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Âê̾ | (´ðÄ´¹Ö±é) Memory Technology Trends and Micron: The Future Starts Here |
Ãø¼Ô | Linda Somerville (Corporate Vice President, Strategy and Operations Technology Development, Micron Technology, Inc.) |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Every day, 2.5 quintillion bytes of data are created, and Micron¡Çs memory devices are where data lives, where data goes to work, and where data becomes intelligence. The advent of 5G, smart cities, smart grids, smart manufacturing and autonomous vehicle ecosystems will transform the memory industry and our daily life. Micron is a critical part of the world¡Çs data economy, meeting our customers¡Ç needs today and tomorrow with the industry¡Çs broadest, most innovative portfolio of memory, storage and accelerator solutions. |
Âê̾ | Ê£¿ô¤Î¥×¥í¥·¥å¡¼¥Þ¦ÃßÅÅÃÓ¤òÍѤ¤¤¿Ãϰ襳¥ß¥å¥Ë¥Æ¥£¥â¥Ç¥ë¤ÈÅÅÎÏÍ»ÄÌ¥·¥ß¥å¥ì¡¼¥·¥ç¥ó |
Ãø¼Ô | *Ê¡¸¶ ÆØ (¹ÅçÂç³Ø ¹©³ØÉô), 벬 ²Âµª, º´¡¹ÌÚ Ë, ¾¾ËÜ ½¡°ìϺ, Mumbere Kihembo Samuel, ´Øùõ ¿¿Ìé, ñ±ÍøÌî ľ¿Í, ¤²ì ˧ʸ (¹ÅçÂç³Ø Âç³Ø±¡¹©³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 1 - 4 |
¥¡¼¥ï¡¼¥É | Ãϰ襳¥ß¥å¥Ë¥Æ¥£, ÃßÅÅÃÓ, ÅÅÎÏÍ»ÄÌÀ©¸æ, ¥×¥í¥·¥å¡¼¥Þ, »ö¶È·Ñ³·×²è |
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Ãø¼Ô | *ËÌÏÆ ½Ù²ð (¹ÅçÂç³ØÂç³Ø±¡ Àè¿ÊÍý¹©·Ï²Ê³Ø¸¦µæ²Ê), ´Øùõ ¿¿Ìé, À¾ùõ °ìϺ, ÎÓÅÄ ÃÒ¹° (¹ÅçÂç³Ø, Àè¿ÊÍý¹©·Ï²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 5 - 8 |
¥¡¼¥ï¡¼¥É | demand response, game theory |
Âê̾ | 1»þ´ÖÀè¼ûµëÉԶѹդËÂбþ¤·¤¿Ä´À°ÎÏÇÛʬˡ¤Î´ðÁø¡Æ¤ |
Ãø¼Ô | *¾å²¬ ¿¿¶×, ºå°æ É¢Ìï, º´¡¹ÌÚ Ë, ñ±ÍøÌî ľ¿Í, ¤²ì ˧ʸ (¹ÅçÂç³ØÂç³Ø±¡ Àè¿ÊÍý¹©·Ï²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 9 - 13 |
¥¡¼¥ï¡¼¥É | ·ÐºÑÉÕ²ÃÇÛʬ, ¥Þ¥¤¥¯¥í¥°¥ê¥Ã¥É, ÂÀÍÛ¸÷ȯÅÅ, ÃßÅÅÃÓ, DR |
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Âê̾ | PVÂçÎÌƳÆþ¤Ë¤è¤ëÉԳμÂÀ¤ò¹Íθ¤·¤¿PSSÀ߷פ˴ؤ¹¤ë¸¦µæ |
Ãø¼Ô | *ÊÆß· ÃλÖ, ¤²ì ˧ʸ, ñ±ÍøÌî ľ¿Í, º´¡¹ÌÚ Ë (¹ÅçÂç³Ø) |
¥Ú¡¼¥¸ | pp. 14 - 17 |
¥¡¼¥ï¡¼¥É | PSS, ¥í¥Ð¥¹¥ÈÀ©¸æ, ÂÀÍÛ¸÷ȯÅÅ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ¶áǯ¡¤ºÆÀ¸²Äǽ¥¨¥Í¥ë¥®¡¼¤Ë¤è¤ëȯÅŤ¬ÃíÌܤµ¤ì¡¤ÂÀÍÛ¸÷ȯÅŤ¬ÅÅÎÏ·ÏÅý¤ËÂçÎÌƳÆþ¤µ¤ì¤Ä¤Ä¤¢¤ë¡£ÂÀÍÛ¸÷ȯÅŤνÐÎϤÏÅ·¸õ¤Ë¤è¤Ã¤Æº¸±¦¤µ¤ì¡¤ÍÍ¡¹¤ÊĬή¥Ñ¥¿¡¼¥ó¤¬ÁÛÄꤵ¤ì¤ë¤¿¤á¡¤·ÏÅý¤ÎÉԳμÂÀ¤¬ÁýÂ礹¤ë¤È¤¤¤Ã¤¿²ÝÂ꤬¤¢¤ë¡£¤³¤Î¤è¤¦¤ÊÇطʤ«¤é¡¤ÂÀÍÛ¸÷ȯÅŤäÉ÷ÎÏȯÅŤʤɤÎÉԳμÂÀ¤¬Â礤ÊÅŸ»¤¬·ÏÅýÆâ¤ËƳÆþ¤µ¤ì¤¿¾ì¹ç¤Î·ÏÅý¾õÂÖ¤â¹Íθ¤¹¤ëɬÍפ¬¤¢¤ê¡¤ÉԳμÂÀ¤¬ÁýÂ礷¤¿·ÏÅý¤Ç¤âŬÀÚ¤ËPSS¤òÀ߷פ¹¤ëɬÍ×À¤¬¤Ç¤Æ¤¤¿¡£ËܹƤǤϡ¤¤Þ¤º PSS¤ÎÀ߷פˤª¤¤¤Æ¹Í¤¨¤é¤ì¤ëÉԳμÂÀ¤òÀ°Íý¤·¡¤¤½¤ì¤¾¤ì¤ÎÉԳμÂÀ¤ËÂбþ¤¹¤ëÀß·×¼êË¡¤È¤·¤ÆPVÂçÎÌƳÆþ¤Ë¤è¤ë·ÏÅý¾õÂÖ¤ÎÉԳμÂÀ¤â¹Íθ¤·¤¿¼êË¡¤òÄó°Æ¤¹¤ë¡£¤½¤·¤ÆÄó°Æ¼êË¡¤Î͸úÀ¤ò³Îǧ¤¹¤ë¤¿¤á¤Ë¡¤ÄêÎÌŪ¤Ëɾ²Á¤·¤¿·ë²Ì¤ÎÈæ³Ó¸¡Æ¤¤ò¹Ô¤¦¡£ |
Âê̾ | Ê£¿ô¤Î¿å¼Ö¤òÍѤ¤¤¿¥Þ¥¤¥¯¥í¿åÎÏȯÅŤˤª¤±¤ëModular Multilevel Converter ¤ÎŬÍѤ˴ؤ¹¤ë´ðÁø¡Æ¤ |
Ãø¼Ô | *Ãæ¼ ¿®Ç·²ð (»³¸ýÂç³Ø ¹©³ØÉô), »³ÅÄ ÍÎÌÀ (»³¸ýÂç³Ø Âç³Ø±¡ÁÏÀ®²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 18 - 21 |
¥¡¼¥ï¡¼¥É | ¥Þ¥¤¥¯¥í¿åÎÏȯÅÅ, MMC, ¼þÇÈ¿ôÀ©¸æ, Àä±ï·¿SEPIC, SEPIC |
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Ãø¼Ô | *²¬ËÜ ½Ù, ÃÓÅÄ É÷²Ö, ²¬ËÜ ¾»¹¬ (±§Éô¹©¶È¹âÅùÀìÌç³Ø¹»), »³ÅÄ ÍÎÌÀ, ÅÄÃæ ½Óɧ (»³¸ýÂç³Ø) |
¥Ú¡¼¥¸ | pp. 22 - 23 |
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Ãø¼Ô | *ºûÌî Í´¿¿, »³ÅÄ ÍÎÌÀ, ÅÄÃæ ½Óɧ (»³¸ýÂç³ØÂç³Ø±¡ÁÏÀ®²Ê³Ø¸¦µæ²Ê), ¸Å²°Éß ·¼°ìϺ, »³ÅÄ ¹¬¼£, ÌîÅÄ Í¦ÆóϺ, ¶â»Ò ¿ÊÞ (±§Éô¶½»º³ô¼°²ñ¼Ò) |
¥Ú¡¼¥¸ | pp. 24 - 27 |
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Ãø¼Ô | *ÅÏî´ ÎèÍ£, ÃÓÅÄ É÷²Ö, ²¬ËÜ ¾»¹¬ (±§Éô¹©¶È¹âÅùÀìÌç³Ø¹») |
¥Ú¡¼¥¸ | pp. 28 - 29 |
¥¡¼¥ï¡¼¥É | GaN¥È¥é¥ó¥¸¥¹¥¿, ¥ï¥¤¥ä¥ì¥¹µëÅÅ, °ìÀз¿¥¤¥ó¥Ð¡¼¥¿ |
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¥Ú¡¼¥¸ | pp. 30 - 31 |
¥¡¼¥ï¡¼¥É | ñÁêPLL²óÏ©, ÅÅ°µ¥Ç¥£¥Ã¥× |
Âê̾ | ¥Æ¥é¥Ø¥ë¥ÄÇÈ¥±¥ß¥«¥ë¸²Èù¶À¤òÍѤ¤¤¿Á´¸ÇÂÎÅÅÃÓÃÇÌ̷׬¼êË¡¤Î¸¡Æ¤ |
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¥Ú¡¼¥¸ | pp. 32 - 35 |
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¥Ú¡¼¥¸ | pp. 36 - 38 |
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¥Ú¡¼¥¸ | pp. 39 - 42 |
¥¡¼¥ï¡¼¥É | ¥Æ¥é¥Ø¥ë¥Ä, ¥Õ¥§¥à¥ÈÉå졼¥¶, ²½¾ÑÉÊɾ²Á, in vivo |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ¶áǯ¤Î²½¾ÑÉʤˤª¤±¤ë»Ô¾ìµ¬ÌϤγÈÂç¤Ë¤è¤ê¡¤²½¾ÑÉʤΰÂÁ´À¤ä¸ú²Ì¤Ø¤ÎÃíÌܤ¬¹â¤Þ¤Ã¤Æ¤¤¤ë¡¥¤³¤ì¤é¤òÄêÎÌŪ¤Ëɾ²Á¤¹¤ë¤¿¤á¤Ë¡¤²½¾ÑÉʤÎÈéÉæ¤ËÂФ¹¤ë¿»Æ©¿¼¤µ¤ª¤è¤Ó¿»Æ©Â®Å٤ϲ½¾ÑÉʶȳ¦¤Ë¤ª¤¤¤Æ½ÅÍפʻØɸ¤È¤Ê¤ë¡¥¸½ºß°ìÈÌŪ¤Ê¼êË¡¤È¤·¤Æ¥Æ¡¼¥×¥¹¥È¥ê¥Ã¥Ô¥ó¥°Ë¡¤¬¤¢¤ë¤¬¡¤¤³¤ÎÊýË¡¤Ë¤ÏÍÍ¡¹¤Ê²ÝÂ꤬¤¢¤ë¡¥¤½¤³¤Ç²æ¡¹¤Ï¡¤¥»¥ó¥·¥ó¥°¥×¥ì¡¼¥È¤òÍѤ¤¤¿¥Æ¥é¥Ø¥ë¥ÄÇȤˤè¤ë¿·¤¿¤Ê²½¾ÑÉʤο»Æ©É¾²ÁË¡¤Î³«È¯¤Ë¼è¤êÁȤó¤Ç¤¤¤ë¡¥in vivo¾õÂ֤ǤÎɾ²Á¤òÁÛÄꤷ¤Æ¡¤ÆÚÈéÉæÁÈ¿¥¤Ë¥Õ¥§¥¤¥¹¥Þ¥¹¥¯¤òÍѤ¤¤Æ²½¾Ñ¿å¤ò¿»Æ©¤µ¤»É½È馤«¤é¬Äꤷ¤¿¤È¤³¤í¡¤¿»Æ©¤ÎÍͻҤò³Îǧ¤Ç¤¤¿¡¥¤³¤Î¤³¤È¤«¤é¥Æ¥é¥Ø¥ë¥ÄÇȤˤè¤ëin vivo¤Ç¤Î²½¾ÑÉÊ¿»Æ©É¾²Á¤ò¹Ô¤¨¤ë²ÄǽÀ¤¬¼¨¤µ¤ì¤¿¡¥ |
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Ãø¼Ô | *À¾Ã« ·Ãµ±, ÌÚ²¼ ÂóÌð, »³ËÜ Æ© (¹ÅçÂç³ØÂç³Ø±¡Àè¿ÊÍý¹©·Ï²Ê³Ø¸¦µæ²ÊÅŵ¤¥·¥¹¥Æ¥àÀ©¸æ¥×¥í¥°¥é¥à) |
¥Ú¡¼¥¸ | pp. 43 - 46 |
