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Ãø¼Ô | ¡ûÃæÀî ¶©¹° (Ĺ²¬µ»½Ñ²Ê³ØÂç³Ø) |
¥Ú¡¼¥¸ | pp. 1 - 6 |
¥¡¼¥ï¡¼¥É | ǾÇÈ, ´¶À, °ûÎÁ, ¥Õ¥é¥¯¥¿¥ë, ·×¬ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Ëܸ¦µæ¤Ç¤Ï¡¢°ûÎÁ¤Î¹á¤ê¤òÓ̤°¡¢°ûÎÁ¤ò°û¤à¤È¤¤¤Ã¤¿¥¿¥¹¥¯¤ò²Ý¤·¤Æ¤¤¤ë¤È¤¤ÎǾÇȤò·×¬¤·¥Õ¥é¥¯¥¿¥ë¼¡¸µ¤Ë´ð¤Å¤¤¤¿´¶À²òÀϤò¹Ô¤¦¤³¤È¤Ç¡¢°ûÎÁ¤Î¹á¤ê¤äÌ£¤¬¿Í¤Î´¶À¤Ë¤É¤Î¤è¤¦¤Ê±Æ¶Á¤òÍ¿¤¨¤Æ¤¤¤ë¤Î¤«¤ò²òÌÀ¤¹¤ë¤³¤È¤òÌÜŪ¤È¤¹¤ë¡£¶ñÂÎŪ¤Ë¤Ï¡¢Ç¾ÇÈ¿®¹æ¤ÎÇÈ·Á¤ÎÊ£»¨À¤ò¥Õ¥é¥¯¥¿¥ë²òÀϤˤè¤êÄêÎ̲½¤·¡¢¤½¤Î»þ¶õ´ÖÆÃÀ¤«¤é´¶À¡Ê¹¬Ê¡´¶¡¢¥ê¥é¥Ã¥¯¥¹´¶¡¢¥»¥ì¥Ö´¶¡¢¤È¤¤á¤´¶¡¢¥ê¥Õ¥ì¥Ã¥·¥å´¶¡Ë¤òÄêÎ̲½¤¹¤ë¤³¤È¤Ë¤è¤ê¡¢°ûÎÁ¤ò°û¤ó¤Ç¤¤¤ë»þ¤Î»þ¡¹¹ï¡¹¤ÈÊÑÆ°¤¹¤ë´¶À¾õÂ֤η׬¤ò¼Â¸½¤¹¤ë¡£ |
Âê̾ | ¾ö¤ß¹þ¤ß¥Ë¥å¡¼¥é¥ë¥Í¥Ã¥È¥ï¡¼¥¯¤òÍøÍѤ·¤¿¸÷ÅÅÍÆÀÑÌ®ÇȤ«¤é¤Î±¿Æ°»þ¿´Çï¿äÄê¼êË¡ |
Ãø¼Ô | ¡ýÃæ¼ ¸øÚö, ×¢ËÜ ÀµÇ·, º´Æ£ ¹â»Ë (µþÅÔÂç³Ø Âç³Ø±¡¾ðÊó³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 7 - 12 |
¥¡¼¥ï¡¼¥É | ¸÷ÅÅÍÆÀÑÌ®ÇÈ, ¿´Çï¿ô¿äÄê, ¾ö¤ß¹þ¤ß¥Ë¥å¡¼¥é¥ë¥Í¥Ã¥È¥ï¡¼¥¯, ±¿Æ°»þ |
Âê̾ | ÎÙÀܲèÁÇ´Ö¤ÎÎà»÷À¤Ë´ð¤Å¤¯º¹Ê¬ÃͤòÍѤ¤¤¿²ÄµÕ¾ðÊóËä¹þ¤ßË¡ |
Ãø¼Ô | ¡ýÀ¥¸ý ÍÛÂÀ (ÀéÍÕÂç³ØÂç³Ø±¡ Í»¹çÍý¹©³ØÉÜ), º£Àô ¾Í»Ò (ÀéÍÕÂç³ØÂç³Ø±¡ ¹©³Ø¸¦µæ±¡) |
¥Ú¡¼¥¸ | pp. 13 - 18 |
¥¡¼¥ï¡¼¥É | ²ÄµÕ¾ðÊóËä¹þ¤ßË¡, °åÍѲèÁü, ¥Ò¥¹¥È¥°¥é¥à°ÜÆ° |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ËܹƤǤϡ¤°åÍѲèÁü¤òÂоݤȤ·¤¿º¹Ê¬ÃͤòÍѤ¤¤¿²ÄµÕ¾ðÊóËä¹þ¤ßË¡¤òÄó°Æ¤¹¤ë¡¥Äó°ÆË¡¤Ï¡¤Ê¬³ä¤·¤¿¥Ö¥í¥Ã¥¯¤´¤È¤ËÎÙÀܲèÁǤκ¹Ê¬Ãͤò»»½Ð¤¹¤ë¤³¤È¤Ç¡¤Â礤ÊÊФê¤ò¤â¤Äº¹Ê¬ÃͤΥҥ¹¥È¥°¥é¥à¤¬ÆÀ¤é¤ì¤ë¡¥ÆÀ¤é¤ì¤¿¥Ò¥¹¥È¥°¥é¥à¤ËÂФ·¤Æ¡¤¥Ò¥¹¥È¥°¥é¥à°ÜÆ°¤Ë¤è¤ë²ÄµÕ¾ðÊóËä¹þ¤ß¼êË¡¤òŬÍѤ¹¤ë¤³¤È¤Ç¡¤¾ðÊóËä¹þ¤ß²èÁü¤Î²è¼Á¤ÎÄã²¼¤òÍÞÀ©¤·¤Ä¤Ä¡¤½¾ÍèË¡¤è¤ê¤âËä¹þ¤ß²ÄǽÍÆÎ̤ò¸þ¾å¤¹¤ë¤³¤È¤¬¤Ç¤¤ë¡¥¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¤è¤ê¡¤Ëä¹þ¤ß²ÄǽÍÆÎ̤θþ¾å¤È¾ðÊóËä¹þ¤ß²èÁü¤Î²è¼Á¤ÎÄã²¼ÍÞÀ©¤ò³Îǧ¤·¤¿¡¥ |
Âê̾ | ¥Ï¡¼¥Õ¥È¡¼¥ó½èÍý¤òÍѤ¤¤¿Æÿ§°õºþ¤Î¤¿¤á¤Î²ÄµÕ¾ðÊóËä¹þ¤ßË¡ |
Ãø¼Ô | ¡ý¾åÅÄ ÈþÆä (ÀéÍÕÂç³ØÂç³Ø±¡ Í»¹çÍý¹©³ØÉÜ), º£Àô ¾Í»Ò (ÀéÍÕÂç³ØÂç³Ø±¡ ¹©³Ø¸¦µæ±¡) |
¥Ú¡¼¥¸ | pp. 19 - 24 |
¥¡¼¥ï¡¼¥É | Æÿ§°õºþ, ²ÄµÕ¾ðÊóËä¹þ¤ß, ¥Ï¡¼¥Õ¥È¡¼¥ó |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ËܹƤǤϡ¤¼«Á³²èÁü¤òÂоݤȤ·¤¿Æÿ§°õºþ¤Ë¤ª¤¤¤Æ¡¤Æÿ§ÈDzèÁü¤ò¥«¥é¡¼ÈDzèÁü¤Ø²ÄµÕ¤ËËä¤á¹þ¤à¼êË¡¤òÄó°Æ¤¹¤ë¡¥Äó°ÆË¡¤Ï¡¤¤Þ¤º¡¤Æÿ§ÈDzèÁü¤ò¥Ï¡¼¥Õ¥È¡¼¥ó½èÍý¤Ë¤è¤ê2 ÃͲèÁü¤ØÊÑ´¹¤·¤¿¸å¡¤¤³¤ì¤ËÂФ·¤ÆJBIG2 ¤òÍѤ¤¤Æ²ÄµÕ°µ½Ì¤ò»Ü¤¹¡¥¼¡¤Ë¡¤°µ½Ì¤µ¤ì¤¿Æÿ§ÈDzèÁü¤Î¾ðÊó¤ò¡¤¥«¥é¡¼ÈDzèÁü¤Ë²ÄµÕ¤ËËä¤á¹þ¤à¡¥¤³¤ì¤Ë¤è¤ê¡¤1 Ëç¤Î²èÁü¤Ç¡¤CMYK ¥¤¥ó¥¯¤Î¤ß¤òÍѤ¤¤¿Ä̾ï°õºþ¤ÈÆÿ§¥¤¥ó¥¯¤òÍѤ¤¤¿Æÿ§°õºþ¤ò¤È¤â¤Ë¼Â¸½¤¹¤ë¡¥Äó°ÆË¡¤Ï¡¤¥Ï¡¼¥Õ¥È¡¼¥ó½èÍý¤òÍѤ¤¤¿µ¼»÷Ãæ´ÖĴɽ¸½¤Ë¤è¤ê¡¤Â¿³¬Ä´É½¸½¤«¤Ä¾¯¤Ê¤¤¾ðÊóÎ̤ÎÆÿ§ÈDzèÁü¤òÀ¸À®¤·¡¤Ëä¹þ¤ß²èÁü¤Î²è¼ÁÄã²¼¤òÍÞÀ©¤¹¤ë¤³¤È¤¬¤Ç¤¤ë¡¥¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¤è¤ê¡¤À¸À®¤µ¤ì¤¿2 ÃͤÎÆÿ§ÈDzèÁü¤Î¾ðÊóÎ̤òɾ²Á¤¹¤ë¤È¤È¤â¤Ë¡¤Ëä¹þ¤ß¸å¤Î²èÁü¤Î²è¼ÁÄã²¼¤¬ÍÞÀ©¤µ¤ì¤Æ¤¤¤ë¤³¤È¤ò³Îǧ¤·¤¿¡¥ |
Âê̾ | Rabin°Å¹æ¤òÆâÊñ¤·¤¿¥«¥ª¥¹¥¹¥È¥ê¡¼¥à°Å¹æ¤Ë´Ø¤¹¤ë¸¦µæ |
Ãø¼Ô | ¡ýº´Æ£ ÂÀ°ì, ¿·°æ µ®»Ë (ÌÀ¼£Âç³ØÍý¹©³Ø¸¦µæ²ÊÅŵ¤¹©³ØÀ칶Çî»ÎÁ°´ü²ÝÄø), ³ùÅÄ ¹°Ç· (ÌÀ¼£Âç³ØÍý¹©³ØÉôÅŵ¤ÅÅ»ÒÀ¸Ì¿³Ø²Ê) |
¥Ú¡¼¥¸ | pp. 25 - 29 |
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Âê̾ | (¾·ÂÔ¹Ö±é) ²»¸»Ê¬Î¥¤Ë¤ª¤±¤ëICA¤ÈNMF¤Î³ÈÄ¥¤ÈÍ»¹ç |
Ãø¼Ô | ¡ûß·ÅÄ ¹¨ (NTT¥³¥ß¥å¥Ë¥±¡¼¥·¥ç¥ó²Ê³Ø´ðÁø¦µæ½ê) |
¥Ú¡¼¥¸ | p. 30 |
