{"id":3910,"date":"2017-01-19T14:00:04","date_gmt":"2017-01-19T05:00:04","guid":{"rendered":"http:\/\/www.is.doshisha.ac.jp\/news\/?p=3910"},"modified":"2017-01-19T14:00:04","modified_gmt":"2017-01-19T05:00:04","slug":"arob-22nd-2017","status":"publish","type":"post","link":"https:\/\/is.doshisha.ac.jp\/news\/?p=3910","title":{"rendered":"AROB 22nd 2017"},"content":{"rendered":"<p>2017\u5e741\u670819\u65e5\u304b\u308921\u65e5\u304b\u3051\u3066\u5927\u5206\u770c\u5225\u5e9c\u5e02\u306eB-Con PLAZA\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305f\uff0c22nd International Symposium on Artificial Life and Robotics\uff08AROB 22nd 2017\uff09\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\uff0c\u5ee3\u5b89\u5148\u751f\u3068\u7530\u4e2d\u90a3\u667a(M2)\uff0c\u5f8c\u85e4(M2)\uff0c\u7530\u6751(M2)\uff0c\u77f3\u539f(M1)\uff0c\u5ca1\u7530(M1)\u304c\u53c2\u52a0\u3057\uff0c\u767a\u8868\u5f62\u5f0f\u306f\u53e3\u982d\u767a\u8868\u3067\u3057\u305f\uff0e\u767a\u8868\u6f14\u984c\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\uff0e<\/p>\n<ul>\n<li>\u201cHelicobacter pylori Infection Identification from Gastroscopy Images: Reliability and Validity of Endoscopy Linked Color Imaging\u201d<br \/>\nTomoyuki Hiroyasu, Yuto Okada, Hiroshi Ichikawa, Nobuaki Yagi, Hiroaki Kitae, Satoru Hiwa, Hiroshi Furutani<\/li>\n<li>\u201cDetection of Mesenteric Blood Vessel in Laparoscopic Video Images -Comparison study of discriminant model between linear and non-linear model-\u201d<br \/>\nTomoyuki Hiroyasu, Nachi Tanaka, Satoru Hiwa, Hisatake Yokouchi, Akio Hagiwara, Hiroshi Furutani<\/li>\n<li>\u201cOptimal motor imagery for EEG-based brain-computer interfaces\u201d<br \/>\nSatoru Hiwa, Tomonori Ishihara, Tomoyuki Hiroyasu<\/li>\n<li>\u201cDiscussion on maintaining spatial information in deep learning of functional brain imaging data\u201d<br \/>\nTomoyuki Hiroyasu, Ryota Tamura, Satoru Hiwa, Keisuke Hachisuka, Hiroshi Furutani<\/li>\n<li>\u201cAutomatic quality evaluation of the cultured in-vivo corneal endothelial cell &#8211; Panorama generated by the partial image -\u201d<br \/>\nTomoyuki Hiroyasu, Yudai Goto, Naoki Okumura, Noriko Koizumi, Satoru Hiwa, Hiroshi Furutani<\/li>\n<\/ul>\n<p><a href=\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/01\/IMG_4719.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-3912\" src=\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/01\/IMG_4719-300x169.jpg\" alt=\"\" width=\"300\" height=\"169\" \/><\/a>\u3000<a href=\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/01\/IMG_4738.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-3911\" src=\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/01\/IMG_4738-300x169.jpg\" alt=\"\" width=\"300\" height=\"169\" \/><\/a><br \/>\n<span style=\"font-weight: 400;\">AROB 22nd 2017\u306f\uff0cISAROB\u304c\u4e3b\u50ac\u3059\u308b<\/span><span style=\"font-weight: 400;\">\u4eba\u5de5\u751f\u547d\u3068\u30ed\u30dc\u30c3\u30c8\u5de5\u5b66\u306e\u65b0\u3057\u3044\u6280\u8853\u3068\u305d\u306e\u5fdc\u7528\u5206\u91ce\u3092\u4e2d\u5fc3\u30c6\u30fc\u30de\u3068\u3057\u3066\uff0c<\/span><span style=\"font-weight: 400;\">\u4eba\u5de5\u751f\u547d\u304a\u3088\u3073\u30ed\u30dc\u30c3\u30c8\u306b\u95a2\u3059\u308b<\/span><span style=\"font-weight: 400;\">\u306e\u7814\u7a76\u8005\u306a\u3069\u304c\u96c6\u3046\u5b66\u4f1a<\/span><span style=\"font-weight: 400;\">\u3067\uff0c\u69d8\u3005\u306a\u5206\u91ce\u306e\u8b1b\u6f14\u3092\u805e\u304f\u3053\u3068\u304c\u51fa\u6765\u307e\u3057\u305f\uff0e \u79c1\u81ea\u8eab\u521d\u3081\u3066\u306e\u56fd\u969b\u5b66\u4f1a\u3067\u521d\u3081\u3066\u306e\u82f1\u8a9e\u3067\u306e\u53e3\u982d\u767a\u8868\u3068\u3044\u3046\u3053\u3068\u3082\u3042\u308a\uff0c\u7dca\u5f35\u3057\u307e\u3057\u305f\u304c\uff0c\u524d\u3082\u3063\u3066\u53c2\u52a0\u30e1\u30f3\u30d0\u30fc\u3067\u767a\u8868\u7df4\u7fd2\u3092\u4f55\u5ea6\u3082\u3057\u305f\u305f\u3081\u304b\uff0c\u5168\u54e1\u304c\u4e0a\u624b\u306b\u767a\u8868\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3057\u305f\uff0e\u4e00\u65b9\u3067\uff0c\u3084\u306f\u308a\u6d77\u5916\u306e\u65b9\u306e\u82f1\u8a9e\u767a\u8868\u306f\u3068\u3066\u3082\u805e\u304d\u3084\u3059\u304f\uff0c\u82f1\u8a9e\u3092\u558b\u308b\u80fd\u529b\u306e\u5fc5\u8981\u6027\u3092\u6539\u3081\u3066\u78ba\u8a8d\u81f4\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u7d4c\u9a13\u3092\u3082\u3068\u306b\u4eca\u5f8c\u3082\u3088\u308a\u4e00\u5c64\u9811\u5f35\u3063\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e<\/span><\/p>\n<p style=\"text-align: center;\"><a href=\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/01\/IMG_AROB.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-3918\" src=\"http:\/\/www.is.doshisha.ac.jp\/news\/wp-content\/uploads\/2017\/01\/IMG_AROB-300x200.png\" alt=\"IMG_AROB\" width=\"300\" height=\"200\" \/><\/a><\/p>\n<p>\u3010\u6587\u8cac\uff1aM1 \u5ca1\u7530\u3011<br \/>\n<!--more--><br \/>\n<strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"147\"><strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"373\">\u7530\u4e2d\u90a3\u667a<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">\u8179\u8154\u93e1\u52d5\u753b\u50cf\u306b\u304a\u3051\u308b\u8178\u9593\u819c\u5185\u8d70\u884c\u8840\u7ba1\u306e\u691c\u51fa \u2013\u7dda\u5f62\u30e2\u30c7\u30eb\u3068\u975e\u7dda\u5f62\u30e2\u30c7\u30eb\u306e\u6bd4\u8f03-<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">Detection of Mesenteric Blood Vessel in Laparoscopic Video Images \u2013Comparison study of discriminant model between linear model and nonlinear model-<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">\u5ee3\u5b89\u77e5\u4e4b, \u7530\u4e2d\u90a3\u667a\uff0c\u65e5\u548c\u609f\uff0c\u6a2a\u5185\u4e45\u731b\uff0c\u8429\u539f\u660e\u65bc\uff0c\u53e4\u8c37\u535a\u53f2<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\"><\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">THE TWENTY-SECOND INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS (AROB 22bd 2017)<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">B-con plaza\u5927\u5206\u770c<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2017\/01\/19-2017\/01\/21<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>2017\/01\/19\u304b\u30892017\/01\/21\u306b\u304b\u3051\u3066\uff0c\u5927\u5206\u770c\u5225\u5e9c\u5e02\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305fTHE TWENTY-SECOND INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS (AROB 22bd 2017)\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u5b66\u4f1a\u306f\uff0cInternational Society of Artificial Life and Robotics (ISAROB)\u306b\u3088\u3063\u3066\u4e3b\u50ac\u3055\u308c\uff0c\u69d8\u3005\u306a\u30d5\u30a3\u30fc\u30eb\u30c9\u3067\u306e\u4eba\u5de5\u751f\u547d\u3068\u30ed\u30dc\u30c3\u30c8\u5de5\u5b66\u3068\u305d\u308c\u3089\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u305f\u3081\u306e\u65b0\u6280\u8853\u306e\u958b\u767a\u3092\u76ee\u7684\u3068\u3057\u3066\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u3059\uff0e<br \/>\n\u79c1\u306f19\u65e5\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u5ee3\u5b89\u5148\u751f\uff0c\u5f8c\u85e4\uff0c\u7530\u6751\uff0c\u77f3\u539f\uff0c\u5ca1\u7530\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u306f19\u65e5\u306e\u5348\u5f8c\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cComputational methods for Human Biological information\u300d\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u53e3\u982d\u767a\u8868\u3067\uff0c10\u5206\u306e\u8b1b\u6f14\u6642\u9593\u30685\u5206\u306e\u8cea\u7591\u5fdc\u7b54\u6642\u9593\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e<br \/>\n\u4eca\u56de\u306e\u767a\u8868\u306f\uff0cDetection of Mesenteric Blood Vessel in Laparoscopic Video Images \u2013Comparison study of discriminant model between linear model and nonlinear model-\u3067\uff0c\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">Laparoscope is commonly used in colorectal cancer surgery as a minimally invasive method. However, colorectal cancer surgery using laparoscope has the following disadvantage. The mesenteric blood vessel that should not be harmed is obscured. Therefore, locating the mesenteric blood vessel increases operation time. To solve this problem, we develop a system that provides the position of the mesenteric blood vessel using only videos. In this study, we examine two methods for extracting the mesenteric blood vessel. In the first method, a regression surface is used for determining the mesenteric blood vessel from training data through a linear model. In the second method, an ellipsoid surface surrounding the pixel values of the mesenteric blood vessel is determined using Mahalanobis\u2019 distance in a nonlinear model. To verify the effectiveness of these methods, we perform an evaluation experiment using six images captured from laparoscopic videos including the mesenteric blood vessel.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<ul>\n<li>\u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n<p>\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<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>1<\/strong><br \/>\n\u8cea\u554f\u8005\u306f\u540c\u7814\u7a76\u5ba4\u306e\u7530\u6751\u3067\u3057\u305f\uff0e\u975e\u7dda\u5f62\u30e2\u30c7\u30eb\u3092\u8aac\u660e\u3059\u308b\u56f3\u306b\u304a\u3051\u308b\u70b9\u306e\u8272\u306e\u610f\u5473\u3092\u554f\u308f\u308c\u307e\u3057\u305f\uff0e\u8272\u306f\u8b58\u5225\u9762\u5185\u3068\u5916\u3092\u793a\u3057\u3066\u304a\u308a\uff0c\u5171\u306b\u8178\u9593\u819c\u5185\u8d70\u884c\u8840\u7ba1\u306e\u753b\u7d20\u5024\u3092\u793a\u3059\u3082\u306e\u3067\u3042\u308b\u3068\u89e3\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\n\u8cea\u554f\u8005\u306f\u5ee3\u5b89\u5148\u751f\u3067\u3057\u305f\uff0e\u76ee\u6a19F\u5024\u306f\u3044\u304f\u3089\u304b\u3068\u3044\u3046\u8cea\u554f\u3067\u3057\u305f\uff0e\u59cb\u3081\u6238\u60d1\u3063\u3066\u3057\u307e\u3044\u307e\u3057\u305f\u304c\uff0c\u76ee\u6a19F\u5024\u306f\u6c7a\u307e\u3063\u3066\u304a\u3089\u305a\uff0c\u4eca\u5f8c\u6c7a\u3081\u3066\u3044\u304f\u5fc5\u8981\u304c\u3042\u308b\u3068\u89e3\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>3<\/strong><br \/>\n\u8cea\u554f\u8005\u306f\u5ee3\u5b89\u5148\u751f\u3067\u3057\u305f\uff0e\u533b\u5e2b\u306f\u5b9f\u969b\u306b\u306f\u7740\u8272\u3055\u308c\u305f\u6620\u50cf\u3067\u306f\u306a\u304f\u5143\u306e\u6620\u50cf\u3092\u898b\u305f\u3044\u6642\u304c\u3042\u308b\u3068\u601d\u3044\u307e\u3059\u304c\uff0c\u3069\u306e\u3088\u3046\u306b\u5bfe\u5fdc\u3059\u308b\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3067\u3057\u305f\uff0e\u63d0\u6848\u624b\u6cd5\u3092\u5b9f\u969b\u306e\u8179\u8154\u93e1\u306b\u5bfe\u3057\u3066\u9069\u7528\u3059\u308b\u305f\u3081\u306e\u30b7\u30b9\u30c6\u30e0\u3092\u8003\u3048\u3066\u304a\u308a\uff0c\u5143\u306e\u6620\u50cf\u3068\u306f\u5225\u306e\u30c7\u30a3\u30b9\u30d7\u30ec\u30a4\u3067\u8868\u793a\u3059\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u4e21\u65b9\u306e\u6620\u50cf\u3092\u540c\u6642\u306b\u78ba\u8a8d\u3067\u304d\u308b\u3068\u89e3\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>4<\/strong><br \/>\n\u8cea\u554f\u8005\u306f\u5ee3\u5b89\u5148\u751f\u3067\u3057\u305f\uff0e\u51e6\u7406\u901f\u5ea6\u306f\u3069\u308c\u304f\u3089\u3044\u304b\u3068\u3044\u3046\u8cea\u554f\u3067\u3057\u305f\uff0e\u73fe\u57284fps\u7a0b\u5ea6\u3067\u3059\uff0e\u5b9f\u7528\u5316\u306e\u305f\u3081\u306b\u306f30fps\u7a0b\u5ea6\u304c\u671b\u307e\u3057\u3044\u305f\u3081\uff0c\u9ad8\u901f\u5316\u306e\u5fc5\u8981\u304c\u3042\u308b\u3068\u89e3\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u00a0<\/strong><\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u4eca\u56de\u306e\u56fd\u969b\u5b66\u4f1a\u306f\uff0c\u6628\u5e74\u5ea6\u306eEMBC\u3088\u308a\u3082\u6210\u9577\u3057\u305f\u3068\u611f\u3058\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u90e8\u5c4b\u306f\u5c0f\u3055\u304f\uff0c\u65e5\u672c\u4eba\u304c\u307b\u3068\u3093\u3069\u306e\u5b66\u4f1a\u3067\uff0c\u767a\u8868\u3092\u805e\u3044\u3066\u3044\u308b\u4e0a\u3067\u306f\u539f\u7a3f\u3092\u4e38\u304b\u3058\u308a\u3067\u8aad\u3093\u3067\u3044\u308b\u4eba\u3082\u591a\u304b\u3063\u305f\u3088\u3046\u306b\u601d\u3044\u307e\u3059\uff0e\u305d\u3093\u306a\u7a7a\u6c17\u306e\u4e2d\u3067\uff0c\u81ea\u5206\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u306e\u6700\u521d\u306e\u30d7\u30ec\u30bc\u30f3\u30bf\u30fc\u306b\u3044\u304d\u306a\u308a\u8cea\u554f\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u306e\u306f\uff0c\u3068\u3066\u3082\u5927\u304d\u306a\u6210\u9577\u3060\u3063\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u305d\u308c\u306b\u3088\u3063\u3066\uff0c\u4ed6\u306e\u30e1\u30f3\u30d0\u30fc\u306b\u3082\u5f71\u97ff\u3092\u4e0e\u3048\u3089\u308c\u305f\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u601d\u3044\u307e\u3059\uff0e\u81ea\u8eab\u306e\u767a\u8868\u3067\u306f\uff0c\u8003\u3048\u3066\u3044\u305f\u6642\u9593\u901a\u308a\u306b\u9032\u3081\u3064\u3064\u3082\uff0c\u30a2\u30c9\u30ea\u30d6\u3092\u4ea4\u3048\u3066\u8a71\u3059\u3053\u3068\u304c\u3067\u304d\uff0c\u826f\u304b\u3063\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u307e\u305f\u30b9\u30e9\u30a4\u30c9\u7b49\u306e\u898b\u305b\u65b9\u7b49\u3082\uff0c\u4eca\u307e\u3067\u3067\u6700\u3082\u898b\u3084\u3059\u304f\u3067\u304d\u305f\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u601d\u3044\u307e\u3059\uff0e\u8cea\u554f\u306b\u6238\u60d1\u3046\u3053\u3068\u3082\u3042\u308a\u307e\u3057\u305f\u304c\uff0c\u5168\u3066\u89e3\u7b54\u3067\u304d\u305f\u306e\u3067\uff0c\u4e0d\u5b89\u8981\u7d20\u3060\u3063\u305f\u8cea\u7591\u5fdc\u7b54\u306b\u3082\u5c11\u3057\u81ea\u4fe1\u304c\u3064\u304d\u307e\u3057\u305f\uff0e\u4fee\u58eb\u8ad6\u6587\u3068\u4e26\u3093\u3067\u81ea\u5206\u306e3\u5e74\u9593\u306e\u7814\u7a76\u751f\u6d3b\u306e\u96c6\u5927\u6210\u306e1\u3064\u3068\u3057\u3066\uff0c\u6e80\u8db3\u3059\u308b\u7d50\u679c\u3092\u6b8b\u305b\u307e\u3057\u305f\uff0e\u6b21\u306b\u3053\u306e\u3088\u3046\u306a\u767a\u8868\u306e\u6a5f\u4f1a\u304c\u5f97\u3089\u308c\u305f\u306a\u3089\uff0c\u3082\u3063\u3068\u30c6\u30f3\u30dd\u3084\u5f37\u8abf\u306a\u3069\uff0c\u65e5\u672c\u8a9e\u767a\u8868\u3068\u540c\u3058\u3088\u3046\u306b\u805e\u304d\u3084\u3059\u3055\u306b\u6c17\u3092\u3064\u3051\u3066\u8a71\u3059\u3053\u3068\u3092\u5fc3\u304c\u3051\u307e\u3059\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e3\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Detection of Fake Bills Using Spectral Band Images<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Sigeru Omatu; Hideo Araki<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Artificial intelligence<br \/>\nAbstract \uff1a In the world, many currencies have been issued and some of them were counterfeited. Especially, by the advances in copying technology and computer ability it is rather easy to make counterfeiting bills. In this paper, we develop a method to use spectral property of the bills and classify the bill whether it is true or fake using spectral sub-band analysis for Singapore dollars.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\u672c\u5b66\u4f1a\u3067\u306f\u73cd\u3057\u3044\u753b\u50cf\u51e6\u7406\u306e\u7814\u7a76\u3067\u3057\u305f\uff0e\u507d\u672d\u306e\u691c\u51fa\u3092\u884c\u3046\u3068\u3044\u3046\u7814\u7a76\u3067\u3057\u305f\u304c\uff0c\u4e3b\u306a\u5185\u5bb9\u306f\u7167\u660e\u74b0\u5883\u306b\u95a2\u308f\u3089\u305a\u691c\u51fa\u3092\u884c\u3046\u305f\u3081\u306eCLAHE\u306e\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u5e73\u5766\u5316\u306b\u985e\u4f3c\u3057\u305f\u624b\u6cd5\u306e\u63d0\u6848\u3092\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u305d\u306e\u305f\u3081\uff0c\u624b\u6cd5\u306b\u95a2\u3057\u3066\u306f\u3042\u307e\u308a\u65b0\u3057\u304f\u3066\u3059\u3054\u3044\u3068\u3044\u3046\u5370\u8c61\u306f\u53d7\u3051\u307e\u305b\u3093\u3067\u3057\u305f\uff0e\u3057\u304b\u3057\u306a\u304c\u3089\uff0c\u507d\u672d\u306e\u691c\u51fa\u3068\u3044\u3046\u30c6\u30fc\u30de\u304c\u9762\u767d\u304f\uff0c\u3082\u3063\u3068\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u5168\u4f53\u3092\u898b\u3066\u307f\u305f\u3044\u3068\u601d\u308f\u305b\u308b\uff0c\u9762\u767d\u3044\u767a\u8868\u306e\u4ed5\u65b9\u3067\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Detecting Texts on the Shirts of Soccer Players<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Phatthanaphong, Chomphuwiset<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Artificial intelligence<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a This paper aims to study and propose a technique to detection texts printed on player uniforms (shirts) in sport events, soccer (football). The detection is considered as a preliminary process for recognizing players. The proposed technique is performed based-on the contextual information obtaining from the alignment of objects in images. There are 4 main processes to achieve the task, i.e. (i) edge detection (ii) candidate text detection, (iii) head detection and (iv) false-positive rejection using contextual information. The candidate texts are identified using am edge and structural-based technique using Stroke Width Transform (SWT). Heads of players are detected using a histogram matching method that projects a head template to the images to form head regions. The template is defined by averaging head images, collected from a separate dataset. The final step of rejecting false detection is performed using the contextual information of spatial location between player heads and text regions. To evaluate the performance of the proposed technique, 250 images of players in soccer games are collected. In addition, 150 images of the head position are separately prepared, to generate a head template. The ground-truth is prepared by an expert using a manual scheme. The experiments are performed and show that the proposed technique provides a promising result.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u3067\u306f\uff0c\u30b5\u30c3\u30ab\u30fc\u30d7\u30ec\u30a4\u30e4\u30fc\u306e\u30e6\u30cb\u30d5\u30a9\u30fc\u30e0\u306e\u6587\u5b57\u3092\u691c\u51fa\u3059\u308b\u7814\u7a76\u3067\u3057\u305f\uff0e\u305d\u3093\u306a\u3082\u306e\u3092\u691c\u51fa\u3057\u3066\u4f55\u306e\u610f\u5473\u304c\u3042\u308b\u306e\u304b\u308f\u304b\u3089\u305a\uff0c\u30bf\u30a4\u30c8\u30eb\u304b\u3089\u3057\u3066\uff0c\u9762\u767d\u304f\u306a\u3055\u305d\u3046\u3067\uff0c\u671f\u5f85\u3057\u3066\u3044\u307e\u305b\u3093\u3067\u3057\u305f\uff0e\u3057\u304b\u3057\uff0c\u5b9f\u969b\u306b\u805e\u3044\u3066\u307f\u308b\u3068\uff0c\u4f55\u6545\u691c\u51fa\u3057\u3066\u3044\u308b\u306e\u304b\u306a\u3069\u304c\u308f\u304b\u308a\uff0c\u9762\u767d\u3044\u5185\u5bb9\u3067\u3057\u305f\uff0e\u52d5\u304f\u30d7\u30ec\u30a4\u30e4\u30fc\u304b\u3089\u306e\u691c\u51fa\u306f\u304b\u306a\u308a\u96e3\u3057\u3044\u3068\u601d\u3046\u306e\u3067\uff0c\u53c2\u8003\u306b\u3057\u305f\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Color image segmentation based on immune mechanism and microsaccades<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Witthaya sitthivet, Suripon somkuarnpanit, Kitti paithoonwattanakij<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Artificial intelligence<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Photoplethysmography is a non-invasive method of measuring the blood volume pulse and can be captured using a low-cost camera and ambient light. We present a proof-of-concept smartphone-based system to detect restricted peripheral blood flow in the legs with the aim of characterizing symptoms of Peripheral Artery Disease. We designed a novel interface to quantify the perfusion signal strength. In initial tests we observed differences between affected and non-affected limbs, such a system could simplify diagnosis in patients at risk.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\uff0c\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u306b\u95a2\u3059\u308b\u767a\u8868\u3067\u3057\u305f\uff0e\u3053\u306e\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u624b\u6cd5\u306f\uff0c\u30d2\u30c8\u306e\u514d\u75ab\u6a5f\u80fd\u3092\u6a21\u64ec\u3057\u305f\u624b\u6cd5\u3067\uff0c\u8a73\u3057\u304f\u306f\u7406\u89e3\u3067\u304d\u306a\u304b\u3063\u305f\u304c\uff0cGA\u306e\u3088\u3046\u306b\u4e16\u4ee3\u3092\u304b\u3051\u3066\u6700\u9069\u306a\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u3092\u884c\u3046\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u3088\u3046\u3067\u3057\u305f\uff0e\u3042\u307e\u308a\u898b\u6163\u308c\u306a\u3044\u30d1\u30bf\u30fc\u30f3\u306e\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u624b\u6cd5\u3067\uff0c\u3053\u3046\u3044\u3046\u65b0\u3057\u3044\u624b\u6cd5\u3068\u3044\u3046\u306e\u306f\uff0c\u3069\u306e\u3088\u3046\u306b\u751f\u307e\u308c\u3066\u304f\u308b\u306e\u304b\uff0c\u81ea\u5206\u3082\u5168\u304f\u65b0\u3057\u3044\u3082\u306e\u3092\u4f5c\u3063\u3066\u307f\u305f\u3044\u3068\u3044\u3046\u6c17\u6301\u3061\u306b\u306a\u308b\u767a\u8868\u3067\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n\u53c2\u8003\u6587\u732e<br \/>\nTHE TWENTY-SECOND INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS (AROB 22nd 2017)<br \/>\n&nbsp;<br \/>\n<strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"147\"><strong>\u00a0<\/strong><br \/>\n<strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"373\">&nbsp;<br \/>\nYuto OKADA<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">\u80c3\u90e8\u5185\u8996\u93e1\u753b\u50cf\u304b\u3089\u306eHp\u611f\u67d3\u8b58\u5225\u306e\u691c\u8a0e\uff5eLCI\u5185\u8996\u93e1\u753b\u50cf\u306e\u6709\u7528\u6027\u306e\u691c\u8a0e\uff5e<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">Helicobacter pylori Infection Identification from Gastroscopy Images: Reliability and Validity of Endoscopy Linked Color Imaging<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">Tomoyuki Hiroyasu, Hiroshi ICHIKAWA, Nobuaki YAGI, Hiroaki KITAE, Satoru HIWA, Hiroshi FURUTANI<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">ISAROB<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">AROB 22<sup>nd<\/sup> 2017<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">Beppu