¥¡¼¥ï¡¼¥É | ¥Ç¡¼¥¿¶îÆ°·¿À©¸æ, PIDÀ©¸æ, ±þÅú¿äÄê, ¥ª¥Õ¥é¥¤¥ó, ÆþÎÏ¿®¹æ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ¶áǯ¡¤¥·¥¹¥Æ¥à¥â¥Ç¥ê¥ó¥°¤¬ÉÔÍפÊÀ©¸æ·ÏÀ߷פȤ·¤Æ¥Ç¡¼¥¿¶îÆ°·¿À©¸æ¤¬Äó°Æ¤µ¤ì¤Æ¤ª¤ê¡¤¥Ç¡¼¥¿¥Ù¡¼¥¹¤òÍѤ¤¤ÆÈóÀþ·Á·Ï¤Ë³ÈÄ¥¤µ¤ì¤Æ¤¤¤ë¡£¤³¤Î¤È¤¡¤Îɹ¥¤ÊÀ©¸æ·ë²Ì¤òÆÀ¤ë¤¿¤á¤Ë¤Ï¡¤ÍÍ¡¹¤Ê¥Ç¡¼¥¿¤ò¼ý½¸¤¹¤Ù¤¤Ç¤¢¤ë¤¬¡¤½½Ê¬¤Ê¥Ç¡¼¥¿¼ý½¸¤Î¤¿¤á¤Ë¤Ï¡¤»þ´ÖŪ¡¦¿ÍŪ¥³¥¹¥È¤¬À¸¤¸¤ë¡£ËܼêË¡¤Ç¤Ï¡¤°ìÁȤγ«¥ë¡¼¥×¥Ç¡¼¥¿¤«¤éÊ£¿ô¤Î¥Ç¡¼¥¿¤òÀ¸À®¤ª¤è¤ÓÀ©¸æ·ÏÀ߷פ¬²Äǽ¤Ê¥ª¥Õ¥é¥¤¥ó¼êË¡¤òÄó°Æ¤·¡¤¤½¤Î͸úÀ¤ò¿ôÃÍÎã¤Ë¤è¤ê¸¡¾Ú¤¹¤ë¡£¤Þ¤¿¡¤½ÐÎϤΤߤò¹Íθ¤·¤ÆÀ©¸æ·Ï¤òÀ߷פ¹¤ë¤È¡¤ÆþÎÏ¿®¹æ¤¬Ì¤ÃΤȤʤꡤ¥¢¥¯¥Á¥å¥¨¡¼¥¿¤Ø¿Âç¤ÊÉéô¤¬¤«¤«¤ë¾ì¹ç¤¬¤¢¤ë¡£¤½¤³¤Ç¡¤ÆþÎÏ¿®¹æ¤ò¹Íθ¤·¤¿À©¸æ·ÏÀ߷פò¹Ô¤¤¡¤½ÐÎϤΤߤò¹Íθ¤·¤¿¾ì¹ç¤ÈÈæ³Ó¡¦É¾²Á¤ò¹Ô¤¦¡£ |
Âê̾ | Investigation of the relationship between CO2 concentration data and weather |
Ãø¼Ô | *Ë̼ Íö´Ý, ¹áÀî ľ¸Ê (Ê¡»³Âç³Ø ¹©³ØÉô) |
¥Ú¡¼¥¸ | pp. 47 - 48 |
¥¡¼¥ï¡¼¥É | CO2, torrential rainfall, sensor network, prediction, vegetation |
¥¢¥Ö¥¹¥È¥é¥¯¥È | CO2 concentration data have a complex set of characteristics that may be attributed to various factors. The photosynthesis of vegetation is particularly significant. In recent years as in July 2018, torrential rains caused many shallow landslides which might have been prevented by the vegetation. In other words, the vegetation has been damaged by something, and CO2 concentration data may indicate this. therefore, we explore characteristics of the CO2 concentration data for predicting any sediment disasters. In this report, we investigated the separation of some elements included in the CO2 concentration data and their relationship to meteorological phenomena. The analytical results show that CO2 concentrations data have a circadian cycle that may be mainly due to vegetation activity. |
Âê̾ | ¥®¥»¥ë¹½À®¤òÍѤ¤¤¿Æ³ÇÈ´É·¿¥Þ¥¤¥¯¥íÇÈÅÅÎÏʬÇÛ/¹çÀ®´ï |
Ãø¼Ô | *·ªÀ¯ ½Õ½¨, º´Æå Ì (²¬»³Âç³ØÂç³Ø±¡¼«Á³²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 49 - 52 |
¥¡¼¥ï¡¼¥É | ¥Þ¥¤¥¯¥íÇÈ, ÅÅÎÏʬÇÛ/¹çÀ®´ï, Êý·ÁƳÇÈ´É, ¥Þ¥¤¥¯¥í¥¹¥È¥ê¥Ã¥×ÀþÏ©, ¥®¥»¥ë¹½À® |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ËÜÊó¹ð¤Ç¤Ï¡¤Åż§³¦¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤ÈÅù²Á²óÏ©¤òÊ»ÍѤ·¤Æ¡¤ÂçÅÅÎÏÍÑÅÓ¤ËŬ¤·¤¿¥®¥»¥ë¹½À®¤òÍѤ¤¤¿Æ³ÇÈ´ÉÅÅÎÏ2ʬÇÛ/¹çÀ®´ï¤Ë¤Ä¤¤¤Æ¸¡Æ¤¤ò¹Ô¤Ã¤¿¡¥Æ³Çȴɥݡ¼¥È¤Ë¤ª¤±¤ëÈ¿¼Í¤Ï¹¤¤¼þÇÈ¿ôÂӤˤ錄¤Ã¤Æ-20dB°Ê²¼¤È¤Ê¤ê¡¤Ê¬ÇÛ½ÐÎÏ/¹çÀ®ÆþÎϥݡ¼¥È´Ö¤Î¥¢¥¤¥½¥ì¡¼¥·¥ç¥ó¤Ï10dB°Ê¾å¤È¤Ê¤ëʬÇÛ¹çÀ®´ï¤òÀ߷פ·¤¿¡¥ |
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Ãø¼Ô | *¸þ°æ Í¥²Ï, ¸¶ÅÄ Ä¾¹¬ (»³¸ýÂç³Ø ÁÏÀ®²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 53 - 55 |
¥¡¼¥ï¡¼¥É | ĶÅÁƳ, »À²½ÊªÄ¶ÅÁƳ¥Æ¡¼¥×Àþºà, ¥³¥¤¥ë |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Bi·Ï¤äY·Ï¤Î»À²½ÊªÄ¶ÅÁƳ¥Æ¡¼¥×Àþºà¤¬³«È¯¤µ¤ì¡¤±ÕÂÎÃâÁǤäÎäÅൡ¤ÇÎäµÑ¤¹¤ë¥³¥¤¥ë¤Ø¤Î±þÍѤ¬¿Ê¤á¤é¤ì¤Æ¤¤¤ë¡¥Ëܸ¦µæ¤Ç¤Ï¡¤Y·ÏĶÅÁƳ¥Æ¡¼¥×Àþºà¤òÍѤ¤¤¿Ä¶ÅÁƳ¥³¥¤¥ë¤ÎÅÅή¡ÝÅÅ°µÆÃÀ¤òͽ¬¤¹¤ë¤³¤È¤òÌÜŪ¤È¤·¤Æ¡¤¥Æ¡¼¥×Àþºà¤ÎÆÃÀ¤È¥³¥¤¥ëÆâÉô¤Î¼§¾ìʬÉۤθ¡Æ¤¤ò¹Ô¤Ã¤¿¡¥ |
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Ãø¼Ô | *²¬ËÜ Î¼Í´, ÉÚΤ ÈË, ÅÄÌî ů, ¾å¸¶ °ì¹À (²¬»³Âç³ØÂç³Ø±¡¼«Á³²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 56 - 58 |
¥¡¼¥ï¡¼¥É | ̵ÀþÄÌ¿®, ²Ä»ë¸÷ÄÌ¿®, LED |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Ëܸ¦µæ¤Ç¤Ï¡¤LED²Ä»ë¸÷ÄÌ¿®¤Ë¤ª¤±¤ë¹â®¡¦¹â¸úΨÅÁÁ÷¤ò¼Â¸½¤¹¤ë¤¿¤á¡¤¶õ´ÖŪÊÂÎó¿®¹æÅÁÁ÷¤Ë¤ª¤¤¤Æ¡¤LED¾ÈÌÀ¤È¼õ¸÷ÁǻҤÎÀßÃÖÊýË¡¤Ë¤è¤ë¼õ¿®ÆÃÀ¤òɾ²Á¤·¡¤¤½¤Î·ë²Ì¤«¤é¼õ¸÷ÁǻҤÇƱ»þ¤Ë¼õ¿®²Äǽ¤Ê¿®¹æ·ÏÎó¿ô¤ò»»½Ð¤¹¤ë¡¥É¾²Á·ë²Ì¤«¤é¡¤¼õ¸÷ÁǻҤ«¤éÅ·°æ¤Þ¤Ç¤Îµ÷Î¥¤ÈLED¾ÈÌÀ¤ÎÀßÃÖ´Ö³Ö¤ËÂФ·¤Æ¼õ¸÷ÁǻҴ֤γÑÅÙ¤òŬÀÚ¤ËÀßÄꤹ¤ë¤³¤È¤Ë¤è¤ê¡¤É¾²Á¥¨¥ê¥¢¤ÎÁ´¤Æ¤ÎÃÏÅÀ¤Ç²Ä»ë¸÷ÄÌ¿®¤¬²Äǽ¤È¤Ê¤ê¡¤97.4¡ó¤ÎÃÏÅÀ¤Ç2·ÏÎó°Ê¾å¤Î¿®¹æ¤òƱ»þ¤Ë¼õ¿®¤Ç¤¤ë¤³¤È¤¬Ê¬¤«¤Ã¤¿¡¥¤³¤ì¤é¤Î·ë²Ì¤«¤é¡¤LED²Ä»ë¸÷ÄÌ¿®¤Ë¤ª¤¤¤Æ¡¤¶õ´ÖŪÊÂÎó¿®¹æÅÁÁ÷¼Â¸½¤Î²ÄǽÀ¤òÌÀ¤é¤«¤Ë¤·¤¿¡¥ |
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Ãø¼Ô | *²¬ÅÄ ¹Ò, ÉÚΤ ÈË, ¾å¸¶ °ì¹À (²¬»³Âç³ØÂç³Ø±¡¼«Á³²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 59 - 62 |
¥¡¼¥ï¡¼¥É | IoT, ÂÓ°èÉýÀ©¸æ, ¥Þ¥ë¥Á¥Ð¥ó¥É, ¼þÇÈ¿ô¶¦ÍÑ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ̵ÀþÄÌ¿®¤Ë¤ª¤±¤ëIoT (Internet of Things) ¤Î¿ÊŸ¤Ë¤è¤ê¡¤Ê£¿ô¤Î¥·¥¹¥Æ¥à¤ÎüËö¤¬Æ±¤¸¼þÇÈ¿ôÂÓ¤ò¶¦ÍѤ¹¤ë¤³¤È¤Ë¤Ê¤ê¡¤¤³¤Î¤¿¤á³Æ¥·¥¹¥Æ¥à¤ÎüËö¤«¤é½ÐÎϤµ¤ì¤ëÂÓ°è³°íռͤÎÃßÀѤ¬Â礤ʴ³¾Ä¤È¤Ê¤ë¡¥¤³¤Î¤è¤¦¤ÊÂÓ°è³°íռͤòÄ㸺¤¹¤ë¤¿¤á¡¤¥¯¥ê¥Ã¥×¤È¥Õ¥£¥ë¥¿¥ê¥ó¥° (Clipping and Filtering: CAF) ¼êË¡¤¬Äó°Æ¤µ¤ì¤Æ¤¤¤ë¡¥ ËÜÏÀʸ¤Ç¤Ï¡¤¼þÇÈ¿ô¶¦ÍÑ¥·¥¹¥Æ¥à¤Ë¤ª¤±¤ë¥·¥¹¥Æ¥à´Ö´³¾Ä¤òÄ㸺¤¹¤ë¤¿¤á¡¤¥Þ¥ë¥Á¥Ð¥ó¥É¤òÍѤ¤¤ëÍ¿´³¾Ä¥·¥¹¥Æ¥à¤ËÅÁÈÂÏ©ÊÑÆ°¤Ë±þ¤¸¤¿ÂÓ°èÉýÀ©¸æ¤ÎŬÍѤò¸¡Æ¤¤·¡¤¤³¤Î¤È¤¤ÎÈï´³¾Ä¥·¥¹¥Æ¥à¤Ë¤ª¤±¤ë´³¾ÄÄ㸺¸ú²Ì¤ÈÍ¿´³¾Ä¥·¥¹¥Æ¥à¤Î¾ì½êΨ²þÁ±¸ú²Ì¤ò·×»»µ¡¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¤è¤êɾ²Á¤¹¤ë¡¥¤½¤·¤Æ¡¤¤½¤Î·ë²Ì¤Ë´ð¤Å¤¤¤Æ¡¤ÅÁÈÂÏ©¾õ¶·¤Ë±þ¤¸¤¿ÂÓ°è»ÈÍÑÊýË¡¤ò³ÎΩ¤¹¤ë¡¥ |
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Ãø¼Ô | *Ãæ²È æÆ, Ê¿Àî Âó»Ö, À¾»³ Å°, ÉÚΤ ÈË, ÅÄÌî ů, ¾å¸¶ °ì¹À (²¬»³Âç³Ø Âç³Ø±¡¼«Á³²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 63 - 65 |
¥¡¼¥ï¡¼¥É | IoT, Eb/N0, ¿®¹æʬΥ, ÆÃħÎÌʬΥ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | IoT»þÂ夬´Ö¶á¤È¤Ê¤ê¡¤Â¿¤¯¤ÎÄ㵡ǽ¤ÎüËö¤¬ÌµÃá½ø¤ËÄÌ¿®¤ò¹Ô¤¦¤È¾×Æͤ䴳¾Ä¤Ë¤è¤ê¼õ¿®¤¬½ÐÍè¤Ê¤¯¤Ê¤ë¤È¤¤¤¦²ÝÂ꤬¤¢¤ë¤¿¤á¡¤ÃßÀÑ°ì³ç¿®¹æ½èÍýµ»½Ñ¤ò³ÎΩ¤·¡¤¾×Æͤ䴳¾Ä¤ò¼õ¤±¤¿¿®¹æ¤ÎʬΥ¡¤ÉüÄ´¤òÌܻؤ·¤Æ¤¤¤ë¡¥ ¤³¤ÎÌäÂê¤ò²ò·è¤¹¤ë¤¿¤á¤Ë¡¤ÆÃħÎÌÃê½Ð¤òÍѤ¤¤Æ¿®¹æʬΥ¤ò¹Ô¤¦ÆÃħÎÌÉüÄ´Ë¡¤Î¸¡Æ¤¤¬¹Ô¤ï¤ì¤Æ¤¤¤ë¡¥ËܹƤǤÏÃßÀÑ°ì³ç¿®¹æ½èÍý¤Ë¤è¤ëÆÃħÎÌÉüÄ´Ë¡¤ÈƱ´ü¸¡ÇÈË¡¤Ë¤Ä¤¤¤Æ¡¤Eb/N0¤Î±Æ¶Á¤ËÃåÌܤ·¡¤BPSK, QPSK, 16QAMÊÑÄ´¤Î¿®¹æ¤ËÂФ·¤Æ·×»»µ¡¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤òÍѤ¤¤Æ¿®¹æʬΥÀǽ¤òɾ²Á¤·¤¿¡¥ ¤½¤Î·ë²Ì¡¤BPSK¡¤QPSKÊÑÄ´¤Î¾ì¹ç¡¤¿®¹æʬΥÀǽ¤¬Æ±ÄøÅÙ¤ÎÀǽ¤Ç¤¢¤ë¤¬¡¤16QAMÊÑÄ´¤Î¾ì¹ç¡¤½¾Íè¼êË¡¤Ç¤ÏʬΥ¤Ç¤¤Ê¤¤¾ò·ï²¼¤Ç¤â¿®¹æʬΥ¤¬²Äǽ¤Ç¤¢¤ë¤³¤È¤òÌÀ¤é¤«¤Ë¤·¤¿¡¥ |
