京都大学 / 新制・課程博士 / 博士(理学) / 甲第24170号 / 理博第4861号 / 新制||理||1695(附属図書館) / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)准教授 松四 雄騎, 准教授 深畑 幸俊, 教授 王 功輝 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24641号 / 工博第5147号 / 新制||工||1983(附属図書館) / 京都大学大学院工学研究科化学工学専攻 / (主査)教授 大嶋 正裕, 教授 佐野 紀彰, 教授 山本 量一 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

為了瞭解影響演化性神經網路（ENT）預測表現的四項重要的機制：輸入資料性質、訓練樣本大小、網路搜尋密度以及控制模型複雜度，進而找出能使ENT充分發揮效果的組合。在本論文中首先設計ENT在模擬資料上的實驗，探討上述四項機制個別對預測表現的影響，再依照實驗結果的建議，設計能讓ENT發揮功效的組合，並以實際金融高頻率資料：香港恆生指數在一九九八年十二月報酬率為標的，探討模擬資料的結果在實際金融資料需要調整的部份。實驗結果顯示，當輸入資料經過線性過濾後，搭配大樣本訓練、高搜尋強度與適當地模型複雜度控制，會是能讓神經網路提高預測能力的組合。在實際金融資料的實驗當中同時發現，資料中偶而出現特別高或特別低的變化，會對ENT的預測表現有相當程度的影響。 / In this thesis, Evolutionary Neural Trees (ENTs) are applied to forecast the artificial data generated by financial and chaos models — iid random, linear process (Auto Regressive-Moving Average；ARMA), nonlinear processes (AutoRegressive Conditional Heteroskedasticity；ARCH, General AutoRegressive Conditional Heteroskedasticity；GARCH, Bilinear), mixed linear and nonlinear process (AR and GARCH). Experiments of the artificial data were conducted to understand the characteristics of ENTs mechanism. – data pre-processing procedures, search intensity, sample size and complexity regularization. From the experiment results of artificial data, the combination of pure linear or nonlinear time series, large sample size, intensive search and simple neural trees are suggested for the parameters setting of ENTs. And for the sake of computational burden, we have a trade-off between search intensity and sample size. Ten experiments are designed for ENTs modeling on the high-frequency stock returns of Heng Sheng stock index on December, 1998, in order to have an efficient combination of the factors of ENTs. The results show that ENTs would perform more efficiently if data are pre-processed by a linear filter, for ENTs will concentrate on searching in the space of nonlinear signals. Also, as is well demonstrated in this study, the infrequent bursts (outliers) appearing in the data set can be very disturbing for the ENTs modeling.

閱聽人抗拒主流意識形態的批判能力﹐一直是閱聽人研究領域的重要議題。但是﹐現存閱聽人研究典範卻無法提出適切的討論框架﹐接收分析典範因時代變遷而使其概念模式難以適用﹐新典範如觀展／表演典範卻不關心閱聽人的抗拒問題。因此﹐本研究以閱聽人的思辨能力指稱閱聽人藉由與媒介影像互動、進而返回自我主體思考的能動性﹐試圖建構適合當代複雜媒介景象之閱聽人思辨能力的理論框架。 為了強調思考過程重於思考結果﹐本研究引入政治哲學家鄂蘭的哲學體系﹐視閱聽人的思辨能力為流動變化的過程﹐並以其提出之想像操作機制、普遍可溝通性、想像式巡訪與無家感思考狀態作為研究觀察的參考框架。於實際經驗資料蒐集分析方面﹐本研究持續觀察四年（1998年至2002年）閱聽人於《村上春樹的網路森林》發表循環文本共1,815篇﹐並從鄂蘭的哲學體系出發﹐試圖描繪閱聽人思辨進行的歷程。 研究首先發現﹐閱聽人研究應當將「閱聽人」的概念回歸到「人」的本質基礎﹐承認閱聽人並非單一、純粹、高同質性的群體﹐並重視其多元、混雜、糾結、交錯的身分認同。再者﹐閱聽人進行思辨時﹐其抗拒解構的對象不只是媒介文本蘊藏的意識形態﹐尚且包括長期自我人生經驗形成的默識與價值體系﹐因此﹐閱聽人思辨能力的呈現是綿延不斷的自我摧毀過程﹐是無所依傍與無家感心靈狀態的保持﹐唯有藉由闡述自我思考結果、與他人辯駁溝通的過程等持續的自我鍛鍊﹐才得以培育養成。

爲規避利率上升風險，市場上有很多避險工具，諸如遠期利率協定、利率交換、我國期交所於2004年1月2日所推出的債券期貨（或稱利率期貨）、歐元期貨契約。本論文所要探討的是平均利率上限選擇權之評價，使用的方法是建構Forward LIBOR Tree之利率樹，再使用Timothy. R. Klassen（2001）評價亞式選擇權的方法來評價平均利率上限選擇權。

