在耐延遲網路(Delay Tolerant Network)中，因為節點具有移動性，因此找不到穩定且持續的點對點資料傳送路徑。常見的路由協定可分為機會路由、基於預測的路由以及調度路由，然而這些路由協定使用在市區環境中，有著些許不足與不適用，因此本論文提出一個適用在市區資料傳送的演算法。 本論文提出之混合階層式路由演算法，是在市區環境中建立一個以公車為基礎的資料傳送架構，包含行人與公車兩種節點。我們建立節點與節點相遇時資料交換傳送規則，例如行人與行人相遇、行人與公車相遇或是公車與公車相遇時各自有不同的資料傳送判斷與限制。 實驗結果也證明所提出之混合階層式演算法，除了可以有效地減少傳送延遲時間並提高訊息傳送成功率，並且在給定節點一定的移動速度與緩衝區大小下，我們的演算法有著最突出的效能。 / In Delay Tolerant Networks (DTNs), there is no guarantee that a stable connected path between source and destination nodes always exists because of high node mobility. The current DTN routing protocols can be summarized into three categories: opportunistic, prediction-based and scheduling protocols. However, these routing protocols have some deficiencies and are not specifically focused on the urban areas which have primarily two hierarchical nodes, namely pedestrian and bus nodes. We proposed a Hybrid Hierarchy Routing Protocol, a bus-based architecture for urban areas. We established the rules of data transmission when one node contacts other nodes. More specifically, Ped-to-Ped, Ped-to-Bus and Bus-to-Bus contacts, have different judgments and restrictions for data forwarding. The simulation results demonstrate that the Hybrid Hierarchy Routing Protocol can effectively reduce the delivery delay and improve the successful delivery rate. And in given certain speed and buffer sizes, our algorithm has the most prominent performance.

GPS 單點定位或稱絕對定位，傳統上使用虛擬距離觀測量，容易受到 電離層延遲影響，導致定位精度較差。因此，本文的目的為構建即時的區 域性電離層模型，以便能夠即時減弱電離層延遲量，提高單頻GPS 單點定 位的精度。 構建電離層模型的方法有很多種，而運用類神經網路為可能方法之一， 但是， 國內較少人探討。本研究嘗詴使用倒傳遞類神經網路(Back-propagation Artificial Neural Network)，構建即時的區域電離層模型，藉由選擇適當的神經訓練函數及隱藏層神經元，利用過去收集的已知參考站的雙頻GPS 資料，計算電離層延遲量，訓練類神經網路，直到精度合乎要求；再以檢核站GPS 資料，檢驗類神經網路預測電離層延遲的功效。 採用的實驗資料為臺南市政府e-GPS 系統所提供六個測站，2008 年1 月3 日到1 月5 日的GPS 資料，計算測站與GPS 衛星連線中假想的電離層 薄殼交點—電離層穿透點(Ionosphere Pierce Point, IPP）之地理位置(緯度φ、經度λ)，及太陽黑子數(sunspot numbers)等當作輸入值，IPP 的垂直電離層延遲當作輸出值，測詴包含單日、兩日以及不同的資料型態(IPP 點、網格點)等情況訓練類神經網路，藉由相對應的驗證資料，檢驗類神經網路的功效，最後將類神經網路的預估成果與全球電離層改正模型、雙頻GPS 資料計算的電離層延遲相比較，並根據改正率與統計特性，評估類神經網 路構建出的區域性電離層模型的成效。 由實驗成果顯示，構建的即時區域性電離層模型的標準差可小於±3TECU，並可改正約80%的電離層延遲誤差，故以類神經網路可有效的構 建出區域性的電離層模型。 / The conventional single point positioning using GPS pseudo rangemeasurements, are vulnerable to ionospheric errors, leading to poor positioningaccuracy. Constructing a real-time ionospheric model is one of the methods that can reduce the ionospheric errors and improve the single point positioning accuracy. Although there are many methods to construct regional ionosphere model,using artificial neural network (ANN) to construct a real-time ionospheric model is less to be mentioned. This study used back-propagation artificial neural network to estimate a regional real-time ionospheric model by selecting the appropriate training functions and the number of hidden layers and its’ nodes. The neural network had to be ‘trained’ by the computed TECs from reference stations’ duel-frequency GPS data until the required accuracy was achieved. The experimental data are collected from 6 e-GPS stations of Tainan city government on January 3 to January 5, 2008. The input values for the ANN includ the geographical location of the ionosphere pierce point (IPP) and solar activity (sunspot number). The output value are those IPPs’ vertical total electron content (VTEC). Different times range and data types (IPPs’ or raster data) for the impact of the ANN are tested. And then compared to Klobuchar model and global ionopheric model, according to the correct rate and the ΔTEC statistic table decide the effectiveness of ANN. According to the test results, the regional ionopheric model constructed by ANN can corrected 80% of the ionospheric errors, the standard deviation of ΔTEC is less than ±3TECU.