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¥Ú¡¼¥¸ | pp. 66 - 68 |
¥¡¼¥ï¡¼¥É | ÃßÀÑ°ì³ç¿®¹æ½èÍý, IoT, û»þ´Ö¥Õ¡¼¥ê¥¨ÊÑ´¹, ¿®¹æʬΥ, Áë´Ø¿ô |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ¥Í¥Ã¥È¥ï¡¼¥¯¾å¤Ç¤ÎÃßÀÑ°ì³ç¿®¹æ½èÍýµ»½Ñ¤ò³ÎΩ¤¹¤ë¤¿¤á¡¤½¾Íè¤Î¼õ¿®µ¡¤Ç¤Ï¼Â¸½¤Ç¤¤Ê¤¤¡¤¾×Æͤ·¤¿¿®¹æ¤ä´³¾Ä¤ò¼õ¤±¤¿IoT/M2MüËö¿®¹æ¤ÎʬΥÊýË¡¤È¿®¹æʬΥ¸å¤Î¥Ç¡¼¥¿¤ÎÉüÄ´ÊýË¡¤È¤·¤Æ¡¤Ã»»þ´Ö¥Õ¡¼¥ê¥¨ÊÑ´¹¤òÍѤ¤¤¿ÆÃħÎÌÃê½Ð¤Ë¤è¤ë¿®¹æʬΥ¤ÈÆÀ¤é¤ì¤¿ÆÃħÎ̤òÉüÄ´¤Ë»ÈÍѤ¹¤ëÊýË¡¤òÄó°Æ¤·¤¿¡¥¤µ¤é¤Ë¿®¹æʬΥ¤Ë»ÈÍѤ¹¤ëû»þ´Ö¥Õ¡¼¥ê¥¨ÊÑ´¹¤Î»þ´ÖÁë¤òÍѤ¤¤ëºÝ¡¤Áë´Ø¿ô¤Î°ã¤¤¤Ë¤è¤Ã¤ÆºÇŬ¤ÊÁëÉýÁªÂò¤ËÍ¿¤¨¤ë±Æ¶Á¤Ë¤Ä¤¤¤Æ·×»»µ¡¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤ò¹Ô¤¤¡¤ºÇŬ¤ÊÁëÉý¤ÎÁªÂò¤ËÁë´Ø¿ô¤Ï±Æ¶Á¤·¤Ê¤¤¤³¤È¤òÌÀ¤é¤«¤Ë¤·¤¿¡¥ |
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Ãø¼Ô | *Ê¡»³ ¹ë, ÉÚΤ ÈË, ÅÄÌî ů, ¾å¸¶ °ì¹À (²¬»³Âç³Ø Âç³Ø±¡¼«Á³²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 69 - 72 |
¥¡¼¥ï¡¼¥É | LED, ²Ä»ë¸÷ÄÌ¿®, ̵ÀþÄÌ¿® |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ̵ÀþÄÌ¿®¥·¥¹¥Æ¥à¤Î¥È¥é¥Õ¥£¥Ã¥¯ÁýÂç¤ËÂбþ¤¹¤ë¤¿¤á¡¤LED¾ÈÌÀ¤òÍѤ¤¤¿¿·¤¿¤Ê̵ÀþÄÌ¿®µ»½Ñ¤Ç¤¢¤ëLED²Ä»ë¸÷ÄÌ¿®¤¬¸¦µæ¤µ¤ì¤Æ¤¤¤ë¡¥Ëܸ¦µæ¤Ç¤Ï¡¤LED²óÏ©¤Î¼þÇÈ¿ôÆÃÀ¤Ë¤è¤ë¿¶ÉýÊк¹¤ÎÊä½þ¤È¡¤¤½¤ÎÊä½þ¤Ë¤è¤êȯÀ¸¤¹¤ëLED²óÏ©½ÐÎϤˤª¤±¤ëÈóÀþ·ÁÀ¤òÊä½þ¤¹¤ë¼êË¡¤òÄó°Æ¤¹¤ë¡¥Äó°Æ¼êË¡¤Ç¤Ï³Æ¥µ¥Ö¥¥ã¥ê¥¢¿®¹æ¤Ë¿¶ÉýÊк¹¤ÎµÕÆÃÀ¤ò¾è»»¤¹¤ë¤³¤È¤ÇLED½ÐÎÏ¿®¹æ¤Ë¤ª¤±¤ë¹â¼þÇÈÎΰè¤ÎÅÅÎÏÄã²¼¤òÍÞ¤¨¿¶ÉýÊк¹¤ÎÊä½þ¤ò¹Ô¤¦¡¥¤³¤Î¤È¤LEDÆþÎÏÅÅÎϤ¬ÁýÂ礷ÈóÀþ·ÁÏĤ¬È¯À¸¤¹¤ë¤¬¡¤¤³¤ÎÈóÀþ·ÁÏĤòÊä½þ¤¹¤ë¤¿¤á¡¤¼õ¿®µ¡Â¦¤Ç¼õ¿®¿®¹æ¤ËLED¤ÎÈóÀþ·ÁÏĤεÕÆÃÀ¤ò¾è»»¤¹¤ë¡¥Ëܸ¦µæ¤Ç¤Ï¡¤Äó°Æ¼êË¡¤òÍѤ¤¤¿¾ì¹ç¤Î¼õ¿®ÆÃÀ²þÁ±¸ú²Ì¤ò·×»»µ¡¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¤è¤êɾ²Á¤¹¤ë¡¥ |
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¥Ú¡¼¥¸ | pp. 80 - 83 |
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Âê̾ | Moving Target Defense¼êË¡¤ÎÄ廡¹¶·â¤ÎÊ£»¨²½¤Ë´Ø¤¹¤ëÈæ³Óɾ²Á |
Ãø¼Ô | *Æⳤ Í´¿¿, Á°ÅÄ ¹á¿¥, ¹âÌî Ãκ´ (¹Åç»ÔΩÂç³ØÂç³Ø±¡ ¾ðÊó²Ê³Ø¸¦µæ²Ê), ÂçÀÐ ¶³¹° (³ô¼°²ñ¼Ò¥¤¥ó¥¿¡¼¥Í¥Ã¥È¥¤¥Ë¥·¥¢¥Æ¥£¥Ö) |
¥Ú¡¼¥¸ | pp. 84 - 86 |
¥¡¼¥ï¡¼¥É | Moving Target Defense, Ä廡¹¶·â, Èæ³Óɾ²Á |
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¥Ú¡¼¥¸ | pp. 87 - 90 |
¥¡¼¥ï¡¼¥É | Å·°æ¥¯¥ì¡¼¥ó, ²»¶Á¿®¹æ½èÍý, ¿¶¤ì³Ñ¿äÄê, °ÌÁ꺹, 2¥Þ¥¤¥¯¥í¥Û¥ó |
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¥Ú¡¼¥¸ | pp. 91 - 95 |
¥¡¼¥ï¡¼¥É | ¿ÍÂÎÄÌ¿®, À¸ÂÎǧ¾Ú, ¿®¹æ½èÍý, ¼±ÊÌ, ¥¹¥Ú¥¯¥È¥ë |
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¥Ú¡¼¥¸ | pp. 96 - 99 |
¥¡¼¥ï¡¼¥É | ¥¢¥×¥ê¥±¡¼¥·¥ç¥ó, ºÒ³², ¥È¥ê¥¢¡¼¥¸ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ºÒ³²ÎóÅç¤È¤¤¤ï¤ì¤ëÆüËܤǤÏÂ絬ÌÏÃϿ̤À¤±¤Ç¤Ï¤Ê¤¯¡¢¶áǯÂæÉ÷¤Ê¤É¤Î°Û¾ïµ¤¾Ý¤ä¥Æ¥í¤Ê¤É¤Î´í¸±¤âÁýÂ礷¤Æ¤¤¤ë¡£¤½¤Î¤è¤¦¤ÊºÒ³²¤¬µ¯¤³¤Ã¤¿ºÝºÒ³²µòÅÀɱ¡¤äÂ絬ÌÏɱ¡¤Ê¤É¤Ç»È¤¨¤ë¥·¥¹¥Æ¥à¤Ï¤¢¤ë¤¬¸½¾ì¤Ç´Ê°×Ū¤Ë»È¤¨¤ë¤è¤¦¤Ê¥·¥¹¥Æ¥à¤Ï¤Ê¤¤¡£Ëܸ¦µæ¤Ç¤Ï¤½¤Î¤¿¤á¤Î¸Ä¿Í¤Ç»È¤¨¤ë¥¢¥×¥ê¥±¡¼¥·¥ç¥ó¤Î³«È¯¤ò¹Ô¤¦¡£ËÜ¥¢¥×¥ê¤Ç¤ÏSTARTË¡¤Ë´ð¤Å¤£²Âò¤Î¼ÁÌä¤ò½Ð¤·¤Æ¤¤¤¡¢¤½¤ì¤ËÅú¤¨¤Æ¤â¤é¤¦¤³¤È¤ÇÂоݼԤΥȥꥢ¡¼¥¸¥ì¥Ù¥ë¤òȽÄꤹ¤ë¡£³ØÀ¸8̾¤È´Ç¸î³Ø¹»¶µÍ¡2̾¤Ë¤è¤ë¥È¥ê¥¢¡¼¥¸¼Â¸³¤Ç¤Ï¬Äê»þ´Ö¤ÏÁ´°÷¤ª¤ª¤à¤ÍƱÄøÅ٤Ȥʤä¿¡£¤³¤Î¤³¤È¤«¤é¡¢¥È¥ê¥¢¡¼¥¸·Ð¸³¤Î¤Ê¤¤ÁǿͤǤâSTARTË¡¤Ë´ð¤Å¤¤¤Æ¥È¥ê¥¢¡¼¥¸¤ò¹Ô¤¦¤³¤È¤Çû»þ´Ö¤ÇȽÄê¤ò¹Ô¤¦¤³¤È¤¬¤Ç¤¤ë¤È¤¤¤¦¤³¤È¤¬¤ï¤«¤ë¡£ |
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Ãø¼Ô | *ÎÓ Äø²Å, Htoo Htoo Sandi Kyaw, çÕÛª ¿®À¸, ·ªÎÓ Ì (²¬»³Âç³Ø Âç³Ø±¡¼«Á³²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 100 - 103 |
¥¡¼¥ï¡¼¥É | JPLAS, JAVA, Fill-in-Blank-Problems, ¶õÍó¸ìÊä½¼ÌäÂê |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ËÜ¥°¥ë¡¼¥×¤Ç¤Ï, Java ¥×¥í¥°¥é¥ß¥ó¥°³Ø½¬»Ù±ç¥·¥¹¥Æ¥à JPLAS(Java Programming Learning Assistant Sys- tem)¤òÄó°Æ¤·, ¤½¤ÎÃæ¤Ç¡¤´ðËÜŪ¤Êʸˡ³Ø½¬¤Î¤¿¤á¤Î ¶õÍó¸ìÊä½¼ÌäÂê¤ÎÄó°Æ¡¦¼ÂÁõ¤ò¿Ê¤á¤Æ¤¤¤ë. ËÜÌäÂê¤Ç ¤Ï, ¶õÍó¸ì¤ò´Þ¤àÌäÂꥳ¡¼¥É¤òÄ󼨤·, ³ØÀ¸¤Ï³Æ¶õÍó¤Ë ÂФ¹¤ëŬÀڤʸì¤ò²òÅú¤¹¤ë. ³Æ²òÅú¤Ï, Àµ²ò¸ì¤È¤Îʸ »ú¥Þ¥Ã¥Á¥ó¥°¤Ë¤è¤êÀµ¸íȽÄ꤬¤Ê¤µ¤ì¤ë. Ëܸ¦µæ¤Ç¤Ï, Java ¥×¥í¥°¥é¥ß¥ó¥°¤Î½ÅÍפʱþÍÑŪ¥È¥Ô¥Ã¥¯¤Ç¤¢¤ë¡¤ ¥³¥ì¥¯¥·¥ç¥ó¥Õ¥ì¡¼¥à¥ï¡¼¥¯¡¤Îã³°¡¤¥ª¥Ö¥¸¥§¥¯¥È¥¯ ¥é¥¹¡¤¥¹¥È¥ê¡¼¥à¤Ë¤ª¤±¤ë¡¤¶õÍó¸ìÊä½¼ÌäÂê¤Î²ÝÂêºî À®¤È¤½¤Îɾ²Á¤ò¹Ô¤¦.À¸À®¤·¤¿ 35 ¸Ä¤Î²ÝÂê¤Îɾ²Á¤È ¤·¤Æ¡¤ÆüËÜ¡¤Ãæ¹ñ¡¤¥ß¥ã¥ó¥Þ¡¼¤Î³ØÀ¸ 15 ̾¤Ë½ÐÂꤷ¡¤ Àµ²òΨ¤ÈÄó½Ð²ó¿ô¤ò»»½Ð¤·¤¿. |
Âê̾ | Convolutional Neural Network-based Indoor Scene Classification for a Wheelchair Robot from Spherical Images |
Ãø¼Ô | *Haolin Yuan, Le Wang, Shigang Li, Takahiro Kosaki (Hiroshima City University) |
¥Ú¡¼¥¸ | pp. 104 - 107 |
¥¡¼¥ï¡¼¥É | Indoor Scene, Classification, Convolutional Neural Network, Spherical Images, Resnet |
¥¢¥Ö¥¹¥È¥é¥¯¥È | For a wheelchair robot moving at indoor environments, when it is going to go straight or make a turn at a junction it needs to know the scene structures. In this paper, scene structure for robots¡Ç moving is classified into four types: corridors, L-type junctions, T-type junctions and halls. The scene is captured by a spherical camera. The captured spherical images are classified using the Resnet (deep residual network). As a preliminary experiment, we compare the performance of the classification for the input of full-view rectangular images, one-third covered rectangular images, upper or lower half covered rectangular images and perspective images, and give the future work about this research. |
