{"id":669,"date":"2014-03-13T15:36:48","date_gmt":"2014-03-13T06:36:48","guid":{"rendered":"http:\/\/cmc.ss.is.nagoya-u.ac.jp\/?page_id=669"},"modified":"2016-04-05T15:30:16","modified_gmt":"2016-04-05T06:30:16","slug":"2013%e5%b9%b4%e5%ba%a6%e8%ab%96%e6%96%87%e3%83%aa%e3%82%b9%e3%83%88","status":"publish","type":"page","link":"https:\/\/www.cmc.is.i.nagoya-u.ac.jp\/?page_id=669","title":{"rendered":"2013\u5e74\u5ea6\u8ad6\u6587\u30ea\u30b9\u30c8"},"content":{"rendered":"<h3>\u8ad6\u6587<\/h3>\n<ul>\n<li>\u6e6f\u6d45\u8fb0\u4e38\uff0c\u9ce5\u6d77\u4e0d\u4e8c\u592b\uff0c\u5e73\u5c71\u9ad8\u55e3\uff0c\u698e\u5800\u512a\uff0c\u9593\u702c\u5065\u4e8c &#8220;\u6226\u7565\u5b66\u7fd2\u304cGARCH\u52b9\u679c\u306b\u53ca\u307c\u3059\u5f71\u97ff\u306e\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3068\u5206\u6790&#8221;, \u96fb\u6c17\u5b66\u4f1a\u8ad6\u6587\u8a8cC\uff08\u96fb\u5b50\u30fb\u60c5\u5831\u30fb\u30b7\u30b9\u30c6\u30e0\u90e8\u9580\u8a8c) , vol.133, no. 9, pp.177-1728. (2013.09).<\/li>\n<li>\u81fc\u4e95\u7fd4\u5e73\uff0c\u9ce5\u6d77\u4e0d\u4e8c\u592b\uff0c\u5e73\u5c71\u9ad8\u55e3\uff0c\u698e\u5800\u512a\uff0c\u9593\u702c\u5065\u4e8c &#8220;\u306a\u305c\u9707\u707d\u5f8c\u30c7\u30de\u304c\u62e1\u6563\u3057\u305f\u306e\u304b\uff5e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020\u306e\u5f71\u97ff\u5206\u6790\uff5e&#8221;\uff0c\u96fb\u6c17\u5b66\u4f1a<\/li>\n<li>Takatsugu Hirayama, Kenji Mase, Kazuya Takeda, &#8220;Analysis of Temporal Relationships between Eye-Gaze and Peripheral Vehicle Behavior for Detecting Driver Distraction&#8221;, International Journal of Vehicular Technology<\/li>\n<li>\u698e\u5800\u512a, \u9593\u702c\u5065\u4e8c, &#8220;\u30a6\u30a7\u30a2\u30e9\u30d6\u30eb\u30bb\u30f3\u30b5\u3092\u7528\u3044\u305f\u719f\u7df4\u6307\u5c0e\u54e1\u306e\u30e4\u30b9\u30ea\u304c\u3051\u6280\u80fd\u4e3b\u89b3\u8a55\u4fa1\u5024\u306e\u518d\u73fe&#8221;, \u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u8ad6\u6587\u8a8c 28(4), pp.391-399, 2013<\/li>\n<li>\u671d\u5009\u6df3\uff0c\u5e73\u5c71\u9ad8\u55e3\uff0c\u4e38\u8c37\u5b9c\u53f2\uff0c\u52a0\u85e4\u30b8\u30a7\u30fc\u30f3\uff0c\u9593\u702c\u5065\u4e8c\uff0c&#8221;\u591a\u8996\u70b9\u6620\u50cf\u306e\u8996\u8074\u5c65\u6b74\u3092\u7528\u3044\u305f\u8996\u70b9\u9077\u79fb\u4e88\u6e2c\u30e2\u30c7\u30eb&#8221;\uff0c\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 29\uff0cNo. 1\uff0cpp.207-212 (2014)<\/li>\n<li>Yu Wang\uff0cJien Kato\uff0cKenji Mase\uff0c&#8221;Summarizing Nursery School Surveillance Videos by Distance Metric Learning&#8221;\uff0cJournal of information Processing\uff0c22(1)\uff0c56-66<\/li>\n<li>\u52a0\u85e4\u30b8\u30a7\u30fc\u30f3\uff0c\u767d\u9808\u907c\uff0c\u738b\u5f67\uff0c\u9593\u702c\u5065\u4e8c\uff0c&#8221;\u591a\u304f\u306e\u753b\u50cf\u304c\u5171\u6709\u3059\u308b\u300c\u4e00\u822c\u30af\u30e9\u30b9\u300d\u306b\u7740\u76ee\u3057\u305f\u8a13\u7df4\u753b\u50cf\u306e\u9078\u629e&#8221;\uff0c\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0c55(1)\uff0c542-552<\/li>\n<\/ul>\n<h3>\u56fd\u969b\u4f1a\u8b70<\/h3>\n<ul>\n<li>Shohei Usui, Fujio Toriumi, Masato Matsuo, Takatsugu Hirayama, Yu Enokibori, and Kenji Mase, &#8220;Proposal for a generalized Network Growth Model of Social Network Service&#8221;, THe Fifth International Workshop on Emergent Intelligence on networked Agents (WEIN&#8217;2013), May 2013.