{"id":870,"date":"2012-06-09T14:02:13","date_gmt":"2012-06-09T05:02:13","guid":{"rendered":"http:\/\/www.is.doshisha.ac.jp\/news\/?p=870"},"modified":"2012-06-09T14:02:13","modified_gmt":"2012-06-09T05:02:13","slug":"%e3%80%90%e9%80%9f%e5%a0%b1%e3%80%91%ef%bd%89%ef%bd%85%ef%bd%85%ef%bd%85-%ef%bd%97%ef%bd%83%ef%bd%83%ef%bd%89%ef%bc%92%ef%bc%90%ef%bc%91%ef%bc%92","status":"publish","type":"post","link":"https:\/\/is.doshisha.ac.jp\/news\/?p=870","title":{"rendered":"\u3010\u901f\u5831\u3011\uff29\uff25\uff25\uff25 \uff37\uff23\uff23\uff29 2012"},"content":{"rendered":"

IEEE WCCI 2012\u304c\u30d6\u30ea\u30b9\u30d9\u30f3\u30fb\u30aa\u30fc\u30b9\u30c8\u30e9\u30ea\u30a2\u3067\u958b\u50ac\u3055\u308c\u307e\u3059\u3002
\n\uff2d\uff12\u306e\u5c71\u53e3\u304f\u3093\u304c\u767a\u8868\u3057\u307e\u3059\u3002<\/p>\n

Friday, IEEE CEC, FrC 4-2, 13:30-14:30, Applications of Evolutionary Computation In Biomedical Engineering, Jose A. Lozano
\n535, Hiroaki Yamaguchi, Tomoyuki Hiroyasu, Sakito Nunokawa, Noriko Koizumi, Naoki Okumura, Hisatake Yokouchi, Mitsunori Miki and Masato Yoshimi, Comparison Study of Controlling<\/p><\/blockquote>\n

\u7d30\u80de\u753b\u50cf\u306e\u51e6\u7406\u306b\u95a2\u3059\u308b\u7814\u7a76\u767a\u8868\u3067\u3059\u3002
\n<\/p>\n

\n

\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/strong><\/p>\n<\/div>\n

\n\n\n\n\n\n\n\n\n\n\n
\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/strong><\/td>\n\u5c71\u53e3 \u6d69\u660e<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/strong><\/td>\n\u7d30\u80de\u753b\u50cf\u5206\u5272\u554f\u984c\u306e\u305f\u3081\u306eGP\u306b\u304a\u3051\u308b\u30d6\u30ed\u30fc\u30c8\u6291\u5236\u30e2\u30c7\u30eb\u306e\u6bd4\u8f03<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/strong><\/td>\nComparison Study of Controlling Bloat Model of GP in Constructing Filter for Cell Image Segmentation Problems<\/td>\n<\/tr>\n
\u8457\u8005<\/strong><\/strong><\/td>\n\u5c71\u53e3\u6d69\u660e\uff0c\u5ee3\u5b89\u77e5\u4e4b\uff0c\u5e03\u5ddd\u5c06\u6765\u4eba\uff0c\u5c0f\u6cc9\u7bc4\u5b50\uff0c\u5965\u6751\u76f4\u7a40\uff0c\u6a2a\u5185\u4e45\u731b\uff0c\u4e09\u6728\u5149\u7bc4\uff0c\u5409\u898b\u771f\u8061<\/td>\n<\/tr>\n
\u4e3b\u50ac<\/strong><\/strong><\/td>\nIEEE<\/td>\n<\/tr>\n
\u8b1b\u6f14\u4f1a\u540d<\/strong><\/strong><\/td>\n2012 IEEE World Congress on Computational Intelligence<\/td>\n<\/tr>\n
\u4f1a\u5834<\/strong><\/strong><\/td>\nBrisbane Convention and Exhibition Centre, Australia<\/td>\n<\/tr>\n
\u958b\u50ac\u65e5\u7a0b<\/strong><\/strong><\/td>\n2012\/06\/10-2012\/06\/15<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n \n<\/div>\n

 
\n1. \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30
\n2012\/06\/10\u304b\u30892012\/06\/15\u306b\u304b\u3051\u3066\uff0c\u30d6\u30ea\u30b9\u30d9\u30f3\uff08\u30aa\u30fc\u30b9\u30c8\u30e9\u30ea\u30a2\uff09\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305f2012 IEEE World Congress on Computational Intelligence (WCCI)\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u4f1a\u8b70\u306f\uff0c2\u5e74\u306b1\u5ea6IEEE\u306b\u3088\u3063\u3066\u4e3b\u50ac\u3055\u308c\uff0c\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u30c6\u30fc\u30de\u306b\u3057\u305fIJCNN\uff0c\u30d5\u30a1\u30b8\u30a3\u306eFUZZ-IEEE\uff0c\u9032\u5316\u8a08\u7b97\u306eCEC\u306e3\u3064\u306e\u4f1a\u8b70\u304c\u540c\u6642\u306b\u884c\u308f\u308c\u307e\u3059\uff0e\u4eca\u56de\uff0c\u79c1\u306fCEC\u306b\u304a\u3044\u3066\u53e3\u982d\u767a\u8868\u3092\u884c\u3044\u307e\u3057\u305f\uff0e\u307e\u305f\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u5ee3\u5b89\u5148\u751f\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e
