當今運動健身蔚為風潮,加上網路資源普及,許多人藉由數位教學影片鍛鍊體 魄,不但能自由安排時間,也能在家參與課程,成為新世代的學習方式。傳統上,運 動健身較好的方式為教練在旁協助,除了口頭給予即時的指示,還能直接以身體觸碰 學員姿勢不良的部位,引導其肢體伸展、調整身體重心、提醒放鬆過於緊繃之部位, 然而新世代的學習方式透過教學影片無法立即對運動者當下的肢體動作做出反應,過 程中全倚賴運動者本身對於肢體的認知,往往和影片中教練的動作有落差而不自知, 因此,本研究期望藉由觸覺回饋輔助使用者做出正確的姿勢或動作。 過去針對觸覺回饋的研究相當地多,甚至可回溯至1950年代,然而將觸覺回饋應 用於運動指引的研究近年才漸漸出現,其應用層面僅限於提示作用,無法引導使用者 該如何動作,且目前回饋方式仍需倚賴受測者記憶動作與觸覺回饋的對應關係,無法 直覺做出反應。本研究模仿肌肉群收縮帶動肢體運動之方式,設計人工外部肌肉引導 手臂旋前旋後動作,人工外部肌肉包含步進馬達產生拉力、魚線及鬆緊帶模擬肌肉分 佈、收縮以及袖套包覆手臂帶動旋轉,系統設計歷經三版本的演進,最終設計出一套 具引導效果的觸覺回饋穿戴式裝置。 系統評估共邀請10位受測者進行實驗,結果證實此套裝置能有效提供方向性指示 (正確率98%),且受測者普遍反應裝置提供的回饋方式相當直覺,手臂會有被帶動 的感覺,能馬上知道該如何轉動手臂。實驗更進一步測試引導手臂轉動特定角度,實 驗結果效果也相當好,平均誤差在3度以內,此外,亦探討實驗過程中受測者對觸覺回 饋的行為反應,作為日後系統改良或觸覺回饋設計的參考。 / Nowadays, exercise and fitness have become a growing trend. Since the access to the internet resources is very easy and popular, many people choose to do exercise through digital online videos, which not only they can arrange their own exercising schedule, but also they can learn the courses at home. Traditionally, a better way for exercise learning is getting assistance from a professional coach, who can give instruction immediately, and adjust by direct body contact right away while the exercisers act incorrectly. However, the online video can not accomplish the purpose. On the condition that the exercisers rely only on the cognition of their own bodies they might not notice their posture different from the video. This research aimed to provide guidelines to do the correct posture or movement through tactile feedback. From past till now, the researches of tactile feedback are of considerable numbers, we can find the related researches back to 1950s. Recently it starts to be applied in exercising guiding. However, the applications only provide passive instructions, which require users to memorize the relationship between the tactile feedbacks and the correspond actions. Users are unable to react by instinct. In this research, we imitate the way of body movement driven by the muscles contraction. We design artificial external muscles on a sleeve to guide forearm pronation and supination. The wearable tactile feedback sleeve consists of stepper motors to provide pulling force, fishing wires and elastics to imitate muscle contraction to drive the forearm to roll. This system design has been revised three times, and we finally established a wearable tactile feedback device which has guiding effect. 10 participants are recruited for the experiments. The result showed that this device can guide forearm rolling successfully (the accuracy is 98%). The participants commented that the feedback is very close to instinct. They felt their arm was guided by the device, and knew the exact moment to roll their forearm. In the second experiment, we tried to guide the forearm rolling for several target angles and the result was quite promising. The mean error is within 3 degrees. We also reported the participants’ reactions through our tactile feedback system. We will expand the system to guide the other parts of human body in the future.

