貼心的智慧型生活環境，必須能在不同的情緒狀態提供適當服務，因此我們希望能開發出一個情緒辨識系統，透過對於形於外的生理感測資料的變化來觀察形於內的情緒狀態。 首先我們採用國際情緒圖庫系統(IAPS: International Affective Picture System) 及維度式分析方法，透過心理實驗的操弄，收集了20位的受測者生理數值與主觀評定情緒的強度與正負向。我們提出了一個情緒辨識學習演算法，經由交叉驗證訓練出每個情緒的特徵，並藉由即時測試資料來修正情緒特徵的個人化，經由學習趨勢的評估，準確率有明顯提升。其次，我們更進一步引用了維度式與類別式情緒的轉換概念來驗證受測者主觀評定的結果。相較於相關研究實驗結果，我們在維度式上的強度與正負向辨識率有較高的表現，在類別式上的驗證我們也達到明顯區分效果。 更重要的是，我們所實作出的系統，是搭載了無線生理感測器，使用時更具行動性，而且可即時反映情緒，提供線上智慧型服務。 / A living smart environment should be able to provide thoughtful services by considering different states of emotions. The goal of our research is to develop an emotion recognition system which can detect the internal emotion states from external varieties of physiological data. First we applied the dimensional analysis approach and adopted IAPS (International Affective Picture System) to manipulate psychological experiments. We collected physiological data and subjective ratings for arousal and valence from 20 subjects. We proposed an emotion recognition learning algorithm. It would extract each pattern of emotions from cross validation training and can further learn adaptively by feeding personalized testing data. We measured the learning trend of each subject. The recognition rate reveals incremental enhancement. Furthermore, we adopted a dimensional to discrete emotion transforming concept for validating the subjective rating. Compared to the experiment results of related works, our system outperforms both in dimensional and discrete analyses. Most importantly, the system is implemented based on wireless physiological sensors for mobile usage. This system can reflect the image of emotion states in order to provide on-line smart services.

由於VANET上的行動節點移動速度快，加上受到道路及交通號制的限制，導致網路拓樸快速改變，容易造成網路斷訊，影響資料封包在網路上的傳送效能。在傳統的MANET上有許多用來傳送資料封包的路由機制，並不直接適用在VANET上。隨著Global Position System (GPS)的普及，越來越多的車輛都具備GPS，用以輔助行車定位之用。在本研究中我們將透過GPS取得車輛的地理資訊，提出一個適用於VANET中以車行方向為基礎的貪婪路由演算法(MDBG)。 本論文目的在強化VANET網路上資料封包的路由選擇策略。所提出的路由機制將會透過hello message來取得相鄰車輛的位置和車行方向，並利用目標要求(DREQ)、目標回應(DREP)來獲得目標車輛的資訊。進而運用車輛的車行方向，選擇適當的相鄰車輛找出一條穩定的路由路徑。當來源車輛和目標車輛的車行方向相同時，AODV能有不錯的效能表現。而我們的路由演算法(MDBG)將強化當來源車輛和目標車輛的車行方向相反，並且逐漸遠離時的效能表現。實驗模擬的結果顯示MDBG在封包到達率、吞吐量和平均端對端延遲上較之於AODV及DSR演算法有更優異的表現。 / Packets transmission over VANET is intermittent due to rapid change of network topology. This comes from both high mobility of mobile nodes and road limitation. Intermittent transmission causes inefficient packet delivery. Those routing protocols applicable to MANET might not be suitable for VANET. On the other hand, Global Position System (GPS) is becoming prevalent in assisting positioning for vehicles. In this research, we develop a Moving Direction Based Greedy (MDBG) routing algorithm for VANET. MDBG algorithm is based on the geographical information collected by GPS. The objective of the thesis is to enhance routing decision in packet delivery. The "hello message" is used to retrieve the locations and moving directions of neighboring vehicles. Destination REQuest (DREQ) and Destination REPly (DREP) messages are used to retrieve target vehicle information. The source vehicle will thus use these information together with its own moving direction information to establish a stable routing path by selecting appropriate neighboring vehicles. AODV algorithm is proved to have good performance as both the source vehicle and target vehicle have the same moving direction. MDBG algorithm is proposed to leverage the problem as source vehicle and target vehicle move far apart in opposite directions. Simulation results show that MDBG outperforms both AODV and DSR in packet arrival rate, throughput and average end-to-end delay.

