{"id":5790,"date":"2022-03-30T12:45:11","date_gmt":"2022-03-30T03:45:11","guid":{"rendered":"https:\/\/www.appi.keio.ac.jp\/?page_id=5790"},"modified":"2024-07-30T14:27:19","modified_gmt":"2024-07-30T05:27:19","slug":"seminar-series-on-instrument-control-systems","status":"publish","type":"page","link":"https:\/\/www.appi.keio.ac.jp\/?page_id=5790","title":{"rendered":"\u30bb\u30df\u30ca\u30fc\uff08\u60c5\u5831\u8a08\u6e2c\u30fb\u60c5\u5831\u5236\u5fa1\u5206\u91ce\uff09"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">2024<\/h2>\n\n\n\n<p><a href=\"https:\/\/llien30.github.io\/\" target=\"_blank\" rel=\"noreferrer noopener\">\u67a1\u7530\u771f\u5948\u6c0f\uff20\u60c5\u5831\u5de5\u5b66\u79d1D2<\/a><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u984c\u76ee<\/strong>\uff1a\u30a4\u30d9\u30f3\u30c8\u30ab\u30e1\u30e9\u3092\u7528\u3044\u305f\u4f4e\u9045\u5ef6\u81ea\u5df1\u4f4d\u7f6e\u63a8\u5b9a\u624b\u6cd5\u3068\u305d\u306e\u5c55\u671b<\/li>\n\n\n\n<li><strong>\u65e5\u6642<\/strong>\uff1a2024.6.18(\u706b)13:00-14:30<\/li>\n\n\n\n<li><strong>\u5834\u6240<\/strong>\uff1a\u30aa\u30f3\u30e9\u30a4\u30f3\uff08Zoom\uff09<\/li>\n\n\n\n<li><strong>\u6982\u8981<\/strong>\uff1a\u30a4\u30d9\u30f3\u30c8\u30ab\u30e1\u30e9\u306f\u753b\u7d20\u3054\u3068\u306e\u8f1d\u5ea6\u5909\u5316\u3092\u9ad8\u3044\u6642\u9593\u5206\u89e3\u80fd\u3067\u51fa\u529b\u3059\u308b\u3002\u3053\u306e\u52d5\u4f5c\u539f\u7406\u304b\u3089\u3001\u7279\u306b\u3053\u306e\u30ab\u30e1\u30e9\u306f\u300c\u52d5\u304d\u300d\u306e\u8a8d\u8b58\u306b\u9577\u3051\u3066\u3044\u308b\u30bb\u30f3\u30b5\u30fc\u3060\u3068\u8003\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3002\u3053\u306e\u7279\u6027\u3092\u6d3b\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u30a4\u30d9\u30f3\u30c8\u30ab\u30e1\u30e9\u306f\u81ea\u52d5\u8d70\u884c\u30ed\u30dc\u30c3\u30c8\u7b49\u3078\u306e\u5fdc\u7528\u304c\u671f\u5f85\u3055\u308c\u3066\u3044\u308b\u3002\u672c\u8b1b\u6f14\u3067\u306f\u7279\u306b\u3001\u30ed\u30dc\u30c3\u30c8\u3078\u306e\u5fdc\u7528\u306e\u969b\u306b\u5fc5\u8981\u3068\u306a\u308b\u81ea\u5df1\u4f4d\u7f6e\u63a8\u5b9a\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u7740\u76ee\u3057\u3001\u305d\u306e\u6982\u8981\u3001\u95a2\u9023\u6280\u8853\u3001\u4eca\u5f8c\u306e\u5c55\u671b\u306b\u3064\u3044\u3066\u89e3\u8aac\u3059\u308b\u3002<\/li>\n\n\n\n<li>\u4e16\u8a71\u4eba\uff1a<a href=\"https:\/\/sites.google.com\/keio.jp\/minoue\/\">\u4e95\u4e0a<\/a><\/li>\n\n\n\n<li>\u8a08\u6e2c\u81ea\u52d5\u5236\u5fa1\u5b66\u4f1a \u5236\u5fa1\u90e8\u9580\u300c<a href=\"https:\/\/sites.google.com\/keio.jp\/minoue\/%E3%83%9B%E3%83%BC%E3%83%A0\/%E8%AA%BF%E6%9F%BB%E7%A0%94%E7%A9%B6%E4%BC%9A%E4%BA%BA%E3%81%AE%E7%90%86%E8%A7%A3%E8%AA%98%E5%B0%8E%E3%81%A7%E5%BC%B7%E5%8C%96%E3%81%95%E3%82%8C%E3%82%8B%E5%88%B6%E5%BE%A1%E3%82%B7%E3%82%B9%E3%83%86%E3%83%A0%E8%AA%BF%E6%9F%BB%E7%A0%94%E7%A9%B6%E4%BC%9A?authuser=0\">\u4eba\u306e\u7406\u89e3\/\u8a98\u5c0e\u3067\u5f37\u5316\u3055\u308c\u308b\u5236\u5fa1\u30b7\u30b9\u30c6\u30e0\u8abf\u67fb\u7814\u7a76\u4f1a<\/a>\u300d\u306e\u652f\u63f4\u306e\u3082\u3068\u3067\u958b\u50ac\u3057\u3066\u3044\u307e\u3059<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p><a href=\"https:\/\/smilab.org\/\">\u6749\u6d66\u5b54\u660e\u6559\u6388\uff20\u60c5\u5831\u5de5\u5b66\u79d1<\/a><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u984c\u76ee<\/strong>\uff1a\u30ed\u30dc\u30c6\u30a3\u30af\u30b9\u306b\u304a\u3051\u308b\u57fa\u76e4\u30e2\u30c7\u30eb\u306e\u5229\u6d3b\u7528<\/li>\n\n\n\n<li><strong>\u65e5\u6642<\/strong>\uff1a2024\u5e746\u67085\u65e5\uff08\u6c34\uff0914:45\uff5e16:15<\/li>\n\n\n\n<li><strong>\u5834\u6240<\/strong>\uff1a\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\uff08\u30c7\u30a3\u30b9\u30ab\u30c3\u30b7\u30e7\u30f3\u30eb\u30fc\u30e0\uff12\uff06Zoom\uff09<\/li>\n\n\n\n<li><strong>\u6982\u8981<\/strong>\uff1a\u30de\u30eb\u30c1\u30e2\u30fc\u30c0\u30eb\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\u7b49\u306e\u57fa\u76e4\u30e2\u30c7\u30eb\u306e\u767a\u5c55\u306f\u793e\u4f1a\u5168\u4f53\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u3066\u304a\u308a\u3001\u30ed\u30dc\u30c6\u30a3\u30af\u30b9\u3082\u5927\u304d\u306a\u8ee2\u63db\u671f\u3092\u8fce\u3048\u3066\u3044\u308b\u3002\u73fe\u72b6\u3067\u306f\u3001\u30b7\u30b9\u30c6\u30e0\u7d71\u5408\u3078\u306e\u57fa\u76e4\u30e2\u30c7\u30eb\u306e\u5229\u6d3b\u7528\u304c\u5148\u884c\u3057\u3066\u3044\u308b\u3082\u306e\u306e\u3001\u81ea\u5df1\u6ce8\u610f\u306b\u4ee3\u308f\u308b\u6a5f\u69cb\u3068\u3057\u3066\u72b6\u614b\u7a7a\u9593\u30e2\u30c7\u30eb\u3092\u5c0e\u5165\u3059\u308b\u306a\u3069\u5236\u5fa1\u7406\u8ad6\u306b\u304a\u3051\u308b\u77e5\u898b\u306e\u5229\u6d3b\u7528\u3082\u9032\u307f\u3064\u3064\u3042\u308b\u3002\u672c\u8b1b\u6f14\u3067\u306f\u3001\u57fa\u76e4\u30e2\u30c7\u30eb\u306e\u6982\u8981\u3001\u30ed\u30dc\u30c6\u30a3\u30af\u30b9\u306b\u304a\u3051\u308b\u57fa\u76e4\u30e2\u30c7\u30eb\u306e\u5229\u6d3b\u7528\u4e8b\u4f8b\u3001\u6211\u3005\u306e\u53d6\u308a\u7d44\u307f\u3068\u5c55\u671b\u306b\u3064\u3044\u3066\u8ff0\u3079\u308b\u3002<\/li>\n\n\n\n<li>\u4e16\u8a71\u4eba\uff1a<a href=\"https:\/\/sites.google.com\/keio.jp\/minoue\/\">\u4e95\u4e0a<\/a><\/li>\n\n\n\n<li>\u8a08\u6e2c\u81ea\u52d5\u5236\u5fa1\u5b66\u4f1a \u5236\u5fa1\u90e8\u9580\u300c<a href=\"https:\/\/sites.google.com\/keio.jp\/minoue\/%E3%83%9B%E3%83%BC%E3%83%A0\/%E8%AA%BF%E6%9F%BB%E7%A0%94%E7%A9%B6%E4%BC%9A%E4%BA%BA%E3%81%AE%E7%90%86%E8%A7%A3%E8%AA%98%E5%B0%8E%E3%81%A7%E5%BC%B7%E5%8C%96%E3%81%95%E3%82%8C%E3%82%8B%E5%88%B6%E5%BE%A1%E3%82%B7%E3%82%B9%E3%83%86%E3%83%A0%E8%AA%BF%E6%9F%BB%E7%A0%94%E7%A9%B6%E4%BC%9A?authuser=0\">\u4eba\u306e\u7406\u89e3\/\u8a98\u5c0e\u3067\u5f37\u5316\u3055\u308c\u308b\u5236\u5fa1\u30b7\u30b9\u30c6\u30e0\u8abf\u67fb\u7814\u7a76\u4f1a<\/a>\u300d\u306e\u652f\u63f4\u306e\u3082\u3068\u3067\u958b\u50ac\u3057\u3066\u3044\u307e\u3059<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p><span style=\"text-decoration: underline;\">Thien Q. Tran \u535a\u58eb\uff20LINE\u30e4\u30d5\u30fc<\/span><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u984c\u76ee<\/strong>\uff1a\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\u306b\u5bfe\u3059\u308bjailbreak prompts\u653b\u6483\u3068\u9632\u5fa1<\/li>\n\n\n\n<li><strong>\u65e5\u6642<\/strong>\uff1a2024\u5e744\u670824\u65e5\uff08\u6c34\uff0910:30\uff5e11:30<\/li>\n\n\n\n<li><strong>\u5834\u6240<\/strong>\uff1a\u30aa\u30f3\u30e9\u30a4\u30f3<\/li>\n\n\n\n<li><strong>\u6982\u8981<\/strong>\uff1a\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\uff08LLM\uff09\u306e\u5b89\u5168\u306a\u904b\u7528\u306b\u306f\u3001\u30b7\u30b9\u30c6\u30e0\u306e\u5b89\u5168\u6027\u3092\u8105\u304b\u3059\u300cjailbreak prompts\u300d\u653b\u6483\u3078\u306e\u5bfe\u7b56\u304c\u5fc5\u8981\u4e0d\u53ef\u6b20\u3067\u3059\u3002\u672c\u8b1b\u6f14\u3067\u306f\u3001jailbreak