簡易檢索 / 詳目顯示

研究生: 林晏竹
Lin, Yen-Chu
論文名稱: 以基於專利探勘與佈局之模糊能力集合擴展規劃定義磁浮離心式壓縮機研發策略
A Patent Mining and Mapping Based Fuzzy Competence Set Expansion Method for Defining Magnetic Floating Centrifugal Compressor Appliance R&D Strategies
指導教授: 黃啟祐
Huang, Chi-Yo
學位類別: 碩士
Master
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2020
畢業學年度: 109
語文別: 英文
論文頁數: 118
中文關鍵詞: 專利檢索專利佈局模糊能力集合擴展決策實驗室分析法磁浮離心式壓縮機
英文關鍵詞: patent search, patent landscaping, fuzzy capability set expansion, decision-making laboratory analysis, magnetic floating centrifugal compressor
DOI URL: http://doi.org/10.6345/NTNU202100033
論文種類: 學術論文
相關次數: 點閱:227下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 近年來,全球暖化日益嚴重,節能減碳意識抬頭,廠商積極導入節能科技於傳統家電之中。傳統冷氣為家電產品中,耗能最為嚴重之電器產品,而壓縮機更為冷氣中消耗能源最嚴重之設備。如何導入新興科技,節約能源,為當前最重要的議題。磁浮離心式壓縮機為最新壓縮機技術,由於其磁浮軸承之技術能克服傳統軸承、齒輪傳動與系統冷凍油交換所造成之能源損失,節能效率較現有壓縮機技術提昇百分之二十至三十,而壓縮機體 亦能縮小為傳統壓縮機之四分之一以下,為當前冷凍空調科技最關鍵技術,布局相關專利以提昇競爭力,為後進冷凍空調廠商最重要之專利策略,但相關研究卻付之闕如。因此,研究擬進行專利分析與布局,並進而定義研發策略。為進行專利佈局,本研究首先分析技術範圍,檢索美國專利商標局(USTPO),並以檢索結果定義功能—功效矩陣。本研究並將進一步以主成份分析,歸納適合後進廠商選擇佈局技術之準則,並進而以混合多準則決策分析法,導入決策實驗室分析法(Decision Making Trial and Evaluation Laboratory,DEMATEL)之分析網路流程(DEMATEL based Analytic Network Process,DANP)整合修正式VIKOR法 (DANP-mV,DEMATEL-based ANP with modified VIKOR model)方法來評估佈局之技術。最後,以能力集合擴展為基礎之多目標決策(FMODM)分析法定義技術路徑圖。本研 究將以我國某冷氣廠商為例,實證本分析架構之有效性。本研究所發展之研究架構,將可作為全球廠商專利佈局與研發策略定義之用,定訂之研發策略,亦可作為冷氣產業後進廠商發展磁浮離心式壓縮機之依據。

    In recent years, global warming has become an increasingly critical issue, which has led to an increase in awareness of energy conservation and carbon reduction. Thus, manufacturers are actively introducing energy-saving technologies into traditional home appliances. Traditional air conditioners consume the most energy among electrical home appliances, and compressors are the most energy-intensive equipment in air-conditioning. Introducing emerging technologies and saving energy is the most important issue at present. The magnetic floating centrifugal compressor is the latest technology in air-conditioning. Due to the magnetic floating technology, the compressor can overcome energy losses being caused by traditional bearing, gear transmission, and refrigeration system oil exchange. Its energy efficiency is 20%–30% higher than that of the existing compressors, while its body can be reduced to less than a quarter of the conventional compressor. As compressors are a critical part of the current refrigeration and air-conditioning technology, patent landscaping to enhance competitiveness is the most important strategy for the downstream refrigeration and air-conditioning manufacturers. However, related research is limited. Therefore, the present study intends to conduct patent analysis and landscaping, so that a research and development (R&D) strategy can be defined accordingly. In order to carry out patent landscaping, the study first analyzes the technical scope, searches the U.S. Patent and Trademark Office (USTPO), and defines the technology-function matrix by searching for the result. The study uses principal component analysis (PCA) to summarize the criteria that manufacturers could use in choosing their patent landscaping techniques. Multiple-criteria decision-making (MCDM) methods integrate the decision-making trial and evaluation laboratory (DEMATEL)–based analytic network process (ANP) and the modified VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) model to evaluate the techniques that could be developed by focal companies. Finally, a competence set expansion based on the fuzzy multiple attribute decision-making method will be used to define the technology roadmap. An empirical study of a newer Taiwanese air-conditioning manufacturer will be used to demonstrate the feasibility of the proposed analytic framework. Based on the analytic results, the roadmap of each technology can easy-catch the strategy. The well-verified analytic framework can serve as a basis for R&D strategy definitions by fast-catching manufacturers of air conditioners for patent landscaping and R&D strategy definitions.

