Gaussian processes-based sampling for scalar field reconstruction considering gradient information = 스칼라 필드 재구성을 위한 변화량 정보를 고려한 가우시안 프로세스 기반 샘플링

We are surrounded by several natural phenomena, which can be studied by reconstructing the field underlying these phenomena for research purposes as a good approach. The field can be restructured accurately because of developments in sensor measurement technology and regression techniques. If the number of sensors required to identify the field is larger than the number of available sensors, it is necessary to identify the optimal locations to which to allocate sensors for reconstruction of the field. Criteria based on uncertainty have been used widely. However, the even distribution of sampling points across the entire field has the disadvantage of reducing the amount of sampling in areas that are particularly informative. We overcome this limitation by suggesting a new criterion that combines gradient information, uncertainty, and an algorithm to control the trade-off between exploitation and exploration. The results are given in terms of sampling locations and the root mean square error between the underlying field and the estimated field. The proposed algorithm increases the amount of sampling that occurs in an informative area, and also ensures that the error is lower compared to the other criteria. Consequently, the proposed algorithm has confirmed the possibility of identifying the informative area.
Advisors
Kim, Jinwhanresearcher김진환researcher
Publisher
한국과학기술원
Issue Date
2016
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 해양시스템대학원, 2016.8 ,[iv, 32 p. :]

Keywords

Optimal Sensor Placement; Gaussian process; Gradient information; Trade-off between exploitation and exploration; Information theory; 최적센서배치; 가우시안 프로세스; 변화량정보; 탐험과탐색; 정보이론

URI
http://hdl.handle.net/10203/222036
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=663528&flag=t
Appears in Collection
OSE-Theses_Master(석사논문)
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