prompts\u306e\u80cc\u666f\u3001\u305d\u308c\u304c\u3082\u305f\u3089\u3059\u8105\u5a01\u3001\u304a\u3088\u3073\u3053\u308c\u306b\u95a2\u9023\u6280\u8853\u306b\u3064\u3044\u3066\u89e3\u8aac\u3057\u307e\u3059\u3002\u3055\u3089\u306b\u3001\u52b9\u7387\u7684\u306ajailbreak prompts\u69cb\u7bc9\u624b\u6cd5\u306b\u95a2\u3059\u308b\u6700\u8fd1\u306e\u7814\u7a76\u6210\u679c\u3092\u7d39\u4ecb\u3057\u3001LLM\u306e\u5b89\u5168\u6027\u3092\u9ad8\u3081\u308b\u305f\u3081\u306e\u4eca\u5f8c\u306e\u8ab2\u984c\u306b\u3064\u3044\u3066\u8ff0\u3079\u307e\u3059\u3002<\/li>\n\n\n\n<li>\u4e16\u8a71\u4eba\uff1a<a href=\"https:\/\/sites.google.com\/keio.jp\/minoue\/\">\u4e95\u4e0a<\/a><\/li>\n\n\n\n<li>\u8a08\u6e2c\u81ea\u52d5\u5236\u5fa1\u5b66\u4f1a \u5236\u5fa1\u90e8\u9580\u300c<a href=\"https:\/\/sites.google.com\/keio.jp\/minoue\/%E3%83%9B%E3%83%BC%E3%83%A0\/%E8%AA%BF%E6%9F%BB%E7%A0%94%E7%A9%B6%E4%BC%9A%E4%BA%BA%E3%81%AE%E7%90%86%E8%A7%A3%E8%AA%98%E5%B0%8E%E3%81%A7%E5%BC%B7%E5%8C%96%E3%81%95%E3%82%8C%E3%82%8B%E5%88%B6%E5%BE%A1%E3%82%B7%E3%82%B9%E3%83%86%E3%83%A0%E8%AA%BF%E6%9F%BB%E7%A0%94%E7%A9%B6%E4%BC%9A?authuser=0\">\u4eba\u306e\u7406\u89e3\/\u8a98\u5c0e\u3067\u5f37\u5316\u3055\u308c\u308b\u5236\u5fa1\u30b7\u30b9\u30c6\u30e0\u8abf\u67fb\u7814\u7a76\u4f1a<\/a>\u300d\u306e\u652f\u63f4\u306e\u3082\u3068\u3067\u958b\u50ac\u3057\u3066\u3044\u307e\u3059<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">2023<\/h2>\n\n\n\n<p><a href=\"http:\/\/www.ids.sys.i.kyoto-u.ac.jp\/hoshino\/index.html\">\u661f\u91ce\u5065\u592a\u52a9\u6559\uff08\u4eac\u90fd\u5927\u5b66\uff09<\/a><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u984c\u76ee<\/strong>\uff1a\u5236\u5fa1\u5de5\u5b66\u306b\u304a\u3051\u308b\u6700\u9069\u8f38\u9001<\/li>\n\n\n\n<li><strong>\u65e5\u6642<\/strong>\uff1a2023\u5e747\u670821\u65e5\uff08\u91d1\uff099:00\uff5e10:30&nbsp;<\/li>\n\n\n\n<li><strong>\u5834\u6240<\/strong>\uff1a\u30aa\u30f3\u30e9\u30a4\u30f3\uff08\u53c2\u52a0\u3092\u5e0c\u671b\u3055\u308c\u308b\u5834\u5408\u306f\u4e95\u4e0a (minoue at appi.keio.ac.jp) \u307e\u3067\u3054\u9023\u7d61\u304f\u3060\u3055\u3044\uff09<\/li>\n\n\n\n<li><strong>\u6982\u8981<\/strong>\uff1a\u672c\u8b1b\u6f14\u3067\u306f\uff0c\u6700\u9069\u8f38\u9001\u306e\u5236\u5fa1\u5de5\u5b66\u3078\u306e\u5fdc\u7528\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3059\u308b\uff0e\u6700\u9069\u8f38\u9001\u3068\u306f\uff0c\u78ba\u7387\u5206\u5e03\u306e\u69d8\u3005\u306a\u6027\u8cea\u3092\u6271\u3046\u305f\u3081\u306e\u67a0\u7d44\u307f\u3092\u63d0\u4f9b\u3059\u308b\u7406\u8ad6\u3067\u3042\u308b\uff0e\u8fd1\u5e74\u306e\u6a5f\u68b0\u5b66\u7fd2\u3084\u30c7\u30fc\u30bf\u79d1\u5b66\u306e\u5206\u91ce\u306b\u304a\u3044\u3066\uff0c\u30c7\u30fc\u30bf\u3092\u6271\u3046\u305f\u3081\u306e\u30c4\u30fc\u30eb\u3068\u3057\u3066\u5229\u7528\u3055\u308c\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u304a\u308a\uff0c\u6575\u5bfe\u7684\u751f\u6210\u30cd\u30c3\u30c8\u30ef\u30fc\u30af(GAN)\u3078\u306e\u5fdc\u7528\u306a\u3069\u304c\u6709\u540d\u3067\u3042\u308b\uff0e\u5236\u5fa1\u5de5\u5b66\u306b\u304a\u3044\u3066\u3082\u78ba\u7387\u7684\u306a\u5236\u5fa1\u5bfe\u8c61\u306e\u78ba\u7387\u6027\u3092\u8a73\u7d30\u306b\u6271\u3046\u305f\u3081\u306b\u7528\u3044\u3089\u308c\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u304a\u308a\uff0c\u78ba\u7387\u7684\u306a\u30b7\u30b9\u30c6\u30e0\u306e\u72b6\u614b\u3092\u6240\u671b\u306e\u78ba\u7387\u5206\u5e03\u3092\u6301\u3064\u3088\u3046\u306b\u5236\u5fa1\u3059\u308b\u554f\u984c\u306a\u3069\u304c\u7814\u7a76\u3055\u308c\u3066\u3044\u308b\uff0e\u672c\u8b1b\u6f14\u3067\u306f\uff0c\u6700\u9069\u8f38\u9001\u7406\u8ad6\u306e\u57fa\u790e\u3092\u8aac\u660e\u3057\uff0c\u305d\u306e\u5f8c\uff0c\u8b1b\u6f14\u8005\u306e\u7814\u7a76\u3092\u4e2d\u5fc3\u3068\u3057\u3066\uff0c\u78ba\u7387\u5206\u5e03\u306e\u5236\u5fa1\u554f\u984c\u3092\u8aac\u660e\u3059\u308b\uff0e\u305d\u3057\u3066\uff0c\u305d\u306e\u5fdc\u7528\u3068\u3057\u3066\uff0c\u30ab\u30fc\u30b7\u30a7\u30a2\u30ea\u30f3\u30b0\u306e\u904b\u7528\u554f\u984c\u3078\u306e\u5fdc\u7528\u304a\u3088\u3073\u6df1\u5c64\u5b66\u7fd2\u306e\u751f\u6210\u30e2\u30c7\u30eb\u3068\u306e\u95a2\u4fc2\u306a\u3069\u306b\u3064\u3044\u3066\u8aac\u660e\u3059\u308b\uff0e\u307e\u305f\uff0c\u6642\u9593\u304c\u8a31\u305b\u3070\uff0c\u305d\u306e\u4ed6\u306e\u5fdc\u7528\u306b\u3064\u3044\u3066\u3082\u7d39\u4ecb\u3059\u308b\u4e88\u5b9a\u3067\u3042\u308b\uff0e<\/li>\n\n\n\n<li><strong>\u7565\u6b74<\/strong>\uff1a2014\u5e74\u5317\u6d77\u9053\u5927\u5b66\u5927\u5b66\u9662\u60c5\u5831\u79d1\u5b66\u7814\u7a76\u79d1\u535a\u58eb\u5f8c\u671f\u8ab2\u7a0b\u4fee\u4e86\uff0e\u540c\u5e744\u6708\u9752\u5c71\u5b66\u9662\u5927\u5b66\u7406\u5de5\u5b66\u90e8\u52a9\u6559\uff0e2019\u5e744\u6708\u3088\u308a\u4eac\u90fd\u5927\u5b66\u5927\u5b66\u9662\u60c5\u5831\u5b66\u7814\u7a76\u79d1\u52a9\u6559\uff0e\u78ba\u7387\u5236\u5fa1\uff0c\u975e\u7dda\u5f62\u5236\u5fa1\u306b\u95a2\u3059\u308b\u7814\u7a76\u306b\u5f93\u4e8b\uff0e\u535a\u58eb\uff08\u60c5\u5831\u79d1\u5b66\uff09\uff0e2020\u5e74 SICE International Young Authors Award\uff0c2022\u5e74 \u8a08\u6e2c\u81ea\u52d5\u5236\u5fa1\u5b66\u4f1a\u5236\u5fa1\u90e8\u9580\u5968\u52b1\u8cde\uff08\u57fa\u790e\u90e8\u9580\uff09\u3092\u53d7\u8cde\uff0e\u8a08\u6e2c\u81ea\u52d5\u5236\u5fa1\u5b66\u4f1a\uff0c\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\uff0cIEEE \u306e\u4f1a\u54e1\uff0e<\/li>\n\n\n\n<li>\u4e16\u8a71\u4eba\uff1a<a href=\"https:\/\/sites.google.com\/keio.jp\/minoue\/\">\u4e95\u4e0a<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p><a href=\"https:\/\/scholar.google.com\/citations?user=OlfrlRkAAAAJ&amp;hl=en\" data-type=\"URL\" data-id=\"https:\/\/scholar.google.com\/citations?user=OlfrlRkAAAAJ&amp;hl=en\">Dr. Mengmou Li<\/a><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Title:<\/strong> Convergence Rate Analysis of the Mirror Descent Method via Integral Quadratic Constraints<\/li>\n\n\n\n<li><strong>Date:<\/strong> 2023.1.30 (Mon)  14:45-16:15<\/li>\n\n\n\n<li><strong>Place:<\/strong> 7F-Forum, 14th bldg, Yagami-Campus<\/li>\n\n\n\n<li><strong>Abstract:<\/strong> This talk focuses on convergence analysis for the mirror descent (MD) method, a well-known algorithm in convex optimization. An analysis framework via integral quadratic constraints (IQCs) is constructed to analyze the convergence rate of the MD method with strongly convex objective functions in both continuous time and discrete time. Finding convergence rates of the MD algorithms can be formulated into feasibility problems of linear matrix inequalities (LMIs) in both schemes. In particular, in continuous time, it is shown that the Bregman divergence function, which is commonly used as a Lyapunov function for this algorithm, is a special case of the class of Lyapunov functions associated with the Popov criterion, when the latter is applied to an appropriate reformulation of the algorithm. Thus, applying the Popov criterion and its combination with other IQCs, can lead to convergence rate bounds with reduced conservatism.