Soft Computing Approach for Mathematical Modeling of Engineering Problems

✍ Scribed by Ali Ahmadian (editor), Soheil Salahshour (editor)

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 267
• Edition: 1
• Category: Library

✦ Synopsis

This book describes different mathematical modeling and soft computing techniques used to solve practical engineering problems. It gives an overview of the current state of soft computing techniques and describes the advantages and disadvantages of soft computing compared to traditional hard computing techniques. Through examples and case studies, the editors demonstrate and describe how problems with inherent uncertainty can be addressed and eventually solved through the aid of numerical models and methods. The chapters address several applications and examples in bioengineering science, drug delivery, solving inventory issues, Industry 4.0, augmented reality and weather forecasting. Other examples include solving fuzzy-shortest-path problems by introducing a new distance and ranking functions. Because, in practice, problems arise with uncertain data and most of them cannot be solved exactly and easily, the main objective is to develop models that deliver solutions with the aid of numerical methods. This is the reason behind investigating soft numerical computing in dynamic systems. Having this in mind, the authors and editors have considered error of approximation and have discussed several common types of errors and their propagations. Moreover, they have explained the numerical methods, along with convergence and consistence properties and characteristics, as the main objectives behind this book involve considering, discussing and proving related theorems within the setting of soft computing. This book examines dynamic models, and how time is fundamental to the structure of the model and data as well as the understanding of how a process unfolds
• Discusses mathematical modeling with soft computing and the implementations of uncertain mathematical models
• Examines how uncertain dynamic systems models include uncertain state, uncertain state space and uncertain state’s transition functions
• Assists readers to become familiar with many soft numerical methods to simulate the solution function’s behavior
This book is intended for system specialists who are interested in dynamic systems that operate at different time scales. The book can be used by engineering students, researchers and professionals in control and finite element fields as well as all engineering, applied mathematics, economics and computer science interested in dynamic and uncertain systems.

