Intelligent Decision Technologies 2019: Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Volume 2 [1st ed.] 978-981-13-8302-1;978-981-13-8303-8

Table of contents :
Front Matter ....Pages i-xxvi
Front Matter ....Pages 1-1
Quality Improvement Through the Preventive Detection of Potentially Defective Products in the Automotive Industry by Means of Advanced Artificial Intelligence Techniques (Marco Vannucci, Valentina Colla)....Pages 3-12
An Efficient Non-Blind Steering Vector Estimation Technique For Robust Adaptive Beamforming With Multistage Error Feedback (Ghattas Akkad, Ali Mansour, Bachar Elhassan, Jalal Srar, Mohamad Najem, Frederic Leroy)....Pages 13-23
The P2P–Grid–Agent Distributed Platform: A Distributed and Dynamic Platform for Developing and Executing Large-Scale Application Based on Deep Learning Techniques (Hamdi Hassen, Trifa Zied, Khemakhem Maher)....Pages 25-35
Recognition of Daily Human Activities Using Accelerometer and sEMG Signals (Giorgio Biagetti, Paolo Crippa, Laura Falaschetti, Simona Luzzi, Claudio Turchetti)....Pages 37-47
Classification of Alzheimer’s Disease from Structural Magnetic Resonance Imaging using Particle-Bernstein Polynomials Algorithm (Giorgio Biagetti, Paolo Crippa, Laura Falaschetti, Simona Luzzi, Riccardo Santarelli, Claudio Turchetti)....Pages 49-62
Front Matter ....Pages 63-63
Selecting Informative Samples for Animal Recognition in the Wildlife (Margarita Favorskaya, Vladimir Buryachenko)....Pages 65-75
Genetic Algorithm for Selecting Relevant Regions in Digital Watermarking Scheme for 2D/3D Medical Images (Margarita Favorskaya, Eugenia Savchina)....Pages 77-87
A Transport Coding Gain Estimation in the Conditions of Time Limitation for Maximum Acceptable Message Delay (Evgenii Krouk, Anton Sergeev, Mikhail Afanasev)....Pages 89-99
Modeling Customers Speed of Movement from POS- and RFID-Data (Marina Kholod, Peter Golubtsov, Arthur Varlamov, Svyatoslav Filatov, Katsutoshi Yada)....Pages 101-111
Discovering and Analyzing Binary Codes Based on Monocyclic Quasi-Orthogonal Matrices (Alexander Sergeev, Vadim Nenashev, Anton Vostrikov, Alexander Shepeta, Daniil Kurtyanik)....Pages 113-123
The Study of Generators of Orthogonal Pseudo-random Sequences (Yuriy Balonin, Leonid Abuzin, Alexander Sergeev, Vadim Nenashev)....Pages 125-133
Portraits of Orthogonal Matrices as a Base for Discrete Textile Ornament Patterns (Alexander Sergeev, Mikhail Sergeev, Anton Vostrikov, Daniil Kurtyanik)....Pages 135-143
Training Sample Generation Software (Anton Vostrikov, Stanislav Chernyshev)....Pages 145-151
Front Matter ....Pages 153-153
Business AI Alignment Modeling Based on Enterprise Architecture (Hironori Takeuchi, Shuichiro Yamamoto)....Pages 155-165
Eye Tracking as a Method of Neuromarketing for Attention Research—An Empirical Analysis Using the Online Appointment Booking Platform from Mercedes-Benz (Veit Etzold, Anika Braun, Tabea Wanner)....Pages 167-182
A Concept of an Interactive Web-Based Machine Learning Tool for Individual Machine and Production Monitoring (Marina Burdack, Manfred Rössle)....Pages 183-193
Benefits of Agile Project Management in an Environment of Increasing Complexity—A Transaction Cost Analysis (Raphael Kaim, Ralf-Christian Härting, Christopher Reichstein)....Pages 195-204
Potential Benefits of Digital Business Models and Its Processes in the Financial and Insurance Industry (Ralf-Christian Härting, Christopher Reichstein, Raffael Sochacki)....Pages 205-216
Front Matter ....Pages 217-217
Risk-Sensitive Decision-Making Under Risk Constraints with Coherent Risk Measures (Yuji Yoshida)....Pages 219-229
Mutual Evaluation and Solution Method (Kazutomo Nishizawa)....Pages 231-240
Visualization of Criteria Priorities Using a Ternary Diagram (Natsumi Oyamaguchi, Hiroyuki Tajima, Isamu Okada)....Pages 241-248
Changes in Organizations Due to Management Mechanization (Case Studies of Life Insurance Companies in Japan) (Shunei Norikumo)....Pages 249-257
Naturality for Ranking from Pairwise Comparisons (Takafumi Mizuno)....Pages 259-266
Upper Level Processes and Projects Model Building (Marina Kholod, Yury Lyandau, Valery Maslennikov, Irina Kalinina, Nikolay Mrochkovskiy)....Pages 267-276
SPCM with Harker Method for MDAHP Including Hierarchical Criteria (Takao Ohya)....Pages 277-283
Front Matter ....Pages 285-285
Building a Telemedicine System for Monitoring the Health Status and Supporting the Social Adaptation of Children with Autism Spectrum Disorders (Georgy Lebedev, Herman Klimenko, Eduard Fartushniy, Igor Shaderkin, Pavel Kozhin, Dariya Galitskaya)....Pages 287-294
Implementation of Digital Technologies in Pharmaceutical Products Lifecycle (Konstantin Koshechkin, German Klimenko, Alexander Polikarpov)....Pages 295-305
Home Hospital e-Health Centers for Barrier-Free and Cross-Border Telemedicine (Ralf Seepold, Maksym Gaiduk, Juan Antonio Ortega, Massimo Conti, Simone Orcioni, Natividad Martínez Madrid)....Pages 307-316
Data-Driven Human Movement Assessment (Danny Dressler, Pavlo Liapota, Welf Löwe)....Pages 317-327
Attitudes to Telemedicine, and Willingness to Use in Young People (Margarita Stankova, Polina Mihova)....Pages 329-336
Posture Tracking Using a Machine Learning Algorithm for a Home AAL Environment (Maksim Sandybekov, Clemens Grabow, Maksym Gaiduk, Ralf Seepold)....Pages 337-347
Back Matter ....Pages 349-350

Citation preview

Smart Innovation, Systems and Technologies 143

Ireneusz Czarnowski Robert J. Howlett Lakhmi C. Jain   Editors

Intelligent Decision Technologies 2019 Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Volume 2

Smart Innovation, Systems and Technologies Volume 143

Series Editors Robert J. Howlett, Bournemouth University and KES International, Shoreham-by-sea, UK Lakhmi C. Jain, Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia

The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability. The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form. Volumes on interdisciplinary research combining two or more of these areas is particularly sought. The series covers systems and paradigms that employ knowledge and intelligence in a broad sense. Its scope is systems having embedded knowledge and intelligence, which may be applied to the solution of world problems in industry, the environment and the community. It also focusses on the knowledgetransfer methodologies and innovation strategies employed to make this happen effectively. The combination of intelligent systems tools and a broad range of applications introduces a need for a synergy of disciplines from science, technology, business and the humanities. The series will include conference proceedings, edited collections, monographs, handbooks, reference books, and other relevant types of book in areas of science and technology where smart systems and technologies can offer innovative solutions. High quality content is an essential feature for all book proposals accepted for the series. It is expected that editors of all accepted volumes will ensure that contributions are subjected to an appropriate level of reviewing process and adhere to KES quality principles. ** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, SCOPUS, Google Scholar and Springerlink **

More information about this series at http://www.springer.com/series/8767

Ireneusz Czarnowski Robert J. Howlett Lakhmi C. Jain •

•

Editors

Intelligent Decision Technologies 2019 Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Volume 2

123

Editors Ireneusz Czarnowski Department of Information Systems Gdynia Maritime University Gdynia, Poland

Robert J. Howlett Bournemouth University and KES International Poole, Dorset, UK

Lakhmi C. Jain University of Canberra Canberra, ACT, Australia Faculty of Science Liverpool Hope University Liverpool, UK Centre for Artificial Intelligence University of Technology Sydney Sydney, NSW, Australia KES International Poole, UK

ISSN 2190-3018 ISSN 2190-3026 (electronic) Smart Innovation, Systems and Technologies ISBN 978-981-13-8302-1 ISBN 978-981-13-8303-8 (eBook) https://doi.org/10.1007/978-981-13-8303-8 © Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

KES-IDT 2019 Organization

Honorary Chairs Lakhmi C. Jain, University of Canberra, Australia, Liverpool Hope University, UK and University of Technology Sydney, Australia Gloria Wren-Phillips, Loyola University, USA General Chair Ireneusz Czarnowski, Gdynia Maritime University, Poland Executive Chair Robert J. Howlett, KES International & Bournemouth University, UK Program Chairs Jose L. Salmeron, University Pablo de Olavide, Seville, Spain Antonio J. Tallón-Ballesteros, University of Seville, Spain Publicity Chairs Izabela Wierzbowska, Gdynia Maritime University, Poland Alfonso Mateos Caballero, Universidad Politécnica de Madrid, Spain Special Sessions Intelligent Data Processing and Software Paradigms Margarita Favorskaya, Reshetnev Siberian State University of Science and Technology, Russian Federation Lakhmi C. Jain, University of Canberra, Australia, and University of Technology Sydney, Australia Mikhail Sergeev, Saint Petersburg State University of Aerospace Instrumentation, Russian Federation