B-CON\u3000Plaza<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2017\/01\/19-2017\/01\/21<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>2017\/01\/19\u304b\u30892017\/01\/21\u306b\u304b\u3051\u3066\uff0c\u5225\u5e9c\u56fd\u969b\u30b3\u30f3\u30d9\u30f3\u30b7\u30e7\u30f3\u30bb\u30f3\u30bf\u30fc\uff0f\u30d3\u30fc\u30b3\u30f3\u30d7\u30e9\u30b6\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305fAROB2017\uff08http:\/\/isarob.org\/symposium\/\uff09\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u5b66\u4f1a\u306f\uff0c\u4eba\u5de5\u751f\u547d\u3068\u30ed\u30dc\u30c3\u30c8\u5de5\u5b66\u306e\u65b0\u3057\u3044\u6280\u8853\u3068\u305d\u306e\u5fdc\u7528\u5206\u91ce\u3092\u4e2d\u5fc3\u30c6\u30fc\u30de\u3068\u3057\u3066\uff0c\u4eba\u5de5\u751f\u547d\u304a\u3088\u3073\u30ed\u30dc\u30c3\u30c8\u306b\u95a2\u3059\u308b\u306e\u7814\u7a76\u8005\u306a\u3069\u304c\u96c6\u3046\u5b66\u4f1a\u3067\u3059\uff0e<br \/>\n\u79c1\u306f\u5168\u65e5\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u5ee3\u5b89\u5148\u751f\uff0c\u53e4\u8c37\u5148\u751f\uff0c\u7530\u4e2d\u90a3\u667a\uff0c\u7530\u6751\u9675\u5927\uff0c\u5f8c\u85e4\u512a\u5927\uff0c\u77f3\u539f\u77e5\u61b2\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u306f19\u65e5\u306e\u5348\u5f8c\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cComputational methods for Human Biological information\u300d\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u53e3\u982d\u767a\u8868\u3067\uff0c10\u5206\u306e\u8b1b\u6f14\u6642\u9593\u30685\u5206\u306e\u8cea\u7591\u5fdc\u7b54\u6642\u9593\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e<br \/>\n\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">Approximately half the world\u2019s population is infected by Helicobacter pylori. Atrophic gastritis and intestinal epithelial metaplasia caused by long-term H. pylori infection develop into gastric cancer in many cases. Therefore, endoscopic examination has attracted attention because it helps eradicate H. pylori infection at an early stage. Diagnosis of the diffuse redness characteristic of H. pylori infection is difficult using conventional white light imaging endoscopy. Linked color imaging (LCI) was developed to emphasize diffuse redness. In this study, using endoscopic images captured by LCI, a method to discriminate the presence or absence of H. pylori infection using color information, were examined. A support vector machine was used for identification, and the usefulness of LCI images for identifying the presence or absence of H. pylori infection was examined.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<ul>\n<li>\u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n<p>\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<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>1<\/strong><br \/>\nWLI\u3067\u306f\u3069\u306e\u3088\u3046\u306b\u30d4\u30ed\u30ea\u83cc\u306e\u8a3a\u65ad\u3092\u3057\u3066\u3044\u308b\u306e\u304b\uff0c\u3068\u3044\u3046\u8cea\u554f\u3092\u9802\u304d\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u7c98\u819c\u306e\u80c3\u708e\u306e\u5fae\u5999\u306a\u9055\u3044\u3092\u76ee\u8996\u306b\u3088\u308a\u5224\u65ad\u3057\u3066\u3044\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\nLCI\u306f\u5e83\u304f\u7528\u3044\u3089\u308c\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u9802\u304d\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u6700\u8fd1\u958b\u767a\u3055\u308c\u305f\u6280\u8853\u3067\u3042\u308a\uff0c\u307e\u3060\u666e\u53ca\u3057\u3066\u3044\u306a\u3044\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>3<\/strong><br \/>\nWLI\u3068LCI\u306f\u3069\u3063\u3061\u306e\u65b9\u304c\u5e83\u304f\u7528\u3044\u3089\u308c\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\uff0c\u307e\u305f\u307e\u3060WLI\u306e\u65b9\u304c\u5e83\u304f\u7528\u3044\u3089\u308c\u3066\u3044\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u4eca\u56de\u521d\u3081\u3066\u306e\u82f1\u8a9e\u3067\u306e\u53e3\u982d\u767a\u8868\u3067\u3057\u305f\uff0e\u4eca\u56de\u306f\u4f1a\u5834\u304c\u305d\u3053\u307e\u3067\u5e83\u304f\u306a\u304f\uff0c\u307e\u305f\u540c\u3058\u7814\u7a76\u5ba4\u306e\u30e1\u30f3\u30d0\u30fc\u3068\u540c\u3058\u30bb\u30c3\u30b7\u30e7\u30f3\u3068\u3044\u3046\u3053\u3068\u3082\u3042\u308a\uff0c\u7dca\u5f35\u305b\u305a\u306b\u767a\u8868\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3057\u305f\uff0e\u4eca\u5f8c\u306f\uff0c\u69d8\u3005\u306a\u8a0e\u8ad6\u304c\u3067\u304d\u308b\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u3082\u82f1\u8a9e\u3067\u884c\u3063\u3066\uff0c\u81ea\u5206\u306e\u80fd\u529b\u3092\u9ad8\u3081\u3066\u3044\u304d\u305f\u3044\u3068\u611f\u3058\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e3\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Color image segmentation based on immune mechanism and microsaccades<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aWitthaya Sitthivet, Suripon Somkuarnpanit, Kitti Paithoonwattanakij<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Artificial intelligence<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Automatic segmentation is still an essential step in image processing. This paper presents a new method for segmenting natural image using artificial immune systems (AIS). AIS is a computational intelligence paradigm inspired by the immune mechanism. Negative selection is a function of AIS helped to select the best seed point, and antibody\u2019s receptors are reinforced with microsaccades and markov chain to find out some feature of region. Clonal selection and immune network are utilized to learn reference patterns for different cases. In order to regeneration matches better for the pixel (antigen) with successive generations, region growing technique is employed to achieve the final segmentation results. The feasibility and effectiveness of this method have been demonstrated by experiment and the segmentation results have been evaluated.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u73fe\u5728\u753b\u50cf\u51e6\u7406\u306e\u5206\u91ce\u3067\u306f\uff0c\u69d8\u3005\u306a\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u624b\u6cd5\u304c\u63d0\u6848\u3055\u308c\u3066\u3044\u307e\u3059\uff0e\u305d\u306e\u4e2d\u3067\u3082\u672c\u767a\u8868\u306fAIS\u3068\u547c\u3070\u308c\u308b\u30b7\u30b9\u30c6\u30e0\u3092\u7528\u3044\u3066\u81ea\u7136\u753b\u50cf\u306e\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u5316\u3092\u63d0\u6848\u3057\u3066\u3044\u307e\u3057\u305f\uff0eAIS\u3067\u306f\uff0c\u30de\u30eb\u30b3\u30d5\u9023\u9396\u3084\u30de\u30a4\u30af\u30ed\u30b5\u30c3\u30ab\u30fc\u30c9\u304c\u4f7f\u7528\u3055\u308c\u308b\u3053\u3068\u3067\uff0c\u9818\u57df\u306e\u7279\u5fb4\u91cf\u3092\u62bd\u51fa\u3057\uff0c\u305d\u306e\u7279\u5fb4\u3092\u57fa\u306b\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u3092\u884c\u3044\u307e\u3059\uff0e\u7d50\u679c\u306e\u753b\u50cf\u3067\u306f\uff0c1\u679a\u306e\u753b\u50cf\u304b\u3089\u8349\u539f\u3068\u68ee\uff0c\u52d5\u7269\u306e\u90e8\u5206\u304c\u304d\u308c\u3044\u306b\u5206\u5272\u3055\u308c\u3066\u304a\u308a\u307e\u3057\u305f\uff0e\u3057\u304b\u3057\uff0c\u4eca\u56de\u4f7f\u7528\u3057\u3066\u3044\u305f\u81ea\u7136\u753b\u50cf\u306f\u4eba\u9593\u306e\u76ee\u3067\u898b\u3066\u3082\u5bb9\u6613\u306b\u5206\u985e\u3067\u304d\u308b\u306e\u3082\u3060\u3063\u305f\u306e\u3067\uff0c\u3053\u308c\u3089\u3092\u81ea\u7136\u753b\u50cf\u4ee5\u5916\u306b\u5fdc\u7528\u3059\u308b\u306e\u306f\u56f0\u96e3\u3067\u3042\u308b\u3068\u601d\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Detection of Fake Bills Using Spectral Band Images<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Sigeru Omatu, Hideo Araki<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Artificial intelligence<br \/>\nAbstruct\u3000\u3000\uff1aIn the world, many currencies have been issued and some of them were counterfeited. Especially, by the advances in copying technology and computer ability it is rather easy to make counterfeiting bills. In this paper, we develop a method to use spectral property of the bills and classify the bill whether it is true or fake using spectral sub-band analysis for Singapore dollars.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u3067\u7740\u76ee\u3057\u305f\u306e\u306f\uff0c\u73fe\u5728\u4eba\u306e\u76ee\u8996\u306b\u3088\u308a\u78ba\u8a8d\u3055\u308c\u3066\u3044\u308b\u507d\u9020\u7d19\u5e63\u3092\u81ea\u52d5\u7684\u306b\u691c\u51fa\u3057\u3088\u3046\u3068\u3057\u3066\u3044\u308b\u3053\u3068\u3067\u3059\uff0e\u753b\u50cf\u51e6\u7406\u3068\u3044\u3046\u5206\u91ce\u306f\u5b8c\u74a7\u306a\u3082\u306e\u3067\u306f\u306a\u304f\uff0c\u6700\u7d42\u7684\u306b\u306f\u305d\u306e\u7d50\u679c\u3092\u898b\u3066\u4eba\u304c\u5224\u65ad\u3059\u308b\u3053\u3068\u306b\u306f\u306a\u308b\u304c\uff0c\u305d\u308c\u3067\u3082\u88dc\u52a9\u304c\u51fa\u6765\u308c\u3070\u8ca0\u62c5\u304c\u6e1b\u308b\u3068\u3044\u3046\u3053\u3068\u304c\u79c1\u306e\u7814\u7a76\u306b\u3064\u306a\u304c\u308b\u3088\u3046\u306b\u611f\u3058\u307e\u3057\u305f\uff0e\u3053\u306e\u5b9f\u9a13\u3067\u306f\u5b9f\u969b\u306b\u69d8\u3005\u306a\u5149\u3092\u7d19\u5e63\u306b\u7167\u5c04\u3057\uff0c\u305d\u306e\u6642\u306e\u30c7\u30fc\u30bf\u3092\u89e3\u6790\u3059\u308b\u3053\u3068\u3067\u81ea\u52d5\u5224\u5225\u3092\u884c\u304a\u3046\u3068\u3057\u3066\u3044\u307e\u3059\uff0e\u3044\u304f\u3089\u9ad8\u5ea6\u306a\u6280\u8853\u3067\u7d19\u5e63\u3092\u4f5c\u308d\u3046\u3068\u3082\u6a21\u5023\u3055\u308c\u3066\u3057\u307e\u3046\u73fe\u5728\u3067\u306f\uff0c\u3053\u306e\u3088\u3046\u306a\u6280\u8853\u304c\u304b\u306a\u308a\u91cd\u8981\u3068\u306a\u3063\u3066\u3044\u304f\u3068\u611f\u3058\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Development of a design support system for pedestrian space using virtual city environment<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Shota Okamoto, Sen Takematsu, Shimpei Matsumoto, Takako Otabe, Takanori Tanaka, Tatsushi Tokuyasu<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Control techniques<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a This paper presents a virtual simulator that supports the safety design of a lane for cyclist and pedestrian. For the background of increasing the number of contact accident between the bicycle and the pedestrian, this study suggests the optimization method of environmental factors on the lane with focusing on the flow of pedestrian. Our simulator aims to optimize the position and size of environment factors such as street trees, guardrails, and planters to minimize the probability of accidents. This paper firstly constructs the lane for cyclist and pedestrian in the virtual space, and makes the multi-agent system in order to simulate the flow of pedestrians and bicycles. This paper introduces the structure of our virtual similar and attempts to extract the behavior pattern of pedestrians by using an actual video.