Sun, Yi
Zhang, Shenhu
Wang, Tianqi
Lou, Feng
Ma, Jingjin
Wang, Chunying
Gui, Chengrong
Funding for this research was provided by:
Hebei Province Key Research and Development Project (No.20313701D, No.19210404D)
National Natural Science Foundation of China (No. U1536112, No. 81673697, No .61872046)
National Social Science; Foundation Key Project (No. 17AJL014)
Blue Fire Project - Huizhou University of Technology Joint Innovation Project (No. CXZJHZ201729)
Industry-University Cooperation Cooperative Education Project of the Ministry of Education (No. 201902218004, No. 201902024006, No. 201901197007)
The Ministry of Education Industry-University Cooperation Collaborative Education Project (No. 201901197001)
Industry-University Cooperation Collaborative Education Project of the Ministry of Education (No. 201901199005)
Educational Reform Project of Beijing University of Posts and Telecommunications (No. 500520096, No: 500519813)
Special project for youth research and innovation: Beijing University of Posts and Telecommunications Project on Tuberculosis (2019 PTB-011)
Fundamental Research Funds for the Central Universities (No.2019RC52)
Article History
Received: 10 June 2021
Accepted: 21 September 2021
First Online: 29 October 2021
Declarations
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: Compared with traditional segmentation or simplification algorithms, the proposed method not only reduces the sensitivity to noise data and improves the accuracy but also solves the defect of fuzzy boundary division, further overcomes the defect of inaccurate curvature weighting of target sample points, and achieves the smooth transition target of curvature of sample points.