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| Title: | 以專利數據進行資料中心冷卻技術之預測分析研究
Technology Forecasting of Data Center Cooling Solutions Through Patent Analysis |
| Authors: | 簡齊萱
Chien, Chi-Hsuan |
| Contributors: | 柯玉佳
Ko, Yu-Chia
簡齊萱
Chien, Chi-Hsuan |
| Keywords: | 資料中心
伺服器冷卻
技術預測
成長曲線
專利分析
Data Center
Server Cooling
Technology Forecasting
Growth Curves
Patent Analysis |
| Date: | 2025 |
| Issue Date: | 2025-09-01 16:08:10 (UTC+8) |
| Abstract: | 隨著AI、大數據、物聯網等高運算科技的崛起，資料中心能耗快速上升，其中冷卻系統即占整體耗能約40%，成為永續營運目標下的關鍵技術。液冷等新興技術因其高效優勢，逐漸成為替代傳統氣冷的解決方案，吸引台灣與全球企業大量投入研發。然而，目前針對資料中心冷卻技術的研究，多聚焦於工程或產業分析，鮮少從技術生命週期角度進行系統性預測，亦未區分不同冷卻技術類型展開專利分析。
本研究旨在探索資料中心伺服器冷卻技術的成長階段與未來發展潛力，並比較氣冷、間接液冷與直接液冷（浸沒式冷卻）三大類型的技術發展差異。研究首先設計專利檢索策略，結合主題關鍵字與IPC分類號進行嚴謹過濾，再依冷卻技術進行類別區分，建立2000年至2022年專利資料集。接著套用Logistic與Gompertz兩種成長曲線模型，分析模型配適度與預測能力。
研究結果顯示，氣冷技術已進入成熟期，增長趨緩；間接液冷技術自2018年後明顯上升，目前處於穩定成長期；浸沒式冷卻則自2020年起快速成長，正值萌芽與成長交界期。在模型預測能力方面，整體冷卻技術與氣冷以Gompertz 模型解釋力較佳；而間接液冷及浸沒式冷卻則以Logistic模型表現較佳，反映其具備高度成長動能的特性。進一步探討專利技術主體，發現氣冷以傳統伺服器商為主，間接液冷吸引晶片大廠參與，浸沒式冷卻由專業新創切入；IPC分類號集中於H05K 7/20與G06F 1/20，顯示專利佈局聚焦於冷卻結構與熱管理。
綜合而言，本研究不僅建立專利導向之冷卻技術分類與檢索框架，亦驗證成長曲線模型於技術預測的適用性，提供研發決策者掌握不同技術成熟度與投入時機的參考依據。未來可進一步結合專家法或情境分析等預測法，深化對市場趨勢的掌握。
With the rise of high-performance computing technologies such as AI, big data, and IoT, energy consumption in data centers has surged. Emerging liquid cooling technologies, known for their high efficiency, are gradually replacing traditional air cooling and have attracted substantial R&D investment globally. However, prior studies often focus on engineering or industry perspectives, lacking systematic technology lifecycle forecasting or comparative analysis across different cooling types.
This study aims to explore the growth stages and future potential of air cooling, indirect liquid cooling, and immersion cooling via patent data （2000–2022）. Both Logistic and Gompertz growth models are applied to assess curve fitting and forecasting accuracy. Results show that air cooling has entered maturity stage, indirect liquid cooling is in stable growth since 2018, and immersion cooling is in a rapid growth stage since 2020. The Gompertz model best fits overall and air cooling trends, while the Logistic model performs better for emerging liquid cooling technologies, reflecting their high growth momentum. Patent assignee analysis reveals different strategic players: traditional server vendors lead air cooling, chipmakers dominate indirect liquid cooling, and immersion cooling is driven by startups. Most patents fall under IPC codes H05K 7/20 and G06F 1/20, indicating a focus on structural cooling and thermal management.