<\/li>\n\n\n\n<li><strong style=\"color: initial;\">Bio:<\/strong><span style=\"color: initial;\"> <\/span>Mengmou Li is currently a postdoc researcher in Tokyo Institute of Technology. He received his B.S. degree in Physics from Zhejiang University, China, in 2016, and the Ph.D. degree in Electrical and Electronic Engineering from the University of Hong Kong, in 2020. From 2021 to 2022 he was a research associate with the Control Group, University of Cambridge. His research interests include robust control, optimization, synchronization, and power systems.<\/li>\n\n\n\n<li><strong>Organizer:<\/strong> <a href=\"https:\/\/sites.google.com\/keio.jp\/minoue-eng\/home\">Masaki Inoue<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">2022<\/h2>\n\n\n\n<p><a href=\"https:\/\/hatanakalab.wixsite.com\/website\">\u7551\u4e2d\u5065\u5fd7\u51c6\u6559\u6388\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\uff09<\/a><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u984c\u76ee<\/strong>\uff1aCyber-Physical-HUMAN Systems\u3000\uff5e\u30ed\u30dc\u30c6\u30a3\u30af\u30b9\u306b\u304a\u3051\u308b\u6b74\u53f2\u304b\u3089\u6700\u524d\u7dda\u307e\u3067\uff5e<\/li>\n\n\n\n<li><strong>\u65e5\u6642<\/strong>\uff1a2022\u5e748\u67083\u65e5\uff08\u6c34\uff0915:00\uff5e16:30&nbsp;<\/li>\n\n\n\n<li><strong>\u5834\u6240<\/strong>\uff1a\u30aa\u30f3\u30e9\u30a4\u30f3\uff08\u53c2\u52a0\u3092\u5e0c\u671b\u3055\u308c\u308b\u5834\u5408\u306f\u4e95\u4e0a (minoue at appi.keio.ac.jp) \u307e\u3067\u3054\u9023\u7d61\u304f\u3060\u3055\u3044\uff09<\/li>\n\n\n\n<li><strong>\u6982\u8981<\/strong>\uff1a\u672c\u8b1b\u6f14\u3067\u306f\u3053\u308c\u307e\u3067\u306e\u30ed\u30dc\u30c6\u30a3\u30af\u30b9\u5206\u91ce\u306b\u304a\u3051\u308bCPHS\u7814\u7a76\u3092Task Architecture\u3068\u4eba\u9593\u30e2\u30c7\u30eb\u306e\u89b3\u70b9\u304b\u3089\u6982\u89b3\u3057\u3001\u6700\u65b0\u306e\u7814\u7a76\u30c8\u30ec\u30f3\u30c9\u3092\u660e\u3089\u304b\u306b\u3059\u308b\u3002<\/li>\n\n\n\n<li><strong>\u7565\u6b74<\/strong>\uff1aTakeshi Hatanaka received the Ph.D. degree in applied mathematics and physics from Kyoto University in 2007. He then held faculty positions at Tokyo Institute of Technology and Osaka University. Since April 2020, he is an associate professor at Tokyo Institute of Technology. He is the coauthor of \u201cPassivity-Based Control and Estimation in Networked Robotics\u201d (Springer, 2015), coauthor of &#8220;Control of Multi-agent Systems&#8221; (Corona Publishing Co., 2015) and the editor of &#8220;Economically-enabled Energy Management: Interplay between Control Engineering and Economics&#8221; (Springer Nature, 2020). His research interests include cyber-physical &amp; human systems and networked robotics. He received the Kimura Award (2017), Pioneer Award (2014), Outstanding Book Award (2016), Control Division Conference Award (2018), Takeda Prize (2020), and&nbsp;Outstanding Paper Awards (2009, 2015, 2020) all from The Society of Instrumental and Control Engineers (SICE). He also received 3rd IFAC CPHS Best Research Paper Award (2020) and 10th Asian Control Conference Best Paper Prize Award (2015). He is serving\/served&nbsp;as an AE for IEEE TSCT and SICE JCMSI, and is a member of the Conference Editorial Board of IEEE CSS. He is a senior member of IEEE.<\/li>\n\n\n\n<li>\u4e16\u8a71\u4eba\uff1a<a href=\"https:\/\/sites.google.com\/keio.jp\/minoue\/\">\u4e95\u4e0a<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p><a href=\"https:\/\/www-robotics.jpl.nasa.gov\/who-we-are\/people\/masahiro_ono\/\">\u5c0f\u91ce\u96c5\u88d5 \u535a\u58eb<\/a><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u984c\u76ee<\/strong>\uff1a\u81ea\u5f8b\u7684\u30ed\u30dc\u30c3\u30c8\u306b\u3088\u308b\u592a\u967d\u7cfb\u63a2\u67fb\u306e\u73fe\u5728\u3068\u672a\u6765<\/li>\n\n\n\n<li><strong>\u65e5\u6642<\/strong>\uff1a2022\u5e747\u67087\u65e5\uff08\u6728\uff0913:00\uff5e14:30&nbsp;<\/li>\n\n\n\n<li><strong>\u5834\u6240<\/strong>\uff1a\u77e2\u4e0a\u30ad\u30e3\u30f3\u30d1\u30b9 \u5275\u60f3\u9928\uff12\u968e 14-204\uff08\u30bb\u30df\u30ca\u30fc\u30eb\u30fc\u30e0\uff14\uff09<\/li>\n\n\n\n<li><strong>\u6982\u8981<\/strong>\uff1a\u672c\u8b1b\u6f14\u306f\u82f1\u8a9e\u306e\u30b9\u30e9\u30a4\u30c9\u3092\u4f7f\u7528\u3057\u65e5\u672c\u8a9e\u306b\u3066\u884c\u308f\u308c\u307e\u3059\u3002\u8cea\u554f\u306f\u4e21\u8a00\u8a9e\u3067\u304a\u53d7\u3051\u3057\u307e\u3059\u3002 This talk will be given in Japanese with English slides. Q&amp;A will be in both languages. Abstract: \u592a\u967d\u7cfb\u63a2\u67fb\u306e\u9ece\u660e\u304b\u308960\u5e74\u304c\u7d4c\u3061\u3001\u8f1d\u304b\u3057\u3044\u767a\u898b\u304c\u7a4d\u307f\u4e0a\u304c\u308b\u4e00\u65b9\u3001\u7c21\u5358\u306b\u30a2\u30af\u30bb\u30b9\u3067\u304d\u308b\u672a\u8e0f\u306e\u63a2\u67fb\u76ee\u6a19\u306f\u6e1b\u308a\u3064\u3064\u3042\u308b\u3002\u672a\u6765\u306e\u63a2\u7d22\u30df\u30c3\u30b7\u30e7\u30f3\u3092\u9042\u884c\u3059\u308b\u30ed\u30dc\u30c3\u30c8\u306f\u56f0\u96e3\u304b\u3064\u672a\u77e5\u306a\u74b0\u5883\u4e0b\u3067\u6d3b\u52d5\u3057\u305f\u308a\uff08\u4f8b: \u6728\u661f\u30fb\u571f\u661f\u306e\u6c37\u885b\u661f\u306e\u5730\u5e95\u306e\u6d77\uff09\u3001\u6975\u3081\u3066\u91ce\u5fc3\u7684\u306a\u76ee\u6a19\u3092\u9054\u6210\u3059\u308b\u3053\u3068\u3092\u6c42\u3081\u3089\u308c\u305f\u308a\u3059\u308b\uff08\u4f8b: \u6708\u9762\u306e1000\u30ad\u30ed\u4ee5\u4e0a\u306e\u8d70\u884c\uff09\u3002\u305d\u306e\u3088\u3046\u306a\u30df\u30c3\u30b7\u30e7\u30f3\u3092\u5b9f\u73fe\u3059\u308b\u305f\u3081\u306e\u9375\u3068\u306a\u308b\u6280\u8853\u306e\u3072\u3068\u3064\u304c\u3001\u81ea\u5f8b\u5316\u3067\u3042\u308b\u3002\u305f\u3068\u3048\u30702021\u5e742\u6708\u306b\u7740\u9678\u3057\u305fNASA\u306e\u706b\u661f\u30ed\u30fc\u30d0\u30fc\u30fb\u30d1\u30fc\u30b5\u30f4\u30a3\u30a2\u30e9\u30f3\u30b9\u306f\u3053\u308c\u307e\u3067\u306e\u30ed\u30fc\u30d0\u30fc\u3067\u6700\u3082\u9ad8\u5ea6\u306a\u81ea\u52d5\u904b\u8ee2\u6a5f\u80fd\u304c\u642d\u8f09\u3055\u308c\u3066\u304a\u308a\u3001\u904e\u53bb\u306e\u706b\u661f\u306b\u5b58\u5728\u3057\u305f\u304b\u3082\u3057\u308c\u306a\u3044\u751f\u547d\u306e\u8a3c\u62e0\u3092\u3055\u304c\u3059\u6311\u6226\u7684\u306a\u30df\u30c3\u30b7\u30e7\u30f3\u306b\u5927\u304d\u304f\u8ca2\u732e\u3057\u3066\u3044\u308b\u3002\u307e\u305f\u3001JPL\u306f\u73fe\u5728EELS (Exobiology Extant Life