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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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Author: search.filters.author.Trappey, Charles V.Subject: search.filters.subject.Data mining

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Now showing 1 - 3 of 3
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    Consumer driven product technology function deployment using social media and patent mining
    (2018-04-01)
    Trappey, Amy J. C.
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    Trappey, Charles V.
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    Fan, Chin-Yuan
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    Lee, Ian J. Y.
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    管理科學系
    The capability of identifying real-time customer needs is critical for manufacturers that provide short life cycle consumer products such as smart phones. Companies need to form research and development (R&D) strategies to improve key functional features for short lifespan products to reflect the adoption of innovative technologies and changing customer expectations. With the pervasive use of the Internet, this research crawls and analyzes the online voice of customers (VoC), overcoming the time lag of offline surveys, to identify and prioritize product functions for deployment using extended quality function deployment (eQFD) models. In this research, the novel analytics of the manufacturer's patent portfolio is added as an additional eQFD dimension to map ranked functional improvements to a manufacturer's R&D capabilities. Thus, a computer supported eQFD system is developed to perform the unique mappings and gap analyses between the VoC, the prioritized product functions, and the manufacturer's patent portfolio. The newly developed eQFD methodology and its novel discoveries are demonstrated in detail using a case study of three smart phones launched during the same time frame. The products include the Samsung Galaxy 57, the Huawei Honor 5X, and the ASUS Zenfone 3. The newly developed methodology is generally applicable to support VoC-centric product function deployment and R&D strategic planning in other domains.
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    Incorporating Data Warehouse Technology into Asset Information Management Systems for Large Assets
    (2016)
    Trappey, Amy J. C.
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    Trappey, Charles V.
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    Ma, Lin
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    Chang, Acer C. C.
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    管理科學系
    Large sized engineering assets such as power transformers are critical parts of the power supply networks. Therefore, focusing on early fault diagnosis to maintain large transformers in good condition is an important operational task to the power companies. In order to manage and fully utilize the big data generated from the large number of transformers, this paper incorporates data warehouse technology to a fault diagnosis system for the entire transformer fleet. The research includes two major parts. First, a data warehouse (DW) is designed for the large assets information management. Then, the DW-based intelligent fault diagnosis system is developed and implemented. The DW stores the complete transformers\' data and then different data cubes are defined according to various applications. The fault diagnosis system for power transformers consists of the condition monitoring module, failure diagnosis module, and intelligent decision supports module. The research methodology and prototype system are verified with real data from a series of 161 kV transformers in operations.
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    CLUSTERING PATENTS USING NON-EXHAUSTIVE OVERLAPS
    (2010-06-01)
    Trappey, Charles V.
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    Trappey, Amy J. C.
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    Wu, Chun-Yi
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    管理科學系
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    Department of Management Science
    Patent documents are unique external sources of information that reveal the core technology underlying new inventions. Patents also serve as a strategic data source that can be mined to discover state-of-the-art technical development and subsequently help guide R&D investments. This research incorporates an ontology schema to extract and represent patent concepts. A clustering algorithm with non-exhaustive overlaps is proposed to overcome deficiencies with exhaustive clustering methods used in patent mining and technology discovery. The non-exhaustive clustering approach allows for the clustering of patent documents with overlapping technical findings and claims, a feature that enables the grouping of patents that define related key innovations. Legal advisors can use this approach to study potential cases of patent infringement or devise strategies to avoid litigation. The case study demonstrates the use of non-exhaustive overlaps algorithm by clustering US and Japan radio frequency identification (RFID) patents and by analyzing the legal implications of automated discovery of patent infringement.