Modeling and Simulation Based Systems Engineering: Theory and Practice

Contents
Preface
Chapter 1. Introduction
References
Chapter 2. Using modeling and simulation and artificial intelligence to improve complex adaptive systems engineering
1. Introduction
2. The Changing Landscape of Systems
3. The Changing Landscape of Systems Engineering
4. Complexity Science, AI, and M&S Methods to Address the New Challenges
4.1. Complexity science
4.1.1. Complexity science definitions
4.1.2. The rise of complex systems
4.1.3. Emergence and collective behavior
4.2. Artificial intelligence
4.2.1. Combinational, exploratory, and transformational creativity
4.2.2. Evaluation
4.3. Modeling and simulation
5. Bringing Designing, Architecting, and Operating Together
5.1. A new systems engineering approach
5.2. Examples
6. Conclusion and Discussion
References
Chapter 3. DEVS and MBSE: A review
1. Introduction
2. Review of DEVS in the MBSE Context
2.1. DEVS formalism
2.2. M&S environment background
2.3. Hierarchy of system specifications
2.4. DEVS simulation protocol
2.5. Timeline history of some key DEVS developments
3. Homomorphic Implementation of DEVS-Like Systems
3.1. Hierarchy of system specification morphisms
3.2. Application to the design of simulation systems for “as-is” software
4. DEVS-Based M&S Capabilities and Tools for MBSE
4.1. Mapping activity diagrams into executable activity-based DEVS models
4.2. DEVS top-to-bottom MBSE capability
5. Summary and Conclusions
References
Chapter 4. XDEVS: A hybrid system modeling framework
1. Introduction
2. XDEVS Specification
2.1. DEVS
2.2. XDEVS
3. Simulation of XDEVS Models
3.1. Simulation of DEVS models
3.2. Simulation engine for XDEVS
4. Case Study
4.1. Model description
4.2. XDEVS simulation result
4.3. Specification comparison
5. Conclusions
Acknowledgment
References
Chapter 5. An integrated intelligent modeling and simulation language for model-based systems engineering
1. Introduction
2. Related Work
2.1. System modeling language
2.2. Physical property modeling language
2.3. Intelligent extension of modeling languages
2.4. Integration of system modeling and simulation
3. Overall Structure of X Language
4. Essential Elements and Grammatical Structure of X Language
4.1. Continuous class
4.2. Discrete class
4.3. Couple class
4.4. Agent class
4.5. Requirement class
4.6. Record class
4.7. Function class
4.8. Connector class
5. Compiler and Simulation Engine
6. Case Study
7. Conclusion
Acknowledgments
Appendix
References
Chapter 6. Modeling for heterogeneous objects based on X language: A modeling method of algorithm-hardware
1. Introduction
2. Background
2.1. X language
2.1.1. Class of model
2.1.2. Graphical model of X language
2.1.3. Text model of X language
2.2. VHDL
3. Methodology
3.1. Modeling hardware-implemented algorithm by X language
3.1.1. Definition diagram
3.1.2. Connection diagram
3.1.3. Equation diagram
3.1.4. State machine diagram
3.2. Model conversion
3.2.1. Model templates of VHDL
3.2.2. Conversion rule
4. Case Study
4.1. Brief of Kalman filter
4.2. Modeling by X language
4.3. Generating VHDL code of Kalman filter
4.4. Verification of algorithm
5. Conclusion
Acknowledgments
References
Chapter 7. Data-driven modeling method with reverse process
1. Introduction
2. Related Work
3. Modeling Based on Partial Least Squares (PLS)
4. Gray Relational Analysis of Characteristic Variables
5. Case Analysis
6. Conclusion
Acknowledgments
References
Chapter 8. Simulation-oriented model reuse in cyber-physical systems: A method based on constrained directed graph
1. Introduction
2. Concepts of Model Reuse and Composition for CPS
2.1. Characteristics of model in CPS
2.2. Constrained directed graph of models
3. Metrics for Model Composition in CPS
3.1. Co-simulation distance of heterogeneous models
3.2. Reusability of model node
3.3. Initialization of constrained directed models graph
4. Model Composition Method Based on Multi-Objective Genetic Algorithm
4.1. Encoding and evolution
4.2. Generating model composition path
4.3. Complexity analysis
5. Experiment and Analysis
6. Conclusion
Acknowledgments
References
Chapter 9. Model maturity towards modeling and simulation: Concepts, index system framework and evaluation method
1. Introduction
2. The Concept of Model Maturity
2.1. Definition
2.2. Features of model maturity compared with other metrics
2.3. Evaluation of model maturity
3. Five Levels of Model Maturity
4. Construction of the Model Maturity Index System
4.1. Principles
4.2. A framework of index system for model maturity evaluation
4.3. Further explanation of model maturity indexes
5. A HEQQ Analysis for Model Maturity
5.1. Main idea of HEQQ
5.2. Mathematical description of HEQQ
5.3. The evaluation process of model maturity based on HEQQ
5.4. Analysis for weight determining methods
5.5. Determining index weights based on entropy weight method
6. Case Study
6.1. Data acquisition
6.2. Comparative experiments
7. Conclusions and Future Work
Acknowledgments
References
Chapter 10. FPGA-based edge computing: Task modeling for cloud-edge collaboration
1. Introduce
2. Task-Based Edge Node Collaboration Method
3. Behavior Analysis of Task
4. Task Critical Attribute Analysis
5. Task Modeling
6. Simulation Experiments
7. Conclusion
Acknowledgment
References
Chapter 11. Hybrid intelligent modeling approach for online predicting and simulating surface temperature of HVs
1. Introduction
2. Mechanism Model for Thermal Conduction of HV
3. Hybrid Intelligent Modeling Approach for Surface Temperature of HV
3.1. Hybrid intelligent modeling strategy
3.2. CBR algorithm for thermal conductivity coefficient and specific heat capacity
3.3. SVR-based error compensation model
4. Experimental Verification
5. Conclusions
Acknowledgments
References
Chapter 12. Knowledge-driven material design platform based on the whole-process simulation and modeling
1. Introduction
2. Multi-Scale Simulation of the Whole Hot Rolling Process
2.1. Production line simulation
2.2. Thermo-mechanical and microstructure simulation
3. Material Design Knowledge Management
4. Data-Driven Models in New Material Development
4.1. Steel grade merging model
4.2. Performance prediction model
4.3. Parameter optimization model
5. System Implementation and Case Study
6. Conclusion
Acknowledgments
References
Chapter 13. A model validation method based on the orthogonal polynomial transformation and area metric
1. Introduction
2. Validation Method Based on Orthogonal Polynomials and Area Metric
2.1. Technology background
2.1.1. Extract coefficients through the discrete orthogonal polynomials
2.1.2. Area metric and u-pooling metric
2.2. Validation method for dynamic response
3. Example
4. Conclusion
Acknowledgments
References
Chapter 14. A mixed reality simulation evaluation method for complex system
1. Introduction
2. Construction of Mixed Reality Cockpit Simulation Scene
3. Mixed Reality Simulation Evaluation Method
4. Simulation Evaluation Test
4.1. Accuracy test of virtual and real scene fusion
4.2. Accuracy test of virtual fusion interaction
4.3. Three human causes of simulation function test
5. Conclusion
References
Index