¥¡¼¥ï¡¼¥É | ¥Ö¥é¥¤¥ó¥É²»¸»Ê¬Î¥, ÆÈΩÀ®Ê¬Ê¬ÀÏ, ÈóÉéÃ͹ÔÎó°ø»Òʬ²ò, ÆÈΩÄã¥é¥ó¥¯¹ÔÎóʬÀÏ, »þ´Ö-¼þÇÈ¿ô-¥Á¥ã¥ó¥Í¥ë¤Î¥Æ¥ó¥½¥ëɽ¸½, Êä½õ´Ø¿ôË¡ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ÆÈΩÀ®Ê¬Ê¬ÀÏICA¤ÈÈóÉéÃ͹ÔÎó°ø»Òʬ²òNMF¤Ï¡¤ÊÌ¡¹¤Î¼êË¡¤È¤·¤Æ¹Í°Æ¤µ¤ì¡¤ÍÍ¡¹¤Ê¿®¹æ½èÍý¤ä¥Ç¡¼¥¿Ê¬ÀϤξõ¶·¤ÇÍѤ¤¤é¤ì¤Æ¤¤¤ë¡¥¼Â´Ä¶¤Ç¤Î²»¸»Ê¬Î¥¤òÌÜŪ¤È¤·¤¿¾ì¹ç¡¤¤³¤ì¤é¤òľÀÜÍѤ¤¤ë¤À¤±¤Ç¤ÏÉÔ½½Ê¬¤Ç¤¢¤ê¡¤³ÈÄ¥¤ò¤·¤Æ¤¤¤¯É¬Íפ¬¤¢¤Ã¤¿¡¥Ëֱܹé¤Ç¤Ï¡¤¤½¤ÎÌÜŪ¤Î¤¿¤á¤ËICA¤ÈNMF¤¬¤É¤Î¤è¤¦¤Ë³ÈÄ¥¤µ¤ì¤Æ¤¤¿¤«¤È¤¤¤¦¤³¤È¤È¡¤¤½¤ÎÀè¤ÎÍ»¹ç¤È¤·¤ÆÄó°Æ¤µ¤ì¤¿ÆÈΩÄã¥é¥ó¥¯¹ÔÎóʬÀÏILRMA¤ò¡¤Ê¬¤«¤ê¤ä¤¹¤¯ÀâÌÀ¤¹¤ë¡¥ |
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Ãø¼Ô | ÃÝÆâ Íø°ì (ÃÝÆâµ»½Ñ»Î»ö̳½ê) |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ËÜ¥»¥ß¥Ê¡¼¤Ç¤Ï¡¤µ»½ÑÄó°Æ½ñ¤òʬÀÏ¡¦À°Íý¤·¤Æ¡¤µºÜÆâÍƤò¸¡Æ¤¤¹¤ë¤³¤È¤Ç¡¤ÀâÆÀÎϤΤ¢¤ëµ»½ÑÄó°Æ½ñ¤òºîÀ®¤¹¤ëÊýË¡¤ò²òÀ⤷¤Þ¤¹¡¥¿½ÀÁ½ñ¤ÎÊ罸Í×¹à¤Ê¤É¤«¤éÍ×µá¹àÌܤò¸¡Æ¤¤·¤Æµ»½ÑÄó°Æ½ñ¤òºîÀ®¤·¡¤Äó°Æ¥Ý¥¤¥ó¥È¤ä¤Þ¤È¤áÊý¤ò³Ø¤Ó¤Þ¤¹¡¥ |
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Ãø¼Ô | °ÂÅÄ ·Ã°ìϺ (¼óÅÔÂç³ØÅìµþ) |
¥Ú¡¼¥¸ | pp. 31 - 36 |
¥¡¼¥ï¡¼¥É | ºÇŬ²½, ¥á¥¿¥Ò¥å¡¼¥ê¥¹¥Æ¥£¥¯¥¹, ¿ôÍý·×²èË¡, ¥·¥ß¥å¥ì¡¼¥·¥ç¥ó, ¥â¥Ç¥ê¥ó¥° |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ºÇŬ²½¤ÎʬÌî¤Ë¤ª¤¤¤Æ¤Ï¡¤1980ǯÂå¸åȾ¤Ë¡Ö¥á¥¿¥Ò¥å¡¼¥ê¥¹¥Æ¥£¥¯¥¹¡×¤È¸Æ¤Ð¤ì¤ë¿·¤¿¤Ê¥Ñ¥é¥À¥¤¥à¤¬À¸¤Þ¤ì¤¿¡£¿ô³Ø¤ò´ðÁäȤ·¤¿¤³¤ì¤Þ¤Ç¤ÎºÇŬ²½¼êË¡¤Ë¤Ï¤Ê¤¤¡¤Â¿Íͤʥ¢¥Ê¥í¥¸¡¼¤¬Â¸ºß¤¹¤ë¤È¤¤¤¦»Â¿·¤Ê¥¤¥ó¥Ñ¥¯¥È¤ò»ý¤Ä¥á¥¿¥Ò¥å¡¼¥ê¥¹¥Æ¥£¥¯¥¹¤Ï¡¤¤µ¤Þ¤¶¤Þ¤ÊʬÌî¤ËŬÍѤµ¤ì¡¤À®²Ì¤òµó¤²¤Æ¤¤¿¡£Ëֱܹé¤Ç¤Ï¡¤¥¢¥Ê¥í¥¸¡¼¤È¤¤¤¦»ëÅÀ¤Î¤ß¤Ê¤é¤ººÇŬ²½¼êË¡¤Î´ðËܹ½Â¤¤ËΩµÓ¤·¤¿¥¢¥ë¥´¥ê¥º¥à¤Î·ÏÅýŪ¤ÊʬÎࡦÀ°Íý¡¤¥á¥¿¥Ò¥å¡¼¥ê¥¹¥Æ¥£¥¯¥¹¤ÎÄêµÁ¤äÆÃħ¡¤¿ôÍý·×²èË¡¤È¤ÎÁê°ã¤ä¶áÀܺÇŬÀ¸¶Íý¤È¤Î´Ø·¸¤Ë´Ø¤¹¤ë¹Í»¡¤ò¹Ô¤Ã¤¿¾å¤Ç¡¤¥³¥ó¥Ô¥å¡¼¥¿¥Ñ¥ï¡¼¤ÎÈôÌöŪÁýÂç¤ä¥â¥Ç¥ê¥ó¥°¡¦¥·¥ß¥å¥ì¡¼¥·¥ç¥óµ»½Ñ¤Î¸þ¾å¤òƧ¤Þ¤¨¤¿¥á¥¿¥Ò¥å¡¼¥ê¥¹¥Æ¥£¥¯¥¹¤Î¾Íè¤òŸ˾¤¹¤ë¡£ |
Âê̾ | Í¥Îɲò½¸¹çõº÷ÌäÂê¤Ë¤ª¤±¤ëÍ¥±Û´Ø·¸¤Ë´ð¤Å¤¯Firefly Algorithm |
Ãø¼Ô | ¡ý²¦ ¹ãdz, Åļ ·ò°ì, ÅÚ²° ½ß°ì, °ÂÅÄ ·Ã°ìϺ (¼óÅÔÂç³ØÅìµþ) |
¥Ú¡¼¥¸ | pp. 37 - 42 |
¥¡¼¥ï¡¼¥É | Í¥Îɲò½¸¹çõº÷ÌäÂê, ¥á¥¿¥Ò¥å¡¼¥ê¥¹¥Æ¥£¥¯¥¹, Firefly Algorithm, Í¥±Û´Ø·¸ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ËÜÏÀʸ¤Ç¤Ï¡¤Í¥Îɲò½¸¹çõº÷ÌäÂ꤬ͤ¹¤ëÀ¼Á¤Ë¤Ä¤¤¤Æ²òÀϤ·¡¤Í¥Îɲò½¸¹çõº÷ÌäÂê¤È¿ÌÜŪºÇŬ²½ÌäÂê¤Î¹½Â¤Åª¤ÊÎà»÷ÅÀ¤ò»ØŦ¤¹¤ë¡£²òÀϤ·¤¿À¼Á¤òƧ¤Þ¤¨¡¤¥æ¡¼¥¶¡¼¤ÎÄêÎÌŪ¤Ê´õµá¿å½à¤òõº÷Àïά¤Ë³èÍѤ¹¤ë¤³¤È¤Ç¡¤Í¥Îɲò½¸¹ç¤òõº÷²Äǽ¤Ê¡ÖÍ¥±Û´Ø·¸¡×¤Ë´ð¤Å¤¯¿·¤¿¤ÊFirefly Algorithm¡ÊFA¡Ë¤òÄó°Æ¤¹¤ë¡£FA¤Ï¡¤ÌäÂê¶õ´Ö¤Îµ÷Î¥¤ò°ÜÆ°µ¡¹½¤ËƳÆþ¤¹¤ë¤³¤È¤ÇÊ£¿ô¤Î·²¤Ëʬ¤«¤ì¤ÆÊ£¿ô¤Î¶É½êŪºÇŬ²ò¤òõº÷²Äǽ¤È¤·¤Æ¤¤¤ë¡£FA¤Î»ý¤Ä¤³¤ÎÀ¼Á¤ò²òÀϤ¹¤ë¤³¤È¤Ç¡¤Í¥Îɲò½¸¹çõº÷ÌäÂê¤ÈFA¤Î¿ÆÏÂÀ¤òÌÀ¤é¤«¤Ë¤¹¤ë¡£¤½¤·¤Æ¡¤Í¥Îɲò½¸¹çõº÷ÌäÂê¤ÈFA¤Ë¤Ä¤¤¤Æ²òÀϤò¹Ô¤¤¡¤Îà»÷¤ÎÌäÂêÀßÄê¤È¹ç¤ï¤»¤Æ¤½¤ÎÀ¼Á¤Ë¤Ä¤¤¤ÆµÄÏÀ¤¹¤ë¡£¤½¤ì¤Ë´ð¤Å¤¡¤Í¥Îɲò½¸¹ç¤Î¤¿¤á¤Î¿·¤¿¤ÊºÇŬ²½¼êË¡¤È¤·¤Æ¡¤¡ÖÍ¥±Û´Ø·¸¡×¤Ë´ð¤Å¤¯FA¤òÄó°Æ¤¹¤ë¡£¤½¤·¤Æ¡¤Í¥Îɲò½¸¹çõº÷ÌäÂê¤òÂоݤ˿ôÃͼ¸³¤ò¹Ô¤¤¡¤Äó°Æ¼êË¡¤È½¾Íè¤ÎFA¤ÎÀǽ¤òÈæ³Ó¤·¤Ê¤¬¤é¡¤Äó°Æ¼êË¡¤ÎÍÍÑÀ¤ò¼¨¤¹¡£ |
Âê̾ | ¹ÔÎó¥Ù¥¯¥È¥ëÀѤÎÊÂÎ󥢥르¥ê¥º¥à¤Î¹çÀ® |
Ãø¼Ô | ¡ýµÜºä ¹¬Íº, Ruitao Gao, Æ£ÅÄ ¾»¹¨ (ÅìµþÂç³Ø) |
¥Ú¡¼¥¸ | pp. 43 - 48 |
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Ãø¼Ô | ¡ýÍû 屸µ, Åļ ·ò°ì, ÅÚ²° ½ß°ì, °ÂÅÄ ·Ã°ìϺ (¼óÅÔÂç³ØÅìµþ ¥·¥¹¥Æ¥à¥Ç¥¶¥¤¥ó¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 49 - 54 |
¥¡¼¥ï¡¼¥É | Áȹ礻ºÇŬ²½, ¥á¥¿¥Ò¥å¡¼¥ê¥¹¥Æ¥£¥¯¥¹, ¶áÀܺÇŬÀ¸¶Íý, ½¸Ãæ²½, ¿ÍͲ½ |
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¥Ú¡¼¥¸ | pp. 55 - 60 |
¥¡¼¥ï¡¼¥É | Áȹ礻ºÇŬ²½, ¥°¥é¥Õ¥¢¥ë¥´¥ê¥º¥à, ¶á»÷¥¢¥ë¥´¥ê¥º¥à |