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u3067\u7740\u76ee\u3057\u305f\u306e\u306f\uff0c\u8fd1\u5e74\u81ea\u8ee2\u8eca\u3084\u6b69\u884c\u8005\u306e\u8eca\u7dda\u306b\u304a\u3051\u308b\u4e8b\u6545\u4ef6\u6570\u304c\u5897\u3048\u3066\u304a\u308a\uff0c\u305d\u308c\u3092\u6e1b\u3089\u305d\u3046\u3068\u3059\u308b\u6280\u8853\u3092\u958b\u767a\u3057\u3066\u3044\u308b\u3053\u3068\u3067\u3059\uff0e\u73fe\u5728\u81ea\u52d5\u904b\u8ee2\u306a\u3069\u306e\u30b7\u30b9\u30c6\u30e0\u306e\u958b\u767a\u304c\u76db\u3093\u306b\u884c\u308f\u308c\u3066\u3044\u307e\u3059\u304c\uff0c\u5168\u90e8\u306e\u81ea\u52d5\u8eca\u306b\u81ea\u52d5\u904b\u8ee2\u304c\u642d\u8f09\u3055\u308c\u308b\u307e\u3067\u306b\u306f\u6642\u9593\u304c\u304b\u304b\u308b\u3068\u8003\u3048\u3089\u308c\u307e\u3059\uff0e\u305d\u306e\u305f\u3081\uff0c\u3053\u306e\u3088\u3046\u306a\u6280\u8853\u306e\u958b\u767a\u304c\u91cd\u8981\u3060\u3068\u8003\u3048\u3089\u308c\u307e\u3059\uff0e\u3053\u306e\u5b9f\u9a13\u3067\u306f\uff0c\u81ea\u8ee2\u8eca\u3084\u6b69\u884c\u8005\u306e\u8eca\u7dda\u306e\u74b0\u5883\u6700\u9069\u5316\u306b\u3088\u308a\uff0c\u4e8b\u6545\u306b\u95a2\u9023\u3059\u308b\u8981\u56e0\u3092\u89e3\u660e\u3057\u3088\u3046\u3068\u3057\u3066\u3044\u307e\u3059\uff0e\u4eca\u5f8c\u3053\u306e\u3088\u3046\u306a\u6280\u8853\u304c\u767a\u5c55\u3059\u308b\u3053\u3068\u3067\uff0c\u81ea\u52d5\u8eca\u3060\u3051\u3067\u306a\u304f\uff0c\u81ea\u8ee2\u8eca\u306a\u3069\u306e\u4e8b\u6545\u4ef6\u6570\u3082\u4e0b\u304c\u3063\u3066\u3044\u304f\u3068\u601d\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n\u53c2\u8003\u6587\u732e<\/p>\n<ul>\n<li>AROB2017,URL\uff1a<a href=\"http:\/\/isarob.org\/symposium\/\">http:\/\/isarob.org\/symposium\/<\/a><\/li>\n<li>AROB2017 proceedings<\/li>\n<\/ul>\n<p><strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"147\"><strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"373\">\u77f3\u539f\u77e5\u61b2<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">Optimal motor imagery for EEG-based brain-computer interfaces<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">\u540c\u4e0a<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">\u65e5\u548c\u609f,\u3000 \u77f3\u539f\u77e5\u61b2,\u3000\u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">Science and International Affairs Bureau, Ministry of Education, Culture, Sports, Science and Technology, Japanese Government<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">AROB 22<sup>nd<\/sup> 2017<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">B-Con PLAZA<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2017\/01\/19\uff5e21<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>The objective of this symposium is to develop new technologies for artificial life and robotics and their applications in various fields listed. Authors are invited to submit papers presenting original research and to discuss development of new technologies concerning artificial life and robotics based on computer simulations and hardware designs of state-of-the-art technologies.<br \/>\n&nbsp;<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u306f19\u65e5\u306e\u5348\u5f8c\u306e\u53e3\u982d\u767a\u8868\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u53e3\u982d\u767a\u8868\u3067\u767a\u8868\u6642\u9593\u304c10\u5206\uff0c\u8cea\u7591\u5fdc\u7b54\u304c5\u5206\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e<br \/>\n\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u00a0This paper investigated how the different motor imagery methods affect the spectral power distribution. We examined the difference in brain activity during motor imagery using a motor imagery task having four patterns: Motor Execution (ME), Observation Of hand Movement (OOM), Kinesthetic Motor Imagery (MIK), Visual Motor Imagery (MIV). Further classification was carried out by comparing five EEG frequency bands (\u03b1, \u03b2,\u03b4-\u03b8, \u03bc, and the full-frequency range). The spectral power distribution of the frequency band with the highest accuracy in each task was analyzed. In ME and OOM, the brain activity reported in previous studies was observed in the spectral power distribution. In MIK, high power of the \u03b4-\u03b8\u3000range was\u3000observed on the forehead. In MIV, the power of the \u03bc\u3000range was high on the visual cortex and sensorimotor cortex. We revealed that MIK was effective for training motor imagery in view of the classification accuracy.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<ul>\n<li>\u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n<p>\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<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>1<\/strong><br \/>\n<strong>\u300c<\/strong>\u00a0how did you choose the channels? what was the criteria?\u00a0<strong>\u300d<\/strong><br \/>\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u56de\u7b54\u3067\u3059\u304ccriteria\u306e\u610f\u5473\u3092\u7406\u89e3\u3067\u304d\u305a\u3001\u8cea\u554f\u306b\u6b63\u3057\u304f\u7b54\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u305b\u3093\u3067\u3057\u305f\u3002\u4ee3\u308f\u308a\u306b\u5ee3\u5b89\u5148\u751f\u306b\u7b54\u3048\u3066\u3044\u305f\u3060\u304d\u307e\u3057\u305f.\u8cea\u554f\u306e\u7b54\u3048\u3068\u3057\u3066\u306f\uff0c\u300c\u8b58\u5225\u7387\u306e\u7b97\u51fa\u306b\u5229\u7528\u3059\u308b\u8133\u6ce2\u306e\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u500b\u6570\u30922\uff5e16\u306e\u9593\u3067\u5909\u5316\u3055\u305b\u307e\u3059\uff0e\u3042\u308b\u30c1\u30e3\u30f3\u30cd\u30eb\u6570\u3067\u8003\u3048\u3089\u308c\u308b\u30c1\u30e3\u30f3\u30cd\u30eb\u4f4d\u7f6e\u306e\u5168\u7d44\u307f\u5408\u308f\u305b\u3092\u8003\u616e\u3057\u3066\uff0c\u7d44\u5408\u305b\u3054\u3068\u306b\u904b\u52d5\u60f3\u8d77\u8b58\u5225\u3092\u884c\u3044\u307e\u3059\uff0e\u904b\u52d5\u60f3\u8d77\u8b58\u5225\u306e\u7d50\u679c\u306e\u4e2d\u3067\uff0c\u6700\u3082\u8b58\u5225\u7387\u306e\u9ad8\u3044\u9078\u629e\u30c1\u30e3\u30f3\u30cd\u30eb\u306e\u7d44\u307f\u5408\u308f\u305b\u3092\uff0c\u8b58\u5225\u306b\u6700\u3082\u6709\u52b9\u306a\u9078\u629e\u30c1\u30e3\u30f3\u30cd\u30eb\u306e\u7d44\u307f\u5408\u308f\u305b\u3068\u5b9a\u7fa9\u3057\u307e\u3057\u305f\uff0e\u5404\u88ab\u9a13\u8005\u30672\uff5e16\u500b\u306e\u9078\u629e\u30c1\u30e3\u30f3\u30cd\u30eb\u3067\u8b58\u5225\u306b\u6709\u52b9\u306a\u9078\u629e\u30c1\u30e3\u30f3\u30cd\u30eb\u306e\u7d44\u307f\u5408\u308f\u305b\u304c\u6c42\u3081\u3089\u308c\u307e\u3059.\u3053\u306e15\u500b\u306e\u8b58\u5225\u7387\u3092\u3055\u3089\u306b\u6bd4\u8f03\u3057\uff0c\u6700\u3082\u8b58\u5225\u7387\u304c\u9ad8\u3044\u30c1\u30e3\u30f3\u30cd\u30eb\u9078\u629e\u6570\u3067\u306e\u30c1\u30e3\u30f3\u30cd\u30eb\u7d44\u307f\u5408\u308f\u305b\u3092\uff0c\u305d\u306e\u88ab\u9a13\u8005\u306b\u5bfe\u3059\u308b\u6700\u9069\u306a\u30c1\u30e3\u30f3\u30cd\u30eb\u7d44\u307f\u5408\u308f\u305b\u3068\u3057\u307e\u3057\u305f\uff0e\u300d\u3068\u306a\u308a\u307e\u3059.<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u4eca\u56de\u306e\u5b66\u4f1a\u306f\u79c1\u306b\u3068\u3063\u3066\u521d\u3081\u3066\u306e\u56fd\u969b\u5b66\u4f1a\u3078\u306e\u53c2\u52a0\u3067\u3057\u305f\uff0eComputational methods for Human Biological information\u30bb\u30c3\u30b7\u30e7\u30f3\u306710\u5206\u9593\u306e\u53e3\u982d\u767a\u8868\u3092\u884c\u3044\u307e\u3057\u305f\uff0e\u5f53\u65e5\u306e\u767a\u8868\u3067\u306f\u69d8\u3005\u306a\u56fd\u7c4d\u306e\u65b9\u306b\u767a\u8868\u3092\u805e\u304d\u306b\u6765\u3066\u3044\u305f\u3060\u304d\uff0c\u7814\u7a76\u306b\u3064\u3044\u3066\u3054\u6307\u5c0e\u3092\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e\u521d\u3081\u3066\u306e\u56fd\u969b\u5b66\u4f1a\u3067\u306e\u53e3\u982d\u767a\u8868\u3068\u3044\u3046\u3053\u3068\u3082\u3042\u308a\u7dca\u5f35\u3082\u3057\u307e\u3057\u305f\u304c\uff0c\u805e\u3044\u3066\u304f\u3060\u3055\u308b\u65b9\u3005\u306b\u81ea\u5206\u306e\u7814\u7a76\u3092\u4f1d\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u672c\u5b66\u4f1a\u3067\u306f\u8cea\u7591\u5fdc\u7b54\u306e\u6642\u9593\u304c5\u5206\u3068\u77ed\u304b\u3063\u305f\u305f\u3081\uff0c\u7814\u7a76\u306b\u95a2\u3059\u308b\u3054\u6307\u6458\u306f\u3042\u307e\u308a\u3044\u305f\u3060\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u305b\u3093\u3067\u3057\u305f\u304c\uff0c\u9650\u3089\u308c\u305f\u6642\u9593\u5185\u3067\u81ea\u5206\u306e\u767a\u8868\u3092\u7406\u89e3\u3057\u3066\u3082\u3089\u3048\u308b\u3088\u3046\u306b\u5de5\u592b\u3059\u308b\u3053\u3068\u306e\u96e3\u3057\u3055\u3092\u611f\u3058\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u4e2d\u3067\uff0c\u6d77\u5916\u304b\u3089\u304a\u8d8a\u3057\u306e\u5148\u751f\u306b\u8cea\u554f\u3092\u3044\u305f\u3060\u304d\u307e\u3057\u305f\u304c\uff0c\u8cea\u554f\u306e\u4e2d\u306e\u82f1\u5358\u8a9e\u306e\u610f\u5473\u304c\u7406\u89e3\u3067\u304d\u305a,\u7b54\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u305a\u6094\u3057\u304b\u3063\u305f\u3067\u3059\uff0e\u307e\u305f\u82f1\u8a9e\u306e\u767a\u8868\u3067\u30bb\u30ea\u30d5\u3092\u8a71\u3059\u3053\u3068\u306b\u5fc5\u6b7b\u306b\u306a\u308a\uff0c\u805e\u3044\u3066\u3044\u308b\u65b9\u3005\u306e\u65b9\u3092\u898b\u306a\u304c\u3089\u767a\u8868\u3067\u304d\u307e\u305b\u3093\u3067\u3057\u305f\uff0e\u7dca\u5f35\u3067\u5927\u4e8b\u306a\u8aac\u660e\u306e\u6587\u8a00\u304c\u98db\u3093\u3067\u3057\u307e\u3063\u305f\u3053\u3068\u3082\u4eca\u56de\u306e\u53cd\u7701\u70b9\u3060\u3068\u611f\u3058\u3066\u3044\u307e\u3059\uff0e\u3067\u304d\u305f\u3053\u3068\u3068\u3057\u3066\uff0c\u7dca\u5f35\u306f\u3042\u3063\u305f\u3082\u306e\u306e\u5168\u54e1\u306b\u805e\u3053\u3048\u308b\u307b\u3069\u5927\u304d\u306a\u58f0\u3067\u767a\u8868\u3067\u304d\u305f\u3053\u3068\u304c\u3042\u3052\u3089\u308c\u307e\u3059\uff0e\u82f1\u8a9e\u306e\u767a\u97f3\u306b\u3064\u3044\u3066\u3082\u305f\u304f\u3055\u3093\u306e\u65b9\u304b\u3089\u307b\u3081\u3066\u3044\u305f\u3060\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u3053\u306e\u5b66\u4f1a\u3067\u306f\u7814\u7a76\u306e\u5185\u5bb9\u306b\u3064\u3044\u3066\u306e\u767a\u898b\u3088\u308a\u3082\u767a\u8868\u306e\u4ed5\u65b9\u3084\uff0c\u82f1\u8a9e\u306e\u5b66\u7fd2\u306b\u3064\u3044\u3066\u5b66\u3076\u3053\u3068\u304c\u305f\u304f\u3055\u3093\u3042\u308a\u307e\u3057\u305f\uff0e\u6b21\u306e\u56fd\u969b\u5b66\u4f1a\u306b\u6311\u6226\u3059\u308b\u3068\u304d\u306b\u306f\uff0c\u4e00\u4eba\u3067\u8cea\u554f\u306b\u6b63\u3057\u304f\u7b54\u3048\u3089\u308c\u308b\u3088\u3046\u82f1\u8a9e\u306e\u52c9\u5f37\u306b\u52b1\u307f\u305f\u3044\u3068\u601d\u3044\u307e\u3059\uff0e\u307e\u305f\u539f\u7a3f\u3092\u4e38\u899a\u3048\u3059\u308b\u306e\u3067\u306f\u306a\u304f\uff0c\u305d\u306e\u5834\u3067\u67d4\u8edf\u306b\u5bfe\u5fdc\u3067\u304d\u308b\u82f1\u8a9e\u529b\u3092\u8eab\u306b\u7740\u3051\u305f\u3044\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u56fd\u969b\u5b66\u4f1a\u306b\u53c2\u52a0\u3057\uff0c\u4eca\u56de\u3082\u305f\u304f\u3055\u3093\u306e\u767a\u898b\u304c\u3042\u308a,\u6bce\u65e5\u304c\u5927\u5909\u6709\u610f\u7fa9\u306a\u6642\u9593\u3067\u3057\u305f\uff0e\u307e\u305f\u5b66\u4f1a\u306b\u53c2\u52a0\u3067\u304d\u308b\u3088\u3046\u306b\u6bce\u65e5\u306e\u7814\u7a76\u3068\u540c\u6642\u306b\uff0c\u82f1\u8a9e\u306e\u30ea\u30b9\u30cb\u30f3\u30b0\u3068\u30b9\u30d4\u30fc\u30ad\u30f3\u30b0\u306e\u5b66\u7fd2\u306b\u3064\u3044\u3066\u3082\u7cbe\u9032\u3057\u3066\u3044\u304d\u305f\u3044\u3068\u8003\u3048\u3066\u3044\u307e\u3059.