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KES-IDT 2019 Organization

Decision-Making Theory for Economics Takao Ohya, Kokushikan University, Japan Intelligent Decision Support in Cybersecurity Leslie F. Sikos, Edith Cowan University, Australia Specialized Decision Techniques for Data Mining, Transportation and Project Management Piotr Jędrzejowicz, Gdynia Maritime University, Poland Ireneusz Czarnowski, Gdynia Maritime University, Poland Dariusz Barbucha, Gdynia Maritime University, Poland Large-Scale Systems for Intelligent Decision-Making and Knowledge Engineering Sergey V. Zykov, National Research University Higher School of Economics and National Research Nuclear University MEPhI, Russia Signal Processing and Pattern Recognition for Decision Making Systems Paolo Crippa, Universita Politecnica delle Marche, Italy Claudio Turchetti, Universita Politecnica delle Marche, Italy Decision Support Systems Wojciech Froelich, University of Silesia, Poland Interdisciplinary Approaches in Data Science and Digital Transformation Practice Ralf-Christian Harting, Aalen University, Germany Ivan Lukovic, University of Novi Sad, Serbia Digital Health, Distance Learning and Decision Support for eHealth Ralf Seepold, HTWG Konstanz, Ubiquitous Computing Lab, Germany Margarita Stankova, New Bulgarian University, Bulgaria Data Selection in Machine Learning Antonio J. Tallón-Ballesteros, University of Seville, Spain Ireneusz Czarnowski, Gdynia Maritime University, Poland International Program Committee Jair M. Abe, University of Sao Paulo, Brazil Witold Abramowicz, Poznan University of Economics, Poland Ari Aharari, Sojo University, Japan Piotr Artiemjew, University of Warmia and Mazury in Olsztyn, Poland Ahmad Taher Azar, Benha University, Egypt Dariusz Barbucha, Gdynia Maritime University, Poland Alina Barbulescu, Ovidius University of Constanta, Romania Farshad Badie, Aalborg University, Denmark Andreas Behrend, University of Bonn, Germany Monica Bianchini, University of Siena, Italy Francesco Bianconi, Università degli Studi di Perugia, Italy Gloria Bordogna, CNR IREA, Italy

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Urszula Boryczka, University of Silesia, Poland János Botzheim, Budapest University of Technology and Economics, Hungary Adriana Burlea-Schiopoiu, University of Craiova, Romania Alfonso Mateos Caballero, Universidad Politécnica de Madrid, Spain Frantisek Capkovic, Slovak Academy of Sciences, Slovakia Wojciech Froelich, University of Silesia, Poland Giovanna Castellano, University of Bari Aldo Moro, Italy Barbara Catania, University of Genoa, Italy Ruay-Shiung Chang, National Taipei University of Business, Taiwan Shyi-Ming Chen, National Taiwan University of Science and Technology, Taiwan Lukasz Chomatek, Lodz University of Technology, Poland Mario Giovanni C. A. Cimino, University of Pisa, Italy Marco Cococcioni, University of Pisa, Italy Angela Consoli, Defence Science and Technology Group, Australia Paulo Cortez, University of Minho, Portugal Paolo Crippa, Universita Politecnica delle Marche, Italy Matteo Cristani, University of Verona, Italy Alfredo Cuzzocrea, University of Trieste, Italy Ireneusz Czarnowski, Gdynia Maritime University, Poland Kusum Deep, Indian Institute of Technology Roorkee, India Dinu Dragan, University of Novi Sad, Serbia Margarita N. Favorskaya, Reshetnev Siberian State University of Science and Technology, Russia Raquel Florez-Lopez, University Pablo Olavide of Seville, Spain Claudia Frydman, Aix-Marseille University, France Rocco Furferi, University of Florence, Italy Mauro Gaggero, National Research Council (CNR), Italy Maksym Gaiduk, Ubiquitous Computing Lab, HTWG Konstanz, Germany Christos Grecos, Central Washington University, USA Foteini Grivokostopoulou, University of Patras, Greece Katarzyna Harezlak, Silesian University of Technology, Poland Ralf-Christian Harting, Aalen University, Germany Ioannis Hatzilygeroudis, University of Patras, Greece Dawn E. Holmes, University of California, USA Katsuhiro Honda, Osaka Prefecture University, Japan Tzung-Pei Hong, National University of Kaohsiung, Taiwan Yuh-Jong Hu, National Chengchi University, Taipei, Taiwan Naohiro Ishii, Aichi Institute of Technology, Japan Yuji Iwahori, Chubu University, Japan Ajita Jain, Seven Steps Physiotherapy, Australia Joanna Jedrzejowicz, University of Gdansk, Poland Piotr Jedrzejowicz, Gdynia Maritime University, Poland Nikos Karacapilidis, University of Patras, Greece Pawel Kasprowski, Silesian University of Technology, Poland Jan Kozak, University of Economics in Katowice, Poland

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Pavel Kozhin, Sechenov University, Russia Marek Kretowski, Bialystok University of Technology, Poland Dalia Kriksciuniene, Vilnius University, Lithuania Aleksandar Kovačević, University of Novi Sad, Serbia Boris Kovalerchuk, Central Washington University, USA Vladimir Kurbalija, University of Novi Sad, Serbia Kazuhiro Kuwabara, Ritsumeikan University, Japan Halina Kwasnicka, Wroclaw University of Technology, Poland Georgiy Lebedev, Sechenov University, Russia Chee-Peng Lim, Deakin University, Australia Pei-Chun Lin, Feng Chia University, Taiwan Mihaela Luca, Romanian Academy, Romania Ivan Luković, University of Novi Sad, Serbia Natividad Martinez Madrid, Reutlingen University, Germany Ewa Magiera, University of Silesia, Poland Neel Mani, Amity University, India Mohamed Arezki Mellal, M’Hamed Bougara University, Algeria Lyudmila Mihaylova, University of Sheffield, UK Polina Mihova, New Bulgarian University, Bulgaria Toshiro Minami, Kyushu Institute of Information Sciences, Japan Michael Mohring, University of Munich, Germany Daniel Moldt, University of Hamburg, Germany Stefania Montani, DISIT, University of Piemonte Orientale, Italy Mikhail Moshkov, KAUST, Saudi Arabia Shastri L. Nimmagadda, Curtin University, Australia Andrzej Obuchowicz, University of Zielona Góra, Poland Marek Ogiela, AGH University of Science and Technology, Poland Takao Ohya, Kokushikan University, Japan Mrutyunjaya Panda, Utkal University, India Petra Perner, Institute of Computer Vision and Applied Computer Sciences, Germany Isidoros Perikos, University of Patras, Greece Georg Peters, Munich University of Applied Sciences, Germany Anitha S. Pillai, Hindustan Institute of Technology & Science, India Camelia Pintea, Technical University Cluj-Napoca, Romania Bhanu Prasad, Florida A&M University, USA Dilip Kumar Pratihar, Indian Institute of Technology Kharagpur, India Radu-Emil Precup, Politehnica University of Timisoara, Romania Jim Prentzas, Democritus University of Thrace, Greece Giuseppe Pronesti, Universita’ Mediterranea Di Reggio Calabria, Italy Malgorzata Przybyla-Kasperek, University of Silesia, Poland Marcos Quiles, UNIFESP, Brazil Milos Radovanovic, University of Novi Sad, Serbia Azizul Azhar Ramli, Universiti Tun Hussein Onn Malaysia Ewa Ratajczak-Ropel, Gdynia Maritime University, Poland Paolo Remagnino, University of Kingston, UK

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Ana Respício, University of Lisbon, Portugal Marina Resta, University of Genoa, Italy Alvaro Rocha, AISTI & University of Coimbra, Portugal John Ronczka, Independent Research Scientist, SCOTTYNCC, Australia Anatoliy Sachenko, Ternopil National Economic University, Ukraine Mika Sato-Ilic, University of Tsukuba, Japan Miloš Savić, University of Novi Sad, Serbia Rafal Scherer, Czestochowa University of Technology, Poland Daniel Scherz, Ubiquitous Computing Lab, HTWG Konstanz, Germany Rainer Schmidt, University of Munich, Germany Ralf Seepold, HTWG Konstanz, Germany Hirosato Seki, Osaka University, Japan Mikhail Sergeev, Saint Petersburg State University of Aerospace Instrumentation, Russian Federation Leslie F. Sikos, Edith Cowan University, Australia Bharat Singh, Hildesheim, Germany Aleksander Skakovski, Gdynia Maritime University, Poland Urszula Stanczyk, Silesian University of Technology, Poland Margarita Stankova, New Bulgarian University, Bulgaria Ulrike Steffens, Hamburg University of Applied Sciences, Germany Ruxandra Stoean, University of Craiova, Romania Mika Sulkava, Natural Resources Institute Finland Piotr Szczepaniak, Lodz University of Technology, Poland Kouichi Taji, Nagoya University, Japan Antonio J. Tallón-Ballesteros, University of Seville, Spain Shing Chiang Tan, Faculty of Information Science and Technology, Multimedia University, Malaysia Dilhan Thilakarathne, VU University Amsterdam, NL Jeffrey Tweedale, DST Group, Australia Marco Vannucci, Scuola Superiore Sant’Anna, Italy Rotaru Virgil, University of Medicine and Pharmacy, Timisoara, Romania Mila Dimitrova Vulchanova, NTNU, Norway Valentin Vulchanov, NTNU, Norway Fen Wang, Central Washington University, USA Junzo Watada, Universiti Teknologi Petronas, Malaysia Gloria Wren, Loyola University Maryland, USA Yoshiyuki Yabuuchi, Shimonoseki City University, Japan Jane You, The Hong Kong Polytechnic University, Hong Kong Cecilia Zanni-Merk, Normandie Universite, INSA Rouen, LITIS, France Gian Pierro Zarri, Sorbonne University, France Krzysztof Zatwarnicki, Opole University of Technology, Poland Lindu Zhao, Southeast University, China Min Zhou, Hunan University of Commerce, China