本文是針對Catalan 族中部分成員：「n個運算符號之結合律運算」、「n組正規中括號」、「n個節點之相異二元樹」、「圓上2n個點可畫幾種不相交之弦」，做彼此間對應關係的探討。 藉由操作方法，找出Catalan 族成員彼此間對應關係，再利用對射函數方法證明是對的。在證明Catalan 族中任兩個成員間的對應關係，我們先造一個對應函數，再證明此函數是一對一且映成（onto），即此函數為對射函數，則我們的操作方法是可行的。

論文名稱：可轉換公司債存續期間之分析 校所組別：國立政治大學金融研究所 畢業時間：九十年度第二學期 提要別：碩士學位論文提要 研究生：陳嘉霖 指導教授：陳松男博士 論文提要及內容： 本研究在分析可轉債的存續期間，在存續期間的衡量上是採用有效存續期間法；而在可轉換公司債的評價上，假設股票價格服從幾何布朗寧運動，無風險利率的變動符合Hu1I-white利率模型，並且考量利率與股票報酬之間的相關性，建立可轉換公司債評價六元樹形圖。 本研究分別針對到期期限長短、價內外程度、股價波動度、利率波動度、股價與利率相關係數及票面利率等六項參數，作可轉換公司債存續期間的敏感度分析，研究結果為：1 加入贖回條款後，可轉債的存續期間高於未加任何條款下的可轉債存續期間。2 加入賣回條款後，可轉債的存續期間低於未加任何條款下的可轉債存續期間。3 加入贖回及賣回候款後，可轉債的存續期間會介於僅含贖回條款與僅含賣回條款的存續期間之中。4 距到期日愈長可轉債的存續期間愈高。5 愈價外的可轉債其存續期間愈高。6 股票波動度愈高，可轉債的存續期間愈低。7 利率波動度增加則可轉債的存續期間上升。8 股票價格與利率相關係數由正至負，可轉債的存續期間上升。9 若贖回權愈小，則票息上升會增加可轉債的存續期間。 關鍵字：可轉換公司債、存續期間、有效存續期間、六元樹、Hull-white、利率模型 / Title of Thesis: Anatomy of the Convertible Bond Duration Name of Institute: Graduate Institute of Money and Banking, NCCU Graduate Date: June, 2002 Name of Student: Chen, Chia-Lin Advisor: Dr. Chen, Son-Nan Abstract: This thesis uses effective duration method to anatomize the convertible bond duration. With the assumptions that stock price follows Geometric Brownian Motion and risk-free interest rate follows Hull and White model, we built a hexanomial tree to value the convertible bond. This thesis analyses the effects of the six parameters . They are maturity date, the ratio of the stock price versus the strike price, the correlation between stock return and interest rate, stock return volatility, interest rate volatility, and coupons. The conclusions include nine points. First, the value of convertible bond duration including call clauses is higher then pure convertible bond duration. Second, the value of convertible bond duration including put clauses is lower than pure convertible bond duration. Third, the value of convertible bond duration including both call and put clauses is between only including call or put clauses ones. Fourth, the longer the time to maturity is, the higher the convertible bond duration is. Fifth, the higher the ratio of the strike price versus the stock price is , the higher the convertible bond duration is. Sixth, the higher the stock volatility is , the lower the convertible bond duration is. Seventh, the higher the interest rate volatility is , the higher the convertible bond duration is. Eighth, the value of the correlation between stock return and interest rate increases from a negative value to a positive one, then the convertible bond duration increases. Ninth, if the value of call right is very small , the convertible bond duration will increase by the increasing of the coupon . Keywords: Convertible Bond, Duration, Effective Duration, Hexanomial Tree, Hull and White Interest Rate Model

