relative to the position of the individual hi, fbest and fworst are the best and worst fitness function values found by the algorithm, which are determined as follows:

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f best ¼ max ðð max ðf ðhi ÞÞÞ Þ

ð3Þ

j2f0::kg

i2f1::Ng

j

f worst ¼ min ðð min ðf ðhi ÞÞÞ Þ j2f0::kg

i2f1::Ng

ð4Þ

j

where k – is the current iteration of the algorithm. Herbivore Herd Model. The herbivore population includes the leader, a group of individuals seeking a position closer to the center of the herd, and individuals that move relatively independently of the pack due to the herd's current internal structure and movement patterns. The herd is led by a leader, an individual with extraordinary survival abilities. During an attack by a predator, the herd leader performs the critical task of choosing a path of retreat or a strategy that all other herd members should use. On the other hand, individuals moving with the pack, trying to reduce predation risk, tend to place other individuals between themselves and attacking predators. At each iteration k, the algorithm determines the only individual pL among the population of the herd as the leader of the selfish herd (P), considering the values of vitality: pkL ¼

pki 2 Pk jSF pki


 ¼ max SF pkj j2f1::N p g

! ð5Þ

The leader has the highest vitality value. Selfish individuals within the herd tend to increase their chances of survival in the event of an attack by a predator, seeking to take a safer position within the herd in relation to the nearest neighboring pkci, which is defined as follows: pkci ¼ ðpkj 2 Pk ; pkj 6¼ ½pki ; pkL jSF pkj [ [ SF pki ; r i;j ¼ min ðk pki  pkj kÞ j2f1::N p g

ð6Þ

where ri,j – Euclidean distance between neighboring individuals of the herd at the kth iteration. The most interesting behavior observed in a population is the decision of individuals to either follow the movement of the herd or leave it and move independently of it. The decision-making criteria underlying such behavior are closely related to the degree of safety of each individual during the attack of predators and the position of the individual in the herd. The algorithm uses decision-making operators that consider the unique survival possibilities of each individual of the herd. As a result, the population P is divided into two subgroups: a subgroup of individuals remaining in the herd PF and a subgroup of deserters, PD. In the algorithm, these subgroups are determined at each iteration k depending on the current survival value as follows: PkF ¼ fpki 6¼ pkL jSF pki  randð0; 1Þg

ð7Þ

PkD ¼ fpki 6¼ pkL jSF pki \randð0; 1Þg

ð8Þ

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Individuals with a higher vitality value have a higher chance of following the herd, in contrast to individuals with a low vitality, the likelihood of desertion of which from the herd is increased. In most cases, the risk of becoming prey increases among individuals at the periphery of the herd and decreases towards the center of such aggregation. In the algorithm, the herd population center is determined as follows: X Np XNp pKM ¼ ð i¼1 SFpki
pKi Þ=ð j¼1 SFpkj Þ

ð9Þ

The same as for (9), the best position of attacking predators is determined: rkM ¼ ð

XNr i¼1

SFrki
rKi Þ=ð

XNr

SFrkj Þ

j¼1

ð10Þ

Herbivore Movement Operators. We use various heuristic operators to simulate the movement of herbivores in the algorithm: the movement of the leader and the operators of the movement of other individuals, including deserters. Individuals in a herd seek to increase their chances of surviving a predator attack by moving so that another individual is between them and the attacking predators. This behavior is modeled as follows: npi ;pj ¼ SF pj
ekpi pj k

2

ð11Þ

where ||pi – pj|| is the Euclidean distance between individuals pi and pj. The value (11) in general form can be calculated for any pair of individuals of the herd, however, the algorithm uses several specific directions of movement: • movement of the individual pi to the leader of the herd npi ;pL ¼ SF pL
ekpi pL k

2

ð12Þ

• movement of an individual pi to the nearest best neighbor npi ;pc ¼ SF pci
ekpi pci k

2

i

ð13Þ

• movement of an individual pi to the center of the herd population pM 2

npi ;pM ¼ SF pM
ekpi pM k

ð14Þ

• movement of the individual pi to the safest position known by the entire aggregation npi ;pbest ¼ SF pbest
ekpi pbest k

2

ð15Þ

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and which is defined as f ðxbest Þ ¼ f best

ð16Þ

where fbest is calculated according to (3). In addition, it should be borne in mind that predators are the main source of danger for herbivores. Therefore, their presence affects the movement of individuals of the herd. This factor is considered in the algorithm as follows: gpi ;rM ¼ SF rM
ekpi rM k

2

ð17Þ

where SFrM is the value of survivability according to (10). The value in (17) is determined between the individual pi and the center of the pack of predators rM, if any individual of the herd will always try to get away from all attacking predators as far as possible. The herd leader occupies the safest position in the herd, but this does not necessarily mean that such an individual is completely protected from predators. At the same time, the leader can exhibit several different types of leadership behavior. For example, in the algorithm, the position of the leader at the next iteration is updated as follows: ( pkL þ 1

¼

pkK þ ck ; if SF pkL ¼ 1 pkK þ sk ; if SF pkL \1

ð18Þ

where ck and sk are leader motion vectors. The top rule of movement chosen by the leader of the herd assumes cooperative leadership, where the leader of the herd is in the current best position and directs the movement of the herd  so that it is potentially beneficial to the herd. Equation (2) k implies that if f pL ¼ f best , then SF pkL ¼ 1. This achieves the removal of the flock from the attacking predators. The motion vector ck is defined as follows:   ck ¼ 2
a
gkpL ;rM
rkM  pkL

ð19Þ

where gpi ;rM is defined according to (17), a is a random number in the interval [0, 1]. On the other hand, if SF pkL \1, т then the leader of the herd chooses a selfish direction of movement, seeking to reduce risk and trying to move to the safest position. His motion vector sk is defined as:   sk ¼ 2
a
nkpL ;xbest
xkbest  pkL

ð20Þ

where nkpL ;xbest is defined according to (15). According to (7) and (8), in its turn, individuals of herd P is divided into a subgroup of PF remaining in the herd and a subgroup of deserters PD, depending on the current value of survival. Taking this into account, at each iteration k of the algorithm, the position of each individual is updated as follows:

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ð21Þ where fki is the vector of movement of individuals within the herd; dki is the vector of movement of deserters. ( f ki

¼¼

  2
ðb
nkpi ;pL
pkL  pki þ c
nkpi ;pci
  2
d
nkpi ;pM
pkM  pki ; if pki 2 Pks  
pkci  pki ; if pki 2 Pkd

ð22Þ

where b, c, d are random numbers from the interval [0, 1].   dki ¼ 2
ðb
nkpi ;xbest
xkbest  pki þ c
ð1  SF pki Þ
br

