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- 저자 : Hanming Guo (검색결과 2 건)
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Wang Hanming,Zhao Jinghong,Guo Guoqiang,Chen Hansi,Xiong Yiyong,Yan Sinian 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.2
Due to its simple structure, the linear induction motor provides a linear driving force without any intermediate conversion devices. Linear induction motors are gradually used in various applications, including rail transit and the national defense industry. However, linear induction motors are affected by the end-effect phenomenon and suffer from problems such as low efficiencies and low power factors. In this paper, to optimize the performance of short primary double-sided linear induction motors (SP-DLIMs), first, a new analytical model is established based on an equivalent circuit considering both transverse and longitudinal end effects. Second, to analyze the impact of main parameters on the output characteristics of SP-DLIMs, a simple iterative design method (IDM) is presented. Third, a multi-objective optimal design method is proposed to optimize the efficiency, power factor and tooth weight of SP-DLIMs, with proposed design constraints and four different optimization problems. Moreover, an improved differential evolutionary (IDE) algorithm is proposed to optimize the design. A comparison of the results proved that the IDE yielded a competitive performance to those of other advanced heuristic algorithms. Finally, a three-dimensional time-stepping finite-element method was utilized to verify the validity of the optimization results.
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Adaptive Subpixel Edge Detection for Locating the Center of Nut Screw Hole
Rui Yang,Hanming Guo,Zhentao Chen,Jiuai Sun 한국정밀공학회 2021 International Journal of Precision Engineering and Vol.22 No.8
An accurate and fast machine vision-based method for locating the center of disk nuts is of tremendous significant for safe installation and maintaining service life of glass curtain wall. However, current manual or image processing approaches suffer from the problems of inaccurate or time consuming. Based on the intensity distribution model around the edge of screw holes within actual captured images, a Gaussian weighted adaptive threshold method is proposed to replace traditional Otsu threshold algorithm to identify the edge of screw holes in a level of subpixel. The identified edge points are used to fi t the center of hole via a least square estimation algorithm. Both simulation and real object evaluation have shown that the proposed algorithm has higher accuracy in locating the center of the screw holes and demonstrated with good tolerance to noise and faster processing speed comparing to that of the traditional algorithm.
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