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Beata Zielosko, University of Silesia in Katowice, Poland Alfred Zimmerman, Reutlingen University, Germany Sergey Zykov, National Research University Higher School of Economics and National Research Nuclear University MEPhI, Russia

Preface

This volume contains the Proceedings (Volume 2) of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019) which will be held in Malta, during June 17–19, 2019. The KES-IDT is a well-established international annual conference organized by KES International. The KES-IDT is a subseries of the KES conference series. The KES-IDT is an interdisciplinary conference and provides opportunities for the presentation of interesting new research results and discussion about them, leading to knowledge transfer and generation of new ideas. This edition, KES-IDT 2019, attracted a number of researchers and practitioners from all over the world. The KES-IDT 2019 Program Committee received papers for the main track and 10 special sessions. Each paper has been reviewed by 2–3 members of the International Program Committee and International Reviewer Board. Following a review process, only the highest-quality submissions were accepted for inclusion in the conference. Sixty-one best papers have been selected for oral presentation and publication in the two volumes of the KES-IDT 2019 proceedings. We are very satisfied with the quality of the program and would like to thank the authors for choosing KES-IDT as the forum for the presentation of their work. Also, we gratefully acknowledge the hard work of the KES-IDT International Program Committee members and of the additional reviewers for taking the time to review the submitted papers and selecting the best among them for presentation at the conference and inclusion in its proceedings. We hope and intend that KES-IDT 2019 significantly contributes to the fulfillment of the academic excellence and leads to even greater successes of KES-IDT events in the future. Gdynia, Poland Poole, UK Canberra, Australia June 2019

Ireneusz Czarnowski Robert J. Howlett Lakhmi C. Jain

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Contents

Part I 1

2

Signal Processing and Pattern Recognition for Decision Making Systems

Quality Improvement Through the Preventive Detection of Potentially Defective Products in the Automotive Industry by Means of Advanced Artificial Intelligence Techniques . . . . Marco Vannucci and Valentina Colla 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 The Problem of Returned Products and the Advantages of Their Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 A Tool for the Preventive Detection of Future Returned Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Experimental Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An Efficient Non-Blind Steering Vector Estimation Technique For Robust Adaptive Beamforming With Multistage Error Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ghattas Akkad, Ali Mansour, Bachar Elhassan, Jalal Srar, Mohamad Najem and Frederic Leroy 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Background Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Least Mean Square Beamformer . . . . . . . . . . . . 2.2.2 Recursive Least Square Beamformer . . . . . . . . . 2.2.3 Minimum Variance Distortionless Response Beamformer . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Kalman-Based MVDR Beamformer . . . . . . . . . . 2.3 Steering Vector Estimation . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Steering Vector Estimation Algorithm . . . . . . . .

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2.3.2 Multistage Error Feedback Beamformer . 2.4 Simulation Results and Discussion . . . . . . . . . . . . 2.5 Conclusion and Future Work . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

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The P2P–Grid–Agent Distributed Platform: A Distributed and Dynamic Platform for Developing and Executing Large-Scale Application Based on Deep Learning Techniques . . . . . . . . . . . . Hamdi Hassen, Trifa Zied and Khemakhem Maher 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Pattern Recognition System (PRS): An Overview . . . . . . . . . 3.3 Existing Handwriting Recognition Systems Based on Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Large-Scale (LS) Applications: Pattern Recognition (PR) Application as a Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 The P2P–Grid–Agent Platform: The Proposed Approach . . . . 3.6 The Experimental Study . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.1 LS-OCR System Based on Clusters Architecture . . 3.6.2 LS-OCR OCR System Based on the P2P–Grid–Agent Distributed Platform . . . . . . . . . . 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recognition of Daily Human Activities Using Accelerometer and sEMG Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Giorgio Biagetti, Paolo Crippa, Laura Falaschetti, Simona Luzzi and Claudio Turchetti 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Classification of Alzheimer’s Disease from Structural Magnetic Resonance Imaging using Particle-Bernstein Polynomials Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Giorgio Biagetti, Paolo Crippa, Laura Falaschetti, Simona Luzzi, Riccardo Santarelli and Claudio Turchetti 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Magnetic Resource Imaging Preprocessing . . . . . . . . . . . . . 5.4 Features Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Classification Based on Particle-Bernstein Polynomials (PBP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part II 6

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Intelligent Data Processing and Software Paradigms

Selecting Informative Samples for Animal Recognition in the Wildlife . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Margarita Favorskaya and Vladimir Buryachenko 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Proposed Algorithm for Selecting Informative Samples . 6.3.1 Collection of Informative Samples . . . . . . . . . 6.3.2 Set of Representative Samples . . . . . . . . . . . . 6.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Genetic Algorithm for Selecting Relevant Regions in Digital Watermarking Scheme for 2D/3D Medical Images . . . . . . . . Margarita Favorskaya and Eugenia Savchina 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Proposed Algorithm for Selecting Relevant Regions . . . . 7.3.1 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . 7.3.2 Selecting Relevant Regions for Watermark Embedding in 2D Medical Images . . . . . . . . . . 7.3.3 Selecting Relevant Regions for Watermark Embedding in 3D Medical Images . . . . . . . . . . 7.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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A Transport Coding Gain Estimation in the Conditions of Time Limitation for Maximum Acceptable Message Delay . . . . . . . . . Evgenii Krouk, Anton Sergeev and Mikhail Afanasev 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 The Transport Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 The Network Model . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 The Message Encoding Procedure . . . . . . . . . . . . . 8.3 The Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 The Traffic Model That Is Critical to Message Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 The Average Packet Delivery Time . . . . . . . . . . . .

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8.3.3 8.3.4

The Direct Problem Statement . . . . . . . The Influence of the Transport Coding on the Message Delay Jitter . . . . . . . . 8.3.5 The Inverse Problem Statement . . . . . . 8.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

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Modeling Customers Speed of Movement from POS- and RFID-Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marina Kholod, Peter Golubtsov, Arthur Varlamov, Svyatoslav Filatov and Katsutoshi Yada 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Related Work Review and Formulation of Hypotheses . . . . . 9.3 Experiment Setting and Data Collection . . . . . . . . . . . . . . . . 9.4 Research Methodology and Empirical Results . . . . . . . . . . . 9.4.1 General Calculations of the Characteristics of the Traffic and Shopping in the Supermarket . . . 9.4.2 Calculation of Motion Characteristics for Each Store Department . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.3 Calculations for Purchases . . . . . . . . . . . . . . . . . . 9.4.4 Analysis of the Dependence of Revenue on Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.5 Grouping by Speed . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10 Discovering and Analyzing Binary Codes Based on Monocyclic Quasi-Orthogonal Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexander Sergeev, Vadim Nenashev, Anton Vostrikov, Alexander Shepeta and Daniil Kurtyanik 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Code Sequences of Lengths 3, 7, and 11 Based on Monocyclic Mersenne Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Comparative Analysis of Autocorrelation Functions of Barker and Mersenne Coding Sequences . . . . . . . . . . . . . . . . . . . . . 10.4 Codes of Lengths 5 and 13 Based on Monocyclic Raghavarao Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 Comparative Analysis of Barker and Raghavarao Coding Sequence ACFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6 Codes of Lengths 2 and 4 Based on Monocyclic Belevitch and Hadamard Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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11 The Study of Generators of Orthogonal Pseudo-random Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuriy Balonin, Leonid Abuzin, Alexander Sergeev and Vadim Nenashev 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Construction of Two-Circulant Hadamard Matrix . . . 11.3 The Study of Parameter Sets . . . . . . . . . . . . . . . . . . 11.4 Two-Circulant Hadamard Matrices H116 . . . . . . . . . . 11.5 Matrix Search Algorithms . . . . . . . . . . . . . . . . . . . . 11.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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12 Portraits of Orthogonal Matrices as a Base for Discrete Textile Ornament Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexander Sergeev, Mikhail Sergeev, Anton Vostrikov and Daniil Kurtyanik 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Matrix Portraits as a Base for Ornament Patterns . . . . . . . . . 12.3 Orthogonal Hadamard and Mersenne Matrices and Patterns of Their “Portraits” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4 Possible Ways of Building a Pattern . . . . . . . . . . . . . . . . . . 12.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Training Sample Generation Software . . . . . . . . . . . . . . . . Anton Vostrikov and Stanislav Chernyshev 13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 Ways to Generate a Dataset . . . . . . . . . . . . . . . . . . . . . 13.3 Programs and Services for the Marking of a Training Sample. Important Requirement . . . . . . . . . . . . . . . . . . 13.4 Creating a Training Sample for Analyzing the Behavior of Drivers and Pilots . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part III

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Interdisciplinary Approaches in Data Science and Digital Transformation Practice

14 Business AI Alignment Modeling Based on Enterprise Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hironori Takeuchi and Shuichiro Yamamoto 14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3 AI System and Research Hypothesis . . . . . . . . . .