Âê̾ | ÃγФǤ¤Ê¤¤¿¶Æ°»É·ã¤Ë¤è¤ëͶȯǾÇȤòÍѤ¤¤¿¸Ä¿Í¼±Ê̤ˤª¤±¤ë¼±ÊÌÀǽ¸þ¾å¤Î»î¤ß |
Ãø¼Ô | *ÃæÅç ¹¨ÃÒ, ¿ÀÆ£ µÁÌÀ (Ä»¼èÂç³ØÂç³Ø±¡»ý³À¼Ò²ñÁÏÀ¸²Ê³Ø¸¦µæ²Ê), ÃæÀ¾ ¸ù (Ä»¼èÂç³Ø¹©³ØÉô) |
¥Ú¡¼¥¸ | pp. 108 - 111 |
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Ãø¼Ô | *»Ö¼ Âóµ¯ (²¬»³¸©Î©Âç³ØÂç³Ø±¡ ¾ðÊó·Ï¹©³Ø¸¦µæ²Ê ¥·¥¹¥Æ¥à¹©³ØÀ칶), Ô¢Åç ¾æÀ¸ (²¬»³¸©Î©Âç³Ø ¾ðÊ󹩳ØÉô ¾ðÊóÄÌ¿®¹©³Ø²Ê) |
¥Ú¡¼¥¸ | pp. 112 - 114 |
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Ãø¼Ô | *Ãö¸¶ ·½°ì, Ê¡»Î ¾ (»³¸ýÂç³Ø Âç³Ø±¡ÁÏÀ®²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 115 - 118 |
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Âê̾ | ¥¿¡¼¥ó²ó¿ô¤ÎÀ©¸Â¤Ë¤è¤ëŬ±þŪ¥ë¡¼¥Æ¥£¥ó¥°¤ÎÄÌ¿®Àǽɾ²Á |
Ãø¼Ô | *ÉÙÅÄ Âç´î, ³áËÜ Âçµ± (»³¸ýÂç³Ø¹©³ØÉôÃÎǽ¾ðÊ󹩳زÊ), ¹õÀî ÍÛÂÀ, Ê¡»Î ¾ (»³¸ýÂç³ØÂç³Ø±¡ÁÏÀ®²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 119 - 122 |
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Âê̾ | ÈòÆñͶƳ¥¢¥×¥êVirtual Wall¤Ë¤è¤ëÈòÆñͶƳ¼Â¸³ |
Ãø¼Ô | *»³¾å À¿¿Í (Ê¡»³Âç³Ø ¹©³ØÉô), ¹õÌÚ ½Õ¼ù (ËÌΦÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø), Éôë ÂÙÀ¸, Ãæ±à ζ¼¡, ÃÓ²¬ ¹¨, ÃæÆ» ¾å (Ê¡»³Âç³Ø ¹©³ØÉô) |
¥Ú¡¼¥¸ | pp. 123 - 126 |
¥¡¼¥ï¡¼¥É | Beacon, ¥Õ¥£¥¸¥«¥ë¶õ´Ö, ¥µ¥¤¥Ð¡¼¶õ´Ö, ¼Â¶õ´Ö, ²¾ÁÛ¶õ´Ö |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ·úʪ¤Ë¤¤¤ë¥¿¥¤¥ß¥ó¥°¤ÇÅÚº½ºÒ³²¤¬È¯À¸¤·¤¿ºÝ¡¤²°Æâ¤ÎÈòÆñ¼Ô¤ÏºÒ³²¤¬¤É¤³¤ÇȯÀ¸¤·¤Æ¤¤¤ë¤«¤òÇÄ°®¤¹¤ë¤³¤È¤¬Æñ¤·¤¯¡¤ÈòÆñ»þ´Ö¤¬ÂçÉý¤ËÃÙ¤ì¤ë²ÄǽÀ¤¬¤¢¤ë¡¥²°Æâ¤Ë¤ª¤¤¤ÆÅÚº½ºÒ³²¤Ë¤è¤ëÆ󼡺ҳ²¤òÈò¤±¤ÆÈòÆñͶƳ¤¹¤ë¤¿¤á¤Ë¡¤Beacon¤Ë¤è¤ëÈòÆñͶƳ¤ËÃåÌܤ·¤¿¡¥Ëܸ¦µæ¤Ç¤Ï¡¤Beacon¤òÍѤ¤¤ÆʪÍýŪ¤ÊÊɤǤ¢¤ëËɲÐÈâ¤Î¤è¤¦¤ËÀßÄê¤Ç¤¤ë²¾ÁÛŪ¤ÊÊÉ¡ÖVirtual Wall¡×¤òÄó°Æ¤¹¤ë¡¥¥Õ¥£¥¸¥«¥ë¶õ´Ö¤È¥µ¥¤¥Ð¡¼¶õ´Ö¤Ë¤ª¤±¤ëÈòÆñͶƳ¼Â¸³¤Î·ë²Ì¡¤¥Õ¥£¥¸¥«¥ë¶õ´Ö¡¦¥µ¥¤¥Ð¡¼¶õ´Ö¤È¤â¤ËÈòÆñͶƳ¥¢¥×¥ê¾å¤ËVirtual Wall¤òɽ¼¨¤¹¤ë¤³¤È¤Ë¤è¤Ã¤ÆÈòÆñ»þ´Ö¤¬Ã»½Ì²Äǽ¤Ç¤¢¤ë¤³¤È¤¬ÌÀ¤é¤«¤È¤Ê¤Ã¤¿¡¥ |
Âê̾ | Evaluations of Air-Conditioning Guidance System in Hot Days |
Ãø¼Ô | *Samsul Huda, Nobuo Funabiki, Minoru Kuribayashi, Masaki Sakagami, Nobuya Ishihara (Okayama University) |
¥Ú¡¼¥¸ | pp. 127 - 130 |
¥¡¼¥ï¡¼¥É | air-conditioning guidance, AC-Guide, Raspberry Pi, discomfort index, API |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Global warming has become the major issue around the world as the serious risk for sustainable societies. Then, the proper use of an air conditioner (AC) is important to reduce the risk by saving energy consumptions. It is also essential to avoid heatstroke of seniors in hot days. To guide the proper use of the AC, we have developed the air-conditioning guidance system (AC-Guide) using Raspberry Pi with the sensors and the weathercast API. It sends alarm messages of requesting turning on/off the AC when the discomfort index (DI) is out of/within the comfortable range. However, due to the seasonal reason, this system has been only evaluated in cold or normal days, not in hot days. In this paper, we evaluate the system in hot days by running it in two rooms at Okayama University in August. It is confirmed that the system outputs the messages properly. |
Âê̾ | ·úʪ²ÐºÒ¤Ë¤ª¤±¤ë¼«Î§Ê¬»¶·¿ÈòÆñͶƳ¥·¥¹¥Æ¥à |
Ãø¼Ô | *À¶¿å ·ò (Ä»¼èÂç³ØÂç³Ø±¡»ý³À¼Ò²ñÁÏÀ¸²Ê³Ø¸¦µæ²Ê), ¶ûÅÄ ÂçÊå (Ä»¼èÂç³Ø¹©³ØÉô(¥¯¥í¥¹¾ðÊó²Ê³Ø¸¦µæ¥»¥ó¥¿¡¼)) |
¥Ú¡¼¥¸ | pp. 131 - 134 |
¥¡¼¥ï¡¼¥É | ÈòÆñͶƳ, ¼«Î§Ê¬»¶, ưŪ |
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¥Ú¡¼¥¸ | pp. 135 - 138 |
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Ãø¼Ô | *Ãæ°æ Þæ´ð, ¿ÀÊÝ ½¨»Ê (²¬»³Âç³Ø¹©³ØÉô¾ðÊó·Ï³Ø²Ê) |
¥Ú¡¼¥¸ | pp. 139 - 141 |
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¥Ú¡¼¥¸ | pp. 142 - 143 |
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¥Ú¡¼¥¸ | pp. 144 - 147 |
¥¡¼¥ï¡¼¥É | ÇÛÅÅ·ÏÅý, Äã°µ·ÏÅý, ÂÀÍÛ¸÷ȯÅÅ, ·ÏÅý¹½À®, ¶è´Ö±é»» |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ¶áǯ¤ÎÄã°µ·ÏÅý¤Ø¤ÎPVÏ¢·ÏÎÌÁý²Ã¤Ë¤è¤ê¡¤¾ï»þ¤ÎÅÅ°µ°Ý»ýÌäÂê¤ËÇï¼Ö¤¬¤«¤«¤Ã¤Æ¤¤¤ë¡£²Ã¤¨¤Æ¡¤±¿ÍѸ½¾ì¤Ç¤ÏPVÏ¢·Ï¤Î¿½¤·¹þ¤ß»þ¡¤ÅÅÎÏÉʼÁ¤Î°Ý»ý¡¤·ÏÅýÍÆÎ̤γÎǧ¤¬Ã༡Ū¤Ë¼Â»Ü¤µ¤ì¤Æ¤ª¤ê¡¤Êñ³çŪ¤Ê¸¡Æ¤¤¬É¬ÍפȤµ¤ì¤Æ¤¤¤ë¡£¤½¤³¤Ç¡¤Ãø¼Ô¤é¤Ï¾Íè¤ÎÄã°µ·ÏÅý¤Î¤¢¤ë¤Ù¤ÀßÈ÷¹½À®¤òÇÄ°®¤¹¤ë¤³¤È¤òÌÜŪ¤È¤·¸¡Æ¤¤ò¹Ô¤Ã¤Æ¤¤¿¡£ËܹƤǤϡ¤º®¹çÀ°¿ôÈóÀþ·Á·×²èÌäÂê¤È¤·¤ÆÄê¼°²½¤µ¤ì¤ëËÜÌäÂê¤Ë¤ª¤¤¤Æ¡¤¤è¤ê¸úΨŪ¤Ë·ÏÅý¹½À®¤ò·èÄꤹ¤ë¤¿¤á¤Ë¡¤PV¤äÉé²Ù¤ÎÉԳμÂÀ¤ËÂФ·¤Æ¶è´Ö±é»»¤òÍøÍѤ·¤¿¼êË¡¤òÄó°Æ¤·¡¤¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¤è¤ë¸¡¾Ú¤ò¹Ô¤¦¤³¤È¤Ç¡¤¼êË¡¤Î͸úÀ¤òɾ²Á¤¹¤ë¡£ |
Âê̾ | ÅÅÎÏ·ÏÅý¤Ë¤ª¤±¤ëºÇŬÅÅ°µÀ©¸æ¼êË¡¤Î¸¡Æ¤ ¡ÁÅÅ°µÀ©¸æµ¡´ï¤òÍѤ¤¤¿ÅÅ°µÀ©¸æÌäÂê¤ÎÄê¼°²½¡Á |
Ãø¼Ô | ñ±ÍøÌî ľ¿Í, *°æ¾å ľµª, ÅÔÅÄ Î¶Ê¿, º´¡¹ÌÚ Ë, ¤²ì ˧ʸ (¹ÅçÂç³Ø Âç³Ø±¡¹©³Ø¸¦µæ²Ê), ¿ÀÅÄ ¸÷¾Ï (Ãæ¹ñÅÅÎϥͥåȥ¥¯³ô¼°²ñ¼Ò ·ÏÅý±¿ÍÑÉô ·ÏÅýµ»½Ñ¥°¥ë¡¼¥×) |
¥Ú¡¼¥¸ | pp. 148 - 151 |
¥¡¼¥ï¡¼¥É | ÅÅÎÏ·ÏÅý, ÅÅ°µ°ÂÄêÀ, ÅÅ°µÀ©¸æ, º®¹çÀ°¿ôÀþ·Á·×²èÌäÂê |
¥¢¥Ö¥¹¥È¥é¥¯¥È | This paper proposes an optimal voltage control method to eliminate voltage violations. An optimization problem is formulated as a MILP problem where the objective is to minimize the control operations subject to operation constraints. In addition , the method is able to deal with infeasibility of voltage regulation caused by insufficient ¡Èvoltage control device¡É capacities. In this case, the total voltage violation is minimized by the available controls. |
