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Data-Driven Methods and Applications...
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Huang, Zhiyuan.
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Data-Driven Methods and Applications for Optimization Under Uncertainty and Rare-Event Simulation.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Data-Driven Methods and Applications for Optimization Under Uncertainty and Rare-Event Simulation./
作者:
Huang, Zhiyuan.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:
162 p.
附註:
Source: Dissertations Abstracts International, Volume: 82-07, Section: B.
Contained By:
Dissertations Abstracts International82-07B.
標題:
Industrial engineering. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28240112
ISBN:
9798684618420
Data-Driven Methods and Applications for Optimization Under Uncertainty and Rare-Event Simulation.
Huang, Zhiyuan.
Data-Driven Methods and Applications for Optimization Under Uncertainty and Rare-Event Simulation.
- Ann Arbor : ProQuest Dissertations & Theses, 2020 - 162 p.
Source: Dissertations Abstracts International, Volume: 82-07, Section: B.
Thesis (Ph.D.)--University of Michigan, 2020.
This item must not be sold to any third party vendors.
For most of decisions or system designs in practice, there exist chances of severe hazards or system failures that can be catastrophic. The occurrence of such hazards is usually uncertain, and hence it is important to measure and analyze the associated risks. As a powerful tool for estimating risks, rare-event simulation techniques are used to improve the efficiency of the estimation when the risk occurs with an extremely small probability. Furthermore, one can utilize the risk measurements to achieve better decisions or designs. This can be achieved by modeling the task into a chance constrained optimization problem, which optimizes an objective with a controlled risk level. However, recent problems in practice have become more data-driven and hence brought new challenges to the existing literature in these two domains. In this dissertation, we will discuss challenges and remedies in data-driven problems for rare-event simulation and chance constrained problems. We propose a robust optimization based framework for approaching chance constrained optimization problems under a data-driven setting. We also analyze the impact of tail uncertainty in data-driven rare-event simulation tasks.On the other hand, due to recent breakthroughs in machine learning techniques, the development of intelligent physical systems, e.g. autonomous vehicles, have been actively investigated. Since these systems can cause catastrophes to public safety, the evaluation of their machine learning components and system performance is crucial. This dissertation will cover problems arising in the evaluation of such systems. We propose an importance sampling scheme for estimating rare events defined by machine learning predictors. Lastly, we discuss an application project in evaluating the safety of autonomous vehicle driving algorithms.
ISBN: 9798684618420Subjects--Topical Terms:
526216
Industrial engineering.
Subjects--Index Terms:
Robust optimization
Data-Driven Methods and Applications for Optimization Under Uncertainty and Rare-Event Simulation.
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