\n 
\n2. \u7814\u7a76\u767a\u8868
\n2.1. \u767a\u8868\u6982\u8981
\n\u79c1\u306f15\u65e5\u306e13:30\u304b\u3089\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cApplications of Evolutionary Computation In Biomedical Engineering\u300d\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u53e3\u982d\u767a\u8868\u3067\uff0c15\u5206\u306e\u8b1b\u6f14\u6642\u9593\u30683\u5206\u306e\u8cea\u7591\u5fdc\u7b54\u6642\u9593\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e
\n\u4eca\u56de\u306e\u767a\u8868\u306f\uff0c\u7d30\u80de\u753b\u50cf\u5206\u5272\u554f\u984c\u3092\u5bfe\u8c61\u306b\uff0cGP\u306e\u30d6\u30ed\u30fc\u30c8\u6291\u5236\u30e2\u30c7\u30eb\u3092\u6bd4\u8f03\u3057\u305f\u7d50\u679c\u3067\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n\n\n\n
\u672c\u7814\u7a76\u3067\u306f\uff0c\u89d2\u819c\u518d\u751f\u533b\u7642\u3092\u652f\u63f4\u3059\u308b\u305f\u3081\u306e\u7d30\u80de\u753b\u50cf\u89e3\u6790\u30b7\u30b9\u30c6\u30e0\u306e\u69cb\u7bc9\u3092\u76ee\u6307\u3057\u3066\u3044\u308b\uff0e\u65e2\u5b58\u306e\u753b\u50cf\u89e3\u6790\u30bd\u30d5\u30c8\u306f\uff0c\u89e3\u6790\u306e\u969b\uff0c\u4f7f\u7528\u8005\u304c\u753b\u50cf\u51e6\u7406\u3092\u81ea\u3089\u7d44\u307f\u5408\u308f\u305b\u308b\u5fc5\u8981\u304c\u3042\u308b\u305f\u3081\u753b\u50cf\u51e6\u7406\u306b\u95a2\u3059\u308b\u77e5\u8b58\u304c\u5fc5\u8981\u3068\u306a\u308b\uff0e\u305d\u3053\u3067\uff0c\u907a\u4f1d\u7684\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0(Genetic Programming:GP)\u3092\u7528\u3044\u81ea\u52d5\u3067\u76ee\u7684\u306e\u753b\u50cf\u51e6\u7406\u3092\u69cb\u7bc9\u3067\u304d\u308b\u65b9\u6cd5\u304c\u6570\u591a\u304f\u63d0\u6848\u3055\u308c\u3066\u3044\u308b\uff0e\u3057\u304b\u3057\uff0c\u305d\u308c\u3089\u306e\u624b\u6cd5\u3067\u306f\u6a19\u6e96\u7684\u306aGP\u30e2\u30c7\u30eb\u306e\u9069\u7528\u3057\u304b\u884c\u308f\u308c\u3066\u3044\u306a\u3044\uff0e\u305d\u3053\u3067\uff0c\u672c\u7a3f\u3067\u306f\uff0c\u63d0\u6848\u3055\u308c\u3066\u3044\u308bGP\u306e\u30d6\u30ed\u30fc\u30c8\u6291\u5236\u30e2\u30c7\u30eb\u3092\u9069\u7528\u3057\uff0c\u7d30\u80de\u9818\u57df\u5206\u5272\u306e\u305f\u3081\u306b\u9069\u3057\u305fGP\u30e2\u30c7\u30eb\u3092\u8abf\u67fb\u3057\u305f\uff0e\u9069\u7528\u3057\u305f\u30e2\u30c7\u30eb\u306f\uff0c\u6a19\u6e96\u7684\u306a\u30e2\u30c7\u30eb\u306b\u52a0\u3048\uff0cDouble Tournament\uff0cTarpeian\uff0cNon-Destructive Crossover (NDC)\uff0cRecombinative Hill-Clibming (RHC)\uff0cSpatial Structure + Elitism (SS+E)\u306e6\u7a2e\u985e\u3067\u3042\u308b\uff0e\u3053\u308c\u3089\u306e\u30e2\u30c7\u30eb\u306b\u5bfe\u3057\u3066\u89d2\u819c\u5185\u76ae\u7d30\u80de\u753b\u50cf\u3092\u5bfe\u8c61\u306b\uff0cGP\u306b\u3088\u3063\u3066\u6c42\u3081\u3089\u308c\u305f\u753b\u50cf\u51e6\u7406\u306e\u7d44\u307f\u5408\u308f\u305b\u3068\uff0c\u305d\u306e\u30ed\u30d0\u30b9\u30c8\u6027\u80fd\u306e\u6bd4\u8f03\u5b9f\u9a13\u3092\u884c\u3063\u305f\uff0e\u5b9f\u9a13\u7d50\u679c\u3067\u306f\uff0cSS+E\u30e2\u30c7\u30eb\u304c\u6728\u306e\u6df1\u3055\u5236\u9650\u3084\u30da\u30ca\u30eb\u30c6\u30a3\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306b\u4f9d\u5b58\u305b\u305a\uff0c\u751f\u6210\u3055\u308c\u305f\u7d30\u80de\u9818\u57df\u5206\u5272\u306e\u305f\u3081\u306e\u753b\u50cf\u51e6\u7406\uff0c\u30ed\u30d0\u30b9\u30c8\u6027\u80fd\u306e\u4e21\u65b9\u306b\u304a\u3044\u3066\u4ed6\u306e\u30e2\u30c7\u30eb\u3088\u308a\u3082\u512a\u308c\u305f\u5024\u3092\u793a\u3057\u305f\uff0e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 
\n2.2. \u8cea\u7591\u5fdc\u7b54
\n\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e
\n 
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>1<\/strong>
\n\u8cea\u554f\u5185\u5bb9\u306f\uff0c\u300c\u4eca\u56de\u6bd4\u8f03\u3057\u305fSS+E\u30e2\u30c7\u30eb\u3067\u306e\u5b50\u500b\u4f53\u306e\u751f\u6210\u65b9\u6cd5\uff0e \u89aa\u3068\u5b50\u306e\u9069\u5408\u5ea6\u304c\u7b49\u3057\u3044\u5834\u5408\u306f\u3069\u3046\u3044\u3046\u51e6\u7406\u3092\u3059\u308b\u304b\uff0e\u300d\u3067\u3057\u305f\uff0e1\u3064\u76ee\u306e\u8cea\u554f\u306b\u306f\uff0c\u5b50\u500b\u4f53\u4e00\u3064\u4e00\u3064\u306f\u305d\u308c\u305e\u308c\u306e\u30b5\u30d6\u6bcd\u96c6\u56e3\u5185\u3067\u751f\u6210\u3055\u308c\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e2\u3064\u76ee\u306e\u8cea\u554f\u306f\uff0c\u305d\u306e\u5834\u3067\u5185\u5bb9\u3092\u7406\u89e3\u3067\u304d\u305a\uff0c\u56de\u7b54\u3067\u304d\u307e\u305b\u3093\u3067\u3057\u305f\uff0e
\n 
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>2<\/strong>
\n\u8cea\u554f\u5185\u5bb9\u306f\uff0c\u300c\u624b\u6cd5\u306b\u3088\u3063\u3066\u306f\u8a2d\u5b9a\u3057\u305f\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u8a2d\u5b9a\u306e\u5f71\u97ff\u304c\u7570\u306a\u308b\u3068\u601d\u3046\u304c\uff0c\u6bd4\u8f03\u5185\u5bb9\u304c\u30d5\u30a7\u30a2\u3067\u306f\u306a\u3044\u306e\u3067\u306f\u306a\u3044\u304b\uff0e\u300d\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0cGP\u306e\u30d6\u30ed\u30fc\u30c8\u6291\u5236\u30e2\u30c7\u30eb\u3092\u6bd4\u8f03\u3057\u3066\u3044\u308b\u8ad6\u6587\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u305d\u306e\u307e\u307e\u53c2\u8003\u306b\u8a2d\u5b9a\u3057\u305f\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n 
\n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>3<\/strong>
\n\u8cea\u554f\u5185\u5bb9\u306f\uff0c\u300c\u5b9f\u969b\u306b\u305d\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306f\u73fe\u5834\u3067\u4f7f\u7528\u3055\u308c\u3066\u3044\u308b\u306e\u304b\uff0e\u300d\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u306f\uff0c\u73fe\u5728\u306f\u4f7f\u7528\u3055\u308c\u3066\u304a\u3089\u305a\uff0c\u305d\u308c\u3092\u76ee\u6a19\u306b\u7814\u7a76\u3057\u3066\u3044\u308b\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e
\n 
\n2.3. \u611f\u60f3