This research builds a patent-driven classification framework and validates growth models for cooling technology forecasting, offering insights into technological maturity and investment timing. |
| Reference: | 中文文獻
朱文伶（2009）。行動電話擴散研究之模型選用及驅動因子分析［未出版之博士論文］。國立政治大學科技管理研究所。
行政院國家永續發展委員會（2022）。2050淨零排放政策路徑藍圖。https://ncsd.ndc.gov.tw/Fore/nsdn/about0/2050Path
技嘉。Hpc server （h262-zl0）。https://www.gigabyte.com/tw/Enterprise/High-Density-Server/H262-ZL0-rev-100
金融監督管理委員會（2013）。2013 強化我國公司治理藍圖。https://www.fsc.gov.tw/fckdowndoc?file=/2013%E5%BC%B7%E5%8C%96%E6%88%91%E5%9C%8B%E5%85%AC%E5%8F%B8%E6%B2%BB%E7%90%86%E8%97%8D%E5%9C%96（1）.pdf&flag=doc
金融監督管理委員會（2018）。新版公司治理藍圖 （2018~2020）。https://www.fsc.gov.tw/uploaddowndoc?file=news/201803281624310.pdf&filedisplay=%E6%96%B0%E8%81%9E%E7%A8%BF%E9%99%84%E4%BB%B61_%E6%96%B0%E7%89%88%E5%85%AC%E5%8F%B8%E6%B2%BB%E7%90%86%E8%97%8D%E5%9C%96.pdf&flag=doc
金融監督管理委員會（2020）。公司治理3.0-永續發展藍圖。https://www.fsc.gov.tw/fckdowndoc?file=/%E5%85%AC%E5%8F%B8%E6%B2%BB%E7%90%863_0-%E6%B0%B8%E7%BA%8C%E7%99%BC%E5%B1%95%E8%97%8D%E5%9C%96.pdf&flag=doc
金融監督管理委員會（2022）。上市櫃公司永續發展路徑圖。https://www.sfb.gov.tw/ch/home.jsp?id=95&parentpath=0,2&mcustomize=news_view.jsp&dataserno=202203030001&dtable=News
建準。全方位散熱解決方案。https://www.sunon.com/Product.aspx?view=view2&oid=B2C2372CCEB5CB77&pid=43444CB2BC90C97C&cid=C1B85CB9AED312BF
施陽正、李智軒、趙楚源（2021）。數據中心自然冷卻方式的探討。冷凍空調&能源科技，（128），28-33。https://doi.org/10.29911/jehvace_new.202107_（128）.0001
涂敏芬、洪世章（2008）。探討技術變遷的週期循環：1976-2003年之顯示器技術發展。管理學報，25（3），291-308。https://doi.org/10.6504/jom.2008.25.03.02
袁建中（2005）。產業分析之技術預測方法與實例。麥格羅希爾。
高力。Rdhx （liquid / air）。https://www.kaori.com.tw/en/modules/news/index.php
張登凱（2020）。透過專利數據進行技術預測：探討自駕車技術之擴散［未出版之碩士論文］。國立政治大學科技管理與智慧財產研究所。
陳牧風（2024）。2024年第一季台灣伺服器產業產銷暨重要趨勢分析。資策會產業情報研究所。https://mic.iii.org.tw/aisp/ReportS?docid=CDOC20240301001
陳牧風、魏傳虔、陳奕伶（2023）。2023年伺服器市場現況暨產業發展議題分析。資策會產業情報研究所。https://mic.iii.org.tw/aisp/ChartS?docid=PPT1120720-1
陳思妤（2023）。利用專利分析進行技術預測：評估浮動式離岸風電技術創新發展［未出版之博士論文］。國立政治大學科技管理與智慧財產研究所。
陳達仁（2013）。專利檢索與專利分析-智慧財產培訓學院教材。經濟部智慧財產局。
富果直送（2023）。 緯穎 2024 年營收預估成長 45%！最新解讀AI伺服器 ODM 大廠緯穎。https://blog.fugle.tw/2024-wiwynn-report/
資策會產業情報研究所 （2024）。 臺灣代工占全球ai伺服器出貨達九成 帶動我國伺服器產業鏈營收成長 2024有兩大成長動能。https://mic.iii.org.tw/news.aspx?id=660
英文文獻
Adamuthe, A. C., & Thampi, G. (2020). Forecasting technology maturity curve of cloud computing with its enabler technologies. Journal of Scientific Research, 64(2), 239-246.