ð23Þ

where xbest is defined accordingly (15), br is a unit vector indicating a random direction of movement. Operators of Movement of a Flock of Predators. Individuals within the herd are safer than single individuals, and it is because the herd's move makes it difficult for a predator to attack a particular individual. However, this level of safety of individuals within the pack depends on their current position. The predator's position relative to the herd is also an essential factor in deciding whether to attack a particular herd. The algorithm simulates the movement of a flock of predators R both considering the survivability of an individual, depending on its position in the herd, and from the distance of an individual of the herd to predators. Initially, it is assumed that each pj of the herd P can be pursued by a predator ri with a certain probability: ð24Þ In (24), xri ;pj means the attractiveness of prey pj for the predator ri. This value considers the position of the individual in the herd, the distance from the individual of the herd to the predators and is calculated using the following formula: xri ;pj ¼ ð1  SF pj Þ
ekri pj k

2

ð25Þ

where SF pj is the value of the vitality of the individual pj, ||ri – pj|| is the Euclidean distance between prey and predator. This means that predators prefer to attack weaker

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and closer prey. Taking this into account, at each iteration k of the algorithm, the pursuit by a predator of a weaker and closer prey is simulated: rki þ 1 ¼ rki þ 2
q
ðpku  rki Þ

ð26Þ

where q is a random number from the interval [0, 1]. The Operator of the Attack of the Predator on the Prey. Let us define the area of danger for an individual of the herd in the form of a circle of an area of a finite radius RAD: RAD ¼

Xn j¼1

jxlow  xhigh j=ð2
nÞ j j

ð27Þ

where n is the number of individuals (for simplicity, the algorithm assumes that the radius of the danger area is the same for all individuals). Before the attack begins, set of victims Vic = ∅. During an attack, it is assumed that the distance between ri and pj is less than or equal to the radius of the RAD, and more than one individual of the herd may be threatened. For each predator ri, the set of potential prey is defined as: n o Qri ¼ pj 2 PjSF pj \SF ri &kri  pj k  RAD&pj 62 Vic

ð28Þ

where SF pj and SF ri are the values of the survival rate of the prey and the predator, respectively, kri  pj k is the Euclidean distance between prey and predator. As soon as for a particular predator ri, according to (28), a set of potential prey Qri is identified, then one of them is selected. In the algorithm, such a decision is made based on the following probability: X ð29Þ P ri ;pj ¼ xri ;pj = ðp 2Q Þ xri ;pm m

ri

where pj 2 Qri , xri ;pj is the attractiveness of prey according to (25). Applying the roulette wheel selection method, according to (29), one of these individuals pj is selected, which is then considered killed by the attacking predator ri and fits in a set of Vic. Note that if Qri ¼ £, then predators do not hunt. Decisions corresponding to the individual of the herd killed during the attack are excluded from this decision space and replaced by new ones using a special crossingover operator. Crossover Operator. In nature, the size of populations of carnivores and herbivores changes dynamically over time. In balanced biosystems, such changes are periodic, and the population is recovering. Therefore, the algorithm includes a computational procedure that allows the population to be restored using the crossing over operator of herd individuals. First, a subset of individuals for crossing over is formed:

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C ¼ fpj 62 Vicg

ð30Þ

The probability of participating in crossing depends on the vitality of the individual and is defined as: X Cpj ¼ SF pj = p 2C SF pm ð31Þ m

j

Individuals with higher survivability values will have a higher chance of generating new offspring solutions. To generate a new descendant solution, randomly, using a roulette wheel, n individuals are selected from the set C, considering the probabilities Cpj according to (31). Using this procedure, we restore the herd population after the attack of predators. 2.3

Co-evolutionary Self-adjusting Optimization Algorithm: Discussion

We represent the co-evolutionary self-adjusting (COSA) algorithm as a general sequence of steps: Step 1. Random population initialization H from N animals, according to (1). Step 2. Population H divided into a herd of herbivores P = {p1, p2,…, pNp} and a flock of predators R = {r1, r2,…, rNr}. Step 3. The vitality estimate for each individual in the population is calculated P and R, according to (2–10). Step 4. Operators (11)–(23) are applied to each individual these are the operators of the movement of the herbivore P. Step 5. Operators (24)–(26) are applied to each individual of the flock these are the operators of the movement of the flock of predators R. Step 6. Estimates of survivability are recalculated for each individual in populations P and R. Step 7. We use operators (27)–(29), that is, the operators of the attack of the predator on the prey. Step 8. A crossing-over operator is performed to restore the population, according to (30) and (31). Step 9. If the stopping criterion is met, then the algorithm stops; otherwise - return to step 4. Finding a balance between the convergence rate of an algorithm and the diversification of the solution search space is an open research problem that is important for ensuring the accuracy and performance of optimization algorithms. Diversification of the solution search space refers to the ability of search agents to visit completely new regions of the search space. At the same time, the convergence of the algorithm emphasizes the ability of these agents to refine the currently known “good” solutions. The balance between them largely depends on the identified patterns of individual and collective behavior of agents, as well as on the mechanism of population selection used, on the attractiveness of operators for finding optimal solutions. For example, in the case of PSOs, crawlers usually aim for the position of the current best agent. It forces the entire population to concentrate on the best solution

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now, contributing to premature convergence [2]. In addition, most swarm bioheuristics use search agents with the same properties and behavior patterns. Under such conditions, the operators of these algorithms lose their attractiveness, do not allow improving the diversity of the population and expanding the search space for optimal solutions. Unlike competing bioheuristics, the COSA algorithm simulates different search agents (predators and prey) with different individual behaviors. In other words, COSA represents a multi-algorithmic approach and is a co-evolutionary algorithm. In addition, it includes self-adjusting computational mechanisms that allow maintaining a balance between the rate of convergence of the algorithm and the diversification of the solution search space: selfish herd behavior, the unique internal social structure of agents, predation, and population recovery mechanisms.

3 Experimental Results and Discussion The COSA algorithm was applied to optimize the 5 test functions presented in [5, 6], and the results of which were also compared with the results obtained using competing bioheuristics of the particle swarm (PSO) [1], bee (ABC), and firefly ( FA) of colonies [7, 8], differential evolution (DE) [9], genetic algorithm (GA) [10], crow algorithm (CSA) [11], moths flying into the light (MFO) [12]. Finding the global minimum for each of the test functions to be optimized is a difficult task. Description of test functions is presented in the Table 1. Here n is the dimension of the function, Bn is the variation interval of the variables xi, X* is the optimal solution, fi(X*) is the minimum value of the function. Rosenbrock's “banana function” has a large, slowly decreasing plateau. The global minimum of the function is inside a highly elongated parabolic surface. The Schweffel function is multi-extreme with an “unpredictable” global minimum. The Zakharov function has no local minima, except for the global one. Salomon, Qing functions are continuous, multi-extremal, differentiable. Table 1. Test functions for experiments. f name Rosen-brock

Test function fi(X) f 1 ðXÞ ¼

nP 1 i¼1

Schwef-fel

f 2 ðXÞ ¼

 2 ½100 xi þ 1  x2i þ ðxi  1Þ2 

2 n P i P ð xj Þ

xi 2 Bn

n

Min

½5; 10n

30

f1(X*) = 0, X* = (1..1)

½100; 100n

30

f2(X*) = 0, X* = (0..0)

½5; 10n

30

f3(X*) = 0, X* = (0..0)

½100; 100n

30

f4(X*) = 0, X* = (0..0)

½500; 500n

30

 pffiffiffi pffiffiffi f5(X*) = 0,X ¼  1 ::ð nÞ

i¼1 j¼1

Zakharov

f 3 ðXÞ ¼

n P

ðxi Þ2 þ ð

i¼1

Salo-mon

n P

n P

4

0; 5ixi Þ

i¼1

sffiffiffiffiffiffiffiffiffiffiffi! sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n n P P f 4 ðXÞ ¼ cos 2p x2i þ 0; 1 x2i þ 1 i¼1

Qing

2

0; 5ixi Þ þ ð

i¼1

f 5 ðXÞ ¼

n P i¼1

ðx2i  iÞ

2

i¼1

To search for solutions using the algorithms COSA, PSO, ABC, FA, DE, GA, CSA, MFO, N = 50 individuals were selected, and the maximum number of iterations T = 1000. Such a stopping criterion was chosen to maintain consistency with other similar works on which are currently reported in [13].