    Abstract i Table of Contents iv List of Figure vi List of Table vii Chapter 1 Introduction 1 1.1 Research Backgrounds 1 1.2 Research Motivations 4 1.3 Research Purpose 4 1.4 Research Methods 5 1.5 Research Limitation 5 1.6 Research Framework 6 1.7 Thesis Structure 7 Chapter 2 Literature review 9 2.1 Data Mining 9 2.2 Patent Data Mining 11 2.3 Patent Mapping 13 Chapter 3 Methodology 17 3.1 Patents Searching 18 3.2 Patent Map 24 3.3 Modified Delphi Method 26 3.4 Fuzzy Competence Set Expansion 29 3.5 D-DANP-mV 38 Chapter 4 Empirical Study 53 4.1 Background of Target Technology 53 4.2 Brain Storming with Experts before Searching Patent 55 4.3 Patent Searching 56 4.4 Construct Patent Map 60 4.5 Technology Selection 66 4.6 The Relationship of Each Expending Technologies 72 4.7 The Roadmap of Each Expending Technologies 83 Chapter 5 Discussion 87 5.1 Implications and Contribution 87 5.2 Limitation 89 5.3 Suggestion for Further Research 90 Chapter 6 Conclusion 91 References 95 Appendix 101

    References
    Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Paper presented at the Proceedings of the 1993 ACM SIGMOD international conference on Management of data, 207-216
    Allen, E., Thallon, R., & Schreyer, A. C. (2017). Fundamentals of residential construction. Hoboken, NJ: John Wiley & Sons.
    Bellman, R. (1952). On the theory of dynamic programming. Proceedings of the National Academy of Sciences, 38(8), 716-719.
    Bergek, A. (2019). Technological innovation systems: a review of recent findings and suggestions for future research. Handbook of Sustainable Innovation, 200-218.
    Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S., & Rickne, A. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research policy, 37(3), 407-429.
    Botkin, D. B., & Keller, E. A. (1998). Environmental science: earth as a living planet. Chichester, England: John Wiley & Sons Ltd.
    Bramer, M. (2007). Principles of data mining. London: Springer.
    Carlsson, C., & Korhonen, P. (1986). A parametric approach to fuzzy linear programming. Fuzzy sets and systems, 20(1), 17-30.
    Chikano, H., Maeda, K., Fujiya, H., Ishibashi, H., Tsuda, K., Kawahara, S., & Tano, A. (2011). Color Production Printer RICOH Pro C751EX/651EX, Ricoh Technical Report, 37, 144-149.
    Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. In Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics, 17(3), 37-37.
    Freimer, M., & Yu, P. (1976). Some new results on compromise solutions for group decision problems. Management science, 22(6), 688-693.
    Gao, L., Porter, A. L., Wang, J., Fang, S., Zhang, X., Ma, T., Huang, L. J. T. F. (2013). Technology life cycle analysis method based on patent documents. Technological Forecasting Social Change, 80(3), 398-407.
    Han, B., Xu, Q., & Yuan, Q. (2015). Multiobjective optimization of a combined radial-axial magnetic bearing for magnetically suspended compressor. IEEE Transactions on Industrial Electronics, 63(4), 2284-2293.
    Han, B., Xue, Q., Liu, X., & Wang, K. (2017). Multi-objective optimization design of a high-speed PM machine supported by magnetic bearings. Mechanical Systems Signal Processing, 92, 349-363.
    Heiyanthuduwage, M., Mounoury, S., & Kovacevic, A. (2011). Performance prediction methods for screw compressors. Paper presented at the Institution of Mechanical Engineers-7th International Conference on Compressors and Their Systems 2011. London, England: Woodhead Publishing Limited
    Ho, T. B., Cheung, D., & Liu, H. (2005). Advances in knowledge discovery and data mining: 9th Pacific-Asia Conference, PAKDD 2005, Hanoi, Vietnam, May 18-20, 2005, Proceedings. Berlin, Germany: Springer.
    Huang, C.-Y., Chung, P.-H., Shyu, J. Z., Ho, Y.-H., Wu, C.-H., Lee, M.-C., & Wu, M.-J. (2018). Evaluation and selection of materials for particulate matter MEMS sensors by using hybrid MCDM methods. Sustainability, 10(10), 3451.
    Huang, C.-Y., Hsieh, H.-L., & Chen, H. (2020). Evaluating the Investment Projects of Spinal Medical Device Firms Using the Real Option and DANP-mV Based MCDM Methods. International Journal of Environmental Research and Public Health, 17(9), 3335.
    Huang, C.-Y., Shyu, J. Z., & Tzeng, G.-H. (2007). Reconfiguring the innovation policy portfolios for Taiwan's SIP Mall industry. Technovation, 27(12), 744-765.