Adamuthe, A. C., & Thampi, G. T. (2019). Technology forecasting: A case study of computational technologies. Technological Forecasting and Social Change, 143, 181-189. https://doi.org/https://doi.org/10.1016/j.techfore.2019.03.002
Almaneea, A., Thompson, H., Summers, J., & Kapur, N. (2014). Cooling system analysis for a data center using liquid immersed servers. International Journal of Thermal Technologies, 4(3), 200-207.
Altuntas, S., Dereli, T., & Kusiak, A. (2015). Forecasting technology success based on patent data. Technological Forecasting and Social Change, 96, 202-214. https://doi.org/https://doi.org/10.1016/j.techfore.2015.03.011
Andersen, B. (1999). The hunt for S-shaped growth paths in technological innovation: a patent study. Journal of evolutionary economics, 9, 487-526.
Avelar, V., Azevedo, D., French, A., & Power, E. N. (2012). PUE: a comprehensive examination of the metric. White paper, 49.
Ayres, R. U. (1969). Technological forecasting and long-range planning.
Bengisu, M., & Nekhili, R. (2006). Forecasting emerging technologies with the aid of science and technology databases. Technological Forecasting and Social Change, 73(7), 835-844.
Bodger, P. S., & H.S, T. (1986). Trend extrapolation in long-term forecasting: An investigation using New Zealand electricity consumption data. Technological Forecasting and Social Change, 30(2), 167-188. https://doi.org/https://doi.org/10.1016/0040-1625(86)90018-1
Cai, Z., Zhou, Y., Xu, Y., & Liu, Z. (2024). Patent Analysis for Identifying Core Technology and Forecasting Promising Technology in Medical Imaging Equipment. IEEE Transactions on Engineering Management.
Calleja-Sanz, G., Olivella-Nadal, J., & Solé-Parellada, F. (2020). Technology Forecasting: Recent Trends and New Methods. In C. Machado & J. P. Davim (Eds.), Research Methodology in Management and Industrial Engineering (pp. 45-69). Springer International Publishing. https://doi.org/10.1007/978-3-030-40896-1_3
Campbell, R. S. (1983). Patent trends as a technological forecasting tool. World patent information, 5(3), 137-143. https://doi.org/10.1016/0172-2190(83)90134-5
Capozzoli, A., & Primiceri, G. (2015). Cooling Systems in Data Centers: State of Art and Emerging Technologies. Energy Procedia, 83, 484-493. https://doi.org/https://doi.org/10.1016/j.egypro.2015.12.168
Chanchetti, L. F., Oviedo Diaz, S. M., Milanez, D. H., Leiva, D. R., de Faria, L. I. L., & Ishikawa, T. T. (2016). Technological forecasting of hydrogen storage materials using patent indicators. International Journal of Hydrogen Energy, 41(41), 18301-18310. https://doi.org/https://doi.org/10.1016/j.ijhydene.2016.08.137
Chang, Q., Huang, Y., Liu, K., Xu, X., Zhao, Y., & Pan, S. (2024). Optimization Control Strategies and Evaluation Metrics of Cooling Systems in Data Centers: A Review. Sustainability, 16(16), 7222.
Chen, H., Zhang, G., Zhu, D., & Lu, J. (2017). Topic-based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014. Technological Forecasting and Social Change, 119, 39-52. https://doi.org/https://doi.org/10.1016/j.techfore.2017.03.009
Cho, Y., & Daim, T. (2013). Technology Forecasting Methods. In T. Daim, T. Oliver, & J. Kim (Eds.), Research and Technology Management in the Electricity Industry: Methods, Tools and Case Studies (pp. 67-112). Springer London. https://doi.org/10.1007/978-1-4471-5097-8_4
Coates, V., Farooque, M., Klavans, R., Lapid, K., Linstone, H. A., Pistorius, C., & Porter, A. L. (2001). On the Future of Technological Forecasting. Technological Forecasting and Social Change, 67(1), 1-17. https://doi.org/https://doi.org/10.1016/S0040-1625(00)00122-0
Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981-1012. https://doi.org/https://doi.org/10.1016/j.techfore.2006.04.004
Dargay, J. M. (2001). The effect of income on car ownership: evidence of asymmetry. Transportation Research Part A: Policy and Practice, 35(9), 807-821.