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The parameter settings in competing algorithms are as follows: • learning coefficients of the algorithm PSO are defined as c1 = 2 and c2 = 2, weight w decreases linearly from 0,9 to 0,2 with increasing number of iterations; • parameters of FA algorithm are a = 0,2 and c = 1,0; • differential weight in the algorithm DE is F = 1, crossing over probability CR = 0,2; • in GA crossover and mutation probabilities CR = 0,8 and MP = 0,2 accordingly; • in CSA the probability of awareness and the flight length of the crow are set to AP = 0,1 and fl = 2 accordingly; • in MFO a constant used to simulate the logarithmic spiral motion of moths, b = 1; • in COSA Np = floor(N⋅rand(0.7, 0.9)), other individuals are predators. These parameters in the above works were determined experimentally as the best for each of the 5 optimized test functions fi(X) [14]. The COSA algorithm was compared against 7 competing algorithms for the following metrics: average best solution so far, median best solution so far, and standard deviation from the best solution so far. The average results corresponding to 30 separate runs are shown in Table 2. In each cell of the table, the mean, median solution, and standard deviation from the best solution now are indicated, respectively. Minimum results for each function are in bold. Table 2. Results of comparing PSO, ABC, FA, DE, GA, CSA, MFO algorithms with the COSA algorithm, averaged over 30 runs with a population size of N = 50 and the maximum number of iterations T = 1000. f1(X) 7.30⋅1001 2.00⋅1002 1.00 1002 ABC 2.20⋅1001 6.80⋅1001 3.10⋅1001 FA 5.70⋅1002 2.40⋅1003 1.60⋅1003 DE 2.99⋅1001 2.65⋅1002 1.19⋅1002 GA 7.05⋅1001 7.18⋅1001 4.46⋅1001 CSA 3.10⋅1001 2.89⋅1002 1.17⋅1002 MFO 1.60⋅1002 8.24⋅1002 3.57⋅1002 COSA 2.40⋅1000 3.70⋅1001 4.10⋅1001

PSO

f2(X)

f3(X)

f4(X)

f5(X)

1.00⋅10−04 1.50⋅10−03 1.20⋅10−03 2.30⋅1000 1.40⋅1001 8.60⋅1000 9.70⋅10−04 2.60⋅10−03 9.80⋅10−03 9.52⋅10−09 8.93⋅10−09 5.30⋅10−09 2.37⋅10−01 9.74⋅10−01 3.97⋅10−01 2.14⋅1001 2.07⋅1002 9.20⋅1001 8.85⋅1001 8.61⋅1002 5.03⋅1002 2.90⋅10−09 5.50⋅10−09 2.30⋅10−09

1.50⋅1002 4.80⋅1002 1.60⋅1002 2.30⋅1002 2.90⋅1002 2.90⋅1001 7.30⋅1001 4.10⋅1003 2.20⋅1001 1.63⋅1002 1.63⋅1003 2.75⋅1002 2.80⋅1002 3.17⋅1002 1.63⋅1002 3.89⋅1001 3.49⋅1001 1.97⋅1001 1.31⋅1002 1.25⋅1003 3.35⋅1002 1.80⋅1001 2.30⋅1001 6.40⋅1000

5.00⋅10−01 1.70⋅1000 2.90⋅1000 3.10⋅1000 4.00⋅1000 4.80⋅10−01 2.30⋅1000 3.60⋅1000 6.10⋅10−01 4.11⋅1000 4.01⋅1000 3.01⋅10−01 5.67⋅10−01 6.00⋅10−01 8.02⋅10−02 1.66⋅1000 1.65⋅1000 3.19⋅10−01 7.08⋅1001 8.83⋅1001 4.02⋅1001 1.57⋅10−01 2.84⋅10−01 6.37⋅10−02

7.60⋅10−04 1.40⋅10−01 3.00⋅10−01 1.87⋅1001 5.94⋅1001 2.08⋅1001 7.60⋅1001 6.20⋅1002 3.60⋅1002 8.97⋅1002 9.03⋅1002 2.81⋅1002 9.20⋅10−02 3.89⋅10−02 1.78⋅10−01 1.61⋅1001 1.14⋅1001 1.28⋅1001 1.08⋅1001 2.18⋅1001 1.68⋅1001 5.30⋅10−04 9.62⋅10−02 3.17⋅10−02

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In most cases, the results obtained by the COSA algorithm on the considered test functions are superior to the results of competing algorithms. It is due to the balance achieved between the algorithm's convergence rate and the diversification of the solution search space. A nonparametric proof of the statistical significance of the results was carried out using the Wilcoxon rank-sum test [15] for independent samples found by each of the compared algorithms on 30 test runs. Significance level 5%. The value T < 0.05 was considered adequate evidence against the null hypothesis, which is rejected, and the proposed algorithm outperforms the competing one. The experimental results obtained allow us to assert that the results according to the COSA algorithm are statistically significant and that they did not happen by chance.

4 Conclusion A co-evolutionary self-adjusting COSA algorithm is proposed for solving global A coevolutionary self-adjusting COSA algorithm is proposed for solving global optimization problems. The algorithm simulates the selfish behavior of herbivores attacked by a herd of predators. Search agents are individually controlled by a set of different attractive search operators based on the patterns of individual and collective behavior of the agents and the used mechanisms of population selection observed in the preypredator relationship. The proposed algorithm uses two types of search agents: herbivorous herds (prey) and predators that hunt them. Depending on their type and internal social structure, each agent moves in the space of solutions to a given optimization problem using a set of operators imitating various kinds of behavior, including selfish ones. Unlike most competing algorithms, COSA allows simulating multiple types of selfish behavior and includes computational mechanisms to maintain a balance between the convergence rate of the algorithm and the diversification of the solution search space. The performance of the COSA algorithm was analyzed using a series of experiments for the problems of finding the global minimum in a set of 5 known test functions. The results were compared with seven competing bioheuristics for indicators such as the average best solution now, the median best solution now, and the standard deviation of the best solution now. The COSA accuracy turned out to be higher than that of competing algorithms. The nonparametric proof of the statistical significance of the results obtained using the Wilcoxon signed-rank test allows us to assert that the results according to the COSA algorithm are statistically significant and did not happen by chance. Acknowledgements. The study was performed by the grant from the Russian Science Foundation № 22–21-00316, https://rscf.ru/project/22-21-00316/ in the Southern Federal University.

