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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

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    異質網路環境之行動搜尋關鍵技術---子計畫一：車用隨意網路存取控制與連結機制之研究
    (2008)
    簡榮宏
    ;
    JAN RONG-HONG
    ;
    國立交通大學資訊工程學系(所)
    隨著無線通訊網路技術的進步，加上電子元件價格漸漸下降以及行車安全問題越來越受重視，各國政府紛紛投入智慧型運輸系統(Intelligent Transport Systems, ITS)的研究。車用隨意網路(Vehicle Ad-hoc Network, VANET)，為當前了為實現ITS所發展出的重要網路架構。在個架構下，每一個搭載無線通訊設備的車輛，都可以透過路旁的路側系統(Road Side Unit, RSU) 連線到Server 端索取所需的資訊。而當車輛距離基地台太遠超出傳訊範圍時，也可透過其它的車輛幫忙轉送。換句話說，每一個車輛都可視為是一部行動無線路由器(mobile wireless router)。因此，這樣的架構可大幅提升網路建置彈性，只要有車輛的地方，即可是VANET的涵蓋範圍。 子計畫一的主要研究目標在於發展VANET網路之存取控制協定(Media Access Control, MAC)及連結機制(Connectivity Support)。分為三個階段:在第一年的計畫中，先針對網路低層的MAC進行設計，我們將基於未來最有可能被廣泛使用的專用短距離通通訊(Dedicated Short Range Communication , DSRC)標準進行發展，以合乎趨勢的走向。接下來，在第二年的研究裡，我們將為高速移動的車輛設計以有效的叢集化及功率指定方法，以減少網路干擾，進而提升流量產出及管理上的效果。基於這些低層的技術及規劃方法後，最後一年，我們將發展上層的路由及配送方法，以可克服VANET網路因高度移動和短距離傳輸所造成間歇性連結特性及其衍伸的延遲問題，達到可靠傳輸的最終目標。總括而言，我們將在三年中階段逐步完成下列三大主題:
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    禪坐腦電波之時變頻譜的空間特性研究(II)
    (2008)
    羅佩禎
    ;
    LO PEI-CHEN
    ;
    國立交通大學電機與控制工程學系(所)
    主持人自 1998 即開始將醫學工程研究經驗投入於禪坐之生理、意識等現象的探 討，已有相當成果。主要以科學化方法來探討禪定過程中之腦電波特性變化，以禪 宗佛法之修行者為主要研究對象；以先進之數位訊號處理的方法理論，從大量記錄 之多通道禪坐腦電波中，進行時域、頻域分析。其中觀察到某些特性，值得進一步 探究其腦部動態機制與空間定位之關聯性。 『壓力』深切影響現代人身心健康，臨床上雖已證實「靜坐」是最佳減壓方法之 一。本實驗群過去從皮膚阻抗、心電圖和心率變異之分析結果，已觀察到禪坐之舒壓 效果；如何進一步建立科學量化機制，來評估禪坐舒壓效益，為時下迫切之課題。 為瞭解禪坐下之腦電波的頻率空間特性如何隨時間演變，本研究計畫分兩年探 討，並已於第一年間（96 年八月至97 年七月）完成實驗環境與流程之規劃，同時 已發展演算法、用之於30 通道禪坐腦電波之時變頻空特性分析。本計畫所提第二年 度之研究重點在於：（一）建立禪坐腦電波之空間特性 (MBM)，（二）探討禪坐過 程中之MBM 演變情形(MBMS)，（三）以MBM 與其他生理參數關聯性，探討禪坐 舒解壓力的機制。研究過程中，除了進行大量腦電波記錄實驗，亦將發展多元化之 數位訊號分析方法，來定性和量化MBM。
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    異質網路環境之行動搜尋關鍵技術-子計畫一---車用隨意網路存取控制與連結機制之研究
    (2009)
    簡榮宏
    ;
    JAN RONG-HONG
    ;
    國立交通大學資訊工程學系(所)
    隨著無線通訊網路技術的進步，加上電子元件價格漸漸下降以及行車安全問題越來越受重視，各國政府紛紛投入智慧型運輸系統(Intelligent Transport Systems, ITS)的研究。車用隨意網路(Vehicle Ad-hoc Network, VANET)，為當前了為實現ITS所發展出的重要網路架構。在個架構下，每一個搭載無線通訊設備的車輛，都可以透過路旁的路側系統(Road Side Unit, RSU) 連線到Server 端索取所需的資訊。而當車輛距離基地台太遠超出傳訊範圍時，也可透過其它的車輛幫忙轉送。換句話說，每一個車輛都可視為是一部行動無線路由器(mobile wireless router)。因此，這樣的架構可大幅提升網路建置彈性，只要有車輛的地方，即可是VANET的涵蓋範圍。 子計畫一的主要研究目標在於發展VANET網路之存取控制協定(Media Access Control, MAC)及連結機制(Connectivity Support)。分為三個階段:在第一年的計畫中，先針對網路低層的MAC進行設計，我們將基於未來最有可能被廣泛使用的專用短距離通通訊(Dedicated Short Range Communication , DSRC)標準進行發展，以合乎趨勢的走向。接下來，在第二年的研究裡，我們將為高速移動的車輛設計以有效的叢集化及功率指定方法，以減少網路干擾，進而提升流量產出及管理上的效果。基於這些低層的技術及規劃方法後，最後一年，我們將發展上層的路由及配送方法，以可克服VANET網路因高度移動和短距離傳輸所造成間歇性連結特性及其衍伸的延遲問題，達到可靠傳輸的最終目標。總括而言，我們將在三年中階段逐步完成下列三大主題:
  • Thumbnail Image
    Item type:Item,
    異質網路環境之行動搜尋關鍵技術---子計畫一：車用隨意網路存取控制與連結機制之研究
    (2010)
    簡榮宏
    ;
    JAN RONG-HONG
    ;
    國立交通大學資訊工程學系（所）
    隨著無線通訊網路技術的進步，加上電子元件價格漸漸下降以及行車安全問題越來越受重視，各國政府紛紛投入智慧型運輸系統(Intelligent Transport Systems, ITS)的研究。車用隨意網路(Vehicle Ad-hoc Network, VANET)，為當前了為實現ITS所發展出的重要網路架構。在個架構下，每一個搭載無線通訊設備的車輛，都可以透過路旁的路側系統(Road Side Unit, RSU) 連線到Server 端索取所需的資訊。而當車輛距離基地台太遠超出傳訊範圍時，也可透過其它的車輛幫忙轉送。換句話說，每一個車輛都可視為是一部行動無線路由器(mobile wireless router)。因此，這樣的架構可大幅提升網路建置彈性，只要有車輛的地方，即可是VANET的涵蓋範圍。 子計畫一的主要研究目標在於發展VANET網路之存取控制協定(Media Access Control, MAC)及連結機制(Connectivity Support)。分為三個階段:在第一年的計畫中，先針對網路低層的MAC進行設計，我們將基於未來最有可能被廣泛使用的專用短距離通通訊(Dedicated Short Range Communication , DSRC)標準進行發展，以合乎趨勢的走向。接下來，在第二年的研究裡，我們將為高速移動的車輛設計以有效的叢集化及功率指定方法，以減少網路干擾，進而提升流量產出及管理上的效果。基於這些低層的技術及規劃方法後，最後一年，我們將發展上層的路由及配送方法，以可克服VANET網路因高度移動和短距離傳輸所造成間歇性連結特性及其衍伸的延遲問題，達到可靠傳輸的最終目標。總括而言，我們將在三年中階段逐步完成下列三大主題:
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    Variance enhanced K-medoid clustering
    (2011-01-01)
    Lai, Por-Shen
    ;
    Fu, Hsin-Chia
    ;
    資訊工程學系
    ;
    Department of Computer Science