本文主要著重在處理條件評估法下，「不知道」受訪者的回應。當「不知道」受訪者的產生機制並未符合完全隨機時，考量他們的真實意向就顯得極為重要。 文中使用中央研究院生醫所在其研究計畫「竹東及朴子地區心臟血管疾病長期追蹤研究」（CardioVascular Disease risk FACtor Two-township Study，簡稱CVDFACTS）第五循環中的研究調查資料。 　　由於以往的文獻對於「不知道」受訪者的處理，皆有不足之處。如Wang (1997)所提出的方法，就只能針對某種特定的「不知道」受訪者來做處理；而Caudill and Groothuis (2005)所提的方法，由於將「不知道」受訪者的差補與願付價格的估計分開，亦使其估計結果不具備一些好的性質。在本文中，我們提出一個能同時處理「不知道」受訪者且估計願付價格的方法。除了使得統計上較有效率外，也保有EM演算法的一個特性：願付價格模型中的估計參數為最大概似估計值。此外，在加入三要素混合模型(Tsai (2005))後，我們也可避免用到極端受訪者的訊息去差補那些「不知道」受訪者的意向。 　　在分析願付價格的過程中，我們發現此筆資料的「不知道」受訪者，其產生的機制為隨機，而非為完全隨機，這意謂著不考量「不知道」受訪者的分析結果，必定會產生偏差。而在比較有考量「不知道」受訪者與沒有的情況後，其結果確實應證了我們的想法：只要「不知道」受訪者不是完全隨機產生的，那麼不考量他們必定會產生某種程度的偏差。 / This paper investigates how to deal with “Don’t Know” (DK) responses in contingent valuation surveys, which must be taken into consideration when they are not completely at random. The data we use is collected from the fifth cycle of the Cardiovascular Disease Risk Factor Two-township Study (CVDFACTS), which is a series of long-term surveys conducted by the Institute of Biomedical Sciences, Academia Sinica. Previous methods used in dealing with DK responses have not been satisfactory because they only focus on some types of DK respondents (Wang (1997)), or separate the imputation of DK responses from the WTP estimation (Caudill and Groothuis (2005)). However, in this paper, we introduce an integrated method to cope with the incomplete data caused by DK responses. Besides being more efficient, the single-step method guarantees maximum likelihood estimates of the WTP model to be obtained due to the good property that the EM algorithm possesses. Furthermore, by adding the concept of the three-component mixture model (Tsai (2005)), some extreme information are drawn out when imputing the DK inclinations. In this hypertension data, the mechanism of the DK responses is “Don’t know at random”, which means the analysis of DK-dropped results in a bias. By using our method, the difference between DK-dropped and DK-included is actually revealed, which proves our suspicion that a DK-dropped analysis is accompanied by a biased result when DK is not completely at random.

綜觀現今全球化的趨勢，世界各國皆進行跨語言的專利文書翻譯工作。在專利文書翻譯及跨語言檢索方面，蒐集大量且正確的專利文書平行語料能夠協助相關研究的進行。利用人工進行平行語料文句的對列工作相當費時，因此，本研究利用斷句、斷詞及英文詞幹還原等前處理技術，搭配中英技術名詞對應表，透過統計詞頻調整對應詞組的權重，並以句子間的餘弦相似度作為輔助，計算中英文句子間的相似度，最後利用動態規劃演算法挑選最佳的對列組合，發展出一套中英文句對列的系統。以精確率及召回率評比對列成效，並將對列後產生的句對作為輔助式機器翻譯系統詞序調動的訓練語料，以2003年國際數學語科學教育成就趨勢調查測驗試題作為翻譯對象，採用BLEU及NIST的評比方式進行評估。實驗結果顯示本系統不僅在1：1對列模式的精確率達到0.995，且利用門檻值篩選出的大量中英文句對，確實能夠提升輔助式機器翻譯系統的翻譯品質。 / The importance of cross-language translation of patent documents has grown substantially as a result of globalization. Accurately aligned parallel corpora help researchers conduct their research projects that depend on bilingual data to develop techniques such as computer-aided translation and cross-language information retrieval. It takes time to collect parallel data manually; therefore, an English-Chinese sentence alignment system was built that will automatically complete this process. A variety of preprocessing techniques for natural language processing were used, such as the stemming of the English words, to build this system. Two parts of scores were considered to align sentences. The first part considered the number and weight of aligned word pairs in the Chinese and English sentences. The second part came from a special way to compute the cosine value of the Chinese and English sentence pairs. Precision and recall rates were used to evaluate the quality of the aligned results and the 1:1 alignment achieved 0.995 precision. In addition, the aligned sentences were used as training data in a machine translation for the TIMSS test items, experimental results show that the aligned sentences are helpful for the translation system.