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References 1. Rodzin, S., Skobtsov, Y., El-Khatib, S.: Bioheuristics - theory, algorithms and applications, monograph, Cheboksary, publishing house “Sreda”, 224 p. (2019) 2. Wang, H., et al.: Diversity enhanced particle swarm optimization with neighborhood search. J. Inf. Sci. 223, 119–135 (2013) 3. Hamilton, W.: Geometry for the selfish herd. J. Theor. Biol. 31(2), 295–311 (1971) 4. Orpwood, J., et al.: Minnows and the selfish herd: effects of predation risk on shoaling behavior are de-pendent on habitat complexity. Animal Behav. 76(1), 143–152 (2008) 5. Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009) 6. Sergienko, A.: Test functions for global optimization, Krasnoyarsk: SSAU, 112 p. (2015) 7. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Math. Comput. 8(1), 687–697 (2008) 8. Yang, X.-S.: Flower pollination algorithm for global optimization. In: Durand-Lose, J., Jonoska, N. (eds.) UCNC 2012. LNCS, vol. 7445, pp. 240–249. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32894-7_27 9. Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997) 10. Mitchell, M.: An Introduction to Genetic Algorithms, 162 p. MIT Press, Cambridge (1996) 11. Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. J. Comput. Struct. 169, 1–12 (2016) 12. Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. J. Knowl. Syst. 89, 228–249 (2015) 13. Shengqi, J., et. al.: Elite opposition-based selfish herd optimizer. International Conference on Intelligent Information Processing, pp 89–98 (2018) 14. Fausto, F., et. al.: A global optimization algorithm inspired in the behavior of selfish herds. J. BioSystems 160, 39–55 (2017) 15. Wilcoxon, F.: Individual comparisons by ranking methods Frank Wilcoxon. Biometrics Bull. 1(6), 80–83 (2006)

Use of Machine Learning to Investigate Factors Affecting Waste Generation and Processing Processes in Russia Yu V. Frolov1, T. M. Bosenko2(&), and M. D. Mironova1 1

2

Institute of Digital Education, Moscow City Pedagogical University, 28, Sheremetyevskaya Street, Moscow 129594, Russia National Research University “Moscow Power Engineering Institute”, 14, Krasnokazarmennaya Street, Moscow 111250, Russia [email protected]

Abstract. The article presents the results of a study of statistical data, during which using machine learning methods, socio-economic factors that significantly affect the share of recycled waste in Russia were studied. The assessment of waste processing (treatment, disposal and disposal) activities is proposed to be carried out using the index of W1 - the share of recycled waste in their total mass. Socio-economic factors that have the greatest impact on the level of recycled waste have been identified. Results of cluster analysis, regression analysis and neural network modeling on statistical data from the field of waste management are compared. #CSOC1120. Keywords: Machine learning methods
Regression
Cluster analysis
Neural networks
Waste generation and processing processes
Statistics
Analysis of research results

1 Introduction The need to recycle production and consumption waste is obvious both from the point of view of environmental safety and from economic feasibility standpoint. Providing for growing scarcity of resources and environmental constraints, humanity is faced with the need to make the transition to the so-called “circular economy”, i.e. to the economy of closed cycles, in which the resources reproduction is carried out to a large extent through the transformation of production waste and population life. The solution to the problem of waste processing should be comprehensive and include: the development of appropriate technologies and equipment for waste processing, adapted to the industry specifics; normative support for the activities of economic entities in the field of waste generation and treatment with the help of Federal laws and government rules and regulations [1]; management of waste sorting processes and their subsequent disposal; logistics processes coordination and much more. An important component of the waste management system is the collection and subsequent analysis of data with the patterns identification in waste generation and processing, for example, in the context of economic sectors. The result of such © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 267–278, 2022. https://doi.org/10.1007/978-3-031-09073-8_23

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analytics should be proactive management decisions aimed at improving the efficiency of activities to reduce non-recyclable waste proportion both in the context of individual enterprises, regions, industries, and at a federal level. There is a wide variety of analytical methods (machine learning methods) for solving problems of identifying patterns hidden in data. Among these methods, the most common are such machine learning methods as cluster analysis, regression analysis, neural networks [2–6].

2 Related Works The dynamics of the generation and use and / or disposal of waste can be tracked by statistical data. However, it should be borne in mind that the system of state statistical observation covers only legal entities and individual entrepreneurs who carry out activities for production and consumption waste management, while the volume of waste generated by the population and enterprises that do not submit reporting in the form 2-TP (waste), are outside the scope of official statistical reporting, due to which the activity of collecting and analyzing data must be divided into two blocks: analytical results based on official statistical data [7, 8] and analytical results representing processes with municipal solid waste (SMW) [9]. According to Rosprirodnadzor [10, 11], in 2020 the country generated 7 billion 232 million tons of industrial and consumer waste. This is a record figure in recent years, twice the level of a decade ago. Of the total amount of waste, 3.526 billion tons of waste were reused, 2.874 billion tons were sent for storage, and 0.832 billion tons - for dumping. The above figure of 7,232 million tons exceeds the figure from the official statistical reporting –6,955 million tons, which is apparently due to errors in the waste data compilation process, as well as to probable additional waste receipts not accounted for in previous years. The most active increase in waste generation occurred in the last five years, which was facilitated by the positive dynamics of industrial production, as well as the increasingly active use of packaging materials (primarily plastic) in the utilities sector [12, 13].

3 Methods The preliminary data analysis, as well as the construction of clustering models by the kmeans method, were carried out in the environment of the IBM SPSS Statistics 26 package [14]. Within the study framework, regression models and neural networks were also formed, with the help of which the links between the W1 index proposed in this work and socio-economic indicators were identified. For these studies, the SPSS Statistics 26 package (data preprocessing - standardization and normalization) and the Statistica 14.0.0.15 Tibco package (statistical and regression analysis of indicators, spectral analysis of time series, the use of a multilayer perceptron to form a neural network model) were used [15].