在目前的企業環境中，很多企業致力於管理和應用組織知識，來維持他們的核心能力和創造競爭優勢。有效率的管理組織知識，能減少解決問題的時間和成本，並增加組織學習和創新能力。並且，由於累積知識資源的需求，很多企業開始發展知識庫，以儲存組織及個人的知識，用來增加組織整體的效率、支援日常的運作以及企業策略的操作。 知識管理是現代的典範，可用來有效管理組織知識，進而改善組織績效。知識管理的目的是強調管理知識的流動及流程。在知識管理流程方面，主要區分為知識擷取、整合、儲存/歸類、散播和應用知識等程序。另外，資訊技術可用來協助知識管理，並能使知識管理更有效率。知識管理的主要議題之ㄧ是知識的擷取，由於目前知識來源的提供，主要是透過知識工作者，可是它對於知識工作者而言，是一種額外的負擔。因此，設計一個有效的方法來自動產生組織知識，以減輕他們的額外負擔，將是一個很重要的課題。第二個相當重要的議題是知識整合，由於不同來源的知識，可能造成組織知識的衝突，因此設計一個知識整合方法，把不同來源的知識整合成一個完整的知識，組織將會逐漸增加這方面的需求。 分類在很多應用中是常遭遇的問題，例如財務預測、疾病診斷等。在過去，分類規則常藉由決策樹的方法所產生，並用於解決分類的問題。在本論文中，提出兩個以遺傳規劃為基礎的知識擷取方法和兩個以遺傳規劃為基礎的知識整合方法，分別支援知識管理流程中的知識擷取和知識整合。 在兩個所提的知識擷取方法中，第一個方法是著重在快速和容易地找到想要的分類樹，但是，此方法可能會產生結構較複雜的分類樹。第二個方法是修正第一個方法，產生一個較精簡和應用性高的分類樹。這些所獲得的分類樹，能被轉換成規則集合，並匯入知識庫中，幫助企業決策的制定和日常的運作。 此外，在兩個所提的知識整合方法中，第一個方法，能自動結合多重的知識來源成為一個整合的知識，並可匯入知識庫中，但是此方法只考慮到單一時間點的整合。第二個方法則是可以解決不同時間點的知識整合問題。另外，本論文提出三個新的遺傳運算子，在演化過程中，可用來解決規則集合中有重複、包含和衝突等常見的問題，因而可以產生較精簡及一致性高的分類規則。最後，本論文採用信用卡資料及乳癌資料來驗證所提方法的可行性，實驗結果皆獲得良好的成效。 / In today’s business environment, many enterprises make efforts in managing and applying organizational knowledge to sustain their core competence and create competitive advantage. The effective management of organizational knowledge can reduce the time and cost of solving problems, improve organizational performance, and increase organizational learning as well as innovative competence. Moreover, due to the need to accumulate knowledge resources, many enterprises have devoted to developing their knowledge repositories. These repositories store organizational and individual knowledge that are used to increase overall organization efficiency, support daily operations, and implement business strategies. Knowledge management (KM) is the modern paradigm for effective management of organizational knowledge to improve organizational performance. The intent of KM is to emphasize knowledge flows and the main process of acquisition, integration, storage/categorization, dissemination, and application. Furthermore, extant information technologies can provide a way of enabling more effective knowledge management. One of the important issues in knowledge management is knowledge acquisition. It is an extra burden for knowledge workers to contribute their knowledge into repositories, such that designing an effective method for abating an extra burden to automatically generate organizational knowledge will play a critical role in knowledge management. A second rather important issue in knowledge management is knowledge integration from different knowledge sources. Designing a knowledge-integration method to combine multiple knowledge sources will gradually become a necessity for enterprises. Classification problems, such as financial prediction and disease diagnosis, are often encountered in many applications. In the past, classification trees were often generated by decision-tree methods and commonly used to solve classification problems. In this dissertation, we propose two GP-based knowledge-acquisition methods and two GP-based knowledge-integration methods to support knowledge acquisition and knowledge integration respectively in the knowledge management processes for classification tasks. In the two proposed knowledge-acquisition methods, the first one is fast and easy to find the desired classification tree. It may, however, generate a complicated classification tree. The second method then further modifies the first method and produces a more concise and applicatory classification tree than the first one. The classification tree obtained can be transferred into a rule set, which can then be fed into a knowledge base to support decision making and facilitate daily operations. Furthermore, in the two proposed knowledge-integration methods, the former method can automatically combine multiple knowledge sources into one integrated knowledge base; nevertheless, it focuses on a single time point to deal with such knowledge-integration problems. The latter method then extends the former one to handle integrating situations properly with different time points. Additionally, three new genetic operators are designed in the evolving process to remove redundancy, subsumption and contradiction, thus producing more concise and consistent classification rules than those without using them. Finally, the proposed methods are applied to credit card data and breast cancer data for evaluating their effectiveness. They are also compared with several well-known classification methods. The experimental results show the good performance and feasibility of the proposed approaches.