<\/li>\n<li>Ryo Shirasu, Jien Kato, Yu Wang and Kenji Mase, &#8220;Common Class Information based Efficient Training Data Selection for Photo Categorization&#8221;, IAPR International Conference On Machine Vision Applications (MVA2013), Kyoto, Japan, 2013.5.<\/li>\n<li>Tomoko Yonezawa, Noriko Suzuki, Kenji Mase, Kiyoshi Kogure, &#8220;Appearance and Physical Presence of Anthropomorphic Media in Parallel with Non-face-to-face Communication&#8221;, 1st International Conference on Human-Agent Interaction,(HAI2013), August 7-9, 2013 Sapporo, Japan.<\/li>\n<li>Masataka Mori, Chiyomi Miyajima, Takatsugu Hirayama, Norihide Kitaoka, Kazuya Takeda, &#8220;Analysis of Lane Change Maneuvers Based on Driver Gaze and Vehicle Operation Behavior&#8221;, International Conference on Driver Distraction and Inattention (DDI 2013), 2013.8.<\/li>\n<li>Shohei Usui, Fujio Toriumi, Takatsugu Hirayama, and Kenji Mase, &#8220;Analysis of Influential Features for Information Diffusion&#8221;, SocialCom 2013.(interactive)<\/li>\n<li>Yu Enokibori, Akihisa Suzuki, Hirotaka Mizuno, Yuuki Shimakami and Kenji. Mase, &#8220;E-Textile Pressure Sensor Based on Conductive Fiber and Its Structure,&#8221; In Adjunct Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), pp. 207-210, 2013.9.<\/li>\n<li>Yu Enokibori, Yoshu Ito, Akihisa Suzuki, Hirotaka Mizuno, Yuuki Shimakami, Tsutomu Kawabe and Kenji Mase, &#8220;SpiroVest: An e-Textile-Based Wearable Spirometer with Posture Change Adaptability,&#8221; In Adjunct Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), pp. 203-206, 2013.9.<\/li>\n<li>Takashi Bando, Masumi Egawa, Hiroyuki Okuda, Hitoshi Terai, Takatsugu Hirayama, Chiyomi Miyajima, Daisuke Deguchi, Katsuhiko Kaji, Kazuya Takeda, Tatsuya Suzuki, &#8220;Repressing Overtrust: Driver Cooperated Driving Support Systems,&#8221; The 2nd International Symposium on Future Active Safety Technology toward Zero Traffic Accidents (FAST-zero&#8217;13),2013.9.<\/li>\n<li>Masataka Mori, Chiyomi Miyajima, Takatsugu Hirayama, Norihide Kitaoka, Kazuya Takeda, &#8220;Comparison of Lane Change Behavior of Expert and Non-Expert Drivers,&#8221; The 2nd International Symposium on Future Active Safety Technology toward Zero Traffic Accidents (FAST-zero&#8217;13), 2013.9.<\/li>\n<li>Masataka Mori, Chiyomi Miyajima, Takatsugu Hirayama, Norihide Kitaoka, Kazuya Takeda, &#8220;Integrated Modeling of Multiple Driving Behavior Signals to Estimate Risk Level of Lane Change Maneuvers&#8221;, The 6th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems, 2013.9.<\/li>\n<li>Kentaro Hitomi, Takashi Bando, Masumi Egawa, Hiroyuki Okuda, Hitoshi Terai, Takatsugu Hirayama, Chiyomi Miyajima, Daisuke Deguchi, Katsuhiko Kaji, Kazuya Takeda, and Tatsuya Suzuki, &#8220;Toward Well-Balanced Man-Machine Cooperation in Vehicle&#8221;, The 6th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems, 2013.9.