\n\u767a\u8868\u306b\u95a2\u3057\u3066\u306f\uff0c2\u56de\u76ee\u306e\u56fd\u969b\u5b66\u4f1a\u3068\u3044\u3046\u3053\u3068\u3082\u3042\u308a\uff0c\u30ea\u30cf\u30fc\u30b5\u30eb\u901a\u308a\u306b\u884c\u3048\u305f\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u601d\u3044\u307e\u3059\uff0e\u3057\u304b\u3057\uff0c\u8cea\u7591\u5fdc\u7b54\u3067\u306f\uff0c\u524d\u56de\u3068\u540c\u69d8\u306b\u8cea\u554f\u5185\u5bb9\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u305a\uff0c\u5ee3\u5b89\u5148\u751f\u306b\u4ee3\u308f\u308a\u306b\u56de\u7b54\u3057\u3066\u9802\u304d\u307e\u3057\u305f\uff0e\u8003\u3048\u3089\u308c\u308b\u9650\u308a\u306e\u8cea\u7591\u5fdc\u7b54\u7528\u306e\u30b9\u30e9\u30a4\u30c9\u306f\u6e96\u5099\u3057\u3066\u3044\u305f\u306e\u3067\u3059\u304c\uff0c\u3084\u306f\u308a\u8cea\u554f\u5185\u5bb9\u3092\u307e\u305a\u805e\u304d\u53d6\u3089\u306a\u3051\u308c\u3070\uff0c\u4f55\u3082\u3067\u304d\u306a\u3044\u3053\u3068\u3092\u6539\u3081\u3066\u75db\u611f\u3057\u307e\u3057\u305f\uff0e\u4ed6\u306e\u65e5\u672c\u306e\u5b66\u751f\u306e\u767a\u8868\u3067\u306f\uff0c\u3084\u306f\u308a\u767a\u8868\u5185\u5bb9\u306e\u30ec\u30d9\u30eb\u304c\u9ad8\u3044\u5b66\u751f\u306f\uff0c\u4e00\u4eba\u3067\u5bfe\u5fdc\u3067\u304d\u3066\u3044\u308b\u5370\u8c61\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u4eca\u56de\u306e\u7d4c\u9a13\u3092\u8e0f\u307e\u3048\uff0c\u7814\u7a76\u306f\u3082\u3061\u308d\u3093\u306e\u3053\u3068\uff0c\u82f1\u8a9e\u306b\u5bfe\u3057\u3066\u306e\u5b66\u7fd2\u3082\u3088\u308a\u7cbe\u9032\u3057\u3066\u3044\u3053\u3046\u3068\u601d\u3044\u307e\u3059\uff0e\u307e\u305f\uff0c\u4eca\u56de\u306e\u767a\u8868\u306b\u3042\u305f\u308a\uff0c\u7814\u7a76\u3092\u6307\u5c0e\u3057\u3066\u9802\u304d\u307e\u3057\u305f\u5ee3\u5b89\u5148\u751f\uff0c\u8cb4\u91cd\u306a\u753b\u50cf\u30c7\u30fc\u30bf\u3092\u63d0\u4f9b\u3057\u3066\u9802\u3044\u305f\u5c0f\u6cc9\u5148\u751f\u3068\u5965\u6751\u5148\u751f\uff0c\u540c\u3058\u7814\u7a76\u73ed\u3067\u8cb4\u91cd\u306a\u610f\u898b\u3092\u9802\u3044\u305f\u7530\u4e2d\u3055\u3093\u3068\u5e03\u5ddd\u304f\u3093\u306b\uff0c\u3053\u306e\u5834\u3092\u501f\u308a\u5fa1\u793c\u3092\u7533\u3057\u4e0a\u3052\u307e\u3059\uff0e\u3069\u3046\u3082\u3042\u308a\u304c\u3068\u3046\u3054\u3056\u3044\u307e\u3057\u305f\uff0e
\n 
\n3. \u8074\u8b1b
\n\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e2\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e
\n <\/p>\n\n\n\n
\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Image Description Generation without Image Processing using Fuzzy Inference
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Naho Ito and Masafumi Hagiwara
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Fuzu IEEE Poster
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a We propose a sentence generation method that describes images. We do not use image processing technique in our proposed method. Human annotated image tags are used as image information to generate sentence. By using human annotated tags, we think this enables to describe image more relevant and user specific. Our method uses Kyoto University\u2019s case frame data and Google N-gram to generate candidate sentences. We extend these candidates to describe images more relevant. To be more precise, we added segments with missing semantic role, and added modification segments. To select one output sentence, we used fuzzy rules to grade naturalness of candidate sentences. To grading image relevance of the sentence, we scored word similarity for each word. The performance of the proposed system has been evaluated by subjective experiments and obtained satisfactory results.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 