Surveyor)\u3068\u547c\u3070\u308c\u308b\u30d8\u30d3\u578b\u306e\u30ed\u30dc\u30c3\u30c8\u3092\u73fe\u5728\u958b\u767a\u3057\u3066\u3044\u308b\u3002\u305d\u306e\u76ee\u7684\u306f\u5c06\u6765\u3001\u571f\u661f\u306e\u885b\u661f\u30a8\u30f3\u30bb\u30e9\u30c9\u30b9\u306e\u6c37\u306e\u88c2\u3051\u76ee\u3092\u964d\u4e0b\u3057\u5730\u5e95\u306e\u6d77\u306b\u5730\u7403\u5916\u751f\u547d\u3092\u63a2\u3059\u3053\u3068\u3092\u53ef\u80fd\u306b\u3059\u308b\u3053\u3068\u3067\u3042\u308b\u3002\u74b0\u5883\u306e\u4e0d\u78ba\u5b9a\u6027\u3068\u5730\u7403\u3068\u306e\u901a\u4fe1\u9045\u5ef6\u306b\u3088\u308a\u3001EELS\u3092\u5730\u7403\u304b\u3089\u624b\u52d5\u3067\u64cd\u4f5c\u3059\u308b\u3053\u3068\u306f\u975e\u73fe\u5b9f\u7684\u3067\u3042\u308b\u3002\u3053\u306e\u8b1b\u6f14\u3067\u306fJPL\u306b\u304a\u3051\u308b\u60d1\u661f\u63a2\u67fb\u30ed\u30dc\u30c3\u30c8\u306e\u81ea\u7acb\u5316\u306e\u7814\u7a76\u958b\u767a\u306e\u4e00\u7aef\u3092\u7d39\u4ecb\u3057\u3001\u672a\u6765\u306e\u30df\u30c3\u30b7\u30e7\u30f3\u306b\u304a\u3051\u308b\u6280\u8853\u30cb\u30fc\u30ba\u306e\u77e5\u898b\u3092\u5171\u6709\u3059\u308b\u3002<\/li>\n\n\n\n<li><strong>\u7565\u6b74<\/strong>\uff1aNASA\u30b8\u30a7\u30c3\u30c8\u63a8\u9032\u7814\u7a76\u6240 Robotic Surface Mobility Group \u30b0\u30eb\u30fc\u30d7\u30ea\u30fc\u30c0\u30fc\u3002Mars 2020\u30ed\u30fc\u30d0\u30fc\u30df\u30c3\u30b7\u30e7\u30f3\u306e\u4e00\u54e1\u3068\u3057\u3066\u3001\u30d1\u30fc\u30b5\u30f4\u30a3\u30a2\u30e9\u30f3\u30b9\u30ed\u30fc\u30d0\u30fc\u306e\u904b\u7528\u306b\u95a2\u308f\u308b\u3002\u904e\u53bb\u306b\u306f\u305d\u306e\u81ea\u52d5\u8d70\u884c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u958b\u767a\u3084\u7740\u9678\u5730\u70b9\u9078\u5b9a\u306b\u643a\u308f\u3063\u305f\u3002\u307e\u305f\u3001EELS\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306e\u4e3b\u4efb\u7814\u7a76\u54e1 (PI)\u3092\u52d9\u3081\u3001\u7d0450\u4eba\u306e\u30c1\u30fc\u30e0\u3092\u7387\u3044\u308b\u3002\u7814\u7a76\u30c6\u30fc\u30de\u306f\u5b87\u5b99\u63a2\u67fb\u7528\u30ed\u30dc\u30c3\u30c8\u306e\u81ea\u5f8b\u5316\u3067\u3001\u3068\u308a\u308f\u3051\u6a5f\u68b0\u5b66\u7fd2\u306e\u8a8d\u77e5\u3001\u30c7\u30fc\u30bf\u89e3\u91c8\u3084\u610f\u601d\u6c7a\u5b9a\u3078\u306e\u5fdc\u7528\u306b\u8208\u5473\u304c\u3042\u308b\u30022005\u5e74\u6771\u4eac\u5927\u5b66\u822a\u7a7a\u5b87\u5b99\u5de5\u5b66\u79d1\u5b66\u58eb\u30012007\u5e74\u30de\u30b5\u30c1\u30e5\u30fc\u30bb\u30c3\u30c4\u5de5\u79d1\u5927\u5b66\u4fee\u58eb\u30012012\u5e74\u540c\u5927\u5b66\u535a\u58eb\u5352\u30022012\u5e74\u3088\u308a\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u7406\u5de5\u5b66\u90e8\u52a9\u6559\u30022013\u5e74\u3088\u308a\u73fe\u8077\u3002\u962a\u795e\u30d5\u30a1\u30f3\u3002\u30df\u30fc\u3061\u3083\u3093\u306e\u30d1\u30d1\u3002<\/li>\n\n\n\n<li>\u4e16\u8a71\u4eba\uff1a<a href=\"https:\/\/arx.appi.keio.ac.jp\/\">\u8db3\u7acb\u4fee\u4e00<\/a><\/li>\n\n\n\n<li>\u53c2\u52a0\u3092\u5e0c\u671b\u3055\u308c\u308b\u65b9\u306f\uff0c\u5fc5\u305a\u8db3\u7acb\uff08adachi[at]appi.keio.ac.jp\uff09\u3068\u6589\u85e4\u79d8\u66f8\uff08elfe[at]appi.keio.ac.jp\uff09\u306b\uff0c\u4e8b\u524d\u306b\uff08\uff17\u6708\uff14\u65e5\u307e\u3067\u306b\uff09e-mail \u3067\uff0c\u6240\u5c5e\uff0c\u6c0f\u540d\uff0c\u30e1\u30fc\u30eb\u30a2\u30c9\u30ec\u30b9\u306a\u3069\u3092\u9023\u7d61\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/li>\n\n\n\n<li>\u30d1\u30f3\u30d5\u30ec\u30c3\u30c8<\/li>\n\n\n\n<li><img loading=\"lazy\" decoding=\"async\" width=\"755\" height=\"1024\" class=\"wp-image-5951\" style=\"color: initial; width: 600px;\" src=\"https:\/\/www.appi.keio.ac.jp\/wordpress\/wp-content\/uploads\/2022\/06\/2020-7-7-poster-755x1024-1.jpg\" alt=\"\" srcset=\"https:\/\/www.appi.keio.ac.jp\/wordpress\/wp-content\/uploads\/2022\/06\/2020-7-7-poster-755x1024-1.jpg 755w, https:\/\/www.appi.keio.ac.jp\/wordpress\/wp-content\/uploads\/2022\/06\/2020-7-7-poster-755x1024-1-221x300.jpg 221w\" sizes=\"auto, (max-width: 755px) 100vw, 755px\" \/><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p><strong><a href=\"https:\/\/www.cmu.edu\/ece\/learning-control\/index.html\">Prof. Yorie Nakahira (Carnegie Mellon University)<\/a><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Title:<\/strong> Myopically verifiable probabilistic certificate for long-term safety and its autonomous driving application<\/li>\n\n\n\n<li><strong>Date:<\/strong> 2022.6.22 (Wed)  15:00-16:00<\/li>\n\n\n\n<li><strong>Room:<\/strong> Seminar Room 4 (14-204; \u30bb\u30df\u30ca\u30fc\u30eb\u30fc\u30e04) <\/li>\n\n\n\n<li><strong>Abstract:<\/strong> In this talk, we will first introduce our recent work that focused on barrier function-based approaches for the safe control problem in stochastic systems. With the presence of stochastic uncertainties, a myopic controller that ensures safe probability in infinitesimal time intervals may allow the accumulation of unsafe probability over time and result in a small long-term safe probability. Meanwhile, increasing the outlook time horizon may lead to significant computation burdens and delayed reactions, which also compromises safety. To tackle this challenge, we define a new notion of forward invariance on \u2018probability space\u2019 as opposed to the safe regions on state space. This new notion allows the long-term safe probability to be framed into a forward invariance condition, which can be efficiently evaluated. We build upon this safety condition to propose a controller that works myopically yet can guarantee long-term safe probability or fast recovery probability. The proposed controller ensures the safe probability does not decrease over time and allows the designers to directly specify safe probability. This framework can also be adapted to characterize the speed and probability of forward convergent behaviors, which can be of use to finite-time Lyapunov analysis in stochastic systems. Building upon the above framework, we will then present an adaptive safe control method that can adapt to changing environments, tolerate large uncertainties, and exploit predictions in autonomous driving. The use of long-term safe probability provides a sufficient outlook time horizon to capture future predictions of the environment and planned vehicle maneuvers and to avoid unsafe regions of attractions. The resulting control action systematically mediates behaviors based on uncertainties and can find safer actions even with large uncertainties. This feature allows the system to quickly respond to changes and risks, even before an accurate estimate of the changed parameters can be constructed. The safe probability can be continuously learned and refined. Using more precise probability avoids over-conservatism, which is a common drawback of the deterministic worst-case approaches. The proposed techniques can also be efficiently computed in real-time using onboard hardware and modularly integrated into existing processes such as predictive model controllers.