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Multiobjective Optimization for Space Systems Architecture: Applying and Extracting Knowledge
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Multiobjective Optimization for Space Systems Architecture: Applying and Extracting Knowledge. |
| 개인저자 | Hitomi, Nozomi. |
| 단체저자명 | Cornell University. Mechanical Engineering. |
| 발행사항 | [S.l.]: Cornell University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 216 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
| ISBN | 9780438026179 |
| 학위논문주기 | Thesis (Ph.D.)--Cornell University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Daniel Selva. |
| 이용제한사항 | This item is not available from ProQuest Dissertations & Theses. |
| 요약 | Distributed spacecraft missions (DSM) are gaining traction in the space community for the potential to deploy multiple simple and low-cost spacecraft to provide high temporal resolution of observations over regions of interest. Designing a DSM, |
| 요약 | This thesis proposes a new tradespace exploration tool that combines the efficiency of expert design heuristics with the explorative power of an MOEA. The tool exploits the available expert knowledge to push the exploration to the most promising |
| 요약 | This thesis also develops an MOEA that can extract new knowledge by applying a data mining algorithm to candidate solutions generated during an optimization run. This tool is useful when there are few or no design heuristics available for a give |
| 요약 | The efficacy of the proposed tradespace exploration tools are demonstrated on a DSM design problem for climate monitoring. The results shows that combining an MOEA with knowledge from experts or data mining algorithms leads to significant improv |
| 일반주제명 | Mechanical engineering. Aerospace engineering. Engineering. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