Ali Ahmadian is a Senior Lecturer at the Institute of IR 4.0, The National University of Malaysia.
Soheil Salahshour is an associate professor at Bahcesehir University.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
Editor Biographies
Contributors
Chapter 1 Soft Computing Techniques: An Overview
1.1 Introduction
1.2 The Concept of Uncertainty: The Role of Fuzzy Logic
1.3 The Concept of Complexity: The Role of Artificial Neural Networks
1.4 The Concept of Optimization: The Role of Evolutionary Algorithms
1.5 Concluding Remarks
Chapter 2 Solution of Linear Difference Equation in Interval Environment and Its Application
2.1 Introduction
2.1.1 Uncertainty via Interval Numbers
2.1.2 Difference Equation Versus Differential Equation
2.1.3 Relevance of Difference Equation Under Interval Uncertainty
2.1.4 Review on Imprecise Difference Equation
2.1.5 Novelties
2.1.6 Arrangement of the Chapter
2.2 Preliminaries
2.2.1 Interval Number
2.2.2 Difference Equation
2.2.3 Stability Analysis of Linear Difference Equation
2.2.4 Stability Analysis of System of Linear Non-homogeneous Difference Equations
2.3 Flowchart of Solution Approach
2.4 Difference Equation with Interval Environment
2.4.1 Solution When µo Is an Interval Number
2.5 Numerical Example and Application
2.5.1 Numerical Example
2.5.2 Applications
2.6 Conclusion
Chapter 3 Industrial Internet of Things and Industry 4.0
3.1 Introduction
3.2 Review of Literature
3.2.1 Augmented Reality
3.2.2 Additive Manufacturing
3.2.3 The Cloud
3.2.4 The Industrial Internet of Things
3.2.5 System Integration—Horizontal, Vertical and End to End
3.2.6 Simulation
3.2.7 Autonomous Robots
3.2.8 Big Data and Analytics
3.2.9 Cyber Physical Systems and Cybersecurity
3.3 Challenges and Fundamental Issues of Industry 4.0
3.4 Future Direction and Scope
3.5 Conclusion
Chapter 4 Industry 4.0 and Its Practice in Terms of Fuzzy Uncertain Environment
4.1 Introduction
4.1.1 Fuzzy Theory and Degree of Uncertainty
4.2 Review of Literature
4.3 Industry 4.0 Barriers
4.4 Conclusion
Chapter 5 Consistency of Aggregation Function-Based m-Polar Fuzzy Digraphs in Group Decision Making
5.1 Introduction
5.1.1 Problem Description
5.2 Fuzzy Ordering Theory
5.2.1 Aggregation of Fuzzy Orderings
5.2.2 Fuzzy Graphs
5.2.3 m-Polar Fuzzy Graphs
5.3 Conjunction-Based Fuzzy Relations
5.4 Conjunction-Based Framework for m-Polar Fuzzy Graphs
5.4.1 Preservation of A-Transitivity for Aggregated Fuzzy Relation
5.4.2 Proposed Algorithm
5.4.3 Numerical Example
5.5 Conclusion
Chapter 6 Path Programming Problems in Fuzzy Environment
6.1 Introduction
6.2 Preliminaries
6.3 The Shortest Path (SP) Problem in Fuzzy Environment
6.3.1 First Approach: The Fuzzy Shortest Path (FSP) Problem by Reliability
6.3.2 Second Approach: The Fuzzy Shortest Path (FSP) Problem by Interval-Valued Arithmetic
6.3.3 Numerical Example
6.4 The Shortest Path (SP) Problem in Hesitant Fuzzy Environment
6.4.1 Mathematical Model of Hesitant Fuzzy Shortest Path (HFSP) Problem
6.4.2 First Approach: HFSP Problem by Reliability
6.4.3 Second Approach: HFSP Problem by Interval-Valued Arithmetic
6.4.4 Numerical Example
6.5 Conclusion
Chapter 7 Weather Forecast and Climate Prediction Using Soft Computing Methods
7.1 Introduction
7.2 Artificial Neural Networks
7.2.1 A Concise Introduction to ANNs
7.2.2 Applications of ANNs in Climatology
7.3 Decision Tree
7.4 Support Vector Machines
7.5 Fuzzy Systems
7.5.1 An Introduction to Fuzzy Set Theory
7.5.2 Applications of Fuzzy Logic in Climatology and Meteorology
7.6 Conclusions
Chapter 8 Color Descriptor for Mobile Augmented Reality
8.1 Introduction
8.2 RGB FREAK Descriptor Framework
8.3 ALOI Dataset
8.4 Tracking Accuracy
8.5 Conclusion
Chapter 9 Cryptosystem for Meshed 3D through Cellular Automata
9.1 Introduction: Background and Driving Forces
9.2 Three Dimensional Objects Defined by Mesh
9.3 Compression of 3D Animated Mesh
9.4 Image Mining in 2D
9.5 Cellular Automata and Cryptography
9.6 Challenges in Using Data
9.7 State-of-the-Art Algorithms and Descriptors
9.8 Flexible FcCA Cryptosystem
9.9 Improved Flexible Cryptosystem (iFcCA)
9.10 Robotic Movement Encryption (Applied Case)
9.11 Previous Work
9.12 Proposed Applied Case (EncKin)
9.13 Conclusion
Chapter 10 Evolutionary Computing and Swarm Intelligence for Hyper Parameters Optimization Problem in Convolutional Neural Networks
10.1 Introduction
10.1.1 Bayesian Optimization
10.1.2 Applications
10.2 Deep Learning Overview
10.2.1 Deep Learning
10.2.2 Convolutional Neural Networks
10.2.3 Hyper Parameters Problem Optimization
10.3 Metaheuristic in Hyper Parameters Optimizations
10.3.1 Evolutionary Commuting
10.3.2 Particle Swarm Intelligence
10.3.3 Evolutionary Computing and Convolutional Neural Network
10.3.4 Swarm Intelligence and Convolutional Neural Network
10.4 Problems and Challenges
10.4.1 Trusting Defaults
10.4.2 Fake Metrics
10.4.3 Overfitting
10.4.4 High Hyperparameters
10.4.5 Hand-Tuning
10.4.6 Random Search
10.5 Concluding Remarks
Chapter 11 New Approach for Efficiently Computing Factors of the RSA Modulus
11.1 Introduction: Background and Driving Forces
11.1.1 Continued Fractions
11.1.2 LLL Algorithm
11.1.3 Coppersmith's Method
11.1.4 Continuous Midpoint Subdivision Analysis
11.1.5 Jochemsz May's Strategy
11.2 Attacking RSA Key Equation Using Arbitrary Parameter
11.2.1 First Attack
11.2.2 Second Attack
11.3 On The Continuous Midpoint Subdivision Analysis Upon RSA Cryptosystem
11.3.1 Our Proposed Attack
11.4 Attack on N = p2q When The Primes Share Least Significant Bits
11.5 Conclusion
Chapter 12 Vision-Based Efficient Collision Avoidance Model Using Distance Measurement
12.1 Introduction
12.2 Research Background
12.3 Research Contribution
12.4 Proposed Model
12.5 Experimental Results
12.5.1 Experimental Set Up
12.5.2 Hardware Set Up
12.5.3 Software Set Up
12.6 Datasets
12.6.1 Results
12.6.2 Comparison with Previous Research Results
12.7 Conclusion
Index