Davidson, F. (1928). Growth and senescence in pure-bred Jersey cows. Bulletin(32), 192-199.
Deshpande, N., Ahmeda, S., & Khodea, A. (2016). Business intelligence through patinformatics: A study of energy efficient data centres using patent data. Journal of intelligence studies in business, 6(3), 13-26. https://doi.org/10.37380/jisib.v6i3.193
Ernst, H. (1997). The Use of Patent Data for Technological Forecasting: The Diffusion of CNC-Technology in the Machine Tool Industry. Small Business Economics, 9(4), 361-381. https://doi.org/10.1023/A:1007921808138
Ernst, H. (2003). Patent information for strategic technology management. World patent information, 25(3), 233-242. https://doi.org/https://doi.org/10.1016/S0172-2190(03)00077-2
FirstResearch. (2024). Managed Application & Network Services.
Fisher, J. C., & Pry, R. H. (1971). A simple substitution model of technological change. Technological Forecasting and Social Change, 3, 75-88.
Foster, R. N. (1986). Innovation: The attacker’s advantage. In: New York: Summit Books.
Gao, L., Porter, A. L., Wang, J., Fang, S., Zhang, X., Ma, T., Wang, W., & Huang, L. (2013). Technology life cycle analysis method based on patent documents. Technological Forecasting and Social Change, 80(3), 398-407. https://doi.org/https://doi.org/10.1016/j.techfore.2012.10.003
Garimella, S. V., Lian-Tuu, Y., & Persoons, T. (2012). Thermal Management Challenges in Telecommunication Systems and Data Centers. IEEE transactions on components, packaging, and manufacturing technology (2011), 2(8), 1307-1316. https://doi.org/10.1109/TCPMT.2012.2185797
Gartner. (2024). Forecast: Public Cloud Services, Worldwide, 2022-2028, 1Q24 Update. https://www.gartner.com/en/newsroom/press-releases/2024-05-20-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-surpass-675-billion-in-2024
Gompertz, B. (1825). On the Nature of the Function Expressive of the Law of Human Mortality, and on a New Mode of Determining the Value of Life Contingencies. Philosophical transactions of the Royal Society of London, 115, 513-583.
Haghshenas, K., Setz, B., Blosch, Y., & Aiello, M. (2023). Enough hot air: the role of immersion cooling. Energy Informatics, 6(1), 14. https://doi.org/10.1186/s42162-023-00269-0
Harhoff, D., Narin, F., Scherer, F. M., & Vopel, K. (1999). Citation frequency and the value of patented inventions. Review of Economics and statistics, 81(3), 511-515.
Harhoff, D., Scherer, F. M., & Vopel, K. (2003). Citations, family size, opposition and the value of patent rights. Research Policy, 32(8), 1343-1363. https://doi.org/https://doi.org/10.1016/S0048-7333(02)00124-5
Haruna, S., Jinji, N., & Zhang, X. (2010). Patent citations, technology diffusion, and international trade: evidence from Asian countries. Journal of Economics and Finance, 34(4), 365-390. https://doi.org/10.1007/s12197-009-9078-2
Huang, J.-Y. (2016). Patent portfolio analysis of the cloud computing industry. Journal of Engineering and Technology Management, 39, 45-64. https://doi.org/https://doi.org/10.1016/j.jengtecman.2016.01.002
Huang, L., & Wang, N. (2013, 2013//). Status and Prospects of Technology Diffusion Research based on Patent Information. Proceedings of the Sixth International Conference on Management Science and Engineering Management, London.
IEA. (2024). Electricity 2024 Analysis and forecast to 2026. https://www.iea.org/reports/electricity-2024
Isenson, R. S. (1966). TECHNOLOGICAL FORECASTING IN PERSPECTIVE. Management Science (pre-1986), 13(2), 14. https://proxyone.lib.nccu.edu.tw/login?url=https://www.proquest.com/scholarly-journals/technological-forecasting-perspective/docview/205826999/se-2?accountid=10067
İskender, C. (2021). Mathematical study of the Verhulst and Gompertz growth functions and their contemporary applications. EKOIST Journal of Econometrics and Statistics(34), 73-102.
