科技與工程學院
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沿革
科技與工程學院（原名為科技學院）於87學年度成立，其目標除致力於科技與工程教育師資培育外，亦積極培育與科技產業有關之工程及管理專業人才。學院成立之初在原有之工業教育學系、工業科技教育學系、圖文傳播學系等三系下，自91學年度增設「機電科技研究所」，該所於93學年度起設立學士班並更名為「機電科技學系」。本學院於93學年度亦增設「應用電子科技研究所」，並於96學年度合併工教系電機電子組成立「應用電子科技學系」。此外，「工業科技教育學系」於98學年度更名為「科技應用與人力資源發展學系」朝向培育科技產業之人力資源專才。之後，本院為配合本校轉型之規劃，增加學生於科技與工程產業職場的競爭，本院之「機電科技學系」與「應用電子科技學系」逐漸朝工程技術發展，兩系並於103學年度起分別更名為「機電工程學系」及「電機工程學系」。同年，本學院名稱亦由原「科技學院」更名為「科技與工程學院」。至此，本院發展之重點涵蓋教育（技職教育/科技教育/工程教育）、科技及工程等三大領域，並定位為以技術為本位之應用型學院。
107學年度，為配合本校轉型規劃，「光電科技研究所」由原隸屬於理學院改為隸屬本（科技與工程）學院，另增設2學程，分別為「車輛與能源工程學士學位學程」及「光電工程學士學位學程」。
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Item 基於ROS之智慧安防自主巡邏履帶式機器人系統(2022) 紀鴻文; Ji, Hong-Wen本論文主要將深度感測器與自主式履帶機器人整合，並針對障礙物偵測與人體動作辨識這兩方面各自提出一種系統。在障礙物偵測系統中，運用深度影像使機器人能夠偵測前方空間中的障礙物，並結合模糊控制器控制機器人安全避開。在人體動作辨識系統中，藉由Kinect v2攝影機取得人體骨架，並透過事先訓練好的模糊類神經網路進行即時動作辨識，以觀察是否作出危險動作。除了以上兩種系統外，還增加監控系統的使用者介面，並透過3台Mesh架構的路由器來跟履帶式機器人相互溝通，以此來傳遞影像資訊、地圖位置、任務要求、顯示警示燈等功能。Item 可適應無人搬運車彈性化設計之學習式導航策略及強健式路徑跟隨控制(2022) 王思涵; Wang, Sih-Han現今無人搬運車(Automated Guided Vehicle，AGV)引入製造工廠和自動化倉儲是邁向工業4.0的必備條件，由於實際工廠生產線環境中高度動態與不確定性，本論文開發一套強化AGV定位精確性與導航策略。首先提出具有低成本效益之反光柱輔助定位技術，利用反光點作為環境中的分離特徵進行重新定位，能有效改善自適應蒙地卡羅定位定位(Adaptive Monte Carlo Localization, AMCL) 演算法在環境特徵不明顯或環境地圖邊界過於破碎，所導致的迷航或定位失效的問題。接著，本論文提出可適應AGV動作的路徑跟隨控制設計，並整合至機器人作業系統(Robot Operating System, ROS)的軟體環境，此種設計除了可延伸應用於相關自主式無人搬運車軌跡追蹤控制策略之外，基於模糊神經網路架構並提出新的誤差計算方式，可以在模擬環境搭配AGV運動模型來預先進行控制參數自動調整。本論文開發的AGV導航控制先使用MATLAB模擬環境來實現所提出的用於導航控制的模糊神經網絡(Fuzzy Neural Network, FNN)策略，對軌跡跟踪中的模擬結果評估，以驗證所提出的AGV控制策略的有效性。由實驗測試結果說明，本論文提出的反光柱輔助定位搭配AMCL定位演算法能有效克服累積定位誤差之外，進一步整合強健式路徑跟隨控制與學習式導航策略，能展現本論文所開發AGV技術在實際工廠生產線環境中之高度應用價值。Item 多重智慧控制器應用於機械手臂定位(2013) 許哲勝本論文的主要目的是設計一個六軸機械手臂，並且實現高精密且高穩定之六軸機械手臂。並且在硬體架構、機械手臂之空間三維座標、機械手臂各關節轉動角度與定位追跡的控制作介紹。在空間座標轉換中，本研究使用了D-H 座標系統來運算，並且求得機械手臂中各軸關節之轉換矩陣，再藉由順向運動學與逆向運動學的理論求得機械手臂之空間三維座標與機械手臂各關節轉動角度之轉換關係，並且再藉由設計控制器完成定位控制與追跡控制。 在控制器設計方面，本論文也設計一個多重人工智慧控制器去控制此六軸機械手臂。在控制的過程中，系統會有外界的干擾與不穩定因素，因此本研究所使用之適應性模糊類神經網路控制器會藉由理想輸出位置與機械手臂實際位置之誤差的回授來調整控制器的內部參數，藉由控制器自行調整其內部參數，則可達到高精密與高穩定度的控制法則。最後也提出李阿普諾函式(Lyapunov function)來證明此控制機械手臂系統之穩定性。Item GA-based learning of BMF fuzzy-neural network(2002-05-17) W.-Y. Wang; T.-T. Lee; C.-C. Hsu; Y.-H. LiAn approach to adjust both control points of B-spline membership functions (BMFs) and weightings of fuzzy-neural networks using a simplified genetic algorithm (SGA) is proposed. The SGA is proposed by using a sequential-search-based crossover point (SSCP) method in which a better crossover point is determined and only the gene at the specified crossover point is crossed as a single point crossover operation. Chromosomes consisting of both the control points of BMFs and the weightings of fuzzy-neural networks are coded as an adjustable vector with real number components and searched by the SGA. Because of the use of the SGA, faster convergence of the evolution process to search for an optimal fuzzy-neural network can be achieved. Nonlinear functions approximated by using the fuzzy-neural networks via the SGA are demonstrated to illustrate the applicability of the proposed methodItem Function approximation using fuzzy neural networks with robust learning algorithm(IEEE Systems, Man, and Cybernetics Society, 1997-08-01) W.