Âê̾ | ÄäÅźî¶È·ÏÅýƳ½Ð¤Ë¤ª¤±¤ëÅÅ°µ¤ò¹Íθ¤·¤¿²á¹ó¾ò·ï¤Î¸¡Æ¤ |
Ãø¼Ô | *½Å¸÷ ¼Ó±Ñ (¹ÅçÂç³Ø), À ¹Ì¼£ (¹Å繩¶ÈÂç³Ø), ¤²ì ˧ʸ, ñ±ÍøÌî ľ¿Í, º´¡¹ÌÚ Ë (¹ÅçÂç³Ø) |
¥Ú¡¼¥¸ | pp. 152 - 154 |
¥¡¼¥ï¡¼¥É | ÄäÅźî¶È, ºî¶È·ÏÅý, ²á¹ó¾ò·ï |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Åö¸¦µæ¼¼¤Ï¡¤ÄäÅźî¶È¤Ë¸þ¤±¤Æºî¶ÈÆü¡¤ºî¶ÈÁȹ礻¡¤·ÏÅý¹½À®¤ò·èÄꤹ¤ëÄäÅźî¶È·×²èÌäÂê¤Ë¼è¤êÁȤó¤Ç¤¤¤ë¡£¶áǯ¤Ç¤Ï¡¤ÂÀÍÛ¸÷ȯÅŤÎÂçÎÌƳÆþ¤äÅÅÎϼ«Í³²½¤Î¿ÊŸ¤Ë¤è¤ê¡¤·ÏÅýÆâ¤ÎÅŸ»·²¤ÎÉÔ³ÎÄêÀ¤¬Áý²Ã¤·¤Æ¤¤¤ë¡£¤½¤³¤Ç¡¤ÄäÅźî¶È¤Ë¤ª¤±¤ë²á¹ó¾ò·ï¤ò²þ¤á¤Æ¹Í¤¨¡¤ÉÔ³ÎÄêÀ¤ËÂбþ¤¹¤ëÊý¿Ë¤òΩ¤Æ¤¿¡£ËܹƤǤϡ¤¹Í¤¨¤ë¤Ù¤²á¹ó¾ò·ï¤òÄê¼°²½¤·¡¤´Ê°×·ÏÅý¥â¥Ç¥ë¤ª¤è¤Ó¼Â·ÏÅý¥â¥Ç¥ë¤Ç¤Î¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤òÄ̤·¤Æ¹Í»¡¤ò¹Ô¤¦¡£ |
Âê̾ | Åù²ÁÄñ¹³À©¸æË¡¤òŬÍѤ·¤¿SEPIC¤òÍѤ¤¤¿¥Ô¥¨¥¾Êý¼°¿¶Æ°È¯ÅÅ¥·¥¹¥Æ¥à |
Ãø¼Ô | *ÃæÅì ¾°·É (»³¸ýÂç³Ø), »³ÅÄ ÍÎÌÀ (»³¸ýÂç³ØÂç³Ø±¡ÁÏÀ®²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 155 - 158 |
¥¡¼¥ï¡¼¥É | Åù²ÁÄñ¹³À©¸æË¡, ¥Ô¥¨¥¾ÁÇ»Ò, SEPIC |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ÃÛǯ¿ô¤¬Ä¹¤¤¶¶Î¤ʤɤΰ۾ï¤ò¸¡ÃΤ¹¤ë¥â¥Ë¥¿¥ê¥ó¥°µ»½Ñ¤¬¶áǯ µá¤á¤é¤ì¤Æ¤¤¤ë¡£ËÜÏÀʸ¤Ç¤Ï,¤³¤Î¤è¤¦¤Ê¥â¥Ë¥¿¥ê¥ó¥°¤ËÍѤ¤¤ë ¥»¥ó¥µ¥Ç¥Ð¥¤¥¹¤ÎÅŸ»¤È¤·¤Æ¥Ô¥¨¥¾Êý¼°¿¶Æ°È¯ÅŤòÍѤ¤¤ë¥·¥¹¥Æ¥à¤Î´ðÁø¡Æ¤¤ò¹Ô¤¦¡£Äó°Æ¤¹¤ë¥·¥¹¥Æ¥à¤Ï¥À¥¤¥ª¡¼¥ÉÀ°Î®²óÏ©¤ÈSEPIC¡¤ÃßÅťǥХ¤¥¹¤«¤é¹½À®¤µ¤ì¤ë¡£SEPIC¤ò¥Ô¥¨¥¾ÁǻҤζ¡µëÅÅÎϤ¬ºÇÂç¤È¤Ê¤ë¡¤¤¢¤ë¤¤¤Ï,Éé²Ù¤ÎÅÅ°µ¤¬°ìÄê¤È¤Ê¤ë¤è¤¦¤ËÆ°ºî¤µ¤»¡¤2ÃʤÎÅÅÎÏÊÑ´¹´ï¤òÍѤ¤¤Æ¶¡µëÅÅÎϤª¤è¤Ó½ÐÎÏÅÅ°µ¤òÀ©¸æ¤¹¤ë½¾ÍèÊý¼°¤ÈÈæ³Ó¤·¤ÆÅÅÎÏÊÑ´¹¸úΨ¤Î¸þ¾å¤¬´üÂԤǤ¤ë¡£ËÜÏÀʸ¤Ç¤ÏPLECS¤Ë¤è¤ë¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤è¤ê¡¤Åù²ÁÄñ¹³À©¸æË¡¤òÍѤ¤¤ë¤³¤È¤Ç¥Ô¥¨¥¾ÁǻҤζ¡µëÅÅÎϤòºÇÂç²½¤Ç¤¤ë¤³¤È¤òÊó¹ð¤¹¤ë¡£ |
Âê̾ | Power Loss Analysis of DAB Converter Based Battery Charger in a Stand-Alone Wind Power Generation System |
Ãø¼Ô | *Yuto Takayama, Hiroaki Yamada (Graduate School of Sciences and Technology for Innovation, Yamaguchi University) |
¥Ú¡¼¥¸ | pp. 159 - 164 |
¥¡¼¥ï¡¼¥É | Dual Actibe Bridge converter, Wind power generation, battery charger |
¥¢¥Ö¥¹¥È¥é¥¯¥È | This paper deals with the power loss evaluation of a dual active bridge (DAB) converter based battery charger in a stand-alone wind power generation system (WPGS). The proposed WPGS with DAB converter based battery charger uses three converters between a generator and AC load. In this paper, we test and evaluate the proposed DAB converter based battery charger in the experimental setup. Experimental results demonstrate that the output voltage of the VSI can be kept constant when the DC-link voltage is changed and the DC-link voltage is kept constant by the DAB converter in battery-discharging mode. |
Âê̾ | ¥¢¥¯¥Æ¥£¥Ö¥Ð¥Ã¥Õ¥¡²óÏ©ÉÕ¤100-WµéLED¶îÆ°ÅŸ»¤Î»¼º¸¡Æ¤ |
Ãø¼Ô | *¾å±ò °ìµ±, »³ÅÄ ÍÎÌÀ (»³¸ýÂç³Ø Âç³Ø±¡ ÁÏÀ®²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 165 - 169 |
¥¡¼¥ï¡¼¥É | LED¶îÆ°ÅŸ», ÅŲò¥¥ã¥Ñ¥·¥¿¥ì¥¹, ¥Õ¥ê¥Ã¥«¥ì¥¹, ¥¢¥¯¥Æ¥£¥Ö¥Ð¥Ã¥Õ¥¡ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | LED¤ÎÅŸ»¤ËñÁê¸òή¤òÍѤ¤¤ë¾ì¹ç¡¤LEDÅÀÅô»þ¤ËÅŸ»¼þÇÈ¿ô¤Î2ÇܤνֻþÅÅÎÏ̮ư¤Ë¤è¤ë¥Õ¥ê¥Ã¥«¤òËɻߤ¹¤ë¤¿¤á¤Ë¡¤ÂçÍÆÎ̤ÎÅŲò¥¥ã¥Ñ¥·¥¿¤¬É¬ÍפȤʤ롥ÅŲò¥¥ã¥Ñ¥·¥¿¤Ï²¹ÅÙ¤ä¥ê¥×¥ëÅÅή¤Î±Æ¶Á¤ò¼õ¤±¤Æ¼÷Ì¿¤¬ÊѲ½¤¹¤ë¤È¤¤¤Ã¤¿ÌäÂ꤬¤¢¤ë¡¥Æäˡ¤ËÜÏÀʸ¤ÇÄó°Æ¤¹¤ë¤è¤¦¤Ê¹âµ±ÅÙLED ¾ÈÌÀ¤Ç¤Ï¡¤LED¤ÎȯǮ¤ò¼õ¤±¤Æ¡¤¶îÆ°ÅŸ»¤Î¼þ°Ï¤¬¹â²¹¤Ë¤Ê¤ë¡¥¤³¤Î¤¿¤á¡¤¹âµ±ÅÙLED ¶îÆ°ÅŸ»¤ÎÅŲò¥¥ã¥Ñ¥·¥¿¥ì¥¹²½¤ª¤è¤ÓLED ÅÅή¤Î̮ư¤òÊä½þ¤¹¤ë¶îÆ°ÅŸ»¤Î³«È¯¤¬É¬ÍפȤʤ롥ËÜÏÀʸ¤Ç¤Ï¡¤¾®ÍÆÎ̤Υ¥ã¥Ñ¥·¥¿¤Ç½Ö»þÅÅÎÏ̮ư¤òÊä½þ¤¹¤ë¥¢¥¯¥Æ¥£¥Ö¥Ð¥Ã¥Õ¥¡²óÏ©¤òÍѤ¤¤¿ÅŲò¥¥ã¥Ñ¥·¥¿¥ì¥¹¤ÎLED¶îÆ°ÅŸ»¤òÄó°Æ¤·¡¤¤½¤Î͸úÀ¤ª¤è¤Ó²óϩ»¼º¤ò¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¤è¤ê³Îǧ¤¹¤ë¡¥ |
Âê̾ | ľÎó·Á¥¢¥¯¥Æ¥£¥Ö¥Õ¥£¥ë¥¿¤òÍѤ¤¤¿ ¥Ï¥¤¥Ö¥ê¥Ã¥ÉÀŻ߷Á̵¸úÅÅÎÏÊä½þÁõÃ֤μÂÍÑŪ¤ÊÀ©¸æË¡ |
Ãø¼Ô | *ÂçË٠͵ÂÀ (»³¸ýÂç³Ø¹©³ØÉô), »³ÅÄ ÍÎÌÀ (»³¸ýÂç³Ø Âç³Ø±¡ÁÏÀ®²Ê³Ø¸¦µæ²Ê), ÃÓÅÄ É÷²Ö, ²¬ËÜ ¾»¹¬ (±§Éô¹©¶È¹âÅùÀìÌç³Ø¹»), ÅÄÃæ ½Óɧ (»³¸ýÂç³Ø Âç³Ø±¡ÁÏÀ®²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 170 - 173 |
¥¡¼¥ï¡¼¥É | ľÎó·Á¥¢¥¯¥Æ¥£¥Ö¥Õ¥£¥ë¥¿, ¥Ï¥¤¥Ö¥ê¥Ã¥ÉÊý¼°SVC, thyristor-controlled reactor, ¿ÊÁꥳ¥ó¥Ç¥ó¥µ, ¹âÄ´ÇÈÊä½þ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ËÜÏÀʸ¤Ç¤Ï¡¤¤Ï¤¸¤á¤Ë¡¤ÅŸ»ÅÅή¤Î¹âÄ´ÇÈÀ®Ê¬¤Î¤ß¤ò¸¡½Ð¤¹¤ë¤¿¤á¤Ë¤Ï»°Áêphase locked loop (PLL) ¤Ë¤è¤êȯÀ¸¤·¤¿Åŵ¤³Ñ¤òÅŸ»Â¦ÁêÅÅ°µ¤ÎÅŵ¤³Ñ¤È°ìÃפµ¤»¤ëɬÍפ¬Ìµ¤¤¤³¤È¤òÍýÏÀŪ¤ËÌÀ¤é¤«¤Ë¤¹¤ë¡¥¼¡¤Ë¡¤Àè¤ËÄó°Æ¤·¤¿¥Ï¥¤¥Ö¥ê¥Ã¥ÉSVC ¤ÎÀ©¸æË¡¤Ë¤ª¤¤¤Æ»°ÁêPLL ¤Ë¤è¤êȯÀ¸¤·¤¿Åŵ¤³Ñ¤òÅŸ»Â¦ÁêÅÅ°µ¤ÎÅŵ¤³Ñ¤È°ìÃפ·¤Æ¤¤¤Ê¤¤¾ì¹ç¤Ç¤âÎɹ¥¤Ê¹âÄ´ÇÈÊä½þ¸ú²Ì¤¬ÆÀ¤é¤ì¤ë¤³¤È¤ò·×»»µ¡¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¤è¤ê³Îǧ¤¹¤ë¡¥ |
Âê̾ | ÅÅ°µ¥Ç¥£¥Ã¥×»þ¤ÎÅŵ¤¼«Æ°¼ÖÍÑ¥¹¥Þ¡¼¥È¥Á¥ã¡¼¥¸¥ã¤òÍѤ¤¤¿ÅÅÎÏÉʼÁÊÝ¾Ú |
Ãø¼Ô | *¼ã¿ù ¿Ô, ÃÓÅÄ É÷²Ö, ²¬ËÜ ¾»¹¬ (±§Éô¹©¶È¹âÅùÀìÌç³Ø¹»), »³ÅÄ ÍÎÌÀ, ÅÄÃæ ½Óɧ (»³¸ýÂç³Ø) |
¥Ú¡¼¥¸ | pp. 174 - 175 |
¥¡¼¥ï¡¼¥É | ñÁê3Àþ¼°ÇÛÅÅÊý¼°, ÅÅ°µ¥Ç¥£¥Ã¥× |
Âê̾ | ¥Æ¥é¥Ø¥ë¥ÄÇÈ¥±¥ß¥«¥ë¸²Èù¶À¤òÍѤ¤¤¿ÇÙ´âºÙ˦¸¡½Ðµ»½Ñ¤Î³«È¯ |
Ãø¼Ô | *èÓÅÄ Í¦°ì, º´Æ£ ¹§Í´ (²¬»³Âç³ØÂç³Ø±¡ ¥Ø¥ë¥¹¥·¥¹¥Æ¥àÅý¹ç²Ê³Ø¸¦µæ²Ê), °æ¾å Çîʸ (²¬»³Âç³ØÂç³Ø±¡ °å»õÌô³ØÁí¹ç¸¦µæ²Ê), ²¦ 璡, ºæ ·ò»Ê, ÄÍÅÄ ·¼Æó, µªÏ Íøɧ (²¬»³Âç³ØÂç³Ø±¡ ¥Ø¥ë¥¹¥·¥¹¥Æ¥àÅý¹ç²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 176 - 178 |