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4 Problem Statement The purpose of the study was the preliminary processing of statistical data on the waste generation and processing, as well as the calculation of indices reflecting the proportion of recycled waste in the Russian Federation. Estimates were also carried out in order to identify the factors to which the waste generation indices are most sensitive, both in the context of individual industries (types of activities) and in the Russian economy as a whole. In the course of the study, the hypothesis was tested about the possible influence of various socio-economic indicators on the processes of waste generation and processing both in the country as a whole and in the context of individual industries. In this work, it is proposed to use the W1 index for the analysis of waste movement processes, indicating recycled waste proportion in their total amount and calculated by the formula: W1 ¼ U1=O1;

ð1Þ

where U1 is the value of processing indicator, recycling and disposal of production and consumption waste in total and by types of economic activity in the Russian Federation (thousand tons), O1 is the value of the indicator of production and consumption waste generation in total and by types of economic activity in the Russian Federation (thousand tons). Data on W1 index dynamics for the Russian economy and for the types of economic activities W1i were used to determine the industries that significantly affect recycled waste proportion. Table 1 shows the main types of economic activities (industries) that were taken into consideration in this study. Table 1. Major waste generating industries in Russia. Indicator code Wokv1 Wokv2 Wokv3 Wokv4 Wokv5 Wokv6 Wokv7 Wokv8 Wokv9 Wokv10 Wokv11 Wokv12 Wokv13 Wokv14

Industry name Agriculture, forestry, hunting, fishing and fish farming Coal mining Crude oil and natural gas extraction Metal ores mining Other minerals mining Provision of services in the field of mining Food production Beverage production Manufacture of tobacco products Manufacture of textiles Manufacture of wearing apparel Manufacture of leather and leather products Woodworking and manufacture of articles of wood and cork, except furniture, manufacture of articles from straw and plaiting materials Manufacture of paper and paper products (continued)

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Indicator code Wokv15 Wokv16 Wokv17 Wokv18 Wokv19 Wokv20 Wokv21 Wokv22 Wokv23 Wokv24 Wokv25 Wokv26 Wokv27 Wokv28 Wokv29 Wokv30 Wokv31 Wokv32

Industry name Printing activities and copying of information carriers Production of coke and petroleum products Production of chemicals and chemical products Production of medicines and materials used for medical purposes Manufacture of rubber and plastic products Manufacture of other non-metallic mineral products Metallurgical production Production of finished metal products, except for machinery and equipment Manufacture of computers, electronic and optical products Manufacture of electrical equipment Manufacture of machinery and equipment not included in other categories Manufacture of motor vehicles, trailers and semi-trailers Manufacture of other vehicles and equipment Furniture manufacture Manufacture of other finished products Repair and installation of machinery and equipment Provision of electricity, gas and steam; air conditioning Water supply; sewerage, organization of waste collection and disposal, activities to eliminate pollution

5 Results In Fig. 1 it is shown the dynamics of W1 index, which illustrates processed, utilized and neutralized waste proportion in Russia.

Fig. 1. Dynamics of W1 index (according to the Federal Statistical Observation Form No. 2-TP (waste) “Information on the formation, use, dumping, transportation and disposal of production and consumption waste”).

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In Fig. 2 it is shown the dynamics of W1i sectoral indices for 2005–2020 (using the example of some activity types).

Fig. 2. Dynamics of sectoral indices of processed waste proportion.

Table 2 illustrates the results of cluster analysis of industry indices showing the proportion of recycled waste in each industry (type of economic activity). Table 2. Clusters, average W1i values and W1i distances from the cluster center. Types of economic activities in According to OKVED Metallurgical production Production of finished metal products, except for machinery and equipment Manufacture of computers, electronic and optical products Manufacture of electrical equipment Manufacture of machinery and equipment not included in other categories Manufacture of motor vehicles, trailers and semi-trailers Manufacture of other vehicles and equipment Furniture manufacture

2016 2017 2018 W1i value

2019 2020 Cluster Distance W1i number to cluster average center value

0.85 0.68

0.78 0.56

0.85 0.53

0.82 0.53

0.75 0.48

1 1

0.49 0.21

0.81 0.56

0.50

0.51

0.57

0.44

0.49

1

0.30

0.50

0.48 0.83

0.50 0.74

0.44 0.01

0.56 0.76

0.65 0.03

1 1

0.28 0.80

0.53 0.48

0.77

0.83

0.75

0.72

0.76

1

0.37

0.76

1.03

1.00

0.91

0.98

0.97

1

0.84

0.98

0.31

0.57

0.42

0.54

0.49

1

0.37

0.46

(continued)

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Y. V. Frolov et al. Table 2. (continued)

Types of economic activities in According to OKVED Manufacture of other finished products Repair and installation of machinery and equipment Provision of electricity, gas and steam; air conditioning Water supply; sewerage,organization of collection And waste disposal, activities to eliminate pollution Manufacture of other non-metallic mineral products Production of medicines and materials used for medical purposes Manufacture of rubber and plastic products Coal mining Extraction of crude oil and natural gas Mining of metal ores Mining of other minerals Provision of services in the field of mining Food production Beverage production Manufacture of tobacco products Manufacture of textiles Manufacture of wearing apparel Manufacture of leather and leather products Woodworking and manufacture of articles of wood and cork, except furniture, manufacture of articles from straw and plaiting materials Manufacture of paper and paper products Printing activities and copying of information carriers Production of coke and petroleum products Production of chemicals and chemical products Agriculture, forestry, hunting, fishing and fish farming

2016 2017 2018 W1i value

2019 2020 Cluster Distance W1i number to cluster average center value

0.44 0.52

0.74 0.49

0.54 0.54

0.92 0.49

0.77 0.48

1 1

0.39 0.28

0.68 0.50

0.41

0.98

0.27

0.21

0.24

1

0.68

0.42

0.46

0.68

0.54

0.59

0.69

1

0.20

0.59

0.37

0.00

2.59

0.98

0.13

1

0

0.81

0.93

0.10

0.03

0.56

1.53

1

0.55

0.63

0.72

0.19

0.16

1.16

0.66

1

0.55

0.58

0.27 0.25 0.22 0.37 0.01

0.23 0.21 0.29 0.29 0.01

0.24 0.24 0.24 0.33 0.07

0.19 0.21 0.47 0.23 0.02

0.31 0.19 0.25 0.38 0.00

2 2 2 2 2

0.11 0.05 0.29 0.27 0.43

0.25 0.22 0.29 0.32 0.02

0.13 0.00 0.34 0.05 0.71 0.19

0.12 0.00 0.03 0.05 0.45 0.13

0.08 0.00 0.39 0.06 0.15 0.58

0.33 0.01 0.33 0.24 0.06 0.16

0.27 0.01 0.35 0.64 0.05 0.10

2 2 2 2 2 2

0.23 0.46 0.32 0.51 0.59 0.39

0.19 0.01 0.29 0.21 0.28 0.23

0.40

0.37

0.37

0.37

0.36

2

0.37

0.38

0.19

0.21

0.24

0.19

0.13

2

0.10

0.19

0.31

0.41

0.15

0.12

0.08

2

0.29

0.21

0.09

0.04

0.08

0.09

0.09

2

0.30

0.08

0.25

3.85

16.09 0.69

8.62

3

0

5.90

2.64

3.26

2.88

6.66

3

0

3.90

4.05

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6 Discussion Results analysis presented in Fig. 1 shows that over the past 15 years there has been a trend towards an increase in recycled waste proportion. At the same time, the volumes of recycled waste in the Russian Federation are low and amount to about 8%. Figure 2 illustrates the contribution of some industries (as an example) to waste treatment and disposal. The minimum volumes of waste processing are observed in the industry (type of activity) - electricity provision, gas and steam. Among the leading industries in waste processing are agriculture, forestry, hunting, fishing and fish farming. The results of cluster analysis based on the value of the industry indices W1i (Table 2) indicate that, firstly, in the studied sample, three clusters were identified, consisting of industries with medium, low and high levels of waste recycling. Secondly, the industries that are typical representatives of the first cluster with an average level of waste processing (W1i from 0.4 to 1.0) and having a minimum distance from the center of the cluster are: production of finished metal products, except for machinery and equipment; water supply, sewerage, waste management and disposal, pollution elimination activities; production of other non-metallic mineral products. In the second cluster, which includes industries with a minimum volume of waste processing (W1i less than 0.4), typical representatives of the cluster are: extraction of crude oil and natural gas; manufacture of paper and paper products; coal mining. In the third cluster, in which W1i has a value above 1, there is a maximum scatter in the W1i values (probably due to errors in collecting and recording statistical data), therefore, the results obtained require rechecking. The performed regression analysis made it possible to identify the following main socio-economic factors that positively and negatively affect the W1 index: W1 ¼ 0:013W1O þ 0:021VD8  0:186VD11 þ 2:145Z4  0:419Z5 þ 0:764Z6 þ 5:165Z8 þ 0:147Z10  1:795Z12  1:076Z14 þ 0:405Z17  0:716Z18  0:248;