\n\u3053\u306e\u767a\u8868\u306f\uff0c\u753b\u50cf\u51e6\u7406\u3092\u7528\u3044\u305a\u306b\u753b\u50cf\u306e\u8aac\u660e\u6587\u3092\u751f\u6210\u3059\u308b\u624b\u6cd5\u3092\u63d0\u6848\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u753b\u50cf\u304c\u3069\u3046\u3044\u3063\u305f\u5185\u5bb9\u3067\u3042\u308b\u306e\u304b\u306f\uff0c\u753b\u50cf\u306e\u30bf\u30b0\u306b\u5165\u529b\u3055\u308c\u3066\u3044\u308b\u30ad\u30fc\u30ef\u30fc\u30c9\u304b\u3089\uff0c\u5019\u88dc\u6587\u3092\u4f5c\u6210\u3057\uff0cFuzzy\u3092\u7528\u3044\u4e00\u3064\u8aac\u660e\u6587\u3092\u9078\u629e\u3059\u308b\u3068\u3044\u3063\u305f\u5185\u5bb9\u3067\u3057\u305f\uff0eFuzzy\u306b\u3064\u3044\u3066\u306e\u77e5\u8b58\u306f\u3042\u307e\u308a\u306a\u3044\u306e\u3067\u3059\u304c\uff0c\u30bf\u30b0\u60c5\u5831\u3092\u7528\u3044\u308b\u3053\u3068\u3067\uff0c\u9762\u5012\u306a\u753b\u50cf\u51e6\u7406\u3092\u884c\u308f\u305a\u306b\u6e08\u3080\u305f\u3081\u9762\u767d\u3044\u7740\u773c\u70b9\u3060\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u4eca\u5f8c\u306f\uff0cFlicker\u306a\u3069\u306e\u30b5\u30fc\u30d3\u30b9\u306e\u753b\u50cf\u306b\u9069\u7528\u3057\u3066\u7cbe\u5ea6\u3092\u78ba\u8a8d\u3059\u308b\u3064\u3082\u308a\u3067\u3042\u308b\uff0e
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\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Estimating Subjective Assessments using a Simple Biosignal Sensor
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Yoshihito Maki, Genma Sano, Yusuke Kobashi, Tsuyoshi Nakamura, Masayoshi Kanoh, Koji Yamada
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a FUZZ IEEE, SS human Symbiotic Systems
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Given the remarkable recent progress in robotics re- search, we can envision the day when robots and humans coexist and robots become closely integrated into our daily lives. This means endowing robots with the ability to communicate so they perceive human emotion, adapt their behavior to humans, and sense situations even without explicit instructions. Meanwhile, affective computing, that interprets emotion or other affective phenomena from human biosignals, has emerged as an area of great interest. In addition to biosignals-brain waves, heart rate, pulse, electrical activity, and the like-affective computing is concerned with facial expressions, gestures, and a wide range of other indicators of emotion. Here we explore the latest insights of affective computing in relation to human-robot interaction (HRI). There is good reason to believe robots will soon have the ability to read human emotions, so here we investigate the feasibility of inferring human psychological states from biosensor signals. Obviously, non-invasive biosensors that don\u2019t interfere with normal everyday activities would be preferable. A number of inexpensive user-friendly brain-wave sensors have been brought to market recently, and we employ one of these devices, the NeuroSky Mindset EEG neuroheadset, in assessment trials to explore the feasibility of inferring subjective assessments. Using our experimental setup, we find that it is indeed possible to infer subjective assessments from biosignals, and this capability could prove immensely useful for future HRI applications.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 