Kheirabadi, A. C., & Groulx, D. (2016). Cooling of server electronics: A design review of existing technology. Applied Thermal Engineering, 105, 622-638. https://doi.org/https://doi.org/10.1016/j.applthermaleng.2016.03.056
Kim, G., & Bae, J. (2017). A novel approach to forecast promising technology through patent analysis. Technological Forecasting and Social Change, 117, 228-237. https://doi.org/https://doi.org/10.1016/j.techfore.2016.11.023
Kim, S., & Cho, K. (2025). Assessing International Technological Competitiveness in Renewable Energy: An IPC-Based Analysis of Granted Patents.
Kucharavy, D., & De Guio, R. (2011). Logistic substitution model and technological forecasting. Procedia Engineering, 9, 402-416. https://doi.org/https://doi.org/10.1016/j.proeng.2011.03.129
Kucharavy, D., & De Guio, R. (2015). Application of Logistic Growth Curve. Procedia Engineering, 131, 280-290. https://doi.org/https://doi.org/10.1016/j.proeng.2015.12.390
Lenz, R. C. (1962). TECHNOLOGICAL FORECASTING.
Lerner, J. (1994). The importance of patent scope: An empirical analysis. The RAND Journal of Economics, 25(2), 319. https://proxyone.lib.nccu.edu.tw/login?url=https://www.proquest.com/scholarly-journals/importance-patent-scope-empirical-analysis/docview/236576287/se-2?accountid=10067
Levitt, T. (1965). Exploit the product life cycle (Vol. 43). Graduate School of Business Administration, Harvard University Cambridge, MA ….
Li, X., Xie, Q., Daim, T., & Huang, L. (2019). Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology. Technological Forecasting and Social Change, 146, 432-449. https://doi.org/https://doi.org/10.1016/j.techfore.2019.01.012
Liu, S.-J., & Shyu, J. (1997). Strategic planning for technology development with patent analysis. International journal of technology management, 13(5-6), 661-680.
Martino, J. P. (1993). Technological Forecasting for Decision Making. McGraw-Hill. https://books.google.com.tw/books?id=lZFRAAAAMAAJ
Martino, J. P. (2003). A review of selected recent advances in technological forecasting. Technological Forecasting and Social Change, 70(8), 719-733. https://doi.org/https://doi.org/10.1016/S0040-1625(02)00375-X
Meade, N., & Islam, T. (1995). Forecasting with growth curves: An empirical comparison. International Journal of Forecasting, 11(2), 199-215. https://doi.org/https://doi.org/10.1016/0169-2070(94)00556-R
Meade, N., & Islam, T. (1998). Technological forecasting--model selection, model stadility, and combining models. Management Science, 44(8), 1115-1130. https://proxyone.lib.nccu.edu.tw/login?url=https://www.proquest.com/scholarly-journals/technological-forecasting-model-selection/docview/213239723/se-2?accountid=10067
Meyer, P. S., & Ausubel, J. H. (1999). Carrying Capacity: A Model with Logistically Varying Limits. Technological Forecasting and Social Change, 61(3), 209-214. https://doi.org/https://doi.org/10.1016/S0040-1625(99)00022-0
Mogee, M. E. (1991). Using Patent Data for Technology Analysis and Planning. Research Technology Management, 34(4), 43. https://proxyone.lib.nccu.edu.tw/login?url=https://www.proquest.com/scholarly-journals/using-patent-data-technology-analysis-planning/docview/213814447/se-2?accountid=10067
Mogee, M. E. (1997). Patents and technology intelligence. Keeping abreast of science and technology: Technical intelligence for business, 295-335.