<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e3\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000An analysis of brain activity in sensory skill learning for braille learning<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Takayo Kawasaki , Hirokazu Miura , Noriyuki Matsuda , Hirokazu Taki<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Brain science<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Feeling transmitted to the learner is very important factor in skill learning. Therefore, it can be possible that learning efficiency is affected by increasing or decreasing the sensation transmitted to the learner. In order to build such a skill learning support system, it is necessary to understand the learning state of the learner. Therefore, we investigate whether the learning state can be analyzed by the EEG analysis under sensory skill learning as target the reading braille. In our EEG analysis, brain activities in the region of the primary somatosensory cortex and the frontal lobe are investigated. Our experimental results show the proportion of \u03b1-waves in mastering skill process and the correlation between the somatosensory cortex and the frontal lobe activities.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\u904b\u52d5\u30b9\u30ad\u30eb\u306e\u5b66\u7fd2\u306b\u304a\u3044\u3066\uff0c\u5b66\u7fd2\u8005\u306e\u611f\u899a\u3092\u5b9a\u91cf\u7684\u306b\u8a55\u4fa1\u3059\u308b\u3053\u3068\u3067\u904b\u52d5\u30b9\u30ad\u30eb\u306e\u5b66\u7fd2\u52b9\u7387\u3092\u4e0a\u3052\u308b\u3053\u3068\u3092\u76ee\u7684\u3068\u3057\u305f\u7814\u7a76\u3067\u3057\u305f\uff0e\u5b66\u7fd2\u8005\u306e\u611f\u899a\u306e\u5b9a\u91cf\u5316\u3092\u56f3\u308b\u305f\u3081\u306b\uff0c\u3053\u306e\u7814\u7a76\u3067\u306f\u8133\u6ce2\u3092\u7528\u3044\u3066\u3044\u307e\u3057\u305f\uff0e\u5177\u4f53\u7684\u306b\u306f\u4f53\u6027\u611f\u899a\u76ae\u8cea\u3068\u524d\u982d\u524d\u91ce\u304b\u3089\u8a08\u6e2c\u3055\u308c\u308b\u8133\u6ce2\u306b\u5468\u6ce2\u6570\u89e3\u6790\u3092\u884c\u3044\uff0c\u89e3\u6790\u3088\u308a\u6c42\u3081\u3089\u308c\u305f\u89d2\u5468\u6ce2\u6570\u6210\u5206\u306e\u76f8\u95a2\u5024\u304b\u3089\u8133\u306e\u6a5f\u80fd\u7684\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u898b\u3066\u3044\u307e\u3057\u305f\uff0e\u8133\u6ce2\u306e\u5468\u6ce2\u6570\u6210\u5206\u3054\u3068\u306b\u76f8\u95a2\u4fc2\u6570\u3092\u6c42\u3081\uff0c\u6a5f\u80fd\u7684\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u89e3\u6790\u3092\u884c\u3046\u624b\u6cd5\u304c\u3042\u308b\u3089\u3057\u304f\u65b0\u3057\u3044\u77e5\u898b\u3067\u3057\u305f\uff0e\u7d50\u679c\u3068\u3057\u3066\u306f\u524d\u982d\u524d\u91ce\u3068\u904b\u52d5\u91ce\u3067\u306e\u76f8\u95a2\u304c\u898b\u3089\u308c\u305f\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\u304c\uff0c\u8133\u5168\u4f53\u3067\u89e3\u6790\u3092\u3057\u3066\u3082\u9762\u767d\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e\u540c\u3058\u751f\u4f53\u4fe1\u53f7\u3067\u3042\u308b\u8133\u6ce2\u3092\u7528\u3044\u305f\u7814\u7a76\u3067\u3057\u305f\u304c\uff0c\u5168\u304f\u7570\u306a\u308b\u89e3\u6790\u624b\u6cd5\uff0c\u30a2\u30d7\u30ed\u30fc\u30c1\u3067\u3042\u3063\u305f\u305f\u3081\u5927\u5909\u8208\u5473\u6df1\u3044\u3082\u306e\u3067\u3057\u305f\uff0e\u7279\u306b\u5468\u6ce2\u6570\u3054\u3068\u306e\u76f8\u95a2\u306b\u7740\u76ee\u3057\u305f\u70b9\u306f,\u81ea\u8eab\u306e\u7814\u7a76\u3092\u9032\u3081\u3066\u3044\u304f\u3046\u3048\u3067\u6709\u76ca\u3067\u3042\u308b\u3068\u611f\u3058\u3089\u308c\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Detection of Fake Bills Using Spectral Band Images<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Sigeru Omatu and Hideo Araki<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Artificial Intelligence<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000In the world, many currencies have been issued and some of them were counterfeited. Especially, by the advances in copying technology and computer ability it is rather easy to make counterfeiting bills. In this paper, we develop a method to use spectral property of the bills and classify the bill whether it is true or fake using spectral sub-band analysis for Singapore dollars.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\u507d\u88c5\u901a\u8ca8\u3092\u8b58\u5225\u3059\u308b\u30a6\u30a7\u30a2\u30e9\u30d6\u30eb\u30c7\u30d0\u30a4\u30b9\u306e\u958b\u767a\u3092\u76ee\u7684\u3068\u3057\u305f\u7814\u7a76\u3067\u3057\u305f\uff0e\u5404\u56fd\u3067\u767a\u884c\u3055\u308c\u308b\u7d19\u5e63\u306f\u4eba\u306e\u76ee\u3060\u3051\u3067\u306f\u8b58\u5225\u4e0d\u53ef\u80fd\u306a\u69d8\u3005\u306a\u7d30\u5de5\u304c\u3055\u308c\u3066\u3044\u308b\u305d\u3046\u3067\u3059\uff0e\u4f8b\u3068\u3057\u3066\u306f\u30d6\u30e9\u30c3\u30af\u30e9\u30a4\u30c8\u3092\u7167\u5c04\u3057\u305f\u3068\u304d\u306b\u6d6e\u304b\u3073\u4e0a\u304c\u308b\u6a21\u69d8\u3084\uff0c\u8d85\u5fae\u7d30\u306a\u7dda\u306a\u3069\u304c\u3042\u3052\u3089\u308c\u307e\u3059\uff0e\u8457\u8005\u306e\u5148\u751f\u306f\u6d77\u5916\u3067\u4e21\u66ff\u3092\u3057\u305f\u969b\u306b\u507d\u672d\u306b\u9a19\u3055\u308c\u305f\u7d4c\u9a13\u304b\u3089\u3053\u306e\u7814\u7a76\u306b\u53d6\u308a\u7d44\u307e\u308c\u305f\u305d\u3046\u3067\u3059\uff0e\u5177\u4f53\u7684\u306b\u306f\u753b\u50cf\u51e6\u7406\u624b\u6cd5\u3092\u7528\u3044\u305f\u3082\u306e\u3067\u3057\u305f\uff0e\u307e\u305a\u7d19\u5e63\u306e\u753b\u50cf\u3092\u30b0\u30ec\u30fc\u30b9\u30b1\u30fc\u30eb\u5316\u3057\u5468\u6ce2\u6570\u6210\u5206\u3054\u3068\u306b\u30b3\u30f3\u30c8\u30e9\u30b9\u30c8\u88dc\u6b63\u3092\u884c\u3044\u307e\u3059\uff0e\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u5e73\u5766\u5316\u306e\u5f8c\u306b\u5f97\u3089\u308c\u305f\u753b\u50cf\u304b\u3089\u7279\u5fb4\u91cf\u3092\u8a08\u7b97\u3057\uff0c\u507d\u88c5\u7d19\u5e63\u3092\u8b58\u5225\u3059\u308b\u3082\u306e\u3067\u3057\u305f\uff0e\u73fe\u5728\u306f\u307e\u30601m\u307b\u3069\u306e\u5927\u304d\u3055\u306e\u30c7\u30d0\u30a4\u30b9\u3067\u5b9f\u7528\u5316\u307e\u3067\u306b\u306f\u6642\u9593\u304c\u304b\u304b\u308b\u3068\u306e\u3053\u3068\u3067\u3057\u305f\u304c\uff0c\u753b\u50cf\u89e3\u6790\u306f\u77ed\u3044\u6642\u9593\u3067\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u308b\u305f\u3081\uff0c\u3042\u3068\u306f\u5c0f\u578b\u5316\u3092\u9032\u3081\u3066\u3044\u304f\u3068\u306e\u3053\u3068\u3067\u3057\u305f\uff0e\u753b\u50cf\u51e6\u7406\u306e\u7814\u7a76\u3092\u82f1\u8a9e\u3067\u805e\u3044\u305f\u305f\u3081\uff0c\u89e3\u91c8\u306e\u9593\u9055\u3044\u3082\u3042\u3063\u305f\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u304c\uff0c\u9762\u767d\u3044\u7814\u7a76\u3060\u3068\u601d\u3044\u307e\u3057\u305f\uff0e\u81ea\u5206\u306e\u56f0\u3063\u305f\u7d4c\u9a13\u304b\u3089\u7814\u7a76\u306b\u7d50\u3073\u3064\u3051\u3066\u7a81\u304d\u8a70\u3081\u308b\u59ff\u52e2\u306f\u7814\u7a76\u8005\u3068\u3057\u3066\u5b66\u3073\u305f\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0 \uff1a\u3000Perception using properties of the real world &#8211; Task to distinguish objects in cans-<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Yuzo Chojin , Kazuyuki Ito<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Learning<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aOur research aimed to develop a new method for robot perception based on that of human beings. In this research, as a typical example, we employ a task involving distinguishing objects in cans, and we demonstrated the importance of motion in utilizing real-world properties. We focused on a learning mechanism to find usable real-world properties and to identify the motion that would enable us to utilize these properties. We employed shaking motion as an example, and we proposed a new learning procedure. Experiments were conducted, and we demonstrated that the difference in mass and material could be distinguished autonomously by the proposed simple procedure.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\u4eba\u9593\u306e\u77e5\u899a\u3092\u30ed\u30dc\u30c3\u30c8\u3067\u518d\u73fe\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u3068\u3057\u305f\u7814\u7a76\u3067\u3057\u305f\uff0e\u30bf\u30b9\u30af\u3068\u3057\u3066\u306f\u30ed\u30dc\u30c3\u30c8\u30a2\u30fc\u30e0\u306b\u7f36\u964d\u3089\u305b\u308b\u30bf\u30b9\u30af\u3067\uff0c\u7f36\u306e\u4e2d\u8eab\u3068\u305d\u306e\u91cf\u3092\u5909\u5316\u3055\u305b\u308b\u3082\u306e\u3067\u3057\u305f\uff0e\u7f36\u306e\u4e2d\u306f\u30ac\u30e9\u30b9\u3068\u30b4\u30e0\u3068\u30d7\u30e9\u30b9\u30c1\u30c3\u30af\u3067,\u8a08\u6e2c\u3055\u308c\u308b\u30ed\u30dc\u30c3\u30c8\u30a2\u30fc\u30e0\u306e\u52d5\u304d\u3092\u8a55\u4fa1\u3059\u308b\u3082\u306e\u3067\u3057\u305f\uff0e\u307e\u305f\u30ed\u30dc\u30c3\u30c8\u30a2\u30fc\u30e0\u306e\u52d5\u304d\u3060\u3051\u3067\u306a\u304f\uff0c\u7f36\u304b\u3089\u8a08\u6e2c\u3055\u308c\u308b\u97f3\u306e\u89e3\u6790\u3082\u540c\u6642\u306b\u3055\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u7d50\u679c\u3068\u3057\u3066\u97f3\u3068\u30ed\u30dc\u30c3\u30c8\u306e\u52d5\u304d\u304b\u3089\u4e2d\u8eab\u306e\u7279\u5b9a\u304c\u9ad8\u3044\u7cbe\u5ea6\u3067\u3067\u304d\u3066\u3044\u308b\u3088\u3046\u3067\u3057\u305f\uff0e\u751f\u4f53\u60c5\u5831\u3067\u306f\u306a\u3044\u3067\u3059\u304c\uff0c\u611f\u899a\u306e\u5b9a\u91cf\u5316\u3068\u3044\u3046\u90e8\u5206\u3067\u306f\u81ea\u5206\u305f\u3061\u306e\u7814\u7a76\u5ba4\u306b\u901a\u305a\u308b\u3082\u306e\u3092\u611f\u3058\u307e\u3057\u305f\uff0e\u500b\u4eba\u3068\u3057\u3066\u306f\u30ed\u30dc\u30c3\u30c8\u3092\u6271\u3046\u3068\u3044\u3046\u610f\u5473\u3067\u91cd\u306a\u308b\u90e8\u5206\u3082\u591a\u304f\uff0c\u30ed\u30dc\u30c3\u30c8\u5236\u5fa1\u306e\u89e3\u6790\u624b\u6cd5\u306e\u898b\u305b\u65b9\u306f\u53c2\u8003\u306b\u306a\u308a\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n\u53c2\u8003\u6587\u732e<\/p>\n<ul>\n<li>AROB2017 \u30d7\u30ed\u30b0\u30e9\u30e0<\/li>\n<li>AROB2017 http:\/\/isarob.org\/symposium\/<\/li>\n<\/ul>\n<p><strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"147\"><strong>\u00a0<\/strong><br \/>\n<strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"373\">&nbsp;<br \/>\n\u7530\u6751\u9675\u5927<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">Deep Learning\u306e\u8133\u6a5f\u80fd\u30a4\u30e1\u30fc\u30b8\u30f3\u30b0\u30c7\u30fc\u30bf\u306b\u304a\u3051\u308b\u7a7a\u9593\u60c5\u5831\u306e\u7dad\u6301\u306b\u3064\u3044\u3066\u306e\u8b70\u8ad6<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">Discussion on maintaining spatial information in deep learning of functional brain imaging data<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">Ryota TAMURA, Tomoyuki HIROYASU, Satoru HIWA, Keisuke HACHISUKA, Hiroshi FURUTANI<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">ISAROB (International Society of Artificial Life and Robotics)<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">22nd International Symposium on Artificial Life and Robotics\u30002017<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">B-Con PLAZA, Beppu, JAPAN<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2017\/01\/19-2017\/01\/21<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>2017\/01\/19\u304b\u30892017\/01\/21\u306b\u304b\u3051\u3066\uff0c\u5927\u5206\u770c\u5225\u5e9c\u5e02\u306e\u30d3\u30fc\u30b3\u30f3\u30d7\u30e9\u30b6\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305f\u7b2c22\u56deAROB 