<\/li>\n<li>Masataka Mori, Chiyomi Miyajima, Takatsugu Hirayama, Norihide Kitaoka, Kazuya Takeda, &#8220;Integrated Modeling of Driver Gaze and Vehicle Operation Behavior to Estimate Risk Level During Lane Changes&#8221;, ITSC 2013, 2013.10.<\/li>\n<li>Yusuke Tanaka, Takashi Bando, Masumi Egawa, Hiroyuki Okuda, Hitoshi Terai, Takatsugu Hirayama, Chiyomi Miyajima, Daisuke Deguchi, Katsuhiko Kaji, Kazuya Takeda, and Tatsuya Suzuki, &#8220;Toward the Development of a Driving Support System for Repressing Overtrust and Overreliance&#8221;, ITS World Congress, 2013.10.<\/li>\n<li>Fumiharu Tomiyasu, Takatsugu Hirayama, and Kenji Mase, &#8220;Wide-range Feature Point Tracking with Corresponding Point Search and Accurate Feature Point Tracking with Mean-Shift,&#8221; The Workshop on Recent Advances in Computer Vision and Pattern Recognition(RACVPR2013), interactive presentation, pp. 907-911, Loisir Hotel, Okinawa, Japan, (November 5th, 2013).<\/li>\n<li>Atsushi Iwatsuki, Takatsugu Hirayama, Kenji Mase &#8220;Analysis of Soccer Coach&#8217;s Eye Gaze Behavior&#8221;, ASVAI 2013.<\/li>\n<li>Kensho Hara, Takatsugu Hirayama, and Kenji Mase, &#8220;Simultaneous Action Recognition and Localization Based on Multi-View Hough Voting&#8221;, ACPR 2013.<\/li>\n<li>Takafumi Suzuki, Jien Kato, Yu Wang, and Kenji Mase, &#8220;Domain Adaptive Action Recognition with Integrated Self-training and Feature Selection&#8221;, ACPR 2013, Loisir Hotel, Okinawa, Japan, (November 7th, 2013).<\/li>\n<li>Guanwen Zhang, Jien Kato, Yu Wang, and Kenji Mase, &#8220;Adaptive Metric Learning in Local Distance Comparison for People Re-identification&#8221;, The 2nd Asian Conference on Pattern Recognition (ACPR\u201913), Okinawa, Japan (Nov 5-8, 2013).<\/li>\n<li>Yu Enokibori, Akihisa Suzuki, Hirotaka Mizuno, Yuuki Shimakami, Tsutomu Kawabe and Kenji Mase, &#8220;An e-Textile-based Wearable Spirometer and Its Adaptability for Context Changes Depending on Sweat and Meal,&#8221; IEEE 24th International Symposium on Micro-NanoMechatronics and Human Science, pp.93-97, 2013.10.<\/li>\n<li>Takatsugu Hirayama, Takafumi Marutani, Daishi Tanoue, Shogo Tokai, Sidney Fels, Kenji Mase, &#8220;Agent-assisted Multi-viewpoint Video Viewer and Its Gaze-Based Evaluation&#8221;, The 6th Workshop on Eye Gaze in Intelligent Human Machine Interaction, 2013.12.<\/li>\n<li>Guanwen Zhang, Jien Kato, Yu Wang, and Kenji Mase, &#8220;People Re-identification Using Deep Convolutional Neural Network&#8221;, The 9th International Conference on Computer Vision Theory and Applications (VISAPP 2014), Lisbon, Portugal (Jan 5-8, 2014).<\/li>\n<li>Masataka Mori, Chiyomi Miyajima, Takatsugu Hirayama, Norihide Kitaoka, Kazuya Takeda, \ufffc&#8221;Use of Driver Gaze Information for Detecting Risky Lane Changes&#8221;, RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing, 2014. 3.