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14.3.1 AI System . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3.2 Research Hypothesis . . . . . . . . . . . . . . . . . . . . 14.4 Modeling Business AI Alignment . . . . . . . . . . . . . . . . . 14.4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.4.2 Business and Application Layers . . . . . . . . . . . 14.4.3 Motivation Extension and Relation . . . . . . . . . 14.4.4 Method for Modeling Business AI Alignement 14.5 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.6 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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15 Eye Tracking as a Method of Neuromarketing for Attention Research—An Empirical Analysis Using the Online Appointment Booking Platform from Mercedes-Benz . . . . . . . . . . . . . . . . . . . . Veit Etzold, Anika Braun and Tabea Wanner 15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.1.1 Goal of the Study . . . . . . . . . . . . . . . . . . . . . . . . . 15.1.2 Study Group and Task Performed . . . . . . . . . . . . . 15.2 Eye Tracking Study of the Service Appointment Booking of Mercedes-Benz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.1 Concept of the Study . . . . . . . . . . . . . . . . . . . . . . 15.2.2 Test Subject . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2.3 Eye Tracking System and Software . . . . . . . . . . . . 15.2.4 Questionnaire Before the Study . . . . . . . . . . . . . . . 15.2.5 Questionnaire After the Study . . . . . . . . . . . . . . . . 15.2.6 Tasks of the Test Persons . . . . . . . . . . . . . . . . . . . 15.3 Implementation of the Study . . . . . . . . . . . . . . . . . . . . . . . . 15.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.4.1 Evaluation and Analysis of the Results . . . . . . . . . 15.4.2 Results of the Questionnaire Before the Study . . . . 15.4.3 Awareness of the Service Appointment Agreement . 15.4.4 Results of the Questionnaire After the Study . . . . . 15.4.5 Eye Tracker Data . . . . . . . . . . . . . . . . . . . . . . . . . 15.4.6 General Heatmap Analysis . . . . . . . . . . . . . . . . . . 15.4.7 Analysis of Attention Distribution by Scanpaths . . . 15.4.8 Scanpath at the ‘Service Selection’ Booking Step . . 15.4.9 Analysis of the Processing Time . . . . . . . . . . . . . . 15.4.10 Significance Tests . . . . . . . . . . . . . . . . . . . . . . . . . 15.4.11 Entry Channels and Insights . . . . . . . . . . . . . . . . . 15.4.12 Resulting Marketing Measures . . . . . . . . . . . . . . . 15.4.13 Development of a Catalogue of Measures . . . . . . . 15.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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16 A Concept of an Interactive Web-Based Machine Learning Tool for Individual Machine and Production Monitoring . . . . . . . . . . Marina Burdack and Manfred Rössle 16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3.1 Master Data Management . . . . . . . . . . . . . . . . . . . 16.3.2 Project Management . . . . . . . . . . . . . . . . . . . . . . . 16.3.3 Data Storage Engine . . . . . . . . . . . . . . . . . . . . . . . 16.3.4 Preprocessing Engine . . . . . . . . . . . . . . . . . . . . . . 16.3.5 Machine Learning Engine . . . . . . . . . . . . . . . . . . . 16.3.6 Live Prediction Engine . . . . . . . . . . . . . . . . . . . . . 16.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4.1 Evaluation Data and Settings for Quality Prediction Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4.2 Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.5 Conclusions and Further Work . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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17 Benefits of Agile Project Management in an Environment of Increasing Complexity—A Transaction Cost Analysis . . . . Raphael Kaim, Ralf-Christian Härting and Christopher Reichstein 17.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2 Growing Complexity Increases Transaction Costs . . . . . . . 17.3 Agile Project Management and Scrum . . . . . . . . . . . . . . . 17.4 Transaction Cost Effects of Scrum . . . . . . . . . . . . . . . . . . 17.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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18 Potential Benefits of Digital Business Models and Its Processes in the Financial and Insurance Industry . . . . . . . . . . . . . . . . . . . Ralf-Christian Härting, Christopher Reichstein and Raffael Sochacki 18.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2 Digitization and Digital Business Models . . . . . . . . . . . . . . . 18.3 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.4 Research Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.5 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contents

Decision Making Theory for Economics

19 Risk-Sensitive Decision-Making Under Risk Constraints with Coherent Risk Measures . . . . . . . . . . . . . . . . . . . . . . . . . Yuji Yoshida 19.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2 Coherent Risk Measures Derived from Risk Averse Utility 19.3 Risk Allocation in Asset Management . . . . . . . . . . . . . . . 19.4 Maximization of Risk-Sensitive Rewards Under a Risk Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.5 Numerical Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Mutual Evaluation and Solution Method Kazutomo Nishizawa 20.1 Introduction . . . . . . . . . . . . . . . . . . 20.2 Proposed Calculation Method . . . . . 20.3 Examples . . . . . . . . . . . . . . . . . . . . 20.3.1 Example 1 . . . . . . . . . . . . 20.3.2 Example 2 . . . . . . . . . . . . 20.4 Conclusion . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . .

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21 Visualization of Criteria Priorities Using a Ternary Diagram . Natsumi Oyamaguchi, Hiroyuki Tajima and Isamu Okada 21.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Methods and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.1 Alternatives Priorities with Respect to Each Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.2 Visualize the Relationship between Criteria Priorities and the Most Favorable Alternatives (Two Criteria) . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.3 Visualize the Relationship Between Criteria Priorities and the Most Favorable Alternatives (Three Criteria) . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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22 Changes in Organizations Due to Management Mechanization (Case Studies of Life Insurance Companies in Japan) . . . . . . . . . . 249 Shunei Norikumo 22.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 22.2 The Mechanization of Management in Japan . . . . . . . . . . . . . . 250

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22.3 Companies with a Long History of Mechanization of Management . . . . . . . . . . . . . . . . . . . . . . . . . . 22.3.1 Nippon Life Insurance . . . . . . . . . . . . . 22.3.2 Asahi Mutual Life Insurance Company (Former Imperial Life) . . . . . . . . . . . . . 22.4 Consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Naturality for Ranking from Pairwise Comparisons . Takafumi Mizuno 23.1 Ranking from Pairwise Comparisons . . . . . . . . . 23.2 Naturality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.3 Ranking Which Satisfies Naturality . . . . . . . . . . 23.4 Rating to Derive Weights . . . . . . . . . . . . . . . . . 23.5 A Link Diagram . . . . . . . . . . . . . . . . . . . . . . . . 23.5.1 An Example . . . . . . . . . . . . . . . . . . . . 23.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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24 Upper Level Processes and Projects Model Building . . . . . . . . . . Marina Kholod, Yury Lyandau, Valery Maslennikov, Irina Kalinina and Nikolay Mrochkovskiy 24.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.2 Strategic and Operational Level of Projects and Processes Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.3 The Methodology of Value Creation and Value-Added Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.3.1 Value-Added Chain in Manufacturing . . . . . . . . . . 24.3.2 Value-Added Chain in Services . . . . . . . . . . . . . . . 24.3.3 Value-Added Chain in Trade . . . . . . . . . . . . . . . . . 24.3.4 Value-Added Chain for Individual Orders . . . . . . . 24.4 Managerial Implications of the Model . . . . . . . . . . . . . . . . . 24.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 SPCM with Harker Method for MDAHP Including Hierarchical Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Takao Ohya 25.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.2 D-AHP and SPCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.2.1 Evaluation in D-AHP . . . . . . . . . . . . . . . . . . . . . . 25.2.2 SPCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.3 Numerical Example of Using SPCM for Calculation of MDAHP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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25.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Part V