在耐延遲網路環境下，訊息資料的傳送依賴於節點間因移動性而產生的間斷性連結，並使用「儲存並攜帶再轉送」的方式傳遞至其目的地，因此網路中各個節點的儲存空間以及與其他節點的「接觸關連性」將扮演訊息傳遞品質的重要因素。 本論文提出一以Flooding-based與Forwarding-based兩類路由協定為基礎設計結構、並加以擴充考量訊息優先權於轉發機制之三階段式路由演算法。其主要概念在於利用網路中節點的移動特性來週期性地預測節點與節點間未來的相遇人氣做為訊息散播時的分配權重、及以累計相遇時間之比率為接觸關聯性做為訊息是否進一步轉送之依據、最後並在訊息傳送順序上加入優先權排序的策略。根據與其他路由演算法的模擬實驗，顯示我們所提的演算法能有較高的訊息傳遞成功率、相對低的資源耗費、以及差異化訊息傳送服務的效能。 / Delay Tolerant Networks (DTNs) use the “Store-Carry-and-Forward” approach to deliver the messages to the destinations. It relies on the intermittent link that occurs when two nodes contact each other due to mobility. Therefore, the buffer and “contact association” of nodes are two important factors that affect the delivery performance. In this thesis, we propose a three-phase algorithm (SFMS: Spray and Forwarding scheme with Message Scheduling) that integrates the concepts of flooding-based and forwarding-based protocols, and considers message priority. The main idea of SFMS is to periodically predict the contact popularity and contact association among nodes, such that we can determine the fast message spraying and efficient forwarding strategy. Furthermore, we come up with a message scheduling mechanism to enhance the resource allocation. Simulation results show that our scheme has a better performance for delivering messages. Besides, it also achieves a differential delivery performance for different priorities of messages while maintaining a better resource allocation.

最近，金融科技(FinTech)和行動金融服務，吸引越來越多的目光。新的創新金融科技服務，改變了金融服務的消費行為。行動網路的發展使人們能夠隨時隨地的享受行動銀行的服務已經是個不爭的事實。然而，由於無線網路先天的特性以及行動裝置的移動性，使行動金融的服務品質受到網路不穩定的影響。而且，隨著Bank 3.0時代的來臨，將會有大量的使用者同時使用行動金融服務，特別是在股市開盤以及重大訊息揭露的時候。因著大量使用者瞬間湧入，以及無線網路不穩定的影響，交易系統的效能很可能會時好時壞，所以無法滿足即時金融市場的需求。 本論文中，我們提出「行動銀行訊息即服務」的框架，使系統能夠很容易的水平擴充，並且能夠輕易的實現雙向通訊和雙向交易等多項行動金融服務。為了達到最少成本追求最大利益的目的，我們發展了能夠適應突發流量以及網路不穩定性的任務分配演算法，使得不用增加額外硬體成本的前提下改善系統效能。然後，為了實驗欲模擬大量行動裝置的使用者，我們觀察真實網路的特性並發明了網路延遲自相關模型來驗證我們提出的任務分配演算法。結果顯示，透過此任務分配演算法，確實能夠有效改善系統資源管理的能力。最後，本研究將系統佈署於真實網路環境當中，並且發現進行同樣實驗的結果與採用網路延遲自相關模型的實驗結果一致。因此，本研究間接驗證了網路自相關模型的正確性，以及證明本任務分配演算法，在突發流量和網路不穩定的即時行動金融服務環境下，能有效降低系統響應時間。 / FinTech (financial technology) and mobile financial services are getting more and more attention recently. New innovative FinTech services change the consumption behavior for financial services. It is an indisputable fact development of the mobile Internet allows people to enjoy mobile banking everywhere and anytime. However, due to the nature of wireless networking and the mobility of the mobile device, the quality of mobile financial service will be affected by network instability. Moreover, with the coming of Bank 3.0, a huge amount of users would be in the mobile service of finance simultaneously, especially when the instances of the stock market opening or disclosure of highly important financial message. As the result of bursty traffic and network instability, the performance of transaction system is up and down, making it tough to satisfy the demand of real-time financial markets. In this thesis, we propose a “Mobile Banking Messaging as a Service Framework” that can easily scale out and fulfill functions comprising Bilateral Communication, Bilateral Trading, and many other mobile financial services. To pursuit of the greatest benefit along with investment of the least resources, we develop the task assignment algorithm which can adapt the system to bursty traffic and unstable networks to improve performance for free. Then, in order to simulate a large number of mobile users, we observe the characteristic of real-world network delay and propose a network delay autocorrelation model to verify our task assignment algorithm. The results of experiment show that we could actually use our task assignment algorithm to improve the ability of the system to manage resource. Finally, we deploy our system in a real-world network delay environment and find that the results obtained in the real condition are the same with our simulation results. Therefore, this research can indirectly verify the correctness of the network delay autocorrelation model, and prove that our task assignment algorithm can effectively reduce the system response time for real-time mobile financial service system under bursty traffic and unstable networks.

