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研究生:林彬儀
研究生(外文):Pin-YiLin
論文名稱:針對非充裕不確定資料設計之最佳取樣與資源配置
論文名稱(外文):Optimal Sampling Augmentation and Resource Allocation for Design with Inadequate Uncertainty Data
指導教授:詹魁元
指導教授(外文):Kuei-Yuan Chan
學位類別:碩士
校院名稱:國立成功大學
系所名稱:機械工程學系碩博士班
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:89
中文關鍵詞:可靠度設計不確定性因素樣本量測貝氏二項式推估資源配置馬可夫鏈蒙地卡羅法
外文關鍵詞:Uncertaintyreliability-based design optimizationBayesian binomial inferencesample measurementresource allocationMarkov chain Monte Carlo
相關次數:
  • 被引用被引用:0
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書名頁 i
論文口試委員審定書 ii
Copyright iii
中文摘要 iv
Abstract v
誌謝 vi
Table of Contents vii
List of Tables xi
List of Figures xiii
List of Symbols xv
1 Introduction and Motivation 1
1.1 Introduction 1
1.2 Motivation and Objective 4
1.3 Organization of the Thesis 5
2 Bayesian Reliability Inference with Sample Data 6
2.1 Data Inference using Bayesian Theory 6
2.1.1 Bayes Theorem 6
2.1.2 Binomial Distribution 9
2.1.3 Bayesian Binomial Inference 10
2.2 Reliability Estimation with Sample Data 13
2.2.1 Bayesian Inference of Constraint Reliability Values 13
2.2.2 Con_dence Range and Con_dence Bound 15
2.2.3 Clari_cation of “Sample” 16
2.2.4 Reliability Estimation Example 17
2.3 Sample Data Filter via Markov Chair Monte Carlo 19
2.3.1 Backgrounds on Markov Chain 19
2.3.2 Markov Chain Monte Carlo by Metropolis-Hasting Algorithm 22
2.3.3 MCMC Modi_cation with Bootstrap 24
3 Optimal Sampling Augmentation and Resource Allocation 29
3.1 RBDO with Inadequate Uncertainty Data 29
3.1.1 Introduction of RBDO 29
3.1.2 Activity of Bayesian Reliability Constraints 30
3.1.3 Generalized Optimization Model of RBDO with Inadequate Uncertainty Data 33
3.2 Optimal Sampling Augmentation for Design 35
3.2.1 Purpose of Sampling Augmentation 35
3.2.2 Sampling Augmentation Process 36
3.3 Resource Allocation Process 41
3.3.1 Sensitivity Analysis 41
3.3.2 Scheme of Resource Allocation 42
4 Case Studies in Single Level Systems 44
4.1 A Mathematical Example 44
4.1.1 Optimization Model of Mathematical Example 45
4.1.2 Optimal Results and Discussions 47
4.1.3 Comparison of Sampling Augmentation with Different Sample Size 50
4.2 Passive Vehicle Suspension Design 51
4.2.1 Optimization Model of Passive Vehicle Suspension Design 51
4.2.2 Optimization Result and Discussions 54
4.2.3 Comparison of MCMC and without MCMC in Passive Vehicle Suspension
Design with different sample size 57
4.3 Summary 59
5 Case Studies in Complex Multilevel System 60
5.1 Introduction to Analytical Target Cascading 60
5.1.1 ATC Problem Decomposition 61
5.1.2 Augmented Lagrangian Method for ATC 63
5.2 Passive Vehicle Suspension Design in Complex System 63
5.2.1 All in One System of Passive Vehicle Suspension Design 64
5.2.2 Multilevel Passive Vehicle Suspension Design 70
5.2.3 Optimal Results and Discussion 73
5.3 Summary 80
6 Conclusions and Future Work 81
6.1 Conclusions 81
6.2 Future Work 82
References 83
Personal Communication 89

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