Digital Health, Distance Learning and Decision Support for eHealth

26 Building a Telemedicine System for Monitoring the Health Status and Supporting the Social Adaptation of Children with Autism Spectrum Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Georgy Lebedev, Herman Klimenko, Eduard Fartushniy, Igor Shaderkin, Pavel Kozhin and Dariya Galitskaya 26.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.2 The Purpose of Research . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.3 Materials and Methods of Research . . . . . . . . . . . . . . . . . . . 26.4 Research Results and Discussion . . . . . . . . . . . . . . . . . . . . . 26.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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27 Implementation of Digital Technologies in Pharmaceutical Products Lifecycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Konstantin Koshechkin, German Klimenko and Alexander Polikarpov 27.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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28 Home Hospital e-Health Centers for Barrier-Free and Cross-Border Telemedicine . . . . . . . . . . . . . . . . . . . . . . . . . . Ralf Seepold, Maksym Gaiduk, Juan Antonio Ortega, Massimo Conti, Simone Orcioni and Natividad Martínez Madrid 28.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.2 Relevance of Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 28.2.1 Rural and Cross-Border . . . . . . . . . . . . . . . . . . . . . 28.2.2 Expected Benefit . . . . . . . . . . . . . . . . . . . . . . . . . . 28.3 Home Hospital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.3.1 General Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 28.3.2 Sleep Hospital in the Home Environment . . . . . . . . . 28.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.5 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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29 Data-Driven Human Movement Assessment . . . . . . . . . . . . Danny Dressler, Pavlo Liapota and Welf Löwe 29.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29.2 Common Preprocessing of the Assessment Approaches . 29.3 Data-Driven Assessment Methods . . . . . . . . . . . . . . . . 29.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29.5 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Attitudes to Telemedicine, and Willingness to Use in Young People . . . . . . . . . . . . . . . . . . . . . . . . . . Margarita Stankova and Polina Mihova 30.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.1.1 Defining the Term Telemedicine . . . . . . . . 30.1.2 Telemedicine Tools Studies . . . . . . . . . . . . 30.2 Participants and Methods . . . . . . . . . . . . . . . . . . . . . 30.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.3.1 Demographic and Computer Literacy Skills Characteristics of the Participants . . . . . . . 30.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.5 Limitations of the Study . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Posture Tracking Using a Machine Learning Algorithm for a Home AAL Environment . . . . . . . . . . . . . . . . . . . . Maksim Sandybekov, Clemens Grabow, Maksym Gaiduk and Ralf Seepold 31.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2 State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . . . 31.3.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . 31.3.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . 31.3.3 Posture Prediction . . . . . . . . . . . . . . . . . . . 31.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.6 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349

About the Editors

Ireneusz Czarnowski is a Professor at the Gdynia Maritime University. He holds B.Sc. and M.Sc. degrees in Electronics and Communication Systems from the same University. He gained the doctoral degree in the field of computer science in 2004 at Faculty of Computer Science and Management of Poznan University of Technology. In 2012, he earned a postdoctoral degree in the field of computer science in technical sciences at Wroclaw University of Science and Technology. His research interests include artificial intelligence, machine learning, evolutionary computations, multi-agent systems, data mining and data science. He is an Associate Editor of the Journal of Knowledge-Based and Intelligent Engineering Systems, published by the IOS Press, and a reviewer for several scientific journals. Dr. Robert J. Howlett is the Executive Chair of KES International, a non-profit organization that facilitates knowledge transfer and the dissemination of research results in areas including Intelligent Systems, Sustainability, and Knowledge Transfer. He is a Visiting Professor at Bournemouth University in the UK. His technical expertise is in the use of intelligent systems to solve industrial problems. He has been successful in applying artificial intelligence, machine learning and related technologies to sustainability and renewable energy systems; condition monitoring, diagnostic tools and systems; and automotive electronics and engine management systems. His current research work is focussed on the use of smart microgrids to achieve reduced energy costs and lower carbon emissions in areas such as housing and protected horticulture. Lakhmi C. Jain BE(Hons), M.E., Ph.D., Fellow (IE Australia) is a member of the Faculty of Education, Science, Technology & Mathematics at the University of Canberra and the University of Technology Sydney, both in Australia. He is a Fellow of the Institution of Engineers Australia. Professor Jain founded KES International, which provides professional communities with opportunities for

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publication, knowledge exchange, cooperation and teaming. His interests include artificial intelligence paradigms and their applications in complex systems, security, e-education, e-healthcare, unmanned aircraft and intelligent agents.

Part I

Signal Processing and Pattern Recognition for Decision Making Systems

Chapter 1

Quality Improvement Through the Preventive Detection of Potentially Defective Products in the Automotive Industry by Means of Advanced Artificial Intelligence Techniques Marco Vannucci and Valentina Colla

Abstract The paper addresses the problem of quality assessment of high-precision automotive components by means of artificial intelligence techniques that aim at the detection of potentially defective products before they are sold to customers. This control is motivated by industrial requirements as it could avoid a number of negative consequences for the company. The problem involves the classification of strongly unbalanced datasets and requires a suitable preprocessing of the data before they are used for models training. Standard classifiers and ensemble methods were used for the detection of defective products. The satisfactory results achieved by the selected approach lead to significant improvement from the industrial point of view.

1.1 Introduction Quality control is a key aspect in most industrial contexts. The quality of a product, when put into the market, influences the customers’ satisfaction, the reputation of the producer, and may determine its success. Further, quality issues may determine production strategies and resources allocation, and involve a number of issues throughout the industrial framework that goes beyond the purely technical aspects. For these reasons, quality control gained interest in the last years in nearly all the industrial frameworks and at different levels of the production chain. For instance, real-time quality controls performed throughout the production line are exploited for scheduling purposes (via linear programming) in order to optimize cost and overall productivity [1]. In [2], quality evaluation is used, together with customers’ feedM. Vannucci (B) · V. Colla Scuola Superiore Sant’Anna, Istituto TeCIP, Pisa, Italy e-mail: [email protected] V. Colla e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 I. Czarnowski et al. (eds.), Intelligent Decision Technologies 2019, Smart Innovation, Systems and Technologies 143, https://doi.org/10.1007/978-981-13-8303-8_1

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back, for the determination of the optimal cost of products by applying concepts taken from game theory. In [3], statistical methods are used in order to infer the quality of electronic products from the quality features of their components. From the more technical side, quality controls based on vision systems are used in the glass industry in order to spot superficial defects [4]. In the automotive industry, advanced 3D vision systems are used to assess metal panels’ quality by investigating shapes and eventual surface defects [5]. Artificial Intelligence (AI) techniques are becoming popular in quality assessment tasks due to their flexibility and capability of learning from data or users’ experience. AI combined with vision systems is used in the steel industry for the detection of superficial defects as in [6] where the images acquired during the manufacturing of flat products are elaborated and the extracted features are fed to Artificial Neural Networks (ANNs)-based system in order to assess the overall quality of the product in real time. In [7], in an analogous context, a set of Artificial Intelligence (AI) techniques is able to determine all the defects on semi-manufactured products and to provide a classification of their types and main features (position, extension, severity, etc.). In this latter application, these information are used in the subsequent manufacturing stages in order to maximize the quality of the final products. In another application related to brakes production, the information acquired during the processes are fed to a Fuzzy Inference System (FIS) whose output is used to control the manufacturing itself and, finally, to evaluate the product quality [8]. In this paper, AI-based techniques are used in order to address a quality assessment problem related to the manufacturing of high-precision automotive components. In this application, although the rate of defective products put into market is extremely low, a system for the preventive identification of the products that will likely lead to problems when installed on vehicles engines is needed for several reasons that include a general quality improvement and a more efficient management of customers’ claims. The industrial problem handled in this work is described in detail in Sect. 1.2; then, in Sect. 1.3 the design of a system for the detection of potentially faulty products is described and in Sect. 1.4 the achieved results are presented and discussed not only from a technical point of view but also analyzing the overall industrial benefits. Finally, conclusions and future perspective of the developed framework are discussed in Sect. 1.5.

1.2 The Problem of Returned Products and the Advantages of Their Identification This work is focused on an engine component manufactured by a major automotive components producer that is not indicated for confidentiality reasons. The considered component is worked out on a dedicated production line of the factory and represents a strategical product from the commercial point of view.

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The production line is quite complex and consists of a set of interconnected sequential sublines, each one devoted to a particular phase of the manufacturing that includes the following: – the assembly operations devoted to the combination of the main components of the product; – the performance of a series of specific tests during which the main functions and features of the products are checked in order to detect eventual defective products that, if needed, are discarded as scraps; – the installation of secondary components; and – the visual examination of sample products and, if this latter test succeed, they are packaged, ready to be put into the market. During all these phases, a huge amount of data on the specific operations and tests are collected and stored in Different Databases (DB) that use as primary key the product IDs. For instance, for all the steps of the assembly phase, the measures provided by sensors and machines that assemble components are stored in the DB as well as the results of the tests performed on single products. Once on the market, components are finally installed on the engines type they are designed for. Although the rate of defective products that reach this stage is extremely low, it is possible that some of them fail when operating before the expiration of warranty. In this case, customers may present an official claim to the producer. The claim results in the return of the presumed defective product (the so-called Returned Product—RA) to the company so as to allow the required tests in order to verify the presence of an actual problem imputable to the producer and eventually to gain knowledge on it in order to improve future quality. The check of the actual defectiveness of a finished part is pursued in a dedicated laboratory where each RP is object of a number of time required and costly tests. The return of a product (whether it is actually defective or not) represents thus a considerable damage for the producer since it implies the performing of the whole set of tests envisaged for the fault assessment: roughly, the examination of every single RP requires 5 h and the use of specific testing machines and consumables. Further, if the defect is confirmed, the producer has to pay some penalty and, in the worst cases, has to change the commercial agreement with the customer (i.e., lower prices). In this context, the strategic value of a tool for the identification of RPs before they are sent to the customers is evident. In order to be effective, such tool should be based on the exploitation of the information on single products collected before the packaging phase. This set of information includes all the characteristics of the components that form each final product, the measures gathered during the assembly phases, the outcome of each test intermediate performed, and the visual quality assessment. The detection of a future RP and its discard as scrap would positively affect the factory performance under two main points of view:

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– sensible reduction of the work burden of the laboratory devoted to tests on RPs; – reduction of RPs leads to a higher satisfaction of the customers. Quality improvements will then influence the commercial agreements between producer and customers. The risk in the use of this technology lies in the possibility of rising the production scrap rate by generating a high number of false alarms: in that case, a non-defective product is wrongly discarded, reducing the overall line productivity. In the next section, the different approaches for development of a software tool for the early identification of future RPs are presented. All the approaches are based on the exploitation of historical data collected by the factory through different AI techniques that put into relation the measures gathered during the production with the future return of products. The design of the system takes into account all the aspects behind the identification of future RPs in terms of accuracy, missed detection of RPs, and false alarms in order to maximize its real efficiency.