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¥Ú¡¼¥¸ | p. 61 |
¥¡¼¥ï¡¼¥É | ¿¼Áسؽ¬, CNN, FPGA |
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¥Ú¡¼¥¸ | pp. 62 - 67 |
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¥Ú¡¼¥¸ | pp. 68 - 72 |
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¥Ú¡¼¥¸ | pp. 73 - 76 |
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¥Ú¡¼¥¸ | pp. 77 - 79 |
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¥Ú¡¼¥¸ | pp. 80 - 83 |
¥¡¼¥ï¡¼¥É | ¥°¥é¥ÕÍýÏÀ, Ãæ¿´À, ÅÁÈÂ¥â¥Ç¥ë |
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¥Ú¡¼¥¸ | pp. 84 - 89 |
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¥¢¥Ö¥¹¥È¥é¥¯¥È | Ëܸ¦µæ¤Ç¤Ï¥ï¡¼¥¯¥Õ¥í¡¼¥Í¥Ã¥È¤ÎÉôʬÎà»÷Å٤˾ÇÅÀ¤òÅö¤Æ¤Æ¤¤¤ë.¥¢¥×¥í¡¼¥Á¤È¤·¤ÆLevenshteinÊÔ½¸µ÷Î¥¤òÍѤ¤¤Æ¥×¥í¥»¥¹¥Ä¥ê¡¼¤Î¥ª¥Ú¥ì¡¼¥¿¡¼¤ò¹Íθ¤¹¤ë¤³¤È¤ÇÊÔ½¸µ÷Î¥¤¬µá¤á¤é¤ì¡¢Â¿¹à¼°»þ´Ö¤ÎÉôʬÎà»÷ÅÙ¤òµá¤á¤ëÊýË¡¤òÄó°Æ¤·¤¿.¤Þ¤¿¡¢¥×¥í¥»¥¹¥Ä¥ê¡¼¤Çɽ¸½¤Ç¤¤ë¥¯¥é¥¹¤Ë¸ÂÄꤹ¤ë¤³¤È¤Ë¤è¤ê¡¢¥ï¡¼¥¯¥Õ¥í¡¼¥Í¥Ã¥È¤ÎÉôʬÎà»÷Å٤Ȥ½¤ÎºÇÂçÃͤò»ý¤ÄÎà»÷Éôʬ¤òÃê½Ð¤¹¤ë¤³¤È¤¬²Äǽ¤Ç¤¢¤ë.¤½¤·¤Æ¡¢ËÜ¥¢¥×¥í¡¼¥Á¤Î͸úÀ¤ò¼¨¤¹¤¿¤á¤Ë¾¤ÎÊýË¡¤È¤Îɾ²Á¤ò¹Ô¤Ã¤¿. |
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Ãø¼Ô | ¡ý¾®»³ ¹ÀÌï (ÌÀ¼£Âç³ØÂç³Ø±¡Íý¹©³Ø¸¦µæ²ÊÅŵ¤¹©³ØÀ칶), ³ùÅÄ ¹°Ç· (ÌÀ¼£Âç³ØÍý¹©³ØÉôÅŵ¤ÅÅ»ÒÀ¸Ì¿³Ø²Ê) |
¥Ú¡¼¥¸ | pp. 90 - 95 |
¥¡¼¥ï¡¼¥É | ÈóÀþ·Á²òÀÏ, ¥Ñ¡¼¥·¥¹¥Æ¥ó¥È¡¦¥Û¥â¥í¥¸¡¼·² |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ǾÇȤä³ô²ÁÊÑÆ°¤Ê¤É¡¤»þ´Ö¤È¤È¤â¤ËÊ£»¨¤ËÊÑÆ°¤¹¤ë¿®¹æ¤ËÂФ·¤Æ¡¤ÈóÀþ·ÁÍýÏÀ¤«¤é¤Î¥¢¥×¥í¡¼¥Á¤òÍѤ¤¤Æ²òÀϤò¤¹¤ë¸¦µæ¤¬¸½ºß¿Ê¤á¤é¤ì¤Æ¤¤¤ë¡¥¤³¤Î²òÀϤǤϡ¤ÃÙ¤ì»þ´Ö¤ÈËä¤á¹þ¤ß¼¡¸µ¤È¤¤¤¦2¤Ä¤Î¥Ñ¥é¥á¡¼¥¿¤ò¼Â¥Ç¡¼¥¿¤«¤é¿äÄꤷ¡¤¥¢¥È¥é¥¯¥¿¤È¸Æ¤Ð¤ì¤ë¥Õ¥í¡¼¤òºÆ¹½À®¤¹¤ëɬÍפ¬¤¢¤ë¡¥¸½ºß¤è¤¯ÍѤ¤¤é¤ì¤Æ¤¤¤ëÃÙ¤ì»þ´Ö¤Î¿äÄê¼êË¡¤Ï»þ·ÏÎó¿®¹æ¤ÎÁê´Ø¤Ë¾ÇÅÀ¤òÅö¤Æ¤¿¤â¤Î¤Ç¤¢¤ë¤¬¤³¤ì¤Ï¥¢¥È¥é¥¯¥¿¤Î´ö²¿³ØŪ¤Ê·Á¾õ¤Ë¤Ä¤¤¤Æ¤Ï¹Í褵¤ì¤Æ¤¤¤Ê¤¤¡¥ËܹƤǤϡ¤¥Ñ¡¼¥·¥¹¥Æ¥ó¥È¡¦¥Û¥â¥í¥¸¡¼·²¤È¤¤¤¦¥Ç¡¼¥¿¤Î¡Ö·Á¡×¤òÆÃħÉÕ¤±¤ë¼êË¡¤òÍѤ¤¤Æ¡¤¥¢¥È¥é¥¯¥¿¤Î´ö²¿³ØŪ·Á¾õ¤Ë¹Íθ¤·¤¿¿·¤¿¤ÊÃÙ¤ì»þ´Ö¤Î¿äÄê¼êË¡¤Ë¤Ä¤¤¤Æ¸¡Æ¤¤·¡¤¤½¤Îɾ²Á¤ò¹Ô¤¦¡¥ |
2019ǯ8·î23Æü(¶â) |
Âê̾ | ¥³¡¼¥¸¥§¥Í¥ì¡¼¥·¥ç¥óÀßÈ÷¤òÂоݤȤ¹¤ëÊ£¿ô¥»¥ó¥µ¤Ë¤è¤ë°Û¾ï¸¡ÃÎË¡ |
Ãø¼Ô | ¡ý³°ÅÄ æû (³ô¼°²ñ¼ÒÌÀÅżË) |
¥Ú¡¼¥¸ | pp. 96 - 98 |
¥¡¼¥ï¡¼¥É | ¿®¹æ½èÍý, ȯÅÅÀßÈ÷, °Û¾ï¸¡ÃÎ |
Âê̾ | Suppressing Racial Bias in Image Recognition via Domain Adaptation |
Ãø¼Ô | ¡ýAntonio Tejero-de-Pablos (The University of Tokyo), ¸¶ÅÄ Ã£Ìé (The University of Tokyo/RIKEN) |
¥Ú¡¼¥¸ | pp. 99 - 104 |
¥¡¼¥ï¡¼¥É | Race-biased datasets, Domain adaptation, Racist computer vision, Image feature distribution, Adapted encoder |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Since the emergence of deep learning, the number of autonomous systems integrated in our society has increased enormously. Deep learning methods are able to leverage huge amount of training data in order to learn how to extract the features that result in highest accuracies. Computer vision applications such as face recognition have been greatly benefited from deep learning. However, this dependency on the training data causes failures when the dataset is biased, that is, some members of the population are less likely to be included than others. For example, cases of face misclassification for minority groups (i.e., black people