2017\uff08http:\/\/isarob.org\/symposium\/\uff09 \u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0eAROB\u306f\uff0c\u4eba\u5de5\u751f\u547d\u3068\u30ed\u30dc\u30c3\u30c8\u56fd\u969b\u5b66\u4f1a\u306b\u3088\u3063\u3066\u4e3b\u50ac\u3055\u308c\u305f\u56fd\u969b\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u3067\uff0c\u65e5\u672c\u4eba\u306e\u5b66\u751f\u3084\u6559\u54e1\uff0c\u7814\u7a76\u8005\uff0c\u307e\u305f\uff0c\u30a2\u30b8\u30a2\u7cfb\u306e\u5916\u56fd\u4eba\u7814\u7a76\u8005\u304c\u53c2\u52a0\u3057\u3066\u304a\u308a\u307e\u3057\u305f\uff0e\u3053\u306e\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u306e\u76ee\u7684\u306f\uff0c\u4eba\u5de5\u751f\u547d\u3068\u30ed\u30dc\u30c3\u30c8\u306b\u95a2\u3059\u308b\u69d8\u3005\u306a\u5fdc\u7528\u5206\u91ce\u306e\u305f\u3081\u306e\u65b0\u6280\u8853\u306e\u958b\u767a\u306e\u4fc3\u9032\u3084\uff0c\u305d\u306e\u7814\u7a76\u306b\u95a2\u3059\u308b\u8b70\u8ad6\u3092\u63a8\u9032\u3057\u3066\u3044\u304f\u3053\u3068\u3092\u76ee\u7684\u3068\u3057\u3066\u3044\u307e\u3059\uff0e<br \/>\n\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u79c1\u306e\u4ed6\u306b\u5ee3\u5b89\u5148\u751f\uff0c\u7530\u4e2d\u90a3\u667a\u304f\u3093\uff0c\u5f8c\u85e4\u304f\u3093\uff0c\u5ca1\u7530\u304f\u3093\uff0c\u77f3\u539f\u304f\u3093\u306e\u8a086\u540d\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u306f19\u65e5\u306e\u5348\u5f8c13\u6642\u304b\u3089\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cComputational methods for Human Biological information\u300d\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u82f1\u8a9e\u306e\u53e3\u982d\u767a\u8868\u3067\uff0c10\u5206\u306e\u8b1b\u6f14\u6642\u9593\u30685\u5206\u306e\u8cea\u7591\u5fdc\u7b54\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e<br \/>\n\u4eca\u56de\u306e\u767a\u8868\u306f\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u984c\u76ee\uff1aDiscussion on maintaining spatial information in deep learning of functional brain imaging data<br \/>\n\u7814\u7a76\u76ee\u7684\uff1aDeep Learning\u3092\u7528\u3044\u305f\u8133\u6a5f\u80fd\u30a4\u30e1\u30fc\u30b8\u30f3\u30b0\u30c7\u30fc\u30bf\u89e3\u6790\u624b\u6cd5\u306e\u63d0\u6848<br \/>\n\u672c\u767a\u8868\u306e\u5185\u5bb9\uff1a3\u6b21\u5143\u306efNIRS\u30c7\u30fc\u30bf\u69cb\u9020\u306e\u89e3\u6790\u306b\u5bfe\u3059\u308bDeep Learning\u624b\u6cd5\u306e\u691c\u8a0e<br \/>\n\u63d0\u6848\u624b\u6cd5\uff1a3Dimensional Convolutional Neural Network\uff083DCNN\uff09\u3092\u7528\u3044\u305ffNIRS\u30c7\u30fc\u30bf\u89e3\u6790\u624b\u6cd5<br \/>\n\u8a55\u4fa1\u5b9f\u9a13\uff1a2\u7a2e\u985e\u306efNIRS\u4eba\u5de5\u30c7\u30fc\u30bf\u3092\u63d0\u6848\u624b\u6cd5\u30683\u6b21\u5143\u30c7\u30fc\u30bf\u306b\u5bfe\u5fdc\u3067\u304d\u306a\u30442DCNN\u306b\u5b66\u7fd2\u3055\u305b\uff0c\u5404\u30e2\u30c7\u30eb\u304b\u3089ROI\u3092\u62bd\u51fa<br \/>\n\u7d50\u679c\uff1a\u63d0\u6848\u624b\u6cd5\u3067\u306f\uff0c2\u7a2e\u985e\u306e\u30c7\u30fc\u30bf\u3067\u5dee\u7570\u306e\u6700\u3082\u5927\u304d\u3044\u9818\u57df\u3092ROI\u3068\u3057\u3066\u62bd\u51fa\u3067\u304d\u3066\u304a\u308a\uff0c\u6bd4\u8f03\u5bfe\u8c61\u306e2DCNN\u306f\u30c7\u30fc\u30bf\u306e\u5927\u90e8\u5206\u3092ROI\u3068\u3057\u3066\u62bd\u51fa<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n\u30fb\u8cea\u7591\u5fdc\u7b54<br \/>\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<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>1<\/strong><br \/>\n\u540c\u5fd7\u793e\u5927\u5b66\u533b\u7642\u60c5\u5831\u30b7\u30b9\u30c6\u30e0\u7814\u7a76\u5ba4\u6240\u5c5e\u306e\u5f8c\u85e4\u304f\u3093\u304b\u3089\u306e\u8cea\u554f\u3067\u3059\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\u300c\u305d\u3082\u305d\u3082Deep Learning\u3068\u306f\u3069\u306e\u3088\u3046\u306a\u3082\u306e\u304b\uff1f\u300d\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u306f\u300cDeep Learning\u306f\u8a8d\u8b58\u5bfe\u8c61\u306e\u91cd\u8981\u306a\u7279\u5fb4\u3092\u524d\u3082\u3063\u3066\u6559\u3048\u8fbc\u307e\u305a\u306b\u8a8d\u8b58\u5bfe\u8c61\u304b\u3089\u7279\u5fb4\u3092\u81ea\u52d5\u3067\u62bd\u51fa\u3059\u308b\u5b66\u7fd2\u6a5f\u80fd\u3092\u5099\u3048\u305f\u30b7\u30b9\u30c6\u30e0\u3067\u3059\u300d<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\u82f1\u8a9e\u3067\u805e\u304d\u53d6\u308a\u3065\u3089\u304b\u3063\u305f\u306e\u3067\u3059\u304c\uff0c\u304a\u305d\u3089\u304f\u300c\u6bce\u56defNIRS\u306e\u30c7\u30fc\u30bf\u306f\u5909\u308f\u3063\u3066\u3044\u304f\u304c\uff0c\u3069\u306e\u3088\u3046\u306bROI\u3092\u8a2d\u5b9a\u3059\u308b\u306e\u304b\uff1f\u300d\u3068\u3044\u3046\u8cea\u554f\u3060\u3063\u305f\u3068\u601d\u3044\u307e\u3059\u3002\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u56de\u7b54\u306b\u624b\u9593\u53d6\u3063\u3066\u3057\u307e\u3044\uff0c\u6642\u9593\u3044\u3063\u3071\u3044\u306b\u306a\u3063\u3066\u3057\u307e\u3044\u307e\u3057\u305f\u306e\u3067\uff0c\u30bb\u30c3\u30b7\u30e7\u30f3\u7d42\u4e86\u5f8c\u304a\u8a71\u306b\u4f3a\u3044\u307e\u3057\u305f\uff0e\u305d\u306e\u6642\u306e\u56de\u7b54\u306f\u300c\u4e88\u3081\u6ce8\u76ee\u3057\u305f\u3044\u30bf\u30b9\u30af\u3084\u72b6\u614b\u306e\u6642\u306e\u30d2\u30c8\u306e\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u5b66\u7fd2\u3055\u305b\u3066\u304a\u304d\uff0c\u5b66\u7fd2\u5f8c\u306e\u30e2\u30c7\u30eb\u3067\u305d\u306e\u7740\u76ee\u3057\u305f\u95a2\u9023\u9818\u57df\u3092\u63a8\u5b9a\u3057\u307e\u3059\u300d<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u4eca\u56de\u767a\u8868\u3044\u305f\u3057\u307e\u3057\u305fAROB2017\u3067\u306f\uff0c\u30ed\u30dc\u30c3\u30c8\u3092\u5236\u5fa1\u3059\u308b\u3053\u3068\u3092\u60f3\u5b9a\u3057\u305f\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u304c\u591a\u3044\u3068\u601d\u3063\u3066\u304a\u308a\u307e\u3057\u305f\u304c\uff0cNeuroscience\u306e\u3088\u3046\u306a\u30bb\u30c3\u30b7\u30e7\u30f3\u3082\u3042\u308a\uff0c\u8133\u6a5f\u80fd\u30a4\u30e1\u30fc\u30b8\u30f3\u30b0\u30c7\u30fc\u30bf\u306e\u5fdc\u7528\u5206\u91ce\u3082\u767a\u8868\u3055\u308c\u3066\u304a\u308a\uff0c\u81ea\u5206\u306e\u7814\u7a76\u306b\u3082\u8208\u5473\u3092\u3082\u3063\u3066\u3044\u305f\u3060\u3051\u305f\u767a\u8868\u4f1a\u306b\u306a\u3063\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u306f\u3058\u3081\u3066\u306e\u82f1\u8a9e\u3067\u306e\u53e3\u982d\u767a\u8868\u3068\u3044\u3046\u3053\u3068\u3082\u3042\u308a\uff0c\u60f3\u5b9a\u8cea\u554f\u3092\u8003\u3048\u3066\u767a\u8868\u306b\u81e8\u307f\u307e\u3057\u305f\u304c\uff0c\u307e\u3060\u307e\u3060\u81ea\u5206\u306e\u82f1\u8a9e\u3067\u306f\u60f3\u5b9a\u3055\u308c\u3066\u3044\u306a\u3044\u8cea\u7591\u5fdc\u7b54\u306f\u96e3\u3057\u3044\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u3053\u306e\u767a\u8868\u3092\u304d\u3063\u304b\u3051\u306b\uff0c\u82f1\u8a9e\u5b66\u7fd2\u306e\u91cd\u8981\u6027\u3092\u518d\u78ba\u8a8d\u3057\uff0c\u4eca\u5f8c\u306f\u66f4\u306b\u82f1\u8a9e\u5b66\u7fd2\u3092\u3057\u3066\u3044\u3053\u3046\u3068\u610f\u6b32\u304c\u308f\u304d\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u81ea\u5206\u306e\u7814\u7a76\u306b\u8208\u5473\u3092\u6301\u3063\u3066\u304f\u3060\u3055\u3063\u305f\u4eba\u304c\u3044\u305f\u305f\u3081\uff0c\u81ea\u5206\u306e\u7814\u7a76\u306b\u5bfe\u3059\u308b\u30e2\u30c1\u30d9\u30fc\u30b7\u30e7\u30f3\u304c\u4e0a\u304c\u308a\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e3\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Thai Printed Recognition from Benchmark Dataset using Deep Learning<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Rapeeporn Chamchong, Kaveepoj Banluawong, Umaporn Saisangchan, and Phatthanaphong Chomphuwiset<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Learning<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u4eca\u65e5\uff0c\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306e\u753b\u50cf\u30c7\u30fc\u30bf\u304c\u5927\u91cf\u306b\u8a18\u9332\u3055\u308c\uff0c\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u3067\u51e6\u7406\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3042\u308b\uff0e\u30bf\u30a4\u3067\u306f\uff0c\u30bf\u30a4\u8a9e\u306e\u5149\u5b66\u5f0f\u6587\u5b57\u8a8d\u8b58\u30b7\u30b9\u30c6\u30e0\uff08OCR\uff09\u3092\u5229\u7528\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3042\u308b\uff0e\u73fe\u5728ThaiOCR\u3084ArnThai\u306a\u3069\u306e\u30bf\u30a4\u306eOCR\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u304c\u5b58\u5728\u3059\u308b\uff0e\u3053\u306e\u30bf\u30a4\u306eOCR\u306e\u7cbe\u5ea6\u3092\u5411\u4e0a\u3055\u305b\u308b\u5fc5\u8981\u304c\u3042\u308b\uff0e\u8fd1\u5e74\u6587\u5b57\u8a8d\u8b58\u3067\u306fDeep Learning\u306e\u5229\u7528\u304c\u63d0\u6848\u3055\u308c\u3066\u3044\u308b\uff0e\u3053\u306e\u6280\u6cd5\u306f\u753b\u50cf\u306e\u7d71\u8a08\u7684\u69cb\u9020\u307e\u305f\u306f\u4f9d\u5b58\u6027\u3092\u5b66\u7fd2\u3059\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u753b\u50cf\u306e\u7279\u5fb4\u3092\u62bd\u51fa\u3057\uff0c\u5206\u985e\u3092\u884c\u3046\u3053\u3068\u304c\u53ef\u80fd\u3068\u306a\u3063\u3066\u3044\u308b\uff0e\u672c\u7a3f\u3067\u306fDeep Learning\u624b\u6cd5\u3067\u3042\u308bStacked AutoEncoder\u3092\u4f7f\u7528\u3057\uff0c\u30bf\u30a4\u306e\u30a2\u30eb\u30d5\u30a1\u30d9\u30c3\u30c8\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5fdc\u3057\u305f\uff0eMulti Layer Perceptron\uff08MLP\uff09\u3068Stacked AutoEncoder\u306e\u7d50\u679c\u306f\u6bd4\u8f03\u3055\u308c\u305f\uff0e\u8b58\u5225\u7387\u306fMLP\u304c83.61\uff05\uff0cStacked AutoEncoder\u306f96.82\uff05\u3068\u306a\u3063\u305f\uff0e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u306f\u30bf\u30a4\u306e\u30a2\u30eb\u30d5\u30a1\u30d9\u30c3\u30c8\u306bDeep Learning\u624b\u6cd5\u3067\u3042\u308bStacked AutoEncoder\u3092\u9069\u5fdc\u3057\uff0c\u6587\u5b57\u8a8d\u8b58\u3092\u884c\u3046\u7814\u7a76\u3067\u3057\u305f\uff0e\u3053\u3053\u3067\u7591\u554f\u306b\u601d\u3063\u305f\u306e\u306f\uff0c\u6587\u5b57\u8a8d\u8b58\u3067\u306f\u4e00\u822c\u7684\u306bConvolutional Neural Network\uff08CNN\uff09\u3092\u7528\u3044\u308b\u306e\u3067\uff0c\u306a\u305cStacked AutoEncoder\u3092\u7528\u3044\u3066\u6587\u5b57\u8a8d\u8b58\u3057\u3066\u3044\u308b\u306e\u304b\u7591\u554f\u306b\u601d\u3044\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u4e2d\u3067\uff0c\u4eca\u56de\u306f\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u4fdd\u7ba1\u3055\u308c\u3066\u3044\u308b\u6587\u5b57\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u884c\u3063\u305f\u305f\u3081\uff0c\u4eca\u5f8c\u306f\u5b9f\u969b\u306e\u624b\u66f8\u304d\u6587\u5b57\u3092\u8a8d\u8b58\u3055\u305b\u308b\u3053\u3068\u3092\u8003\u3048\uff0c\u305d\u306e\u6642\u306fCNN\u3092 \u7528\u3044\u308b\u3053\u3068\u3082\u8003\u3048\u3066\u3044\u304f\u3068\u304a\u3063\u3057\u3083\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aPerception using properties of the real world -Task to distinguish objects in cans-<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Yuzo Chojin and Kazuyuki