Innovation and Competitiveness in Industry 4.0 Based on Intelligent Systems

Preface
Contents
Part I Artificial Intelligence and Fuzzy Techniques Applications in the Industry 4.0
Machine Learning and Edge Computing for Industry 4.0 Applications: Concepts and Extensive Review
1 Introduction
2 Concepts of Industry 4.0
3 Concepts of Edge Computing in Industry 4.0
4 Concepts of ML in Industry 4.0
5 Image Processing and Computer Vision
6 Sensors in Industry 4.0
7 Edge Computing in Industry 4.0
8 Conclusions
References
Failure Detection System Controlled by a Mixed Reality Interface
1 Introduction
1.1 State of the Art
2 Problem Analysis
2.1 Assembly Process for Medical Instruments
2.2 Failures in the Assembly Process
2.3 Proposed Innovation
3 Methodology
3.1 Materials and Equipment
3.2 System Characterization
3.3 Computer Vision Algorithm
3.3.1 Dataset
3.3.2 Training
3.4 Control Interface
3.5 HoloLens Configuration
4 Results
4.1 HoloLens App
4.2 Failure Detection System Powered by Azure
5 Conclusion and Future Work
Appendix
References
Industry 4.0 in the Health Sector: System for Melanoma Detection
1 Introduction
2 Machine Learning Methods for Melanoma Diagnoses
2.1 Machine Learning
2.1.1 Supervised Learning
2.1.2 Unsupervised Learning
2.1.3 Reinforcement Learning
2.1.4 Supervised Learning Algorithms
2.1.5 Neural Networks
2.1.6 Multilayer Neural Network
2.1.7 Convolutional Neural Network
2.1.8 CNN Architecture
2.1.9 Activation Functions
2.1.10 Sigmoid Function
2.1.11 Hyperbolic Tangent Function
2.1.12 Función ReLU—Rectified Lineal Unit
2.1.13 Función Leaky ReLU—Rectified Lineal Unit
2.1.14 Softmax Function
2.1.15 Loss Function
2.2 Evaluation Parameters
2.2.1 Precision
2.2.2 Sensitivity or Completeness
2.2.3 Specificity
2.2.4 Accuracy
2.2.5 F1-Score
2.2.6 ROC Curve
3 Proposed Architecture for Classification
3.1 ResNet50 Architecture
4 Development of the Methodology for Injury Classification
4.1 Materials Used
4.2 Database Selection
4.2.1 Image Selection and Classification
4.3 Dataset Preparation
4.3.1 Selected Injuries
4.3.2 Preprocessing and Image Processing
4.4 Model Construction
4.5 Architecture Definition
4.6 Model Training
4.6.1 Visualization of the Training Model
4.7 Model Evaluation
5 Results
5.1 Results of the ResNet 50 Model
6 Conclusions
References
Assistive Device for the Visually Impaired Based on Computer Vision
1 Introduction
1.1 Background and State of the Art
1.2 Proposed System
2 Methodology
2.1 Algorithms Comparison
2.2 Objective Function
3 Materials and Methods
3.1 Dataset
3.2 Hardware and Software
3.3 Support Vector Machine
3.4 Problem Characterization
3.5 Algorithm Implementation
3.6 Proposed Design of Experiments
4 Results
4.1 Android App
4.2 Classification Results
4.3 Metrics Comparison
5 Conclusion and Future Scope
References
Part II Analytical Strategies for Productive Processes Based on Industry 4.0
Development and Evaluation of a Machine Learning Model for the Prediction of Failures in an Injection Moulding Process
1 Introduction
1.1 Injection Moulding
1.2 Artificial Intelligence
1.3 Machine Learning
1.4 Unsupervised Machine Learning Algorithms
1.5 Machine Learning Algorithm Applied to Injection Moulding Process for Fault Diagnosis
2 Methodology
3 Results and Discussion
4 Conclusions
References
An Approach to Select an Open Source ERP for SMEs Based on Industry 4.0 and Digitization Considering the SHERPA and WASPAS Methods
1 Introduction
2 Literature Review
3 Methodology
4 Case Study
5 Conclusion
References
The Technological Role of Steepest Ascent Optimization in Industry 4.0 Modeling
1 Introduction
2 Method
2.1 Production Simulated Data Consideration
2.2 Designed Experimentation Development
2.3 Automation Process Modeling
2.4 Statistical Analysis with R Software and Minitab®
2.5 Human Intervention
3 Results
3.1 Optimization Through SADM
4 Discussion and Conclusion
References
The Role of Industry 4.0 Technologies in the Energy Transition: Conceptual Design of Intelligent Battery Management System Based on Electrochemical Impedance Spectroscopy Analysis
Nomenclature
1 Introduction
2 Theoretical Background