in the USA) have been already reported. While such misjudgment may not have a great impact in the overall performance of the method, it can lead to unethical situations such as racism and sexism. This paper tackles the recently-emerged problem of racist computer vision systems. We propose a domain adaptation methodology in order to adapt the features extracted for one majority racial group to other underrepresented groups. Our experimental results show the validity of our approach, opening a path for future research towards racial bias-free computer vision. |
Âê̾ | A Study for Designing Elderly People Assistive Robot |
Ãø¼Ô | ¡ýYihsin Ho (Takushoku University) |
¥Ú¡¼¥¸ | pp. 105 - 110 |
¥¡¼¥ï¡¼¥É | Data Analysis, Assistive robot |
Âê̾ | ʬ³ä¦ÂÓÇȤε÷Î¥¾ÇÅÀ·ÁÀ®¤Ë¤è¤ë¥Ñ¥é¥á¥È¥ê¥Ã¥¯¥¢¥ì¡¼¥¹¥Ô¡¼¥«¶á˵ºÆÀ¸¤Î¸¡Æ¤ |
Ãø¼Ô | ¡ý²¼Êý À¿ (Ω̿´ÛÂç³ØÂç³Ø±¡), Ã滳 ²í¿Í (Âçºå»º¶ÈÂç³Ø/Ω̿´ÛÂç³Ø), À¾±º ·É¿® (Ω̿´ÛÂç³ØÂç³Ø±¡) |
¥Ú¡¼¥¸ | pp. 111 - 116 |
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¥Ú¡¼¥¸ | pp. 117 - 122 |
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Ãø¼Ô | ¡ý¾¾±Ê ·ý, Æ«»³ ·ò¿Î (ÅìµþÅŵ¡Âç³Ø/¹©³ØÉôÅŵ¤ÅŻҹ©³Ø²Ê) |
¥Ú¡¼¥¸ | pp. 123 - 128 |
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Ãø¼Ô | ¡ýË̸¶ ÂçÃÏ, ¿¹Àî ÐÒÆà, Ê¿ÎÓ ¹¸ (Ω̿´ÛÂç³Ø ¾ðÊóÍý¹©³ØÉô), µÈÀî ±É°ì (±§Ãè¹Ò¶õ¸¦µæ³«È¯µ¡¹½ ¹Ò¶õµ»½ÑÉôÌç), µÆÃÓ Çî»Ë (Åŵ¤ÄÌ¿®Âç³Ø ±§Ã衦Åż§´Ä¶¸¦µæ¥»¥ó¥¿¡¼), µíÈø ÃÎͺ (¼óÅÔÂç³ØÅìµþ ¹Ò¶õ±§Ã襷¥¹¥Æ¥à¹©³Ø°è) |
¥Ú¡¼¥¸ | pp. 129 - 134 |
¥¡¼¥ï¡¼¥É | ¥Õ¥§¡¼¥º¥É¥¢¥ì¥¤µ¤¾Ý¥ì¡¼¥À, ÅÅÎÏ¥¹¥Ú¥¯¥È¥ëÌ©ÅÙ, ¥Ô¥ê¥ª¥É¥°¥é¥à, MAP¿äÄê, EM¥¢¥ë¥´¥ê¥º¥à |
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Âê̾ | ¥Þ¥ë¥Á¥Ñ¥é¥á¡¼¥¿¥Õ¥§¡¼¥º¥É¥¢¥ì¥¤µ¤¾Ý¥ì¡¼¥À¤Ø¤ÎMMSE¥Ó¡¼¥à¥Õ¥©¡¼¥ß¥ó¥°Å¬ÍÑ·ë²Ì |
Ãø¼Ô | ¡ýÂìß· ľÌé, ÆâÅÄ É¢Ê¿ (¼óÅÔÂç³ØÅìµþ), µÈÀî ±É°ì (±§Ãè¹Ò¶õ¸¦µæ³«È¯µ¡¹½), µÆÃÓ Çî»Ë (Åŵ¤ÄÌ¿®Âç³Ø), ºÊ¼¯ ͧ¾¼, µíÈø ÃÎͺ (¼óÅÔÂç³ØÅìµþ) |
¥Ú¡¼¥¸ | pp. 135 - 137 |
¥¡¼¥ï¡¼¥É | ¥Õ¥§¡¼¥º¥É¥¢¥ì¥¤µ¤¾Ý¥ì¡¼¥À, ¥¢¥ì¥¤¿®¹æ½èÍý, Ŭ±þ¿®¹æ½èÍý |
¥¢¥Ö¥¹¥È¥é¥¯¥È | µ¤¾ÝºÒ³²¤Îͽ¬ÀºÅ٤θþ¾å¤ÏɬÍ×ÉԲķç¤Ç¤¢¤ë¡¥¤·¤«¤·, ½¾Íè¤Îµ¤¾Ý¥ì¡¼¥À¤Ï»þ´Ö¡¦¶õ´Öʬ²òǽ¤ÎÉÔ¤ˤè¤ê¥·¥Ó¥¢¸½¾Ý¤Î´Ñ¬¤ËÉÔ¸þ¤¤Ç¤¢¤ë¤¿¤á, ¥Þ¥ë¥Á¥Ñ¥é¥á¡¼¥¿¥Õ¥§¡¼¥º¥É¥¢¥ì¥¤µ¤¾Ý¥ì¡¼¥À(MP-PAWR)¤¬³«È¯¤µ¤ì¤¿¡¥MP-PAWR¤Ï, ¶Ä³ÑÊý¸þ¤«¤é¤Î¼õ¿®¿®¹æ¤ËÂФ·¤ÆDigital Beam Forming(DBF)½èÍý¤ò¹Ô¤¦¤³¤È¤Ç¹â»þ´Öʬ²òǽ¤ò¼Â¸½¤·¤Æ¤¤¤ë¡¥½¾ÍèDBF½èÍý¼êË¡¤Ë¤ÏŬÍѤ¬Íưפʥա¼¥ê¥¨¥Ó¡¼¥à¥Õ¥©¡¼¥ÞË¡¡ÊFRË¡)¤¬ºÎÍѤµ¤ì¤Æ¤¤¿¤¬, FRË¡¤Ï´Ñ¬ÎΰèÆâ¤Ë¥Ó¥ëÅù¤Î¾ã³²Êª(¥°¥é¥ó¥É¥¯¥é¥Ã¥¿)¤¬¤¢¤ë¾ì¹ç, ¥¢¥ó¥Æ¥Ê¥µ¥¤¥É¥í¡¼¥Ö¤Î±Æ¶Á¤ò¼õ¤±¤ä¤¹¤¤ÌäÂ꤬¤¢¤ë¡¥¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¿·¤¿¤ËºÇ¾®Æó¾èÊ¿¶Ñ¸íº¹Ë¡(MMSEË¡)¤Ë¤è¤ëDBF½èÍý¤òMP-PAWR¤ËŬÍѤ·¤¿. ·ë²Ì¤È¤·¤Æ, MMSEË¡¤ÏFRË¡¤ÈÈæ³Ó¤·¤Æ10-20[dB]ÄøÅÙ¥µ¥¤¥É¥í¡¼¥Ö¤òÍÞ°µ¤Ç¤, ¥¯¥é¥Ã¥¿¤Î±Æ¶Á¤òÄ㸺¤¹¤ë¤³¤È¤¬¤Ç¤¤¿. |
Âê̾ | Comb Imaging on a Phased Array Weather Radar |
Ãø¼Ô | ¡ûEiichi Yoshikawa (Japan Aerospace Exploration Agency), Tomoo Ushio (Tokyo Metropolitan University) |
¥Ú¡¼¥¸ | pp. 138 - 141 |