MorganStanley. (2024). Powering the AI Revolution https://www.morganstanley.com/ideas/ai-energy-demand-infrastructure
Nadjahi, C., Louahlia, H., & Lemasson, S. (2018). A review of thermal management and innovative cooling strategies for data center. Sustainable Computing: Informatics and Systems, 19, 14-28. https://doi.org/https://doi.org/10.1016/j.suscom.2018.05.002
Nutt, A. B., Lenz, R. C., Lanford, H. W., & Cleary, M. J. (1976). Data sources for trend extrapolation in technological forecasting. Long Range Planning, 9(1), 72-76. https://doi.org/https://doi.org/10.1016/0024-6301(76)90169-2
Omdia. (2024). Omdia research predicts data center cooling market to reach $16.87 billion in 2028. https://omdia.tech.informa.com/pr/2024/jun/omdia-research-predicts-data-center-cooling-market-to-reach-16point87-billion-dollars-in-2028
Opiah, A. (2020). GRC, TDCS partner to expand data centre liquid immersion cooling in Europe
Ott, B., Wenzel, P. M., & Radgen, P. (2024). Analysis of Cooling Technologies in the Data Center Sector on the Basis of Patent Applications. Energies, 17(15), 3615. https://doi.org/https://doi.org/10.3390/en17153615
Pambudi, N. A., Sarifudin, A., Firdaus, R. A., Ulfa, D. K., Gandidi, I. M., & Romadhon, R. (2022). The immersion cooling technology: Current and future development in energy saving. Alexandria Engineering Journal, 61(12), 9509-9527. https://doi.org/https://doi.org/10.1016/j.aej.2022.02.059
Pearl, R., & Reed, L. J. (1920). On the rate of growth of the population of the United States since 1790 and its mathematical representation. Proceedings of the national academy of sciences, 6(6), 275-288.
Perni, S., Andrew, P. W., & Shama, G. (2005). Estimating the maximum growth rate from microbial growth curves: definition is everything. Food Microbiology, 22(6), 491-495. https://doi.org/https://doi.org/10.1016/j.fm.2004.11.014
Porter, A. (1991). Forecasting and management of technology. In: Wiley.
Porter, A. L., Rossini, F. A., Carpenter, S. R., Roper, A. T., Larson, R. W., & Tiller, J. S. (1984). A Guidebook for Technology Assessment and Impact Analysis. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie, 15(2), 369-371.
Prescott, R. B. (1922). Law of growth in forecasting demand. Journal of the American Statistical Association, 18(140), 471-479.
Ranaei, S., Karvonen, M., Suominen, A., & Kässi, T. (2016). Patent-based technology forecasting: case of electric and hydrogen vehicle. International Journal of Energy Technology and Policy, 12(1), 20-40. https://doi.org/10.1504/ijetp.2016.074490
Roberts, E. B. (1969). Exploratory and normative technological forecasting: a critical appraisal. Technological forecasting, 1(2), 113-127.
Robertson, T. B. (1923). The chemical basis of growth and senescence. JB Lippincott Company.
Rogers, E. M. (1962). Diffusion of innovations.
Rota, M. F., Carcedo, J. M., & García, J. P. (2016). Dual approach for modelling demand saturation levels in the automobile market. The Gompertz curve: Macro versus micro data. Investigación Económica, 75(296), 43-72. https://doi.org/https://doi.org/10.1016/j.inveco.2016.07.003
Roussel, P. A. (1984). Technological Maturity Proves a Valid and Important Concept. Research management, 27(1), 29-34. https://doi.org/10.1080/00345334.1984.11756815
Sandberg, A., Armstrong, S., Gorman, R., & England, R. (2021). Sigmoids behaving badly: why they usually cannot predict the future as well as they seem to promise. arXiv preprint arXiv:2109.08065.
Shirmohammadi, B., Moradi, H., Moosavi, V., Semiromi, M. T., & Zeinali, A. (2013). Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran). Natural Hazards, 69(1), 389-402. https://doi.org/https://doi.org/10.1007/s11069-013-0716-9
Sudtasan, T., & Mitomo, H. (2017). Comparison of diffusion models for forecasting the growth of broadband markets in Thailand.