where: W1O - W1 for the manufacturing industry; VD8 - Gross Domestic Product by type of economic activity “Transportation and storage”; VD11 - the same for the type of economic activity “Financial and insurance activities”; Z4 - Structure of the employed population by type of economic activity (on average per year) “Supply of electricity, gas and steam; air conditioning”; Z5 - the same for the type of economic activity “Water supply; sewerage, waste collection and disposal, pollution elimination activities “; Z6 - the same for the type of economic activity “Construction”; Z8 - the same for the type of economic activity “Transportation and storage”;

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Z10 - the same for the type of economic activity “Activities in the field of information and communication”; Z12 - the same for the type of economic activity “Activities in transactions with real estate”; Z14 - the same for the type of economic activity “State administration and military security; social Security”; Z17 - the same for the type of economic activity “Activities in the field of culture, sports, organization of leisure and entertainment”; Z18 - the same for the type of economic activity “Provision of other types of services”. Based on the results of the regression analysis, the factors in the order of increasing their influence on recycled waste proportion can be arranged in the following row: VD8; Z10; Z17; Z6; Z4; Z8. The types of economic activities that are leaders in terms of recycled waste level are: proportion of employees engaged in electricity provision, gas and steam, and proportion of employees engaged in transportation and storage activities. On the other hand, according to the results of regression analysis, the factors that negatively affect the proportion of processed waste are (in increasing order of their negative impact): VD11, Z5, Z18, Z14, Z12. Regression analysis ranked first in this row the proportion of workers engaged in activities in the field of real estate transactions. It should be noted that the regression model showed the presence of some asymmetry in the magnitude of the positive and negative influence of various factors. Thus, the number of people employed in the field of transportation and storage increases the proportion of recycled waste by about 2.9 times more intensively than the number of people employed in the field of operations with real estate - the leader in the negative impact on recycled waste proportion. At the next stage of the study, a sensitivity analysis was performed on a trained and tested neural network [6] in order to determine the magnitude and sign of some socioeconomic factors influence on the target function W1, followed by a comparison of regression results and neural network modeling. In order to determine time interval duration, which was then necessary to use when training the neural network, the spectral Fourier method was applied. Using this method, a periodogram was formed (Fig. 3), from which it follows that the period with the maximum values of the parameter W1 is 4–5 years. Consequently, the time interval for assessing the sign and magnitude of socio-economic factors influence on the target function W1 on a trained neural network is 4–5 years.

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Fig. 3. Analysis of W1variation period using the spectral Fourier method.

For studies on the training and testing of the neural network, the time interval from 2016 to 2020 was chosen. The value of W1 index was used as the objective function, which makes it possible to determine the proportion of recycled waste in their total amount. The results of the sensitivity analysis of the trained neural network model are presented in Table 3 and Fig. 4. Table 3. Results of sensitivity analysis on a trained neural network model. Factor code Socio-economic factors W1O Z8 (1+) Z17 (4+) Z6 (3+) Z4 (2+) Z18 (3–) Z10 (5+)

The value of W1i for the type of activity manufacturing Proportion of employed in the industry Transportation and storage Proportion of people employed in culture, sports, leisure and entertainment Proportion of employees in the construction industry Proportion of the employed in the industry Supply of electricity, gas and steam; air conditioning Proportion of employees in the industry “Provision of other types of services” The proportion of employees in the industry “Activities in the field of information and communication”

Contribution of the factor to W1 index change 0.486 0.424 0.294 0.208 0.178 0.155 0.145

(continued)

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Factor code Socio-economic factors VD11 (5–) Proportion in gross income in the sector “Financial and insurance activities” W1S The value of W1i for the type of activity “Agriculture, hunting and forestry” W1W The value of W1i for the type of activity “Water supply; sewerage, organization of collection and waste disposal, pollution activities “ Z14 (2–) The proportion of people employed in the sector “Public administration and military security; social Security” VD14 Proportion in gross income in the sector “Activities of households as employers; undifferentiated activities of private households for the production of goods and the provision of services for their own consumption” VD8 (6+) Proportion in gross income in the “Transportation and storage” industry Z5 (4–) The proportion of people employed in the industry “Water supply; sewerage, organization of collection and disposal of waste, activities to eliminate pollution” Z12 (1–) Proportion of people employed in the “Real estate activities” industry

Contribution of the factor to W1 index change 0.132 0.091 0.060

–0.021

–0.090

–0.123 –0.255

–0.440

Fig. 4. Results of W1 index sensitivity analysis to socio-economic factors on a trained neural network (multilayer perceptron model).

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Table 3 in the “code” column in parentheses are marked the ranks of the positive (with a “+” sign) and negative (with a “–” sign) influence of socio-economic factors on the W1 index, obtained as a result of the regression model formation. These results show that, in general, the magnitude and sign of factors influence on waste recycling degree in Russia, based on the results of the study on the regression model and the neural network model, coincide. Also, a certain asymmetry of factors influence in the direction of a greater contribution of factors that increase W1 index value is repeated.

7 Conclusion Based on the results of the study, the following main results were obtained: • In this paper, it is proposed to use W1 index for the analysis of waste movement processes, which determines the proportion of recycled waste in their total amount both in the Russian Federation as a whole and in the context of industries; • The dynamics of W1 indices in the range of 2005–2020 was investigated; • A cluster analysis was carried out, according to the results of which industriestypical representatives of three clusters were identified, including industries with high, medium and low levels of waste processing; • Types of economic activity - the leaders in increasing the proportion of recycled waste are the proportion of employees engaged in the provision of electricity, gas and steam and the proportion of employees engaged in transportation and storage; • Based on the results of regression application and neural network analysis, it was found that the magnitude and sign of the influence of socio-economic factors on the degree of waste recycling in Russia approximately coincide in the regression model and the neural network model.