<\/li>\n<li>Michimasa Inaba, Naoyuki Iwata, Fujio Toriumi, Takatsugu Hirayama, Yu Enokibori, Kenichi Takahashi, Kenji Mase, &#8220;Constructing a Non-task-oriented Dialogue Agent using Statistical Response Method and Gamification&#8221;, International Conference on Agents and Artificial Intelligence, 2014. 3.<\/li>\n<li>Yuki Muramatsu, Takatsugu Hirayama and Kenji Mase, &#8220;Video Generation Method Based on User\u2019s Tendency of Viewpoint Selection for Multi-view Video Contents&#8221;, The Fifth Augmented Human International Conference (AH 2014), Kobe, Japan (Mar 7-9, 2014).<\/li>\n<li>Toshiya Ohira, Takatsugu Hirayama, Shohei Usui, Shota Sato and Kenji Mase, &#8220;Top-down Visual Attention Computational Model Using Visual Feature Distribution of Search Target&#8221;, 9th ACM\/IEEE International Conference on Human-Robot Interaction (HRI 2014), Bielefeld, Deutschland (Mar 3-6, 2014).<\/li>\n<li><span style=\"font-size: 13px;\">Michimasa Inaba, Naoyuki Iwata, Fujio Toriumi, Takatsugu Hirayama, Yu Enokibori, Kenichi Takahashi, Kenji Mase, &#8220;Constructing a Non-task-oriented Dialogue Agent using Statistical Response Method and Gamification&#8221;, International Conference on Agents and Artificial Intelligence, 2014.3.<\/span><\/li>\n<li><span style=\"font-size: 13px;\">Yuki Muramatsu, Takatsugu Hirayama and Kenji Mase, &#8220;Video Generation Method Based on User\u2019s Tendency of Viewpoint Selection for Multi-view Video Contents&#8221;, The Fifth Augmented Human International Conference (AH 2014), 2014.3. (SCOPE, NICT)<\/span><\/li>\n<li><span style=\"font-size: 13px;\">Takatsugu Hirayama, Takafumi Marutani, Sidney Fels, Kenji Mase, &#8220;Analysis of Gaze Behavior while using a Multi-Viewpoint Video Viewer&#8221;, 2014 Symposium on Eye Tracking Research and Applications (ETRA2014), pp.211-214, 2014.3. (SCOPE, NICT, \u79d1\u7814\u82e5\u624bB_No.23700168)<\/span><\/li>\n<\/ul>\n<h3>\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u30fb\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7<\/h3>\n<ul>\n<li>\u539f\u5065\u7fd4\uff0c\u5e73\u5c71\u9ad8\u55e3\uff0c\u9593\u702c\u5065\u4e8c\uff0c&#8221;\u591a\u8996\u70b9\u6620\u50cf\u306b\u304a\u3051\u308bHough Forests\u3092\u7528\u3044\u305f\u000b\u4eba\u7269\u884c\u52d5\u8a8d\u8b58\u3068\u4f4d\u7f6e\u63a8\u5b9a\u306e\u540c\u6642\u51e6\u7406&#8221;, MIRU 2013 \u7b2c16\u56de \u753b\u50cf\u306e\u8a8d\u8b58\u30fb\u7406\u89e3\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0,\u6771\u4eac \u56fd\u7acb\u60c5\u5831\u5b66\u7814\u7a76\u6240,2013.7 (\u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u63a1\u629e)<\/li>\n<li>\u51a8\u5b89\u53f2\u967d, \u5e73\u5c71\u9ad8\u55e3, \u9593\u702c\u5065\u4e8c\uff0c&#8221;\u5bfe\u5fdc\u70b9\u63a2\u7d22\u3068Mean-Shift\u63a2\u7d22\u306e\u9010\u6b21\u51e6\u7406\u306b\u3088\u308b\u7279\u5fb4\u70b9\u8ffd\u8de1,&#8221; MIRU 2013 \u7b2c16\u56de \u753b\u50cf\u306e\u8a8d\u8b58\u30fb\u7406\u89e3\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0, (\u82f1\u8a9e\u67fb\u8aad\u21d2\u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u63a1\u629e),\u6771\u4eac \u56fd\u7acb\u60c5\u5831\u5b66\u7814\u7a76\u6240,2013.7<\/li>\n<li>\u9234\u6728\u5d69\u53f2\uff0c\u52a0\u85e4\u30b8\u30a7\u30fc\u30f3\uff0c\u30ef\u30f3\u30e6\uff0c\u9593\u702c\u5065\u4e8c\uff0c&#8221;ISFS\u3092\u7528\u3044\u305f\u884c\u52d5\u8a8d\u8b58\u5668\u306e\u30c9\u30e1\u30a4\u30f3\u9069\u5408\u578b\u5b66\u7fd2&#8221;, MIRU 2013 \u7b2c16\u56de \u753b\u50cf\u306e\u8a8d\u8b58\u30fb\u7406\u89e3\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0, (\u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u63a1\u629e\u6c7a\u5b9a),\u6771\u4eac \u56fd\u7acb\u60c5\u5831\u5b66\u7814\u7a76\u6240,2013.7<\/li>\n<li>\u698e\u5800\u512a, \u4f0a\u85e4\u967d\u8129, \u5e73\u5c71\u9ad8\u55e3, \u9593\u702c\u5065\u4e8c, &#8220;\u59ff\u52e2\u5909\u52d5\u3092\u8003\u616e\u3057\u305f\u4e0a\u4f53\u5468\u56f2\u9577\u8a08\u6e2c\u306b\u57fa\u3065\u304f\u547c\u5438\u63db\u6c17\u91cf\u63a8\u5b9a&#8221;, \u7b2c23\u56de\u30d0\u30a4\u30aa\u30e1\u30ab\u30cb\u30ba\u30e0\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0, pp.139-147, 2013.7.<\/li>\n<li>\u81fc\u4e95\u7fd4\u5e73, \u9ce5\u6d77\u4e0d\u4e8c\u592b, \u5e73\u5c71\u9ad8\u55e3, \u9593\u702c\u5065\u4e8c, &#8220;\u60c5\u5831\u62e1\u6563\u306b\u95a2\u3059\u308b\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020\u8981\u7d20\u306e\u5206\u6790&#8221;, 2013\u5e74\u5ea6\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u751f\u614b\u5b66\u7814\u7a76\u4f1a\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0(\u7b2c10\u56de)(NetEcoSinp2013),\u30dd\u30b9\u30bf\u30fc\u767a\u8868, \u6709\u99ac, 2013.9.<\/li>\n<li>\u68ee\u771f\u8cb4\uff0c\u5bae\u5cf6\u5343\u4ee3\u7f8e\uff0c\u5e73\u5c71\u9ad8\u55e3\uff0c\u5317\u5ca1\u6559\u82f1\uff0c\u6b66\u7530\u4e00\u54c9\uff0c&#8221;\u8996\u884c\u52d5\u3068\u904b\u8ee2\u64cd\u4f5c\u884c\u52d5\u306e\u7d71\u5408\u30e2\u30c7\u30eb\u306b\u3088\u308b\u5371\u967a\u306a\u8eca\u7dda\u5909\u66f4\u306e\u691c\u51fa&#8221;\uff0c2013\u5e74\u81ea\u52d5\u8eca\u6280\u8853\u4f1a\u79cb\u5b63\u5927\u4f1a\uff0c2013.10.<\/li>\n<li>\u5927\u5e73\u96bc\u4e5f\uff0c\u5e73\u5c71\u9ad8\u55e3\uff0c\u698e\u5800\u512a\uff0c\u9593\u702c\u5065\u4e8c\uff0c&#8221;\u63a2\u7d22\u76ee\u6a19\u306e\u8996\u899a\u7279\u5fb4\u5206\u5e03\u3092\u8003\u616e\u3057\u305f\u8a98\u76ee\u5ea6\u63a8\u5b9a\u30e2\u30c7\u30eb&#8221;\uff0cHCG\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0 2013\uff0c\u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u767a\u8868\uff0c\u677e\u5c71\uff0c2013.12.<\/li>\n<li>\u51a8\u5b89\u53f2\u967d\uff0c\u6751\u677e\u7950\u5e0c\uff0c\u98ef\u7530\u6dbc\u592a\u90ce\uff0cWangXueting\uff0c\u7c73\u6fa4\u670b\u5b50\uff0c\u5e73\u5c71\u9ad8\u55e3\uff0c\u9593\u702c\u5065\u4e8c\uff0c&#8221;\u88ab\u5199\u4f53\u8ffd\u5f93\u8996\u8074\u306e\u305f\u3081\u306e\u8996\u70b9\u63a8\u85a6\u578b\u591a\u8996\u70b9\u6620\u50cf\u8996\u8074\u30b7\u30b9\u30c6\u30e0&#8221;\uff0c\u7b2c17\u56de\u4e00\u822c\u793e\u56e3\u6cd5\u4eba\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0 \u30a4\u30f3\u30bf\u30e9\u30af\u30b7\u30e7\u30f32014\uff0c\u30a4\u30f3\u30bf\u30e9\u30af\u30c6\u30a3\u30d6\u767a\u8868(\u30d7\u30ec\u30df\u30a2\u30e0\u67a0)\uff0c\u65e5\u672c\u79d1\u5b66\u672a\u6765\u9928\uff0c\u6771\u4eac\uff0c2014.2.28\uff5e3.1<\/li>\n<\/ul>\n<h3>\u7814\u7a76\u4f1a<\/h3>\n<ul>\n<li>\u51a8\u5b89\u53f2\u967d, \u5e73\u5c71\u9ad8\u55e3, \u9593\u702c\u5065\u4e8c, &#8220;Kalman-filter\u4e88\u6e2c\u3092\u7528\u3044\u305f\u7279\u5fb4\u70b9\u30de\u30c3\u30c1\u30f3\u30b0\u3068Mean-Shift\u63a2\u7d22\u306e\u7d71\u5408\u306b\u3088\u308b\u5e83\u57df\u7279\u5fb4\u70b9\u8ffd\u8de1,&#8221; \u7814\u7a76\u5831\u544a\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30d3\u30b8\u30e7\u30f3\u3068\u30a4\u30e1\u30fc\u30b8\u30e1\u30c7\u30a3\u30a2\u7814\u7a76\u4f1a(CVIM), vol. 2013-CVIM-188, pp. 1-8, \u9ce5\u53d6\u770c, \u9ce5\u53d6\u5927\u5b66, 2013.9<\/li>\n<li>\u81fc\u4e95\u7fd4\u5e73\uff0c\u9ce5\u6d77\u4e0d\u4e8c\u592b\uff0c\u5e73\u5c71\u9ad8\u55e3\uff0c\u9593\u702c\u5065\u4e8c\uff0c&#8221;\u9707\u707d\u306b\u304a\u3051\u308b\u60c5\u5831\u62e1\u6563\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u8a55\u4fa1\u53ca\u3073\u5206\u6790&#8221;\uff0c\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5408\u540c\u7814\u7a76\u4f1a \u793e\u4f1a\u306b\u304a\u3051\u308bAI\u7814\u7a76\u4f1a(SIG-SAI)\uff0c\u53e3\u982d\u767a\u8868\uff0c\u795e\u5948\u5ddd\u770c\uff0c\u6176\u61c9\u7fa9\u587e\u5927\u5b66\uff0c2013.10.<\/li>\n<li>\u6751\u677e\u7950\u5e0c, \u5e73\u5c71\u9ad8\u55e3, \u9593\u702c\u5065\u4e8c, &#8220;\u591a\u8996\u70b9\u6620\u50cf\u306b\u304a\u3051\u308b\u500b\u4eba\u306e\u8996\u8074\u55dc\u597d\u306b\u5373\u3057\u305f\u8996\u70b9\u753b\u50cf\u63a8\u5b9a\u624b\u6cd5,&#8221; \u753b\u50cf\u96fb\u5b50\u5b66\u4f1a\u7b2c267\u56de\u7814\u7a76\u4f1a, \u5927\u962a\u5927\u5b66, \u5927\u962a\u5e9c\u5439\u7530\u5e02, 2013.10.<\/li>\n<li>\u5927\u6a4b\u52c7\u4ecb\uff0c\u698e\u5800\u512a\uff0c\u9593\u702c\u5065\u4e8c\uff0c&#8221;\u8db3\u5727\u5e03\u30bb\u30f3\u30b5\u3092\u7528\u3044\u305f\u8def\u9762\u5f62\u72b6\u5224\u5225\u306e\u691c\u8a0e&#8221;\uff0c\u7b2c40\u56deUBI\u7814\u7a76\u4f1a\uff0c\u795e\u5948\u5ddd\u770c\uff0c\u9752\u5c71\u5b66\u9662\u5927\u5b66\uff0c2013.11.<\/li>\n<li>\u68ee\u7950\u99ac\uff0c\u698e\u5800\u512a\uff0c\u9593\u702c\u5065\u4e8c\uff0c&#8221;\u30a6\u30a7\u30a2\u30e9\u30d6\u30eb\u52a0\u901f\u5ea6\u30bb\u30f3\u30b5\u3092\u7528\u3044\u305f\u59ff\u52e2\u8a55\u4fa1\u306e\u691c\u8a0e&#8221;\uff0c\u7b2c40\u56deUBI\u7814\u7a76\u4f1a\uff0c\u795e\u5948\u5ddd\u770c\uff0c\u9752\u5c71\u5b66\u9662\u5927\u5b66\uff0c2013.11.<\/li>\n<li>\u539f\u5065\u7fd4\uff0c\u5e73\u5c71\u9ad8\u55e3\uff0c\u9593\u702c\u5065\u4e8c\uff0c&#8221;Trend-Sensitive Hough Forests\u306e\u63d0\u6848\u3068\u884c\u52d5\u691c\u51fa\u3078\u306e\u5fdc\u7528&#8221;\uff0c\u30d1\u30bf\u30fc\u30f3\u8a8d\u8b58\u30fb\u30e1\u30c7\u30a3\u30a2\u7406\u89e3\u7814\u7a76\u4f1a(PRMU)\uff0c\u5927\u962a\u5e9c\uff0c\u5927\u962a\u5927\u5b66\uff0c2014.1.<\/li>\n<li>Guanwen Zhang, Jien Kato, Yu Wang, Kenji Mase, &#8220;People Re-identification with Auxiliary Knowledge&#8221;, \u30d1\u30bf\u30fc\u30f3\u8a8d\u8b58\u30fb\u30e1\u30c7\u30a3\u30a2\u7406\u89e3\u7814\u7a76\u4f1a(PRMU)\uff0c\u5927\u962a\u5e9c\uff0c\u5927\u962a\u5927\u5b66\uff0c2014.1.