¥¡¼¥ï¡¼¥É | ¥Æ¥é¥Ø¥ë¥Ä, ¥Æ¥é¥Ø¥ë¥ÄÇÈ¥±¥ß¥«¥ë¸²Èù¶À, ÇÙÁ£´âÇÝÍܺÙ˦, ¥Õ¥§¥à¥ÈÉå졼¥¶¡¼ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ¶áǯ¡¤¤¬¤ó¼À´µ¤ËÂФ·¤Æ¥²¥Î¥à¤Ë´ð¤Å¤¤¤¿¿ÇÃǤ¬¹Ô¤ï¤ì¤ë¤è¤¦¤Ë¤Ê¤Ã¤Æ¤¤¤ë¡¥¤³¤ÎºÝ¡¤°ìÄê¿å½à¤òËþ¤¿¤¹¸¡ÂÎÁÈ¿¥¤¬É¬ÍפȤʤ롥¤½¤³¤Ç¡¤¸¡ÂÎÁÈ¿¥¤Î´Ñ»¡¤¬¹Ô¤¦¤³¤È¤Ç¡¤¤½¤ÎȽÃǤò¤·¤Æ¤¤¤ë¡¥¤·¤«¤·¡¤¸¡ÂÎÁÈ¿¥¤ò´Ñ»¡¤¹¤ë¤¿¤á¤Î½èÍý¤Ë»þ´Ö¤òÍפ¹¤ë¤³¤È¤äµ»Î̤ˤè¤Ã¤Æ·ë²Ì¤¬º¸±¦¤µ¤ì¤ë¤È¤¤¤Ã¤¿²ÝÂ꤬¤¢¤ë¡¥¤½¤³¤Ç¡¤¥Æ¥é¥Ø¥ë¥ÄÇÈ¥±¥ß¥«¥ë¸²Èù¶À(TCM)¤òÍѤ¤¤Æ¡¤²ÝÂê²ò·è¤Ë¼è¤êÁȤó¤Ç¤¤¤ë¡¥Ëܸ¦µæ¤Ç¤ÏÇÙÁ£´âÇÝÍܺÙ˦¤Î¸¡½Ð¤ò¹Ô¤¤¡¤ºÇŬ¤ÊÇÙÁ£´âÇÝÍܺÙ˦¤Î¸¡Æ¤¤ò¹Ô¤Ã¤¿¡¥¤½¤Î·ë²Ì¡¤¸ÇÄê½èÍý¤ò¤·¤¿ÇÙÁ£´âÇÝÍܺÙ˦¤òÍѤ¤¤ë¤³¤È¤Ç¡¤¥Æ¥é¥Ø¥ë¥ÄÇȶ¯ÅÙ¤ÎÊѲ½Î̤¬2.3~2.6Çܾ徺¤¹¤ë¤³¤È¤¬³Îǧ¤Ç¤¤¿¡¥¤½¤Î¤¿¤á¡¤¸ÇÄê½èÍý¤ò¤·¤¿ÇÙÁ£´âÇÝÍܺÙ˦¤¬ºÇŬ¤Ç¤¢¤ë¤³¤È¤¬¼¨¤µ¤ì¤¿¡¥ |
Âê̾ | ¾®Æ°Êª¤ÎõÃΤòÁÛÄꤷ¤¿Ä¶²»Çȥ졼¥À¥·¥¹¥Æ¥à¤Î³«È¯ |
Ãø¼Ô | *ÉÚÌÚÅÄ ÍªÀ¸, Àîµ×ÊÝ µ®»Ë (¹áÀî¹âÅùÀìÌç³Ø¹»ÅŻҾðÊóÄÌ¿®¹©³ØÀ칶) |
¥Ú¡¼¥¸ | pp. 179 - 181 |
¥¡¼¥ï¡¼¥É | Ķ²»ÇÈ¥»¥ó¥µ, Aruduino, processing |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ¶áǯ¡¤¥Í¥º¥ß¤ä¥¤¥¿¥Á¡¤¥Ï¥¯¥Ó¥·¥óÅù¤ÎÍÍ¡¹¤ÊÌîÀ¸Æ°Êª¡Ê³²½Ã¡Ë¤¬²È²°¤Î²°º¬Î¢¤ä¾²²¼¤Ë½»¤ßÃ夯»öÎ㤬ȯÀ¸¤·¤Æ¤¤¤ë¡¥¤³¤ì¤é¤Î³²½Ã¤Ï¡¤Áû²»¤ä°½¤Î¸¶°ø¤È¤Ê¤ê¡¤²È²°¤ÎÉå¿©¤äÇ˲õ¡¤½»Ì±¤Ø¤Î·ò¹¯Èï³²¤ò°ú¤µ¯¤³¤¹²ÄǽÀ¤¬¤¢¤ë¡¥³²½Ã¤Ë¤è¤ëÈï³²¤òËɤ°¤Ë¤Ï¡¤¤³¤ì¤é¤Îưʪ¤¬²È²°Æâ¤Î¤É¤³¤ËÀ¸Â©¤·¤Æ¤¤¤ë¤«¡¤¤Þ¤¿¡¤¤½¤ÎÆþ½Ð·ÐÏ©¤òÄ´ºº¤·¡¤¶î½ü¤¹¤ëɬÍפ¬¤¢¤ë¡¥Ëܸ¦µæ¤Ç¤Ï¡¤¤³¤ì¤é²È²°¤ËÀø¤à³²½Ã¤Îõºº¤òÌÜŪ¤È¤·¡¤Ä¶²»ÇÈ¥»¥ó¥µ¤ò2¤ÄÍѤ¤¤Æ2¤Ä¤Î¥ì¡¼¥À¤«¤é°ÌÃÖ¿äÄꤹ¤ë¥·¥¹¥Æ¥à¤Î³«È¯¤ò»î¤ß¤¿¡¥¤Þ¤¿¡¤Ä¶²»ÇȤϿʹ֤ˤÏʹ¤³¤¨¤Ê¤¤²»ÇȤǤ¢¤ë¤¬¡¤¥Í¥º¥ßÅù¤Îưʪ¤Ë¤Ïʹ¤³¤¨¤ë¤³¤È¤¬ÃΤé¤ì¤Æ¤¤¤ë¡¥¤½¤Î¤¿¤á¡¤Ä¶²»ÇȤòÍѤ¤¤ë¤³¤È¤Ç³²½Ã¤Î²È²°¤Ø¤ÎΩ¤ÁÆþ¤ê¤ò̤Á³¤ËËɤ°¤³¤È¤â´üÂԤǤ¤ë¤È¹Í¤¨¤¿¡¥ |
Âê̾ | ²»¶Á¿®¹æ¥Ç¡¼¥¿¤ÎÆâÀÑ·×»»¤Ë´ð¤Å¤¯¥¸¥Ö¥¯¥ì¡¼¥ó¿¶¤ì³Ñ¿äÄê |
Ãø¼Ô | *ËÙ¹¾ ľµ®, ÃæËÜ ¾»Í³, »³ËÜ Æ© (¹ÅçÂç³Ø) |
¥Ú¡¼¥¸ | pp. 182 - 185 |
¥¡¼¥ï¡¼¥É | ¥¸¥Ö¥¯¥ì¡¼¥ó, 3¥Þ¥¤¥¯¥í¥Û¥ó, ²°³°¼Â¸³, ÆâÀÑ, ¿¶¤ì³Ñ¿äÄê |
Âê̾ | Improvement of Laser-tracking Algorithm for Spectral Sensing with Free Space Optics and its Performance Evaluation |
Ãø¼Ô | *Tomohiro Shinki, Naoki Kagawa (Ê¡»³Âç³Ø) |
¥Ú¡¼¥¸ | pp. 186 - 187 |
¥¡¼¥ï¡¼¥É | Algorithm, beam, arc, translation, rotation |
¥¢¥Ö¥¹¥È¥é¥¯¥È | We develop a system for measuring the concentration of greenhouse gases in the air and communicating as the free space optics, simultaneously. The core technology of the system is the ray-tracking. In this paper, we report a developed algorithm for tracking beams with the same angular velocity even if different optical path lengths, i.e. radius of curvature. |
Âê̾ | RF-DC ÊÑ´¹²óÏ©¤Ë¤ª¤±¤ë¥À¥¤¥ª¡¼¥É¤Î¸úΨ¬Äê |
Ãø¼Ô | *Çß¼ã ˼£ (²¬»³Âç³Ø ¹©³ØÉôÅŵ¤ÄÌ¿®·Ï³Ø²Ê), ¾®ÎÓ ·òÊå, Æ£¿¹ ÏÂÇî (²¬»³Âç³ØÂç³Ø±¡ ¼«Á³²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 188 - 189 |
¥¡¼¥ï¡¼¥É | ̵ÀþÅÅÎÏÅÁÁ÷, ¥ì¥¯¥Æ¥Ê, RF-DCÊÑ´¹²óÏ© |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ̵ÀþÅÅÎÏÅÁÁ÷¤Ë»ÈÍѤµ¤ì¤ë¹â¸úΨ¤ÊRF-DCÊÑ´¹²óÏ©¤Ë¤ª¤¤¤Æ¡¤¥À¥¤¥ª¡¼¥É¤ÎÆ°ºî¥¤¥ó¥Ô¡¼¥À¥ó¥¹¤ò¬Äꤹ¤ë¤¿¤á¡¤¥À¥¤¥ª¡¼¥É¤ÎºÇ¹â¸úΨ¤ò¬Äꤷ¤¿¡¥ |
Âê̾ | ¥Û¡¼¥ëÁǻҤòÍѤ¤¤ë¤³¤È¤Ë¤è¤ëĶÅÁƳ¥Æ¡¼¥×Àþºà¤ÎÀܳÄñ¹³¤Î¬ÄêÊýË¡¤Î¸¡Æ¤ |
Ãø¼Ô | *ÂçÀ¾ ·É²ð, ¾åÌî Í¥²ð, ¸¶ÅÄ Ä¾¹¬ (»³¸ýÂç³ØÂç³Ø±¡ÁÏÀ®²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 190 - 192 |
¥¡¼¥ï¡¼¥É | ĶÅÁƳ, ¥³¥¤¥ë |
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¥Ú¡¼¥¸ | pp. 196 - 198 |
¥¡¼¥ï¡¼¥É | IoT/M2MüËö, ´³¾Ä, ¿®¹æʬΥ¡¦ÉüÄ´, D/U |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Áý²Ã¤¹¤ë̵Àþµ¡Ç½¤òÅëºÜ¤·¤¿¥Ç¥Ð¥¤¥¹¤Î̵Ãá½ø¤ÊÄÌ¿®¤Ë¤è¤ê¡¤¿®¹æ¤Î´³¾Ä¤¬µ¯¤³¤ë²ÝÂ꤬¹Í¤¨¤é¤ì¤Æ¤¤¤ë¡¥ËܹƤǤϴû¸µ»½Ñ¤Ç¤Ïº¤Æñ¤Ê´³¾Ä¤ò¼õ¤±¤¿IoT/M2MüËö¿®¹æ¤ÎʬΥ¡¤ÉüÄ´¤ò²Äǽ¤È¤¹¤ëÃßÀÑ°ì³ç¿®¹æ½èÍýµ»½Ñ¤Î³ÎΩ¤òÌܻؤ·¡¤Æ±´ü¸¡ÇȤòÍѤ¤¤ë½¾Íè¤Î¿®¹æʬΥ¡¦ÉüÄ´ÊýË¡¤È¡¤Äó°Æ¤µ¤ì¤Æ¤¤¤ëÆÃħÎÌÉüÄ´Ë¡¤Ë¤Ä¤¤¤Æ¡¤´³¾Ä¿®¹æ¤ÎÅÅÎϤαƶÁ¤òƧ¤Þ¤¨Ê¬Î¥¡¦ÉüÄ´Àǽ¤ò·×»»µ¡¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Çɾ²Á¤·¤¿¡¥¤½¤Î·ë²Ì¡¤D/U¤¬-3dB¤Ë¤ª¤¤¤Æ8PSK¿®¹æ¤ÏξÉüÄ´Ë¡¤ÇʬΥ²Äǽ¤Ç¤¢¤ë¤¬¡¤16QAM¿®¹æ¤Ï½¾Íè¼êË¡¤Ç¤ÏʬΥÉÔ²Äǽ¤ËÂФ·¤ÆÄó°Æ¼êË¡¤Ç¤ÏʬΥ²Äǽ¤È¤Ê¤ê¡¤Äó°Æµ»½Ñ¤ÎÍ¥°ÌÀ¤ò³Îǧ¤·¤¿¡¥¤Þ¤¿STFT¤Ë¤ª¤±¤ëÁëÉý¡¤Áë´Ø¿ô¤ÎÊѹ¹¤Ë¤è¤êD/U¤¬-10dB¤Ç¤âʬΥ²Äǽ¤È¤Ê¤ë¤³¤È¤òÌÀ¤é¤«¤Ë¤·¤¿¡¥ |
Âê̾ | Dive into Convolutional Neural Network with Constellation Diagram for Automatic Modulation Classification |
Ãø¼Ô | *Yutao Liu, Toru Nishiyama, Shigeru Tomisato, Kazuhiro Uehara (²¬»³Âç³Ø¼«Á³²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 199 - 203 |
¥¡¼¥ï¡¼¥É | Automatic Modulation Classification, Convolutional Neural Network, Constellation Diagram |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Automatic Modulation Classification (AMC) of the transmitted signals remains a challenging area in modern intelligent communication systems such as cognitive radio system. There are some problems such as low efficiency and low accuracy for traditional classification systems. Therefore, we propose a new system for AMC using Convolutional Neural Network (CNN) with constellation diagrams in Additive White Gaussian Noise channel and use the trained model to achieve modulation recognition with constellation diagrams. After using new CNN models, improved input image size, number of dropout layers and simulation hyperparameters, we have achieved high efficiency and high precision recognition of modulation methods. |
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¥Ú¡¼¥¸ | pp. 204 - 207 |
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¥Ú¡¼¥¸ | pp. 208 - 210 |
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¥Ú¡¼¥¸ | pp. 226 - 229 |
