KNN regression model-based refinement of thermohaline data

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 机器人学研究室

	KNN regression model-based refinement of thermohaline data
	Zhang, Tong1 ; Gou, Yu 1; Liu J(刘军)1,2
	2018
会议日期	December 3-5, 2018
会议地点	Shenzhen, China
关键词	KNN BOA_ARGO thermohaline data granularity thermocline
页码	1-8
英文摘要	This paper carries out a renement on the basis of existing data sets, whose level of granularity is not available for some experimental analysis such as thermocline research. The thermocline is sensitive to thermohaline data granularity for sudden sea temperature changes. We rened the data with the KNN regression method and managed to choose the optimal parameters for the construction of a prediction model. We also rened the temperature and salinity data in BOA_Argo using the regression forecast model. The original data, whose horizontal resolution is 1 °x 1 °and vertically divided into uneven 58 layers from the sea surface to 1,975 meters underwater, has been rened into a new set with the resolution of 1 °x 1 °horizontally and 1-meter interval vertically. At each point, we rened the previously uneven 58 temperature data samples into 1,976 evenly distributed data samples. The rened data sets can be used in experimental analysis, and the validity of this method has been veried by regional data.
源文献作者	CSSC Systems Engineering Research Institute ; Institute of Acoustics, Chinese Academy of Sciences ; Jilin University ; Northwestern Polytechnical University ; Shenzhen University
产权排序	2
会议录	Proceedings of the 13th ACM International Conference on Underwater Networks and Systems, WUWNet 2018
会议录出版者	ACM
会议录出版地	New York
语种	英语
ISBN号	978-1-4503-6193-4
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/24036]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Zhang, Tong
作者单位	1.Jilin University, Changchun City, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
推荐引用方式 GB/T 7714	Zhang, Tong,Gou, Yu,Liu J. KNN regression model-based refinement of thermohaline data[C]. 见:. Shenzhen, China. December 3-5, 2018.