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研究生:陳煒如
研究生(外文):CHEN,WEI-RU
論文名稱:運用文字探勘和文字分析方法來探索科技技術發展機會-以美國無線人體區域網路專利資料為例
論文名稱(外文):Applying Text Mining and Text Analysis Methods to Discover the Technological Chance - A Case of Wireless Body Network Patents in the United States
指導教授:余心淳
指導教授(外文):YU,HSIN-CHUN
口試委員:邱紹豐余心淳張榮庭
口試委員(外文):CHIOU, ANDY S.YU, HSIN-CHUNCHANG, JUNG-TING
口試日期:2020-06-19
學位類別:碩士
校院名稱:東海大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:87
中文關鍵詞:無線人體區域網路機會探索文字探勘文字分析
外文關鍵詞:wireless body area networkchance discoverytext miningtext analysis
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近年來在無線通訊、微機電系統和積體電路等相關技術的最新發展和進步,使得低功耗、智能化、微型化和奈米技術融合的感測器節點能夠廣泛地植入在人體或周邊的可穿戴式設備,並隨著多樣化的醫療、保健與消費性人體感測應用產品市場的迅速興起,無線人體區域網路(Wireless Body Area Network, WBAN)的發展也越來越受到歡迎。無可否認,美國在物聯網和感測器方面都具有一定的科技優勢與領先的地位,也是WBAN技術發展的先驅者。因此本論文為了發掘美國在WBAN專利創新領域的關鍵技術,針對美國專利及商標局資料庫內所收集到至2019年12月31日為止的104筆WBAN相關的專利文件,利用文字探勘KeyGraph演算法來發掘美國WABN專利裡關鍵的技術群體與可能潛藏且具影響力的技術機會,並進一步利用文字分析針對技術機會的專利摘要及專利申請範圍的內容,透過文字主題生成模型、找出專利裡具影響力的單詞以及找尋具有洞察力的單詞等方法,並將文字分析結果與技術群體結合,以發掘WBAN未來的研究趨勢與熱點。本研究結果一共找到三個技術機會,機會一主要是討論貼片式的WBAN感測器能應用於心室,再不侵入身體的情形下測量皮膚深處的動脈,比起傳統的測量方式,更能準確地測量血壓、心率、血氧以及心房顫動,並且也發現到胰島素泵和化療泵能識別出可穿戴式電子設備上的節點,未來研究能針對胰島素泵和化療泵跟貼片做結合。機會二主要是講述WBAN用於測量不同運行狀況參數的節點會產生異質性,並且具有不同功率和計算級別來執行各種任務,未來應該針對此問題解決異質性網路的干擾問題和挑戰,機會三主要是講述WBAN可穿戴式設備與行動電話建立起無線區域網路,並利用在人體可穿戴的多個無線電子設備之間提供通信的方法,同時提供強化通信認證機制等安全性相關的解決方案。
Wireless communication, microelectromechanical systems, integrated circuits, and related technologies have progressed in recent years, making it possible for sensor nodes that combine low energy, intelligence, micromation, and nano technologies to be widely implanted in human bodies or auxiliary wearable equipment. With the rapid rise of product markets for diverse medical, health maintenance, or consumable human sensors, the wireless body area network (WBAN) has grown in popularity. There is no denying that the United States has enjoyed a technological advantage and is the world leader in the Internet of Things (IoT) and sensors, and it is at the forefront of technological development for WBAN. Therefore, this thesis set out to explore the key technologies in patented WBAN innovations in the United States, and through December 31, 2019, there were 104 patent records about WBAN found in the database of the United States Patent and Trademark Office (USPTO). The method proposed in this thesis first uses KeyGraph algorithm, a text-mining and keyword extraction method to analyze the US WBAN patents to discover diverse key technological clusters and potential but influential technology opportunities, and it further uses multiple computerized text analysis methods to better understand patent documents about those discovered technology opportunities, including using text themes to form models, finding influential words in patents, and identifying insightful words to reveal WBAN research trends and hot spots in the future.This research has identified three technology opportunities. The first technology opportunity: without being invasive, adhesive WBAN sensors may be used in heart chambers to measure arteries deep under the skin. This measurement method is more accurate than the traditional method in measuring blood pressure, heart rate, oxygen saturation, and atrial fibrillation. It was also discovered that insulin pumps and chemotherapy pumps can be identified on nodes of wearable electronic devices. Future research may combine adhesive insulin pumps and chemotherapy pumps.The second technology opportunity is about using WBN to measure differences in nodes arising from operating under different parameters and carrying out missions under different power and calculation grades. The issues and challenges of heterogeneous networks should be addressed in the future.The third technology opportunity is about establishing wireless LANs with wearable devices and mobile phones, using multiple wearable devices on human bodies to provide communication, and using strengthened communication authentication mechanisms to solve security-related problems.
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 6
第三節 論文架構 6
第二章 文獻探討 8
第一節 機會探索 8
第二節 文字探勘KeyGraph演算法 9
第三節 隱含狄利克雷分佈 13
第四節 介數中心性 14
第五節 Louvain社區檢測演算法 15
第三章 研究方法 16
第一節 使用KeyGraph進行WBAN專利分析 16
第二節 研究架構 19
第四章 研究案例分析 27
第一節 收集WBAN專利 27
第二節 文字探勘KeyGraph 28
第五章 結果分析 39
第一節 技術群體CPC分類號統計 39
第二節 技術機會文字分析 43
第三節 技術機會應用在技術群體 58
第六章 結論 60
第一節 整體研究回顧 60
第二節 研究貢獻 63
參考文獻 64
附錄 72

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