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¥¢¥Ö¥¹¥È¥é¥¯¥È | ²ÐºÒ¤¬È¯À¸¤·¤¿ºÝ¡¤ÄäÅŤäÄÌ¿®¥¢¥¯¥»¥¹¤Î½¸Ãæ¤Ë¤è¤Ã¤ÆÄÌ¿®¥¤¥ó¥Õ¥é¤¬ÍøÍѤǤ¤Ê¤¤²ÄǽÀ¤¬¤¢¤ë¡¥¤³¤Î¤è¤¦¤Ê¾õ¶·¤ËÂбþ¤¹¤ë¤¿¤á¡¤MANET¤òÍѤ¤¤¿ÈòÆñ»Ù±ç¥·¥¹¥Æ¥à¤Î¸¦µæ¤¬¹Ô¤ï¤ì¤Æ¤¤¤ë¡¥¤³¤ÎÈòÆñ»Ù±ç¥·¥¹¥Æ¥à¤ÏMANET¤òÍѤ¤¤Æ¡¤ÈòÆñ¼Ô´Ö¤ÇºÒ³²¾ðÊó¤ò¶¦Í¤¹¤ë¡¥¤½¤·¤Æ¡¤¤½¤Î¾ðÊó¤ò´ð¤ËÈòÆñ·ÐÏ©¤òõº÷¤·¡¤ÈòÆñ¼Ô¤ò·úʪ³°¤ËͶƳ¤¹¤ë¡¥¤³¤ì¤Þ¤Ç¤ÎÈòÆñ»Ù±ç¥·¥¹¥Æ¥à¤Ç¤Ï°Üư®ÅÙ¤ò¹Íθ¤·¤Æ¤¤¤Ê¤«¤Ã¤¿¡¥¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¹âÎð¼Ô¤Ë°ÜÆ°µ÷Î¥¤Îû¤¤·ÐÏ©¤òÍ¥À褷¤ÆÁªÂò¤µ¤»¡¤¼ã¼Ô¤Ë¹âÎð¼Ô¤¬¾¯¤Ê¤¯¡¤º®»¨¤·¤Æ¤¤¤Ê¤¤·ÐÏ©¤òÍ¥À褷¤ÆÁªÂò¤µ¤»¤ëÈòÆñ·ÐÏ©ÁªÂò¼êË¡¤òÄó°Æ¤¹¤ë¡¥¤³¤ÎÄó°Æ¼êË¡¤òɾ²Á¤¹¤ë¤¿¤á¡¤¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¼Â¸³¤ò¹Ô¤Ã¤¿¡¥¤½¤Î·ë²Ì¡¤¥æ¡¼¥¶¤ÎÈòÆñ´°Î»»þ´Ö¤òû½Ì¤¹¤ë¤³¤È¤¬¤Ç¤¤¿¡¥ |
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Ãø¼Ô | *ÃÝë ±Ñů (¹Åç»ÔΩÂç³Ø¾ðÊó²Ê³ØÉô), Ã滳 ¿Î»Ë, Àи÷ ½Ó²ð (¹Åç»ÔΩÂç³ØÂç³Ø±¡) |
¥Ú¡¼¥¸ | pp. 230 - 231 |
¥¡¼¥ï¡¼¥É | ¥¢¥Ç¥Î¥¤¥É, À¼Æ»¥â¥Ç¥ë, ÅÁã´Ø¿ô, TSP¿®¹æ, ¥¯¥í¥¹¥¹¥Ú¥¯¥È¥ë |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ²æ¡¹¤ÏÈ󿯽±¤ÊÊýË¡¤È¤·¤Æ¡¤²»¤Ë¤è¤ë¥¢¥Ç¥Î¥¤¥ÉÈîÂç²½¸¡½Ð¤Î¸¡Æ¤¤ò¹Ô¤Ã¤Æ¤¤¤ë¡¥Ëܸ¦µæ¤Ç¤Ï»°¼¡¸µ¥â¥Ç¥ë¤òÂоݤȤ·¤¿·×¬¤Þ¤¿É¾²Á¤ò¹Ô¤¦½àÈ÷¤È¤·¤Æ, ±ßÅû´É¥â¥Ç¥ë¤ò´ð¤Ë¥Ç¥¶¥¤¥ó¤µ¤ì¤¿À¼Æ»¥â¥Ç¥ë¤òÂоݤËÅÁã´Ø¿ô¤Î¿äÄêÀºÅÙ¤ò³Îǧ¤¹¤ë¡¥À¼Æ»¥â¥Ç¥ë¤ËÂФ·¤Æ³Æ¼êË¡¤Ë¤è¤ëÅÁã´Ø¿ô¤ò¿äÄꤷ¤¿¤È¤³¤í¡¤¶¦¿¶¼þÇÈ¿ô¤Îº¹¤Ï¾¯¤Ê¤«¤Ã¤¿¤â¤Î¤Î¥²¥¤¥óº¹¤¬¤¢¤ë¤³¤È¤¬³Îǧ¤µ¤ì¤¿¡¥ |
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Ãø¼Ô | *ÀµÈª ÃÒÆÁ (Ê¡»³Âç³Ø¹©³ØÉô), ÉðÅÄ Í´¼ù (Ê¡»³Âç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê), ÃæÆ» ¾å (Ê¡»³Âç³Ø¹©³ØÉô), ÅÏÊÕ ·ÃÂÀ (¥¨¥à¥¹¥ê¡¼³ô¼°²ñ¼Ò), »³ÅÄ ½ÓºÈ (NTT¥Æ¥¯¥Î¥¯¥í¥¹³ô¼°²ñ¼Ò) |
¥Ú¡¼¥¸ | pp. 232 - 233 |
¥¡¼¥ï¡¼¥É | »Ê½ñ, ¿Þ½ñ´Û, »Øº¹¤·, ¥¸¥§¥¹¥Á¥ã¡¼ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ¡Ö»°Ì©¡×(Ì©ÊÄ¡¤Ì©½¸¡¤Ì©ÀÜ)¤ò²óÈò¤·¡¤¿Í¤È¤ÎÀÜ¿¨¤ò¸º¤é¤¹¤³¤È¤¬µá¤á¤é¤ì¤Æ¤¤¤ë¡¥¿Þ½ñ°ÆÆâ¤Ë¤ª¤¤¤Æ¡¤Ì©ÀܤˤĤ¤¤Æ¤Ï¡¤Å°Ä줹¤ë¤³¤È¤¬Æñ¤·¤¤¾ì¹ç¤¬¤¢¤ë¡¥Ëܸ¦µæ¤Ç¤Ï¡¤¥¹¥Ý¥Ã¥È¥é¥¤¥È·¿¥Ý¥¤¥ó¥Æ¥£¥ó¥°´Ä¶¤Ç¤¢¤ëScale Gesture SpotLighting¤òÄó°Æ¤¹¤ë¡¥Äó°Æ¥·¥¹¥Æ¥à¤Ï»Øº¹¤·¥¸¥§¥¹¥Á¥ã¡¼¤Ë¤è¤ë¥Ý¥¤¥ó¥Æ¥£¥ó¥°Áàºî¤Ë²Ã¤¨¡¤²¾ÁÛ¥¿¥Ã¥Á¥Ñ¥Í¥ë¤Ë¤è¤ë¥Õ¥©¡¼¥«¥¹¥¨¥ê¥¢¤Î°ÜÆ°¡¦¸ÇÄ굡ǽ¡¤¼ê¤Î¤Ò¤é¤ò²óž¤µ¤»¤ë¥¸¥§¥¹¥Á¥ã¡¼¤Ë¤è¤Ã¤Æ¤½¤Î¾ì¤Ç¥Õ¥©¡¼¥«¥¹¥¨¥ê¥¢¤Î¥µ¥¤¥º¤òÊѹ¹¤Ç¤¤ë³ÈÂ硦½Ì¾®µ¡Ç½¤«¤é¹½À®¤µ¤ì¤ë¡¥Ê¡»³Âç³Ø¿Þ½ñ´Û¤Ë¤ÆÄó°Æ¥·¥¹¥Æ¥à¤Î´Ä¶¤òÀ°È÷¤·¡¤»Ê½ñ¤¬»Øº¹¤·¤Ë¤è¤Ã¤ÆÌ©ÀܤòÈò¤±¤¿¤Þ¤ÞÍøÍѼԤÎÌÜŪ¤È¤¹¤ë¿Þ½ñ¤ò°ÆÆ⤹¤ë¤³¤È¤¬²Äǽ¤Ç¤¢¤ë¤³¤È¤ò³Îǧ¤·¤¿¡¥ |
Âê̾ | ¥½¡¼¥·¥ã¥ë¥Ç¥£¥¹¥¿¥ó¥¹¤Î´¶³Ð¤Ë¤è¤ëǧ¼±¤Îº¹°Û |
Ãø¼Ô | *üâÅÄ ²Â¸ã, ÀµÈª ÃÒÆÁ, ÃæÆ» ¾å (Ê¡»³Âç³Ø ¹©³ØÉô) |
¥Ú¡¼¥¸ | pp. 234 - 237 |
¥¡¼¥ï¡¼¥É | Âп͵÷Î¥, ²Ä»ë²½, µ÷Î¥´¶³Ð |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ¿··¿¥³¥í¥Ê¥¦¥¤¥ë¥¹´¶À÷¾É(COVID-19)¤Ë¤À¤ì¤¬´¶À÷¤·¤Æ¤¤¤ë¤«Ê¬¤«¤é¤Ê¤¤º£¡¤¿Í¤È¤Î´Ö³Ö¤ò2m°Ê¾å¶õ¤±¤ë¥½¡¼¥·¥ã¥ë¥Ç¥£¥¹¥¿¥ó¥¹¤È¤¤¤¦¹Í¤¨¤¬µá¤á¤é¤ì¤ë¡¥Ëܸ¦µæ¤Ç¤Ï¿Í¤È¤ÎÂп͵÷Υǧ¼±¤Îº¹°Û¤ò¸¡¾Ú¤¹¤ë¤³¤È¤¬ÌÜŪ¤Ç¤¢¤ë¡¥Kinect¥»¥ó¥µ¡¼¤òÍѤ¤¤¿SD(Social Distance)¥Ä¡¼¥ë¤ò³«È¯¤·¡¤Âç³ØÀ¸61¿Í¤Ëµ÷Î¥´¶³Ð¤Î·×¬¼Â¸³¤ò¹Ô¤Ã¤¿¡¥¤½¤Î·ë²Ì¡¤ÂÐÌÌ¡¦²£Îó(º¸¡§»²²Ã¼Ô)¡¦²£Îó(±¦¡§»²²Ã¼Ô)¡¦½ÄÎó¤ÎÂâ·Á¤Ë¤ª¤¤¤Æ¡¤´Ö³Öµ÷Î¥¤ÎÊ¿¶Ñ2m°Ê¾å¤ò°Ý»ý¤Ç¤¤Æ¤¤¤ëÂâ·Á¤Ï¡¤²£Îó(º¸¡§»²²Ã¼Ô)¡¤²£Îó(±¦¡§»²²Ã¼Ô)¤Ç¤¢¤Ã¤¿¡¥¤³¤Î¤³¤È¤«¤éµ÷Î¥¤ò¼è¤ëºÝ¤Ë»²²Ã¼Ô¤´¤È¤Ë²£Îó(º¸¡§»²²Ã¼Ô)¡¤²£Îó(±¦¡§»²²Ã¼Ô)¤È½ÄÎó¡¤ÂÐÌ̤Ǥϵ÷Î¥¤ËÂФ¹¤ëǧ¼±¤¬°Û¤Ê¤ë²ÄǽÀ¤¬¤¢¤ë¤³¤È¤òÌÀ¤é¤«¤Ë¤·¤¿¡¥ |
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Ãø¼Ô | *ÀîÉû ÌÀ²ÂÍü, ¹õÀî ÍÛÂÀ, Ê¡»Î ¾ (»³¸ýÂç³ØÂç³Ø±¡) |
¥Ú¡¼¥¸ | pp. 238 - 242 |
¥¡¼¥ï¡¼¥É | ¥Í¥Ã¥È¥ï¡¼¥¯¥ª¥ó¥Á¥Ã¥×, ÂѸξã¥ë¡¼¥Æ¥£¥ó¥°Ë¡ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ËÜÊó¹ð¤Ç¤Ï¡¤¸Î¾ã¥Î¡¼¥É¤ÎÄ̲᤬²Äǽ¤ÊŬ±þŪ¤ÊÂѸξã¥ë¡¼¥Æ¥£¥ó¥°Ë¡¤òÄó°Æ¤·¡¤¤½¤ÎÄÌ¿®Àǽ¤òɾ²Á¤¹¤ë¡¥½¾Íè¤ÎÂѸξã¥ë¡¼¥Æ¥£¥ó¥°Ë¡¤Ç¤Ï¡¤¸Î¾ã¥Î¡¼¥É¤ò±ª²ó¤¹¤ë¤Î¤¬¾ï¼±¤Ç¤¢¤Ã¤¿¤¬¡¤¶áǯ¡¤Ä̲᤹¤ë¤³¤È¤¬²Äǽ¤Ê¿·¤¿¤Ê¼êË¡¤¬Äó°Æ¤µ¤ì¤¿¡¥¤·¤«¤·¡¤¤³¤Î¼êË¡¡ÊËÜÊó¹ð¤Ç¤ÏPassge-Y¤È¸Æ¤Ö¡Ë¤Ç¤Ï¡¤¥Ñ¥±¥Ã¥È¤Î½ÐȯÃϤÈÌÜŪÃϤ¬·è¤Þ¤ë¤È¡¤ÅÓÃæ·ÐÏ©¤¬°ì°Õ¤Ë¸ÇÄꤵ¤ì¤ë·èÄêŪ¤Ê¥ë¡¼¥Æ¥£¥ó¥°¤ò¹Ô¤¦¤¿¤á¡¤íÕíÔ¤ò²óÈò¤Ç¤¤º¡¤¥ì¥¤¥Æ¥ó¥·¤¬Â礤¯¤Ê¤ëÌäÂ꤬¤¢¤ë¡¥Äó°Æ¼êË¡¤Ç¤Ï¡¤´û¸¤ÎŬ±þŪ¤Ê¼êË¡¤ÈPassge-Y¤òÊ»ÍѤ·¡¤²¾ÁÛ¥Á¥ã¥Í¥ë¤òÍѤ¤¤Æ¥Ç¥Ã¥É¥í¥Ã¥¯¤ÎȯÀ¸¤òËɻߤ¹¤ë¥ë¡¼¥Æ¥£¥ó¥°¥ë¡¼¥ë¤òÄêµÁ¤¹¤ë¡¥É¾²Á¤Ë¤è¤ê¡¤Passge-Y¤ÈÈæ³Ó¤·¤Æ¡¤Ê¿¶Ñ¥ì¥¤¥Æ¥ó¥·¤òÌó21.5%ºï¸º²Äǽ¤Ç¤¢¤ë¤³¤È¤òÌÀ¤é¤«¤Ë¤·¤¿¡¥ |
Âê̾ | Ķ²»ÇȲèÁü¤òÍѤ¤¤¿´Î¹ÅÊÑ¿ÇÃǤΤ¿¤á¤Î CNN¤ÎMultiple Instance LearningË¡ |
Ãø¼Ô | *ÅÄ¿¬ ·½Î¼, Æ£ÅÄ Íª²ð (»³¸ýÂç³ØÂç³Ø±¡ÁÏÀ®²Ê³Ø¸¦µæ²Ê), »°Ã« ˧¹° (±§Éô¹©¶È¹âÅùÀìÌç³Ø¹»), ÉÍËÜ µÁɧ (»³¸ýÂç³ØÂç³Ø±¡ÁÏÀ®²Ê³Ø¸¦µæ²Ê), À¥Àî À¿ (»³¸ýÂç³Ø°å³ØÉô), »û°æ ã®Æó (¿·³ãÂç³ØÂç³Ø±¡°å»õ³ØÁí¹ç¸¦µæ²Ê), ºä°æÅÄ ¸ù (»³¸ýÂç³ØÂç³Ø±¡°å³Ø·Ï¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 243 - 247 |
¥¡¼¥ï¡¼¥É | Ķ²»ÇȲèÁü, ´Î¹ÅÊÑ, ¾ö¤ß¹þ¤ß¥Ë¥å¡¼¥é¥ë¥Í¥Ã¥È¥ï¡¼¥¯, Multiple Instance Learning |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Ķ²»ÇȲèÁü¤òÍѤ¤¤¿´Î¹ÅÊÑ¿ÇÃǤΤ¿¤á¤ÎÄêÎÌŪ¤Êɾ²ÁË¡¤Î³ÎΩ¤¬µá¤á¤é¤ì¤Æ¤¤¤ë¡¥É®¼Ô¤é¤Î¤³¤ì¤Þ¤Ç¤Î¸¦µæ¤Ç¤Ï¡¤GaborÆÃħ¤Ë¤è¤ëÆÃħÃê½Ð¤ª¤è¤ÓBayes2¼¡¼±ÊÌ´Ø¿ô¤Ë¤è¤ë¼±Ê̤ò¹Ô¤Ã¤Æ¤¤¤ë¤¬¡¤¤è¤ê͸ú¤ÊÆÃħÎ̤ÎÃê½Ð¤È¼±ÊÌ´ï¤ÎÀ߷פòÌÜŪ¤È¤·¤Æ¡¤¿¼Áسؽ¬¥â¥Ç¥ë¤òŬÍѤ¹¤ë¡¥Ëܼ±ÊÌÌäÂê¤Ç¤Ï¡¤È︳¼Ôñ°Ì¤Ç¥¯¥é¥¹¥é¥Ù¥ë¤¬ÉÕÍ¿¤µ¤ì¤¿³Ø½¬¥Ç¡¼¥¿¤òÍѤ¤¤Æ¼±ÊÌ¥â¥Ç¥ë¤ò¹½ÃÛ¤¹¤ë¤¿¤á¡¤CNN¤Ç¤Î¼±ÊÌÂоݤȤʤëROI¤Î¥¯¥é¥¹¥é¥Ù¥ë¤¬¤¢¤¤¤Þ¤¤¤Ê¾õ¶·¤Ç¡¤CNN¤Î³Ø½¬¤ò¹Ô¤ï¤Ê¤±¤ì¤Ð¤Ê¤é¤Ê¤¤¡¥¤½¤³¤Ç¡¤ËܹƤǤÏCNN¤Î³Ø½¬¤ËMultiple Instance Learning¤Î¼êË¡¤ò¼è¤êÆþ¤ì¤¿³Ø½¬ÊýË¡¤òÄó°Æ¤·¡¤ËܼêË¡¤Î͸úÀ¤òɾ²Á¤·¤¿¼Â¸³¤Ë¤Ä¤¤¤ÆÊó¹ð¤¹¤ë¡¥ |