半導體生產的流程可分成四階段：晶圓生產(fabrication)、測試分類(sorting)、封裝(assembly)與檢驗(testing)。每個階段都有不同的廠商可提供服務。當晶圓生產廠商接獲訂單，其供應鍊管理者會根據產能、需求量、交貨日、技術水準與成本等考慮因素，決定此訂單應由何晶圓廠區生產、由何測試分類廠做分類、由何封裝廠做封裝與最後由何檢驗廠做檢驗。本研究的主要目的為在各種限制條件下，以最小成本為目標，找出完成客戶訂單的最佳廠商組合。可能的限制包括產能限制、交貨日的滿足、各廠區的技術水準及需求量的大小。本問題可視為產品組合、廠商組合與生產排程的綜合問題，過去常用的解決方法為整數與線性規劃的混合應用，但是由於牽涉的因素太多，常常問題的模式中變數與限制式過多導致無法解決。本研究先以資料的收集與模式的建構為主，利用並修改現有的產品結構樹模型使其變成供應鏈網路模式，並加入半導體產業供應鏈相關特性，建立一個以時間軸為機制的混合整數線性模式。並且以時間成本的概念來衡量整個半導體供應鏈的效能。 混合整數線性模式常會面臨許多問題，由於模式的複雜，變數與限制式過多，造成求解的困難。對電腦資源的需求很大，花費的時間也很長。同時對於問題的規模亦造成制限。於是本研究藉著修改Kim (1995) 的 backward list scheduling 演算法概念，建構一個解決問題的啟發式演算法，可快速求得一組近似最佳解之可行解。 由於供應鏈所面對的是隨機環境，因此必須以模擬的方式對上述模型進行檢驗，確認其有效性及適用的範圍。利用系統模擬方法，確定隨機變數與其分配，以建立模擬模型程式。實際進行模擬，以驗證上述供鏈模型之有效性，並瞭解、分析模型之適用性及應用方式。 對於半導體產業供應鏈廠商指派與資源分配之網路管理方面，提供一數量化的思考邏輯。運用數量化的模式表現出不同的半導體產業供應鏈廠商指派與資源分配之網路管理的問題，並提出解決問題的演算機制。

近幾年來，非線性時間數列轉折點的研究愈來愈受到重視，學者們也提出許多關於轉折點的偵測及檢定方法。若考慮實際資料走勢轉變的情形，“轉折區間”的概念更可以解釋結構改變的現象。但文獻中對於如何找尋時間數列結構改變之轉折區間的研究並不多。本文擬以時間數列統計模式及模糊學理論的角度來研究，並結合遺傳演算的規則而提出主導模式的概念，來架構出時間數列遺傳模式，再藉由轉折區間決策法則來找出數列的轉折區間。其中，我們以統計模式為遺傳演化過程中的染色體，而以候選模式之隸屬度函數為衡量染色體適應能力的指標。最後，我們舉出臺灣股價收盤指數之實例，分別以我們所提出的方法及其他方法找出數列的轉折區間及轉折點，並做比較。 / For recent years, the research of change point in nonlinear time series has been considered to be more and more important. Scholars have proposed a lot of detecting and testing methods about change points.If considering the trend of real situation, the concept of change period will show the phenomena of structure change.But there are not many researches about how to find change period in time series.My paper is based on the points of time series models and fuzzy theory.Besides,it combines the rules of genetic algorithm and provides the concepts of leading model to construct time seriep genetic model and to find out change period by decision rule.ln this paper, we use time series statistical models as chromosome in procedure of genetic evolution, and we also use membership function of selected models as pereformance: index of chromosome.Finally, the empirical application about change periods and change points detecting by our method and other's for Taiwan stock closing prices is demonstrated and make a comparision with these results.