Ito<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Learning<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u7814\u7a76\u76ee\u7684\u306f\u4eba\u9593\u306e\u77e5\u899a\u306b\u57fa\u3065\u3044\u305f\uff0c\u30ed\u30dc\u30c3\u30c8\u306e\u65b0\u3057\u3044\u77e5\u899a\u65b9\u6cd5\u306e\u958b\u767a\u3067\u3042\u308b\uff0e\u3053\u306e\u7814\u7a76\u3067\u306f\uff0c\u5178\u578b\u7684\u306a\u4f8b\u3068\u3057\u3066\u7f36\u306e\u4e2d\u306b\u5165\u3063\u3066\u3044\u308b\u7269\u4f53\u3092\u533a\u5225\u3059\u308b\u3068\u3044\u3046\u30bf\u30b9\u30af\u3092\u7528\u3044\u308b\uff0e\u5b9f\u7528\u7684\u306a\u5b66\u7fd2\u30e1\u30ab\u30cb\u30ba\u30e0\u306b\u6ce8\u76ee\u3057\u305f\uff0e\u7f36\u306e\u5185\u90e8\u306b\u5165\u3063\u3066\u3044\u308b\u7269\u4f53\u3092\uff0c\u7f36\u3092\u632f\u3063\u305f\u6642\u306b\u751f\u3058\u308b\u97f3\u3067\u7f36\u306e\u4e2d\u8eab\u3068\u305d\u306e\u91cf\u3092\u63a8\u5b9a\u3059\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u63d0\u6848\u3059\u308b\uff0e\u7f36\u306e\u632f\u308a\u65b9\u306f\u4eba\u306e\u7f36\u3092\u632f\u308b\u52d5\u4f5c\u306b\u57fa\u3065\u3044\u3066\u8a2d\u8a08\u3055\u308c\u3066\u3044\u308b\uff0e\u5b9f\u9a13\u3067\u306f\uff0c\u7f36\u306e\u4e2d\u306b\u30ac\u30e9\u30b9\uff08\u30d3\u30fc\u7389\uff09\uff0c\u30d7\u30e9\u30b9\u30c1\u30c3\u30af\uff08BB\u5f3e\uff09\uff0c\u30b4\u30e0\uff08\u30b4\u30e0\u5f3e\uff09\u3092\u5165\u308c\uff0c\u7f36\u3092\u632f\u3063\u305f\u6642\u306e\u97f3\u306e\u5468\u6ce2\u6570\u3067\u4e2d\u8eab\u3068\u8cea\u91cf\u3092\u63a8\u5b9a\u3057\u305f\uff0e\u305d\u306e\u7d50\u679c\uff0c\u5404\u6750\u8cea\u3068\u8cea\u91cf\u3092\u6b63\u78ba\u306b\u5224\u65ad\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3042\u3063\u305f\uff0e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n\u3053\u306e\u767a\u8868\u3067\u306f\uff0c\u5b9f\u4e16\u754c\u306b\u304a\u3044\u3066\u3088\u308a\u5b9f\u7528\u7684\u306a\u65b0\u3057\u3044\u30ed\u30dc\u30c3\u30c8\u306e\u77e5\u899a\u3092\u958b\u767a\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u3068\u3057\u3066\u304a\u308a\uff0c\u305d\u306e\u4e2d\u3067\u3082\u30d2\u30c8\u306e\u8074\u899a\u306b\u57fa\u3065\u304d\uff0c\u7269\u4f53\u306e\u8cea\u91cf\u3084\u6750\u8cea\u3092\u63a8\u5b9a\u3059\u308b\u3053\u3068\u306b\u8208\u5473\u3092\u5f15\u304b\u308c\u307e\u3057\u305f\uff0e\u305f\u3057\u304b\u306b\u30d2\u30c8\u306f\u4e2d\u8eab\u304c\u4e0d\u660e\u306a\u7bb1\u3084\u7f36\u3092\u898b\u305f\u6642\uff0c\u305d\u308c\u3092\u624b\u306b\u6301\u3063\u3066\u632f\u3063\u305f\u6642\u306e\u97f3\u3067\u3042\u308b\u7a0b\u5ea6\u4e2d\u8eab\u3092\u63a8\u6e2c\u3059\u308b\u3053\u3068\u304c\u3067\u304d\uff0c\u305d\u308c\u304c\u30ed\u30dc\u30c3\u30c8\u3067\u3082\u51fa\u6765\u305f\u306e\u306a\u3089\uff0c\u30ed\u30dc\u30c3\u30c8\u306e\u884c\u52d5\u30d1\u30bf\u30fc\u30f3\u304c\u5897\u3048\u308b\u3068\u601d\u3044\u307e\u3057\u305f\uff0e\u30ed\u30dc\u30c3\u30c8\u306e\u884c\u52d5\u3082\u3053\u306e\u3088\u3046\u306a\u7814\u7a76\u304c\u9032\u3081\u3070\uff0c\u3088\u308a\u4eba\u9593\u306b\u8fd1\u3065\u3044\u3066\u3044\u304f\u3068\u8003\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aApplying Overextension to First Language Acquisition in a Joint Attention Frame<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Ryuichi Matoba, Yuya Hayashi, and Shingo Hagiwara<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Artificial intelligence<br \/>\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u8a00\u8a9e\u304c\u305d\u306e\u5b9f\u969b\u306e\u610f\u5473\u306e\u7bc4\u56f2\u5916\u3067\u4f7f\u7528\u3055\u308c\u308b\u73fe\u8c61\u3092\u904e\u4f38\u5c55\u3068\u547c\u3076\uff0e\u904e\u4f38\u5c55\u306f\u5e7c\u5150\u306e\u7b2c\u4e00\u8a00\u8a9e\u7372\u5f97\u306b\u304a\u3044\u3066\u983b\u7e41\u306b\u767a\u751f\u3059\u308b\u50be\u5411\u304c\u3042\u308b\uff0e\u5e7c\u5150\u306e\u8a9e\u5f59\u306f\u751f\u5f8c18\u30f6\u6708\u3054\u308d\u306b\u6025\u901f\u306b\u5909\u5316\u3059\u308b\uff0e\u4e00\u65b9\uff0c\u5e7c\u5150\u306f\u672a\u77e5\u306e\u767a\u8a71\u53ef\u80fd\u306a\u610f\u5473\u306e\u7bc4\u56f2\u3092\u5236\u9650\u3059\u308b\u8a8d\u77e5\u30d0\u30a4\u30a2\u30b9\u306e\u5f71\u97ff\uff0c\u304a\u3088\u3073\u8a8d\u77e5\u30d0\u30a4\u30a2\u30b9\u306e\u30bf\u30a4\u30d7\u306f\uff0c\u3042\u308b\u7a0b\u5ea6\u306e\u8a00\u8a9e\u8a8d\u8b58\u304c\u306a\u3051\u308c\u3070\u52b9\u679c\u304c\u306a\u3044\uff0e\u3057\u305f\u304c\u3063\u3066\uff0c\u5e7e\u3064\u304b\u306e\u7a2e\u985e\u306e\u8a8d\u77e5\u30d0\u30a4\u30a2\u30b9\u306f\uff0c\u6700\u521d\u306e\u8a00\u8a9e\u7372\u5f97\u521d\u671f\u306b\u52b9\u679c\u7684\u306b\u6a5f\u80fd\u3057\u306a\u3044\u53ef\u80fd\u6027\u304c\u3042\u308b\uff0e\u7b2c\u4e00\u8a00\u8a9e\u7372\u5f97\u306e\u521d\u671f\u306e\u5e7c\u5150\u304c\u81ea\u5df1\u306e\u8a00\u8a9e\u3092\u5897\u52a0\u3055\u305b\u308b\u3053\u3068\u3092\u793a\u5506\u3057\u3066\u3044\u308b\uff0e\u69d8\u3005\u306a\u80fd\u529b\u3092\u7528\u3044\u305f\u8a00\u8a9e\u8a8d\u8b58\u304c\u8a00\u8a9e\u306e\u904e\u5ea6\u306a\u62e1\u5f35\u3092\u3082\u305f\u3089\u3059\uff0e\u672c\u7814\u7a76\u306e\u76ee\u7684\u306f\uff0c\u8a00\u8a9e\u7372\u5f97\u6642\u306e\u8a00\u8a9e\u306e\u904e\u5270\u62e1\u5f35\u306e\u5f71\u97ff\u3092\u78ba\u8a8d\u3059\u308b\u3053\u3068\u3067\u3042\u308b\uff0e\u6211\u3005\u306e\u4eee\u8aac\u3092\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u306b\u3088\u3063\u3066\u30c6\u30b9\u30c8\u3057\u305f\uff0e\u7d50\u679c\u306f\uff0c\u904e\u5270\u62e1\u5f35\u304c\u6587\u6cd5\u306e\u8a00\u8a9e\u7372\u5f97\u306e\u52a0\u901f\u3092\u793a\u3057\u305f\uff0e\u3055\u3089\u306b\uff0c\u672c\u30e2\u30c7\u30eb\u306e\u5e7c\u5150\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u306f\u8868\u73fe\u529b\u306e\u9ad8\u3044\u6587\u6cd5\u3092\u7372\u5f97\u3057\u305f\uff0e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n\u3053\u306e\u7814\u7a76\u306f\uff0c\u5e7c\u5150\u306e\u521d\u671f\u306e\u8a00\u8a9e\u7372\u5f97\u6642\u306b\u751f\u3058\u308b\u8a00\u8a9e\u306e\u904e\u4f38\u5c55\u304c\uff0c\u3069\u306e\u3088\u3046\u306b\u3057\u3066\u6210\u308a\u7acb\u3063\u3066\u3044\u308b\u304b\u4eee\u8aac\u3092\u7acb\u3066\uff0c\u305d\u308c\u3092\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u306b\u3088\u3063\u3066\u793a\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u5e7c\u5150\u306f\uff0c\u899a\u3048\u305f\u3066\u306e\u8a00\u8a9e\u3092\u69d8\u3005\u306a\u5834\u9762\u3067\u7528\u3044\u307e\u3059\uff0e\u5f93\u6765\u307e\u3067\u306f\uff0c\u8a8d\u77e5\u30d0\u30a4\u30a2\u30b9\u3068\u3044\u3046\u8003\u3048\u65b9\u3067\u5e7c\u5150\u306f\u8a00\u8a9e\u7372\u5f97\u3092\u884c\u3063\u3066\u3044\u308b\u3068\u8003\u3048\u3089\u308c\u3066\u304d\u307e\u3057\u305f\uff0e\u3057\u304b\u3057\u306a\u304c\u3089\uff0c\u8a8d\u77e5\u30d0\u30a4\u30a2\u30b9\u306f\u3042\u308b\u7a0b\u5ea6\u306e\u8a00\u8a9e\u8a8d\u8b58\u80fd\u529b\u304c\u306a\u3051\u308c\u3070\u305d\u306e\u52b9\u679c\u3092\u5f97\u3089\u308c\u306a\u3044\u3068\u8003\u3048\u3089\u308c\u307e\u3059\uff0e\u3053\u306e\u7814\u7a76\u3067\u306f\uff0c\u81ea\u5df1\u306e\u8a00\u8a9e\u3092\u904e\u5270\u306b\u62e1\u5f35\u3055\u305b\u308b\u3053\u3068\u3067\uff0c\u5e7c\u5150\u306f\u7b2c\u4e00\u8a00\u8a9e\u306e\u8868\u73fe\u65b9\u6cd5\u3092\u8eab\u306b\u3064\u3051\u308b\u3068\u8003\u3048\u3066\u3044\u307e\u3059\uff0e\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3067\u306f\uff0c\u5e7c\u5150\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u306b\u8a00\u8a9e\u3092\u63d0\u793a\u3057\uff0c\u305d\u308c\u3092\u69d8\u3005\u306a\u4f1a\u8a71\u306b\u591a\u7528\u3057\u3066\u3044\u3063\u305f\u7d50\u679c\uff0c\u6700\u7d42\u7684\u306b\u5e7c\u5150\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u306f\u8868\u73fe\u529b\u306e\u9ad8\u3044\u6587\u6cd5\u3092\u8eab\u306b\u3064\u3051\u3066\u3044\u307e\u3057\u305f\uff0e\u3053\u306e\u7814\u7a76\u304c\u9032\u3081\u3070\uff0c\u5c06\u6765\u7684\u306b\u306f\u30ed\u30dc\u30c3\u30c8\u306e\u8a00\u8a9e\u7372\u5f97\u304c\u3088\u308a\u81ea\u7136\u306b\u306a\u3063\u3066\u3044\u304f\u3068\u8003\u3048\u3089\u308c\u307e\u3059\uff0e\u4eca\u5f8c\u3082\u3053\u306e\u3088\u3046\u306a\u7814\u7a76\u306b\u3082\u7740\u76ee\u3057\u3066\u3044\u3053\u3046\u3068\u611f\u3058\u3055\u305b\u3089\u308c\u308b\u767a\u8868\u3067\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\nURL<br \/>\n22nd International Symposium on Artificial Life and Robotics\u30002017 &#8211; AROB,\u3000http:\/\/isarob.org\/symposium\/<br \/>\n<strong>\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"147\"><strong>\u00a0<\/strong><br \/>\n<strong>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"373\">&nbsp;<br \/>\nYudai Goto<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">\u57f9\u990a\u751f\u4f53\u5185\u89d2\u819c\u5185\u76ae\u7d30\u80de\u306e\u81ea\u52d5\u8a55\u4fa1<br \/>\n-\u90e8\u5206\u753b\u50cf\u306b\u3088\u308b\u30d1\u30ce\u30e9\u30de\u751f\u6210-<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">Automatic quality evaluation of the cultured in-vivo corneal endothelial cell<br \/>\n&#8211; Panorama generated by the partial image &#8211;<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">Tomoyuki Hiroyasu, Yudai Goto, Naoki Okumura, Koizumi Noriko, Satoru Hiwa, Hiroshi Furutani<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">ISAROB<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">AROB 22<sup>nd<\/sup> 2017<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">Beppu B-CON\u3000Plaza<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2017\/01\/19-2017\/01\/21<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>2017\/01\/19\u304b\u30892017\/01\/21\u306b\u304b\u3051\u3066\uff0c\u5225\u5e9c\u56fd\u969b\u30b3\u30f3\u30d9\u30f3\u30b7\u30e7\u30f3\u30bb\u30f3\u30bf\u30fc\uff0f\u30d3\u30fc\u30b3\u30f3\u30d7\u30e9\u30b6\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305fAROB2017\uff08http:\/\/isarob.org\/symposium\/\uff09\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u5b66\u4f1a\u306f\uff0c\u4eba\u5de5\u751f\u547d\u3068\u30ed\u30dc\u30c3\u30c8\u5de5\u5b66\u306e\u65b0\u3057\u3044\u6280\u8853\u3068\u305d\u306e\u5fdc\u7528\u5206\u91ce\u3092\u4e2d\u5fc3\u30c6\u30fc\u30de\u3068\u3057\u3066\uff0c\u4eba\u5de5\u751f\u547d\u304a\u3088\u3073\u30ed\u30dc\u30c3\u30c8\u306b\u95a2\u3059\u308b\u7814\u7a76\u8005\u306a\u3069\u304c\u96c6\u3046\u5b66\u4f1a\u3067\u3059\uff0e<br \/>\n\u79c1\u306f\u5168\u65e5\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u5ee3\u5b89\u5148\u751f\uff0c\u7530\u4e2d\u90a3\u667a\uff0c\u7530\u6751\u9675\u5927\uff0c\u5ca1\u7530\u96c4\u6597\uff0c\u77f3\u539f\u77e5\u61b2\uff0c\u53e4\u8c37\u5148\u751f\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ol start=\"2\">\n<li>\u7814\u7a76\u767a\u8868\n<ul>\n<li>\u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u79c1\u306f19\u65e5\u306e\u5348\u5f8c\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cComputational methods for Human Biological information\u300d\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u53e3\u982d\u767a\u8868\u3067\uff0c10\u5206\u306e\u8b1b\u6f14\u6642\u9593\u30685\u5206\u306e\u8cea\u7591\u5fdc\u7b54\u6642\u9593\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e<br \/>\n\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">For eye drop treatment, observation of the culturing corneal endothelial cells is critical. Observing and evaluating the cell\u2019s proliferation around the entire corneal is necessary. A contact specular microscope can observe a much wider range than the conventional optical microscope can. The quality of the panorama becomes worse when there are unfocused images. In this study, a method for extracting only focused images from the movie of corneal endothelial cells is proposed. In the proposed method, the target images are Fourier transformed and the image features are extracted. A threshold is established for the feature values detected from the focused images is prepared, and only focused images are obtained. Furthermore, here, we discuss the threshold, the accuracy of the results, and another method for improving the efficiency of the proposed method.