References 1. Order of the Government of the Russian Federation of 25.01.2018 N 84-r: On approval of the Strategy for the development of industry for the processing, disposal and disposal of production and consumption waste for the period up to 2030. https://sudact.ru/law/ rasporiazhenie-pravitelstva-rf-ot-25012018-n-84-r/strategiia-razvitiia-promyshlennosti-poobrabotke/. Accessed 01 N0v 2021 2. Frolov, Y.V., Bosenko, T.M.: Training of personnel for the development of innovative entrepreneurship. Acad. Entrepreneur. J. 26(1), 1–6 (2020). https://www.abacademies.org/ articles/training-of-personnel-for-the-development-of-innova-tive-entrepreneurship-9065. html. Accessed 09 Oct 2021 3. Frolov, Y., Bosenko, T.M.: Statistical data research on staff training for the digital economy in the Russian federation. High. Educ. Russ. 30(11), 29–41 (2021). https://doi.org/10.31992/ 0869-3617-2021-30-11-29-41 4. Bosenko, T.M., Konopelko, E.S., Lavrenova, E.V., Frolov, Y.V.: Application of cluster analysis for the study of factors affecting the rating of schools in Moscow. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds.) CoMeSySo 2021. LNNS, vol. 231, pp. 281–295. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90321-3_23

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5. Lavrenova, E.V., Frolov, Y.V., Bosenko, T.M.: Analytical studies of behavior of users of the Moscow electronic school service. In: Silhavy, R. (ed.) CSOC 2021. LNNS, vol. 229, pp. 121–132. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77445-5_11 6. Frolov, Y.: Intelligent Systems and Management Solutions, p. 294. Moscow City Pedagogical University, Moscow (2000) 7. Indicators affecting waste generation and waste treatment in the Russian Federation. https:// rosstat.gov.ru/folder/11194. Accessed 01 Nov 2021 8. Federal State Statistics Service. Environment. Waste from production and consumption. https://rosstat.gov.ru/folder/11194. Accessed 01 Nov 2021 9. Shilkina, S.V.: Global trends in waste management and analysis of the situation in Russia. Russ. J. Resour. Conserv. Recycl. 1(7), (2020). https://resources.today/PDF/05ECOR120. pdf. https://doi.org/10.15862/05ECOR120 10. State cadastre of waste. Waste data bank. https://rpn.gov.ru/activity/regulation/kadastr/bdo/. Accessed 01 Nov 2021 11. Volkova, A.V.: Annual reviews of key industries and markets. Waste disposal market 2018. National Research University Higher School of Economics. Institute Development Center. https://dcenter.hse.ru/news/221399866.html. Accessed 01 Nov 2021 12. Portal of Open Data of the Russian Federation. Waste Data Bank. https://data.gov.ru/ opendata/7703381225-bankdannih/data-20201214T1021-structure-20201214T1021.csv. Accessed 01 Nov 2021 13. Environmental protection in Russia. Stat. cb.. Rosstat 0–92 M., 113 (2020). https://rosstat. gov.ru/storage/mediabank/nmV0UuE3/Ochrana_2020.pdf. Accessed 01 Dec 2021 14. Software Platform for Statistical Analysis, IBM SPSS Statistics 26. https://www.ibm.com/ruru/products/spss-statistics 15. STATISTICA TIBCO® Data Science Software Platform for Statistical Analysis and Machine Learning. https://www.tibco.com/products/data-science/downloads

Improved Genetic Local Search Heuristic for Solving Non-permutation Flow Shop Scheduling Problem Sabrine Chalghoumi(B)

and Talel Ladhari

BADEM, University of Tunis, Tunis, Tunisia [email protected]

Abstract. In this paper we address the two-machine flow shop scheduling problem for minimizing the total completion times under release dates. We used two phases versions of genetic local search algorithms. The first phase is used to find a good permutation schedule. The second phase explores non-permutation schedules to improve the initial solution. In our experiment we compare the quality of permutation and nonpermutation schedules. As result, we prove that the improved genetic local search method is able to improve the sum of the completion times solution comparing with the state-of-the-art permutation solution.

Keywords: Non-permutation flow shop Release date · Genetic local search

1

· Total completion time ·

Introduction

In this paper, we stressed the advantage to consider the two-machine nonpermutation flowshop with the objective of minimizing the total completion times subject to release dates. In the traditional permutation flow shop scheduling problems, n jobs are processed on m machines in the same order. The aim is to find the best sequence of jobs to be processed. The total number of possible schedules is equal to n! where n is the number of jobs to schedule. In the nonpermutation flow shop scheduling problem each job sequence can be different through each of the machines. The number of possible schedules is then equal to (n!)m where m is the number of machines. This problem can be defined with the following assumptions: – A set of n jobs J = {1, 2, . . . .n} processed on two machines M1 and M2 in that order. – Each of n jobs must be processed during p1j time units without preemption firstly on machine M1 , and then during p2j time units on machine M2 . – The processing of job j cannot be started before a release date rj . – The processing of more than one job at a time on a machine is not possible and two machines cannot process a job simultaneously. – Each machine is available at time zero. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  R. Silhavy (Ed.): CSOC 2022, LNNS 503, pp. 279–289, 2022. https://doi.org/10.1007/978-3-031-09073-8_24

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– The objective is to find a nonpreemptive schedule that minimizes the total completion times. According problems are  to Pinedo 2012, The considered  denoted by F 2|rj , prmu| Cj and F 2|rj , non − prmu| Cj for the nonpermutation case.  Cj is a Ladhari and Rakrouki (2009) proved that the problem F 2|rj , prmu|  strongly N P-hard problem. Obviously, the F 2|rj , non − prmu| Cj is more complex and is N P-hard too. 1.1

Literature Review

As previous work, we stress on papers dealing with the non-permutation flowshop scheduling problem (NPFSP). Tandon et al. (1991) considered the nonpermutation flowshop scheduling problem minimizing the makespan. They proposed a simulated annealing algorithm and a heuristic called greedy rapid access extensive search. According to their computational experiments, the average percentage improvement of non-permutation schedule optimum over permutation schedule optimum is usually more than 1.5. Potts et al. (1991) studied the same problem. Later, Koulamas (1998) proposed a simple constructive heuristic to produce non-permutation schedules for the flowshop, makespan problem. A flow line-based manufacturing system is a general flowshop which allows some jobs having missing operations on some machines. Pugazhendhi et al. (2003) developed a heuristic to derive NPFS with makespan and flow time criteria. The authors proved that the effect on improving the schedule performance is significant. Pugazhendhi et al. (2004) considered also the NPFSP with the total flowtime objective in the particular case where some jobs may not require processing on some machines. They proposed a heuristic procedure to derive nonpermutation schedules from a given permutation schedule and evaluate its effectiveness in improving the objective value. Moreover, Rebaine (2005) evaluated a ratio between the best solution of there non-permutation flowshop problem and the permutation flowshop with time delays. The author proved that, in the two-machine case, the performance ratio between the best solution of the permutation flow shop problem and the best solution of non permutation flowshop problem is bounded by 2. When the operations of the n jobs are restricted to be unit execution time, this ratio is reduced to (2 − (3/n + 2)) for the two-machine case, and is m for the m-machine case. In 2006, Liao et al. compared permutation and non-permutation schedules obtained in the problems of the makespan, total tardiness and total weighted tardiness. The computational results show that the percentage improvement is quite small (less than 0.5) with respect to the makespan criteria, but it is significant (in the range of 1–5) with respect to the total tardiness, and the weighted total tardiness. Ying (2007) proposed an iterated greedy heuristic for NPFSP minimizing the makespan. According to the computational experiments based on a comparison with existing approximate approaches developed for the permutation case, the authors proved the dominance of the presented heuristic in terms of solution quality and computational time. Lin and Ying (2008) proposed a hybrid approach, a

