電機工程學系

Permanent URI for this communityhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/85

歷史沿革

本系成立宗旨在整合電子、電機、資訊、控制等多學門之工程技術,以培養跨領域具系統整合能力之電機電子科技人才為目標,同時配合產業界需求、支援國家重點科技發展,以「系統晶片」、「多媒體與通訊」、與「智慧型控制與機器人」等三大領域為核心發展方向,期望藉由學術創新引領產業發展,全力培養能直接投入電機電子產業之高級技術人才,厚植本國科技產業之競爭實力。

本系肇始於民國92年籌設之「應用電子科技研究所」,經一年籌劃,於民國93年8月正式成立,開始招收碩士班研究生,以培養具備理論、實務能力之高階電機電子科技人才為目標。民國96年8月「應用電子科技學系」成立,招收學士班學生,同時間,系所合一為「應用電子科技學系」。民國103年8月更名為「電機工程學系」,民國107年電機工程學系博士班成立,完備從大學部到博士班之學制規模,進一步擴展與深化本系的教學與研究能量。

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    Global Optimization Using Novel Randomly Adapting Particle Swarm Optimization Approach
    (2011-10-12) Nai-Jen Li; Wen-June Wang; Chen-Chien Hsu; Chih-Min Lin
    This paper proposes a novel randomly adapting particle swarm optimization (RAPSO) approach which uses a weighed particle in a swarm to solve multi-dimensional optimization problems. In the proposed method, the strategy of the RAPSO acquires the benefit from a weighed particle to achieve optimal position in explorative and exploitative search. The weighed particle provides a better direction of search and avoids trapping in local solution during the optimization process. The simulation results show the effectiveness of the RAPSO, which outperforms the traditional PSO method, cooperative random learning particle swarm optimization (CRPSO), genetic algorithm (GA) and differential evolution (DE) on the 6 benchmark functions.
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    Localization of Mobile Robots via an Enhanced Particle Filter
    (2010-05-06) Chen-Chien Hsu; Ching-Chang Wong; Hung-Chih Teng; Nai-Jen Li; Cheng-Yao Ho
    A self-localization method entitled enhanced particle filter incorporating tournament selection and Nelder-Mead simplex search (NM-EPF) for autonomous mobile robots is proposed in this paper. To evaluate the performance of the localization scheme, an omnidirectional vision device is mounted on top of the robot to analyze the environment of a soccer robot game field. Through detecting the white boundary lines relative to the robot in the game field, weighting for each particle representing the robot's pose can be updated via the proposed NM-EPF algorithm. Because of the efficiency of the NM-EPF, particles converge to the correct location of the robot in a responsive way while tackling uncertainties. Simulation results have shown that efficiency in robot self-localization can be significantly improved while maintaining a relatively smaller mean error in comparison to that via conventional particle filter.