¥¡¼¥ï¡¼¥É | ¥Õ¥§¡¼¥º¥É¥¢¥ì¥¤, µ¤¾Ý¥ì¡¼¥À, ¥³¥à¥Ó¡¼¥à |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ¶áǯ³«È¯¤µ¤ì¤¿¥Õ¥§¡¼¥º¥É¥¢¥ì¡¼µ¤¾Ý¥ì¡¼¥À¤Ï¡¢£³£°ÉäȤ¤¤¦¶Ë¤á¤Æû»þ´Ö¤Ç¤ÎÁ´Å·¥¹¥¥ã¥ó¤ò¼Â¸½¤·¡¢Îµ´¬¤äÆÍÉ÷¤¬¤â¤¿¤é¤¹Èï³²¤ò·Ú¸º¤¹¤ë¤¿¤á¤ËɬÍ×ÉԲķç¤Ê´Ñ¬¾ðÊó¤òÆÀ¤é¤ì¤ë¤È¤¤¤¦ÅÀ¤Ç¡¢ÃíÌܤòÍá¤Ó¤Æ¤¤¤ë¡£½¾Íè¤Îµ¤¾Ý¥ì¡¼¥À¤¬¶¹³Ñ¤Î¥Ó¡¼¥à¤òÍѤ¤¤Æ°ìÊý¸þ¤º¤Ä´Ñ¬¤¹¤ë¤Î¤ËÂФ·¤Æ¡¢¥Õ¥§¡¼¥º¥É¥¢¥ì¥¤µ¤¾Ý¥ì¡¼¥À¤Ï¥Õ¥¡¥ó¡Ê¹³Ñ¡Ë¥Ó¡¼¥à¤òÍѤ¤¤ÆÊ£¿ôÊý¸þ¤òƱ»þ¤Ë´Ñ¬¤¹¤ë¤³¤È¤Çû»þ´Ö´Ñ¬¤ò¼Â¸½¤·¤Æ¤¤¤ë¡£¤·¤«¤·¤Ê¤¬¤é¡¢¥Õ¥¡¥ó¡Ê¹³Ñ¡Ë¥Ó¡¼¥à»ÈÍѤ¹¤ë¤³¤È¤Ë¤è¤ë´Ñ¬¸íº¹¤¬Â礤¤¾ì¹ç¤¬¤¢¤ë¤³¤È¤¬»ØŦ¤µ¤ì¤Æ¤¤¤ë¡£Ëܹà¤Ç¤Ï¥Õ¥¡¥ó¥Ó¡¼¥à¤ÎÂå¤ï¤ê¤Ë¡¢¶û¾õ¤Î¥Ó¡¼¥à¤ò°ÕÌ£¤¹¤ë¥³¥à¥Ó¡¼¥à¤òÍѤ¤¤ë¤³¤È¤Ë¤è¤Ã¤Æ¡¢´Ñ¬ÀºÅÙ¤¬²þÁ±¤µ¤ì¤ë¤³¤È¤ò¿ôÃÍ¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¤è¤Ã¤Æ¼¨¤·¤¿¡£ |
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¥Ú¡¼¥¸ | pp. 142 - 145 |
¥¡¼¥ï¡¼¥É | ¥Ç¥£¥¸¥¿¥ë¥Õ¥£¥ë¥¿, ¥Õ¥£¥ë¥¿ºÇŬ²½GA |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ËÜÏÀʸ¤Ç¤Ï¡¤Ê£¿ô½¸ÃÄGA¤òÍѤ¤¤¿CSD (Canonic Signed Digit) ·¸¿ôFIR (Finite Impulse Response) ¥Õ¥£¥ë¥¿¤ÎÀ߷פˤĤ¤¤Æ¸¡Æ¤¤¹¤ë¡¥ CSD·¸¿ôFIR¥Õ¥£¥ë¥¿¤ÎÀß·×Ë¡¤È¤·¤Æ¡¤ACO (Ant Colony Optimization) ¤Ë¤è¤ë Àß·×Ë¡¤¬Í¸ú¤Ç¤¢¤ë¤³¤È¤¬ÃΤé¤ì¤Æ¤¤¤ë¤¬¡¤ACO¤Ï¶áÀܺÇŬÀ¸¶Íý¤Ë´ð¤Å¤¯Ãµ º÷¤ò¹Ô¤Ê¤¦¤¿¤á¡¤¿¶ÉýÆÃÀ¤Î³µ·Á¤ò·è¤á¤ëÈæ³ÓŪÂ礤¤Ãͤò¤â¤Ä¥Õ¥£¥ë¥¿·¸¿ô ¤Î¤ß¤¬Í¥ÀèŪ¤Ëõº÷¤µ¤ì¤ë¡¥¤·¤«¤·¡¤¹â¼¡¿ô¥Õ¥£¥ë¥¿¤Î¾ì¹ç¤ÏÃͤµ¤¤¥Õ¥£ ¥ë¥¿·¸¿ô¤¬±Ô¤¤¼×ÃÇÆÃÀ¤Î·ÁÀ®¤Ë½ÅÍפȤʤ뤿¤á¡¤·Ñ³Ū¤Êõº÷¤Î¿ÍͲ½¤¬µá ¤á¤é¤ì¤ë¡¥Ëܸ¦µæ¤Ç¤Ï¡¤ÍøÍѲÄǽÈóÎí·å¿ô¤Î°Û¤Ê¤ëÊ£¿ô¤Î½¸ÃĴ֤θòºµ¤ò¹Ô¤Ê ¤¦MPGA (Multiple Population GA) ¤òÍѤ¤¤ÆÀ߷פò¹Ô¤Ê¤¦¡¥Àß·×Îã¤è¤ê¡¤¹â¼¡ ¿ô¥Õ¥£¥ë¥¿À߷פǤÏMPGA¤¬ACO¤è¤ê͸ú¤Ç¤¢¤ë¤³¤È¤ò¼¨¤¹¡¥ |
Âê̾ | Ääα²óÈò¼êË¡¤Î¥Ñ¥é¥á¡¼¥¿¤¬IIR¥Õ¥£¥ë¥¿À߷פËÍ¿¤¨¤ë¸ú²Ì |
Ãø¼Ô | ¡ý¹âÀ¥ ͵Ìð, Æ«»³ ·ò¿Î (ÅìµþÅŵ¡Âç³Ø/¹©³ØÉôÅŵ¤ÅŻҹ©³Ø²Ê) |
¥Ú¡¼¥¸ | pp. 146 - 150 |
¥¡¼¥ï¡¼¥É | ¥Ç¥£¥¸¥¿¥ë¥Õ¥£¥ë¥¿, PSO |
¥¢¥Ö¥¹¥È¥é¥¯¥È | IIR¥Õ¥£¥ë¥¿¤ÎÌÜŪ´Ø¿ô¤Ï¿ÊöÀ´Ø¿ô¤Ç¤¢¤ê¡¤¶É½ê²ò¤¬Â¿¿ô¸ºß¤¹¤ë¤¿¤á¡¤IIR¥Õ¥£¥ë¥¿Àß·×ÌäÂê¤ÏÈóÀþ·ÁºÇŬ²½ÌäÂê¤Ç¤¢¤ë¡¥ ¤½¤³¤Ç¡¤PSO¤òÍѤ¤¤ÆÌÜŪ´Ø¿ô¤ÎÌäÂê¶õ´Ö¤òõº÷¤·¡¤¶É½ê²ò¤òÎóµó¤¹¤ë¡¥ PSO¤Ï¶É½ê²ò¤Ø¤Î¶¯¤¤»Ø¸þÀ¤ò¤â¤Á¡¤¹â®¤Ë²ò¸õÊä¤òÎóµó²Äǽ¤Ç¤¢¤ë¡¥ ¤·¤«¤·¡¤¤½¤Î¶¯¤¤½¸Ã沽ǽÎϤˤè¤Ã¤ÆÁá´ü¼ý«¤·Ääα¤¹¤ë·¹¸þ¤¬¤¢¤ë¡¥ Ääα¤òPSO¤ÎÆÃħ¤Î°ì¤Ä¤È¤·¤Æ¡¤Ääα²óÈò¤ò¹Ô¤Ê¤¤PSO¤Î½¸Ã沽ǽÎϤò³è¤«¤·¤¿Ääα²óÈò¼êË¡¤Î°ì¤Ä¤ËPSS-PSO¤¬Äó°Æ¤µ¤ì¤Æ¤¤¤ë¡¥ PSS-PSO¤Ç¤Ï½ê˾ÆÃÀ¤Î¥²¥¤¥ó¤òÊѹ¹¤¹¤ë¡¥ ¤½¤Î·ë²Ì¡¤ÌäÂê¶õ´Ö¤ò¿½Ì¤·Ääα²ò¤ÎÌÜŪ´Ø¿ôÃͤ¬ÊѲ½¤¹¤ë¤¿¤á¡¤¸ÄÂΤÏÊ̤ζɽê²ò¤Îõº÷¤ò¹Ô¤Ê¤¦¡¥ Ëܸ¦µæ¤Ç¤Ï¡¤PSS-PSO¤Î½ê˾ÆÃÀ¤Î¥²¥¤¥ó¤òÊѹ¹¤¹¤ë¥Ñ¥é¥á¡¼¥¿¤Ë¤Ä¤¤¤Æ¸¡¾Ú¤¹¤ë¡¥ |
Âê̾ | CSO¤òÍѤ¤¤¿·Ñ³Ū¤Êõº÷¤Ë¤è¤ëIIR¥Õ¥£¥ë¥¿Àß·× |
Ãø¼Ô | ¡ýÄ¥ÂØ ÍµÂÀ, Æ«»³ ·ò¿Î (ÅìµþÅŵ¡Âç³Ø/¹©³ØÉôÅŵ¤ÅŻҹ©³Ø²Ê) |
¥Ú¡¼¥¸ | pp. 151 - 155 |
¥¡¼¥ï¡¼¥É | ¥Ç¥£¥¸¥¿¥ë¥Õ¥£¥ë¥¿, ºÇŬ²½, ÈóÀþ·Á´Ø¿ô |
¥¢¥Ö¥¹¥È¥é¥¯¥È | PSO (Particle Swarm Optimization) ¤òÍѤ¤¤¿IIR¥Õ¥£¥ë¥¿¤ÎÀß·×Ë¡¤¬Äó°Æ¤µ¤ì¤Æ¤¤¤ë¡¥PSO¤Ï¹â®¤Ë²ò¸õÊä¤òÎóµó²Äǽ¤Ç¤¢¤ë¤¬¡¤¶É½ê²ò¤Ø¤Î»Ø¸þÀ¤Î¶¯¤µ¤Î¤¿¤á¡¤¶É½ê²òÄä᤬À¸¤¸¤ë·¹¸þ¤¬¤¢¤ë¡¥¤½¤Î¤¿¤á¡¤¶É½ê²òÄäα²óÈò¼êË¡¤¬Äó°Æ¤µ¤ì¤Æ¤¤¤ë¡¥¤·¤«¤·¤Ê¤¬¤é¡¤ºÇÂç¸íº¹ºÇ¾®²½´ð½à¤Ë¤è¤ëIIR¥Õ¥£¥ë¥¿Àß·×ÌäÂê¤Î¸íº¹´Ø¿ô¤ÏÀ߷ץѥé¥á¡¼¥¿¤ËÂФ·¤Æ¡¤°ÈÅÀ¤Ë»÷¤¿´Ë¤ä¤«¤Ê·Á¾õ¤òͤ¹¤ë¡¥¤½¤Î¤¿¤á¡¤Ääα²óÈò¤·¤Æ¤âºÆ¤Ó¶É½ê²òÄäα¤¹¤ë¾ì¹ç¤¬Â¿¿ôÀ¸¤¸¤ë¡¥Ëܸ¦µæ¤Ç¤Ï¡¤CSO (Cat Swarm Optimization) ¤òÍѤ¤¤¿·Ñ³Ū¤Êõº÷¤Ë¤è¤ëÀß·×Ë¡¤òÄó°Æ¤¹¤ë¡¥Àß·×Îã¤è¤ê¡¤CSO¤Î͸úÀ¤ò¼¨¤¹¡¥ |
Âê̾ | (¾·ÂÔ¹Ö±é) ¥¹¡¼¥Ñ¡¼¥³¥ó¥Ô¥å¡¼¥¿¡ÖÉٳ١׳«È¯¤Ë¤ª¤±¤ëCPUÏÀÍýÉʼÁÊÝ¾Ú |
Ãø¼Ô | ¡û¾¾°æ À빬, Ô¢ÎÎ ÂöÌé, ´Ø¸ý ½¨Ç·, ÀéÂå±ä ¾¼¹¨, »³Â¼ ¼þ»Ë, µÈÀî δ±Ñ, ÅÏÊÕ ¿µ¸ã (ÉÙ»ÎÄÌ), ¾®¾¾ δ±û, ÎëÌÚ ¿®ÂÀϺ (ÉÙ»ÎÄÌ¥³¥ó¥Ô¥å¡¼¥¿¥Æ¥¯¥Î¥í¥¸¡¼¥º) |
¥Ú¡¼¥¸ | pp. 156 - 161 |
