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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

Author: search.filters.author.Chen, Ruey-ShunSubject: search.filters.subject.data mining

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Now showing 1 - 6 of 6
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    Using Cache Optimization Method to Reduce Network Traffic in Communication Systems Based on Cloud Computing
    (2019-01-01)
    Kang, Lanlan
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    Chen, Ruey-Shun
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    Chen, Yeh-Cheng
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    Wang, Chung-Chei
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    Li, Xingguan
    The overwhelming increase of population will lead to an increase network traffic usage. In most products, local cache mechanism is implemented for the purpose of reducing the network traffics. However, Due to storage space limitations, lot of times, cache will get purged when there are lots of queries sent and replaced by other newest queries. It is not an efficient by using traditional methodology to build or forming cache. In order to reduce the network traffic usage, the paper propose a solution, which utilizes data mining technique with clustering concept, by gathering the current feedback data that have from our SPN (Smart Protection Network). we are able to form these data in groups with similarity, and by deploying these data to client side, to achieve the reduction of traffic usage. In prototype, this design can really reduce network traffic more than 8%.
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    Using data mining to provide recommendation service
    (2007-01-01)
    Chen, Ruey-Shun
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    Tsai, Yung-Shun
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    Yeh, K. C.
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    Sun, Chia-Ming
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    Yu, D. H.
    This research introduces personalized recommendation service into library services. Using the borrowing record of the library as basis, the association rules of data mining technique are used to look for book association by focusing on reader's borrowing mode, personal interest and trait in order to simplify the complexity of recommendation structure. The Bayesian network concept is used to build up a personalized book recommender system in order to generate different book recommendations, ranking from high to low, to help reader to locate book information most suitable to his requirement. Meanwhile we use user satisfaction questionnaire to understand the accuracy of recommended books and further to feedback information in order to help the post learning of Bayesian network parameter. This is for the perfection of the overall structure of recommender system so that readers could make use of the resource of the library more effectively and the value of the library system could be further improved.
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    Design and implementation of an intelligent manufacturing execution system for semiconductor manufacturing industry
    (2006)
    Chen, Ruey-Shun
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    Tsai, Yung-Shun
    ;
    Chang, Chan-Chine
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    資訊管理與財務金融系 註：原資管所+財金所
    ;
    Department of Information Management and Finance
    This paper proposed an intelligent MES by integrating manufacturing execution system, data warehouse, online analytical processing and data mining system. The data warehouse for this system is provided by the massive amounts of data gathered from a MES. Through some process of integrating MES with data warehouses, data warehouses with decision analysis, and decision analysis with data mining systems. A three-tiered web-based systematic framework has been established. The result of this study is the integration of the MES and data mining system. According to experimental analysis, the product yield and the manufacturing cycle time all have been improved for manufacturing industry.
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    Using data mining technology to improve manufacturing quality - A case study of LCD driver IC packaging industry
    (2006)
    Chen, Ruey-Shun
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    Yeh, Kun-Chieh
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    Chang, Chan-Chine
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    Chien, H. H.
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    資訊管理與財務金融系 註：原資管所+財金所
    In recent year, because of the professional teamwork, to improve the qualification percentage of products, to accelerate the acknowledgement of product defects and to find out the solution, the LCD driver IC packaging factories have to establish an analysis mode for quality problems of product for more effective and quicker acquisition of needed information and to improve the customer's satisfaction for information system. The past information system used neural network to improve the yield rate of production. In this research employs the star schema of data warehousing as the base of line analysis, and uses decision tree in data mining to establish a quality analysis system for the defects found in the production processes of package factories in order to provide an interface for problem analysis, enabling quick judgment and control over the cause of problem to shorten the time solving the quality problem. The result of research shows that the use of decision tree algorithm reducing the numbers of defected inner leads and chips has been improved, and using decision tree algorithm is more suitable than using neural network in quality problem classification and analysis of the LCD driver IC packaging industry.
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    Using data mining technology to build an intelligent manufacturing system for semiconductor industry
    (2006)
    Chen, Ruey-Shun
    ;
    Chang, Chan-Chine
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    ;
    Department of Information Management and Finance
    In this paper, we propose a flexible three-tier architecture for data mining in semiconductor manufacturing. The architecture allows a high-performance implementation of data mining techniques using association rule. The prototype is able to mine patterns from large data sets that reside in databases. There are benefits to use the data mining control system that is implemented by association rule in computer integrated manufacturing (CIM) environment. The result shows that the proposed intelligent manufacturing system can improve product yield rate and reduce cycle time in semiconductor manufacturing. It could find the best machines of process machine sets that affect processes. It also enhances wafer yield accuracy, reduces production costs and promotes the enterprise competitiveness.
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    Using Bayesian networks to build data mining applications for a semiconductor cleaning process
    (2007)
    Chen, Ruey-Shun
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    Chang, Chan-Chine
    ;
    資訊管理與財務金融系 註：原資管所+財金所
    ;
    Department of Information Management and Finance
    Data mining is part of the knowledge discovery process that offers a new way to look at data. It consists of the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It is the discovery of patterns that lie hidden among a set of data. A Bayesian networks is a specific model of knowledge network. It offers useful information about the mutual dependencies among the features in the application domain. Such information can be used for gaining better understanding about the dynamics of the process under observation. Manufacturing process monitoring using Bayesian networks is a quite useful example. In this paper, we provide Bayesian networks to extract knowledge from data. We present an approach for the Bayesian networks to implement a data mining task for computer integrated manufacturing (CIM). It could find the cause factors in various parameters that affect in semiconductor cleaning process.