Innovation and Competitiveness in Industry 4.0 Based on Intelligent Systems

This book presents a series of applications of different techniques found in Industry 4.0 with relation to productivity, continuous improvement, quality, decision systems, software development, and automation systems. The techniques used throughout this book allow the reader to replicate the results obtained towards different types of companies that wish to undertake in the new era of the digital industrial revolution. This book can also help students from different areas of engineering to understand how the use of new technologies is applied to solve current relevant problems and how they give the possibility of constant innovation in the different industrial sectors. This is accomplished through the analysis of illustrative case studies, descriptive methodologies and structured insights that are provided through the different considered techniques.

Chapter 1. Machine Learning and Edge Computing for Industry 4.0 Applications: Concepts and Extensive Review.- Chapter 2. Failure detection system controlled by a mixed reality interface.- Chapter 3. Industry 4.0 in the health sector: System for Melanoma Detection.- Chapter 4. Assistive device based on computer vision.- Chapter 5. Development and Evaluation of a Machine-Learning Model for Prediction of Failures in an Injection Molding Process.- Chapter 6. An approach to select an open source ERP for SMEs based on industry 4.0 and digitization considering the SHERPA and WASPAS method.- Chapter 7. The Technological Role of Steepest Ascent Optimization in Industry 4.0 modeling.- Chapter 8. The role of industry 4.0 technologies in the energy transition: conceptual design of intelligent battery management system based on electrochemical impedance spectroscopy analysis.- Chapter 9. Performance analysis of 8-channel WDM optical network with different Optical Amplifiers for Industry 4.0.- Chapter 10. Traffic signs configuration with a geo-simulation approach.- Chapter 11. Emotional diagnosis for employees within the framework of Industry 4.0: a case study in Ciudad Juarez.- Chapter 12. Architecture for Initial States Algorithm for Blockchain Scalability in Local OnPrem IIoT Environments.- Chapter 13. Distribution route optimization using Floyd-Warshall weighted graph analysis algorithm with Google Maps integration in industry 4.0 context.- Chapter 14. Feature Selection in Electroencephalographic Signals Using a Multicriteria Decision Analysis Method.

Dr. Luis Carlos Méndez-González is a research professor at the Autonomous University of Ciudad Juarez. He is recognized by the National Research Systems in México and has several years of experience in the automotive, medical, and manufacturing industries. His research areas and publications include reliability analysis, statistical modeling, automation, and Industry 4.0.

 Dr. Luis Alberto Rodríguez-Picón is a researcher-professor in the Autonomous University of Ciudad Juarez. He is recognized by the National Research Systems in México and has several years of experience in the automotive industry. His areas of interest include statistical modeling, applied statistics, and stochastic modeling. He has published several research papers in important research journals in areas related to statistical modeling. 

Dr. Iván Juan Carlos Pérez Olguín is a researcher-professor at the Autonomous University of Ciudad Juarez. He is recognized by the National Research Systems in México and has several years of experience in the automotive industry. His contribution to the industry includes patents and software developments. His areas of interest include operation research, robust optimization, multi-criteria decision-making, lean manufacturing, stochastic modeling, and process improvement. He has published several research papers in important research journals related to industrial engineering.