    Hunt, D., Nguyen, L., & Rodgers, M. (2012). Patent searching: Tools & techniques. Hoboken, NJ: John Wiley & Sons.
    Inenaga, S., & Bannai, H. (2012). Finding characteristic substrings from compressed texts. International Journal of Foundations of Computer Science, 23(02), 261-280.
    Kumar, A., & Dixit, G. (2019). A novel hybrid MCDM framework for WEEE recycling partner evaluation on the basis of green competencies. cleaner production, 241, 118017.
    Lai, Y. J., & Hwang, C. L. (1994). Fuzzy multiple objective decision making. In Fuzzy Multiple Objective Decision Making. Berlin, Germany: Springer.
    Lin, C.-L., & Tzeng, G.-H. J. E. s. w. a. (2009). A value-created system of science (technology) park by using DEMATEL. Expert systems with applications, 36(6), 9683-9697.
    Lin, S.-H., Zhao, X., Wu, J., Liang, F., Li, J.-H., Lai, R.-J., Tzeng, G.-H. (2020). An evaluation framework for developing green infrastructure by using a new hybrid multiple attribute decision-making model for promoting environmental sustainability. Socio-economic planning sciences, 100909.
    Madani, F., & Weber, C. (2016). The evolution of patent mining: Applying bibliometrics analysis and keyword network analysis. World Patent Information, 46, 32-48.
    Magdy, W., Lopez, P., & Jones, G. J. (2011). Simple vs. sophisticated approaches for patent prior-art search. Advances in Information Retrieval, 6611, 725-728.
    Nakano, T., Okamoto, M., & Kisen, T. (2018). U.S. Patent No. 15/754,319. Washington, DC: U.S. Patent and Trademark Office.
    Nehra, V., Prajapati, S., Tankwal, P., Zilic, Z., Kumar, T. N., & Kaushik, B. (2020). Energy-Efficient Differential Spin Hall MRAM-Based 4-2 Magnetic Compressor. IEEE Transactions on Magnetics, 56(1), 1-11.
    Onat, N. C., Kucukvar, M., & Tatari, O. (2014). Scope-based carbon footprint analysis of US residential and commercial buildings: An input–output hybrid life cycle assessment approach. Building Environment, 72, 53-62.
    Opricovic, S. (1998). Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade, 2(1), 5-21.
    Qu, G.-B., Zhao, T.-Y., Zhu, B.-W., Tzeng, G.-H., & Huang, S.-L. (2019). Use of a Modified DANP-mV Model to Improve Quality of Life in Rural Residents: The Empirical Case of Xingshisi Village, China. Environmental Research and Public Health, 16(1), 153.
    Rogers, T. W., Rogers, T. S., Stoner, M. H., Sellgren, K. L., Lynch, B. J., Forbis-Stokes, A. A., Hawkins, B. T. (2018). A granular activated carbon/electrochemical hybrid system for onsite treatment and reuse of blackwater. Water research, 144, 553-560.
    Saaty, T., & Vargas, L. (2006). Decision making with the analytic network process: Economic, Political, Social and Technological Applications with Benefits, Opportunities, Costs and Risks. New York, NY, U.S.A.: Springer.
    Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European journal of operational research, 48(1), 9-26.
    Shi, D., & Yu, P. (1996). Optimal expansion and design of competence sets with asymmetric acquiring costs. Journal of Optimization theory and Applications, 88(3), 643-658.
    Tzeng, G.-H., & Huang, C.-Y. (2012). Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing&logistics systems. Annals of Operations Research, 197(1), 159-190.
    Tzeng, G. H., Chiang, C., & Li, C. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert systems with applications, 32(4), 1028-1044.
    Tzeng, G. H., & Huang, J. J. (2016). Fuzzy Multiple Objective Decision Making. CRC Press. London, England: Taylor & Francis.
    Yang, C.-L., Huang, C.-Y., Kao, Y.-S., & Tasi, Y.-L. (2017). Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data. Eurasia Journal of Mathematics, Science and Technology Education, 13(8), 4553-4589.
    Yang, C.-L., Yuan, B. J., & Huang, C.-Y. (2015). Key determinant derivations for information technology disaster recovery site selection by the multi-criterion decision making method. Sustainability, 7(5), 6149-6188.
    Yu, P., & Zhang, D. (1992). Optimal expansion of competence sets and decision support. INFOR: Information Systems and Operational Research, 30(2), 68-84.
    Yu, P.-L. (1973). A class of solutions for group decision problems. Management Science, 19(8), 936-946.
    Yu, P.-L. (1991). Habitual domains. Operations Research, 39(6), 869-876.
    Zeleny, M., & Cochrane, J. L. (1982). Multiple Criteria Decision Making. New York, N.Y: McGraw-Hill.

    無法下載圖示 本全文未授權公開
    QR CODE