{"id":6582,"date":"2020-01-26T11:11:38","date_gmt":"2020-01-26T02:11:38","guid":{"rendered":"http:\/\/www.is.doshisha.ac.jp\/news\/?p=6582"},"modified":"2021-03-09T11:09:15","modified_gmt":"2021-03-09T02:09:15","slug":"%e3%80%90%e9%80%9f%e5%a0%b1%e3%80%91arob-25th-2020","status":"publish","type":"post","link":"https:\/\/is.doshisha.ac.jp\/news\/?p=6582","title":{"rendered":"\u3010\u901f\u5831\u3011AROB 25th 2020"},"content":{"rendered":"<p>AROB 25th 2020\u304c\u5225\u5e9c\u3000B-Con plaza\u3067\u958b\u50ac\u3055\u308c\u307e\u3057\u305f\u3002<br \/>\nComputational intelligence and cognitive science for human biosignals and human well-being<br \/>\n\u3068\u3044\u3046\u30bb\u30c3\u30b7\u30e7\u30f3\u3092\u4f01\u753b\u3057\u307e\u3057\u305f\u3002<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u3067\u306f\uff15\u4ef6\u306e\u767a\u8868\u304c\u884c\u308f\u308c\u307e\u3057\u305f\u3002<br \/>\nOS5-1 Runtime analysis of linear functions using Markov chain method<br \/>\nSatoshi Ikeda, Yu-an Zhang, Hiroshi Furutani, Satoru Hiwa, Tomoyuki Hiroyasu<br \/>\nOS5-2 Mindful driving: The relationship between mindful driving and mind-wandering based upon biological information<br \/>\nKoma Yoshioka, Yuta Kawaratani, Hiroshi Furutani, Tomoyuki Hiroyasu, Satoru Hiwa<br \/>\nOS5-3 Brain functional response to different categories of image stimulation using fNIRS<br \/>\nRyosuke Shimizu, Hiroshi Furutani, Satoru Hiwa, Tomoyuki Hiroyasu<br \/>\nOS5-4 Effect of two-dimensional crossover on search performance of genetic algorithm<br \/>\nAkihiro Fujii, Hiroshi Furutani, Satoru Hiwa, Tomoyuki Hiroyasu<br \/>\nOS5-5 Molecular graph discovery using machine learning approach<br \/>\nYutaka Tsujimoto, Yohei Oe, Hiroshi Furutani, Tomoyuki Hiroyasu, Satoru Hiwa<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\">\u5409\u5ca1\u6602\u99ac<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">\u30de\u30a4\u30f3\u30c9\u30d5\u30eb\u30c9\u30e9\u30a4\u30d3\u30f3\u30b0\uff1a \u751f\u4f53\u60c5\u5831\u306b\u57fa\u3065\u304f\u30de\u30a4\u30f3\u30c9\u30d5\u30eb\u30c9\u30e9\u30a4\u30d3\u30f3\u30b0\u3068\u30de\u30a4\u30f3\u30c9\u30ef\u30f3\u30c0\u30ea\u30f3\u30b0\u306e\u95a2\u4fc2<\/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\">Mindful driving: The relationship between mindful driving\u3000and mind-wandering based upon biological information<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">\u5409\u5ca1\u6602\u99ac\uff0c\u74e6\u8c37\u512a\u592a,\u53e4\u8c37\u535a\u53f2\uff0c\u5ee3\u5b89\u77e5\u4e4b, \u65e5\u548c\u609f<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">AROB<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">AROB202020<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">\u5225\u5e9c\u56fd\u969b\u30a4\u30ce\u30d9\u30fc\u30b7\u30e7\u30f3\u30bb\u30f3\u30bf\u30fc\u3000B-CON PLAZA<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2020\/01\/22-2020\/01\/24<\/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>2020\/01\/23\u304b\u30892020\/01\/24\u306b\u304b\u3051\u3066\uff0c\u5225\u5e9c\u56fd\u969b\u30a4\u30ce\u30d9\u30fc\u30b7\u30e7\u30f3\u30bb\u30f3\u30bf\u30fc\u3000B-CON PLAZA\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305fAROB2020\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306eAROB2019\u306f\uff0cAROB\u306b\u3088\u3063\u3066\u4e3b\u50ac\u3055\u308c\u305f\u4eba\u5de5\u751f\u547d\u3068\u30ed\u30dc\u30c3\u30c8\u306b\u95a2\u3059\u308b\u56fd\u969b\u4f1a\u8b70\u3067\uff0c\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\uff0c\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u8a2d\u8a08\uff0c\u304a\u3088\u3073\u7814\u7a76\u8005\u3078\u306e\u6700\u5148\u7aef\u6280\u8853\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u5316\u306b\u95a2\u9023\u3057\u305f\u4eba\u5de5\u751f\u547d\uff0c\u304a\u3088\u3073\u30ed\u30dc\u30c3\u30c8\u5de5\u5b66\uff0c\u30cd\u30c3\u30c8\u30ef\u30fc\u30ad\u30f3\u30b0\uff0c\u8907\u96d1\u3055\u304a\u3088\u3073\u4eba\u5de5\u751f\u547d\u6280\u8853\u306b\u304a\u3051\u308b\u9032\u6b69\u304c\u3044\u304b\u306b\u3057\u3066\u5b66\u969b\u7684\u306a\u30a2\u30d7\u30ed\u30fc\u30c1\u306b\u95a2\u9023\u3059\u308b\u304b\u306b\u95a2\u3059\u308b\u77e5\u898b\u3092\u5171\u6709\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u306b\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u3059\uff0e\u79c1\u306f23\u65e5\u306824\u65e5\u306e\u4e8c\u65e5\u9593\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u5ee3\u5b89\u5148\u751f\uff0cM2\u306e\u6e05\u6c34\u541b\uff0c\u85e4\u4e95\u541b\uff0cM1\u306e\u8fbb\u672c\u541b\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\u306f24\u65e5\u306e\u5348\u524d\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cOS5 Computational intelligence and cognitive science for human biosignals and human well-being\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\uff0c\u300cMindful driving: The relationship between mindful driving\u3000and mind-wandering based upon biological information\u300d\u3068\u3044\u3046\u30bf\u30a4\u30c8\u30eb\u3067\u767a\u8868\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">Distracted driving is a major cause of traffic accidents and injuries. We aim to construct a mindful driving system that detects driver&#8217;s mind wandering (MW) and promotes a focused attention to driving. In this study, heart rate variability (HRV) analysis based on electrocardiography was used to gauge the driver&#8217;s attentional state. Using the driving simulator, the heart rate was measured during the driving task where the driver was required to maintain the appropriate headway distance from the preceding vehicle. The brake reaction time (BRT) in case of sudden deceleration of the preceding vehicle was also measured as a behavioral metric. A probe tone was also used to signal the driver to report their MW state. The HRV metrics between mindful and MW conditions, determined by BRT and self-reports, were also compared. The results suggested that BRT and self-report MW were correlated and LF\/HF could be useful in differentiating between MW and mindful conditions.<\/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\uff11<\/strong><br \/>\n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u305d\u3053\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\uff0e\u8cea\u554f\u306f\u300c\u30b7\u30df\u30e5\u30ec\u30fc\u30bf\u3067\u306f\u5b9f\u8eca\u3068\u306f\u74b0\u5883\u304c\u7570\u306a\u308b\u3053\u3068\u306b\u3064\u3044\u3066\u3069\u3046\u8003\u3048\u3066\u3044\u308b\u304b\u300d\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u78ba\u304b\u306b\u30b7\u30df\u30e5\u30ec\u30fc\u30bf\u3067\u306f\u5b9f\u969b\u306e\u518d\u73fe\u5ea6\u304c\u4f4e\u3044\u305f\u3081\uff0c\u4eca\u5b9f\u8eca\u3067\u3082\u540c\u69d8\u306e\u5b9f\u9a13\u3092\u3057\u3066\u3044\u308b\u3068\u3044\u3046\u65e8\u3092\u304a\u4f1d\u3048\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\n\u672c\u7814\u7a76\u5ba4\u306e\u8fbb\u672c\u541b\u304b\u3089\u306e\u8cea\u554f\u3067\u3059\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0c\u300c\u5b9f\u9a13\u8a2d\u8a08\u3067\u30d6\u30ec\u30fc\u30ad\u30bf\u30b9\u30af\u3068\u30d7\u30ed\u30fc\u30d6\u30c8\u30fc\u30f3\u30bf\u30b9\u30af\u304c\u3042\u308b\u304c\uff0c\u3069\u306e\u3088\u3046\u306a\u9806\u5e8f\u3067\u63d0\u793a\u3055\u308c\u308b\u306e\u304b\u300d\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u3067\u306f\uff0c\u30d6\u30ec\u30fc\u30ad\u30bf\u30b9\u30af\u306f30\u00b110s\u306e\u9593\u9694\u3067\u30e9\u30f3\u30c0\u30e0\uff0c\u30d7\u30ed\u30fc\u30d6\u30c8\u30fc\u30f3\u306f60\u00b110\uff53\u306e\u9593\u9694\u3067\u30e9\u30f3\u30c0\u30e0\u3068\u3044\u3046\u65e8\u3092\u304a\u4f1d\u3048\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u611f\u60f3<br \/>\n\u53bb\u5e74\u306b\u5f15\u304d\u7d9a\u304d\u4e8c\u5ea6\u76ee\u306e\u5b66\u4f1a\u53c2\u52a0\u3060\u3063\u305f\u3081\uff0c\u6bd4\u8f03\u7684\u843d\u3061\u7740\u3044\u3066\u767a\u8868\u3067\u304d\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u7279\u306b\uff0c\u53bb\u5e74\u306f\u7dca\u5f35\u3067\u65e9\u53e3\u3068\u306a\u3063\u3066\u3057\u307e\u3044\u307e\u3057\u305f\u304c\uff0c\u4eca\u5e74\u306f\u305d\u306e\u53cd\u7701\u3092\u3044\u304b\u3057\u7df4\u7fd2\u3092\u53bb\u5e74\u4ee5\u4e0a\u306b\u5ff5\u5165\u308a\u306b\u884c\u306a\u3063\u305f\u305f\u3081\uff0c\u3044\u3044\u30c6\u30f3\u30dd\u3067\u8a71\u305b\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u3057\u304b\u3057\uff0c\u3084\u306f\u308a\u8cea\u554f\u3092\u805e\u304d\u53d6\u308b\u80fd\u529b\u3084\u5fdc\u7b54\u3068\u3044\u3046\u70b9\u3067\u82f1\u8a9e\u80fd\u529b\u306e\u4e4f\u3057\u3055\u3092\u611f\u3058\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u5916\u90e8\u306e\u65b9\u3005\u3092\u898b\u3066\u3044\u308b\u3068\u82f1\u8a9e\u3092\u30b9\u30e9\u30b9\u30e9\u3068\u8a71\u3059\u65b9\u304c\u591a\u304f\uff0c\u3084\u306f\u308a\u82f1\u8a9e\u529b\u306f\u4eca\u5f8c\u3082\u5fc5\u8981\u3060\u3068\u5f37\u304f\u611f\u3058\u307e\u3057\u305f\uff0e<\/li>\n<\/ul>\n<p>&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\u306e2\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\u3000New approach to describing Collaborative robot behavior based on process building block<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Jay Cheong<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Plenary Speech 2<br \/>\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Collaborative robots, that work together with the human, are becoming more and more popular in the industrial world. And you know that, the human and the robot complement each other. In this session, introducing process building blocks for designing collaborative robot interaction work systems and calculating accurate cycle times. We must accurately understand the structure and requirements of the upcoming Smart Factory and transform it into a robot of the desired shape in the industrial field. Because of this, recently developed collaborative robots have many sensors that can move with people. In the future, factories will change significantly as consumer patterns change. At the heart of that change is a smart factory. It should be easy to separate and assemble and convenient at the same time. The most important factor is to be with people.<br \/>\nIn the coming future, collaborative robots will be exposed to public places as well as industrial sites and work with people. The smart factory strategy is different for each country or factory, but I want to talk about the components that a collaborative robot must have. One of its components is to present an approach to describing robot behavior based on process building blocks. The new collaborative architecture can be combined with existing process building block systems to reflect human-robot interaction and extract accurate cycle times. This method will present a new approach to describing robot behavior based on process building blocks.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u3067\u306f\uff0c\u4eba\u3068\u5f37\u8abf\u3059\u308b\u30ed\u30dc\u30c3\u30c8\u306b\u7740\u76ee\u3057\u3066\u304a\u308a\uff0c\u4eba\u3068\u30ed\u30dc\u30c3\u30c8\u304c\u4e92\u3044\u306b\u88dc\u5b8c\u3057\u5408\u3046\u3053\u3068\u3067\u3088\u308a\u826f\u3044\u30b7\u30b9\u30c6\u30e0\u306e\u63d0\u6848\u3092\u3059\u308b\u5185\u5bb9\u3068\u306a\u3063\u3066\u3044\u307e\u3057\u305f\uff0e\u7279\u306b\uff0c\u5c06\u6765\u306e\u5354\u8abf\u30ed\u30dc\u30c3\u30c8\u306e\u6d3b\u8e8d\u306e\u5834\u3068\u3057\u3066\u306f\uff0c\u516c\u5171\u306e\u5834\u3084\u5de5\u696d\u7528\u5730\u304c\u671f\u5f85\u3055\u308c\u3066\u3044\u308b\u3068\u3044\u3046\u3053\u3068\u3067\u3057\u305f\uff0e\u30d5\u30a1\u30ca\u30c3\u30af\u306e\u5354\u8abf\u30ed\u30dc\u30c3\u30c8\u306e\u30a4\u30e1\u30fc\u30b8\u52d5\u753b\u3067\u81ea\u52d5\u8eca\u306e\u30bf\u30a4\u30e4\u3092\u4eba\u3068\u30ed\u30dc\u30c3\u30c8\u304c\u5354\u529b\u3057\u3066\u306f\u3081\u8fbc\u3093\u3067\u3044\u304f\u90e8\u5206\u304c\u5370\u8c61\u7684\u3067\u3057\u305f\uff0e\u4eba\u3068\u30ed\u30dc\u30c3\u30c8\u306e\u5354\u8abf\u3068\u3044\u3046\u89b3\u70b9\u3084\uff0c\u30ed\u30dc\u30c3\u30c8\u304c\u62c5\u3046\u5f79\u5272\u30fb\u4eba\u304c\u62c5\u3046\u5f79\u5272\u7b49\u306e\u5206\u3051\u65b9\u304c\u8208\u5473\u6df1\u304b\u3063\u305f\u3067\u3059\uff0e<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 Exploring Parameters for Generating Semi-Realistic Traffic Flow with Data Assimilation Approach<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Taro NISHIURA, Hiromitsu HATTORI<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a OS13 Social simulation and supercomputers<br \/>\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Multi-Agent Social Simulation (MAAS) can model complex systems and compute social phenomena within the system, and traffic is one of the most popular subjects. When we conduct multiagent-based traffic simulations, it is typical to be required to achieve the reproduction of real-life traffic flow in MAAS. Since large-scale traffic phenomena are consisting of individuals, each of which has his\/her preferences on its behaviors, it is still difficult to successfully reproduce the realistic traffic flows. One simple approach is to conduct a simulation using precise behavioral models constructed personal data on the movement of the entity though, obtaining personal data on the daily move is difficult even in the current big data-era. Thus, authors have tried to develop a method to generate semi-realistic traffic flow based on data assimilation perspective. In the proposed method, we first carry out a simulation with arbitrary simulation parameters, and then compare the simulation results with the actual data on the cross-section traffic volume at several intersections to assess the difference. After that, the approximation to the actual data is tried by repeating the change of the parameters, execution of the simulation, and comparison of the cross-section traffic volume. In this paper, we realize traffic flow in the parts of Kusatsu City in Shiga Prefecture using multi-agent traffic simulator on GAMA and show quality evaluation of generated traffic flow based on the proposed method.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u3067\u306f\uff0c\u8907\u96d1\u306a\u30b7\u30b9\u30c6\u30e0\u3092\u30e2\u30c7\u30eb\u5316\u3057\u30b7\u30b9\u30c6\u30e0\u5185\u306e\u793e\u4f1a\u73fe\u8c61\u3092\u8a08\u7b97\u53ef\u80fd\u306a\u30de\u30eb\u30c1\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u793e\u4f1a\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\uff08MAAS\uff09\u3092\u4ea4\u901a\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u306b\u5bfe\u3057\u3066\u9069\u5fdc\u3057\u305f\u5185\u5bb9\u304c\u5831\u544a\u3055\u308c\u307e\u3057\u305f\uff0e\u81ea\u52d5\u8eca\u306b\u95a2\u308f\u308b\u3068\u3044\u3046\u70b9\u3067\uff0c\u8208\u5473\u6df1\u304f\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u5b9f\u884c\u3057\u3066\u3044\u304f\u4e0a\u3067\uff0c\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u5909\u66f4\u3084\u65ad\u9762\u4ea4\u901a\u91cf\u306e\u5909\u66f4\u30fb\u6bd4\u8f03\u3092\u7e70\u308a\u8fd4\u3059\u3053\u3068\u3067\u5b9f\u969b\u306e\u30c7\u30fc\u30bf\u3078\u306e\u8fd1\u4f3c\u304c\u884c\u308f\u308c\u308b\u3068\u3044\u3046\u70b9\u304c\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u306e\u96e3\u3057\u3044\u3068\u3053\u308d\u3060\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u3057\u304b\u3057\uff0c\u904b\u8ee2\u7d4c\u8def\u306e\u6700\u9069\u5316\u306f\u6e0b\u6ede\u306e\u7de9\u548c\u7b49\u306b\u3082\u3064\u306a\u304c\u308b\u305f\u3081\uff0c\u4eca\u5f8c\u306e\u5c55\u958b\u306b\u671f\u5f85\u304c\u9ad8\u307e\u308a\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n\u53c2\u8003\u6587\u732e<\/p>\n<ul>\n<li>AROB 25<sup>th<\/sup> 2020, https:\/\/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>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"373\">\u85e4\u4e95\u3000\u5149\u592e<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u304a\u3051\u308b\u4e8c\u6b21\u5143\u4ea4\u53c9\u304c\u63a2\u7d22\u6027\u80fd\u306b\u4e0e\u3048\u308b\u5f71\u97ff<\/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\">Effect of two-dimensional crossover on search performance of genetic algorithm<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">Akihiro Fujii, Hiroshi Furutani, Satoru Hiwa, Tomoyuki Hiroyasu<\/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>\u5b66\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">AROB 25<sup>th<\/sup> 2020<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">\u5225\u5e9c\u56fd\u969b\u30b3\u30f3\u30d9\u30f3\u30b7\u30e7\u30f3\u30bb\u30f3\u30bf\u30fc B-CON PLAZA<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2020\/01\/22-2020\/01\/24<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<\/p>\n<ol>\n<li>\u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n<p>2020\/01\/23\u304b\u30892020\/01\/24\u306b\u304b\u3051\u3066The International Society of Artificial Life and Robotics\uff08AROB2020\uff09\u306b\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u306f\uff0c\u4eba\u5de5\u751f\u547d\u3084\u30ed\u30dc\u30c6\u30a3\u30af\u30b9\u306b\u5bfe\u3059\u308b\u65b0\u305f\u306a\u6280\u8853\u306e\u767a\u5c55\u3068\uff0c\u305d\u308c\u3089\u306e\u5e45\u5e83\u3044\u30c8\u30d4\u30c3\u30af\u3078\u306e\u5fdc\u7528\u3092\u76ee\u6307\u3057\u3066\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u3059\uff0e<br \/>\n\u4eca\u56de\u306e\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u306f\u7b2c25\u56de\u76ee\u3067\uff0c\u4f8b\u5e74\u540c\u69d8\uff0c\u5927\u5206\u770c\u5225\u5e9c\u5e02\u56fd\u969b\u30b3\u30f3\u30d9\u30f3\u30b7\u30e7\u30f3\u30bb\u30f3\u30bf\u30fc B-CON PLAZA\u3067\u958b\u50ac\u3055\u308c\u307e\u3057\u305f\uff0e\u79c1\u305f\u3061\u306e\u7814\u7a76\u5ba4\u304b\u3089\u306fM2\u306e\u85e4\u4e95\uff0c\u6e05\u6c34\uff0c\u5409\u5ca1\u3068M1\u306e\u8fbb\u672c\u304c\u53c2\u52a0\u3057\uff0c\u82f1\u8a9e\u3067\u306e\u53e3\u982d\u767a\u8868\u3092\u884c\u3044\u307e\u3057\u305f\uff0e<br \/>\nISAROB\u30db\u30fc\u30e0\u30da\u30fc\u30b8 <a href=\"https:\/\/isarob.org\/symposium\/\">https:\/\/isarob.org\/symposium\/<\/a><\/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\u306f24\u65e5\u306e10:45\uff5e12:00\u306b\u304b\u3051\u3066\u306eorganized session: Computational intelligence and cognitive science for human biosignals and human well-being\u306b\u53c2\u52a0\u81f4\u3057\u307e\u3057\u305f\uff0e<br \/>\n\u767a\u8868\u306f10\u5206\u9593\u306e\u53e3\u982d\u767a\u8868\u3068\uff0c5\u5206\u9593\u306e\u8cea\u7591\u5fdc\u7b54\u5f62\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e\u4eca\u56de\u306fEffect of two-dimensional crossover on search performance of genetic algorithm\u3000\u3068\u3044\u3046\u30bf\u30a4\u30c8\u30eb\u3067\u767a\u8868\u3057\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">In the Genetic Algorithm (GA), the role of crossover in search performance is significant. Generally, if distance and geographic information suitable for the problem are used at the time of the crossover, then the crossover will be enhanced. In this research, we approached the improvement of the search performance using a GA was approached by expressing a genotype in two dimensions rather than in one dimension, as is typically performed in a conventional GA. In this study, we investigated the effects of two-dimensional genes and two-dimensional crossover on solution search performance was investigated. As a numerical experiment, two types of benchmark problems were proposed in which geographic information and variable dependencies were assigned to genes arranged in a two-dimensional matrix. In addition, a performance comparison between two-dimensional crossover and general crossover operators was performed. As a result of numerical experiments, GA using block crossover, one of the two-dimensional crossover methods, showed the best performance in the proposed benchmark problem.<\/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\u30d6\u30ed\u30c3\u30af\u30e2\u30c7\u30eb\u306eNKL\uff08K=15\uff09\u306b\u304a\u3051\u308b\u6bd4\u8f03\u3067\u30d6\u30ed\u30c3\u30af\u4ea4\u53c9\u306e\u6027\u80fd\u3060\u3051\u304c\u826f\u3044\u7406\u7531\u306f\u4f55\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u5404\u4ea4\u53c9\u306e\u4ea4\u53c9\u9818\u57df\u5185\u306b\u304a\u3051\u308b\u5909\u6570\u306e\u6570\u3092\u793a\u3059\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u3092\u53c2\u7167\u3057\u306a\u304c\u3089\uff0c\u4e00\u5ea6\u306e\u4ea4\u53c9\u306e\u5b9f\u884c\u306b\u3088\u3063\u3066\u4ea4\u63db\u3055\u308c\u308b\u5909\u6570\u306e\u6570\u304c\u591a\u304f\u306a\u308b\u306b\u3064\u308c\u3066\uff0cGA\u306e\u6027\u80fd\u304c\u4f4e\u4e0b\u3057\u3066\u3044\u304f\u306e\u3060\u3068\u8003\u3048\u3089\u308c\u308b\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u5165\u308c\u66ff\u308f\u308b\u907a\u4f1d\u5b50\u6570\u304c\u63a2\u7d22\u6027\u80fd\u306b\u4e0e\u3048\u308b\u5f71\u97ff\u3092\u8abf\u67fb\u3059\u308b\u305f\u3081\u306b\uff0c\u4ea4\u53c9\u306a\u3057\u7a81\u7136\u5909\u7570\u306e\u307f\u306eGA\u306e\u5229\u7528\u3084\uff0c\u30d6\u30ed\u30c3\u30af\u4ea4\u53c9\u306e\u4ea4\u53c9\u7bc4\u56f2\u306e\u5927\u304d\u3055\u3092\u30d1\u30e9\u30e1\u30fc\u30bf\u3067\u8abf\u6574\u3057\u305f\u5834\u5408\u306a\u3069\uff0c\u8ffd\u52a0\u306e\u5b9f\u9a13\u3092\u884c\u3046\u5fc5\u8981\u304c\u3042\u308b\u3068\u8003\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\nNKL\u306b\u304a\u3044\u3066\uff0cK=15\u4ee5\u5916\u306e\u7d50\u679c\u306f\u3069\u3046\u306a\u3063\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\uff0eK\u304c\u5897\u52a0\u3059\u308b\u306b\u3064\u308c\u3066\uff0c\u30d6\u30ed\u30c3\u30af\u4ea4\u53c9\u306e\u6027\u80fd\u304c\u9ad8\u304f\u306a\u308b\u50be\u5411\u306f\u3042\u308b\u304c\uff0c\u305d\u308c\u305e\u308c\u306eK\u3067\u306e\u8003\u5bdf\u306f\u672a\u3060\u884c\u3048\u3066\u3044\u306a\u3044\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e\u4eca\u5f8c\u306f\u307e\u305a\uff0c\u4e21\u30e2\u30c7\u30eb\uff0c\u5404K\u6570\u306b\u304a\u3051\u308b\u8003\u5bdf\u3092\u9032\u3081\u308b\u5fc5\u8981\u304c\u3042\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u30d6\u30ed\u30c3\u30af\u30e2\u30c7\u30eb\u306eNKL\u306b\u304a\u3044\u3066\u306f\uff0cK\u306e\u5897\u52a0\u306b\u5bfe\u3057\u3066\uff0c\u30d6\u30ed\u30c3\u30af\u4ea4\u53c9\u3060\u3051\u3067\u306a\u304f\u4e8c\u70b9\u4ea4\u53c9\u3082\u30ed\u30d0\u30b9\u30c8\u306b\u826f\u597d\u306a\u89e3\u3092\u7372\u5f97\u3057\u3066\u3044\u307e\u3059\uff0e\u3053\u308c\u306f\uff0c\u4e8c\u70b9\u4ea4\u53c9\u306b\u3088\u3063\u3066\u4ea4\u63db\u3055\u308c\u308b\u5909\u6570\u306e\u9818\u57df\u304c\u30d6\u30ed\u30c3\u30af\u4ea4\u53c9\u306b\u3088\u308b\u3082\u306e\u3068\u985e\u4f3c\u3057\u3066\u3044\u308b\u304b\u3089\u3067\u3042\u308b\u3068\u8003\u3048\u3066\u3044\u307e\u3059\uff08\u4ea4\u53c9\u70b9\u304c\u5c11\u306a\u3044\u5834\u5408\uff0c\u4ea4\u53c9\u9818\u57df\u306f\u30d6\u30ed\u30c3\u30af\u72b6\u306b\u8fd1\u3065\u304f\uff09\uff0e\u305d\u306e\u305f\u3081\uff0c\u4e8c\u70b9\u4ea4\u53c9\u3060\u3051\u3067\u306a\u304f\uff0c\u4e00\u70b9\u4ea4\u53c9\u306e\u5229\u7528\u3084\u4ea4\u53c9\u70b9\u3092\u5897\u3084\u3057\u305fn\u70b9\u4ea4\u53c9\u3092\u5229\u7528\u3057\u305f\u6570\u5024\u5b9f\u9a13\u3092\u884c\u3046\u5fc5\u8981\u304c\u3042\u308b\u3068\u8003\u3048\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u4eca\u56de\u306f\u521d\u3081\u3066\u306e\u82f1\u8a9e\u3067\u306e\u53e3\u982d\u767a\u8868\u3068\u3044\u3046\u3053\u3068\u3082\u3042\u308a\uff0c\u975e\u5e38\u306b\u7dca\u5f35\u611f\u306e\u3042\u308b\u767a\u8868\u3068\u306a\u308a\u307e\u3057\u305f\uff0e\u500b\u4eba\u7684\u306b\u306f\uff0cGL\u73ed\u3067\u306e\u6d3b\u52d5\uff0c\u30bc\u30df\u62c5\u5f53\u3067\u306e\u6d3b\u52d5\u7b49\uff0c\u82f1\u8a9e\u529b\u306e\u5f37\u5316\u306b\u529b\u3092\u5165\u308c\u3066\u304d\u305f\u306e\u3067\uff0c\u305d\u306e\u6210\u679c\u3092\u6d3b\u304b\u3059\u826f\u3044\u6a5f\u4f1a\u3068\u306a\u308a\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u6700\u4e2d\u306f\uff0c\u30b9\u30e9\u30a4\u30c9\u3092\u8aad\u307f\u4e0a\u3052\u308b\u3060\u3051\u3067\u306a\u304f\uff0c\u305d\u306e\u5834\u3067\u8a00\u3044\u305f\u3044\u3053\u3068\u3092\u8003\u3048\u306a\u304c\u3089\u8a71\u3057\u305f\u308a\uff0c\u8cea\u554f\u306b\u7b54\u3048\u305f\u308a\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u305f\u306e\u306f\uff0cGL\u73ed\u3067\u6d3b\u52d5\u3057\u3066\u3044\u305f\u6642\u3068\u6bd4\u8f03\u3057\u3066\uff0c\u82f1\u8a9e\u529b\u306e\u5411\u4e0a\u304c\u611f\u3058\u3089\u308c\uff0c\u975e\u5e38\u306b\u3046\u308c\u3057\u304f\u601d\u3044\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u540c\u3058\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u767a\u8868\u3057\u305f\u4e09\u4eba\u306e\u5b66\u751f\u3068\u306f\uff0c\u767a\u8868\u524d\u65e5\u306e\u6df1\u591c\u307e\u3067\uff0c\u5171\u306b\u7df4\u7fd2\u3084\u6e96\u5099\u3092\u884c\u3044\uff0c\u3088\u308a\u826f\u3044\u767a\u8868\u304c\u3067\u304d\u308b\u3088\u3046\u5207\u78cb\u7422\u78e8\u3057\u3042\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u304e\u308a\u304e\u308a\u307e\u3067\u6e96\u5099\u3092\u3057\u3066\u3044\u305f\u5206\uff0c\u4f53\u529b\u7684\uff0c\u7cbe\u795e\u7684\u306b\u3064\u3089\u3044\u90e8\u5206\u3082\u3042\u308a\u307e\u3057\u305f\u304c\uff0c\u307f\u3093\u306a\u3067\u5354\u529b\u3059\u308b\u3053\u3068\u3067\u4e57\u308a\u8d8a\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u5927\u5b66\u9662\u751f\u6d3b\u3067\u6700\u5f8c\u306e\u5b66\u4f1a\u3068\u306a\u308a\u307e\u3057\u305f\u304c\uff0c\u3053\u306e\u56db\u4eba\u3067\u767a\u8868\u3092\u7121\u4e8b\u7d42\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u305f\u306e\u306f\uff0c\u975e\u5e38\u306b\u826f\u3044\u601d\u3044\u51fa\u306b\u306a\u3063\u305f\u3068\u601d\u3044\u307e\u3059\uff0e<\/p>\n<ol start=\"3\">\n<li>\u8074\u8b1b<\/li>\n<\/ol>\n<p>\u4eca\u56de\u306e\u5b66\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e2\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n\u4eba\u9593\u306e\u4f5c\u696d\u3092\u52a9\u3051\u308b\u305f\u3081\u306b\u8a2d\u8a08\u3055\u308c\u305f\uff0c\u4eba\u9593\u3068\u3068\u3082\u306b\u50cd\u304f\u30ed\u30dc\u30c3\u30c8\u306b\u3064\u3044\u3066\u306e\u767a\u8868\u3067\u3057\u305f\uff0e\u767a\u8868\u306e\u4e2d\u3067\u306f\u30b9\u30de\u30fc\u30c8\u30d5\u30a1\u30af\u30c8\u30ea\u30fc\u306e\u4e2d\u3067\u4f7f\u7528\u3055\u308c\u308b\u30ed\u30dc\u30c3\u30c8\u306b\u5fc5\u8981\u3068\u3055\u308c\u308b\u8981\u4ef6\uff0c\u6a5f\u69cb\u306a\u3069\u304c\u7d39\u4ecb\u3055\u308c\u3066\u304a\u308a\uff0c\u6700\u5148\u7aef\u306e\u30ed\u30dc\u30c3\u30c8\u3068\u3057\u3066\uff0c\u30d5\u30a1\u30af\u30c8\u30ea\u30fc\u30aa\u30fc\u30c8\u30e1\u30fc\u30b7\u30e7\u30f3\u306e\u4ee3\u8868\u7684\u306a\u4f01\u696d\u3067\u3042\u308bFANUC\u306e\u30ed\u30dc\u30c3\u30c8\u304c\u7d39\u4ecb\u3055\u308c\u3066\u3044\u305f\u306e\u304c\u5370\u8c61\u7684\u3067\u3057\u305f\uff0e\u4eca\u5f8c\uff0c\u30ed\u30dc\u30c3\u30c8\u306f\u5de5\u5834\u5185\u3084\u7523\u696d\u306e\u5834\u306e\u307f\u306a\u3089\u305a\uff0c\u8eab\u8fd1\u306a\u3068\u3053\u308d\u3067\u3082\u6d3b\u8e8d\u3059\u308b\u3088\u3046\u306b\u306a\u308b\u3068\u3044\u308f\u308c\u3066\u3044\u307e\u3059\u304c\uff0c\u4eba\u3068\u3088\u308a\u5bc6\u63a5\u306b\u5354\u696d\u3059\u308b\u969b\u306b\u5fc5\u8981\u3068\u306a\u308b\u30ed\u30dc\u30c3\u30c8\u306e\u6280\u8853\u304c\u8aac\u660e\u3055\u308c\u3066\u304a\u308a\uff0c\u8208\u5473\u6df1\u3044\u767a\u8868\u5185\u5bb9\u3067\u3057\u305f\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"41\"><\/td>\n<\/tr>\n<tr>\n<td><\/td>\n<td width=\"512\">\n<table width=\"100%\">\n<tbody>\n<tr>\n<td>\n<h2>\u767a\u8868\u30bf\u30a4\u30c8\u30eb\uff1aExploring Parameters for Generation Semi-Realistic Traffic Flow with Data Assimilation Approach<\/h2>\n<h2>\u8457\u8005\uff1aTaro Nishiura, Hiromitsu Hattori<\/h2>\n<p>\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\uff1a Social simulation and supercomputers<br \/>\nAbstruct: Mul-Agent Social Simulation (MASS) can model complex systems and compute social phenomena within the system, and traffic is one of the most popular subjects. When we conduct multiagent-based traffic simulations, it is typical to be required to achieve the reproduction of real-life traffic flow in MASS. Since large-scale traffic phenomena are consisting of individuals, each of which has his\/her performances on its behavior, it is still difficult to successfully reproduce the realistic traffic flows. One simple approach is to conduct a simulation using precise behavioral models constructed personal data on the movement of the entity though, obtaining personal data on the daily move is difficult even in the current big data-era. Thus, authors have tried to develop a method to generate semi-realistic traffic flow based on data assimilation perspective. In the proposed method, we first carry out a simulation with arbitrary simulation parameters, and then compare the simulation results with the actual data on the cross-section traffic volume at several intersections to assess the differences. After that, the approximation to the actual data is tried by repeating the change of the parameters, execution of the simulation, and comparison of the cross-section traffic volume. In this paper, we realize traffic flow in the parts of Kusatsu City in Shiga Prefecture using multi-agent traffic simulator on GAMA and show quality evaluation of generated traffic flow based on the proposed method.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n&nbsp;<br \/>\n\u7acb\u547d\u9928\u5927\u5b66\u306e\u5b66\u751f\u306b\u3088\u308b\u767a\u8868\u3067\uff0c\u6ecb\u8cc0\u770c\u8349\u6d25\u5e02\u306e\u4e00\u90e8\u306e\u4ea4\u901a\u91cf\u3092multi-agent traffic simulation\u306b\u3088\u3063\u3066\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3059\u308b\u3068\u3044\u3046\u7814\u7a76\u3067\u3057\u305f\uff0e\u9053\u8def\u72b6\u6cc1\u306e\u6b63\u78ba\u306a\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u306b\u306f\uff0c\u30c9\u30e9\u30a4\u30d0\u30fc\u500b\u3005\u4eba\u306e\u597d\u307f\uff08\u3069\u306e\u9053\u8def\u3092\u901a\u308b\u306e\u304b\uff09\u306a\u3069\uff0c\u81a8\u5927\u306a\u30c7\u30fc\u30bf\u3092\u96c6\u3081\u308b\u5fc5\u8981\u304c\u3042\u308a\u975e\u5e38\u306b\u56f0\u96e3\u3067\u3042\u308b\u3053\u3068\u304c\u8a00\u53ca\u3055\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u30e2\u30c7\u30eb\u306e\u9053\u8def\u72b6\u6cc1\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u5909\u3048\u306a\u304c\u3089\uff0c\u5b9f\u969b\u306e\u4ea4\u901a\u72b6\u6cc1\u30c7\u30fc\u30bf\u3092\u8003\u616e\u3057\u3066\uff0c\u8fd1\u3065\u3051\u3066\u3044\u304f\u3068\u3044\u3046\u4f5c\u696d\uff08data assimilation\uff09\u3068\u30d1\u30e9\u30e1\u30fc\u30bf\u8abf\u6574\u306e\u82e6\u52b4\u3092\u77e5\u308a\uff0c\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u30e2\u30c7\u30eb\u3092\u4f5c\u308b\u82e6\u52b4\u304c\u308f\u304b\u308b\u3088\u3046\u306a\u767a\u8868\u3067\u3057\u305f\uff0e<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 \/>\n\u6e05\u6c34\u4eae\u4f51<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">fNIRS\u3092\u7528\u3044\u305f\u3055\u307e\u3056\u307e\u306a\u30ab\u30c6\u30b4\u30ea\u306e\u753b\u50cf\u523a\u6fc0\u306b\u5bfe\u3059\u308b\u8133\u306e\u6a5f\u80fd\u7684\u5fdc\u7b54<\/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\">Brain functional response to different categories of image stimulation using fNIRS<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">\u6e05\u6c34\u4eae\u4f51\uff0c\u5ee3\u5b89\u77e5\u4e4b\uff0c\u53e4\u8c37\u535a\u53f2\uff0c\u65e5\u548c\u609f<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">AROB<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">AROB2020<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">\u5225\u5e9c\u56fd\u969b\u30b3\u30f3\u30d9\u30f3\u30b7\u30e7\u30f3\u30bb\u30f3\u30bf\u30fc B-CON PLAZA<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2020\/01\/22-2020\/01\/24<\/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>2020\/01\/22-24\u306b\u5225\u5e9c\u56fd\u969b\u30b3\u30f3\u30d9\u30f3\u30b7\u30e7\u30f3\u30bb\u30f3\u30bf\u30fc B-CON PLAZA \u306b\u3066\u958b\u50ac\u3055\u308c\u305fThe International Society of Artificial Life and Robotics\uff08AROB2020\uff09\u306b\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u5b66\u4f1a\u306f\u6700\u5148\u7aef\u306e\u6280\u8853\u306e\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30fc\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3068\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u8a2d\u8a08\u306b\u57fa\u3065\u3044\u3066\u3001\u4eba\u5de5\u751f\u547d\u3068\u30ed\u30dc\u30c3\u30c8\u5de5\u5b66\u306b\u95a2\u3059\u308b\u65b0\u3057\u3044\u6280\u8853\u306e\u958b\u767a\u306b\u3064\u3044\u3066\u8b70\u8ad6\u3057\u3001\u4eba\u5de5\u751f\u547d\u3068\u30ed\u30dc\u30c3\u30c8\u5de5\u5b66\u306e\u6280\u8853\u306e\u9032\u6b69\u306b\u95a2\u3059\u308b\u77e5\u898b\u3092\u5171\u6709\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u306b\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u3059\uff0e<br \/>\n\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\uff0c\u5ee3\u5b89\u5148\u751f\uff0cM2 \u5409\u5ca1\uff0c\u6e05\u6c34\uff0c\u85e4\u4e95\uff0cM1\u8fbb\u672c\uff0c\u304c\u53c2\u52a0\u3057\uff0c\u767a\u8868\u3092\u884c\u3044\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\u306f24\u65e5\u306e\u5348\u524d\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cOS5 Computational intelligence and cognitive science for<br \/>\nhuman biosignals and human well-being\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\u516c\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\u306f\uff0cBrain functional response to different categories of image stimulation using fNIRS\u3068\u3044\u3046\u30bf\u30a4\u30c8\u30eb\u3067\u767a\u8868\u3057\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u3092\u8a18\u8f09\u3044\u305f\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"566\">Emotion is the movement of the mind in response to external stimuli. It\u00a0is accompanied by physical expressions such as changes in the heartbeat and in the facial expressions. To elucidate emotional function, it is important to use more appropriate emotion-inducing stimulation. However, the mechanism of emotion is not yet clear. In emotion-related research, the method of selecting each image that induces positive, neutral, and negative emotions in a dataset is such that the variance of the valence value, which is the emotion dimension, is equal to the variance of the arousal value. In most cases, the effect of the content of visual emotion stimulus on emotion is not considered. In this study, it was confirmed that the brain function status differed according to the category of the presented stimulus image. It is suggested that the region that controls emotion may differ depending on the category of the image presented.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&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\u4f7f\u7528\u3055\u308c\u3066\u3044\u308bface\u306e\u753b\u50cf\u3067\u306f\u753b\u50cf\u306b\u5199\u3063\u3066\u3044\u308b\u4eba\u306e\u9854\u306b\u52a0\u3048\u3066\u80cc\u666f\u306e\u753b\u50cf\u3082\u8003\u616e\u3057\u3066\u3044\u308b\u306e\u304b\u3068\u3044\u3046\u8cea\u554f\u3092\u3044\u305f\u3060\u304d\u307e\u3057\u305f<br \/>\n&nbsp;<br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u79c1\u306f\uff0c\u4f7f\u7528\u753b\u50cf\u306fNAPS\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304cvalence\u3084arousal\u3092\u7528\u3044\u3066\u5b9a\u91cf\u7684\u306b\u8a55\u4fa1\u3055\u308c\u3066\u3044\u308b\u3082\u306e\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u305f\u3081\uff0c\u753b\u50cf\u5185\u90e8\u306e\u9055\u3044\u306b\u3064\u3044\u3066\u306f\u8003\u616e\u3057\u3066\u3044\u306a\u3044\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\n\u5148\u884c\u7814\u7a76\u3088\u308a\u6c7a\u5b9a\u3057\u305f\u95a2\u5fc3\u9818\u57df\u4ee5\u5916\u306e\u9818\u57df\u3067\u306f\u3069\u306e\u3088\u3046\u306a\u9818\u57df\u306b\u76f8\u95a2\u304c\u307f\u3089\u308c\u305f\u306e\u304b\uff0e<br \/>\n&nbsp;<br \/>\n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u79c1\u306f\uff0cobject positive\u30bf\u30b9\u30af\u3067\u306f\u3001\u8996\u899a\u91ce\u306eIOG\u3068\u793e\u4f1a\u7684\u611f\u60c5\u306e\u5c0f\u8133\u3067\u76f8\u95a2\u304c\u898b\u3064\u304b\u308a\uff0cface\u30bf\u30b9\u30af\u3067\u306f\u3001\u793e\u4f1a\u7684\u611f\u60c5\u306b\u95a2\u3059\u308b\u5c0f\u8133\u3067\u76f8\u95a2\u304c\u307f\u3089\u308c\u305f\uff0e\u3068\u56de\u7b54\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u30b3\u30e1\u30f3\u30c8<\/strong><br \/>\n\u3053\u306e\u611f\u60c5\u306b\u95a2\u3059\u308b\u7814\u7a76\u306f\u753b\u50cf\u3060\u3051\u3067\u306a\u304f\u52d5\u753b\u306b\u304a\u3051\u308b\u8a55\u4fa1\u3067\u3082\u7528\u3044\u3089\u308c\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u3044\u3046\u30b3\u30e1\u30f3\u30c8\u3092\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n\u3053\u306e\u30b3\u30e1\u30f3\u30c8\u306b\u5bfe\u3057\u3066\u79c1\u306f\uff0c\u60c5\u52d5\u306e\u523a\u6fc0\u304c\u753b\u50cf\u3067\u306f\u306a\u304f\u52d5\u753b\u306b\u306a\u3063\u3066\u3057\u307e\u3046\u3068\uff0c\u52d5\u753b\u306e\u3069\u306e\u90e8\u5206\u304c\u523a\u6fc0\u3068\u3057\u3066\u50cd\u3044\u3066\u3044\u308b\u306e\u304b\uff0c\u7279\u5b9a\u304c\u56f0\u96e3\u306b\u306a\u3063\u3066\u3057\u307e\u3046\u554f\u984c\u304c\u3042\u308a\uff0c\u73fe\u72b6\u523a\u6fc0\u3092\u7279\u5b9a\u53ef\u80fd\u306a\u753b\u50cf\u306b\u3088\u308b\u7814\u7a76\u3092\u884c\u3063\u3066\u3044\u308b\u3068\u30b3\u30e1\u30f3\u30c8\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u611f\u60f3<\/li>\n<\/ul>\n<p>\u79c1\u306b\u3068\u3063\u3066\u521d\u3081\u3066\u306e\u5b66\u4f1a\u3067\u3042\u308a\uff0c\u5c1a\u4e14\u3064\u82f1\u8a9e\u3067\u306e\u53e3\u982d\u767a\u8868\u3068\u3044\u3046\u3053\u3068\u3082\u3042\u308a\uff0c\u8cea\u7591\u5fdc\u7b54\u3092\u805e\u304d\u53d6\u308c\u308b\u306e\u304b\uff0c\u6163\u308c\u306a\u3044\u82f1\u8a9e\u306e\u767a\u8868\u306b\u3088\u308a\u767a\u8868\u6642\u9593\u304c\u5b88\u308c\u308b\u306e\u304b\uff0c\u306a\u3069\u591a\u304f\u306e\u4e0d\u5b89\u3068\u5fc3\u914d\u304c\u3042\u308a\u307e\u3057\u305f\uff0e\u3057\u304b\u3057\uff0c\u4e00\u7dd2\u306b\u767a\u8868\u3057\u305f\u30e1\u30f3\u30d0\u30fc\u306b\u4f55\u5ea6\u3082\u7df4\u7fd2\u306b\u4ed8\u304d\u5408\u3063\u3066\u3082\u3089\u3063\u305f\u3053\u3068\u3084\uff0c\u4e8b\u524d\u306e\u30ea\u30cf\u30fc\u30b5\u30eb\u3067\u306e\u53cd\u7701\u70b9\u3092\u8003\u616e\u3057\u305f\u3053\u3068\u306b\u3088\u308a\uff0c\u6e80\u8db3\u306e\u3044\u304f\u767a\u8868\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u3053\u306e\u5b66\u4f1a\u53c2\u52a0\u3092\u901a\u3058\u3066\u82f1\u8a9e\u306b\u3088\u308b\u30d7\u30ec\u30bc\u30f3\u306e\u96e3\u3057\u3055\u3092\u5b66\u3076\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e<br \/>\n\u8ab2\u984c\u3068\u3057\u3066\u306f\uff0c\u767a\u97f3\u306b\u95a2\u3057\u3066\u306f\u4e8b\u524d\u6e96\u5099\u3067\u5bfe\u5fdc\u3067\u304d\u305f\u3082\u306e\u306e\uff0c\u8cea\u7591\u5fdc\u7b54\u3067\u306e\u30ea\u30b9\u30cb\u30f3\u30b0\u306b\u95a2\u3057\u3066\u306f\u6642\u9593\u3092\u304b\u3051\u3066\u4eca\u5f8c\u52c9\u5f37\u3057\u3066\u3044\u304b\u306a\u3044\u3068\u3044\u3051\u306a\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\u306e2\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\">\n<h2>\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a New approach to describing Collaborative robot behavior based on process building block<\/h2>\n<p>\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Jay Cheong<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Plenary Speech 2<br \/>\nAbstruct \uff1a Collaborative robots, that work together with the human, are becoming more and more popular in the industrial world. And you know that, the human and the robot complement each other. In this session, introducing process building blocks for designing collaborative robot interaction work systems and calculating accurate cycle times. We must accurately understand the structure and requirements of the upcoming Smart Factory and transform it into a robot of the desired shape in the industrial field. Because of this, recently developed collaborative robots have many sensors that can move with people. In the future, factories will change significantly as consumer patterns change. At the heart of that change is a smart factory. It should be easy to separate and assemble and convenient at the same time. The most important factor is to be with people.<br \/>\nIn the coming future, collaborative robots will be exposed to public places as well as industrial sites and work with people. The smart factory strategy is different for each country or factory, but I want to talk about the components that a collaborative robot must have. One of its components is to present an approach to describing robot behavior based on process building blocks. The new collaborative architecture can be combined with existing process building block systems to reflect human-robot interaction and extract accurate cycle times. This method will present a new approach to describing robot behavior based on process building blocks.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n\u3053\u306e\u767a\u8868\u3067\u306f\uff0c\u4eba\u3068\u30ed\u30dc\u30c3\u30c8\u304c\u4e92\u3044\u306b\u88dc\u5b8c\u3057\u5408\u3046\u305f\u3081\u306e\u30d7\u30ed\u30bb\u30b9\u306b\u95a2\u3059\u308b\u5185\u5bb9\u3067\u3057\u305f\uff0e\u4eca\u5f8c\u306e\u5354\u8abf\u30ed\u30dc\u30c3\u30c8\u306f\u4eba\u3068\u4e00\u7dd2\u306b\u50cd\u304f\u305f\u3081\u591a\u304f\u306e\u30bb\u30f3\u30b5\u30fc\u3092\u642d\u8f09\u3057\uff0c\u5c06\u6765\u7684\u306b\u306f\u516c\u5171\u306e\u5834\u3084\u5de5\u696d\u7528\u5730\u306b\u304a\u3044\u3066\u3001\u4eba\u3005\u3068\u4ed5\u4e8b\u3092\u3059\u308b\u3053\u3068\u304c\u8a9e\u3089\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u4eba\u3068\u30ed\u30dc\u30c3\u30c8\u306e\u5354\u8abf\u306e\u305f\u3081\uff0c\u6570\u591a\u304f\u306e\u30bb\u30f3\u30b5\u30fc\u3092\u7528\u3044\u308b\u3053\u3068\uff0c\u4eba\u9593\u3068\u30ed\u30dc\u30c3\u30c8\u306e\u76f8\u4e92\u4f5c\u7528\u3092\u53cd\u6620\u3057\u3001\u6b63\u78ba\u306a\u30b5\u30a4\u30af\u30eb\u30bf\u30a4\u30e0\u3092\u62bd\u51fa\u3059\u308b\u624b\u6cd5\u306b\u95a2\u3057\u3066\u8208\u5473\u6df1\u304b\u3063\u305f\u3067\u3059\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 Analysis of OD Data of Ondemand Transportation by Non-negative Matrix Factorization<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Itsuki Noda, Junichi Ochiai,<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Social simulation and supercomputers<br \/>\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a We propose a method to analyze OD data of on-demand transportation services like Smart Access Vehicle Service (SAVS) and AI-bus by non-negative matrix factorization (NMF), which enables to visualize and investigate features od data and OD places for design of future transportation service. OD data represents properties of usage of transportation services, but is composed by complicated and mixtured way with many noise and uncertainly. NMF can decompose OD data into few bases, by which we can get abstracted properties of usage of transportation services. The abstracted properties provides a way to understand the difference among dates or places and to enable to plan for future operation of SAVS and AI bus.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u3067\u306f\u30b9\u30de\u30fc\u30c8\u30a2\u30af\u30bb\u30b9\u8eca\u4e21\u30b5\u30fc\u30d3\u30b9\uff08SAVS\uff09\u3084AI\u30d0\u30b9\u306a\u3069\u306e\u30aa\u30f3\u30c7\u30de\u30f3\u30c9\u8f38\u9001\u30b5\u30fc\u30d3\u30b9\u306eOD\u30c7\u30fc\u30bf\u3092\u975e\u8ca0\u884c\u5217\u56e0\u5b50\u5206\u89e3\uff08NMF\uff09\u3067\u5206\u6790\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8a9e\u3089\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u307f\u306a\u3068\u307f\u3089\u3044\u3067\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u884c\u3063\u3066\u3044\u307e\u3057\u305f\uff0e\u30d0\u30b9\u306e\u4e57\u8eca\u4f4d\u7f6e\u3068\u964d\u8eca\u4f4d\u7f6e\u3092\u793a\u3059\u30de\u30c8\u30ea\u30c3\u30af\u30b9\u89e3\u6790\u3092\u884c\u3044\uff0c\u4f11\u65e5\u3068\u5e73\u65e5\u3067\u6027\u80fd\u8a55\u4fa1\u3092\u884c\u3063\u3066\u3044\u307e\u3057\u305f\uff0e\u4f11\u65e5\u3068\u5e73\u65e5\u306e\u307f\u306a\u3089\u305a\uff0c\u30a4\u30d9\u30f3\u30c8\u3084\u5929\u6c17\u306a\u3069\u5916\u7684\u8981\u56e0\u306b\u304a\u3051\u308b\u5f71\u97ff\u304c\u5927\u304d\u3044\u3068\u3053\u308d\u304c\u96e3\u3057\u3044\u3068\u3053\u308d\u3060\u3068\u611f\u3058\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n\u53c2\u8003\u6587\u732e<br \/>\n1) AROB 25th 2020, https:\/\/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>\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n<td width=\"373\">\u8fbb\u672c\u8c4a<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n<td width=\"373\">\u6a5f\u68b0\u5b66\u7fd2\u624b\u6cd5\u3092\u5229\u7528\u3057\u305f\u5206\u5b50\u30b0\u30e9\u30d5\u306e\u767a\u898b<\/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\">Molecular graph discovery using machine learning approach<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8457\u8005<\/strong><\/td>\n<td width=\"373\">\u8fbb\u672c\u8c4a, \u5927\u6c5f\u6d0b\u5e73, \u53e4\u8c37\u535a\u53f2, \u5ee3\u5b89\u77e5\u4e4b, \u65e5\u548c\u609f<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4e3b\u50ac<\/strong><\/td>\n<td width=\"373\">AROB<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\n<td width=\"373\">AROB202020<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u4f1a\u5834<\/strong><\/td>\n<td width=\"373\">\u5225\u5e9c\u56fd\u969b\u30a4\u30ce\u30d9\u30fc\u30b7\u30e7\u30f3\u30bb\u30f3\u30bf\u30fc\u3000B-CON PLAZA<\/td>\n<\/tr>\n<tr>\n<td width=\"147\"><strong>\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n<td width=\"373\">2020\/01\/22-2020\/01\/24<\/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>2020\/01\/22\u304b\u30892020\/01\/24\u306b\u304b\u3051\u3066\uff0c\u5225\u5e9c\u56fd\u969b\u30a4\u30ce\u30d9\u30fc\u30b7\u30e7\u30f3\u30bb\u30f3\u30bf\u30fc\u3000B-CON PLAZA\u306b\u3066\u958b\u50ac\u3055\u308c\u305fAROB2020\u306b\u53c2\u52a0\u81f4\u3057\u307e\u3057\u305f\uff0e\u4eca\u56de\u306e\u958b\u50ac\u306f\u672c\u5b66\u4f1a\u306e25\u56de\u76ee\u306e\u958b\u50ac\u306b\u3042\u305f\u308a\u307e\u3059\uff0e<br \/>\n\u3053\u306e\u5b66\u4f1a\u306f\uff0c\u4eba\u5de5\u751f\u547d\u3068\u30ed\u30dc\u30c3\u30c8\u306b\u95a2\u3059\u308b\u56fd\u969b\u4f1a\u8b70\u3067\u3059\uff0e\u3053\u306e\u5b66\u4f1a\u3067\u306f\uff0c\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\uff0c\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u8a2d\u8a08\uff0c\u304a\u3088\u3073\u6700\u5148\u7aef\u6280\u8853\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u5316\u306b\u95a2\u9023\u3057\u305f\u4eba\u5de5\u751f\u547d\uff0c\u304a\u3088\u3073\u30ed\u30dc\u30c3\u30c8\u5de5\u5b66\u306b\u304a\u3051\u308b\u9032\u6b69\u304c\u3044\u304b\u306b\u3057\u3066\uff0c\u5b66\u8853\u7684\u306a\u624b\u6cd5\u306b\u95a2\u9023\u3059\u308b\u304b\u306e\u77e5\u898b\u3092\u5171\u6709\u3059\u308b\u3053\u3068\u3092\u76ee\u7684\u306b\u958b\u50ac\u3055\u308c\u3066\u3044\u307e\u3059\uff0e\u79c1\u306f23\u65e5\u306824\u65e5\u306e\u4e8c\u65e5\u9593\u53c2\u52a0\u3057\uff0c23\u65e5\u306f\u8074\u8b1b\uff0c24\u65e5\u306b\u53e3\u982d\u767a\u8868\u3092\u884c\u3044\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u5ee3\u5b89\u5148\u751f\uff0cM2\u306e\u6e05\u6c34\u3055\u3093\uff0c\u85e4\u4e95\u3055\u3093\uff0c\u5409\u5ca1\u3055\u3093\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\u306f24\u65e5\u306e\u5348\u524d\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cOS5 Computational intelligence and cognitive science for human biosignals and human well-being\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\uff0c\u300cMolecular graph discovery using machine learning approach\u300d\u3068\u3044\u3046\u30bf\u30a4\u30c8\u30eb\u3067\u767a\u8868\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"529\">Discovering new organic compounds with desired chemical properties is an important task in drug discovery and material design. However, discovering new compounds requires a significant amount of time. As an alternative, research for artificially generating graphs representing compounds using deep learning has been actively conducted in recent years. In this paper, <u>MolGAN<\/u> will be introduced; it is a method used to artificially generate graphs representing organic compounds. <u>MolGAN<\/u> uses a general adversarial network\u00a0 and it directly generates molecular graphs having the desired chemical properties. Furthermore, we aim to generate a new molecular graph with a desired chemical property, and a larger molecules than before, by applying a learning method that considers graph connectivity using <u>MolGAN<\/u>. Using <u>MolGAN<\/u> and the proposed mechanism, a new molecular graph with desired chemical properties was generated using ZINC-<u>250k<\/u>, a molecular data set, and the results were 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\uff11<\/strong><br \/>\n\u7814\u7a76\u5ba4\u306e\u5409\u5ca1\u3055\u3093\u304b\u3089\u306e\u8cea\u554f\u3067\u3059\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0c\u300c\u5206\u5b50\u3092\u30b0\u30e9\u30d5\u3068\u3057\u3066\u8868\u73fe\u3059\u308b\u4ee5\u5916\u306e\u8868\u73fe\u65b9\u6cd5\u306f\u5b58\u5728\u3059\u308b\u306e\u304b\u300d\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u3067\u306f\uff0c\u6587\u5b57\u5217\u3092\u30d9\u30fc\u30b9\u306b\u3057\u305f\u8868\u73fe\u65b9\u6cd5\u304c\u3042\u308a\uff0c\u305d\u306e\u4ee3\u8868\u4f8b\u3067\u3042\u308bSMILES\u306e\u6982\u8981\u3092\u304a\u4f1d\u3048\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\n\u53e4\u8c37\u535a\u53f2\u5148\u751f\u304b\u3089\u306e\u8cea\u554f\u3067\u3059\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0c\u300c\u751f\u6210\u3055\u308c\u305f\u5206\u5b50\u304c\u5316\u5b66\u7684\u306b\u610f\u5473\u306e\u3042\u308b\u3082\u306e\u304b\uff0c\u307e\u305f\u751f\u6210\u3055\u308c\u305f\u5206\u5b50\u306e\u8a55\u4fa1\u65b9\u6cd5\u306e\u8a08\u753b\u306f\u3042\u308b\u306e\u304b\u300d\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u3067\u306f\uff0c\u751f\u6210\u3055\u308c\u305f\u5206\u5b50\u306f\u5c02\u9580\u7684\u306a\u77e5\u8b58\u3092\u6709\u3059\u308b\u5316\u5b66\u8005\u306b\u8a55\u4fa1\u3057\u3066\u9802\u304f\u8a08\u753b\u304c\u3042\u308b\u3068\u3044\u3046\u65e8\u3092\u304a\u4f1d\u3048\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n<strong>\u30fb\u8cea\u554f\u5185\u5bb9<\/strong><strong>2<\/strong><br \/>\n\u7814\u7a76\u5ba4\u306e\u6e05\u6c34\u3055\u3093\u304b\u3089\u306e\u8cea\u554f\u3067\u3059\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0c\u300c\u85ac\u3089\u3057\u3055\u3092\u793a\u3059QED\u30b9\u30b3\u30a2\u306f\u3069\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u3066\u3044\u308b\u306e\u304b\u300d\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u3067\u306f\uff0c\u30b1\u30e2\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u30c4\u30fc\u30eb\u306b\u3088\u308a\u81ea\u52d5\u3067\u8a08\u7b97\u3055\u308c\u308b\u4e8b\u3068\uff0c\u5206\u5b50\u306e\u5206\u5b50\u91cf\uff0c\u6975\u6027\u8868\u9762\u7a4d\uff0clogP\u306a\u3069\u306e8\u7a2e\u985e\u306e\u7269\u6027\u5024\u3092\u5143\u306b\u8a08\u7b97\u3057\u3066\u3044\u308b\u3068\u3044\u3046\u65e8\u3092\u304a\u4f1d\u3048\u3057\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<\/p>\n<ul>\n<li>\u611f\u60f3<br \/>\n\u4eca\u56de\uff0c\u521d\u3081\u3066\u5b66\u4f1a\u3092\u53c2\u52a0\u3059\u308b\u306b\u3042\u305f\u308a\uff0c\u82f1\u8a9e\u3067\u306e\u539f\u7a3f\u4f5c\u6210\u3084\u53e3\u982d\u767a\u8868\u306a\u3069\uff0c\u666e\u6bb5\u306e\u7814\u7a76\u5ba4\u306e\u751f\u6d3b\u3067\u306f\u7d4c\u9a13\u3067\u304d\u306a\u3044\u4e8b\u304c\u591a\u304f\u3042\u3063\u305f\u3068\u611f\u3058\u307e\u3059\uff0e\u5f53\u65e5\u306e\u767a\u8868\u3067\u306f\u5ff5\u5165\u308a\u306b\u7df4\u7fd2\u3092\u884c\u3063\u305f\u304a\u304b\u3052\u304b\uff0c\u7df4\u7fd2\u901a\u308a\u306e\u767a\u8868\u6642\u9593\u3067\u7d42\u308f\u308b\u4e8b\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u305f\u3060\uff0c\u767a\u8868\u6642\u306b\u8a70\u307e\u308b\u4e8b\u3084\uff0c\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u3059\u3050\u306b\u7b54\u3048\u304c\u3067\u306a\u304b\u3063\u305f\u308a\u7b49\u306e\u82f1\u8a9e\u306b\u95a2\u3059\u308b\u80fd\u529b\u306e\u8ab2\u984c\u3092\u6539\u3081\u3066\u5b9f\u611f\u3057\u307e\u3057\u305f\uff0e\u4eca\u56de\u306e\u5b66\u4f1a\u3067\u82f1\u8a9e\u306b\u3088\u308b\u6210\u679c\u767a\u8868\u306e\u7d4c\u9a13\u306f\u81ea\u8eab\u306e\u4e2d\u3067\u5927\u5909\u8cb4\u91cd\u306a\u7d4c\u9a13\u3068\u306a\u308a\uff0c\u82f1\u8a9e\u306e\u80fd\u529b\u306e\u5411\u4e0a\u306e\u5fc5\u8981\u6027\u3092\u5b9f\u611f\u51fa\u6765\u307e\u3057\u305f\uff0e<\/li>\n<\/ul>\n<p>&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\u306e2\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\u3000New approach to describing Collaborative robot behavior based on process building block<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Jay Cheong<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Plenary Speech 2<br \/>\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Collaborative robots, that work together with the human, are becoming more and more popular in the industrial world. And you know that, the human and the robot complement each other. In this session, introducing process building blocks for designing collaborative robot interaction work systems and calculating accurate cycle times. We must accurately understand the structure and requirements of the upcoming Smart Factory and transform it into a robot of the desired shape in the industrial field. Because of this, recently developed collaborative robots have many sensors that can move with people. In the future, factories will change significantly as consumer patterns change. At the heart of that change is a smart factory. It should be easy to separate and assemble and convenient at the same time. The most important factor is to be with people.<br \/>\nIn the coming future, collaborative robots will be exposed to public places as well as industrial sites and work with people. The smart factory strategy is different for each country or factory, but I want to talk about the components that a collaborative robot must have. One of its components is to present an approach to describing robot behavior based on process building blocks. The new collaborative architecture can be combined with existing process building block systems to reflect human-robot interaction and extract accurate cycle times. This method will present a new approach to describing robot behavior based on process building blocks.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<br \/>\n&nbsp;<br \/>\n\u3053\u306e\u767a\u8868\u306f\uff0c\u4eba\u9593\u3068\u4e00\u7dd2\u306b\u50cd\u304fCollaborative robots\u306b\u95a2\u3059\u308b\u767a\u8868\u3067\u3057\u305f\uff0e\u3053\u306e\u30ed\u30dc\u30c3\u30c8\u306f\u7523\u696d\u754c\u3067\u307e\u3059\u307e\u3059\u5fc5\u8981\u3068\u3055\u308c\u3066\u3044\u308b\u4e8b\u304c\u5206\u304b\u308a\u307e\u3057\u305f\uff0e\u7279\u306b\u3053\u306e\u767a\u8868\u3067\u306f\u30d5\u30a1\u30ca\u30c3\u30af\u304c\u958b\u767a\u3057\u3066\u3044\u308b\u30ed\u30dc\u30c3\u30c8\u3068\u4eba\u9593\u304c\u81ea\u52d5\u8eca\u306e\u30bf\u30a4\u30e4\u3092\u5354\u50cd\u3057\u3066\u30bf\u30a4\u30e4\u3092\u306f\u3081\u8fbc\u3080\u52d5\u753b\u304c\u7d39\u4ecb\u3055\u308c\u307e\u3057\u305f\uff0e\u3053\u306e\u3088\u3046\u306b\u4eba\u9593\u3068\u30ed\u30dc\u30c3\u30c8\u304c\u4e92\u3044\u306b\u88dc\u5b8c\u3059\u308b\u4e8b\u3067\uff0c\u3088\u308a\u826f\u3044\u30b7\u30b9\u30c6\u30e0\u304c\u69cb\u7bc9\u3055\u308c\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u3053\u306e\u30ed\u30dc\u30c3\u30c8\u306f\u30ed\u30dc\u30c3\u30c8\u5de5\u5b66\u3060\u3051\u3067\u306a\u304f\uff0c\u753b\u50cf\u8a8d\u8b58\u3084\u30bb\u30f3\u30b5\u30fc\u6280\u8853\u304c\u91cd\u8981\u3067\u3042\u308b\u3068\u7d39\u4ecb\u3055\u308c\u307e\u3057\u305f\uff0e\u3053\u306e\u3088\u3046\u306b\u4e00\u3064\u306e\u6280\u8853\u3060\u3051\u3067\u306a\u304f\uff0c\u6280\u8853\u3092\u4e0a\u624b\u304f\u7d44\u307f\u5408\u308f\u305b\u308b\u4e8b\u3067\u4e00\u3064\u306e\u5148\u7aef\u6280\u8853\u304c\u69cb\u7bc9\u3055\u308c\u308b\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 Application of deep neural network to traffic simulation<br \/>\n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Luning Zhang, Naoki Yoshioka, Daigo Umemoto, Nobuyasu, Ito<br \/>\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a OS13 Social simulation and supercomputers<br \/>\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Simulation results from an agent-based traffic simulator of Kobe city were used as learning data for deep autoencoding neural network(DANN). Numbes of cars passed though more than 33,000 road segments were one set of learning data, and thousands sets from simulations were used. The trained DANN successfully reproduce input data set, and a k-means clustering of the inner-most neural layer activities classified different traffic parameters.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u3053\u306e\u767a\u8868\u3067\u306f\uff0c\u795e\u6238\u5e02\u306e\u9053\u8def\u4ea4\u901a\u7db2\u3092\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3057\u305f\u30c7\u30fc\u30bf\u3068\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u3092\u5229\u7528\u3057\u3066\u6df7\u96d1\u5ea6\u3068\u3044\u3063\u305f\u30c8\u30e9\u30d5\u30a3\u30c3\u30af\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u4e88\u6e2c\u3067\u304d\u308b\u4e8b\u304c\u767a\u8868\u3055\u308c\u307e\u3057\u305f\uff0e\u795e\u6238\u5e02\u306e\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3067\u306f\u7121\u6570\u306e\u8eca\u304c33,000\u3092\u8d85\u3048\u308b\u9053\u8def\u30bb\u30b0\u30e1\u30f3\u30c8\u3092\u901a\u904e\u3059\u308b\u72b6\u6cc1\u3092\u518d\u73fe\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u3053\u306e\u3088\u3046\u306b\u5b9f\u793e\u4f1a\u3092\u8a08\u7b97\u6a5f\u3067\u30e2\u30c7\u30ea\u30f3\u30b0\u3057\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u7d50\u679c\u3092\u5b9f\u793e\u4f1a\u306b\u9084\u5143\u3059\u308b\u4e8b\u304c\u5c06\u6765\u7684\u306b\u91cd\u8981\u3067\u3042\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u3053\u306e\u7814\u7a76\u306f\u904b\u8ee2\u7d4c\u8def\u306e\u6700\u9069\u5316\u306f\u6e0b\u6ede\u306e\u7de9\u548c\u7b49\u306b\u3082\u3064\u306a\u304c\u308b\u305f\u3081\uff0c\u4eca\u5f8c\u306e\u5c55\u958b\u306b\u671f\u5f85\u304c\u9ad8\u307e\u308a\u307e\u3057\u305f\uff0e<br \/>\n&nbsp;<br \/>\n\u53c2\u8003\u6587\u732e<\/p>\n<ul>\n<li>AROB 25<sup>th<\/sup> 2020, https:\/\/isarob.org\/symposium\/<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AROB 25th 2020\u304c\u5225\u5e9c\u3000B-Con plaza\u3067\u958b\u50ac\u3055\u308c\u307e\u3057\u305f\u3002 Computational intelligence and cognitive science for human biosignals a &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/is.doshisha.ac.jp\/news\/?p=6582\" class=\"more-link\"><span class=\"screen-reader-text\">&#8220;\u3010\u901f\u5831\u3011AROB 25th 2020&#8221; \u306e<\/span>\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-6582","post","type-post","status-publish","format-standard","hentry","category-6"],"_links":{"self":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/6582","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6582"}],"version-history":[{"count":1,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/6582\/revisions"}],"predecessor-version":[{"id":7017,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/6582\/revisions\/7017"}],"wp:attachment":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6582"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6582"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6582"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}