<\/li>\n<li>\u98ef\u7530\u6dbc\u592a\u90ce\uff0c\u9593\u702c\u5065\u4e8c\uff0c&#8221;\u30b5\u30c3\u30ab\u30fc\u306b\u304a\u3051\u308b\u9078\u624b\u79fb\u52d5\u8ecc\u8de1\u3092\u7528\u3044\u305f\u30d1\u30b9\u30b3\u30fc\u30b9\u3092\u4f5c\u308b\u884c\u52d5\u306e\u691c\u51fa&#8221;\uff0c\u30d1\u30bf\u30fc\u30f3\u8a8d\u8b58\u30fb\u30e1\u30c7\u30a3\u30a2\u7406\u89e3\u7814\u7a76\u4f1a(PRMU)\uff0c\u798f\u5ca1\u770c\uff0c\u798f\u5ca1\u5927\u5b66\uff0c2014.2.<\/li>\n<li>\u4f50\u85e4\u7fd4\u592a\uff0c\u5e73\u5c71\u9ad8\u55e3\uff0c\u9593\u702c\u5065\u4e8c\uff0c\u5bae\u5cf6\u5343\u4ee3\u7f8e\uff0c\u6b66\u7530\u4e00\u54c9\uff0c&#8221;\u904b\u8ee2\u8005\u306e\u8996\u7dda\u5909\u5316\u6642\u306b\u304a\u3051\u308b\u5468\u8fba\u8eca\u72b6\u6cc1\u306e\u5206\u6790 \uff5e\u96c6\u4e2d\u72b6\u614b\u3068\u6ce8\u610f\u6563\u6f2b\u72b6\u614b\u306e\u5dee\u7570\uff5e&#8221; \uff0cITS\u7814\u7a76\u4f1a(ITS)\uff0c\u5317\u6d77\u9053\uff0c\u5317\u6d77\u9053\u5927\u5b66\uff0c2014.2.<\/li>\n<li>\u85ea\u572d\u8f14\uff0c\u698e\u5800\u512a\uff0c\u9593\u702c\u5065\u4e8c\uff0c&#8221;\u30ec\u30a4\u30ea\u30fc\u5206\u5e03\u3092\u7528\u3044\u305fWi-Fi\u96fb\u6ce2\u5f37\u5ea6\u5206\u5e03\u306b\u57fa\u3065\u304f\u5c11\u6570\u6a19\u672c\u6642\u4f4d\u7f6e\u63a8\u5b9a&#8221;\uff0c\u7b2c41\u56deUBI\u7814\u7a76\u4f1a\uff0c\u795e\u5948\u5ddd\u770c\uff0c\u6176\u5fdc\u7fa9\u587e\u5927\u5b66\uff0c2014.3.<\/li>\n<\/ul>\n<h3>\u5168\u56fd\u5927\u4f1a<\/h3>\n<ul>\n<li>\u671d\u5009\u6df3\uff0c\u5e73\u5c71\u9ad8\u55e3\uff0c\u4e38\u8c37\u5b9c\u53f2\uff0c\u52a0\u85e4\u30b8\u30a7\u30fc\u30f3\uff0c\u9593\u702c\u5065\u4e8c\uff0c&#8221;\u591a\u8996\u70b9\u6620\u50cf\u306e\u8996\u8074\u5c65\u6b74\u3092\u7528\u3044\u305f\u8996\u70b9\u9077\u79fb\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u691c\u8a0e&#8221;\uff0c2013\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a(\u7b2c27\u56de)(JSAI2013)\uff0c2013.6.<\/li>\n<li>\u81fc\u4e95\u7fd4\u5e73, \u9ce5\u6d77\u4e0d\u4e8c\u592b, \u677e\u5c3e\u771f\u4eba\u0003, \u5e73\u5c71\u9ad8\u55e3, \u9593\u702c\u5065\u4e8c, &#8220;\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020\u304c\u60c5\u5831\u62e1\u6563\u306b\u4e0e\u3048\u308b\u5f71\u97ff\u306e\u5206\u6790&#8221;, 2013\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a(\u7b2c27\u56de)(JSAI2013), 2B4-NFC-02b-2\uff0c\u53e3\u982d\u767a\u8868\uff0c\u30dd\u30b9\u30bf\u30fc\u767a\u8868\uff0c\u5bcc\u5c71\u56fd\u969b\u4f1a\u8b70\u5834, \u5bcc\u5c71\u770c\u5bcc\u5c71\u5e02, 2013.6.<\/li>\n<li>\u58c1\u8c37\u52c7\u78e8\uff0c\u539f\u5065\u7fd4\uff0c\u9593\u702c\u5065\u4e8c\uff0e&#8221;\u6620\u50cf\u7279\u5fb4\u3092\u7528\u3044\u305f\u30b5\u30c3\u30ab\u30fc\u9078\u624b\u306e\u30dc\u30fc\u30eb\u30bf\u30c3\u30c1\u30d7\u30ec\u30fc\u8a8d\u8b58&#8221; \uff0c2014\u5e74\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u7dcf\u5408\u5927\u4f1a\uff0c\u65b0\u6f5f\u5927\u5b66\uff0c\u65b0\u6f5f\u770c\uff0cD-12-77\uff0c2014.3.<\/li>\n<li>\u694a\u83f2\uff0c\u5e73\u5c71\u9ad8\u55e3\uff0c\u9593\u702c\u5065\u4e8c\uff0e&#8221;\u30b5\u30c3\u30ab\u30fc\u9078\u624b\u4f4d\u7f6e\u306e\u30d1\u30bf\u30fc\u30f3\u89e3\u6790\u306b\u3088\u308b\u30dc\u30fc\u30eb\u5b58\u5728\u9818\u57df\u306e\u63a8\u5b9a&#8221; \uff0c2014\u5e74\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u7dcf\u5408\u5927\u4f1a\uff0c\u65b0\u6f5f\u5927\u5b66\uff0c\u65b0\u6f5f\u770c\uff0cD-12-14\uff0c2014.3.