Reliability Engineering and Computational Intelligence for Complex Systems: Design, Analysis and Evaluation (Studies in Systems, Decision and Control, 496)

✍ Scribed by Coen van Gulijk (editor), Elena Zaitseva (editor), Miroslav Kvassay (editor)

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 224
• Edition: 1st ed. 2023
• Category: Library

✦ Synopsis

This book offers insight into the current issues of the merger between reliability engineering and computational intelligence. The intense development of information technology allows for designing more complex systems as well as creating more detailed models of real-world systems which forces traditional reliability engineering approaches based on Boolean algebra, probability theory, and statistics to embrace the world of data science. The works deal with methodological developments as well as applications in the development of safe and reliable systems in various kinds of distribution networks, in the development of highly reliable healthcare systems, in finding weaknesses in systems with the human factor, or in reliability analysis of large information systems and other software solutions.

In this book, experts from various fields of reliability engineering and computational intelligence present their view on the risks, the opportunities and the synergy between reliability engineering and computational intelligence that have been developed separately but in recent years have found a way to each other. The topics addressed include the latest advances in computing technology to improve the real lives of millions of people by increasing safety and reliability of various types of real-life systems by increasing the availability of software services, reducing the accident rate of means of transport, developing high reliable patient-specific health care, or generally, save cost and increase efficiency in the work and living environment. Though this book, the reader has access to professionals and researchers in the fields of reliability engineering and computational intelligence that share their experience in merging the two as well as an insight into the latest methods, concerns and application domains.