Tjørve, K. M. C., & Even, T. (2017). The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family. PLoS One, 12(6). https://doi.org/https://doi.org/10.1371/journal.pone.0178691
Trajtenberg, M. (1990). A Penny for Your Quotes: Patent Citations and the Value of Innovations. The RAND Journal of Economics, 21(1), 172-187. https://doi.org/10.2307/2555502
Trappey, A. J. C., Lin, G.-B., & Hung, L.-P. (2024). Intelligent Text Mining for Ontological Knowledge Graph Refinement and Patent Portfolio Analysis—Case Study of Net-Zero Data Center Innovation Management. Information, 15(7), 374. https://www.mdpi.com/2078-2489/15/7/374
Trappey, C. V., Wu, H.-Y., Taghaboni-Dutta, F., & Trappey, A. J. C. (2011). Using patent data for technology forecasting: China RFID patent analysis. Advanced Engineering Informatics, 25(1), 53-64. https://doi.org/https://doi.org/10.1016/j.aei.2010.05.007
Tseng, Y.-H., Lin, C.-J., & Lin, Y.-I. (2007). Text mining techniques for patent analysis. Information Processing & Management, 43(5), 1216-1247. https://doi.org/https://doi.org/10.1016/j.ipm.2006.11.011
Utsunomiya, Y. T., Utsunomiya, A. T. H., Torrecilha, R. B. P., Paulan, S. d. C., Milanesi, M., & Garcia, J. F. (2020). Growth Rate and Acceleration Analysis of the COVID-19 Pandemic Reveals the Effect of Public Health Measures in Real Time [Original Research]. Frontiers in Medicine, Volume 7 - 2020. https://doi.org/10.3389/fmed.2020.00247
Verhulst, P. (1838). Notice on the law that the population follows in its growth. Corresp Math Phys, 10, 113-126.
VERTIV. (2025). High-density cooling: A guide to advanced thermal solutions for AI and ML workloads in data centers https://www.vertiv.com/en-asia/about/news-and-insights/articles/educational-articles/high--density-cooling-a-guide-to-advanced-thermal-solutions-for-ai-and-ml-workloads-in-data-centers/
Watson, B., & Venkiteswaran, V. K. (2017). Universal Cooling of Data Centres: A CFD Analysis. Energy Procedia, 142, 2711-2720. https://doi.org/https://doi.org/10.1016/j.egypro.2017.12.215
Weymouth, F., McMillin, H., & Rich, W. H. (1931). Latitude and relative growth in the razor clam, Siliqua patula. Journal of Experimental Biology, 8(3), 228-249.
Yıldırım, M. (2023). COVID-19 Pandemic and Technological Change: Analysis of Patent Applications. Verimlilik Dergisi, 57(3), 549-562.
Yoon, B. (2008). On the development of a technology intelligence tool for identifying technology opportunity. Expert Systems with Applications, 35(1), 124-135. https://doi.org/https://doi.org/10.1016/j.eswa.2007.06.022
Young, P. (1993). Technological growth curves: A competition of forecasting models. Technological Forecasting and Social Change, 44(4), 375-389. https://doi.org/https://doi.org/10.1016/0040-1625(93)90042-6
Yuan, X., & Cai, Y. (2021). Forecasting the development trend of low emission vehicle technologies: Based on patent data. Technological Forecasting and Social Change, 166, 120651. https://doi.org/https://doi.org/10.1016/j.techfore.2021.120651
Zhang, Y., Zhao, Y., Dai, S., Nie, B., Ma, H., Li, J., Miao, Q., Jin, Y., Tan, L., & Ding, Y. (2022). Cooling technologies for data centres and telecommunication base stations – A comprehensive review. Journal of Cleaner Production, 334, 130280. https://doi.org/https://doi.org/10.1016/j.jclepro.2021.130280
Zimmermann, S., Meijer, I., Tiwari, M. K., Paredes, S., Michel, B., & Poulikakos, D. (2012). Aquasar: A hot water cooled data center with direct energy reuse. Energy, 43(1), 237-245. https://doi.org/https://doi.org/10.1016/j.energy.2012.04.037 |
| Description: | 碩士
國立政治大學
科技管理與智慧財產研究所
112364136 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112364136 |
| Data Type: | thesis |
| Appears in Collections: | [科技管理與智慧財產研究所] 學位論文
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