\n\u3053\u306e\u767a\u8868\u306f\uff0c\u751f\u4f53\u30bb\u30f3\u30b5\u3092\u7528\u3044\u4eba\u9593\u306e\u611f\u60c5\u3092\u63a8\u5b9a\u3059\u308b\u3068\u3044\u3063\u305f\u5185\u5bb9\u3067\u3057\u305f\uff0e\u751f\u4f53\u30bb\u30f3\u30b5\u306b\u306fNeuro Sky2)\u3068\u547c\u3070\u308c\u308b\u30d8\u30c3\u30c9\u30d5\u30a9\u30f3\u578b\u306e\u8133\u6ce2\u6e2c\u5b9a\u88c5\u7f6e\u3092\u7528\u3044\u3066\u304a\u308a\uff0c\u79c1\u305f\u3061\u306e\u7814\u7a76\u5ba4\u3068\u4f3c\u305f\u3088\u3046\u306a\u7814\u7a76\u3067\u3057\u305f\uff0e\u82f1\u8a9e\u304c\u805e\u304d\u53d6\u308c\u305a\uff0c\u8a73\u3057\u3044\u5185\u5bb9\u306b\u3064\u3044\u3066\u306f\u6b8b\u5ff5\u306a\u304c\u3089\u308f\u304b\u3089\u306a\u304b\u3063\u305f\u306e\u3067\u3059\u304c\uff0cNeuro Sky\u306fNIRS\u4ee5\u4e0a\u306b\u4f4e\u62d8\u675f\u6027\u3067\u3042\u308b\u305f\u3081\uff0c\u3088\u308a\u666e\u6bb5\u306e\u751f\u6d3b\u306b\u8fd1\u3044\u884c\u52d5\u3092\u8a08\u6e2c\u3059\u308b\u306e\u304c\u53ef\u80fd\u306b\u306a\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e
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\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Probabilistic Model Building GP with Belief Propagation
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Hiroyuki Sato, Yoshihiko Hasegaa, Danushaka Bollegala, Hitoshi Iba
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a IEEE CEC, Estimation of distribution algorithms
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Estimation of distribution algorithms (EDAs) which deal with tree structures as GP are called as probabilistic model building GPs (PMBGPs), and they show better search performance than GP in many problems. A problem of prototype tree-based method, a type of PMBGPs, is that samplings do not always generate the most probable solution, which is the individual with the highest probability and reflects a learned distribution most. This problem wastes a part of learning and increases the number of evaluations to get an optimum solution. In order to overcome this difficulty, this paper proposes a hybrid approach using Belief propagation (BP) in sampling process. BP is an inference algorithm on graphical models and can generate the most probable solution. By applying our approach to benchmark tests, we show that the proposed method is more effective than PLS alone.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 
\n\u3053\u306e\u767a\u8868\u306f\uff0cGP\u306e\u78ba\u7387\u30e2\u30c7\u30eb\u624b\u6cd5\u306e\u4e00\u3064\u3067\u3042\u308bPOLE\u306b\u5bfe\u3057\u3066\uff0cBelief propagation\u3068\u547c\u3070\u308c\u308b\u4f1d\u642c\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3092\u63d0\u6848\u3057\u3066\u3044\u307e\u3059\uff0e\u5bfe\u8c61\u554f\u984c\u306f\u308f\u304b\u3089\u306a\u304b\u3063\u305f\u306e\u3067\u3059\u304c\uff0c\u63d0\u6848\u624b\u6cd5\u304cSGP\u3084\u5f93\u6765\u306ePOLE\u3088\u308a\u3082\u512a\u308c\u305f\u6027\u80fd\u3092\u793a\u3057\u3066\u304a\u308a\uff0c\u4eca\u5f8c\u78ba\u7387\u30e2\u30c7\u30eb\u306b\u3064\u3044\u3066\u79c1\u306e\u7814\u7a76\u306b\u53d6\u308a\u5165\u308c\u3088\u3046\u3068\u8003\u3048\u3066\u3044\u308b\u306e\u3067\uff0c\u518d\u5ea6\u6df1\u304f\u8abf\u67fb\u3057\u305f\u304f\u601d\u3044\u307e\u3057\u305f\uff0e