1.3 A Tool for the Preventive Detection of Future Returned Products As introduced, the tool for the characterization of future RPs exploits the historical data collected on the production plant through the years for the training of AI-based models. More in detail, the dataset is formed by merging a number of data sources taken from the plant, namely, the online databases (those that collect data from the assembly, test and dressing lines), the outcomes of the visual check and the database that collects all the evaluations of the laboratory tests performed on the RPs once returned to the company. This latter database is used also in order to select the RPs that are returned for reasons attributable to the producer (the others are not a problem for the company). In this work, the data collected in more than 1 year have been used resulting in a dataset containing about 4 millions of entries. Due to the very low number of RPs, the database is extremely unbalanced so as to heavily compromise the performance of any standard data-driven classifier [9]. The detection of rare events in this case would be in facts hard due to classifier that, trained by using a dataset formed by almost totally non-RPs observations, would be biased toward their identification in order to maximize the overall performance. To limit this effect, the training dataset has been rebalanced by reducing the unbalance ratio through the removal of part of the non-RPs samples. This operation is called under-sampling. In this problem, a special under-sampling procedure is adopted. Production is organized in batches of similar components (in terms of type and characteristics) for the satisfaction of customers’ orders, thus, for each RP in the dataset, a set of non-RPs produced just before and after the RP is selected. This procedure grants the selection of non-RPs that share the characteristics of each RP in the dataset. The use of a so-formed dataset should allow the identification of the characteristics of RPs that deviate from those

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7

of non-RPs. According to the suggestions of the lines personnel, for each RP the 20 previous and successive produced components were included in the dataset. It leads to a dataset including more than 40,000 observations whose unbalance ratio is about 2.5%. The number of variables considered in the database, coming from the whole manufacturing line, is higher than 500. Before being fed to the classifiers, data have been preprocessed with the aim of removing outliers and selecting the most suitable input variables for the problem. Outliers’ removal is devoted to the improvement of models’ performance by means of the elimination of misleading information (unreliable data) that in this framework can be due, for instance, to sensors malfunctions. The algorithm employed in this work for this operation is described in [10, 11]. The method combines through a FIS several techniques for outliers identification and resulted very efficiently when managing industrial data. Variable selection is used on data-driven applications’ dataset for leveraging the training of the models and avoiding the disturbance of unnecessary variables for the learning task that can be even detrimental for models’ performance. In this work, the first selection was performed by expert company personnel who reduced the number of variables to slightly more than 200 (most of them are related to the tests performed on the almost finished products), then the second selection is done through the GiveA-Gap method [12, 13]. This method, belonging to the wrappers class, is a special version for classification problems of the one presented in [14] that finds the input variables that are most related to a target by means of genetic algorithms, evaluating the candidate solutions (i.e., subsets of the whole set of input variables) through the following fitness function, presented in [15], and specifically designed for coping with unbalanced datasets’ classification: f itness(s) =

γU n f Det (s) − F A(s) OvCorr (s) + μF A(s)

(1.1)

In Eq. 1.1, a candidate solution s is evaluated in terms of its unfrequent samples detection ratio (UnfDet), false alarms (FA), and overall correct predictions (OvCorr). The equation encourages the detection of the salient events rather than the correctness of the overall classification and, at the same time, aims at avoiding false alarms. The parameters γ and μ are used to fine-tune the evaluation process as described in detail in [15]. The result of the application of the algorithm leads to the selection of 31 variables that will be exploited by the tested classifiers. The achieved dataset was used in Sect. 1.4 for training a number of different types of classifiers and measures their performance. Due to the nature of the problem and the different weights of the two possible misclassification (a false alarm when a non-RP is classified as an RP and a missed detection when an RP is classified as non-RP), an asymmetric cost matrix was adopted during the training procedure of the classifiers. More in detail, the worst case in this application is represented by the FA that corresponds to the wrong discard of a number of finished products. Keeping constant to 1 the cost for a missed detection, the following costs for the FA were tested: [0.5, 0.75, 1.25, 1.5, 1.75, 2.5].

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Here is a list and a short description of the details of the set of classifiers that were evaluated: – Decision tree (DT) is straightforward decision tree trained by means of the C4.5 algorithm and optimized by pruning for the improvement of generalization capabilities; – Artificial Neural Networks are two-layer feedforward neural networks with two outputs (one for each class, the most active wins). The activation function of both layers is the logistic function. Several ANNs were tested with a different number of hidden neurons (from 5 to 30, the best performing architecture will be reported among the results in the next section); – Thresholded Neural Net (TANN) is an evolution of standard two-layer ANN for the classification of unbalanced datasets [15] that exploit a threshold operator at the output of a single output network. The threshold is automatically optimized on the basis of training data in order to maximize the classifier performance in the presence of unbalanced datasets according to Eq. 1.1; – Random forest (RF) is based on a different number of decision trees obtained by means of the bagging ensemble method [16]. Different numbers of trees were tested (from 10 to 40) although the best performing configuration is reported in Sect. 1.4 – Hybrid Ensemble (HyEM) is an ensemble method based on the combination of decision trees (in the ensemble methods Jargo, the weak learners) for the main classification tasks and a set of neural networks that, through the estimation of single weak classifiers reliability, manage the aggregation of their outputs [17]. Different HyEM architectures, varying the number of weak classifiers, were tested. The best performing one is discussed in the next section. In the next section, the performances of the listed approaches are shown, for each FA misclassification cost, in terms of rates of FA and correct RP detections. The results are analyzed from the point of view of the industrial benefits in order to select the most promising classifier and assess its expected impact in the production chain.

1.4 Experimental Tests In this section, the performance of the classifiers mentioned in the previous section is reported. The classifiers are tuned and tested using the dataset formed as described in Sect. 1.3. Throughout all the tests, the 10-fold cross-validation technique was used for the fair and reliable comparison among the different methods. The total number of tests performed is huge, due to the different employed cost matrices and parameter configurations for the different methods (i.e., number of hidden neurons for the ANN-based approaches, number of weak learners in the ensemble methods, etc.). For the sake of synthesis, the best performing setup for each classifier is presented and compared to the others for the different FA misclassification costs considered. In Table 1.1, the results obtained by the assessed approaches are

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Table 1.1 Performance in terms of TPR and FPR of the tested approaches for the identification of RPs according to different costs of the FA misclassification. In each row, the results achieved by the best performing setup of the classifiers is shown. The information in brackets are the number of neurons in the hidden layer for ANN e TANN approaches and the number of weak learners for random forest and HyEM Cost Method and params KPI 0.5 0.75 1 1.25 1.5 1.75 2 2.5 DT

TPR FPR ANN (22 hid.) TPR FPR TANN (19 hid.) TPR FPR Rand. Forest (41 wl) TPR FPR HyEM (35 wl) TPR FPR

19.1 0.23 21.4 0.28 39.8 1.1 38.6 0.31 42.6 0.41

16.5 0.2 21.6 0.29 36.5 0.95 38.3 0.22 40.9 0.24

14.2 0.11 20.9 0.27 33.3 0.9 37.9 0.12 39.9 0.19

13.9 0.08 16.9 0.25 33.0 0.86 36.1 0.11 37.3 0.16

9.3 0.03 15.5 0.18 32.3 0.65 36.0 0.11 36.4 0.11

7.2 0.03 8.4 0.13 27.9 0.47 32.3 0.09 35.6 0.09

2.1 0.02 5.2 0.09 26.1 0.36 28.4 0.08 34.1 0.08

0.8 0.001 1.8 0.04 22.8 0.19 26.2 0.05 34.7 0.06

presented in terms of True Positive Rate (TPR—sensitivity) and False Positive Rate (FPR) calculated as the percentage of correctly predicted RPs with respect to the number of actually returned RPs and the percentage of FA with respect to the number of non-RP samples, respectively. These measures reflect the Key Performance Indicators (KPIs) commonly taken into account by the producer for its internal evaluations and decision-making processes. The results reported in Table 1.1 are shown also in Fig. 1.1 where the comparison among the classifier is clearer for varying values of the FA misclassification cost. According to the industrial requirements of the problem discussed in Sect. 1.2 and to these results, it stands out that the simpler approaches based on the use of DT or ANN do not represent a satisfactory solution to this problem; in facts, although on one hand these methods are capable of keeping very low the rate of FA (especially when the cost of this type of error is higher—left part of the chart in Fig. 1.1); on the other hand, the rate of detection of future RPs is very low and it grows only (but not sufficiently) when the rate of FA is higher. The TANN method performance is not satisfactory as well since it brings in any case to the generation of a rate of false alarms too high for the problem requirements since every single FA corresponds to an erroneously discarded product that directly affects costs and productivity. The two tested ensemble methods—the random forest and the HyEM—achieve much better results: both methods are able to keep low the rate of FA and acceptable the rate of detected RPs. Among the different tests, the best performance is achieved by the HyEM formed by 35 weak learners with FA misclassification cost set to 2.5; in this case, only 0.06% of false alarms have risen and more than one-third of RPs are avoided. These figures represent a satisfactory trade-off for the problem from the industrial point of view. Other results achieved by these classifiers characterized by