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combination of a simulated annealing and tabu search, for the non-permutation flowshop scheduling with the objective of minimizing the makespan. Lin et al. (2009) studied the NPFSP with sequence dependent family setup times minimizing either a completion time related or a due date related criterion. The considered completion time related criteria are the makespan, the total completion time and the total weighted completion time. The considered due date related criteria are the maximum tardiness, the total tardiness and the total weighted tardiness. They proposed three types of metaheuristics which are simulated annealing, genetic algorithm and tabu search. Liao et al. (2010) considered the NPFSP minimizing the total tardiness. They proposed three mixed integer linear programming formulations and two tabu search based algorithms. Computational experiments proved that the non-permutation schedules are better than the permutation schedules for flowshop total tardiness problems. Nouri et al. (2013) considered the NPFSP with the objective of minimizing the total flow time. They developed a mixed integer programming model and an improvement heuristic. Computational results of the heuristic procedure showed its effectiveness for solving this problem. Gharbi et al. (2014) presents lower and upper bounds for the makespan minimization in a flowshop scheduling problem. Benavides et al. (2015) proposed Iterated Local Search Heuristics for Minimizing Total Completion Time in Permutation and Non-permutation scheduling problems. In Benavides et al. (2016), the authors proposed a constructive iterated local search heuristic for solving a NPFS problem. Benavides et al. (2018) developed fast heuristics for minimizing the makespan in NPFSP. Daniel et al. (2019) presented a combinatorial analysis of the permutation and non-permutation flow shop problems. Recently, Bruma et al. (2022) deal with the NPFSP for minimizing the total completion time. The authors proposed a template to generate iterated greedy algorithms for solving the considered problem. The experimental study provide promising results.

2

Improvement Genetic Local Search Heuristic

The proposed Improvement heuristic includes two basic phases: – First phase: Generation of initial permutation solutions To generate these per mutation solutions, the F 2|rj , prmu| Cj is solved using the Genetic Local Search algorithm of (Ladhari and Rakrouki 2009). – Second phase: Improving the initial permutation solutions In the second phase, the solutions obtained in the first phase are improved by solving the  subproblem 1|rj , prmu| Cj in the second machine where for each j ∈ J, the release date is equal toC1j respectively. This problem will be solved  using a GLS algorithm applied to the single machine problem 1|rj , prmu| Cj . The used GLS method is described in the following section. 2.1

Genetic Local Search

Genetic algorithms (GAs) are general search methods based on the mechanism of natural selection and genetic operators and it is based on different operations

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that control the search strategies. Since their introduction by Holland (1975), genetic algorithms have been used successfully to find near optimal solutions for complex combinatorial optimization problems such as scheduling problems. However, their performance depends on the different chosen operators and parameters. Goldberg 1989 fixed five properties for the GLS algorithm that should be defined: The representation mechanism: it consists on how to encode problems solutions to artificial chromosomes. Initial population: A way of initializing the population of chromosomes. An evaluation mechanism: How to compute the fitness function for each chromosome. Genetic operators: like crossover or mutation. These operators are applied to the population in order to generate new populations with better chromosomes. Values of some control parameters (e.g. population size, crossover and mutation rates, fitness scaling) which control how the various components of the GA combine and operate. InitPopulation(X). We denote by X the value of the population size. In our algorithm the initial population consists of X permutations. The first permutation is generated using H8 heuristic. However X−1 permutations are generated randomly. Fitness Computation. In order to evaluate the X invidious of the generated population, we calculate a fitness value by the function assignFitness. This function assign to each individual the total completion time of the corresponding permutation. Selection. Selection is the process of choosing a target individual from the population. A random selection is executed to decide which individual will play the role of the target solution. Mutation Operators. The mutation operators are applied with a certain probability to generate a different solution from the target solution. The perturbation schemes used in the mutation operators are: exchange two random jobs and insert a random job in a random position. Crossover Operators. Crossover is the process of taking two parent solutions and producing from them a child. The crossover operator is applied between the target and the mutant solutions to creates a trial solution. Regarding the permutation-based representation, the following crossovers are used. – Two-point crossover: A pair of crossing points is randomly selected along the length of the first target permutation. Two versions of the operator are implemented. In the first version, the jobs outside the selected two points are copied into the proto trial permutation and the remaining jobs are copied from the second permutation (mutant) in the order of their appearance. In the second version of the operator, the sequence of the jobs between the selected

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crossing points are copied into the trial solution, and the remaining jobs are taken from the second permutation in the order of their appearance. – Three-point crossover: Three crossing points are randomly selected along the length of the first solution. The permutation is then divided into four distinct sections. Copy the first (second) and third (fourth) sub-sections of jobs into the same places of the offspring. Fill up the remaining empty locations of the trial with jobs from the second parent according to their order of appearance. Again, no duplication of jobs is permitted. Local Search Procedure. After the mutation step, we apply local search phase using two procedures. The first procedure generates k (k= 30) neighbors for each solution in the population using random exchanging and adjacent swapping, and insert the best to the population. The second procedure improves each solution in the population by means of random exchanging. Algorithm describe the GLS algorithm.

3

Computational Results

This section presents an evaluation of the performances of the Improved Genetic Local search method through computational experiments, where the algorithm is coded in C and run on an Intel Core 2 CPU 1.6 GHz processor with 2 GB RAM computer. The experimental study is done and the improved heuristic is evaluated in term of solution quality. More precisely, the non-permutation solutions will be compared with respect to the initial permutation solutions. 3.1

Test Problems

The method is tested on two classes A and B of problem instances. The processing times in class A are drawn from a discrete uniform distribution on [1, 100] and the release dates are uniformly distributed between [1, 100R]. In this way we obtain three subsets A1, A2, and A3 respectively, where R = 2, n, 2n. The second class B consists of four different randomly generated problem sets. The release dates and the processing times are, respectively, uniformly distributed between [1, α] and [1, β]. In this way, we generated all combinations of long short release dates and processing times. The characteristics of these sets are provided in Table 1. The number of jobs n is taken equal to 10; 20; 30; 50; 100. For each size n, 30 instances are generated. 3.2

Evaluation of the Improved Genetic Local Search Heuristic

In order to evaluate the performance of the proposed improved heuristic, we look at the mean of improvement percent of initial solutions of each instance. Further, IGLS is evaluated by average relative percentage improvement (API)

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Input : FileIn, MaxIterationsWithoutImprv, MaxIterations, popSize, MutationProbability, CrossoverProbability Output: BestChromosome pop←InitPopulation(popSize, FileIn); BestChromosome←SearchBestFitness(pop) ; newfitness←BestChromosome.fitness; while NbIter