Search

Your search keyword '"Huang, Chuyuan"' showing total 107 results
Start Over Author "Huang, Chuyuan" Publication Type Academic Journals
107 results on '"Huang, Chuyuan"'

49. An improved gene expression programming algorithm for function mining of map-reduce job execution in catenary monitoring systems.

Author

Ding, Jin, Jiang, Tianyu, Tan, Ping, Wang, Yi, Fei, Zhenshun, Huang, Chuyuan, Ma, Jien, and Fang, Youtong

Subjects
CATENARY, GENE expression, HIGH speed trains, ALGORITHMS, DEMOGRAPHIC change
Abstract

Gene expression programming (GEP) is one of the most prominent algorithms in function mining. In order to obtain a more accurate function model in configuration parameters-execution efficiency (CP-EE) of map-reduce job in the high-speed railway catenary monitoring system, this paper proposes a novel algorithm, called GEP based on multi-strategy (MS-GEP). Compared to traditional GEP, the proposed algorithm can escape premature convergence and jump out of local optimum. First, an adaptive mutation rate is designed according to the evolutionary generations, population diversity, and individual fitness values. A manual intervention strategy is then proposed to determine whether the algorithm enters the dilemma of local optimum based on the generations of population evolutionary stagnation. Finally, the average quality of the population is changed by randomly replacing individuals, and the ancestral population is traced to change the evolutionary direction. The experimental results on the benchmarks of function mining show that the proposed MS-GEP has better solution quality and higher population diversity than other GEP algorithms. Furthermore, the proposed MS-GEP has higher accuracy on the function model of CP-EE of high-speed railway catenary monitoring system than other commonly used algorithms in the field of function mining. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

50. Pantograph Detection Algorithm with Complex Background and External Disturbances.

Author

Tan, Ping, Cui, Zhisheng, Lv, Wenjian, Li, Xufeng, Ding, Jin, Huang, Chuyuan, Ma, Jien, and Fang, Youtong

Subjects
PANTOGRAPH, ALGORITHMS, ELECTRIC power, PROBLEM solving, VIDEO surveillance
Abstract

As an important equipment for high-speed railway (HSR) to obtain electric power from outside, the state of the pantograph will directly affect the operation safety of HSR. In order to solve the problems that the current pantograph detection method is easily affected by the environment, cannot effectively deal with the interference of external scenes, has a low accuracy rate and can hardly meet the actual operation requirements of HSR, this study proposes a pantograph detection algorithm. The algorithm mainly includes three parts: the first is to use you only look once (YOLO) V4 to detect and locate the pantograph region in real-time; the second is the blur and dirt detection algorithm for the external interference directly affecting the high-speed camera (HSC), which leads to the pantograph not being detected; the last is the complex background detection algorithm for the external complex scene "overlapping" with the pantograph when imaging, which leads to the pantograph not being recognized effectively. The dirt and blur detection algorithm combined with blob detection and improved Brenner method can accurately evaluate the dirt or blur of HSC, and the complex background detection algorithm based on grayscale and vertical projection can greatly reduce the external scene interference during HSR operation. The algorithm proposed in this study was analyzed and studied on a large number of video samples of HSR operation, and the precision on three different test samples reached 99.92%, 99.90% and 99.98%, respectively. Experimental results show that the algorithm proposed in this study has strong environmental adaptability and can effectively overcome the effects of complex background and external interference on pantograph detection, and has high practical application value. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results