<\/li>\n\n\n\n<li><strong>Bio<\/strong>: Yorie Nakahira is an Assistant Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University. She received B.E. in Control and Systems Engineering from Tokyo Institute of Technology and Ph.D. in Control and Dynamical Systems from California Institute of Technology. Her research interests include the fundamental theory of optimization, control, and learning and its application to neuroscience, cell biology, smart grid, cloud computing, finance, and autonomous driving. Her group website can be found here: <a href=\"https:\/\/www.cmu.edu\/ece\/learning-control\/index.html\">https:\/\/www.cmu.edu\/ece\/learning-control\/index.html<\/a><\/li>\n\n\n\n<li><strong>Host<\/strong>\uff1a<a href=\"https:\/\/hori.appi.keio.ac.jp\/en\">Yutaka Hori<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p>\u5bae\u4e0b\u4ee4\u592e \u7279\u4efb\u8b1b\u5e2b (\u6771\u4eac\u5927\u5b66)<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u984c\u76ee:<\/strong> \u9ad8\u901f\u753b\u50cf\u51e6\u7406\u306b\u3088\u308b\u8996\u899a\u60c5\u5831\u64cd\u4f5c<\/li>\n\n\n\n<li><strong>\u65e5\u6642:<\/strong> 2022.5.25 (Wed)  16:00-17:00<\/li>\n\n\n\n<li><strong>\u5834\u6240:<\/strong> \u30bb\u30df\u30ca\u30fc\u30eb\u30fc\u30e04 (14-204) <\/li>\n\n\n\n<li><strong>\u6982\u8981:<\/strong> \u8a08\u7b97\u6a5f\u306e\u767a\u5c55\u306b\u3088\u308a\u60c5\u5831\u51e6\u7406\u306e\u9ad8\u901f\u5316\u304c\u9032\u3093\u3067\u3044\u308b\u3002\u9ad8\u901f\u306a\u60c5\u5831\u51e6\u7406\u3092\u7528\u3044\u3066\u30a2\u30af\u30c1\u30e5\u30a8\u30fc\u30bf\u3092\u5236\u5fa1\u3059\u308b\u3053\u3068\u3067\u3001\u4eba\u9593\u306e\u52d5\u4f5c\u901f\u5ea6\u3092\u8d85\u3048\u308b\u9ad8\u901f\u30ed\u30dc\u30c3\u30c8\u30b7\u30b9\u30c6\u30e0\u304c\u69cb\u7bc9\u3055\u308c\u3001\u751f\u7523\u6027\u306e\u5411\u4e0a\u3084\u52d5\u7684\u5bfe\u8c61\u306e\u64cd\u4f5c\u304c\u5b9f\u73fe\u3055\u308c\u3066\u304d\u3066\u3044\u308b\u3002\u4e00\u65b9\u3067\u3001\u4eba\u9593\u3092\u5bfe\u8c61\u3068\u3059\u308b\u5fdc\u7528\u3067\u306f\u3001\u4eba\u9593\u306e\u77e5\u899a\u901f\u5ea6\u3092\u8d85\u3048\u308b\u9ad8\u901f\u30b7\u30b9\u30c6\u30e0\u3092\u69cb\u7bc9\u3057\u3001\u6ca1\u5165\u611f\u306e\u9ad8\u3044\u62e1\u5f35\u73fe\u5b9f\u7a7a\u9593\u3092\u5275\u9020\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3002\u3059\u306a\u308f\u3061\u3001\u5b9f\u4e16\u754c\u306e\u73fe\u8c61\u3092\u6349\u3048\u3001\u60c5\u5831\u51e6\u7406\u3092\u65bd\u3057\u305f\u5f8c\u3001\u5b9f\u4e16\u754c\u306b\u53cd\u6620\u3059\u308b\u64cd\u4f5c\u3092\u4eba\u9593\u304c\u6c17\u3065\u304b\u306a\u3044\u901f\u5ea6\u3067\u884c\u3046\u30b7\u30b9\u30c6\u30e0\u3092\u7d44\u307f\u8fbc\u3080\u3053\u3068\u3067\u3001\u5b9f\u4e16\u754c\u306e\u7269\u7406\u306b\u76f8\u5f53\u3059\u308b\u90e8\u5206\u3092\u4eee\u60f3\u7684\u306b\u518d\u5b9a\u7fa9\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3042\u308b\u3068\u8003\u3048\u3089\u308c\u308b\u3002\u3055\u3089\u306b\u3001\u7269\u7406\u3092\u4eee\u60f3\u7684\u306b\u518d\u5b9a\u7fa9\u3059\u308b\u3053\u3068\u306f\u3001\u4eba\u9593\u304c\u7269\u7406\u3092\u901a\u3057\u3066\u7269\u8cea\u3092\u6349\u3048\u3066\u3044\u308b\u4ee5\u4e0a\u3001\u7269\u8cea\u3092\u3082\u62e1\u5f35\u3059\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u3066\u3044\u308b\u3002\u672c\u8b1b\u6f14\u3067\u306f\u3001\u9ad8\u901f\u753b\u50cf\u51e6\u7406\u306b\u3088\u308b\u9ad8\u901f\u30bb\u30f3\u30b7\u30f3\u30b0\u3068\u9ad8\u901f\u30c7\u30a3\u30b9\u30d7\u30ec\u30a4\u3092\u7528\u3044\u3066\u3001\u4eba\u9593\u306e\u77e5\u899a\u901f\u5ea6\u3092\u8d85\u3048\u308b\u9ad8\u901f\u60c5\u5831\u30b7\u30b9\u30c6\u30e0\u3092\u69cb\u7bc9\u3057\u3001\u5b9f\u4e16\u754c\u306e\u7269\u8cea\u3084\u7269\u7406\u3092\u8996\u899a\u7684\u306b\u66f8\u304d\u63db\u3048\u3001\u62e1\u5f35\u3059\u308b\u7814\u7a76\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3059\u308b\u3002<\/li>\n\n\n\n<li><strong>\u7565\u6b74<\/strong>: \uff12\uff10\uff11\uff12\u5e74\u6771\u4eac\u5927\u5b66\u5de5\u5b66\u90e8\u8a08\u6570\u5de5\u5b66\u79d1\u5352\u3001\uff12\uff10\uff11\uff17\u5e74\u6771\u4eac\u5927\u5b66\u60c5\u5831\u7406\u5de5\u5b66\u7cfb\u7814\u7a76\u79d1\u30b7\u30b9\u30c6\u30e0\u60c5\u5831\u5b66\u5c02\u653b\u535a\u58eb\u8ab2\u7a0b\u4fee\u4e86\u3001\u535a\u58eb\uff08\u60c5\u5831\u7406\u5de5\u5b66\uff09\u3001\uff12\uff10\uff12\uff10\u5e74\u3088\u308a\u6771\u4eac\u5927\u5b66\u60c5\u5831\u57fa\u76e4\u30bb\u30f3\u30bf\u30fc\u7279\u4efb\u8b1b\u5e2b\u3002\u6620\u50cf\u60c5\u5831\u30e1\u30c7\u30a3\u30a2\u5b66\u4f1a \u4e39\u7fbd\u9ad8\u67f3\u8cde \u8ad6\u6587\u8cde\u3001\u4e95\u4e0a\u79d1\u5b66\u632f\u8208\u8ca1\u56e3 \u4e95\u4e0a\u7814\u7a76\u5968\u52b1\u8cde\u3001\u8239\u4e95\u60c5\u5831\u79d1\u5b66\u632f\u8208\u8ca1\u56e3 \u7814\u7a76\u5968\u52b1\u8cde\u306a\u3069\u53d7\u8cde\u591a\u6570\u3002\u9ad8\u901f\u753b\u50cf\u51e6\u7406\u3092\u7528\u3044\u305f\u9ad8\u901f\u30bb\u30f3\u30b7\u30f3\u30b0\u30b7\u30b9\u30c6\u30e0\u3001\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0XR\u30b7\u30b9\u30c6\u30e0\u306e\u958b\u767a\u306b\u5f93\u4e8b\u3002<\/li>\n\n\n\n<li><a href=\"https:\/\/www.appi.keio.ac.jp\/?p=5922\"><strong>\u30d6\u30ed\u30b0\u8a18\u4e8b<\/strong><\/a><\/li>\n\n\n\n<li><strong>\u4e16\u8a71\u4eba<\/strong>\uff1a<a href=\"https:\/\/hori.appi.keio.ac.jp\/en\">\u5800\u8c4a<\/a> &amp; <a href=\"https:\/\/sites.google.com\/keio.jp\/minoue-eng\/home\">\u4e95\u4e0a\u6b63\u6a39<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p><a style=\"font-weight: bold; text-decoration-line: underline;\" href=\"https:\/\/hirotsukamoto.com\/\">Mr. Hiroyasu Tsukamoto (California Institute of Technology)<\/a><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Title:<\/strong> Contraction Theory for Designing Safe, Stable, and Robust Learning-based GNC: A Tutorial Overview<\/li>\n\n\n\n<li><strong>Date:<\/strong> 2022.5.11 (Wed)  9:00-10:30<\/li>\n\n\n\n<li><strong>Place:<\/strong> Zoom <\/li>\n\n\n\n<li><strong>Abstract:<\/strong> AI and machine learning technologies have been utilized for achieving safe and stable autonomy of aerospace and robotic systems. Stability and safety are typically research problems of control theory, while conventional black-box AI approaches lack much-needed robustness, scalability, and interpretability, which are indispensable to designing control and autonomy engines for safety-critical robotic missions on land, in water, or in deep space. This talk gives a brief tutorial overview of contraction theory for deriving formal robustness and stability guarantees of various learning-based and data-driven automatic control problems, with some illustrative examples including the recent NASA JPL-Caltech RTD project on learning-based Interstellar Object (ISO) exploration. This talk is based on a tutorial session, Contraction Theory for Machine Learning, which we organized at the 2021 IEEE Conference on Decision and Control.