<\/li>\n<\/ul>\n<h3>\u652f\u90e8\u5927\u4f1a<\/h3>\n<h3>\u7d00\u8981<\/h3>\n<h3>\u89e3\u8aac\u8a18\u4e8b\u3001\u30ec\u30d3\u30e5\u30fc\u3001\u5831\u544a\u66f8<\/h3>\n<h3>\u5c55\u793a\u4f1a<\/h3>\n<ul>\n<li>CEATEC(\u5e55\u5f35), \u591a\u8996\u70b9\u6620\u50cf\u8996\u8074\u652f\u63f4Web\u30a4\u30f3\u30bf\u30d5\u30a7\u30fc\u30b9, 2013.10.1-5.<\/li>\n<\/ul>\n<h3>\u62db\u5f85\u8b1b\u6f14<\/h3>\n<ul>\n<li>\u5e73\u5c71\u9ad8\u55e3\uff0c&#8221;\u4eba\u9593\u306e\u5185\u90e8\u72b6\u614b\u3092\u9855\u5728\u5316\u3059\u308b\u8996\u899a\u7684\u30a4\u30f3\u30bf\u30e9\u30af\u30b7\u30e7\u30f3&#8221;\uff0cCVIM\/IBISML\/PRMU\uff0c2013.9.<\/li>\n<li><span style=\"font-size: 13px;\">Kenji Mase, &#8220;e-Coaching: Wearable and Ubiquitous Sensing for Coaching in Sports, Manufacturing Skill and Health Care&#8221;, Intel Research, 2013.5.13(SCOPE, NICT, \u77e5\u306e\u62e0\u70b9\uff09<\/span><\/li>\n<li><span style=\"font-size: 13px;\">Kenji Mase, &#8220;e-Coaching: Wearable and Ubiquitous Sensing for Coaching in Sports, Manufacturing Skill and Health Care&#8221;, Samsung Research, 2013.5.13(SCOPE, NICT, \u77e5\u306e\u62e0\u70b9\uff09<\/span><\/li>\n<\/ul>\n<h3><\/h3>\n","protected":false},"excerpt":{"rendered":"<p>\u8ad6\u6587 \u6e6f\u6d45\u8fb0\u4e38\uff0c\u9ce5\u6d77\u4e0d\u4e8c\u592b\uff0c\u5e73\u5c71\u9ad8\u55e3\uff0c\u698e\u5800\u512a\uff0c\u9593\u702c\u5065\u4e8c &#8220;\u6226\u7565\u5b66\u7fd2\u304cGARCH\u52b9\u679c\u306b\u53ca\u307c\u3059\u5f71\u97ff\u306e\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3068\u5206\u6790&#8221;, \u96fb\u6c17\u5b66\u4f1a\u8ad6\u6587\u8a8cC\uff08\u96fb\u5b50\u30fb\u60c5\u5831\u30fb\u30b7\u30b9\u30c6\u30e0\u90e8\u9580\u8a8c) , vol.133, n [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":26,"menu_order":-2013,"comment_status":"open","ping_status":"open","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www.cmc.is.i.nagoya-u.ac.jp\/index.php?rest_route=\/wp\/v2\/pages\/669"}],"collection":[{"href":"https:\/\/www.cmc.is.i.nagoya-u.ac.jp\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.cmc.is.i.nagoya-u.ac.jp\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.cmc.is.i.nagoya-u.ac.jp\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.cmc.is.i.nagoya-u.ac.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=669"}],"version-history":[{"count":12,"href":"https:\/\/www.cmc.is.i.nagoya-u.ac.jp\/index.php?rest_route=\/wp\/v2\/pages\/669\/revisions"}],"predecessor-version":[{"id":802,"href":"https:\/\/www.cmc.is.i.nagoya-u.ac.jp\/index.php?rest_route=\/wp\/v2\/pages\/669\/revisions\/802"}],"up":[{"embeddable":true,"href":"https:\/\/www.cmc.is.i.nagoya-u.ac.jp\/index.php?rest_route=\/wp\/v2\/pages\/26"}],"wp:attachment":[{"href":"https:\/\/www.cmc.is.i.nagoya-u.ac.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=669"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}