近幾年來，非線性時間數列分析有快速的發展。其中的門檻自迴歸模式(SETAR)，以具有許多線性ARIMA模式所不能配適的特性而受到重視。但是，自1978年Tong建立SETAR模式以來，門檻參數估計的問題一直是SETAR模式在發展應用上的一個瓶頸。本文將探討以實數編碼遺傳演算法，結合統計學上的模式選取準則，建構SETAR模式門檻與延遲參數估計程序的可行性。並從這個基礎上，進一步地研究較精確的門檻參數估計法。 / Non-linear time series analysis has rapidly developed in recent years. Self-exciting threshold autoregression(SETAR) model of non-linear time series models is attentive, because it has some characters which linear ARIMA model fail to fit. But, It has not yet been applied widely because the question of estimation of threshold parameter limits its development and application since Tong proposed SETAR model in 1978. In this paper, we will study the feasibility which constructs a procedure of estimation of SETAR's threshod and delay parameters with real-coded genetic algorithm and statistical criterion of model selection, and develop a more precise estimation of threshold parameter in the basis.

近幾年來，非線性時間數列分析一直是時間數列及計量經濟學者所熱衷的研究主題之一，而非線性時間數列結構改變的研究也越來越受到重視。其中的門檻自迴歸模式，雖具有線性模式所不能配適的特性，但模式建構的問題，一直是其在發展應用上的瓶頸。本研究擬以門檻自迴歸模式建構的流程並結合遺傳演算法的最佳化搜尋技術，架構出時間數列遺傳演算法，藉此演算法則及程序，全域性地搜尋最佳的門檻自迴歸模式。 / Non-linear time series analysis is a research topic which the schalors of time series and econometrics are intent on, and the research of structure change of non-linear time series is attentive. Threshold autoregressive model (TAR model) of non-linear time series has some characters which linear model fail to fit while the problem of how to find an appropriate threshold value is still attracted many researchers attention. In this paper, we present about searching the parameters for a TAR model by genetic algorithms.

在線性模型中以計算容易，解釋方便為著稱，但是比須加入許多嚴格限制 ，而對於事後之模型檢測亦要花費番功夫。，而可加性模型只要函數給定 ，backfitting 演算法收歛即可。可加性模型除了保留線性模型的加法性 及解釋能力外，尚且提高了估計準度。在美、日、中、英、德五個國家的 保險市場中，雖然判定係數的提升亦大有斬獲 (0.85->0.9957)，然而在 台灣我們根據實證 一、提升統計應用水準，大幅提高模型變數的解釋能 力，模型內MSE(Me Square Error)大幅降低。(見表5-1、表5-2、表5-3、 表5-4、表5-5、表5-6、二、維持了線性模型方便的解釋能力。三、提升 估計水準，用以比較二種模型之優劣時，採1991年保費收入之實際值與估 計值之比較(見表 5-3，表 5-6，表 5-9，表 5-12，表 5- 15)，可發現 線性模型誤差率與可加性模型誤差率的比值美國為2倍、日本為12倍、臺 灣為4.55倍、英國為2.95倍、德國為2.95倍。四、函數以圖形方式表示顯 而易見。可加性模型所做的保費收入估計模型 / An Application of Insurance in Additive Model：United States's, Japan's,Taiwan's,England's and germnany's Life Insurance Model between Premiums and Macro-variables comparison.

隨著產品生命週期的縮短和環保法規的訂定，逆向物流成為21世紀重要的課題，而越來越多的企業也注意到逆向物流的重要性，他們開始將產品復原(recovery of product)納入整體企業作業的考量，本次的研究特別注重於產品復原中的再製造活動。我們假設一製造商有一再製造中心，製造商面對的是不確定的產品需求率和回收率，為最小化總存貨管理成本，製造商所關心的決策為在每次的再製造和再訂購點時應向供應商訂購多少零件及應有多少的回收品須被再製造。換句話說，我們處理的是一個同時考量最佳經濟製造批量(EPQ)和最佳經濟訂購批量(EOQ)的模式，在此模式中不管是經再製造處理的零件或全新訂購的零件皆可滿足需求且將經再製造處裡的零件視為全新的零件。首先我們混合模糊控制和基因演算法的概念建立一基因-模糊控制架構，藉此架構發展一基因-模糊控制系統，靠著此系統我們能得到一套決定最佳EPQ和EOQ的規則。依照規則，根據不同的回收率和需求率就能同時得到不同的EPQ和EOQ。最後我們會測試由模糊控制系統所得的EOQ和EPQ在存貨管理成本上的績效。