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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    改良式非同步並行處理之粒子群聚最佳化法
    (2008-06-07) 許陳鑑; 林耕宇
    本文提出ㄧ種 改良式非同步並行處理之粒子群聚最佳化法 ,以提升粒子群聚最佳化法在不同質(heterogeneous)的計算環境中之計算效率。作法上係綜合傳統的同步與非同步並行處理計算法,以僕工作端(slave)之性能為基準,分配適當的粒子數量,以減少工作站等待時間的浪費,使計算效能得以提升。為評估本文所提出方法之有效性,我們將以minimax 最佳化問題及系統模型降階的問題作為標的,分別使用傳統的同步並行處理、非同步並行處理、ㄧ台獨立電腦、以及本文所提出之方法做比較。實驗結果指出,我們所提出的方法在兩個範例都有較好的性能展現。
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    Minimum-Phase Criterion on Sampling Time for Sampled-Data Interval Systems Using Genetic Algorithms
    (Elsevier, 2008-09-01) Chen-Chien Hsu; Tsung-Chi Lu
    In this paper, a genetic algorithm-based approach is proposed to determine a desired sampling-time range which guarantees minimum phase behaviour for the sampled-data system of an interval plant preceded by a zero-order hold (ZOH). Based on a worst-case analysis, the identification problem of the sampling-time range is first formulated as an optimization problem, which is subsequently solved under a GA-based framework incorporating two genetic algorithms. The first genetic algorithm searches both the uncertain plant parameters and sampling time to dynamically reduce the search range for locating the desired sampling-time boundaries based on verification results from the second genetic algorithm. As a result, the desired sampling-time range ensuring minimum phase behaviour of the sampled-data interval system can be evolutionarily obtained. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature which requires undertaking a large number of evolution cycles, parallel computation for the proposed genetic algorithm is therefore proposed to accelerate the derivation process. Illustrated examples in this paper have demonstrated that the proposed GA-based approach is capable of accurately locating the boundaries of the desired sampling-time range.