2.1 Battery System and I4.0 Relationship
2.2 EIS and Battery Parameter Estimation
2.3 Artificial Intelligence to Estimate EIS Battery Parameters
3 Methodology
4 Results
5 Conclusions
References
Performance Analysis of Eight-Channel WDM Optical Network with Different Optical Amplifiers for Industry 4.0
1 Introduction
1.1 Advantages of WDM Optical Networks [4]
1.1.1 Large Capacity
1.1.2 Transparent Transmission of Data
1.1.3 Simplified Operations
1.1.4 Flexibility
1.1.5 Overcome Distance Limitations
1.1.6 Maximize Dark Fiber Utilization
1.1.7 Scalability
2 Optical Amplifiers
2.1 Raman Amplifiers (RA)
2.2 Erbium-Doped Fiber Amplifiers
2.3 Semiconductor Optical Amplifiers
3 Industry 4.0
3.1 Optical Networks for Industry 4.0
4 Simulation Model
5 Results and Discussion
5.1 Max Q Factor
5.2 Min BER
5.3 Eye Height
5.4 OSNR
6 Conclusion
References
Part III Soft Computing Application in the Industry 4.0
Traffic Signs Configuration with a Geo-simulation Approach
1 Introduction
2 Related Studies
3 Methodology
4 Traffic Signs Configuration
4.1 Traffic Congestion
4.2 Economic Losses
4.3 Traffic Accidents
5 Agent-Based Simulation and Its Application for Traffic Problems
5.1 Agent-Based Traffic Simulators
6 Simulation Proposed Model
6.1 Geo-spatial Information
6.2 Factors That Influence Traffic Safety
6.2.1 Physical Condition of Road Pavement
6.2.2 Environmental Sensation
6.2.3 Animals in the Road
6.2.4 Vehicles Overtaking Lanes
6.2.5 Vehicle Characteristics
6.2.6 Human Factor on the Road
6.3 Different Agents' Behaviors
6.3.1 Agent Traffic Light
6.3.2 Agent Vehicle
6.3.3 Agent Driver
6.3.4 Agent Walker
6.3.5 Agent Pavement
6.3.6 Agent Environment
6.4 Integration with Intelligent Traffic System
6.4.1 Internet of Things for Traffic Signaling
6.4.2 Possible Integration for Simulation Model Proposed
6.5 Case Study Ciudad Deportiva, Havana
6.5.1 Planning Phase
6.5.2 Design Phase
6.5.3 Conduction Phase
6.5.4 Analysis Phase
7 Discussion
7.1 Limitation of the Study
8 Conclusions
9 Recommendations
References
Emotional Diagnosis for Employees Within the Framework of Industry 4.0: A Case Study in Ciudad Juarez
1 Introduction
2 Method
2.1 Selection of Sensorial Tools
2.2 Development of Data Acquisition System
2.3 System Programming
2.3.1 Data Repair and Preparation
2.3.2 External Variable Analysis
2.3.3 Definition of Objective Values for Data
2.3.4 Construction of a Base Classifier
2.3.5 Evaluation of Base Classifier
2.3.6 Optimization Algorithm Coding
2.3.7 Learning System for Emotional Characterization
2.3.8 Algorithm Integration
3 Results
3.1 Case of Implementation
3.1.1 Result of Equipment Selection
3.1.2 Signal Input
3.1.3 Data Processing
3.1.4 Data Gathering
3.1.5 Data Classification and Case Results
3.1.6 Deep Learning Classification
3.1.7 Case Final Diagnosis
4 Discussion
References
Architecture for Initial States Algorithm for Blockchain Scalability in Local OnPrem IIoT Environments
1 Introduction
2 Background
2.1 Blockchain
2.2 Industrial/Internet of Things (IIoT/IoT)
2.3 Initial State
3 Methodology and Materials
3.1 Network Architecture
3.2 Initial States Algorithm
4 Results
5 Conclusions and Future Work
References
Distribution Route Optimization Using Floyd-Warshall Weighted Graph Analysis Algorithm with Google Maps Integration in Industry 4.0 Context
1 Introduction
2 Literature Review
2.1 Supply Chain in Transportation and Applications
2.2 Internet of Things and Its Impact on Supply Chain Management
2.3 Optimization Algorithms
2.4 Fundamental Principles of Graph Theory
2.5 Floyd-Warshall Algorithm and Its Application
3 Methodology
3.1 Floyd-Warshall Algorithm Applied to Information Technology and the Supply Chain in Transportation and Distribution
4 Conclusion
References
Feature Selection in Electroencephalographic Signals Using a Multicriteria Decision Analysis Method
1 Introduction
2 Related Concepts
2.1 Industry 4.0
2.2 Electroencephalographic Signals
2.3 Motor Imagery
2.4 Multicriteria Decision-Making Methods
2.5 TOPSIS
2.6 Testors Theory
2.7 Artificial Neural Network
3 Methodology
4 Results and Conclusions
References
Index