本研究將探討非等效平行機台問題中具備順序相依整備時間及不同開始工作時間（Unequal ready-time）之情況，並以最小化總延遲工件權重數為目標值，其目的在改善非等效平行機台問題應用於實際產業中製造環境裡所面對的各項挑戰，如印刷電路板的鑽孔和半導體的測試製程。因本研究欲求解之問題是屬於NP - Hard problems 性質之尋優問題，故利用啟發式方法(heuristics)求解為合適的選擇。此外，本研究計畫開發混合式巨集啟發式方法來求解非等效平行機台問題，主要以禁忌搜尋法為主，在鄰域的搜尋上，也藉由變動鄰域尋優法能夠透過鄰域轉換的機制，進而找出更多好的解。由於啟發式方法對於尋優問題常需花費許多時間來計算才能獲得更好的解，為確保增進求解效率與品質，將針對問題特性開發數種初始解產生法，並也嘗試定義幾個能夠減少尋找鄰近解之鄰域。在後續求解改善的過程中，主要整合變動鄰域(VND)及禁忌(TS)巨集啟發式演算法搜尋最佳解。此外，為了評估本文推導之演算法效能，本研究利用設定之條件隨機產生適量模擬現場狀況的測試情境，進而比較本研究所提出之混合式巨集啟發式方法及標準禁忌搜尋法在不同情境下之表現。 / The problem considered in this paper is a set of independent jobs on unrelated parallel machines with sequence-dependent setup times and with unequal ready times so as to minimize total weighted tardy jobs. These problems can be found in real-life manufacturing environments, such as PCB fabrication drilling operations and semiconductor wafer manufacturing dicing. Since the problems are NP-hard in the strong sense, heuristics are an acceptable practice to finding good solutions. A hybrid meta-heuristics are proposed to solve this scheduling problem. The proposed heuristics belong to a type of solution improvement heuristic; therefore, the heuristics start with an effective initial feasible solution then a meta-heuristic is applied to improve the solution. To enhance both the efficiency and efficacy of the heuristics, several different initial solution generators, based on the characteristics of problems, are developed. The meta-heuristic is a hybrid heuristic integrating the principles of Variable Neighborhood Descent approach (VND) and Tabu Search (TS). In order to evaluate the performance of the proposed heuristics, two sets of large number test scenarios will be designed to simulate practical shop floor problems. Computational experiments will be performed to compare the performance of the proposed heuristics, and a basic tabu search algorithm. The results show the proposed heuristic perform better than the basic tabu search algorithm.

面對通訊與資訊科技的大幅進步，通訊網路正在進行一個巨大的變革，要將電信網路與數據網路整合成一個單一的All-IP網路以支援所有網路應用服務。欲達到整合型網路的理想，仍有許多困難尚待克服，而服務品質問題是其中最關鍵的問題之一。因為受限於封包交換網路之原有的特性，All-IP網路有影響服務品質的三項因素：過長的延遲時間、抖動以及封包遺失。首先，我們利用了達成度函數(achievement function)來處理單位的轉換，使得能夠同時考量此三項不同單位的因素。接著，本文中提出一套方法來解決All-IP網路上端對端(end-to-end)的資源配置及路徑規劃問題。在分配資源時，我們企圖提供一種成比例的公平性給各個不同等級。此公平性的精神是要使得所有網路使用者的滿足程度相當，而非各個不同等級的使用者分配到相同的資源。我們將以預算方式控制端對端品質管理以追求使用者之整體最大滿意程度。 本論文的規劃概念是將網路規劃分成兩個階段。第一階段是在一筆給定的總預算底下，以成比例的方式去分配資源給各個不同等級，並建置網路上的頻寬，使各等級能依其需求拿到適當的頻寬，確保滿足程度相當。 接下來第二階段則是在第一部份已完成的規劃基礎下，做路徑規劃，指派新進入的使用者到一條較好的路徑，在滿足此使用者的延遲時間要求下，使此系統的壅塞程度越小越好。路徑規劃的概念為如何挑選最佳網路路徑，以規劃具服務品質之端對端路徑，並可達到資源之最有效利用。網路營運者將可運用此套方法來調校自身所營運的網路以追求使用者最高滿意度。 / We present an approach for the fair resource allocation problem and QoS routing in All-IP networks that offer multiple services to users. The objective of the optimization problem is to determine the amount of required bandwidth for each link and each class to maximize the sum of the users' utility. In this work, we focus on approaches that, while allocating bandwidth, attempt to provide a proportionally fair treatment of all the competing classes. First, we will show that an achievement function can map different criteria subject to various utility onto a normalized scale. It may be interpreted as a measure of QoS (Quality of Service) on All-IP networks. Using the bandwidth allocation model, we can find a Pareto optimal allocation of bandwidth on the network under a limited available budget. This allocation can provide the so-called proportional fairness to every class, that is, this allocation can provide the similar satisfaction to each user. Next, we present a routing scheme under consideration of the delay. Such an optimal path provides the end-to-end QoS guarantees to each user. Finally, a numerical example is given to illustrate how to solve the fair resource allocation problem and how to modify the nonlinear parts.