¥¡¼¥ï¡¼¥É | HPC, CPU, ¸¡¾Ú, ÏÀÍý¥·¥ß¥å¥ì¡¼¥·¥ç¥ó |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ¥¹¡¼¥Ñ¡¼¥³¥ó¥Ô¥å¡¼¥¿¡ÖÉٳ١פϿ·µ¬³«È¯¤·¤¿¹âÀǽ¡¦¹â¿®ÍêÀ¡¦Äã¾ÃÈñÅÅÎϤÎCPU¡ÖA64FX¡×¤ò»ÈÍѤ·¤Æ¤¤¤ë¡£¤³¤ÎÍͤÊÊ£»¨¡¦Â絬ÌϤÊCPU³«È¯¤Ë¤ª¤¤¤Æ¡¢¥ê¥¹¥Ô¥ó¤ÎȯÀ¸¤Ï¹©Äø¡¦ÈñÍÑξÌ̤ÇÈó¾ï¤ËÂ礤ʥ¤¥ó¥Ñ¥¯¥È¤ò»ý¤Ä¤¿¤á¡¢¥×¥ì¥·¥ê¥³¥ó¥Õ¥§¡¼¥º¤Ç¤Î½½Ê¬¤ÊÉʼÁ³ÎÊݤ¬½ÅÍפǤ¢¤ë¡£ËÜȯɽ¤Ç¤Ï¡¢¡ÖA64FX¡×Á´ÂΤª¤è¤Ó³Æµ¡Ç½¥Ö¥í¥Ã¥¯¤Î³µÍפȡ¢CPU³«È¯¤Ë¤ª¤±¤ëÏÀÍýÉʼÁÊݾڤˤĤ¤¤Æ¤Î³µÍפò¾Ò²ð¤¹¤ë¡£¼¡¤¤¤Ç¡¢¥×¥ì¥·¥ê¥³¥ó¥Õ¥§¡¼¥º¤Ç¤Î¥·¥¹¥Æ¥àÏÀÍý¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¾ÇÅÀ¤òÅö¤Æ¡¢CPU³«È¯¤Ë¤ª¤±¤ëÏÀÍý¸¡¾Ú¡¢¤ª¤è¤ÓÂÅÅöÀ³Îǧ¼êË¡¤È¤½¤ÎÀ®²Ì¤ò¾Ò²ð¤¹¤ë¡£ |
Âê̾ | (¾·ÂÔ¹Ö±é) ¼«Æ°±¿Å¾¤Î¤¿¤á¤Î¥Æ¥¹¥È¡¦¸¡¾Úµ»½Ñ¤Î¿¼²½ ¡Á Î¥»¶¤«¤éϢ³¤Ø¤ÎȯŸ¤Èµ¡³£³Ø½¬¹©³Ø |
Ãø¼Ô | ¡ûÀÐÀî Åß¼ù (¹ñΩ¾ðÊó³Ø¸¦µæ½ê¥¢¡¼¥¥Æ¥¯¥Á¥ã²Ê³Ø¸¦µæ·Ï) |
¥Ú¡¼¥¸ | pp. 162 - 166 |
¥¡¼¥ï¡¼¥É | Falsification, ¥µ¡¼¥Á¥Ù¡¼¥¹¥É¥Æ¥¹¥Æ¥£¥ó¥°, µ¡³£³Ø½¬¹©³Ø, ¥µ¥¤¥Ð¡¼¥Õ¥£¥¸¥«¥ë¥·¥¹¥Æ¥à, ¼«Æ°±¿Å¾ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Ëֱܹé¤Ç¤Ï¡¤¼«Æ°±¿Å¾¤ËÂåɽ¤µ¤ì¤ë¼«Î§Åª¤Ê¥µ¥¤¥Ð¡¼¥Õ¥£¥¸¥«¥ë¥·¥¹¥Æ¥à¤ËÂФ¹¤ë¥Æ¥¹¥È¡¦¸¡¾Úµ»½Ñ¤ÎÆ°¸þ¤Ë¤Ä¤¤¤Æ2¤Ä¤Î´ÑÅÀ¤«¤é¾Ò²ð¤¹¤ë¡¥1¤Ä¤Î´ÑÅÀ¤Ï¡¤½¾ÍèÎ¥»¶Åª¤Ç¤¢¤ë¥½¥Õ¥È¥¦¥§¥¢¤Îµ»½Ñ¤ò¡¤Ï¢Â³·Ï¤ò°·¤¦¤è¤¦³ÈÄ¥¤¹¤ëÅÀ¤Ç¤¢¤ë¡¥¤â¤¦1¤Ä¤Î´ÑÅÀ¤Ï¡¤µ¡³£³Ø½¬¤òÍѤ¤µóÆ°¤ò¥Ç¡¼¥¿¤«¤éƳ¤¤¤¿¥½¥Õ¥È¥¦¥§¥¢¤ò°·¤¦¤¿¤á¤Î¿·¤·¤¤¥¢¥×¥í¡¼¥Á¤Ç¤¢¤ë¡¥Ëֱܹé¤Ë¤ª¤¤¤Æ¤Ï¡¤¤³¤ì¤é2¤Ä¤Î´ÑÅÀ¤Ë¤Ä¤¤¤Æ¡¤·Á¼°¼êË¡¡¤¥Æ¥¹¥Æ¥£¥ó¥°¡¤À©¸æ¹©³Ø¡¤¿Í¹©ÃÎǽ¡Ê¥á¥¿¥Ò¥å¡¼¥ê¥¹¥Æ¥£¥Ã¥¯¡¦µ¡³£³Ø½¬¡Ë¤Ê¤ÉÉý¹¤¤µ»½Ñ¥¢¥×¥í¡¼¥Á¤Ë¿¨¤ì¤Ä¤ÄÏÀ¤¸¤ë¡¥ |
Âê̾ | A spanning sink mobility on mWSN with grid topology |
Ãø¼Ô | ¡ûYoshihiro Kaneko (Gifu University) |
¥Ú¡¼¥¸ | pp. 167 - 172 |
¥¡¼¥ï¡¼¥É | WSN, mobile sink, grid stucture, energy, betweenness |
¥¢¥Ö¥¹¥È¥é¥¯¥È | In wireless sensor network WSN, collected data at sensor nodes are usually transmitted toward a static sink node. There nodes close to sink are always forced to transmit more data and/or more often and thus might first dry out energy in WSN. Such critical situation is issued as a hotspot. In order to mitigate hotspots, a model mWSN where sink moves on WSN has been remarkable. In the paper, we restrict the structure of mWSN to grid graph and let a single sink move around node to node. We define three sink mobility pattern types and two kinds of energy-based and betweenness-based next hop selections. As the first result of the paper, it turns out that mWSN lifetime increases as the structure is closer to square grid. Second, the energy-based next hop selection provides better results than betweenness-based selection. Third, M-type mobility patterns provides better results in betweenness-based selection. |
Âê̾ | Information vitality¤òÍѤ¤¤¿¾ðÊó³È»¶¥â¥Ç¥ë¤ÎÄó°Æ |
Ãø¼Ô | ¡ýÂÀÅÄ ÍµÌé, ¼ÄµÜ µªÉ§ (ÁϲÁÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 173 - 176 |
¥¡¼¥ï¡¼¥É | ¾ðÊó³È»¶, Information vitality, ÆÈΩ¥«¥¹¥±¡¼¥É¥â¥Ç¥ë |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ½¾Íè¤Î¾ðÊó³È»¶¥â¥Ç¥ë¤Ç¤Ï, ¾ðÊó¤Î³È»¶³ÎΨ¤Ï, ³È»¶¤Ë´Ø¤ï¤ëÎÙÀܤ·¤¿¥Î¡¼¥É´Ö¤Î¥Ñ¥é¥á¡¼¥¿¤Î¤ß¤ò¹Íθ¤·¤Æ·èÄꤵ¤ì¤Æ¤¤¤¿. ¤·¤«¤·, ¥¤¥ó¥¿¡¼¥Í¥Ã¥È¾å¤Î¥½¡¼¥·¥ã¥ë¥Í¥Ã¥È¥ï¡¼¥¯¤Ë¤ª¤¤¤Æ, ¾ðÊó¤Ï¥Î¡¼¥É¤òÄ̲᤹¤ë¤¿¤Ó¤ËÉղòÁÃͤòÆÀ¤Æ¤ª¤ê, ³È»¶³ÎΨ¤Ï¤½¤ì¤ò¹Íθ¤·¤Æ·èÄꤵ¤ì¤ë. Î㤨¤ÐTwitter¾å¤Ç¤Ï, ¤¢¤ë¾ðÊó¤ËÂФ·¤Æ¥ê¥Ä¥¤¡¼¥È¥Ü¥¿¥ó¤ò²¡¤¹¤³¤È¤Ç³È»¶¤¬µ¯¤¤ë¤¬, Ʊ»þ¤ËÉղòÁÃͤȤ·¤Æ¥ê¥Ä¥¤¡¼¥È¿ô¤¬Áý²Ã¤¹¤ë. ¤½¤Î·ë²Ì, ¼¡¤Î¾ðÊó¼õ¿®¼Ô¤Ï, Áý²Ã¤·¤¿¥ê¥Ä¥¤¡¼¥È¿ô¤ò¸«¤ë¤³¤È¤Ç, ¤½¤Î¾ðÊó¤Î²ÁÃͤ¬¹â¤¤¤ÈȽÃǤ·, ¤è¤ê¹â¤¤³ÎΨ¤Ç¥ê¥Ä¥¤¡¼¥È¥Ü¥¿¥ó¤ò²¡¤¹. ¤½¤Î¤¿¤áËܸ¦µæ¤Ç¤Ï, ¹âÀºÅ٤ξðÊó³È»¶Í½Â¬¡¦À©¸æ¤òÌÜŪ¤È¤·¤Æ, ¾ðÊó¤ÎÉղòÁÃͤò¹Íθ¤·¤¿¥â¥Ç¥ë¹½ÃÛ¤ò¹Ô¤¦¡£ |