<br>For more information, see https:\/\/sites.google.com\/view\/contractiontheory.<\/li>\n\n\n\n<li><strong>Bio:<\/strong> Hiroyasu Tsukamoto is an aerospace Ph.D. student at GALCIT, Caltech. His research interest includes deep learning-based robust optimal control, estimation, and motion planning for general nonlinear systems, aerial swarms, and autonomous aerospace systems. (Google Scholar: https:\/\/scholar.google.com\/citations?user=G9iATfcAAAAJ&amp;hl=ja, LinkedIn: https:\/\/www.linkedin.com\/in\/hiroyasu-tsukamoto-32500a1a5\/)<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/www.appi.keio.ac.jp\/?p=5895\">Blog<\/a><\/strong><\/li>\n\n\n\n<li><strong>Organizers:<\/strong> <a href=\"https:\/\/hori.appi.keio.ac.jp\/en\">Yutaka Hori<\/a> &amp; <a href=\"https:\/\/sites.google.com\/keio.jp\/minoue-eng\/home\" data-type=\"URL\" data-id=\"https:\/\/sites.google.com\/keio.jp\/minoue-eng\/home\">Masaki Inoue<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p><strong><span style=\"text-decoration: underline;\"><a href=\"https:\/\/shumon0423.github.io\/\">\u53e4\u8cc0\u6731\u9580\u535a\u58eb (University of California, San Diego)<\/a><\/span><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u984c\u76ee\uff1a<\/strong>SLAM\u306e\u305f\u3081\u306e\u6700\u9069\u5236\u5fa1\u3068\u5f37\u5316\u5b66\u7fd2<\/li>\n\n\n\n<li><strong>\u65e5\u4ed8\uff1a<\/strong>2022.4.26 (\u706b)13:00-14:30<\/li>\n\n\n\n<li><strong>\u5834\u6240\uff1a<\/strong>\u30bb\u30df\u30ca\u30fc\u30eb\u30fc\u30e0\uff11<\/li>\n\n\n\n<li><strong>\u6982\u8981\uff1a<\/strong>\u81ea\u52d5\u904b\u8ee2\u8eca\u3084\u30c9\u30ed\u30fc\u30f3\u306a\u3069\u306e\u79fb\u52d5\u578b\u30ed\u30dc\u30c3\u30c8\u304c\u8fd1\u5e74\u76ee\u899a\u307e\u3057\u3044\u767a\u5c55\u3092\u9042\u3052\u3066\u3044\u308b\u3002\u7279\u306b\u3001GPS\u304c\u906e\u65ad\u3055\u308c\u305f\u74b0\u5883\u4e0b\u3067\u3001\u79fb\u52d5\u578b\u30ed\u30dc\u30c3\u30c8\u306b\u7d44\u307f\u8fbc\u307e\u308c\u3066\u3044\u308b\u30bb\u30f3\u30b5\uff08\u4f8b\uff1a\u30ab\u30e1\u30e9\u3001\u30e9\u30a4\u30c0\u30fc\uff09\u3092\u7528\u3044\u3066\u3001\u4e0d\u78ba\u5b9a\u304b\u3064\u8907\u96d1\u306a\u74b0\u5883\u3092\u518d\u73fe\u3059\u308b\u6280\u8853\u304c\u6ce8\u76ee\u3092\u6d74\u3073\u3066\u3044\u308b\u3002\u305d\u306e\u3088\u3046\u306a\u8ab2\u984c\u306f\u3001Simultaneous Localization and Mapping (SLAM) \u3068\u3044\u3046\u554f\u984c\u306b\u5e30\u7740\u3055\u308c\u3001\u707d\u5bb3\u6551\u52a9\u3084\u60d1\u661f\u63a2\u7d22\u3001\u307e\u305f\u62e1\u5f35\u73fe\u5b9f\u306a\u3069\u306e\u5e45\u5e83\u3044\u5fdc\u7528\u306b\u9ad8\u3044\u671f\u5f85\u304c\u3055\u308c\u3066\u3044\u308b\u3002\u672c\u8b1b\u6f14\u3067\u306f\u3001SLAM\u306e\u57fa\u790e\u304b\u3089\u306f\u3058\u3081\u3001\u307e\u305f\u305d\u306e\u7cbe\u5ea6\u3092\u5411\u4e0a\u3055\u305b\u308b\u3088\u3046\u306a\u30ed\u30dc\u30c3\u30c8\u306e\u8ecc\u9053\u8a2d\u8a08\u306b\u3064\u3044\u3066\u304a\u8a71\u3057\u3059\u308b\u3002\u6570\u5b66\u7684\u306b\u306f\u3001SLAM\u3092\u30ab\u30eb\u30de\u30f3\u30d5\u30a3\u30eb\u30bf\u306b\u3088\u3063\u3066\u884c\u3044\u3001\u3042\u308b\u60c5\u5831\u7406\u8ad6\u7684\u306a\u8a55\u4fa1\u95a2\u6570\u304c\u6700\u5c0f\u306b\u306a\u308b\u3088\u3046\u306a\u30ed\u30dc\u30c3\u30c8\u306e\u5165\u529b\u3092\u6c42\u3081\u308b\u6700\u9069\u5236\u5fa1\u554f\u984c\u306b\u53d6\u308a\u7d44\u3080\u3002\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u304f\u5236\u5fa1\u6cd5\u304b\u3089\u3001\u30e2\u30c7\u30eb\u306b\u4f9d\u5b58\u3057\u306a\u3044\u5f37\u5316\u5b66\u7fd2\u307e\u3067\u306e\u624b\u6cd5\u3092\u63d0\u6848\u3057\u3001\u5b9f\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u6027\u80fd\u306e\u6709\u52b9\u6027\u3092\u7acb\u8a3c\u3059\u308b\u3002<\/li>\n\n\n\n<li><strong>\u7565\u6b74\uff1a<\/strong>\uff12\uff10\uff11\uff14\u5e74\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u7406\u5de5\u5b66\u90e8\u7269\u7406\u60c5\u5831\u5de5\u5b66\u79d1\u5352\u3001\uff12\uff10\uff12\uff10\u5e74\u30ab\u30ea\u30d5\u30a9\u30eb\u30cb\u30a2\u5927\u5b66\u30b5\u30f3\u30c7\u30a3\u30a8\u30b4\u6821\u6a5f\u68b0\u822a\u7a7a\u5de5\u5b66\u79d1\u535a\u58eb\u8ab2\u7a0b\u4fee\u4e86\u3002\u535a\u58eb\u8ab2\u7a0b\u5728\u5b66\u4e2d\u306b\u30ec\u30f3\u30bb\u30e9\u30fc\u5de5\u79d1\u5927\u5b66\u306b\u3066\u8a2a\u554f\u7814\u7a76\u3001\u307e\u305fNASA\u30b8\u30a7\u30c3\u30c8\u63a8\u9032\u7814\u7a76\u6240\u3001\u4e09\u83f1\u96fb\u6a5f\u7814\u7a76\u6240\u306b\u3066\u30a4\u30f3\u30bf\u30fc\u30f3\u3092\u7d4c\u9a13\u3002\uff12\uff10\uff12\uff10\u5e74\u304b\u3089\u3001\u30ab\u30ea\u30d5\u30a9\u30eb\u30cb\u30a2\u5927\u5b66\u30b5\u30f3\u30c7\u30a3\u30a8\u30b4\u6821\u96fb\u6c17\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u5de5\u5b66\u79d1\u306b\u3066\u30dd\u30b9\u30c9\u30af\u7814\u7a76\u54e1\u3002\u30a2\u30e1\u30ea\u30ab\u5236\u5fa1\u5b66\u4f1a\u306e\u6700\u512a\u79c0\u8ad6\u6587\u8cde\u3001\u30ab\u30ea\u30d5\u30a9\u30eb\u30cb\u30a2\u5927\u5b66\u30b5\u30f3\u30c7\u30a3\u30a8\u30b4\u6821\u306e\u5236\u5fa1\u5206\u91ce\u306b\u304a\u3051\u308b\u6700\u512a\u79c0\u535a\u58eb\u8ad6\u6587\u8cde\u306a\u3069\u3092\u53d7\u8cde\u3002\u535a\u58eb\u8ab2\u7a0b\u3067\u306f\u504f\u5fae\u5206\u65b9\u7a0b\u5f0f\u306e\u5236\u5fa1\u7406\u8ad6\u3001Extremum Seeking\u306b\u3088\u308b\u6700\u9069\u5316\u3068\u5b66\u7fd2\u3001\u307e\u305f\u305d\u308c\u3089\u306e\u69d8\u3005\u306a\u5fdc\u7528\uff08\uff13D\u30d7\u30ea\u30f3\u30bf\u3001\u30ea\u30c1\u30a6\u30e0\u30a4\u30aa\u30f3\u96fb\u6c60\u3001\u71b1\u30a8\u30cd\u30eb\u30ae\u30fc\u8caf\u8535\u3001\u4ea4\u901a\u6e0b\u6ede\u3001\u6c17\u5019\u5909\u52d5\uff09\u306b\u5f93\u4e8b\u3002\u73fe\u5728\u306f\u30ed\u30dc\u30c6\u30a3\u30af\u30b9\u306e\u6700\u9069\u5316\u3068\u6a5f\u68b0\u5b66\u7fd2\u3001\u7279\u306b\u4f4d\u7f6e\u5730\u56f3\u540c\u6642\u63a8\u5b9a\uff08SLAM\uff09\u3084\u8ecc\u9053\u8a2d\u8a08\u306a\u3069\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306b\u643a\u308f\u308b\u3002<\/li>\n\n\n\n<li><a href=\"https:\/\/www.appi.keio.ac.jp\/?p=5887\">\u30d6\u30ed\u30b0\u8a18\u4e8b<\/a><\/li>\n\n\n\n<li><strong>\u4e16\u8a71\u4eba\uff1a<\/strong><a href=\"https:\/\/hori.appi.keio.ac.jp\/en\">Yutaka Hori<\/a>\uff0c<a href=\"https:\/\/sites.google.com\/keio.jp\/minoue-eng\/home\" data-type=\"URL\" data-id=\"https:\/\/sites.google.com\/keio.jp\/minoue-eng\/home\">Masaki Inoue<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p><strong><span style=\"text-decoration: underline;\"><a href=\"https:\/\/ide-research.net\/\">\u4e95\u624b\u525b\u535a\u58eb (IBM Thomas J Watson Research Center) <\/a><\/span><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u984c\u76ee\uff1a<\/strong>\u4eba\u5de5\u77e5\u80fd\u30d6\u30fc\u30e0\u306e\u73fe\u5728\u3068\u6700\u8fd1\u306e\u7814\u7a76\u304b\u3089<\/li>\n\n\n\n<li><strong>\u65e5\u4ed8\uff1a<\/strong>2022.4.5 (\u706b)10:00-12:00<\/li>\n\n\n\n<li><strong>\u5834\u6240\uff1a<\/strong>Zoom<\/li>\n\n\n\n<li><strong>\u6982\u8981\uff1a<\/strong>\u8a00\u8a9e\u3001\u753b\u50cf\u3001\u97f3\u58f0\u306e\u5206\u91ce\u3067\u306e\u6df1\u5c64\u5b66\u7fd2\u306e\u30d6\u30ec\u30a4\u30af\u30b9\u30eb\u30fc\u3092\u304d\u3063\u304b\u3051\u306b\u3001AI (artificial intelligence) \u304c\u4e00\u822c\u30e1\u30c7\u30a3\u30a2\u306e\u8a71\u984c\u306b\u306a\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u3057\u3070\u3089\u304f\u7d4c\u3061\u307e\u3059\u3002\u3053\u306e\u8b1b\u6f14\u3067\u306f\u6700\u521d\u306b\u3001\u5c11\u3057\u524d\u306b\u76db\u3093\u306b\u8a00\u308f\u308c\u3066\u3044\u305fAI\u306b\u5bfe\u3059\u308b\u671f\u5f85\u3068\u30012022\u5e74\u306b\u304a\u3051\u308b\u73fe\u5b9f\u3092\u4fef\u77b0\u3057\u307e\u3059\u3002\u305d\u306e\u5f8c\u3001AI\u8105\u5a01\u8ad6\u306e\u6d41\u308c\u3067\u6ce8\u76ee\u3092\u96c6\u3081\u3066\u304d\u305f\u300cAI\u306e\u8aac\u660e\u53ef\u80fd\u6027\u300d\u306b\u3064\u3044\u3066\u306e\u6240\u898b\u3092\u8ff0\u3079\u3001\u6700\u8fd1\u306e\u7814\u7a76\u6210\u679c\uff08Ide et al., AAAI 21; NeurIPS 21\uff09 \u306b\u3064\u3044\u3066\u6982\u8981\u3092\u7d39\u4ecb\u3057\u305f\u3044\u3068\u601d\u3044\u307e\u3059\u3002<\/li>\n\n\n\n<li><a href=\"https:\/\/arx.appi.keio.ac.jp\/2022\/04\/05\/%e8%b6%b3%e7%ab%8b%e7%a0%94%e3%82%bb%e3%83%9f%e3%83%8a%e3%83%bc%e3%81%a7%e4%ba%95%e6%89%8b%e5%89%9b%e5%8d%9a%e5%a3%ab%e3%81%8c%e8%ac%9b%e6%bc%94\/\">\u30d6\u30ed\u30b0\u8a18\u4e8b<\/a><\/li>\n\n\n\n<li><strong>\u4e16\u8a71\u4eba\uff1a<\/strong><a href=\"https:\/\/arx.appi.keio.ac.jp\/\">\u8db3\u7acb\u4fee\u4e00<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">2019<\/h2>\n\n\n\n<p><strong><span style=\"text-decoration: underline;\"><a href=\"https:\/\/github.com\/zoltuz\/\" data-type=\"URL\" data-id=\"https:\/\/github.com\/zoltuz\/\">Dr. Zoltan Tuza (Imperial College London)<\/a><\/span><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Title: <\/strong>Characterization of Biologically Relevant Network Structures from Time Series Data<\/li>\n\n\n\n<li><strong>Date: <\/strong>2019.11.8 (Fri) 16:30-18:00<\/li>\n\n\n\n<li><strong>Abstract:<\/strong> High-throughput data acquisition in synthetic biology leads to an abundance of data that need to be processed and aggregated into useful biological models. Building dynamical models based on this wealth of data is of paramount importance to understand and optimize designs of synthetic biology constructs. However, building models manually for each data set is inconvenient and might become infeasible for highly complex synthetic systems. In this talk, we present state-of-the-art system identification techniques and combine them with chemical reaction network theory (CRNT) to generate dynamic models automatically. On the system identification side, Sparse Bayesian Learning offers methods to learn from data the sparsest set of base functions necessary to capture the dynamics of the system into ODE models; on the CRNT side, building on such sparse ODE models, all possible network structures within a given parameter uncertainty region can be computed. Additionally, the system identification process can be complemented with constraints on the parameters to, for example, enforce stability or non-negativity&#8212;thus offering relevant physical constraints over the possible network structures. In this way, the wealth of data can be translated into biologically relevant network structures, which then steers the data acquisition, thereby providing a vital step for closed-loop system identification.<\/li>\n\n\n\n<li><strong>Organizer:<\/strong> <a href=\"https:\/\/hori.appi.keio.ac.jp\/\" data-type=\"URL\" data-id=\"https:\/\/hori.appi.keio.ac.jp\/\">Yutaka Hori<\/a><\/li>\n<\/ul>\n\n\n\n<p><strong><span style=\"text-decoration: underline;\"><a href=\"http:\/\/jmmaestre.net\/\">Prof. Jos\u00e9 Mar\u00eda Maestre Torreblanca (University of Seville)<\/a><\/span><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Title: <\/strong>Centralized, Distributed, and Coalitional Model Predictive Control<\/li>\n\n\n\n<li><strong>Date: <\/strong>2019.9.5 (Thurs)<\/li>\n\n\n\n<li><strong>Abstract:<\/strong> Model predictive control (MPC) has become of the most popular control techniques due its flexibility. Issues such as constraints on the control problem variables, delays in the system dynamics, and multiple objectives can be handled explicitly in the MPC framework. The evolution of computer, information and communication technologies has motivated the application of MPC to problems beyond its scope years ago and the development multiple noncentralized MPC approaches. The goal of this talk is to present a coherent and easily accessible overview regarding model predictive control and some of the latest developments regarding its application to large-scale cyber physical systems, including topics such as coalitional control and human in the loop.<\/li>\n\n\n\n<li><strong>Bio: <\/strong>J.M. Maestre received the Ph.D. degree in automation and robotics from the University of Seville, where he works as associate professor. He has also worked in LTH at Lund University, TU Delft, and Tokyo Institute of Technology, where he is currently funded by the Japanese Society for the Promotion of Science. Besides his PhD, which was awarded with the extraordinary prize of the University of Seville, he also has master degrees in intelligent buildings and economics. His main research interests are the control of distributed systems and the integration of service robots in the smart home. He has authored and coauthored more than one hundred publications regarding these topics. He is also editor of the books \u201cService robotics within the Digital Home: Applications and Future Prospects\u201d (Springer, 2011), \u201cDistributed Model Predictive Control Made Easy\u201d (Springer, 2014), and \u201cDomotica para Ingenieros\u201d (Paraninfo, 2015). Finally, he is one of the founders of the technological firms Idener and Eskesso.