認知功能的提升是當今神經科學領域中的研究重點之一，但其神經機制尚有待釐清。本研究利用一種用於改善阿茲海默症臨床的非競爭型N-甲基-D-天門冬胺酸受體拮抗劑memantine，檢測其對於大白鼠在不同時距相關操作式制約行為及空間工作記憶行為之影響效果。實驗一為針對時間屬性的操作式制約行為實驗，運用大白鼠的區辯性增強低頻反應作業（DRL 10秒行為）與固定時距作業（FI 30秒行為）之行為作業，並操弄連續訓練與間歇訓練的兩種不同模式，測試memantine對前述四組受試的操作式制約行為在表現、消除與自發恢復等三階段之劑量反應。實驗二利用配對性延遲T迷津作業區分出不等基準線(表現好與表現差)之受試，再加以藥理實驗，測試memantine對於前述兩組受試之劑量反應。實驗一結果顯示，受試在兩種不同訓練模式下經十五次習得訓練後，在兩種操作式壓桿行為的壓桿反應相關指標中都有明顯的差異，這證實不同的行為訓練模式會導致學習後的表現有差異之別。memantine藥理實驗結果顯示，此藥對於上述四組受試的操作式行為之三階段的影響效果，會因為不同訓練模式與不同作業而異。實驗二結果顯示，memantine提高空間工作記憶的正確率在表現不好的組別有很顯著的藥效，這證實memantine對於空間式工作記憶行為的影響，也會因學習基準線的不同水平而異。在行為實驗後所進行的蛋白質表現量檢測中，memantine（5 mg/kg）只對五個測試腦區中的背側紋狀體中ERK1磷酸化程度有明顯上升的影響，而其對ERK2及CREB的磷酸化在所有腦組織中皆沒有顯著的影響。綜合以上結果，memantine影響時間與空間屬性的相關行為之藥理效果，會依行為的不同習得歷程(或行為背景經驗)及基準線表現程度而異，而此項行為藥理效果，可能與紋狀體中ERK1的磷酸化有關。 / The neural basis of cognitive enhancement is one of the intriguing topics in neuroscience research; however, the underlying neural mechanisms remain to be elucidated. This study examined the effects of memantine, a non-competitive N-methyl-D-aspartate (NMDA) receptor antagonist which is used to treat Alzheimer’s disease in clinic, on operant behaviors and spatial working memory. In Experiment 1, using the differential reinforcement for low-rate-response 10 sec (DRL 10s) and the fixed-interval 30 sec (FI 30s) operant tasks, and with the manipulation of two different training regimens (continuous vs. intermittent) in the acquisition phase, the effects of memantine were evaluated in three stages of behavioral tests including the performance (right after the end of 15-day acquisition), the extinction, and the spontaneous recovery (after the extinction). In Experiment 2, memantine were tested in the subjects with different level of baseline performance (good vs. bad) on the distinctive patterns of operant responding in four different groups which received DRL 10s and FI 30s with different training regimens; indicating that behavioral task and training background are critical to the operant performance of temporal operant behaviors. Such behavioral outcomes led the dissociable effects of memantine appeared in between the four groups as tested in all three different stages. The results of Experiment 2 showed a profound improvement of the correct responses rate on spatial working memory in the low-baseline group as compared to the higher-baseline group. With a pretreatment of memantine (5 mg/kg), brain tissues in five selected areas were collected for western blot assays of ERK 1, ERK 2, and CREB. The results only revealed a significant increase of ERK 1 phosphorylation in the dorsal striatum. Together, the effects of memantine to improve cognition-associated processes in the temporal operant behaviors and the baseline of performance, and the present observation of cognition-enhancing effects of memantine may be resulted by the ERK 1 phosphorylation in the dorsal striatum.