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\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Brain Signal Pattern of Engrossed Subjects using Near Infrared Spectroscopy (NIRS) and its Application to TV Commercial Evaluation
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Mototaka Yoshioka, Tsuyoshi Inoue and Jun Ozawa
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a IEEE IJCNN, Brain Machines Interfaces
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a In this paper, we present near infrared spectroscopy (NIRS) signal patterns of subjects when they are focused on specific tasks. We determined that oxygenated hemoglobin in the frontal cortex decreased when the subjects were engrossed in tasks, and we propose an evaluation method for TV commercials based on the results. TV commercials are produced to be as attractive as possible and can increase consumer awareness of a particular product or its features. Our idea is based on the assumption that the attractiveness of a commercial can be estimated by the extent of decrease in oxygenated hemoglobin using NIRS, and the consumer\u2019s awareness of the product\u2019s features in TV commercials can be measured by analyzing the subject\u2019s glancing regions using an eye-tracking system. We obtained good agreement between the correlation of awareness and focus, and the possibility of estimating these parameters using NIRS is suggested.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 
\n\u3053\u306e\u767a\u8868\u306f\uff0c\u4eba\u304c\u30bf\u30b9\u30af\u306b\u5922\u4e2d\u306b\u306a\u3063\u305f\u3068\u304d\u306b\uff0c\u3042\u308b\u8133\u90e8\u4f4d\u304c\u6d3b\u6027\u5316\u3059\u308b\u3068\u3044\u3046\u7d50\u679c\u3092\uff0cTV\u306e\u30b3\u30de\u30fc\u30b7\u30e3\u30eb\u3092\u7528\u3044\u3066\u4eba\u304c\u8208\u5473\u3092\u3082\u3064CM\u304c\u3069\u306e\u3088\u3046\u306a\u3082\u306e\u306a\u306e\u304b\u3092\u8abf\u67fb\u3057\u305f\u5185\u5bb9\u3067\u3057\u305f\uff0e\u6e2c\u5b9a\u88c5\u7f6e\u306b\u306fNIRS\u304c\u3082\u3061\u3044\u3089\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u7d50\u679c\u3067\u306f\uff0c\u4eca\u56de\u306e\u30bf\u30b9\u30af\u8a2d\u8a08\u3067\u306f\uff0c\u3042\u307e\u308a\u6d3b\u6027\u304c\u898b\u3089\u308c\u3066\u304a\u3089\u305a\uff0c\u5358\u7d14\u306b\u8208\u5473\u3092\u3082\u3064CM\u3068\u3044\u3063\u3066\u3082\u591a\u304f\u306e\u8981\u7d20\u304c\u7d61\u307f\u5408\u3046\u3053\u3068\u304c\u8003\u3048\u3089\u308c\u308b\u306e\u3067\uff0c\u4eca\u5f8c\u306f\u305d\u308c\u3089\u306e\u8981\u7d20\u3092\u5206\u985e\u3059\u308b\u5fc5\u8981\u304c\u8003\u3048\u3089\u308c\u307e\u3057\u305f\uff0e
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\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Adaptive Formation Behaviors of Multi-robot for Cooperative Exploration
\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Yutaka Yasuda, Naoyuki Kubota and Yuichiro Toda
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Hybrid, Computational Intelligence for Cognitive Robotics