    This paper proposes new variance enhanced clustering methods to improve the popular K-medoid algorithm by adapting variance information in data clustering. Since measuring similarity between data objects is simpler than mapping data objects to data points in feature space, these pairwise similarity based clustering algorithms can greatly reduce the difficulty in developing clustering based pattern recognition applications. A web-based image clustering system has been developed to demonstrate and show the clustering power and significance of the proposed methods. Synthetic numerical data and real-world image collection are applied to evaluate the performance of the proposed methods on the prototype system. As shown as the web-demonstration, the proposed method, variance enhanced K-medoid model, groups similar images in clusters with various variances according to the distribution of image similarity values. (C) 2010 Elsevier Ltd. All rights reserved.
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    Using patent data for technology forecasting: China RFID patent analysis
    (2011-01-01)
    Trappey, Charles V.
    ;
    Wu, Hsin-Ying
    ;
    Taghaboni-Dutta, Fataneh
    ;
    Trappey, Amy J. C.
    ;
    管理科學系
    China is one of the world's largest manufacturers and consumers of Radio Frequency Identification (RFID) applications. Current estimates show that China will need over 3 billion RFID tags to satisfy demand in the year 2009. The applications for RFID patents have spread across a very diverse range of inventions and in the future it is likely that most products manufactured in China will contain an RFID tag. China's RFID industry has grown along with the demand and researchers are making significant technological advances. In this research, patent data from the State Intellectual Property Office of the People's Republic of China (SIPO) have been used to explore RFID technology development and its trends. Patent abstracts containing the keyword and phrase "RFID" and "Radio Frequency Identification" were collected for analysis, content extraction, and clustering. In total, 1389 patents from the SIPO database covering the years 1995-2008 were retrieved and archived for analysis. Patents provide exclusive rights and legal protection for inventors, play an important role in the development and fair diffusion of technology, and contain detailed specifications necessary to define and protect the boundaries of an invention. Through patent analysis, companies monitor the development of technology and evaluate the position of potential competitors in the market. This research introduce a methodology which combines patent content clustering and technology life cycle forecasting to find a niche space of RFID technology development in China. A patent content clustering method is used to cluster different patent documents into homogenous groups, and then technology forecasting is applied to evaluate possible market opportunities for future inventors and investors. The results suggest that the cluster called RFID wireless communication devices has entered the saturation stage and thus provides limited opportunity for development. Four other clusters; RFID concepts and applications, RFID architecture, RFID tracking implementation, and RFID transmission apparatus, have entered the mature stage. The RFID frequency and waves cluster appears to be in early growth stage with good development potential. Since the technology related to basic RFID concepts and devices has reached a mature stage in China, the research and development seems to be targeting the improvement of the RFID frequencies and waves as a means to develop more reliable RFID systems and applications. (C) 2010 Elsevier Ltd. All rights reserved.