✦ Table of Contents

Preface
Contents
Mathematical Methods for Reliability Engineering and Computational Intelligence
Experimental Survey of Algorithms for the Calculation of Node Traversal Probabilities in Multi-valued Decision Diagrams
1 Introduction
2 Reliability Analysis
2.1 Structure Function
2.2 Basic Reliability Measures
2.3 Series–Parallel Systems
3 Decision Diagrams
3.1 Node Traversal Probability
3.2 Depth-First Search
3.3 Breadth-First Search
4 Experimental Comparison
5 Conclusion
References
Reliability Analysis of Data Storage Using Survival Signature and Logic Differential Calculus
1 Introduction
2 Redundant Array of Independent Disks
3 Mathematical Background
4 Case Study
5 Conclusion
References
Digital Techniques for Reliability Engineering and Computational Intelligence
Software Tests Quality Evaluation Using Code Mutants
1 Introduction
2 Mutation Testing
2.1 Mutation Testing Metrics
2.2 Algorithm of Mutation Testing
3 Software Tests Quality Evaluation
4 Model Example
5 Conclusion
References
Hacking DCNs
1 Introduction
2 Previous Work
3 Background
4 Methodology
4.1 Classification Evaluation
4.2 Misclassification Label Prediction
5 Experimental Results
5.1 Age and Gender Sensitivity
5.2 Prediction of Label Changes
6 Discussion
7 Conclusion
References
Markov Model of PLC Availability Considering Cyber-Attacks in Industrial IoT
1 Introduction
2 PLC Architecture
3 Evaluation of the Dependability of the PLC Considering Dos-Attacks on Its Components
4 Simulation of the Markov Model of PLC Availability
5 Conclusion
References
Advanced Networking and Cybersecurity Approaches
1 Motivation
2 State-Of-The-Art
2.1 Firewall Techniques
2.2 Blockchain Techniques
2.3 CIDN Deployment
3 Network Planning with Segmenting Within a Campus LAN
3.1 Unsegmented Networks
3.2 Segmenting Best Practices
3.3 Conventional Cybersecurity Approaches
4 Foundations for Advanced Cybersecurity
4.1 Open Web Application Security Project
4.2 MITRE Corporation
4.3 SIEM Market
5 Honeypotting for Advanced Security
5.1 Honeypotting with Gateways and Firewalls
5.2 Honeypotting and Vulnerability Monitoring
5.3 Production Honeypots
5.4 Research Honeypots
5.5 Practical Honeypotting
6 Conclusion
References
Use Cases for Reliability Engineering and Computational Intelligence
Application of Machine Learning Techniques to Solve the Problem of Skin Diseases Diagnosis
1 Introduction
2 Theoretical Background
3 Research Methods
3.1 Input
3.2 Pre-processing with Sobel
3.3 Brightness Normalization
3.4 Pre-processing with PCA
3.5 CNN for Image Detection and Classification
4 Results
4.1 Input Data Preprocessing
5 Conclusions
References
Analyzing Biomedical Data by Using Classification Techniques
1 Introduction
2 Metabolomics
2.1 Analyzing of Metabolomic Data
3 Datamining Techniques
3.1 Tools for Analyze Metabolomics Data
3.2 Glioblastoma Multiforme Data Analysis
4 Decision Tree Induction
4.1 Experimental Settings
5 Conclusion
References
Wildfire Risk Assessment Using Earth Observation Data: A Case Study of the Eastern Carpathians at the Slovak-Ukrainian Frontier
1 Introduction
2 Risk Assessment Methodology
2.1 Approach Concept
2.2 Applying Earth Observation Data
2.3 Risk Evaluation
3 A Case Study of the Eastern Carpathians at the Slovak-Ukrainian Frontier
3.1 Study Area
3.2 Fuels Data
3.3 Earth Observation Data Time Series
3.4 Risk Map
4 Discussion
5 Conclusions
References
Digital Safety Delivery: How a Safety Management System Looks Different from a Data Perspective
1 Introduction
2 Data Analysis and BowTies
2.1 Time-Series Data Analysis
2.2 BowTies
2.3 Complex Barriers
3 Online Process Safety Performance Indicators and Safety Management Systems Using Big Data
3.1 Online PSPIs
3.2 Safety Management Systems with Big Data
4 Conclusion
References
Reliability Optimization of New Generation Nuclear Power Plants Using Artificial Intelligence
1 Introduction
1.1 New Generation Nuclear Power Plants
1.2 Risk
1.3 Probabilistic Risk Analysis
1.4 Artificial Intelligence. Evolutionary Algorithms
1.5 Contributions from Different Perspectives
1.6 The Focus on This Work
2 Definition of the Objective Function and Its Constraints
3 Definition of Artificial Intelligence
4 Results
5 Discussion and Conclusions
References
Algorithmic Management and Occupational Safety: The End Does not Justify the Means
1 Introduction
1.1 Reliability Engineering, Predictive-Based Safety and Algorithmic Management
1.2 Responsible Use of Algorithmic Management
2 The Many Forms of Algorithmic Management
2.1 Personal Protective Equipment and Computer Vision
2.2 Control on the Shop Floor
2.3 Safe Driving Behaviour
3 Guidelines for Responsible Algorithmic Management in Safety
3.1 The Conflicting Goals of Health and Productivity
3.2 Fighting the System
3.3 The Desired and Mandatory Transparency
3.4 What Laws Apply Now
4 Conclusion
5 Discussion
References
Technologies and Solutions for Smart Home and Smart Office
1 Motivation and the Aims of the Work
2 Challenges for Smart Home and Smart Office
3 System Integrators for Smart Office
3.1 Secure IoT Platforms for Smart Office
3.2 Scenario 1: Automatization Sensors via NB-IoT
3.3 Scenario 2: Energy-Efficient EnOcean Sensor Constellation
4 Platforms for Easy Smart Home Integration
4.1 Tuya IoT Development Platform
4.2 Home Assistant
4.3 Azure IoT Hub
4.4 Heterogenous Automatization Example for Smart Home
4.5 Testing of MAKS PRO System Based on LoRaWAN for Deployment of Smart Homes and Smart Offices
5 Advanced Security for IoT and IIoT
6 Designing a Unique IoT System Using Edge/Cloud Computing and Artificial Intelligence
6.1 Configuring Data Collection and Analysis for the Designed IoT System
6.2 IoT System Testing
6.3 Testing an Intelligent IoT System for Temperature Forecasting in the Smart Office Server Room
7 Conclusion
References