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Fig. 1.1 Overview of the results of achieved by the best configurations of the tested approaches for the different FA misclassification costs

a higher rate of RPs detection are not considered as good as the selected one due to the higher number of FA. The practical impact of the adoption of this classifier on the production line can be quantified as follows considering a base production of 1 million good parts sold. In this context, 0.0610% of the finished parts are discarded according to the suggestion of the selected classifier: among them, 600 are false alarms and represent a tolerable cost for the producer (in relative terms 0.06%) but the remaining 10 are correctly detected future RPs and thus reduce by one-third the number of future RPs sent to the customers which represent a noticeable saving in terms of resources (the laboratory devoted to such tests) and money (i.e., commercial relation, penalties).

1.5 Conclusions and Future Work In this work, an industrial problem related to quality assessment of manufactured automotive components is solved through the use of AI-based techniques that is able to detect future Returned Products (RPs) before they are sent to the final customer, with a positive impact on several aspects of the production framework. The problem is characterized by a strong unbalance in the available dataset as the number of RPs is extremely low with respect to the whole production and required the application of suitable techniques for the formation and preprocessing of the classifiers training datasets.

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Several classifiers were tested, and their performance was evaluated according to industrially driven criteria in order to maximize their impact on the actual production: some simpler approaches based on DT and ANNs and some more complex ones based on ensemble methods. The selected approach exploits a particular kind of ensemble method, the HyEM, which is able to reduce of one-third the number of RPs raising a marginal number of false alarms. This result is satisfactory from the industrial point of view as it significantly reduces the cost related to the RPs management without affecting the productivity. In the future, the developed software will be directly installed on the production line and its performance will be assessed on the basis of the actual future production. Further, newly collected data will be used for the continuous update of underlying models.

References 1. Peng, W., Zhang, Z., Tian, Z.: The scheduling of project time, cost and product quality. In: 2010 Chinese Control and Decision Conference, Xuzhou, pp. 150–154 (2010) 2. Fan, J., Ni, D., Tang, X.: Product recall decisions in supply chains under product liability. In: 2015 12th International Conference on Service Systems and Service Management (ICSSSM), Guangzhou, pp. 1–5 (2015) 3. Siyun, C., Wen, S., Simin, S., Lanbo, Z.: The conceptions, principles and systems for automotive materials purchasing quality decision and management based on total quality management theory. In: 2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering, Xian, pp. 117–121 (2013) 4. Adamo, F., Attivissimo, F., Di Nisio, A., Savino, M.: An online defects inspection system for satin glass based on machine vision. In: 2009 IEEE Instrumentation and Measurement Technology Conference, Singapore, pp. 288–293 (2009) 5. Abdul Razak, T.A., Shafee, K.S., Shamsuddin, K.A., Ibrahim, M.R., Baharuddin, B.T.H.T.: Application of 3D scanning onto automotive door panel for quality. In: 2016 International Conference on Robotics and Automation Engineering (ICRAE), Jeju, pp. 31–34 (2016) 6. Borselli, A., Colla, V., Vannucci, M.: Surface defects classification in steel products: a comparison between different artificial intelligence-based approaches. In: Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Applications, AIA, pp. 129– 134 (2011) 7. Vannucci, M., Colla, V., Nastasi, G.: Detection of rare events within industrial datasets by means of data resampling and specific algorithms. Int. J. Simul. Syst. Sci. Technol. 11(3), 1–11 (2010) 8. Gai, C.: Design of product quality process control system based on fuzzy logic. In: 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS), Changsha (2018) 9. Vannucci, M., Colla, V.: Classification of unbalanced datasets and detection of rare events in industry: issues and solutions. In: Proceedings of Engineering Applications of Neural Networks, EANN 2016, Aberdeen, UK, 2–5 Sept 2016 10. Cateni, S., Colla, V., Vannucci, M.: A fuzzy system for combining different outliers detection methods. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA 2009), vol. 1618 (2009) 11. Cateni, S., Colla, V., Nastasi, G.: A multivariate fuzzy system applied for outliers detection. J. Intell. Fuzzy Syst. 24(4), 889–903 (2013)

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12. Cateni, S., Colla, V., Vannucci, M.: Variable selection through genetic algorithms for classification purposes. In: Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010, vol. 1, pp. 6–11 (2010) 13. Cateni, S., Colla, V., Vannucci, M.: A genetic algorithm-based approach for selecting input variables and setting relevant network parameters of a SOM-based classifier. Int. J. Simul. Syst. Sci. Technol. 12(2), 30–37 (2011) 14. Cateni, S., Colla, V., Vannucci, M.: General purpose input variables extraction: a genetic algorithm based procedure GIVE a GAP. In: ISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications, art. no. 5364011, pp. 1278–1283 (2009) 15. Vannucci, M., Colla, V., Sgarbi, M., Toscanelli, O.: Thresholded neural networks for sensitive industrial classification tasks. In: Proceedings of International Work-Conference on Artificial Neural Networks, IWANN 2009, vol. 1, pp. 1320–1327. Springer, Berlin, Heidelberg 16. Kam Ho, T.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998) 17. Vannucci, M., Colla, V., Cateni, S.: Learners reliability estimated through neural networks applied to build a novel hybrid ensemble method. Neural Process. Lett. 46(3), 791–809 (2017)

Chapter 2

An Efficient Non-Blind Steering Vector Estimation Technique For Robust Adaptive Beamforming With Multistage Error Feedback Ghattas Akkad, Ali Mansour, Bachar Elhassan, Jalal Srar, Mohamad Najem and Frederic Leroy

Abstract Adaptive beamforming methods, when applied to practical problems, are known to suffer severe performance degradation if a mismatch occurs between the presumed and actual steering vectors. Such mismatch results in the suppression of the desired signal component, i.e., signal self-nulling phenomenon. Hence, robust approaches to adaptive beamforming and efficient steering vector estimation techniques are required. In this paper, a non-blind steering vector estimation technique is developed as a solution to the signal mismatch problem. The proposed technique extends the least mean square (LMS) and recursive least square (RLS) algorithms to estimate the array steering vector from the desired signal and array weights for robustness against possible mismatch. The resulting array vector is tested using the Kalman filter-based minimum variance distortionless response algorithm (KMVDR) by constraining the desired look direction, i.e., estimated steering vector, in the presence of additive white Gaussian noise. Moreover, a fast converging adaptive algorithm is G. Akkad (B) · A. Mansour · F. Leroy Lab-STICC, UMR 6285, ENSTA Bretagne, Brest, France e-mail: [email protected] A. Mansour e-mail: [email protected] F. Leroy e-mail: [email protected] B. Elhassan Faculty of Engineering, Lebanese University, Tripoli, Lebanon e-mail: [email protected] J. Srar Electrical and Electronic Department, Misratah University, Misratah, Libya e-mail: [email protected] M. Najem CCE, Lebanese International University, Mount Lebanon, Lebanon e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 I. Czarnowski et al. (eds.), Intelligent Decision Technologies 2019, Smart Innovation, Systems and Technologies 143, https://doi.org/10.1007/978-981-13-8303-8_2

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proposed based on cascaded stages and error feedback. Experimental results have shown satisfactory convergence given the estimated steering vector, hence validating the proposed approach.