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<ul>\n<li>\u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n<p>\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<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>1<\/strong><br \/>\n\u89d2\u819c\u5185\u76ae\u518d\u751f\u533b\u7642\u306b\u304a\u3044\u3066\u30d1\u30ce\u30e9\u30de\u753b\u50cf\u306f\u3069\u306e\u7a0b\u5ea6\u91cd\u8981\u306a\u306e\u304b\uff0e<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\n\u5f93\u6765\u306e\u30b9\u30da\u30ad\u30e5\u30e9\u30fc\u30de\u30a4\u30af\u30ed\u30b9\u30b3\u30fc\u30d7\u306b\u5bfe\u3057\u3066\u3069\u306e\u7a0b\u5ea6\u306e\u7d30\u80de\u3092\u64ae\u50cf\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u3063\u305f\u306e\u304b\uff0e<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u4eca\u56de\u521d\u306e\u56fd\u969b\u5b66\u4f1a\u53c2\u52a0\u3067\uff0c\u82f1\u8a9e\u3067\u306e\u53e3\u982d\u767a\u8868\u3067\u3057\u305f\uff0e\u6d77\u5916\u306e\u4eba\u3068\u8b70\u8ad6\u3059\u308b\u3053\u3068\u3067\u7814\u7a76\u3084\u8a00\u8a9e\u306b\u5bfe\u3057\u3066\u96e3\u3057\u3055\u3092\u611f\u3058\u3064\u3064\u3082\uff0c\u81ea\u5206\u81ea\u8eab\u306e\u7814\u7a76\u304c\u518d\u751f\u533b\u7642\u306b\u5f79\u306b\u7acb\u3064\u3068\u3044\u3046\u5b9f\u611f\u3092\u5f97\u3089\u308c\u307e\u3057\u305f\uff0e\u4eca\u5f8c\u306f\u672c\u5b66\u4f1a\u306e\u7d4c\u9a13\u3092\u6d3b\u304b\u3057\uff0c\u80fd\u529b\u3092\u9ad8\u3081\u3066\u3044\u304d\u305f\u3044\u3068\u611f\u3058\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n&nbsp;<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e3\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Analysis of Bus Transportation Planning by using Traffic Simulation<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Hiroyasu Matsushima, Tomohisa Yamashita, and Itsuki Noda3<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Multiscale Social Simulation<br \/>\nAbstract\uff1a\u3000In this paper, we address to optimize transportation plan for big events based on traffic simulation by multi-objective optimization method. In large-scale event, many people gather in an event site. Since many people have to be carried to the event venue, it is necessary that transportation is planned in consideration of traffic influence and management of time schedule. Planning of transportation for large-scale event has potential to be included above trade off relationship between conflicting objects (ex. peak time of traffic congestion and concentration of bus arrival time). As first step of applying to real cases, through traffic simulation which is constructed from simple traffic network, we try to show Pareto surface of transportation plans by using Multi-objective Optimization Evolutionary Algorithm (MOEA).<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u73fe\u5728\uff0c\u6700\u9069\u5316\u306f\u69d8\u3005\u306a\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u304c\u63d0\u6848\u3055\u308c\u3066\u3044\u308b\uff0e\u672c\u767a\u8868\u306f\u4ea4\u901a\u4e8b\u60c5\u3084\u30bf\u30a4\u30e0\u30bf\u30a4\u30e0\u30b9\u30b1\u30b8\u30e5\u30fc\u30eb\u306a\u3069\u306e\u76ee\u7684\u95a2\u6570\u304b\u3089\u591a\u76ee\u7684\u4ea4\u901a\u306a\u30b7\u30df\u30ec\u30fc\u30b7\u30e7\u30f3\u306b\u57fa\u3065\u304f\u4ea4\u901a\u8a08\u753b\u306e\u6700\u9069\u5316\u306e\u63d0\u6848\u3092\u884c\u3063\u3066\u3044\u305f\uff0e\u5927\u898f\u6a21\u306a\u8f38\u9001\u8a08\u753b\u306f\uff0c\u7af6\u5408\u3059\u308b\u30c8\u30ec\u30fc\u30c9\u30aa\u30d5\u306b\u542b\u307e\u308c\uff0c\u591a\u76ee\u6700\u9069\u5316\u9032\u5316\u8a08\u7b97\u3092\u7528\u3044\u3066\u4ea4\u901a\u8a08\u753b\u306e\u30d1\u30ec\u30fc\u30c8\u5e73\u9762\u3092\u63d0\u793a\u3057\u3066\u3044\u305f\u3002\u591a\u76ee\u7684\u6700\u9069\u5316\u306f\u69d8\u3005\u306a\u554f\u984c\u306b\u7528\u3044\u3089\u308c\u6c4e\u7528\u6027\u304c\u3042\u308b\u3068\u601d\u3044\u307e\u3057\u305f\u3002<br \/>\n&nbsp;<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Player Tracking in Sport Events using Condensation and Color\u3000Region-based\u3000Techniques<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Piya Kaewboudee and Phatthanaphong Chomphuwiset<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Artificial intelligence<br \/>\nAbstract\u3000\u3000\uff1aPlayer tracking system (PTS) in the videos of sport events such as football, basketball and hockey can be useful for post-processing of analyzing players and game strategies. Usually, video images contain multiple moving objects. Therefore, PTS is prone to occlusion, identification and trajectory tracking problems, which can affect the accuracy of the system. Thus, many researchers are trying to develop new techniques in order to increase the effectiveness of the tracking systems . This paper presents a tracking technique that applies condensation with color feature using a sampling based on image regions. Performance evaluation is carried out using a set of standard parameters . The experiments conducted on the data show that the proposed technique can perform the tracking well.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u767a\u8868\u3067\u7740\u76ee\u3057\u305f\u306e\u306f\uff0c\u30b9\u30dd\u30fc\u30c4\u9078\u624b\u306e\u30d3\u30c7\u30aa\u306b\u304a\u3044\u3066\u306e\u8ffd\u8de1\u30b7\u30b9\u30c6\u30e0\u3067\u3042\u308b\uff0e\u3053\u306e\u30b7\u30b9\u30c6\u30e0\u304c\u78ba\u7acb\u3055\u308c\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u9078\u624b\u306e\u30b2\u30fc\u30e0\u6226\u7565\u3092\u5206\u6790\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\uff0e\u753b\u50cf\u51e6\u7406\u306e\u89b3\u70b9\u3067\u306f\uff0c\u753b\u50cf\u9818\u57df\u306b\u5bfe\u3057\u3066\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3092\u7528\u3044\u3066\u8272\u7279\u5fb4\u91cf\u3092\u9069\u7528\u3057\u3066\u3044\u305f\u306e\u304c\u826f\u3044\u89b3\u70b9\u3067\u7814\u7a76\u3057\u3066\u3044\u308b\u3068\u601d\u3063\u305f\uff0e\u753b\u50cf\u306b\u5bfe\u3057\u3066\u8ffd\u8de1\u51e6\u7406\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u9069\u7528\u3059\u308b\u306e\u306f\u5bb9\u6613\u306b\u3067\u304d\u305d\u3046\u3060\u304c\uff0c\u5b9f\u969b\u306b\u52d5\u753b\u3092\u7528\u3044\u3066\u51e6\u7406\u3092\u884c\u3046\u6642\uff0c\u51e6\u7406\u304c\u91cd\u304f\u306a\u308a\u5b9f\u969b\u306b\u4f7f\u3048\u308b\u306e\u304b\u5c11\u3057\u96e3\u3057\u3044\u5370\u8c61\u3082\u6301\u3063\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Geometric somersaults of polymer chains through twist propagation<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aShiori Uda, Mengyun Li, and Tomohiro Yanao<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Natural and biological systems<br \/>\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a This study is concerned with the rotary motions of polymer chains that play essential roles in the functions of biological\u3000molecular motors. While the standard pictures for the rotary motions of biological molecular motors may be more or\u3000less like the rotations of rigid bodies, the present study explores a qualitatively different mechanism for the rotary motions. We\u3000take a simple model of a polymer chain and highlight its geometric angle shifts arising from the propagation of twisting waves.\u3000Such angle shifts, which we call geometric somersaults, generally arise even under the conditions of zero total angular momentum\u3000and are thereby analogous to the somersault of a falling cat. As an application, we argue that the geometric somersault of\u3000the polymer chain may serve as a prototypical model for the rotary motions of the central shaft of ATP synthase.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u3067\u7740\u76ee\u3057\u305f\u306e\u306f\uff0c\u9ad8\u5206\u5b50\u9396\u306e\u56de\u8ee2\u904b\u52d5\u306b\u95a2\u9023\u3059\u308b\u751f\u4f53\u5206\u5b50\u30e2\u30fc\u30bf\u30fc\u306b\u3064\u3044\u3066\u3067\u3059\uff0e<br \/>\n\u56de\u8ee2\u904b\u52d5\u306f\u5206\u5b50\u306e\u6a5f\u80fd\u306b\u504f\u5728\u3057\u3066\u3044\u308b\u751f\u6d3b\u30b7\u30b9\u30c6\u30e0\u306e\u30e2\u30fc\u30bf\u30fc\u3067\u3042\u308b\uff0e\u4f8b\u3068\u3057\u3066\u3001\u30ed\u30fc\u30bf\u30ea\u30fc<br \/>\nATP\u30b7\u30f3\u30bf\u30fc\u30bc\u306e\u4e2d\u5fc3\u8ef8\u306e\u904b\u52d5\u304a\u3088\u3073\u97ad\u6bdb\u30d5\u30a3\u30e9\u30e1\u30f3\u30c8\u3067\u3059\uff0e\u6a19\u6e96\u7684\u306a\u5199\u771f\u5206\u5b50\u30e2\u30fc\u30bf\u30fc\u306e\u56de\u8ee2\u904b\u52d5\u304c\u3088\u308a\u591a\u304f\u306e\u525b\u4f53\u306e\u56de\u8ee2\u306e\u3088\u3046\u306b\u3001\u6b63\u78ba\u306a\u30e1\u30ab\u30cb\u30ba\u30e0\u56de\u8ee2\u904b\u52d5\u304c\u307e\u3060\u5b8c\u5168\u306b\u7406\u89e3\u3055\u308c\u305f\u5206\u91ce\u306e\u7814\u7a76\u3092\u884c\u3063\u3066\u3044\u3066\uff0e\u7814\u7a76\u3068\u3057\u3066\u306f\u96e3\u3057\u3044\u5370\u8c61\u3092\u53d7\u3051\u305f\u304c\uff0c\u89e3\u660e\u3055\u308c\u3066\u3044\u306a\u3044\u70b9\u3092\u7814\u7a76\u5bfe\u8c61\u306b\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u9762\u767d\u3044\u3068\u611f\u3058\u305f\uff0e<br \/>\n&nbsp;<br \/>\n\u53c2\u8003\u6587\u732e<\/p>\n<ul>\n<li>AROB2017,URL\uff1a<a href=\"http:\/\/isarob.org\/symposium\/\">http:\/\/isarob.org\/symposium\/<\/a><\/li>\n<li>AROB2017 proceedings<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>2017\u5e741\u670819\u65e5\u304b\u308921\u65e5\u304b\u3051\u3066\u5927\u5206\u770c\u5225\u5e9c\u5e02\u306eB-Con PLAZA\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305f\uff0c22nd International Symposium on Artificial Life and Robotics\uff08ARO &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/is.doshisha.ac.jp\/news\/?p=3910\" class=\"more-link\"><span class=\"screen-reader-text\">&#8220;AROB 22nd 2017&#8221; \u306e<\/span>\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10],"tags":[],"class_list":["post-3910","post","type-post","status-publish","format-standard","hentry","category-10"],"_links":{"self":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/3910","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\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3910"}],"version-history":[{"count":0,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/3910\/revisions"}],"wp:attachment":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3910"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3910"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3910"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}