<\/li>\n\n\n\n<li><a href=\"https:\/\/www.appi.keio.ac.jp\/?p=4612\">Blog<\/a><\/li>\n\n\n\n<li><strong>Organizer:<\/strong><a href=\"https:\/\/sites.google.com\/keio.jp\/minoue-eng\/home\" data-type=\"URL\" data-id=\"https:\/\/sites.google.com\/keio.jp\/minoue-eng\/home\"> Masaki Inoue<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p><strong><span style=\"text-decoration: underline;\"><a href=\"https:\/\/www.ahmet.ac\/\">Prof. Ahmet Cetinkaya (National Institute of Informatics)<\/a><\/span><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Title:<\/strong> Randomized Communication Protocols for Secure Networked Control Under Jamming Attacks<\/li>\n\n\n\n<li><strong>Date: <\/strong>2019.8.1 (Thurs)<\/li>\n\n\n\n<li><strong>Abstract: <\/strong>Recent control architectures in cyber physical systems utilize wireless communication technologies for transmission of measurement and control data packets to remote locations. As the Internet of Things is becoming more popular, the use of wireless technologies in networked control systems is expected to increase even more. These new developments are bringing efficiency to control systems, but they are also expected to introduce vulnerabilities that can be exploited by cyber-attackers. For instance, jamming attackers may be able block the transmission of data packets on a wireless channel by emitting strong interference signals. It has been shown recently that wireless networked control systems that are designed based on classical periodic (or event-driven) sampled-data control approaches may suffer from jamming attacks and face instability even under attacks coming from an energy-constrained attacker. To avoid instability under such control approaches, a restriction on the attack frequency becomes necessary. Specifically, the frequency at which the jamming attacks can be turned on and off is required to be less than the frequency of the communication attempts. In this talk, we will look at a new randomized control &amp; communication approach that allows secure operation under jamming attacks with arbitrarily large frequencies. In this approach, control and measurement data packets are attempted to be transmitted at random time instants with a fixed expected interval between them. We show that this randomized scheme guarantees infinitely many successful transmissions in the long run as long as the intervals of jamming attacks do not cover the entire time domain. In multi-agent systems with marginally-stable integrator agent dynamics, this suffices for achieving consensus. To handle networked control of plants with unstable dynamics, we further look at the long run average number of successful transmissions. We show by employing tail probability bounds that this average number is actually lower-bounded by a constant that depends solely on the attack durations and not on the attack frequency. By using a recent result on the stability of networked control systems, we show that our randomized approach can guarantee almost-sure asymptotic stabilization if the average jamming duration is sufficiently small regardless of how frequent the attacks may occur.<\/li>\n\n\n\n<li><strong>Organizer:<\/strong><a href=\"https:\/\/sites.google.com\/keio.jp\/minoue-eng\/home\" data-type=\"URL\" data-id=\"https:\/\/sites.google.com\/keio.jp\/minoue-eng\/home\"> Masaki Inoue<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">2018<\/h2>\n\n\n\n<p><strong><a href=\"https:\/\/sites.google.com\/view\/iperc-sawada-lab\">\u6fa4\u7530\u8ce2\u6cbb\u51c6\u6559\u6388 (\u96fb\u6c17\u901a\u4fe1\u5927\u5b66)<\/a><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u984c\u76ee<\/strong>\uff1a\u5236\u5fa1\u5de5\u5b66\u3067\u5236\u5fa1\u30b7\u30b9\u30c6\u30e0\u306e\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u306f\u5b88\u308c\u308b\u306e\u304b\uff1f<\/li>\n\n\n\n<li><strong>\u65e5\u6642<\/strong>\uff1a2018.6.27<\/li>\n\n\n\n<li><strong>\u5834\u6240<\/strong>\uff1a\u30bb\u30df\u30ca\u30fc\u30eb\u30fc\u30e0\uff11<\/li>\n\n\n\n<li><strong>\u6982\u8981<\/strong>\uff1a2010\u5e74\u306e\u30a4\u30e9\u30f3\u6838\u71c3\u6599\u65bd\u8a2d\u306eStuxnet\u611f\u67d3\u4e8b\u4ef6\u304b\u3089\u59cb\u307e\u308a\uff0c2017\u5e74\u306e\u30e9\u30f3\u30b5\u30e0\u30a6\u30a7\u30a2\u5927\u898f\u6a21\u611f\u67d3\u306f\u300c\u5236\u5fa1\u30b7\u30b9\u30c6\u30e0\u304c\u60aa\u610f\u3042\u308b\u653b\u6483\u8005\u306b\u72d9\u308f\u308c\u3066\u3044\u308b\u3053\u3068\u300d\u304c\u4e8b\u5b9f\u3067\u3042\u308b\u3053\u3068\u4e16\u754c\u306b\u5f37\u70c8\u306b\u5370\u8c61\u4ed8\u3051\u305f\uff0e\u672c\u8b1b\u7fa9\u3067\u306f\uff0c\u4e16\u754c\u304c\u5236\u5fa1\u30b7\u30b9\u30c6\u30e0\u306e\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u89e3\u6c7a\u306b\u82e6\u52b4\u3057\u3066\u3044\u308b\u72b6\u6cc1\u3068\u305d\u306e\u7406\u7531\u3092\u89e3\u8aac\u3057\u3064\u3064\uff0c\u8b1b\u6f14\u8005\u304c\u95a2\u308f\u308b\u5236\u5fa1\u30b7\u30b9\u30c6\u30e0\u306e\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u6280\u8853\u52d5\u5411\u3092\u89e3\u8aac\u3059\u308b\u4e88\u5b9a\u3067\u3042\u308b\uff0e<\/li>\n\n\n\n<li><strong>\u7565\u6b74<\/strong>\uff1a2009\u5e74\u5927\u962a\u5927\u5b66\u5927\u5b66\u9662\u5de5\u5b66\u7814\u7a76\u79d1\u6a5f\u68b0\u5de5\u5b66\u5c02\u653b\u535a\u58eb\u5f8c\u671f\u8ab2\u7a0b\u4fee\u4e86\uff0e\u540c\u5e74\u96fb\u6c17\u901a\u4fe1\u5927\u5b66\u30b7\u30b9\u30c6\u30e0\u5de5\u5b66\u79d1\u52a9\u6559\uff0c22015\u5e74\u540c\u5927\u5b66 i-\u30d1\u30ef\u30fc\u30c9\u30a8\u30cd\u30eb\u30ae\u30fc\u30fb\u30b7\u30b9\u30c6\u30e0\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u51c6\u6559\u6388\u3068\u306a\u308a\u73fe\u5728\u306b\u81f3\u308b\uff0e\u535a\u58eb\uff08\u5de5\u5b66\uff09\uff0e\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u30b7\u30b9\u30c6\u30e0\u3084\u5236\u5fa1\u7cfb\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u306b\u95a2\u3059\u308b\u7814\u7a76\u306b\u5f93\u4e8b\uff0e2016\u5e74\u3088\u308a\u5236\u5fa1\u30b7\u30b9\u30c6\u30e0\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3\u30bb\u30f3\u30bf\u30fc\u9867\u554f\uff0e<\/li>\n\n\n\n<li><strong>\u4e16\u8a71\u4eba\uff1a<\/strong><a href=\"https:\/\/sites.google.com\/keio.jp\/minoue-eng\/home\" data-type=\"URL\" data-id=\"https:\/\/sites.google.com\/keio.jp\/minoue-eng\/home\">Masaki Inoue<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>2024 \u67a1\u7530\u771f\u5948\u6c0f\uff20\u60c5\u5831\u5de5\u5b66\u79d1D2 \u6749 &hellip; <\/p>\n","protected":false},"author":34,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-5790","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.appi.keio.ac.jp\/index.php?rest_route=\/wp\/v2\/pages\/5790","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.appi.keio.ac.jp\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.appi.keio.ac.jp\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.appi.keio.ac.jp\/index.php?rest_route=\/wp\/v2\/users\/34"}],"replies":[{"embeddable":true,"href":"https:\/\/www.appi.keio.ac.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5790"}],"version-history":[{"count":27,"href":"https:\/\/www.appi.keio.ac.jp\/index.php?rest_route=\/wp\/v2\/pages\/5790\/revisions"}],"predecessor-version":[{"id":6529,"href":"https:\/\/www.appi.keio.ac.jp\/index.php?rest_route=\/wp\/v2\/pages\/5790\/revisions\/6529"}],"wp:attachment":[{"href":"https:\/\/www.appi.keio.ac.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5790"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}