2.1 Introduction Adaptive beamforming is a spatial multiplexing technique, for antenna arrays, used in performing directional signal reception and transmission. It is achieved by continuously generating a main lobe in the direction of the desired signal component while steering nulls in the direction of interference, thus resulting in frequency reuse and increased signal plus interference-to-noise ratio (SINR) [1–3], as described in Fig. 2.1. In wireless communication and multiple-input multiple-output (MIMO) systems, a major requirement for adaptive beamforming, when employed in practical applications, is robustness against the mismatch between the desired signal steering vector and the actual steering vector resulting in suppression of the desired signal component, i.e., signal self-nulling phenomenon [4]. Such mismatch occurs from incorrect environment assumption, imperfect array calibration, look direction errors, and finite precision arithmetic [5]. Hence, it is of great interest to formulate a low complexity solution for the signal mismatch and steering vector estimation problems. Several approaches exist to solve the problem of signal mismatch and provide robustness against some of possible causes. A classical, robust, approach is the linearly constrained minimum variance (LCMV) beamformer [4–7]. Even though the LCMV beamformer provides a robust solution, it still requires an estimate of the correlation matrix and its performance for moving targets which depends greatly on the performance of the direction of arrival estimation algorithm. Moreover, other popular approaches include the quadratically constrained beamformer (QCB) [8–10], eigenspace-based beamformer [11, 12], worst-case optimization-based beamformer

Fig. 2.1 Adaptive beamforming array

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[4, 5], and steering vector and covariance matrix estimation [13]. While the presented techniques offer general form solution for the unknown signal mismatch problem, they experience some drawbacks. First, the QCB lacks an optimized way for obtaining the optimal diagonal loading factor based on the uncertainty of the signal steering vector. Second, the eigenspace-based beamformer have degraded performance at low signal-to-noise ratio (SNR). Finally, the worst-case optimization solution and estimation technique are presented in [13]; even though it presents the best solution, it shows a greater increase in computational complexity [4, 5]. In this paper, a non-blind steering vector estimation technique for robust adaptive beamforming is presented. The suggested approach extends the LMS and the RLS algorithms to estimate the employed steering vector, i.e., array image, from the desired signal, input signal, and array weights while conserving the low computational complexity trait of these algorithms. In addition to presenting a cascaded, fast converging adaptive beamformer is based on error feedback and simulated using Kalman filter-based minimum variance distortionless response (MVDR).

2.2 Background Review This section presents a background review on the LMS, RLS, and the Kalmanbased MVDR beamformers for narrowband complex signals. Let the input vector x(k) = [x1 (k), x2 (k), . . . , x N (k)]T to the narrowband beamformer [14] be x(k) = ad sd (k) + ai si (k) + n(k)

(2.1)

at the discrete time instant k with the first antenna element acting as a reference. Hence, the general form of the steering vectors ad and ai is given by a = [1, e− j2π[

D sin(θ ) ] λ

, . . . , e− j (N −1)2π[

D sin(θ ) ] λ

]T

(2.2)

where [.]T represents the matrix transpose, N is the number of antenna elements, θ is the angle of arrival, D is the distance between consecutive antenna elements, λ is the signals wavelength, sd (k) and si (k) are the desired and interfering signals, ad and ai are the [N × 1] complex array steering vectors for the desired signal and interference, and n(k) stands for the complex additive white Gaussian noise (AWGN) vector [15]. Thus, the output of the beamformer subject to a linear combiner is given by Eq. (2.3) where [.] H represents the matrix Hermitian transpose and w(k) is the phase array weight vector. (2.3) y(k) = wH (k)x(k)

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2.2.1 Least Mean Square Beamformer The least mean square (LMS) algorithm is a stochastic gradient algorithm proposed by Widrow and Hoff as an implementation of the steepest descent optimization technique [14]. The LMS is a computationally efficient technique used to estimate the gradient of the error signal and update the weight vector of the phased array. It is applied to minimize the mean square error of the input and desired signals x(k) and d(k), respectively. The LMS algorithm may be defined by e(k) = d(k) − y(k)

(2.4) ∗

w(k + 1) = w(k) + μe (k)x(k)

(2.5)

where, μ, is the step size. While LMS offers minimal computational complexity and small residual error, it suffers from its slow convergence and is dependent on the eigenvalue spread variation of the input signal [14].

2.2.2 Recursive Least Square Beamformer The recursive least square (RLS) algorithm utilizes the method of least squares, i.e., the sum of error squares over a specified window, to update its weight vector [15]. Compared to the LMS, the RLS algorithm provides greater convergence at the cost of increased computational complexity. The RLS update formula is given by [15] w(k) = w(k − 1) + K (k)x(k)e(k) −1

α Q (k − 1) 1 + α −1 + xH (k)Q−1 (k − 1)x(k) e(k) = d ∗ (k) − wH (k − 1)x(k) K (k) =

(2.6)

−1

(2.7) (2.8)

where α is the forgetting factor and Q(k) is the approximation at time k of the input signal autocorrelation matrix [15] given by Q(k) =

k 

α k−i x(k)xH (k)

(2.9)

i=1

with i being the iteration index. While the RLS provides greater convergence independent of the input signals eigenvalue spread variation, it suffers from increased computational complexity and lacks robustness in fixed-point arithmetic.

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2.2.3 Minimum Variance Distortionless Response Beamformer The MVDR computes the optimal weight vector w by maximizing the beamformer output SINR, i.e., minimizing the interference and noise variance/power, while assuring a distortionless response toward the desired look direction a [16]. The beamformer output SINR is given by SI N R =

σ 2 |wH a(θs )|2 E[|wH sd (k)|2 ] = s H H 2 E[|w (i(k) + n(k))| ] w Ri+n w

(2.10)

where i and n are the interference and noise, respectively, σs2 is the desired signal power, E[.] denotes the expectation operator, and Ri+n is the N × N interference plus noise covariance matrix defined as Ri+n = E[(i(k) + n(k))(i(k) + n(k))H ] [16]. Thus the optimization problem can be formulated as min wH Ri+n w w

s.t. wH a(θs ) = 1

(2.11)

Hence, the optimal weight vector of the MVDR beamformer is given by wM V D R =

−1 a(θs ) Ri+n −1 a H (θs )Ri+n a(θs )

(2.12)

While the MVDR beamformer presents a SINR-based technique to formulate a distortionless response toward a given look direction, it still requires the estimation of the unknown interference plus noise covariance matrix Ri+n . The latter can be replaced ˆ i+n , which also includes the by the estimate of the data sample covariance matrix R desired signal component. The accuracy of the estimation depends on the training data samples available [4, 16]. The data sample covariance matrix estimate is computed using Eq. (2.13), where K is the number of training data samples. K  ˆ i+n = 1 R x(k)xH (k) K k=1

(2.13)

2.2.4 Kalman-Based MVDR Beamformer This section details an online computational form of the MVDR beamformer based on Kalman’s filter (KMVDR) derived in [16, 17]. Furthermore, the state space model of the constrained Kalman filters measurement equation is given by p = BH (k)w(k) + V(k)

(2.14)

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where p = [0 1]T , B and V are defined as the input vector and the measurement noise vector respectively and given by  H  x (k) B (k) = aH   H   −x (k)w(k) v1 (k) = V(k) = v2 (k) 1 − aH w(k) H

(2.15) (2.16)

where v1 (k) and v2 (k) are the residual and constraint error, respectively, modeled as zero-mean white-noise sequence [17]. Moreover, the correlation matrix of V(k) is given by   2 σ 0 (2.17) C = v1 2 0 σv2 Since the environment is considered stationary, the weight vector is assumed deterministic and the process equation is given by [17] w(k) = w(k − 1)

w(0) = w0

(2.18)

where w0 is the optimum weight vector. Applying the Kalman filter in discrete form yields the following equations ˆ − 1)] ˆ ˆ − 1) + G(k)[p − BH (k)w(k w(k) = w(k

(2.19)

G(k) = Z(k − 1)B(k)[BH (k)Z(k − 1)B(k) + C]−1

(2.20)

Z(k) = [I − G(k)B (k)]Z(k − 1)

(2.21)

H

where G(k) is the Kalman gain, Z (k) is the weight error correlation matrix, and I is a [N × N ] identity matrix [17].

2.3 Steering Vector Estimation This section extends the LMS and RLS non-blind techniques to estimate the array steering vector.

2.3.1 Steering Vector Estimation Algorithm A simple method for estimating the steering vector and track the angle of arrival of an adaptive beamformer is described in function of the input signal, array weights, and array output. Moreover, rearranging the input signal to the narrowband beamformer in

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element form at the time instant k at the mth antenna element with m = 1, 2, 3 . . . N , we obtain (2.22) xm (k) = Ad,m sd (k) + Ai,m si (k) + n m (k) where Ad,m and Ai,m are the mth elements of the complex steering vectors ad and ai . Thus the output of the individual taps of the beamformer are given by 

xm (k) = wm xm (k)

(2.23)

When an adaptive algorithm (LMS or RLS) converges, the output y tends to approach sd (k) by suppressing the interference and noise signals [14, 15]. Therefore, let the beamformer output be defined by y(k)  sd (k)

(2.24)

by applying the expectation operator to both sides of Eq. (2.22) at convergence, we get (2.25) E[xm (k)]  Ad,m E[sd (k)]  Ad,m E[y(k)] Assuming that after convergence, we can approximate E[y(k)]  y(k)

(2.26)

thus Eq. (2.25) can be rewritten as E[xm (k)]  Ad,m y(k)

(2.27)

assuming that the input signal and the available tap weights are uncorrelated the expectation of Eq. (2.23) can be written as 

E[xm (k)]  E[wm ]E[xm (k)]

(2.28)

therefore, from Eqs. (2.27) and (2.28), we can estimate the array steering vector in expectation and instantaneous form as 



xm (k) E[xm (k)]  Ad,m (k)  E[wm ]y(k) + ε wm (k)y(k) + ε

(2.29)

where ε is chosen as a small constant to prevent the division by zero 0