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Ãø¼Ô | *°²ÅÄ Ëþ´î, ¹Âü μ²í (¹Åç»ÔΩÂç³Ø¾ðÊó²Ê³ØÉô), »Ô¸¶ ±Ñ¹Ô, °æ¾å ÃÒÀ¸ (¹Åç»ÔΩÂç³ØÂç³Ø±¡¾ðÊó²Ê³Ø¸¦µæ²Ê) |
¥Ú¡¼¥¸ | pp. 248 - 251 |
¥¡¼¥ï¡¼¥É | ¶á»÷¾è»», ÊäÀµ¥Ó¥Ã¥È, ¥Ï¥¤¥Ö¥ê¥Ã¥É¶á»÷¾è»»´ï, LC²Ã»»´ï, NC²Ã»»´ï |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Æ°²èÁü½èÍý¤äµ¡³£³Ø½¬¤Ç¤Ï,·×»»¤Ë¤ª¤¤¤Æ¿¾¯¤Î¸íº¹¤òµöÍƤǤ¤ë¾ì¹ç¤¬¤¢¤ë.¤½¤Î¤¿¤á¶áǯ,·×»»·ë²Ì¤Î¸íº¹¤òµöÍƤ¹¤ë¤³¤È¤Ç,²óÏ©¤ÎÌÌÀѺ︺,¹â®²½,¾ÃÈñÅÅÎÏÄ㸺¤òÌܻؤ¹¶á»÷±é»»¤¬ÃíÌܤµ¤ì¤Æ¤¤¤ë.2¤Ä¤Î°Û¤Ê¤ë¹½Â¤¤Î¶á»÷²Ã»»´ï¤òÁȤ߹ç¤ï¤»¤ë¤³¤È¤Ç,¥¢¥×¥ê¥±¡¼¥·¥ç¥ó¤¬Í׵᤹¤ëÀºÅÙ¤ËŬÀڤ˱þ¤¸¤ë¤³¤È¤¬²Äǽ¤Ê¥Ï¥¤¥Ö¥ê¥Ã¥É¶á»÷¾è»»´ï¤¬Äó°Æ¤µ¤ì¤Æ¤¤¤ë.Ëܸ¦µæ¤Ç¤Ï¡¤¤³¤Î¥Ï¥¤¥Ö¥ê¥Ã¥É¶á»÷¾è»»´ï¤òÂоݤË,2¤Ä¤Î¶á»÷²Ã»»´ï¤ÎÁȹ礻¤ÎÊýË¡¤Ë¤Ä¤¤¤Æ¼Â¸³Åª¹Í»¡¤ò¹Ô¤¤¡¤¥¢¥×¥ê¥±¡¼¥·¥ç¥ó¤ÎÍ×µáÀºÅ٤ˤè¤êŬ¤·¤¿¥Ï¥¤¥Ö¥ê¥Ã¥É¶á»÷¾è»»´ï¤òµÄÏÀ¤¹¤ë. |
Âê̾ | Superpixel¤Ë´ð¤Å¤¯°ÜưʪÂÎÃê½Ð¼êË¡¤ÎGPU¤Ë¤è¤ë¹â®²½ |
Ãø¼Ô | *ËüÌî ±¦´´, Ê¡»Î ¾ (»³¸ýÂç³Ø Âç³Ø±¡ÁÏÀ®²Ê³Ø¸¦µæ²Ê), °¤Éô µü (ÅìËÌÂç³Ø¥µ¥¤¥Ð¡¼¥µ¥¤¥¨¥ó¥¹¥»¥ó¥¿¡¼) |
¥Ú¡¼¥¸ | pp. 252 - 256 |
¥¡¼¥ï¡¼¥É | GPU, Superpixel, SLIC, Çطʺ¹Ê¬, °ÜưʪÂÎÃê½Ð |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ËÜÊó¹ð¤Ç¤Ï¡¤ Superpixel¤Ë´ð¤Å¤¯°ÜưʪÂÎÃê½Ð¼êË¡¤Î¹â®²½¤òÌÜŪ¤È¤·¤Æ¡¤ GPU¤Ë¤è¤ëÊÂÎó²½¼êË¡¤òÄó°Æ¤¹¤ë¡¥ ÂоݤȤ¹¤ëÃê½Ð¼êË¡¤Ç¤Ï¡¤ Superpixel¤È¸Æ¤Ð¤ì¤ëʪÂΤÎÎسԤ˱è¤Ã¤¿¾®Îΰè¤òÀ¸À®¤·¡¤ Superpixelñ°Ì¤ÇÆþÎϲèÁü¤ÈÊ£¿ô¤Î»²¾È²èÁü¤È¤ÎÈæ³Ó¤ò¹Ô¤¦¤³¤È¤Ç¡¤ ¥«¥á¥é¤ÎÊÑÆ°¤äÌÀ¤ë¤µ¤ÎÊѲ½¤Ë´è·ò¤ÊÃê½Ð¤ò²Äǽ¤È¤¹¤ë¡¥ ¤·¤«¤·¡¤Superpixel¤ÎÀ¸À®¤Ë¤ÏɬÍפʷ׻»¤¬Â¿¤¯¡¤½èÍý»þ´Ö¤¬¤«¤«¤ë¤È¤¤¤¦ÌäÂ꤬¤¢¤ë¡¥ Äó°Æ¼êË¡¤Ç¤Ï¡¤Superpixel¤ÎÀ¸À®¿ôʬ¤Î¥¯¥é¥¹¥¿¤òºîÀ®¤·¡¤ Superpixel¤ÎÀ¸À®½èÍý¤¬¸Ä¡¹¤Î¥¯¥é¥¹¥¿¤Î½Å¿´Ã±°Ì¤Ç¹Ô¤ï¤ì¤ë¤³¤È¤ËÃåÌܤ·¤¿¡¥ ½Å¿´¤È¥¯¥é¥¹¥¿Æâ¤Î³Æ²èÁǤȤε÷Î¥¤òµá¤á¤ë·×»»¤Ë¤ª¤¤¤Æ¡¤ ·×»»Îΰè¤òÉôʬÎΰè¤È¤·¤Æʬ³ä¤·¤Æ¡¤ÉôʬÎΰ褴¤È¤ËGPU¤Î¥³¥¢¤Ë³ä¤êÅö¤Æ¤ë¤³¤È¤Ç¡¤ ºÙγÅÙ¤ÊÊÂÎó½èÍý¤ò¹Ô¤¦¡¥ ɾ²Á¤ò¹Ô¤Ã¤¿·ë²Ì¡¤Ã༡½èÍý¤ÈÈæ³Ó¤·¤Æ¡¤ ºÇÂç¤ÇÌó65\%¤Î½èÍý»þ´Ö¤Îºï¸º¤¬²Äǽ¤Ç¤¢¤ë¤³¤È¤òÌÀ¤é¤«¤Ë¤·¤¿¡¥ |
Âê̾ | ¥Ü¥¯¥»¥ë¥Ù¡¼¥¹¤Î¿´ã¥¹¥Æ¥ì¥ªË¡¤ËÂФ¹¤ëGPU¤òÍѤ¤¤¿¹â®²½ |
Ãø¼Ô | *¾±Åç ľ´õ, Ê¡»Î ¾ (»³¸ýÂç³Ø Âç³Ø±¡ÁÏÀ®²Ê³Ø¸¦µæ²Ê), °¤Éô µü (ÅìËÌÂç³Ø ¥µ¥¤¥Ð¡¼¥µ¥¤¥¨¥ó¥¹¥»¥ó¥¿¡¼) |
¥Ú¡¼¥¸ | pp. 257 - 260 |
¥¡¼¥ï¡¼¥É | ÊÂÎó½èÍý |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ËÜÏÀʸ¤Ç¤Ï¡¤¥Ü¥¯¥»¥ë¥Ù¡¼¥¹¤Î¿´ã¥¹¥Æ¥ì¥ªË¡¤ËÂФ¹¤ëGPU(Graphics Processing Unit)¤òÍѤ¤¤¿¹â®²½¼êË¡¤òÄó°Æ¤¹¤ë¡¥ Äó°Æ¼êË¡¤Ç¤Ï¡¤³Æ¥Ü¥¯¥»¥ë¤ËɬÍפʽèÍý¤òGPU¤Î³Æ¥³¥¢¤ËʬÇÛ¤¹¤ë¤³¤È¤Ç¡¤ºÙγÅÙ¤ÊÊÂÎó½èÍý¤Ë¤è¤ê¡¤¹â®²½¤ò¿Þ¤ë¡¥ °ìÈÌŪ¤ÊPC¤ËÅëºÜ¤µ¤ì¤Æ¤¤¤ë¥Þ¥ë¥Á¥³¥¢CPU¤ËÈæ¤Ù¡¤GPU¤ÏÂçÎ̤Υ³¥¢¤¬ÅëºÜ¤µ¤ì¤Æ¤¤¤ë¤¿¤á¡¤CPU¤Ç¼Â¸½²Äǽ¤ÊÊÂÎó¿ô¤è¤ê¤â¡¤Â¿¤¯¤ÎÊÂÎó¿ô¤ò²Äǽ¤Ë¤·¤¿¡¥ Àǽɾ²Á¤Ë¤è¤ê¡¤CPU¤ÇÃ༡½èÍý¤·¤¿¾ì¹ç¤ÈÈæ³Ó¤·¤Æ¡¤ºÇÂç¤Ç½èÍý»þ´Ö¤òÌó98%ºï¸º²Äǽ¤Ç¤¢¤ë¤³¤È¤ò¼¨¤·¤¿¡¥ ¤³¤Î·ë²Ì¤Ï¡¤Äó°Æ¼êË¡¤¬¡¤¥Ü¥¯¥»¥ë¥Ù¡¼¥¹¤Î¿´ã¥¹¥Æ¥ì¥ªË¡¤ò°Â²Á¤Ë¹â®¤Ë¹Ô¤¦ÍÑÅÓ¤ËÍÍѤǤ¢¤ë¤³¤È¤ò¼¨¤·¤Æ¤¤¤ë¡¥ |
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¥Ú¡¼¥¸ | pp. 261 - 264 |
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¥Ú¡¼¥¸ | pp. 265 - 268 |
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¥Ú¡¼¥¸ | pp. 269 - 272 |
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Ãø¼Ô | *³ÑÏÆ ±ÊµÈ (Ä»¼èÂç³ØÂç³Ø±¡ »ý³À¼Ò²ñÁÏÀ¸²Ê³Ø¸¦µæ²Ê), ¶ûÅÄ ÂçÊå (Ä»¼èÂç³Ø ¹©³ØÉô(¥¯¥í¥¹¾ðÊó²Ê³Ø¸¦µæ¥»¥ó¥¿¡¼)) |
¥Ú¡¼¥¸ | pp. 273 - 277 |
¥¡¼¥ï¡¼¥É | EMG, ¶ÚÀþ°Ý»ÈÍѳä¹ç, Type I(ÃÙ¶ÚÀþ°Ý)), Type IIb(®¶ÚÀþ°Ý)), ¿ÈÂÎŪÆÃħ |
¥¢¥Ö¥¹¥È¥é¥¯¥È | ¶ÚÆù²óÉü¤Î¤¿¤á¤Î¥ê¥Ï¥Ó¥ê¥Æ¡¼¥·¥ç¥ó¤ä¶ÚÆù¥È¥ì¡¼¥Ë¥ó¥°¤Ë¤ª¤¤¤Æ¤Ï¡¤¶Ú¤Îɾ²Á¤¬·ç¤«¤»¤Ê¤¤¡¥ °ìÈÌŪ¤Ë¤ÏÍý³ØÎÅË¡¤Ë´ð¤Å¤¯¥ê¥Ï¥Ó¥ê¥¿¥¹¥¯¤ä¥¹¥Ý¡¼¥ÄÍýÏÀÅù¤Ë´ð¤Å¤¤¤¿¥È¥ì¡¼¥Ë¥ó¥°¤¬¹Ô¤ï¤ì¤Æ¤¤¤ë¡¥ ¶ÚÈèÏ«¤Ë¤Ä¤¤¤Æ¤Ï¡¤EMG¤Î¼þÇÈ¿ô²òÀϤòÄ̤·¤Æ¶ÚÀþ°Ý¥¿¥¤¥×¤Î³èÆ°³ä¹ç¤«¤é¶ÚÈèÏ«¤ò¿äÄꤹ¤ëÊýË¡¤òÄó°Æ¤·¤Æ¤¤¤¿¤¬¡¤¶ÚÀþ°Ý¥¿¥¤¥×¤Î³èÆ°³ä¹ç¤Ï¸Ä¿Íº¹¤¬Â礤¯¡¤¿äÄêÀºÅÙ¤ËÌäÂ꤬¤¢¤Ã¤¿¡¥ ËÜÏÀʸ¤Ç¤Ï¡¤¿ÈÂÎŪÆÃħ¤¬¶ÚÈèÏ«»þ¤Î¶ÚÀþ°Ý¥¿¥¤¥×¤Î³èÆ°³ä¹ç¤Ë±Æ¶Á¤òÍ¿¤¨¤ë¸¶°ø¤È²¾Äꤷ¡¤¤½¤Î²¾Àâ¤Î¼Â¾Ú¤ò¹Ô¤Ã¤¿¡¥4¼ïÎà¤Î¿ÈÂÎŪÆÃħ¤È¶Ú³èÆ°ÅṲ̋δط¸¤òÄ´ºº¤·¤¿·ë²Ì¡¤ÂλéËÃΨ¤¬¶ÚÀþ°Ý¥¿¥¤¥×¤Î³èÆ°³ä¹ç¤Ë´Ø¤ï¤Ã¤Æ¤¤¤ë¤³¤È¤ò¸«½Ð¤·¤¿¡¥ |
Âê̾ | Biometric Authentication Using Evoked EEG by Invisible Visual Stimulation ¡ÁEffect of Splitting the Waveband for Synchronization of Scalograms¡Á |
Ãø¼Ô | *Md Atikur Rahman, »°Âð ¿ò¹° (Graduate School of Sustainability Science, Tottori University), ÃæÀ¾ ¸ù (Faculty of Enigneering¡¢Tottori University) |
¥Ú¡¼¥¸ | pp. 278 - 281 |
¥¡¼¥ï¡¼¥É | Biometric authentication, Electroencephalogram (EEG), Invisible visual stimuli, Tine-frequency analysis, Scalogram |
¥¢¥Ö¥¹¥È¥é¥¯¥È | Electroencephalogram (EEG) signals of a brain attract for biometric authentication recently. This document presents a study on the suitability of induced electroencephalograms (EEGs) for implementing high-quality, practical, real-time biometric authentication systems. We use an evoked electroencephalogram (EEG) by invisible visual stimulation. In a previous study, by using Euclidean distance matching, the equal error rate (EER) was 9.4 %. In this study, we applied the concept of splitting the alpha band into three sub-bands for synchronizing scalogram and applied time-frequency analysis. We found that verification performance is improved significantly, and the best and smallest EER is 4.6 %. |
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Ãø¼Ô | *ÃÝÆâ À²¿Í, ¿¹ÊÝ ½ÙÌé, ÂçÌÚ À¿ (Ä»¼èÂç³Ø¹©³ØÉô) |
¥Ú¡¼¥¸ | pp. 282 - 285 |
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