台灣在過去的數十年內，罹患失智症人口逐漸增多，其中的多數皆有接受了各層面的照護，舉凡藥物治療、醫護治療、復健治療以及職能治療，然其中的成效與需求之研究仍相當缺乏。故本研究採以質性與量性研究方法，以便於探索目前失智症患者家屬照護時所面臨的實際抉擇歷程與主要需求，並同時探索個案醫院內的治療效果與病患入院時狀況之關係，本研究希望藉由中部地區失智症病患照護的需求及機構之供給的角度來探索研究所能增進其醫療服務品質之處。 在質性研究方法部分，本研究以民族誌決策樹研究法來洞悉與探索家屬在面臨照護失智症病患時是否要採行機構式照護的決策歷程以及決策條件。藉由深度訪談結果粹取出的判斷準則發現，影響家屬決策之最主要考量為失智症病患者的失智程度，其餘包含道德規範、照護負擔、病患是否需要騎他的專業醫療照護以及照護中心的軟硬體環境。本研究整合考量這些判斷準則的優先順序、輕重緩急以及因果關係後將之建立決策樹，並以另外五十名家屬驗證該模型之預測力，得到預測準確率為92%。 此外，本研究再以量性方法來探索治療對於不同失智症病患的成效。結果顯示入住時狀況較好的失智症住民會以更積極的態度來接受職能治療，也因此他們擁有較大的改善或控制病情的機會，然而當住民以消極的態度接受職能治療時，則其治療效果遠不及積極治療者，也因此病情退步的機會較大，主要原因在於多數情況較差的住民具有攻擊、抗拒治療的傾向，使得照護工作變得更為艱鉅，故本研究建議家屬應重視職能治療以及與病人互動之重要性，不論是在居家照護亦或是機構式照護 / Over the past decade, the number of long-term care (LTC) residents has increased, and many have accepted treatments such as medication, rehabilitation and occupational therapy. This study employs both qualitative and quantitative techniques in order to discuss senile dementia patient care in long-term care institutions, and we use a supply and demand viewpoint to explore what services are really necessary for the patient and their family. In qualitative method, the main purpose of this stage is to use the ethnographic decision tree model to understand and explore the decision criteria of the subject. Our study found that the degree of dementia of the patient always affects the decisions made by family members – in fact, this is the most important of all criteria elicited from the interviews with family members. There are also ethical constraints, care burden, norm of filial obligation, patient need professional medical care and institutional environment, etc. which mentioned by families. We linked together the decision criteria considered most important in accounting for the decision-making sequence of family members to be the ethnographic decision tree model which predictive power is 92%. In quantitative stage, our study discussed the effectiveness of occupational therapy when given to dementia patients of different contexts. The results of this stage showed that patients of a good condition in the first stage present a more positive attitude towards participation in the occupational therapy designed by the institution; therefore, they have a greater chance of their condition improving or remaining the same. However, patients of an average condition have a more passive attitude towards taking part in any therapy; therefore, they have a greater chance of their condition deteriorating, because of their violent tendencies and their resistance to care, the task of caring for these patients is more difficult than caring for patients in the other groups. Above all, we suggest that families adopt the therapies no matter in homecare or institutionalization, as early as possible in order to improve the likelihood of being able to control the patient’s condition. It is understandable that accepting more therapies and interaction in the early stage of dementia, having higher chance to go well, however, by waiting until then they also miss the best opportunity to attempt to improve the patient’s condition, it is really not the good way we suggest to be.

無線網狀網路(WMNs)為目前熱門的廣域無線網路接取技術。使用者可以透過WMNs隨時在各處使用即時影音播放的服務。相較於傳統的主從式架構，低成本且容易建置的點對點架構更適用於影音串流的應用；在進行即時影音播放的時候，影音播放的品質便為相當重要的目標。因為多媒體應用服務對於延遲及網路傳輸效能相當敏感，且WMNs的傳輸過程中常會面臨同頻道干擾的問題而使得傳輸的效能銳減，當每個網路節點都具有多張無線網路卡時，如何善用WMNs多頻道傳輸的特性提升效能更是顯得特別重要。在本篇論文中，我們利用WMNs多頻道傳輸的特性進行多媒體群播傳輸，參考史坦納樹的概念來改善現有的MAODV路由演算法，以傳輸品質較佳的鏈結改良原本尋找最小跳躍數路徑的方式，建立兩棵完全互斥的群播樹作為點對點傳輸的骨幹網路，並以MDC的概念將影像串流編碼成兩份獨立的子串流分別經由不同的群播樹傳輸。經實驗評估，我們的方法在網路負載較高的環境下能有效的降低延遲並提高整體系統的效能。 / In WMNs, users can enjoy the real-time video streaming service anytime and anywhere through the services. Compared to the client/server model, P2P approaches is more suitable for video streaming applications because of its low cost and easy deployment. But when using the real-time multimedia service in WMNs, the multimedia applications are very sensitive to delay time and the performance of packets transmission. And the performance is significantly influenced by the co-channel interference, so that it is important to know how to transmit by multi-channel to enhance the performance. In our approach, we choose the better quality links for routing instead of the minimum hop-count path in MAODV. Then we distribute the video streaming to receivers by multicast in multi-channel WMNs, and refer to the Steiner tree concept to modify the MAODV routing protocol to construct two disjoint multicast trees as the backbone for the P2P structure. Therefore, we can adopt the MDC scheme to encode the video into two independent sub-streams and transmit separately along these trees. Experiment results show that in higher network traffic load environment, our scheme is more effective to reduce the latency and improve overall system performance.