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a This paper proposes a method for constituting the formation of a multi-robot system according to dynamically changing environments. First, we apply a method of multi- objective behavior coordination for integrating behavior outputs from the fuzzy control for collision avoidance and target tracing. Second, we apply a spring model to calculate the temporary target position of each robot for the formation behavior. Third, we discuss multi-robot behaviors based on the concept of coupling. The tight coupling is realized by the spring model while the loose coupling is realized by the individual decision making based on connection and disconnection with other robots. Furthermore, the proposed method is applied to the exploration in unknown environments. Finally, we discuss the effectiveness of the proposed method through several simulation results.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 
\n\u3053\u306e\u767a\u8868\u306f\uff0c\u8907\u6570\u306e\u30ed\u30dc\u30c3\u30c8\u304c\u52d5\u7684\u306b\u5909\u5316\u3059\u308b\u74b0\u5883\u306b\u5fdc\u3058\u305f\u64cd\u4f5c\u306e\u305f\u3081\u306e\u624b\u6cd5\u3092\u63d0\u6848\u3057\u3066\u3044\u307e\u3057\u305f\uff0e \u30d5\u30a1\u30b8\u30a3\u3084\u30ed\u30dc\u30c3\u30c8\u5de5\u5b66\u306e\u77e5\u8b58\u304c\u306a\u3044\u305f\u3081\uff0c\u7406\u89e3\u304c\u96e3\u3057\u304b\u3063\u305f\u306e\u3067\u3059\u304c\uff0c\u672c\u624b\u6cd5\u3067\u306f\u758e\u7d50\u5408\u3060\u3051\u3067\u306a\u304f\uff0c\u5bc6\u7d50\u5408\u306e\u5834\u5408\u306b\u304a\u3044\u3066\u3082\u826f\u597d\u306a\u7d50\u679c\u3092\u793a\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u7d50\u679c\u306f\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3067\u3042\u3063\u305f\u306e\u3067\uff0c\u5b9f\u74b0\u5883\u3067\u306e\u5c55\u671b\u304c\u8003\u3048\u3089\u308c\u307e\u3059\uff0e\u307e\u305f\uff0c\u65e5\u672c\u4eba\u5b66\u751f\u306e\u767a\u8868\u3067\u3057\u305f\u304c\uff0c\u8cea\u7591\u5fdc\u7b54\u3067\u306f\u82e6\u52b4\u3057\u306a\u304c\u3089\u81ea\u8eab\u3067\u5bfe\u5fdc\u3057\u3066\u304a\u308a\uff0c\u826f\u3044\u767a\u8868\u3067\u3057\u305f\uff0e
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\n1)\u00a0\u00a0\u00a0 2012 IEEE WCCI\uff0chttp:\/\/www.ieee-wcci2012.org\/<\/a>
\n2)\u00a0\u00a0\u00a0 Neuro Sky, http:\/\/www.neurosky.jp\/<\/a>
\n <\/p>\n","protected":false},"excerpt":{"rendered":"

IEEE WCCI 2012\u304c\u30d6\u30ea\u30b9\u30d9\u30f3\u30fb\u30aa\u30fc\u30b9\u30c8\u30e9\u30ea\u30a2\u3067\u958b\u50ac\u3055\u308c\u307e\u3059\u3002 \uff2d\uff12\u306e\u5c71\u53e3\u304f\u3093\u304c\u767a\u8868\u3057\u307e\u3059\u3002 Friday, IEEE CEC, FrC 4-2, 13:30-14:30, Applications of Ev … <\/p>\n

“\u3010\u901f\u5831\u3011\uff29\uff25\uff25\uff25 \uff37\uff23\uff23\uff29 2012” \u306e<\/span>\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-870","post","type-post","status-publish","format-standard","hentry","category-6"],"_links":{"self":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/870","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=870"}],"version-history":[{"count":0,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/870\/revisions"}],"wp:attachment":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=870"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=870"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=870"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}