路燈巡修作業的落實與否，影響路燈維護的效率及品質，為能有效提升路燈管理之效能，近來管理階層逐漸重視路燈巡修區域的規劃。然而巡修區域的劃分，多依據主管人員之經驗調派，缺乏系統化、科學化的分析與評估，往往使人力資源無法有效運用，形成勞逸不均的現象，進而影響維護品質，因此如何以科學的方法劃分路燈巡修區域是個重要的課題。 本研究的重點在於針對現行路燈巡修區域劃分之缺點，提出一個新的方法，使各區域管理員巡修時間差達到最小化，以解決現行區域劃分的不合理現象。我們所提出的劃分法，以基因演算法進行演算，並加入禁忌名單改善基因演算法區域搜尋效率不佳的缺點，提升整體的求解速度，同時將路燈維護數量、故障率、維護時間、交通時間、巡修次數等影響因子，納入巡修時間的計算公式中，使劃分後各區的巡修時間差達到最小化。 本研究以台北市政府公園路燈工程管理處的路燈東區分隊為實作對象，在考慮不同的基因演化條件下，分別比較巡修區域劃分前後之變化情況，由實驗結果顯示，我們提出的劃分方法，確實使各區管理員的巡修時間差不超過3%，並且滿足巡修不跨區作業之需求。 / The efficiency and quality of street lights maintenance is influenced by the operation of patrolling and maintaining. In order to raise the working efficiency of maintenance crew, the supervisors pay more attention to region redistricting recently. The formor region districting methods normally base on human experiences without systematic or scientific evaluations, These facts, not only result in human resources wasting and uneven labor allocations, but also affecting the maintenance qualities. Therefore, it is a crucial issue to make region redistricting more scientifically. The key point of this research is to provide a systematic redistricting mechanism to minimize the patrolling time variation for all the districts. Our mechanism is based on genetic algorithm to reduce the patrolling time differences. Tabu search list is used to improve the searching efficiency of general genetic algorithms. Various factors were integrated in our mechanism to minimize the patrolling time variations. These factors include total number of street lights, average failure rate, average maintenance time, traffic delay, patrolling and maintaining frequency, etc. We used districts covered by the East Branch of SET/PSO of Taiepi City Government as the examples in our studies. The experimental results show that, using our mechanism, the patrolling time difference is reduced to 3% and maintenance crews can perform their duty without crossing region boundary.

在音樂中，組曲是一種特別的創作形式。組曲將多首音樂段落組合排列，並且在音樂段落之間加入間奏，形成一首音樂組曲。組曲的編曲重點在於音樂段落的編排順序及段落之間的連結。平時在宴會、舞會、餐廳、賣場等場合中，往往都會連續播放多首流行音樂。利用電腦編曲自動產生流行音樂組曲，將可提升播放音樂的銜接與流暢感。 因此，本研究利用資料探勘技術及音樂編曲理論，將多首音樂重新改編成一首組曲。系統首先將每首音樂分段並找出每首音樂的代表段落。接著，系統根據代表段落間的相似度編排順序。最後，為了達到組曲中音樂段落連接的流暢性，我們以模型訓練的方式在段落連結間加入間奏。系統從訓練資料學習產生旋律發展、和弦進程與節奏的模型，接著分析代表段落的動機、旋律、和弦及節奏，使得組曲編曲後的段落連結更為流暢且完整。本研究以流行音樂鋼琴伴奏曲為測試資料，我們分別邀請三十四位受過音樂訓練與未受音樂訓練的測試者，針對本研究所提出的鋼琴伴奏節奏辨識、代表段落萃取、段落順序編排及間奏產生，評估其效果。實驗結果顯示，本研究所提出的順序編排與間奏產生技術，對於組曲的流暢感，有著相當大的幫助。 / In music, a medley is an organized piece composed from segments of existing pieces. Ordering and bridge for connection between segments are the important elements for medley arrangement. Automatic medley arrangement is helpful for playing a set of music continuously which is common in banquet, party, restaurant, shopping mall, etc.. This thesis aims to develop the automatic medley arrangement method by using data mining techniques and music arrangement theory. The proposed method first segments each music and discovers the significant segment from each music. Then, the linear arrangement based on the similarities between significant segments is generated. Finally, in order to connect segments smoothly in the medley, the bridge between two segments is generated and inserted by using model training. Three models, melody progression, chord progression and rhythm models are learned from training data. For the experiments, testing data is collected from popular piano music and thirty-four people are invited to evaluate the effectiveness of the rhythm recognition of accompaniment, the extraction of significant segment, the linear arrangement of segments, and the creation of bridge. Experimental results show that the proposed medley arrangement method performs well.

The CUSUM control charts have been widely used in detecting small process shifts since it was first introduced by Page (1954). And recent studies have shown that adaptive charts can improve the efficiency and performance of traditional Shewhart charts. To monitor the process mean and variance in a single chart, the loss function is used as a measure statistic in this article. The loss function can measure the process quality loss while the process mean and/or variance has shifted. This study combines the three features: adaption, CUSUM and the loss function, and proposes the optimal VSSI, VSI, and FP CUSUM Loss chart. The performance of the proposed charts is measured by using Average Time to Signal (ATS) and Average Number of Observations to Signal (ANOS). The ATS and ANOS calculations are based on Markov chain approach. The performance comparisons between the proposed charts and some existing charts, such as X-bar+S^2 charts and CUSUM X-bar+S^2 charts, are illustrated by numerical analyses and some examples. From the results of the numerical analyses, it shows that the optimal VSSI CUSUM Loss chart has better performance than the optimal VSI CUSUM Loss chart, optimal FP CUSUM Loss chart, CUSUM X-bar+S^2 charts and X-bar+S^2 charts. Furthermore, using a single chart to monitor a process is not only easier but more efficient than using two charts simultaneously. Hence, the adaptive CUSUM Loss charts are recommended in real process. / The CUSUM control charts have been widely used in detecting small process shifts since it was first introduced by Page (1954). And recent studies have shown that adaptive charts can improve the efficiency and performance of traditional Shewhart charts. To monitor the process mean and variance in a single chart, the loss function is used as a measure statistic in this article. The loss function can measure the process quality loss while the process mean and/or variance has shifted. This study combines the three features: adaption, CUSUM and the loss function, and proposes the optimal VSSI, VSI, and FP CUSUM Loss chart. The performance of the proposed charts is measured by using Average Time to Signal (ATS) and Average Number of Observations to Signal (ANOS). The ATS and ANOS calculations are based on Markov chain approach. The performance comparisons between the proposed charts and some existing charts, such as X-bar+S^2 charts and CUSUM X-bar+S^2 charts, are illustrated by numerical analyses and some examples. From the results of the numerical analyses, it shows that the optimal VSSI CUSUM Loss chart has better performance than the optimal VSI CUSUM Loss chart, optimal FP CUSUM Loss chart, CUSUM X-bar+S^2 charts and X-bar+S^2 charts. Furthermore, using a single chart to monitor a process is not only easier but more efficient than using two charts simultaneously. Hence, the adaptive CUSUM Loss charts are recommended in real process.