Âê̾ | An analysis on payoff distribution across prosumers in resource sharing model based on a potluck problem |
Ãø¼Ô | ¡ûRyo Hase, Mitsue Imahori, Norihiko Shinomiya (Graduate School of Engineering, Soka University) |
¥Ú¡¼¥¸ | pp. 177 - 182 |
¥¡¼¥ï¡¼¥É | Minority Game, Potluck Problem, Graph Theory |
¥¢¥Ö¥¹¥È¥é¥¯¥È | For reducing environmental impacts, many countries have promoted the shift from a centralized energy system toward a distributed energy system. As the number of prosumers using renewable resources increases, it is required to consider a mechanism for enabling prosumers to share their electricity each other in distributed energy systems. In this paper, we propose a resource sharing model based on potluck problem for analyzing prosumers' benefit in energy trading. Experimental results demonstrate the difference in efficiency and fairness of prosumers' payoff resulted from the strategy chosen by prosumers. |
Âê̾ | ¥¹¥Ý¡¼¥Ä¥¹¥±¥¸¥å¡¼¥ê¥ó¥°¤Ë¤ª¤±¤ë¥Ö¥ì¡¼¥¯¿ô¾å¸ÂÉÕ¤¤Î¥Û¡¼¥à¥¢¥¦¥§¥¤¥Æ¡¼¥Ö¥ë¤ÎÎóµó |
Ãø¼Ô | ¡ýÃ滳 ¹À¾´, ËÙ»³ µ®»Ë (ºë¶ÌÂç³Ø Íý¹©³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 183 - 188 |
¥¡¼¥ï¡¼¥É | Îóµó¥¢¥ë¥´¥ê¥º¥à, ¥¹¥Ý¡¼¥Ä¥¹¥±¥¸¥å¡¼¥ê¥ó¥°, ¥Û¡¼¥à¥¢¥¦¥§¥¤¥Æ¡¼¥Ö¥ë, ZDD |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Ëܸ¦µæ¤Ç¤Ï, ¥¹¥Ý¡¼¥Ä¥¹¥±¥¸¥å¡¼¥ê¥ó¥°¤Ë¤ª¤¤¤Æ, ¤¢¤ë¥Á¡¼¥à¤ÎÏ¢ ³¤·¤Æɽ¤ì¤ë¥Ö¥ì¡¼¥¯¤¬»ØÄꤵ¤ì¤¿¸Ä¿ô°Ê²¼¤È¤Ê¤ëHAT (¥Û¡¼¥à ¥¢¥¦¥§¥¤¥Æ¡¼¥Ö¥ë) ¤ÎÎóµó¤ò¹Ô¤¦. HAT ¤ÎÎóµó¤Ë¤ÏZDD (¥¼¥í ¥µ¥×¥ì¥¹·¿Æóʬ·èÄꥰ¥é¥Õ) ¤òÍøÍѤ¹¤ë. ZDD¤ÏÊÑ¿ô½ç½ø¤Ë¤è¤Ã ¤ÆÀáÅÀ¿ô¤¬Â礤¯ÊѲ½¤¹¤ë¤¿¤á, ÊÑ¿ô½ç½ø¤¬½Ä¤È²£¤Î¤½¤ì¤¾¤ì¤Î¾ì ¹ç¤Ë¤Ä¤¤¤Æ¹½ÃÛ¥¢¥ë¥´¥ê¥º¥à¤òÄó°Æ¤¹¤ë. Äó°Æ¥¢¥ë¥´¥ê¥º¥à¤ÎÊÑ¿ô ½ç½ø¤¬½Ä¤Î¾ì¹ç¤¬ºÇ¤â¹â®¤«¤Ä¥á¥â¥ê»ÈÍÑÎ̤⾯¤Ê¤«¤Ã¤¿. »Þ´¢¤ê ÉÕ¤Á´Ãµº÷¤ÈÈæ¤Ù¤Æ·×»»»þ´Ö¤ä¥á¥â¥ê»ÈÍÑÎ̤θúΨ¤ÏÎɹ¥¤Ç, Î㤨 ¤Ð¥Á¡¼¥à¿ô¤¬10¤Î¾ì¹ç¤Ç¤Ï, Ìó5,400Çܤι⮲½¤òãÀ®¤·, ¤Þ¤¿Ìó8,600Çܤξʥá¥â¥ê²½¤¬´üÂԤǤ¤ë¼Â¸³·ë²Ì¤¬ÆÀ¤é¤ì¤¿. |
Âê̾ | ²èÌÌÁ«°Ü¤È»²¾È¹àÌܤÎÁàºîÍúÎò¤Ë´ð¤Å¤¯²èÌÌÀ߷׸ÄÊ̲½¼êË¡¤ÎÄó°Æ-ÅÅÎϼûÍ×ͽ¬¥·¥¹¥Æ¥à¤òÂоݤȤ·¤Æ- |
Ãø¼Ô | ¡ûËÅè °ËÃÎϺ (Åì¼Ç¥¨¥Í¥ë¥®¡¼¥·¥¹¥Æ¥à¥º³ô¼°²ñ¼Ò/ÉÜÃ湩¾ìÅÅÎÏ·ÏÅý¥·¥¹¥Æ¥àÉô), ¹ÓÅÄ ÂöÌé, »³¸ý ¿¿¸ç (»³¸ýÂç³Ø/Âç³Ø±¡ÁÏÀ®²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 189 - 194 |
¥¡¼¥ï¡¼¥É | ²èÌÌÁ«°Ü, ÁàºîÍúÎò, ÅÅÎϼûÍ×ͽ¬, ×Ç×ÓÁàºî |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ¶È̳·Ï¾ðÊó¥·¥¹¥Æ¥à¤Ë¤ª¤¤¤Æ¡¤¤½¤Î²èÌÌÁ«°Ü¤¬»È¤¤¤ä¤¹¤¤¤³¤È¤Ï½ÅÍפǤ¢¤ë¡¥°ìÈ̤˾ðÊó¥·¥¹¥Æ¥à¤Ï¡¤´Ä¶ÊѲ½¤ä»ÈÍѼԤΤФé¤Ä¤¤Ë¤è¤ê¡¤Ä¹´ü´Ö¤ËÅϤäÆËü¿Í¤¬»È¤¤¤ä¤¹¤¤¥æ¡¼¥¶¡¼¥¤¥ó¥¿¡¼¥Õ¥§¡¼¥¹¤òÈ÷¤¨¤ë¤³¤È¤ÏÆñ¤·¤¤¡¥Ëܸ¦µæ¤Ç¤Ï²èÌÌÁ«°ÜÀ߷פò¥°¥é¥Õ¤È¤·¤Æ¥â¥Ç¥ë²½¤·¡¤±¿ÍѼԤÎÁàºîÍúÎò¤òÍѤ¤¤Æ²èÌÌÁ«°Ü¤òµ¡³£Åª¤Ë²þÁ±¤¹¤ë²èÌÌÀ߷׸ÄÊ̲½¼êË¡¤òÄó°Æ¤¹¤ë¡¥¥Ç¡¼¥¿¤Ë´ð¤¯¤³¤È¤Ç¡¤´Ä¶ÊѲ½¤ä¸Ä¿Í¤Î»ÅÍÍ·¹¸þ¤ËÂбþ¤¹¤ë¤³¤È¤¬²Äǽ¤Ë¤Ê¤ë¡¥Äó°Æ¼êË¡¤ÏÅÅÎϼûÍ×ͽ¬¥·¥¹¥Æ¥à¤Ë¤Æɾ²Á»î¸³¤ò¼Â»Ü¤·¡¤Í¸úÀ¤ò³Îǧ¤·¤¿¡¥ |
Âê̾ | ÅÅÎϻԾì¤Ë¤ª¤±¤ë¼ûÍײȤÎÀáÅÅ·¹¸þÊѲ½¤ò¹Íθ¤·¤¿ÀáÅÅÍ×ÀÁ¼êË¡¤ÎÄó°Æ |
Ãø¼Ô | ¡ýÅļ Í¥¿Í, ¼ÄµÜ µªÉ§ (ÁϲÁÂç³Ø¹©³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 195 - 200 |
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¥Ú¡¼¥¸ | pp. 260 - 263 |
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¥Ú¡¼¥¸ | pp. 264 - 265 |
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¥Ú¡¼¥¸ | pp. 266 - 267 |
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¥¢¥Ö¥¹¥È¥é¥¯¥È | ̵ÀþÄÌ¿®¤Ë¤ª¤¤¤Æ°ÂÄꤷ¤¿ÄÌ¿®¤ò¹Ô¤¦¤¿¤á¤Ë¤ÏÅÅÇȤÎÅÁÈ·ÐÏ©¤¬Ê£¿ô¤¢¤ë¤³¤È¤Ëµ¯°ø¤¹¤ë¥Þ¥ë¥Á¥Ñ¥¹´³¾Ä¤òÍÞ°µ¤¹¤ë¤³¤È¤¬Èó¾ï¤Ë½ÅÍפǤ¢¤ë¡¥¥Þ¥ë¥Á¥Ñ¥¹´³¾Ä¤ÎÍÞ°µ¤Ë¤Ï¥Þ¥ë¥Á¥Ñ¥¹ÅÁÈÂÏ©¤òFIR¥Õ¥£¥ë¥¿¤È¤ß¤Ê¤·¡¤¤½¤Î¼þÇÈ¿ô±þÅú¤òÍѤ¤¤ÆÅù²½´ï¤òÀ߷פ¹¤ë¤¬¡¤¤³¤ì¤Ë¤Ï¼þÇÈ¿ô±þÅú¤Î¿äÄê(¥Á¥ã¥Í¥ë¿äÄê)¤¬É¬ÍפǤ¢¤ë¡¥´û¸¤Î¥Ö¥é¥¤¥ó¥É¼êË¡¤ÎÃæ¤Ç¤â¡¤±é»»¥³¥¹¥È¤È¿äÄêÀºÅ٤δÑÅÀ¤ÇÍ¥¤ì¤¿¼êË¡¤È¤·¤ÆÈóÂоΤʿ®¹æ¶õ´Ö¥À¥¤¥¢¥°¥é¥à¤òÍѤ¤¤¿¼êË¡¤¬¤¢¤ë¡¥¤³¤Î¼êË¡¤Ï¿®¹æ¶õ´Ö¥À¥¤¥¢¥°¥é¥à¾å¤Ç±ß¤Ë¤è¤ëïçÃÍȽÄê¤ò¹Ô¤¦¡¥¤·¤«¤·¡¤±ß¤Ë¤è¤ëïçÃÍȽÄê¤Ç¤Ï»¨²»ÅÅÎϤ¬Â礤¤¾ì¹ç¤ËµÕ°ÌÁê¤Î¼õ¿®¥·¥ó¥Ü¥ë¤â´Þ¤á¤Æ´üÂÔÃÍ·×»»¤ò¹Ô¤Ã¤Æ¤·¤Þ¤¦¤¿¤á¡¤Àµ³Î¤Ê¥Á¥ã¥Í¥ë¿äÄê¤ò¹Ô¤¨¤Ê¤¤¤È¤¤¤¦ÌäÂêÅÀ¤¬¤¢¤ë¡¥ËܹƤǤϼõ¿®¥·¥ó¥Ü¥ë¤ËÂФ·¤ÆľÀþ¤Ë¤è¤ëïçÃÍȽÄê¤Ë¤è¤ê½ê˾¤Î´üÂÔÃͤò»ý¤Ä¼õ¿®¥·¥ó¥Ü¥ë¤ò¸¡½Ð¤¹¤ë¼êË¡¤òÄó°Æ¤¹¤ë¡¥ |
Âê̾ | SVD-Free Algorithm with Norm Convergence Guarantee for Robust Principal Component Analysis |
Ãø¼Ô | ¡ý»³´ß ¾»É×, »³ÅÄ ¸ù (Åìµþ¹©¶ÈÂç³Ø ¹©³Ø±¡) |
¥Ú¡¼¥¸ | pp. 272 - 273 |
¥¡¼¥ï¡¼¥É | Robust Principal Component Analysis, Iterative algorithm |
Âê̾ | ¿¥Á¥ã¥ó¥Í¥ë¹â®1bit¿®¹æ¤òÍѤ¤¤¿Æ°Åª¶É½ê²»¾ì¹çÀ®¥·¥¹¥Æ¥à¤Î¹½ÁÛ |
Ãø¼Ô | ¡ý¹õÀî æÆÎÜ, ÄÅÔ¢ ÏÂÀô, ÃÓÅÄ Íº²ð, ¾®ºä ľÉÒ (ÅìµþÅŵ¡Âç³ØÂç³Ø±¡ ̤Íè²Ê³Ø¸¦µæ²Ê), µÚÀî Ì÷¹ (Áá°ðÅÄÂç³Ø ´ð´´Íý¹©³ØÉô) |
¥Ú¡¼¥¸ | pp. 274 - 275 |
¥¡¼¥ï¡¼¥É | ʪÍý²»¾ì¹çÀ®, Local Sound Field Synthesis, ARM/FPGA SoC, Digital Loudspeaker, Head Tracking |
¥¢¥Ö¥¹¥È¥é¥¯¥È | °ìÈ̤ˡ¢Â¿¿ô¤Î¥¹¥Ô¡¼¥«¤ò¹ÈϰϤËÀßÃÖ¤¹¤ëɬÍפ¬¤¢¤ëʪÍý²»¾ìºÆ¸½¥·¥¹¥Æ¥à¤ÏÈÑ»¨¤«¤Ä¹â¥³¥¹¥È¤Ç¤¢¤ë¡£°ìÊý¤Ç¡¢ºÆ¸½Îΰè¤ò¶É½ê¤Ë¸ÂÄꤷ¥¹¥Ô¡¼¥«¥æ¥Ë¥Ã¥È¤ÎʪÍýŪÀ©Ìó¤òĶ¤¨¤Æ¡¤ÇÈÌ̤κƸ½ÀºÅÙ¤ò¹â¤á¤ë¶É½ê²»¾ì¹çÀ®¤¬Äó°Æ¤µ¤ì¤Æ¤¤¤ë¡£ËܹƤǤϡ¤Â¿¥Á¥ã¥ó¥Í¥ë¹â®1bit¿®¹æ¤òÍѤ¤¤¿Æ°Åª¶É½ê²»¾ì¹çÀ®¥·¥¹¥Æ¥à¤Î¹½ÁÛ¤ò¾Ò²ð¤¹¤ë¡£¹â®1bit¿®¹æ¤Ë¤è¤ë¥¹¥Ô¡¼¥«Ä¾ÀܶîÆ°¤òºÎÍѤ·¤¿Ã±½ã¤Ê¹½À®¤«¤Ä¹âÉʼÁ¤Ê¿¥Á¥ã¥ó¥Í¥ëºÆÀ¸¥·¥¹¥Æ¥à¤ò¹½ÃÛ¤·¡¤¥»¥ó¥µ¤Ë¤è¤Ã¤Æ¸¡½Ð¤µ¤ì¤¿Æ¬Éô°ÌÃÖ¤ËÄɽ¾¤·¤Æ¶É½ê²»¾ìºÆ¸½¤ò¹Ô¤¦¤³¤È¤Ç¹âÀºÅ٤ʲ»¾ì¹çÀ®¤òÌܻؤ¹¡£¤Þ¤¿¡¢¹çÀ®»þ¤Ë¹¤¤ÂÓ°è¤Çξ¼ª´Ö²»°µ¥ì¥Ù¥ëº¹¤¬°ÂÄꤹ¤ëÆ°ºî¾ò·ï¤Î¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¤è¤ë¸¡Æ¤¤ò¹Ô¤¦¡£ |
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Ãø¼Ô | ¡ýÄÅÔ¢ ÏÂÀô, ¹õÀî æÆÎÜ, ÃÓÅÄ Íº²ð, ¾®ºä ľÉÒ (ÅìµþÅŵ¡Âç³Ø) |
¥Ú¡¼¥¸ | pp. 276 - 277 |
¥¡¼¥ï¡¼¥É | ¥¤¥ó¥Ñ¥ë¥¹±þÅú, ¥¹¥Ñ¡¼¥¹ºÇŬ²½, ¥¹¥Ô¡¼¥«¥â¥Ç¥ê¥ó¥°, equivalent source |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ²»¶Áµ»½ÑÁ´È̤ˤª¤¤¤Æ¥¤¥ó¥Ñ¥ë¥¹±þÅú¤Î¬Äê¤Ï½ÅÍפÊÌò³ä¤òô¤Ã¤Æ¤ª¤ê¡¤ÍÍ¡¹¤Ê¬ÄêË¡¤¬Äó°Æ¤µ¤ì¤Æ¤¤¤ë¡£¤·¤«¤·¡¤²»¾ìÀ©¸æ¤äʬÀϤòÌÜŪ¤È¤·¤¿Â¿ÅÀ¥Þ¥¤¥¯¥í¥Û¥ó·×¬¤ò¸úΨŪ¤ËÀºÅÙÎɤ¯·×¬¤¹¤ë¤³¤È¤ÏÍưפǤϤʤ¤¡£ ¤½¤³¤Ç¡¤Ëܸ¦µæ¤Ç¤Ï¡¤¾¯¿ôÅÀ¤Ø¤ÎÅÁã´Ø¿ô¤Î¬Äê·ë²Ì¤«¤é¡¤Ã±°ì¤Î¥¹¥Ô¡¼¥«¤«¤é¶É½êÎΰè¤Ø¤ÎÅÁã´Ø¿ô¤ò¥â¥Ç¥ë²½¤¹¤ë¼êË¡¤Ë´Ø¤¹¤ë´ðÁÃŪ¤Ê¸¡Æ¤¤ò¹Ô¤¦¡£¥¹¥Ô¡¼¥«¼þ°Ï¤ËÊ£¿ô¤ÎÅÀ²»¸»¤òÁÛÄꤷ¡¤¥¹¥Ñ¡¼¥¹ºÇŬ²½¤Ë¤è¤Ã¤ÆÁªÂò¤µ¤ì¤¿¾¯¿ô¤ÎÅÀ²»¸»¤Î¤ß¤Ç¡¤·×¬ÅÀ¤Î¼þ°ÏÎΰè¤Ë¤ª¤¤¤ÆÅÁã´Ø¿ô¤¬¹âÀºÅ٤˿äÄê²Äǽ¤Ç¤¢¤ë¤³¤È¤¬¼¨º¶¤µ¤ì¤¿¡£ |
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¥Ú¡¼¥¸ | pp. 278 - 283 |
¥¡¼¥ï¡¼¥É | ¥Ú¥È¥ê¥Í¥Ã¥È, ¹½Â¤ÅªÀ¼Á, ¼«Í³ÁªÂò¥Í¥Ã¥È, ¥Õ¥í¡¼¥Í¥Ã¥È, ¥È¡¼¥¯¥ó¥Õ¥í¡¼»»½Ð |
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¥Ú¡¼¥¸ | pp. 284 - 288 |
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¥Ú¡¼¥¸ | pp. 289 - 292 |
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