農林水産研究情報センターで作成したPDFファイルを使用している。

農林水産研究情報センターで作成したPDFファイルを使用している。

2017年英國《經濟學人》雜誌曾提出，「世界上最寶貴的資源不再是石油，而是數據」。隨著物聯網時代來臨，工業應用領域也開始整合各種技術而掀起新一波工業革命。因為大量自動化及數據化，除了升級自動化設備、整合網通系統，監控設備產生的大數據，透過工業電腦進行分析，經由人工智能判斷邏輯產生條件，再由設備自主處理各種生產問題。除去大量勞動，專注於大數據自動化處理，即能生產更優質的產品，並且優化流程，降低企業成本。 自動化流程機器人(Robotic Process Automation)能自動的管理並執行企業大量耗費時間與人力的業務流程，可用於客戶服務、人力管理、供應鏈管理、採購、會計等範疇。物聯網(IoT)時代下的機器人自動化流程加入了認知運算等新興技術，更能進一步提升企業效率並降低成本。自動化流程機器人(Robotic Process Automation)儼然成下一個新的生產力革命。 市場研究機構IDC預測，2017年全球在認知和人工智慧系統支出將達到125億美元，和2016年相比成長達59.3%。Google母公司Alphabet公開測試無人駕駛汽車、阿里宣佈投資千億成立達摩院、百度機器人入駐肯德基等等。人工智慧(Artificial Intelligence)將顛覆商業思維、改寫商業模式。在2020年，人工智慧(Artificial Intelligence)將成為市場上真正的「主流」技術思維。IDC並且認為亞洲將在2020年成為全球第二大認知與人工智慧輸出區域。 本文探討自動化流程機器人與人工智慧之間的關聯，以及流程優化後對企業所產生的影響與變革．並且針對個案的自動化解決方案所達到的效益與後續發展進行評估與檢討，藉以提升自動化解決方案，協助企業在未來挑戰的競爭環境中創造最佳化優勢． / “The Economist” stated in 2017 that “the world’s most precious resource is no longer oil but data”. With the advent of the Internet of Things, industrial applications have begun to integrate various technologies and set off a new wave of industrial revolution. Because of a large amount of automation and data, in addition to upgrading automation soluitons, integrating netcom systems, and monitoring the big data generated by the solutions, analysis is performed through industrial computers, and conditions are generated through the logic judgment of artificial intelligence, and then the solutions autonomously handles various processes. It can produce better products, optimize the process and reduce business costs to focus on automation of big data and to save a lot of labor hiring. Robotic Process Automation can automate the management and execution of a large number of business processes that consume time and manpower, and can be used in areas such as customer service, manpower management, supply chain management, procurement, finance and accounting. The robotic automation process in the Internet of Things (IoT) era has added emerging technologies such as cognitive computing to further enhance the efficiency of enterprises and to reduce costs. Robotic Process Automation becomes the next new productivity revolution. In 2017, marketing research firm, IDC, predicts that global spendings on cognitive and artificial intelligence systems will reach US$12.5 billion, which represents a growth of 59.3% compared to 2016. Google, the parent company of Alphabet, publicly tests driverless cars, Ali announced that it has invested 100 billion to establish Daruma House, Baidu Robots has settled in Kentucky. Artificial Intelligence will disrupt business thinking and rewrite business models. In 2020, Artificial Intelligence will become the real "mainstream" technical thinking in the market. IDC also believes that Asia will become the world’s second largest cognitive and artificial intelligence output region in 2020. The article discusses the relationships between robotic process automation and artificial intelligence, and also the impact and changes after implementing the solutions. It has also evaluated and reviewed the effectiveness and following development of the automated solutions, so as to enhance the values of automation solutions and to help companies create optimal advantages in the future challenging and competitive environment.

There were many technical events held in 2017, like 2017 COMPUTEX, Google I/O, Microsoft Design Forum…etc., and Artificial intelligence (AI) is one of the most concerned and hottest topics in these events. There is no doubt that in the next few years, AI technology and its further developments will be the major focus in the science and technology industry. As AI technology brings in a lot of new applications and develops commercial values, for the server industry and corresponding supply chain, it will lead to a new wave and benefit the whole ecosystem. Although the growth rate of desktop and laptop computer slows down and smartphone market starts to saturate, the demand for data processing and computing continues to grow. Large datacenter, server hardware and cloud applications supported at the back-end could still keep their momentum of growth. Taiwan-based original design manufacturers (ODMs) maintain a stable growth rate in 90% of the worldwide server manufacturing and many new business models evolved in order to fulfill the demands brought by artificial intelligence (AI). The aim of this study is to analyze the positive and negative impacts of AI technology on server industry, and will focus on four major groups in the industry: datacenter customers, OEM, ODM and component suppliers.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23466号 / 医博第4773号 / 新制||医||1053(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 柳田 素子, 教授 髙折 晃史, 教授 江藤 浩之 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18579号 / 工博第3940号 / 新制||工||1605(附属図書館) / 31479 / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 田村 正行, 教授 川﨑 雅史, 准教授 須﨑 純一 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(エネルギー科学) / 甲第19089号 / エネ博第313号 / 新制||エネ||64(附属図書館) / 32040 / 京都大学大学院エネルギー科学研究科エネルギー基礎科学専攻 / (主査)教授 森井 孝, 教授 片平 正人, 教授 佐川 尚 / 学位規則第4条第1項該当 / Doctor of Energy Science / Kyoto University / DGAM

京都大学 / 新制・課程博士 / 博士(法学) / 甲第25054号 / 法博第300号 / 京都大学大学院法学研究科法政理論専攻 / (主査)教授 船越 資晶, 教授 佐々木 健, 准教授 近藤 圭介 / 学位規則第4条第1項該当 / Doctor of Laws / Kyoto University / DGAM

京都大学 / 新制・課程博士 / 博士(医科学) / 甲第25200号 / 医科博第156号 / 京都大学大学院医学研究科医科学専攻 / (主査)教授 村川 泰裕, 教授 竹内 理, 教授 後藤 慎平 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM