{"id":4603,"date":"2017-11-25T16:22:20","date_gmt":"2017-11-25T07:22:20","guid":{"rendered":"http:\/\/www.is.doshisha.ac.jp\/news\/?p=4603"},"modified":"2017-11-25T16:22:20","modified_gmt":"2017-11-25T07:22:20","slug":"%e3%80%90%e9%80%9f%e5%a0%b1%e3%80%91ieee-ssci","status":"publish","type":"post","link":"https:\/\/is.doshisha.ac.jp\/news\/?p=4603","title":{"rendered":"\u3010\u901f\u5831\u3011IEEE SSCI"},"content":{"rendered":"

IEEE SSCI<\/a>\u304cHawaii\u3067\u958b\u50ac\u3055\u308c\u307e\u3059\u3002
\n2017\/11\/30
\n11:15AM Adaptive Weight Vector Assignment Method for MOEA\/D [#1355]
\nKei Harada, Satoru Hiwa and Tomoyuki Hiroyasu
\nDoshisha University, Japan
\n2:00PM Sparse Feature Selection Method by Pareto-front Exploration -Extraction of functional brain network and ROI for fMRI data- [#1453]
\nTomoyuki Hiroyasu, Yuuki Kohri and Satoru Hiwa
\nDoshisha University, Japan
\n
\n\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
\u00a0<\/strong>
\n\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n		 
\n\u90e1\u3000\u60a0\u5e0c<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n\u30d1\u30ec\u30fc\u30c8\u30d5\u30ed\u30f3\u30c8\u63a2\u7d22\u306b\u3088\u308bSparse\u7279\u5fb4\u9078\u629e \uff5efMRI \u30c7\u30fc\u30bf\u306b\u5bfe\u3059\u308b\u8133\u6a5f\u80fd\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068ROI\u306e\u62bd\u51fa\uff5e<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\nSparse Feature Selection Method by Pareto-front Exploration\u00a0 \uff5eExtraction of functional brain network and ROI for fMRI data\uff5e<\/td>\n<\/tr>\n
\u8457\u8005<\/strong><\/td>\nTomoyuki HIROYASU, Yuuki KOHRI, satoru HIWA<\/td>\n<\/tr>\n
\u4e3b\u50ac<\/strong><\/td>\nThe IEEE Computational Intelligence Society<\/td>\n<\/tr>\n
\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\nThe 2017 IEEE Symposium Series on Computational Intelligence
\n(http:\/\/www.ele.uri.edu\/ieee-ssci2017\/index.html)<\/td>\n<\/tr>\n
\u4f1a\u5834<\/strong><\/td>\nThe Hilton Hawaiian Village Waikiki Resort, Honolulu, HAWAII, USA<\/td>\n<\/tr>\n
\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n2017\/11\/27-2017\/12\/1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 
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  1. \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n

    2017\/11\/27\u304b\u30892017\/12\/01\u306b\u304b\u3051\u3066\uff0c\u30db\u30ce\u30eb\u30eb\uff08\u30cf\u30ef\u30a4\uff09\u306eHilton Hawaiian Village Waikiki Resort\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305f2017 IEEE Symposium Series on Computational Intelligence\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\uff0c\u90e1(M2)\uff0c\u539f\u7530\uff08M2\uff09\uff0c\u5ee3\u5b89\u5148\u751f\u306e\u8a083\u540d\u304c\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e
    \n\u3053\u306e\u5b66\u4f1a\u306f\uff0cIEEE Computational Intelligence Society\u304c\u4e3b\u50ac\u3059\u308b\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30fc\u30a4\u30f3\u30c6\u30ea\u30b8\u30a7\u30f3\u30b9\u306b\u95a2\u3059\u308b\u4e16\u754c\u7684\u306b\u91cd\u8981\u306a\u5b66\u4f1a\u3067\uff0c\u7406\u8ad6\u3084\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u8a2d\u8a08\u3001\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306a\u3069\u69d8\u3005\u306a\u5206\u91ce\u306e\u65b0\u3057\u3044\u6280\u8853\u306b\u3064\u3044\u3066\u30bb\u30c3\u30b7\u30e7\u30f3\u304c\u958b\u304b\u308c\uff0c\u69d8\u3005\u306a\u5206\u91ce\u306e\u65b9\u3005\u304c\u96c6\u307e\u3063\u3066\u3044\u307e\u3057\u305f\uff0e
    \n <\/p>\n

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    1. \u7814\u7a76\u767a\u8868\n
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      • \u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n

        \u79c1\u306f30\u65e5\u5348\u5f8c\u306b\uff0c\u753b\u50cf\u3084\u30d1\u30bf\u30fc\u30f3\u8a8d\u8b58\u306b\u304a\u3051\u308b\u7279\u5fb4\u5206\u6790\u3001\u9078\u629e\u3001\u5b66\u7fd2\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cFASLIP Session 2\u300d\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u53e3\u982d\u767a\u886830\u5206(\u8cea\u7591\u5fdc\u7b54\u542b\u3080)\u3067\u3057\u305f\uff0e
        \n\u4eca\u56de\u306f\uff0c\u30d1\u30ec\u30fc\u30c8\u30d5\u30ed\u30f3\u30c8\u63a2\u7d22\u306b\u3088\u308b\u30b9\u30d1\u30fc\u30b9\u7279\u5fb4\u9078\u629e\u3067fMRI\u30c7\u30fc\u30bf\u304b\u3089\u91cd\u8981\u306a\u8133\u6a5f\u80fd\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u62bd\u51fa\u3059\u308b\u624b\u6cd5\u306e\u691c\u8a0e\u306b\u3064\u3044\u3066\u767a\u8868\u81f4\u3057\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n\n\n\n
        We propose a method to automatically determine important features for the classification of two states by optimizing a selection of features that maximize the classification accuracy of these states. When there are many feature candidates, this result in a trade-off between minimizing the number of selections of features and maximizing classification accuracy. In this study, Pareto solutions are obtained using a multi-objective genetic algorithm. Next, by examining the Pareto solutions, we propose a method of selecting as few sparse features as possible. In the experiments, functional brain imaging data for two states obtained by fMRI was used, and the proposed method was applied to extract important brain regions and their cooperative networks. An analysis of the results shows that selecting sparse features mean that the characteristics of such features are more easily grasp, and this research deepens understanding in the region associated with brain status.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

         
        \n <\/p>\n

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        • \u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n

          \u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e
          \n 
          \n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>1<\/strong>
          \n\u540d\u53e4\u5c4b\u5927\u5b66\u306e\u5409\u5ddd\u5148\u751f\u304b\u3089\u306e\u8cea\u554f\u3067\u3059\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\u4eca\u56de\u306e\u624b\u6cd5\u3067\u306f\u30d1\u30ec\u30fc\u30c8\u30d5\u30ed\u30f3\u30c8\u304b\u3089\u91cd\u8981\u306a\u8133\u6a5f\u80fd\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u898b\u3064\u3051\u3066\u3044\u308b\u304c\uff0c\u30d1\u30ec\u30fc\u30c8\u30d5\u30ed\u30f3\u30c8\u3060\u3051\u306b\u9650\u3089\u305a\u63a2\u7d22\u3055\u308c\u305f\u89e3\u3067\u691c\u8a0e\u3057\u3066\u307f\u3066\u3082\u3044\u3044\u306e\u3067\u306f\u306a\u3044\u304b\uff1f\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e
          \n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\uff0c\u89e3\u62bd\u51fa\u5f8c\u306e\u89e3\u6790\u306e\u4ed5\u65b9\u306f\u96e3\u3057\u3044\u554f\u984c\u3067\u4eca\u5f8c\u691c\u8a0e\u3057\u3066\u3044\u304f\u5fc5\u8981\u304c\u3042\u308b\u3068\u3044\u3063\u305f\u69d8\u306a\u56de\u7b54\u3092\u5148\u751f\u306b\u3057\u3066\u3044\u305f\u3060\u3044\u305f\u3088\u3046\u306b\u601d\u3044\u307e\u3059\uff0e
          \n 
          \n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>2<\/strong>
          \n\u8cea\u554f\u8005\u306e\u6c0f\u540d\u3092\u63a7\u3048\u640d\u306d\u3066\u3057\u307e\u3044\u307e\u3057\u305f\u304c\uff0c\u304a\u305d\u3089\u304fSchool of Engineering and Computer Science, Victoria University of Wellington\u306eMengjie Zhang\u5148\u751f\u304b\u3089\u306e\u8cea\u554f\u3067\u3059\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\uff0c\u4eca\u56deNSGA-II\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u304c\u4ed6\u306e\u624b\u6cd5\u3068\u6bd4\u8f03\u306f\u3057\u306a\u304b\u3063\u305f\u306e\u304b\u3068\u3044\u3063\u305f\u3082\u306e\u3067\u3057\u305f\uff0e
          \n\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u56de\u7b54\u306f\uff0cNSGA-II\u3060\u3051\u3067\u306a\u304fMOEA\/D\u3067\u3082\u8a66\u3057\u3066\u307f\u305f\u304c\uff0c\u3053\u306e\u554f\u984c\u3067\u306fNSGA-II\u306e\u63a2\u7d22\u6027\u80fd\u304c\u826f\u304b\u3063\u305f\u3068\u5148\u751f\u306b\u56de\u7b54\u3057\u3066\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u3053\u306e\u554f\u984c\u306f\u4ea4\u53c9\u65b9\u6cd5\u304c\u3068\u3066\u3082\u91cd\u8981\u3067\uff0c\u4eca\u306f1\u6b21\u5143\u3067\u8003\u3048\u3066\u3044\u308b\u3051\u308c\u3069\u4eca\u5f8c\u6539\u5584\u304c\u5fc5\u8981\u3060\u3068\u5148\u751f\u306b\u56de\u7b54\u3057\u3066\u3044\u305f\u3060\u304d\u307e\u3057\u305f\uff0e
          \n 
          \n <\/p>\n

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          • \u611f\u60f3<\/li>\n<\/ul>\n

            \u4eca\u56de2\u56de\u76ee\u306e\u56fd\u969b\u5b66\u4f1a\u3067\u3057\u305f\u304c\uff0c\u82f1\u8a9e\u3067\u306e\u53e3\u982d\u767a\u8868\u3068\u3044\u3046\u3053\u3068\u3082\u3042\u308a\u3068\u3066\u3082\u7dca\u5f35\u3057\u307e\u3057\u305f\uff0e\u4eca\u56de\u306f\u524d\u56de\u3068\u6bd4\u3079\u3066\uff0c\u7d50\u679c\u3092\u51fa\u3059\u3053\u3068\u306b\u6642\u9593\u304c\u304b\u304b\u308a\uff0c\u767a\u8868\u6e96\u5099\u304c\u5341\u5206\u306b\u51fa\u6765\u306a\u304b\u3063\u305f\u306e\u3067\u7df4\u7fd2\u901a\u308a\u306b\u306f\u306a\u304b\u306a\u304b\u767a\u8868\u3067\u304d\u307e\u305b\u3093\u3067\u3057\u305f\u304c\uff0c\u6642\u9593\u304c\u306a\u3044\u4e2d\u3067\u3082\u81ea\u5206\u306a\u308a\u306e\u5168\u529b\u306f\u51fa\u3057\u5207\u308c\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u8cea\u554f\u306e\u82f1\u8a9e\u3092\u805e\u304d\u53d6\u308b\u3053\u3068\u304c\u51fa\u6765\u305a\uff0c\u7b54\u3048\u306a\u308c\u306a\u304b\u3063\u305f\u3053\u3068\u304c\u3068\u3066\u3082\u6b8b\u5ff5\u3067\u3057\u305f\uff0e\u3057\u304b\u3057\uff0cbanquet\u3084\u65e5\u5e38\u751f\u6d3b\u306e\u4e2d\u3067\u306f\u53bb\u5e74\u306b\u6bd4\u3079\u3066\u7a4d\u6975\u7684\u306b\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u53d6\u308c\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u307e\u305f\u672c\u5b66\u4f1a\u3067\u306f\uff0c\u79c1\u306e\u7814\u7a76\u5ba4\u3067\u884c\u3063\u3066\u3044\u308b\u7814\u7a76\u3068\u4f3c\u3066\u3044\u308b\u3082\u306e\u3084\u5168\u304f\u7570\u306a\u308b\u5206\u91ce\u306e\u8a71\u3092\u805e\u304f\u3053\u3068\u304c\u51fa\u6765\u3066\u3068\u3066\u3082\u9762\u767d\u304b\u3063\u305f\u3067\u3059\uff0e<\/p>\n

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            1. \u8074\u8b1b<\/li>\n<\/ol>\n

              \u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e5\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e
              \n <\/p>\n\n\n\n
              \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Lung segmentation on x-ray images with neural validation
              \n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Dawid Po\u0142ap and Marcin Wo\u00b4zniak
              \n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Computational Intelligence for Human-like Intelligence II
              \nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Lung segmentation on x-ray images is an important part in the process of feature extraction for recognition purposes. Using it we can extract specific data from the input image. Segmentation allows to remove unnecessary elements such as bones and spine, leaving in the image only the lungs. This solution reduces the area of the image subjected to further analysis in terms of disease detection. In this paper, segmentation technique based on graphics processing methods and swarm algorithm was presented. A swarm methodology was used for extraction of particular portions of the information for which we have applied convolutional neural network as a detector. For the composed method we have performed tests to show and discuss the results.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

              \u3053\u306e\u767a\u8868\u306fX\u7dda\u753b\u50cf\u304b\u3089\u80ba\u9818\u57df\u3092\u62bd\u51fa\u3059\u308b\u624b\u6cd5\u306e\u63d0\u6848\u3067\u3057\u305f\uff0e\u5f93\u6765\uff0c\u80ba\u306a\u3069\u306e\u547c\u5438\u5668\u75be\u60a3\u306e\u8a3a\u65ad\u306f\u533b\u5e2b\u304c\u7d44\u7e54\u306e\u69cb\u9020\u306e\u5909\u5316\u3092\u57fa\u306b\u8a3a\u65ad\u3092\u884c\u3063\u3066\u304d\u307e\u3057\u305f\u304c\uff0c\u8fd1\u5e74\u3067\u306f\u533b\u5e2b\u306e\u8a3a\u65ad\u652f\u63f4\u3068\u3044\u3046\u5f62\u3067\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30fc\u304c\u4f7f\u7528\u3055\u308c\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u304d\u307e\u3057\u305f\uff0e\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30fc\u8a3a\u65ad\u652f\u63f4\u30b7\u30b9\u30c6\u30e0\u3067\u306f\uff0c\u3044\u304f\u3064\u304b\u306e\u5de5\u7a0b\u304c\u542b\u307e\u308c\u307e\u3059\u304c\uff0c\u3053\u306e\u767a\u8868\u3067\u306f\u9818\u57df\u306e\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u306b\u7126\u70b9\u3092\u5f53\u3066\u3066\u3044\u307e\u3057\u305f\uff0e\u8fd1\u5e74\u306e\u80ba\u9818\u57df\u306e\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u3067\u306f\u8272\u3084\u30c6\u30af\u30b9\u30c1\u30e3\uff0c\u305d\u306e\u4ed6\u306e\u7279\u5fb4\u304c\u7528\u3044\u3089\u308c\u3066\u3044\u307e\u3059\uff0e\u3053\u306e\u624b\u6cd5\u3067\u306f\uff0c\u307e\u305aX\u7dda\u753b\u50cf\u3092\u4e8c\u5024\u5316\u3057\uff0c\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u7528\u3044\u3066\u80ba\u9818\u57df\u3092\u7279\u5b9a\uff0c\u80ba\u9818\u57df\u4ee5\u5916\u3092\u9664\u53bb\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u305d\u3057\u3066\uff0c\u7c21\u5358\u306a\u30ce\u30a4\u30ba\u9664\u53bb\u3092\u884c\u3044\u80ba\u9818\u57df\u306e\u307f\u3092\u62bd\u51fa\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u9818\u57df\u306e\u62bd\u51fa\u3068\u3044\u3046\u70b9\u3067\u306f\u7814\u7a76\u5ba4\u3067\u884c\u308f\u308c\u3066\u3044\u308b\u753b\u50cf\u51e6\u7406\u306e\u7814\u7a76\u3068\u3082\u5c11\u3057\u95a2\u9023\u304c\u3042\u308b\u3088\u3046\u306b\u611f\u3058\u307e\u3057\u305f\uff0e
              \n\u307e\u305f\uff0c\u3053\u306e\u624b\u6cd5\u306e\u30ce\u30a4\u30ba\u9664\u53bb\u3067\u884c\u308f\u308c\u3066\u3044\u308b\u624b\u6cd5\u304c\uff0c\u79c1\u304c\u753b\u50cf\u51e6\u7406\u306e\u7814\u7a76\u3092\u884c\u3063\u3066\u3044\u305f\u9803\u306b\u7528\u3044\u3066\u3044\u305f\u624b\u6cd5\u3092\u540c\u3058\u3067\uff0c\u5b9f\u969b\u306eX\u7dda\u306b\u3082\u7528\u3044\u308b\u3053\u3068\u306e\u51fa\u6765\u308b\u624b\u6cd5\u3068\u3044\u3046\u3053\u3068\u304c\u5206\u304b\u308a\u3068\u3066\u3082\u5b09\u3057\u304f\u601d\u3044\u307e\u3057\u305f\uff0e\u4ee5\u524d\u53c2\u52a0\u3057\u305fJAMIT\u3067\u3082X\u7dda\u753b\u50cf\u306b\u5bfe\u3059\u308b\u753b\u50cf\u51e6\u7406\u306e\u7814\u7a76\u3092\u8272\u3005\u3068\u8074\u8b1b\u3057\u307e\u3057\u305f\u304c\uff0c\u3042\u307e\u308a\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u7528\u3044\u3066\u3044\u305f\u7814\u7a76\u306f\u7121\u304b\u3063\u305f\u306e\u3067\u50be\u5411\u306e\u5909\u5316\u3092\u611f\u3058\u307e\u3057\u305f\uff0e
              \n 
              \n <\/p>\n\n\n\n
              \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Using Matching Substructures as an Optimization Objective for
              \nRNA Design
              \n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a David J. D. Hampson and Herbert H. Tsang
              \n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Computational Intelligence in Healthcare and E-Health V
              \nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a RNA design is a problem that has been shown to be NP-Hard. It is best described as the inverse of RNA folding. RNA folding describes the process of calculating the most likely secondary structure that a strand of nucleotides will fold into. Inversely, RNA design describes the process of designing a strand of nucleotides that will fold into a given secondary structure. The problem is made more difficult by the presence of a second objective, structural stability. Free energy is a measure of structural stability. In previous research, we have attempted to solve this problem using SIMARD (Simulated Annealing RNA Design). SIMARD employs a simulated annealing framework alongside a preselection strategy to design high-quality sequences in a reasonable amount of time. In this paper, we introduce the integration of BEAR (Brand nEw Alphabet for RNAs) to SIMARD as a way of notating secondary structures for quality evaluation. We attempt to design sequences with four different experimental configurations across two data sets. We find that representing our sequences with the BEAR grammar allows us to improve the average structural similarity of our generated sequences. We also find that SIMARD outperforms six other algorithms when running on the Eterna100 benchmark in terms of successfully designed structures.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

              \u3053\u306e\u767a\u8868\u3067\u306fNP\u56f0\u96e3\u306a\u554f\u984c\u3067\u3042\u308b\u3068\u793a\u3055\u308c\u3066\u3044\u308bRNA\u8a2d\u8a08\u306b\u5bfe\u3057\u3066\uff0c\u65b0\u305f\u306a\u4e8c\u6b21\u69cb\u9020\u306e\u8868\u8a18\u65b9\u6cd5\u3092\u63d0\u6848\u3057\uff0c\u6bd4\u8f03\u691c\u8a0e\u3092\u884c\u3063\u3066\u3044\u307e\u3057\u305f\uff0eRNA\u8a2d\u8a08\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u306f\u521d\u3081\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3084\u52d5\u7684\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u304c\u7528\u3044\u3089\u308c\u3066\u304d\u307e\u3057\u305f\u304c\uff0c\u3053\u308c\u3089\u306fRNA\u8a2d\u8a08\u304c\u591a\u76ee\u7684\u3067\u3042\u308b\u4e8b\u3092\u8003\u616e\u3057\u3066\u3044\u306a\u304b\u3063\u305f\u305f\u3081\uff0c\u8fd1\u5e74\u3067\u306f\u591a\u76ee\u7684\u907a\u4f1d\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u304c\u7528\u3044\u3089\u308c\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u304d\u307e\u3057\u305f\uff0e\u3057\u304b\u3057\uff0c\u3053\u308c\u3089\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3082\u5b9f\u884c\u6642\u9593\u304c\u9577\u3044\u3068\u3044\u3046\u554f\u984c\u70b9\u304c\u3042\u308b\u305f\u3081\uff0c\u3053\u306e\u7814\u7a76\u306e\u8457\u8005\u3089\u306fSimulated annealing\u3092\u57fa\u306b\u3057\u305fSIMARD\u3068\u3044\u3046\u624b\u6cd5\u3092\u958b\u767a\u3057\u3066\u3044\u307e\u3057\u305f\uff08\u3053\u306e\u624b\u6cd5\u306b\u3064\u3044\u3066\u306f\uff0c\u201dSIMARD: A simulated annealing based RNA design algorithm with quality pre-selection strategies\u201d \u7b49\u3092\u53c2\u7167\uff0e\u6628\u5e74\u306eSSCI2016\u3067\u767a\u8868\uff09\uff0e\u3053\u306e\u767a\u8868\u3067\u306f\uff0c\u4e3b\u306b\u3053\u306e\u624b\u6cd5\u306e\u6709\u7528\u6027\u3092\u691c\u8a0e\u3057\u3066\u3044\u307e\u3057\u305f\uff0e
              \n\u3053\u306e\u767a\u8868\u3067\u306f\uff0c\u8907\u96d1\u306a\u69cb\u9020\u3092\u3082\u3064RNA\u8a2d\u8a08\u306b\u5bfe\u3057\u3066\u3069\u306e\u7a0b\u5ea6\u6b63\u78ba\u306a\u69cb\u9020\u3092\u5f97\u308b\u3053\u3068\u304c\u51fa\u6765\u308b\u304b\u304c\u8b70\u8ad6\u3055\u308c\u3066\u304a\u308a\uff0c\u8a73\u3057\u304f\u306f\u8ad6\u6587\u3092\u3044\u304f\u3064\u304b\u8aad\u3080\u5fc5\u8981\u306f\u3042\u308a\u307e\u3059\u304c\uff0c\u3053\u306e\u8868\u73fe\u65b9\u6cd5\u3084\u8003\u3048\u65b9\u306f\u79c1\u306e\u624b\u6cd5\u306e2\u6b21\u5143\u4ea4\u53c9\u306a\u3069\u306b\u3082\u5fdc\u7528\u3057\u3066\u3044\u3051\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e
              \n 
              \n <\/p>\n\n\n\n
              \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000A Comparative Study of CNN, BoVW and LBP for
              \nClassification of Histopathological Images\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Meghana Dinesh Kumar, Morteza Babaie, Shujin Zhu, Shivam
              \nKalra, and H.R.Tizhoosh
              \n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Computational Intelligence in Healthcare and E-Health V
              \nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Despite the progress made in the field of medical imaging, it remains a large area of open research, especially due to the variety of imaging modalities and disease-specific characteristics. This paper is a comparative study describing the potential of using local binary patterns (LBP), deep features and the bag-of-visual words (BoVW) scheme for the classification of histopathological images. We introduce a new dataset, KIMIA Path960, that contains 960 histopathology images belonging to 20 different classes (different tissue types). We make this dataset publicly available. The small size of the dataset and its interand intra-class variability makes it ideal for initial investigations when comparing image descriptors for search and classification in complex medical imaging cases like histopathology. We investigate deep features, LBP histograms and BoVW to classify the images via leave-one-out validation. The accuracy of image classification obtained using LBP was 90.62% while the highest accuracy using deep features reached 94.72%. The dictionary approach (BoVW) achieved 96.50%. Deep solutions may be able to deliver higher accuracies but they need extensive training with a large number of (balanced) image datasets.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

              \u3053\u306e\u767a\u8868\u3067\u306f20\u306e\u7570\u306a\u308b\u30af\u30e9\u30b9\uff08\u7570\u306a\u308b\u7d44\u7e54\u30bf\u30a4\u30d7\uff09\u306b\u5c5e\u3059\u308b960\u679a\u306e\u7d44\u7e54\u75c5\u7406\u5b66\u7684\u753b\u50cf\u306e\u5206\u985e\u306e\u305f\u3081\u306b\u3069\u306e\u69d8\u306a\u7279\u5fb4\u91cf\u3092\u7528\u3044\u308b\u3068\u826f\u3044\u304b\u3092\u6bd4\u8f03\u691c\u8a0e\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u307e\u305f\uff0cKIMIA Path960\u3068\u3044\u3046\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3082\u516c\u958b\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u3053\u306e\u7814\u7a76\u3067\u306f\uff0clocal binary patterns (LBP)\u3084deep features(CNN\u306b\u7528\u3044\u308b)\uff0cthe bag-of-visual words (BoVW) scheme\u3092\u7528\u3044\u3066\u6bd4\u8f03\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u3069\u306e\u7279\u5fb4\u91cf\u3067\u3082SVM\u306e\u8b58\u5225\u7cbe\u5ea6\u306f90\uff05\u3092\u8d85\u3048\u3066\u3044\u307e\u3057\u305f\u304c\uff0c\u3053\u306e\u753b\u50cf\u30bb\u30c3\u30c8\u3067\u306fBoVW\u3092\u7528\u3044\u305f\u5834\u5408\u304c\u6700\u3082\u9ad8\u304f\u306a\u3063\u3066\u3044\u307e\u3057\u305f\uff0e\u307e\u305f\uff0cDeep learning\u3092\u7528\u3044\u308b\u5834\u5408\uff0c\u9ad8\u3044\u7cbe\u5ea6\u3092\u5f97\u308b\u3053\u3068\u306f\u3067\u304d\u308b\u304c\u591a\u6570\u306e\u30d0\u30e9\u30f3\u30b9\u306e\u3068\u308c\u305f\u753b\u50cf\u30bb\u30c3\u30c8\u3092\u7528\u3044\u305f\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u304c\u5fc5\u8981\u3067\u3042\u308b\u3068\u3057\u3066\u3044\u307e\u3057\u305f\uff0e
              \n\u3053\u306e\u7814\u7a76\u306f\uff0c\u5bfe\u8c61\u753b\u50cf\u306f\u9055\u3044\u307e\u3059\u304c\u79c1\u306e\u7814\u7a76\u5ba4\u3067\u884c\u308f\u308c\u3066\u3044\u308b\u753b\u50cf\u51e6\u7406\u306e\u7814\u7a76\u3068\u624b\u6cd5\u304c\u4f3c\u3066\u3044\u308b\u3068\u601d\u3044\u307e\u3057\u305f\uff0e\u3053\u306e\u5b66\u4f1a\u3067\u3082\u4ed6\u306b\u4f55\u4ef6\u304b\u3053\u306e\u69d8\u306a\u767a\u8868\u3092\u805e\u304d\uff0c\u753b\u50cf\u51e6\u7406\u3068\u6a5f\u68b0\u5b66\u7fd2\u306e\u7d44\u307f\u5408\u308f\u305b\u3082\u4e3b\u6d41\u306b\u306a\u3063\u3066\u304d\u3066\u3044\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u79c1\u306e\u7814\u7a76\u3067\u3082\u624b\u6cd5\u306e\u6bd4\u8f03\u306f\u3082\u3061\u308d\u3093\u3067\u3059\u304c\u7279\u5fb4\u91cf\u306e\u6bd4\u8f03\u3082\u884c\u3063\u3066\u3044\u304f\u3068\u691c\u8a0e\u3059\u308b\u9805\u76ee\u3082\u5897\u3048\uff0c\u3088\u308a\u8133\u6a5f\u80fd\u306b\u3064\u3044\u3066\u7406\u89e3\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u308b\u3088\u3046\u306b\u306a\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e
              \n <\/p>\n\n\n\n
              \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Classification of the Estrous Cycle Through Texture and Shape
              \nFeatures\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Meghana Dinesh Kumar, Morteza Babaie, Shujin Zhu, Shivam
              \nKalra, and H.R.Tizhoosh
              \n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Computational Intelligence for Multimedia, Signal and Vision
              \nProcessing I
              \nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a We show, for the first time, an autonomous classification of the estrous cycle (the reproductive cycle in rats), This cycle consists of 4 stages: Proestrus, Estrus, Metestrus and Diestrus. The short duration of the cycle in rats makes them an ideal model for research about changes that occur during the reproductive cycle. Classification is based on the cytology shown by vaginal smear. For this reason, we used texture and shape features on the gray level color space and CIELAB color space on channels A and B, which were classified using support vector machines (SVM) and the artificial neural network multilayer perceptron (MLP). As dataset of 412 images of estrous cycle was used. It was divided into two sets. The first contains all four stages, the second contains two classes. The first class is formed by the stages Proestrus and Estrous and the second class is formed by the stages Metestrus and Diestrus. The two sets were formed to solve the main problems, the research of the reproductive cycle and the reproduction control of rodents. For the first set, we obtained an 87% of validation accuracy and 100% of validation accuracy for the second set using the multilayer perceptron. The results were validated through cross validation using 5 sets and F1 metric.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

              \u3053\u306e\u767a\u8868\u3067\u306f\u30e9\u30c3\u30c8\u3092\u7528\u3044\u3066\u30d2\u30c8\u306e\u751f\u6b96\u5468\u671f\u3092\u30e2\u30c7\u30ea\u30f3\u30b0\u3057\u3066\u3044\u307e\u3057\u305f\uff0e\u30e9\u30c3\u30c8\u306e\u751f\u6b96\u5468\u671f\u306f\u30d2\u30c8\u3088\u308a\u3082\u77ed\u304f\uff0c4\u3064\u306e\u6bb5\u968e\u3067\u5206\u3051\u3089\u308c\u307e\u3059\uff0e\u63d0\u6848\u624b\u6cd5\u3067\u306f\u3053\u306e4\u3064\u306e\u6bb5\u968e\u3092\u5206\u3051\u308b\u305f\u3081\u306b\u753b\u50cf\u51e6\u7406\u3068SVM\uff0c\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u7528\u3044\u3066\u3044\u307e\u3057\u305f\uff0e\u8b58\u5225\u306f\u30b0\u30ec\u30fc\u30b9\u30b1\u30fc\u30eb\u3068CIELAB\u8272\u7a7a\u9593\u4e0a\u306e\u30c6\u30af\u30b9\u30c1\u30e3\u3068\u5f62\u72b6\u7279\u5fb4\u3092\u7528\u3044\u3066\u884c\u3063\u3066\u304a\u308a\uff0c\u672c\u7814\u7a76\u5ba4\u306e\u753b\u50cf\u51e6\u7406\u73ed\u304c\u884c\u3063\u3066\u3044\u308b\u7814\u7a76\u3068\u985e\u4f3c\u3057\u3066\u3044\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u3053\u306e\u7814\u7a76\u3067\u306fSVM\u306e\u30ab\u30fc\u30cd\u30eb\u3092\u4f55\u7a2e\u985e\u304b\u8a66\u3057\u6bd4\u8f03\u3057\u3066\u304a\u308a\uff0c\u3088\u308a\u8ad6\u6587\u306b\u8aac\u5f97\u529b\u3092\u6301\u305f\u305b\u3066\u3044\u307e\u3057\u305f\uff0e\u79c1\u306e\u7814\u7a76\u767a\u8868\u306e\u8cea\u7591\u5fdc\u7b54\u3067\u3082\u3042\u308a\u307e\u3057\u305f\u304c\uff0c\u3053\u306e\u8ad6\u6587\u306e\u69d8\u306b\u540c\u7b49\u306e\u624b\u6cd5\u306a\u3069\u3067\u6bd4\u8f03\u691c\u8a0e\u3059\u308b\u3053\u3068\u306f\u91cd\u8981\u3060\u3068\u6539\u3081\u3066\u611f\u3058\u307e\u3057\u305f\uff0e
              \n 
              \n <\/p>\n\n\n\n
              \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Brain Machine Interface for Useful Human Interaction Via
              \nExtreme Learning Machine and State Machine Design
              \n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Garrett Sargent, Haotian Zhang, Alyssa Morgan, Adam Van
              \nCamp, Adam Van Camp, Adam Cassedy, Emma Romstadt,
              \nVictoria Dicillo,
              \n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Computational Intelligence in Healthcare and E-Health V
              \nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a The idea of brain machine interface (BMI) is to provide a source of interaction between a person and a machine via thought. Three major parts to an effective BMI are determined and handled in this paper: classifying a thought, doing a useful activity, and providing an efficient user interface (UI). This paper proposes an effective way of classifying thoughts and an approach for providing useful activities given a sequence of signals. We demonstrate the effectiveness of an extreme learning machine (ELM) for classifying a number of different thoughts when given a relatively small amount of training samples from a 5-channel EEG headset. We transform the electroencephalograph (EEG) data to a set of features for the input of the ELM model by estimating the logarithmic power (LP) of the discrete wavelet transform (DWT) coefficients, which corresponds to five different frequency bands. The ELM provides up to a 90% to 100% classification accuracy depending on training samples and number of hidden nodes, compared to 52% to 60% for a multi-layer perceptron (MLP). We also present a UI based on a state machine design that allows a person to accomplish certain activities via thought with a robotic arm.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

              \u3053\u306e\u767a\u8868\u306fEEG\u3092\u7528\u3044\u3066\u8133\u6ce2\u3092\u8a08\u6e2c\u3057\uff0c\u96e2\u6563\u30a6\u30a7\u30fc\u30d6\u30ec\u30c3\u30c8\u5909\u63db\u3092\u7528\u3044\u3066\u7570\u306a\u308b5\u3064\u306e\u5468\u6ce2\u6570\u5e2f\u57df\u306b\u5206\u96e2\uff0c\u3053\u306e\u5bfe\u6570\u30d1\u30ef\u30fc\u3092\u5165\u529b\u3068\u3057\u3066extreme learning machine (ELM)\u3000\u3092\u7528\u3044\u3066\u4eba\u306e\u601d\u8003\u3092\u5206\u985e\uff0c\u30ed\u30dc\u30c3\u30c8\u30a2\u30fc\u30e0\u3092\u52d5\u304b\u3059\u3068\u3044\u3046\u7814\u7a76\u3067\u3057\u305f\uff0e\u8fd1\u5e74\uff0cBMI\u306f\u3068\u3066\u3082\u7814\u7a76\u3055\u308c\u3066\u3044\u3066\u6d41\u884c\u306b\u306e\u3063\u305f\u7814\u7a76\u3060\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u3053\u306e\u7814\u7a76\u3067\u306f5\u30c1\u30e3\u30f3\u30cd\u30eb\u306eEEG\u3092\u7528\u3044\u3066\u304a\u308a\uff0c5\u30c1\u30e3\u30f3\u30cd\u30eb\u3068\u5c11\u306a\u3044\u5165\u529b\u304b\u3089\u884c\u52d5\u3092\u8b58\u5225\u3057\u3066\u3044\u3066\u9a5a\u304d\u307e\u3057\u305f\uff0e\u3053\u306e\u7814\u7a76\u306e\u69d8\u306b\uff0c\u7c21\u6613\u306e\u8a08\u6e2c\u6a5f\u304b\u3089\u8133\u72b6\u614b\u3092\u8b58\u5225\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u308c\u3070\uff0c\u3088\u308a\u65e5\u5e38\u306b\u53d6\u308a\u5165\u308c\u3066\u3044\u304f\u4e8b\u304c\u51fa\u6765\u308b\u3088\u3046\u306b\u306a\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\uff0c\u3068\u3066\u3082\u671f\u5f85\u306e\u6301\u3066\u308b\u7814\u7a76\u3067\u3057\u305f\uff0e\u7814\u7a76\u306e\u5185\u5bb9\u304c\u79c1\u306e\u7814\u7a76\u5ba4\u3068\u3082\u4f3c\u3066\u3044\u305f\u306e\u3067\uff0c\u4eca\u5f8c\u3053\u306e\u7814\u7a76\u306e\u52d5\u5411\u3092\u78ba\u8a8d\u3059\u308b\u5fc5\u8981\u3082\u3042\u308b\u3068\u611f\u3058\u307e\u3057\u305f\uff0e\u307e\u305f\uff0c\u3053\u306e\u7814\u7a76\u3067\u3082\u8907\u6570\u65e5\u306b\u6e21\u3063\u3066\u8a08\u6e2c\u3092\u884c\u3044\uff0c\u8b58\u5225\u306e\u5b89\u5b9a\u6027\u3092\u6539\u5584\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3068\u3057\u3066\u304a\u308a\uff0c\u751f\u4f53\u8a08\u6e2c\u3092\u884c\u3046\u4e0a\u3067\u306f\u500b\u4eba\u5dee\u306f\u3082\u3061\u308d\u3093\u500b\u4eba\u5185\u3067\u306e\u30d0\u30e9\u3064\u304d\u3082\u8003\u616e\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3068\u6539\u3081\u3066\u611f\u3058\u307e\u3057\u305f\uff0e
              \n\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
              \u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n\u539f\u7530\u572d<\/td>\n<\/tr>\n
              \u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n\u52d5\u7684\u306a\u91cd\u307f\u5272\u308a\u5f53\u3066\u3092\u884c\u3046MOEA\/D<\/td>\n<\/tr>\n
              \u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\nAdaptive Weigh Vector Assignment Method for MOEA\/D<\/td>\n<\/tr>\n
              \u8457\u8005<\/strong><\/td>\n\u539f\u7530\u572d, \u65e5\u548c\u609f, \u5ee3\u5b89\u77e5\u4e4b<\/td>\n<\/tr>\n
              \u4e3b\u50ac<\/strong><\/td>\nThe IEEE Computational Intelligence Society<\/td>\n<\/tr>\n
              \u56fd\u969b\u5b66\u4f1a\u540d<\/strong><\/td>\nThe 2017 IEEE Symposium Series on Computational Intelligence<\/td>\n<\/tr>\n
              \u4f1a\u5834<\/strong><\/td>\nHonolulu, Hawaii, USA, The Hilton Hawaiian Village Waikiki Resort<\/td>\n<\/tr>\n
              \u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n2017\/11\/27-2017\/12\/1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

               
              \n <\/p>\n

                \n
              1. \u56fd\u969b\u5b66\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n

                The 2017 IEEE Symposium Series on Computational Intelligence(IEEE SSCI 2017)\u304c2017\u5e7411\u670827\u65e5\uff5e12\u6708\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 1\u65e5\u306b\u304b\u3051\u3066\u30a2\u30e1\u30ea\u30ab\u306e\u30cf\u30ef\u30a4\u306eThe Hilton Hawaiian Village Waikiki Resort\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305f\u3002\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u3001\u5ee3\u5b89\u5148\u751f\u3001\u539f\u7530\u572d\uff08M2\uff09\u3001\u90e1\u60a0\u5e0c\uff08M2\uff09\u306e3\u540d\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\u3002\u767a\u8868\u5f62\u5f0f\u306f\u53e3\u982d\u767a\u8868\u3067\u3001\u300cAdaptive Weigh Vector Assignment Method for MOEA\/D\u300d\u3068\u3044\u3046\u984c\u76ee\u3067\u5b66\u4f1a4\u65e5\u76ee\u306e11\u670830\u65e5\u306e\u5348\u524d11\u664215\u5206\u304b\u308911\u664245\u5206\u306b\u304b\u3051\u3066\u53e3\u982d\u767a\u8868\u81f4\u3057\u307e\u3057\u305f\u3002\u5408\u8a08\u306710\u540d\u524d\u5f8c\u306e\u65b9\u306b\u8db3\u3092\u904b\u3093\u3067\u3082\u3089\u3044\u3001\u767a\u8868\u5f8c\u306b\u306f\u8cea\u554f\u3082\u9802\u304d\u56de\u7b54\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3057\u305f\u3002
                \n\u79c1\u305f\u3061\u304c\u53c2\u52a0\u3057\u305fIEEE SSCI 2017\u306f\u3001\u8a08\u7b97\u77e5\u80fd\u306e\u7406\u8ad6\u3084\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u8a2d\u8a08\u3084\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u5206\u91ce\u3092\u4e2d\u5fc3\u306b\u8b70\u8ad6\u304c\u884c\u308f\u308c\u308b\u5b66\u4f1a\u3067\u3042\u308a\u306a\u304c\u3089\u3001\u305d\u308c\u3089\u6280\u8853\u306b\u95a2\u9023\u3059\u308b\u5168\u3066\u306e\u65b0\u8208\u6280\u8853\u306b\u95a2\u3059\u308b\u7814\u7a76\u3082\u767a\u8868\u3055\u308c\u308b\u5834\u3067\u3057\u305f\u3002\u305d\u306e\u305f\u3081\u3001\u79c1\u306e\u5c02\u9580\u5206\u91ce\u3067\u3042\u308b\u9032\u5316\u8a08\u7b97\u5206\u91ce\u3084\u3001\u95a2\u9023\u5206\u91ce\u3067\u3042\u308b\u8133\u3084\u8a8d\u77e5\u306b\u95a2\u308f\u308b\u7814\u7a76\u767a\u8868\u3092\u8074\u8b1b\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3057\u305f\u3002\u307e\u305f\u3001\u6ede\u5728\u3057\u305f\u671f\u9593\u3001\u30cf\u30ef\u30a4\u306f\u96e8\u671f\u306b\u3042\u305f\u308a\u3001\u65ad\u7d9a\u7684\u306b\u96e8\u3084\u98a8\u304c\u5439\u304f\u5929\u5019\u3067\u3057\u305f\u304c\u3001\u79c1\u305f\u3061\u306e\u767a\u8868\u65e5\u306f\u7d20\u6674\u3089\u3057\u3044\u6674\u308c\u6a21\u69d8\u3068\u306a\u308a\u307e\u3057\u305f\u3002
                \n\u5b66\u4f1a\u4e2d\u306f\u3001\u4eca\u307e\u3067\u306e\u5b66\u4f1a\u3067\u51fa\u4f1a\u3063\u305f\u69d8\u3005\u306a\u5148\u751f\u65b9\u3082\u304a\u308a\u3001\u518d\u4f1a\u3092\u679c\u305f\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002\u307e\u305f\u3001\u4ed6\u5b66\u751f\u306e\u767a\u8868\u3067\u306f\u7a4d\u6975\u7684\u306b\u8cea\u554f\u3057\u3001\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u6642\u306b\u306f\u5b66\u751f\u306e\u7814\u7a76\u3092\u5bc6\u306b\u805e\u304f\u3053\u3068\u304c\u51fa\u6765\u307e\u3057\u305f\u3002
                \nIEEE SSCI 2017 \u30db\u30fc\u30e0\u30da\u30fc\u30b8 http:\/\/www.ele.uri.edu\/ieee-ssci2017\/
                \n <\/p>\n

                  \n
                1. \u7814\u7a76\u767a\u8868\n
                    \n
                  • \u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n

                    \u79c1\u306f\u300111\u670830\u65e5\u306e\u5348\u524d11\u664215\u5206\u304b\u308911\u664245\u5206\u306b\u304b\u3051\u3066\u53e3\u982d\u767a\u8868\u81f4\u3057\u307e\u3057\u305f\u3002\u4eca\u56de\u306e\u5b66\u4f1a\u767a\u8868\u3067\u306f\uff0c\u63a2\u7d22\u72b6\u6cc1\u306b\u5fdc\u3058\u3066\u8a08\u7b97\u8cc7\u6e90\u306e\u52d5\u7684\u306a\u5272\u308a\u632f\u308a\u3092\u884c\u3046MOEA\/D\u3092\u63d0\u6848\u81f4\u3057\u307e\u3057\u305f\u3002\u63d0\u6848MOEA\/D\u306f\u3001\u76ee\u7684\u95a2\u6570\u9593\u306e\u96e3\u6613\u5ea6\u306b\u5dee\u304c\u5b58\u5728\u3059\u308b\u91cd\u8981\u306a\u8133\u90e8\u4f4d\u3092\u9078\u629e\u3059\u308b\u5b9f\u554f\u984c\u306b\u9069\u7528\u3055\u308c\u3001\u63a2\u7d22\u306b\u504f\u308a\u306e\u306a\u3044\u6027\u80fd\u3092\u793a\u3059\u3053\u3068\u3092\u78ba\u8a8d\u3057\u307e\u3057\u305f\u300215\u5206\u306e\u53e3\u982d\u767a\u8868\u306e\u5f8c\u3001\u540d\u53e4\u5c4b\u5927\u5b66\u306e\u5409\u5ddd\u5148\u751f\u3068\u96fb\u6c17\u901a\u4fe1\u5927\u5b66\u306e\u4f50\u85e4\u5148\u751f\u304b\u3089\u591a\u304f\u306e\u8cea\u554f\u3092\u9802\u304d\u3001\u305d\u306e\u56de\u7b54\u3092\u884c\u3046\u3053\u3068\u304c\u51fa\u6765\u307e\u3057\u305f\u3002\u4ee5\u4e0b\u306b\u6284\u9332\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n\n\n\n
                    MOEA\/D is one of the multiobjective optimization methods in which an optimization problem is decomposed into subproblems and searches are performed. When the search difficulty of each subproblem is equal, the obtained Pareto solutions are evenly distributed. In contrast, in real problems, the search complexity of each subproblem is often not equal. In that case, a Pareto solution set to a difficult subproblem cannot be found by an easy search. To solve this problem, a method that adaptively assigns the weight vectors of MOEA\/D according to the search situation is proposed. In the proposed method, subproblems that are difficult to search are divided into more subproblems, the search speeds of subproblems with different search difficulties are hence equalized, and solutions over a wider range should be found. The proposed method was used to a real-world problem, and its effectiveness is discussed. The target problem is to identify important brain regions using real data from a noninvasive functional brain imaging device. Identifying important brain regions is expected to promote elucidation of brain functions and contribute to effective training and therapeutic methods to improve human cognitive function. Compared to conventional MOEA\/D, good solutions were obtained by the proposed method in the objective function space that is difficult to search. In addition, the influence of the proposed adaptive weight vector assignment was investigated, and it was confirmed that the proposed method adaptively allocates many weight vectors to the difficult search areas. Hence, the proposed method in this paper will extend the range of real problems that can be addressed using MOEA\/D.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                     
                    \n <\/p>\n

                      \n
                    • \u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n

                      \u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e
                      \n 
                      \n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>1(<\/strong>\u540d\u53e4\u5c4b\u5927\u5b66\u306e\u5409\u5ddd\u5148\u751f<\/strong>)<\/strong>
                      \n\u300c\u306a\u305c\u30c1\u30e3\u30f3\u30cd\u30eb\u9078\u629e\u3092\u6700\u5c0f\u5316\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u306e\u304b\u300d\u3068\u3044\u3046\u8cea\u554f\u3092\u9802\u304d\u307e\u3057\u305f\u3002\u305d\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u3001\u300c\u5168\u3066\u306e\u8133\u6a5f\u80fd\u3092\u8003\u3048\u308b\u306b\u306f\u8907\u96d1\u306a\u306e\u3067\u3001\u79c1\u305f\u3061\u304c\u8133\u6a5f\u80fd\u3092\u7c21\u5358\u306b\u7406\u89e3\u3059\u308b\u305f\u3081\u306b\u3001\u5c11\u6570\u306e\u8133\u9818\u57df\u3092\u62bd\u51fa\u3057\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u3002\u3057\u304b\u3057\u3001\u5c11\u6570\u306e\u8133\u90e8\u4f4d\u306e\u60c5\u5831\u3067\u306f\u8b58\u5225\u8aa4\u5dee\u7387\u304c\u60aa\u5316\u3059\u308b\u3068\u3044\u3046\u30c8\u30ec\u30fc\u30c9\u30aa\u30d5\u95a2\u4fc2\u304c\u3042\u308b\u306e\u3067\u3001\u591a\u76ee\u7684\u6700\u9069\u5316\u3092\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3002\u300d\u3068\u56de\u7b54\u81f4\u3057\u307e\u3057\u305f\u3002
                      \n 
                      \n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>2(<\/strong>\u540d\u53e4\u5c4b\u5927\u5b66\u306e\u5409\u5ddd\u5148\u751f<\/strong>)<\/strong>
                      \n\u300c\u3082\u3057\u3001\u6700\u9069\u89e3\u304c\u63d0\u6848\u624b\u6cd5\u306b\u3088\u3063\u3066\u5206\u5272\u3055\u308c\u305f\u9818\u57df\u306b\u306a\u3051\u308c\u3070\u3001\u305d\u306e\u9818\u57df\u306f\u3069\u306e\u3088\u3046\u306a\u6271\u3044\u306b\u306a\u308b\u306e\u304b\u3002\u300d\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\u3002\u305d\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u3001\u300c\u63d0\u6848\u624b\u6cd5\u3067\u306f\u3001\u3082\u3057\u6700\u9069\u89e3\u304c\u305d\u306e\u30b5\u30d6\u9818\u57df\u306b\u306a\u304b\u3063\u305f\u5834\u5408\u3067\u3082\u3001\u305d\u306e\u9818\u57df\u3092\u63a2\u7d22\u56f0\u96e3\u306a\u9818\u57df\u3068\u3057\u3066\u8a8d\u8b58\u3057\u3066\u3057\u307e\u3044\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u3001\u3053\u306e\u70b9\u306b\u304a\u3044\u3066\u306f\u3001\u4eca\u5f8c\u6539\u5584\u3059\u3079\u304d\u70b9\u3067\u3042\u308b\u3002\u300d\u3068\u56de\u7b54\u81f4\u3057\u307e\u3057\u305f\u3002
                      \n 
                      \n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>3(<\/strong>\u96fb\u6c17\u901a\u4fe1\u5927\u5b66\u306e\u4f50\u85e4\u5148\u751f<\/strong>)<\/strong>
                      \n\u300c\u63d0\u6848\u624b\u6cd5\u306b\u304a\u3051\u308b\u8ca2\u732e\u5ea6C\u306e\u8a08\u7b97\u65b9\u6cd5\u3092\u3082\u3046\u4e00\u5ea6\u8a73\u7d30\u306b\u6559\u3048\u3066\u4e0b\u3055\u3044\u3002\u300d\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\u3002\u305d\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u306f\u30b9\u30e9\u30a4\u30c9\u3092\u898b\u305b\u306a\u304c\u3089\u3001\u8ca2\u732e\u5ea6C1\u306e\u8a08\u7b97\u4f8b\u3092\u6b21\u306e\u3088\u3046\u306b\u8aac\u660e\u81f4\u3057\u307e\u3057\u305f\u3002\u300c\u5168\u3066\u306eEP\u304c\u4eca6\u500b\u3042\u308a\u307e\u3059\u3002\u3053\u3053\u3067\u3001\u30b5\u30d6\u9818\u57dfD1\u306b\u5c5e\u3059\u308bEP\u306e\u6570\u304c3\u3067\u3042\u308b\u305f\u3081\u30013\/6\u306e\u5272\u5408\u3092\u8ca2\u732e\u5ea6C1\u3068\u3057\u3066\u8a08\u7b97\u3057\u307e\u3059\u3002\u300d\u3068\u56de\u7b54\u81f4\u3057\u307e\u3057\u305f\u3002
                      \n 
                      \n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>4(<\/strong>\u96fb\u6c17\u901a\u4fe1\u5927\u5b66\u306e\u4f50\u85e4\u5148\u751f<\/strong>)<\/strong>
                      \n\u300c\u63d0\u6848\u624b\u6cd5\u306f2\u76ee\u7684\u3067\u306f\u6709\u52b9\u7684\u304b\u3082\u3057\u308c\u306a\u3044\u304c\u30013\u76ee\u7684\u4ee5\u4e0a\u306e\u554f\u984c\u306b\u5bfe\u3057\u3066\u306f\u3069\u306e\u3088\u3046\u306b\u3057\u3066\u62e1\u5f35\u30fb\u9069\u7528\u3059\u308b\u306e\u304b\u3002\u300d\u3068\u3044\u3046\u8cea\u554f\u3092\u53d7\u3051\u307e\u3057\u305f\u3002\u305d\u306e\u8cea\u554f\u306b\u5bfe\u3057\u3066\u3001\u300c\u4eca\u306e\u63d0\u6848\u624b\u6cd5\u3067\u306f3\u76ee\u7684\u4ee5\u4e0a\u306b\u62e1\u5f35\u306f\u3067\u304d\u307e\u305b\u3093\u3002\u306a\u306e\u3067\u3001\u305d\u308c\u306f\u4eca\u5f8c\u8003\u3048\u308b\u3079\u304d\u4e8b\u9805\u3067\u3059\u3002\u300d\u3068\u56de\u7b54\u81f4\u3057\u307e\u3057\u305f\u3002
                      \n <\/p>\n

                        \n
                      • \u611f\u60f3<\/li>\n<\/ul>\n

                        \u672c\u5b66\u4f1a\u306f\u79c1\u306b\u3068\u3063\u3066\u306e\u521d\u306e\u56fd\u969b\u5b66\u4f1a\u306e\u53e3\u982d\u767a\u8868\u3067\u3042\u308a\u3001\u5927\u5909\u7dca\u5f35\u81f4\u3057\u307e\u3057\u305f\u3002\u305d\u306e\u305f\u3081\u3001\u73fe\u5730\u306b\u5230\u7740\u5f8c\u3082\u5148\u751f\u3068\u30df\u30fc\u30c6\u30a3\u30f3\u30b0\u3092\u91cd\u306d\u3001\u767a\u8868\u7df4\u7fd2\u3082\u5341\u4e8c\u5206\u306b\u884c\u3044\u3001\u6e96\u5099\u3092\u6574\u3048\u307e\u3057\u305f\u3002\u305d\u306e\u7d50\u679c\u300120\u5206\u306e\u767a\u8868\u6642\u9593\u306b\u5bfe\u3057\u306615\u5206\u3067\u767a\u8868\u3092\u7d42\u3048\u3066\u3057\u307e\u3044\u3001\u81ea\u8eab\u306e\u8a08\u753b\u6027\u306e\u8a70\u3081\u306e\u7518\u3055\u3092\u5b9f\u611f\u3057\u307e\u3057\u305f\u3002\u3057\u304b\u3057\u3001\u305d\u306e\u6b8b\u3063\u305f\u6642\u9593\u3067\u591a\u304f\u306e\u8cea\u554f\u3092\u9802\u304d\u3001\u8b70\u8ad6\u3092\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u3001\u6709\u610f\u7fa9\u306a\u6642\u9593\u3092\u904e\u3054\u3059\u3053\u3068\u304c\u51fa\u6765\u307e\u3057\u305f\u3002\u305f\u3060\u3057\u3001200\u540d\u53ce\u5bb9\u306e\u4f1a\u5834\u306710\u540d\u524d\u5f8c\u306e\u8074\u8846\u3060\u3063\u305f\u306e\u3067\u3001\u3082\u3046\u5c11\u3057\u5b66\u751f\u306b\u547c\u3073\u639b\u3051\u3066\u3001\u767a\u8868\u306b\u8db3\u3092\u904b\u3093\u3067\u3082\u3089\u3046\u52aa\u529b\u3092\u3059\u3079\u304d\u3067\u3042\u3063\u305f\u3068\u75db\u611f\u3057\u307e\u3057\u305f\u3002\u307e\u3068\u3081\u3068\u3057\u3066\u306f\u3001\u81ea\u8eab\u306e\u767a\u8868\u3092\u56fd\u969b\u5b66\u4f1a\u3067\u3001\u82f1\u8a9e\u3067\u767a\u8868\u3057\u3001\u8cea\u554f\u306b\u3082\u5ee3\u5b89\u5148\u751f\u306b\u3082\u52a9\u3051\u3066\u3044\u305f\u3060\u304d\u306a\u304c\u3089\u3067\u306f\u3042\u308a\u307e\u3059\u304c\u3001\u81ea\u8eab\u3067\u5bfe\u5fdc\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u3001\u672c\u5b66\u4f1a\u306e\u5927\u76ee\u6a19\u306f\u9054\u6210\u3067\u304d\u305f\u3068\u8003\u3048\u3066\u304a\u308a\u307e\u3059\u3002
                        \n <\/p>\n

                          \n
                        1. \u8074\u8b1b<\/li>\n<\/ol>\n

                          \u4eca\u56de\u306e\u56fd\u969b\u5b66\u4f1a\u3067\u306f\u3001\u4e0b\u8a18\u306e5\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
                          \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Investigation of particles behaviors of piecewise-linear particle swarm optimizer
                          \n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Tomoyuki Sasaki, Hidehiro Nakano
                          \n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Single objective bound constrained optimization
                          \nAbstract : In our previous study, we have proposed piecewiselinear particle swarm optimizer (PPSO). The dynamics of each particle in PPSO has two search modes that are dynamically switchable to each other. PPSO has better search performance to solve non-separable problems than the classical PSO. However, since it was not clarified why PPSO was effective in solving such problems, we have now studied whether particle behavior in PPSO affects the search performance, focusing on search direction of particles to compare the search direction of PPSO particles with that of PSO particles. We further compared the search performance for parameter pattern of PPSO with the classical PSO based on numerical data. Here, we suggest that PPSO particles can move in solution space freely and that the behavior of PPSO particles is effective for solving the non-separable problems.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                          \u3053\u306e\u8074\u8b1b\u306f\u5236\u7d04\u4ed8\u304d\u5358\u76ee\u7684\u6700\u9069\u5316\u306e\u77e5\u8b58\u3092\u6df1\u3081\u308b\u3079\u304f\u3001\u307e\u305fPSO\u306b\u95a2\u3059\u308b\u77e5\u8b58\u3092\u5f97\u305f\u304b\u3063\u305f\u305f\u3081\u805e\u304d\u307e\u3057\u305f\u3002\u767a\u8868\u3067\u306f\u3001\u4ee5\u524d\u306b\u63d0\u6848\u3057\u305f\u624b\u6cd5\u306b\u3064\u3044\u3066\u3001\u305d\u306e\u63a2\u7d22\u6027\u80fd\u306e\u5f71\u97ff\u3092\u691c\u8a0e\u3059\u308b\u305f\u3081\u306b\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u554f\u984c\u3092\u7528\u3044\u305f\u7d50\u679c\u3092\u8aac\u660e\u3057\u3066\u3044\u307e\u3057\u305f\u3002\u307e\u305f\u3001\u69d8\u3005\u306a\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u7528\u3044\u3066\u5f93\u6765\u624b\u6cd5\u3068\u306e\u6bd4\u8f03\u3092\u3057\u3066\u3044\u307e\u3057\u305f\u3002\u7d50\u679c\u3067\u306f\u300120\u4ee5\u4e0a\u306e\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u554f\u984c\u306b\u5bfe\u3057\u3066\u3001\u305d\u306e\u307b\u3068\u3093\u3069\u306e\u554f\u984c\u306b\u304a\u3044\u3066\u63d0\u6848\u624b\u6cd5\u306e\u3042\u308b\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u6027\u80fd\u304c\u5f93\u6765\u624b\u6cd5\u3088\u308a\u9ad8\u3044\u3053\u3068\u304c\u793a\u3055\u308c\u3066\u3044\u305f\u4e00\u65b9\u3067\u3001\u3044\u304f\u3064\u304b\u306e\u554f\u984c\u306b\u304a\u3044\u3066\u306f\u5f93\u6765\u624b\u6cd5\u306e\u65b9\u304c\u9ad8\u304b\u3063\u305f\u3002\u3053\u3053\u304b\u3089\u3001\u30d1\u30e9\u30e1\u30fc\u30bf\u306b\u3088\u3063\u3066\u63a2\u7d22\u6027\u80fd\u304c\u5927\u304d\u304f\u5909\u308f\u308b\u4e8b\u3068\u3001\u5168\u3066\u306e\u8a55\u4fa1\u9805\u76ee\u306b\u304a\u3044\u3066\u512a\u308c\u305f\u624b\u6cd5\u3092\u63d0\u6848\u3059\u308b\u3053\u3068\u306e\u96e3\u3057\u3055\u3092\u518d\u8a8d\u8b58\u3057\u307e\u3057\u305f\u3002
                          \n 
                          \n <\/p>\n\n\n\n
                          \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a A Type-2 Fuzzy Set induced Classification of Cognitive Load in Inter-individual Working Memory Performance based on Hemodynamic Response
                          \n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Amiyangshu De, Tanuka Bhattacharjee, Amit Konar, Anca L. Ralescu, Atulya K. Nagar
                          \n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Computational Intelligence, Cognitive Algorithms, Mind, and Brain I
                          \nAbstract : The crucial role of working memory for cognitive performance based on temporary information processing has been studied for decades. Behavioral tests and functional magnetic resonance imaging studies have already established a relation between cortical activation and working memory performance. However, dearth of literature is observed on functional near infrared spectroscopy based assessment of working memory. This paper provides a novel study of cerebral oxygenation as a basis of working memory performance considering verbal working memory task. The recorded signal is filtered and processed for extraction of 96 dimensional features, which is reduced to 24 by means of a meta-heuristic optimization technique. Next, we transfer it to a type-2 fuzzy classifier for classifying into three different cognitive load classes: high, moderate and low. Experimental instances show that the type-2 fuzzy classifier with the proposed artificial bee colony optimization induced feature selection technique, yields high classification accuracy, which tends to reach above 86 percent. Additionally, experimental evidence suggests that low working memory performance is, possibly, due to poor activation of certain regions of frontal cortex.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                          \u3053\u306e\u7814\u7a76\u306f\u3001\u30ef\u30fc\u30ad\u30f3\u30b0\u30e1\u30e2\u30ea\u8ab2\u984c\u4e2d\u306e\u8133\u72b6\u614b\u3092fNIRS\u306e16\u30c1\u30e3\u30f3\u30cd\u30eb\u3067\u8a08\u6e2c\u3057\u305f\u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u3066\u304a\u308a\u3001\u305d\u306e\u30c7\u30fc\u30bf\u3092\u8b58\u5225\u3057\u3066\u3044\u307e\u3057\u305f\u3002\u307e\u305f\u8cea\u7591\u5fdc\u7b54\u3067\u306f\u3001\u30d5\u30ea\u30fc\u30c9\u30de\u30f3\u691c\u5b9a\u306b\u3088\u308b\u63d0\u6848\u624b\u6cd5\u306e\u6709\u52b9\u6027\u304c\u59a5\u5f53\u304b\u306b\u3064\u3044\u3066\u8b70\u8ad6\u3055\u308c\u3001\u81ea\u8eab\u3082\u624b\u6cd5\u3092\u63d0\u6848\u3059\u308b\u7814\u7a76\u3092\u884c\u3063\u3066\u3044\u308b\u3060\u3051\u306b\u3001\u3088\u308a\u6df1\u3044\u691c\u5b9a\u306b\u5bfe\u3059\u308b\u7406\u89e3\u304c\u5fc5\u8981\u3060\u3068\u518d\u8a8d\u8b58\u3057\u307e\u3057\u305f\u3002\u307e\u305f\u3001\u30cb\u30e5\u30fc\u30ed\u30b5\u30a4\u30a8\u30f3\u30c6\u30a3\u30b9\u30c8\u3068\u3057\u3066\u65b0\u305f\u306a\u77e5\u898b\u3092\u5f97\u308b\u306b\u306ffNIRS\u306e16\u30c1\u30e3\u30f3\u30cd\u30eb\u306e\u8a08\u6e2c\u3067\u306f\u8db3\u308a\u306a\u3044\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u3044\u3063\u305f\u8b70\u8ad6\u304c\u884c\u308f\u308c\u3001\u8208\u5473\u3092\u3082\u3063\u3066\u8074\u8b1b\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002
                          \n 
                          \n <\/p>\n\n\n\n
                          \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aHow to Select a Winner in Evolutionary Optimization?
                          \n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aRisto Miikkulainen, Hormoz Shahrzad, Nigel Duffy and Phil Long
                          \n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Computational Intelligence in Dynamic and Uncertain Environments I
                          \nAbstract : In many evolutionary optimization domains evaluations are noisy. The candidates are tested on a number of randomly drawn samples, such as different games played, different physical simulations, or different user interactions. As a result, selecting the winner is a multiple hypothesis problem: The candidate that evaluated the best most likely received a lucky selection of samples, and will not perform as well in the future. This paper proposes a technique for selecting the winner and estimating its true performance based on the smoothness assumption: Candidates that are similar perform similarly. Estimated fitness is replaced by the average fitness of candidate\u2019s neighbors, making the selection and estimation more reliable. Simulated experiments in the multiplexer domain show that this technique is reliable, making it likely that the true winner is selected and its future performance is accurately estimated.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                          \u30bf\u30a4\u30c8\u30eb\u304b\u3089\u3001\u3069\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u304c\u3044\u3044\u306e\u304b\u6c7a\u5b9a\u3059\u308b\u65b9\u6cd5\u3092\u77e5\u308c\u308c\u3070\u3068\u601d\u3044\u8074\u8b1b\u3057\u307e\u3057\u305f\u3002\u3044\u308d\u3044\u308d\u306a\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u8a66\u3057\u3066\u3044\u308b\u306e\u304b\u3068\u601d\u3063\u305f\u306e\u3067\u3059\u304c\u3001\u4eca\u56de\u306f\u8a55\u4fa1\u65b9\u6cd5\u306e\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u306e\u307f\u306e\u767a\u8868\u3067\u3057\u305f\u3002\u8a55\u4fa1\u65b9\u6cd5\u306f\u8fd1\u508d\u30b5\u30a4\u30ba\u3092\u4efb\u610f\u3067\u6c7a\u5b9a\u3057\u3001\u305d\u308c\u306b\u57fa\u3065\u3044\u305f\u6027\u80fd\u8a55\u4fa1\u3092\u3057\u3066\u3044\u307e\u3057\u305f\u3002\u3057\u304b\u3057\u3001\u305d\u306e\u8fd1\u508d\u30b5\u30a4\u30ba\u306e\u4efb\u610f\u6c7a\u5b9a\u306f\u96e3\u3057\u3044\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u601d\u3044\u3001\u8cea\u554f\u3057\u305f\u3068\u3053\u308d\u3001\u305d\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u6700\u9069\u5316\u3082\u4eca\u5f8c\u306f\u5fc5\u8981\u3060\u3068\u56de\u7b54\u3055\u308c\u307e\u3057\u305f\u3002\u4eca\u56de\u306e\u7814\u7a76\u767a\u8868\u3092\u805e\u304d\u3001\u4f7f\u7528\u3059\u308b\u6700\u9069\u5316\u624b\u6cd5\u306e\u6027\u80fd\u3092\u516c\u5e73\u306b\u6bd4\u8f03\u3059\u308b\u305f\u3081\u306e\u65b9\u6cd5\u3092\u8003\u3048\u308b\u3053\u3068\u306f\u91cd\u8981\u3060\u3068\u611f\u3058\u307e\u3057\u305f\u3002
                          \n <\/p>\n\n\n\n
                          \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aNeuro-Energetic Aspects of Cognition – The Role of Pulse-Wave-Pulse Conversion in the\u3000Interpretation of Brain Imaging Data
                          \n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aRaymond Noack, Joshua Davis, Chetan Manjesh and Robert Kozma
                          \n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Computational Intelligence, Cognitive Algorithms, Mind, and Brain II
                          \nAbstract : In the last decade, neuro-energetics has become an important research topic, which can contribute to better understanding and interpreting brain imaging data. We need to understand how the brain encodes information coming from the environment, and how this information is converted to knowledge and meaning useful for intentional action and decision making. Valuable information can be derived from both single neuron and population (neuropil) recording in order to investigate the cognitive cycle. Usually pulses are measured with electrodes placed intracellularly while oscillations are measured through ECoG. Our main interest here is to investigate the relationship between the creation of knowledge and meaning and the metabolic cycle in neural populations, as well as the conversion of incoming action potentials to the dendritic structure of the neuron into currents which will contribute to new action potentials. This process we call the pulse-wave-pulse conversion. We model the coupling the energy consumption associated with new action potentials and the metabolic cycle, and the conclusions for future large-scale neuro-energetic models.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                          \u3053\u306e\u7814\u7a76\u306f\u3001\u8133\u306e\u795e\u7d4c\u30a8\u30cd\u30eb\u30ae\u30fc\u30e2\u30c7\u30eb\u306b\u3064\u3044\u3066\u691c\u8a0e\u3057\u3066\u3044\u307e\u3057\u305f\u3002100billion\u306e\u30cb\u30e5\u30fc\u30ed\u30f3\u306e\u8a71\u3092\u524d\u7f6e\u304d\u3068\u3057\u3066\u3001\u795e\u7d4c\u306e\u69cb\u6210\u8981\u7d20\u3092\u8a9e\u308a\u3001\u305d\u306e\u30e2\u30c7\u30eb\u5316\u3092\u884c\u3063\u3066\u3044\u307e\u3057\u305f\u3002\u8cea\u7591\u5fdc\u7b54\u3067\u306f\u3001\u795e\u7d4c\u306e\u30b9\u30d1\u30a4\u30af\u30e2\u30c7\u30eb\u306b\u3064\u3044\u3066\u3001\u306a\u305c\u305d\u306e\u30b9\u30d1\u30a4\u30af\u30e2\u30c7\u30eb\u3092\u9078\u629e\u3057\u305f\u304b\u3001\u305d\u3057\u3066\u305d\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306f\u3069\u3046\u3084\u3063\u3066\u6c7a\u5b9a\u3057\u305f\u306e\u304b\u3068\u3044\u3046\u8b70\u8ad6\u304c\u884c\u308f\u308c\u3001\u6700\u3082\u30b7\u30f3\u30d7\u30eb\u306a\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3001\u4eca\u5f8c\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u3092\u884c\u3044\u306a\u304c\u3089adaptive\u306b\u6c7a\u5b9a\u3057\u3066\u3044\u304f\u3088\u3046\u306b\u3059\u308b\u3068\u56de\u7b54\u3057\u3066\u3044\u307e\u3057\u305f\u3002\u3053\u306e\u5206\u91ce\u306e\u7814\u7a76\u8005\u306f\u3001\u4ed6\u306e\u767a\u8868\u3067\u3082\u898b\u3089\u308c\u307e\u3057\u305f\u304c\u3001\u30d1\u30e9\u30e1\u30fc\u30bf\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u306b\u8a71\u984c\u304c\u4e0a\u304c\u308a\u3001\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u6c7a\u5b9a\u65b9\u6cd5\u3092\u610f\u8b58\u3057\u306a\u304c\u3089\u7814\u7a76\u3092\u3059\u308b\u91cd\u8981\u6027\u3092\u6539\u3081\u3066\u75db\u611f\u3057\u307e\u3057\u305f\u3002
                          \n 
                          \n <\/p>\n\n\n\n
                          \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aComparison Study of Large-scale Optimisation Techniques on the LSMOP Benchmark Functions
                          \n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aHeiner Zille and Sanaz Mostaghim
                          \n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aComputational Intelligence in Multicriteria Decision-Making II
                          \nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 : In this paper, we study the performance of three popular large-scale optimisation algorithms on the recently proposed large-scale many-objective optimisation problems (LSMOP). We briefly explain the three methods (MOEA\/DVA, LMEA and WOF) and give an overview of their use and performance in the literature. For the Weighted Optimization Framework (WOF), we propose a new transformation function to eliminate the parameter needed in its previous version. In our experiments, we compare the three algorithms on the LSMOP1-9 functions with 2 and 3 objectives and up to 1006 decision variables. The special focus of our study is on the convergence speed and behaviour, since MOEA\/DVA and LMEA, in contrast to WOF, need huge computational budgets to obtain variable groups prior to optimisation. Our experiments show that MOEA\/DVA and WOF perform significantly better than LMEA on almost all instances and WOF further outperforms MOEA\/DVA significantly in most of the 1006-variable problems, in solution quality as well as convergence speed. In most instances the WOF only needs 0:1% to 10% of the total evaluations to outperform the final solution sets obtained by LMEA and MOEA\/DVA.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                          \u3053\u306e\u767a\u8868\u306f\u304b\u306d\u3066\u304b\u3089\u89aa\u4ea4\u306e\u3042\u308bSanaz\u6559\u6388\u3068Heiner\u306e\u7814\u7a76\u767a\u8868\u3067\u3042\u308a\u3001\u540c\u3058\u591a\u76ee\u7684\u6700\u9069\u5316\u3092\u4e3b\u984c\u3068\u3057\u3066\u3044\u308b\u305f\u3081\u3001\u8208\u5473\u3092\u6301\u3063\u3066\u8074\u8b1b\u3057\u307e\u3057\u305f\u3002\u7814\u7a76\u3067\u306f\u3001\u6bcd\u96c6\u56e3\u306e\u30b5\u30d6\u30b0\u30eb\u30fc\u30d7\u5316\u3092\u884c\u3046\u624b\u6cd5\u306e\u63d0\u6848\u3092\u884c\u3044\u3001\u305d\u306e\u3046\u3048\u3067\u8a2d\u8a08\u5909\u6570\u7a7a\u9593\u3068\u76ee\u7684\u95a2\u6570\u7a7a\u9593\u3092\u8003\u616e\u3055\u308c\u3066\u3044\u307e\u3057\u305f\u3002\u8a2d\u8a08\u5909\u6570\u306e\u8003\u616e\u306f\u6628\u5e74\u306e\u79c1\u306e\u7814\u7a76\u8ab2\u984c\u306b\u3082\u3042\u305f\u308a\u3001\u307e\u305f3\u624b\u6cd5\u306b\u3088\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u6027\u80fd\u6bd4\u8f03\u3092\u884c\u3063\u3066\u3044\u305f\u3053\u3068\u304b\u3089\u3001\u3088\u308a\u7406\u89e3\u3057\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u7814\u7a76\u3067\u3042\u308b\u3053\u3068\u3092\u518d\u78ba\u8a8d\u81f4\u3057\u307e\u3057\u305f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

                          IEEE SSCI\u304cHawaii\u3067\u958b\u50ac\u3055\u308c\u307e\u3059\u3002 2017\/11\/30 11:15AM Adaptive Weight Vector Assignment Method for MOEA\/D [#1355] Kei Ha … <\/p>\n

                          “\u3010\u901f\u5831\u3011IEEE SSCI” \u306e<\/span>\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10,3],"tags":[],"class_list":["post-4603","post","type-post","status-publish","format-standard","hentry","category-10","category-3"],"_links":{"self":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/4603","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=4603"}],"version-history":[{"count":0,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/4603\/revisions"}],"wp:attachment":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4603"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4603"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4603"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}