-Y. Wang; T.-T. Lee; C.-L. Liu; C.-H. WangThe paper describes a novel application of the B-spline membership functions (BMF's) and the fuzzy neural network to the function approximation with outliers in training data. According to the robust objective function, we use gradient descent method to derive the new learning rules of the weighting values and BMF's of the fuzzy neural network for robust function approximation. In this paper, the robust learning algorithm is derived. During the learning process, the robust objective function comes into effect and the approximated function will gradually be unaffected by the erroneous training data. As a result, the robust function approximation can rapidly converge to the desired tolerable error scope. In other words, the learning iterations will decrease greatly. We realize the function approximation not only in one dimension (curves), but also in two dimension (surfaces). Several examples are simulated in order to confirm the efficiency and feasibility of the proposed approach in this paperItem Evolutionary learning of BMF fuzzy-neural networks using a reduced-form genetic algorithm(IEEE Systems, Man, and Cybernetics Society, 2003-12-01) W.-Y. Wang; Y.-H. LiIn this paper, a novel approach to adjust both the control points of B-spline membership functions (BMFs) and the weightings of fuzzy-neural networks using a reduced-form genetic algorithm (RGA) is proposed. Fuzzy-neural networks are traditionally trained by using gradient-based methods, which may fall into local minimum during the learning process. To overcome the problems encountered by the conventional learning methods, genetic algorithms are adopted because of their capabilities of directed random search for global optimization. It is well known, however, that the searching speed of the conventional genetic algorithms is not desirable. Such conventional genetic algorithms are inherently incapable of dealing with a vast number (over 100) of adjustable parameters in the fuzzy-neural networks. In this paper, the RGA is proposed by using a sequential-search-based crossover point (SSCP) method in which a better crossover point is determined and only the gene at the specified crossover point is crossed, serving as a single gene crossover operation. Chromosomes consisting of both, the control points of BMFs and the weightings of the fuzzy-neural network are coded as an adjustable vector with real number components that are searched by the RGA. Simulation results have shown that faster convergence of the evolution process searching for an optimal fuzzy-neural network can be achieved. Examples of nonlinear functions approximated by using the fuzzy-neural network via the RGA are demonstrated to illustrate the effectiveness of the proposed method.