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    Semi-supervised Linear Discriminant Clustering
    (2014-07-01)
    Liu, Chien-Liang
    ;
    Hsaio, Wen-Hoar
    ;
    Lee, Chia-Hoang
    ;
    Gou, Fu-Sheng
    ;
    交大名義發表
    This paper devises a semi-supervised learning method called semi-supervised linear discriminant clustering (Semi-LDC). The proposed algorithm considers clustering and dimensionality reduction simultaneously by connecting K-means and linear discriminant analysis (LDA). The goal is to find a feature space where the K-means can perform well in the new space. To exploit the information brought by unlabeled examples, this paper proposes to use soft labels to denote the labels of unlabeled examples. The Semi-LDC uses the proposed algorithm, called constrained-PLSA, to estimate the soft labels of unlabeled examples. We use soft LDA with hard labels of labeled examples and soft labels of unlabeled examples to find a projection matrix. The clustering is then performed in the new feature space. We conduct experiments on three data sets. The experimental results indicate that the proposed method can generally outperform other semi-supervised methods. We further discuss and analyze the influence of soft labels on classification performance by conducting experiments with different percentages of labeled examples. The finding shows that using soft labels can improve performance particularly when the number of available labeled examples is insufficient to train a robust and accurate model. Additionally, the proposed method can be viewed as a framework, since different soft label estimation methods can be used in the proposed method according to application requirements.
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    Network security management with traffic pattern clustering
    (2014-09-01)
    Chiou, Tao-Wei
    ;
    Tsai, Shi-Chun
    ;
    Lin, Yi-Bing
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Profiling network traffic pattern is an important approach for tackling network security problem. Based on campus network infrastructure, we propose a new method to identify randomly generated domain names and pinpoint the potential victim groups. We characterize normal domain names with the so called popular 2gram (2 consecutive characters in a word) to distinguish between active and nonexistent domain names. We also track the destination IPs of sources IPs and analyze their similarity of connection pattern to uncover potential anomalous group network behaviors. We apply the Hadoop technique to deal with the big data of network traffic and classify the clients as victims or not with the spectral clustering method.
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    Constructing a dental implant ontology for domain specific clustering and life span analysis
    (2013-08-01)
    Trappey, Charles V.
    ;
    Wang, Tong-Mei
    ;
    Hoang, Sean
    ;
    Trappey, Amy J. C.
    ;
    管理科學系
    Dental implant and prosthetics is a growing industry that follows the increasing aged populations that incur a higher percentage of tooth loss [1]. The dental implant sector is one of the most technical oriented fields in dentistry with many new techniques, devices, and materials being invented and put to clinical trials. Most innovations and technologies tend to be protected by intellectual property rights (IPRs) through patents. Thus, this research identifies the life spans of dental implant (DI) key technologies using patent analysis. Key patents and their frequently appearing phrases are analyzed for the construction of the DI ontology. Afterward, the life spans of DI technical clusters are defined based on the ontology schema. This research demonstrates the feasibility of using text mining and data mining techniques to extract key phrases from a set of DI patents with different patent classifications (e.g., UPC, IPC) as the basis for building a domain-specific ontology. The case study of ontological sub-clustering for dental implants demonstrates life span mapping of the technology and the ability to use clusters to represent stages of development and maturity in specific technology life cycles. (C) 2013 Elsevier Ltd. All rights reserved.
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    Pattern discovery from patient controlled analgesia demand behavior
    (2012-10-01)
    Hu, Yuh-Jyh
    ;
    Ku, Tien-Hsiung
    ;
    分子醫學與生物工程研究所
    ;
    資訊工程學系
    ;
    Institute of Molecular Medicine and Bioengineering
    Unlike previous research on patient controlled analgesia, this study explores patient demand behavior over time. We apply clustering methods to disclose demand patterns among patients over the first 24 h of analgesic medication after surgery. We consider demographic, biomedical, and surgery-related data in statistical analyses to determine predictors for patient demand behavior, and use stepwise regression and Bayes risk analysis to evaluate the influence of demand pattern on analgesic requirements. We identify three demand patterns from 1655 patient controlled analgesia request log files. Statistical tests show correlations of gender (p=.0022), diastolic blood pressure (p =.025), surgery type (p =.0028), and surgical duration (p <.0095) with demand patterns. Stepwise regression and Bayes risk analysis show demand pattern plays the most important role in analgesic consumption prediction (p=0.E+0). This study suggests analgesia request patterns over time exist among patients, and clustering can disclose demand behavioral patterns. (C) 2012 Elsevier Ltd. All rights reserved.