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1D-4 (»þ´Ö: 14:50 - 15:10)
Âê̾An Implementation of PQI-based Surveillance Systems Changing Transaction Intervals Cyclically
Ãø¼ÔChaxiong Yukonhiatou (ÂçºåÂç³Ø), Àî¾å ÊþÌé (Ê¡°æÂç³Ø/ÂçºåÂç³Ø), *µÁµ× ÃÒ¼ù (ÂçºåÂç³Ø), »ûÀ¾ ͵°ì (¾ðÊóÄÌ¿®¸¦µæµ¡¹½/ÂçºåÂç³Ø), ²¼Ûê ¿¿»Ê (ÂçºåÂç³Ø)
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¥­¡¼¥ï¡¼¥ÉStreaming, IoT, Video
¥¢¥Ö¥¹¥È¥é¥¯¥ÈDue to the widespread monitoring spaces such as banks, airports, building lobbies, stores, and other public places. In these systems, each camera continuously sends its recorded video data to the processing computers to detect the specific objects. The effectiveness of surveillance systems can be enhanced by speeding up the transaction rate which means the number of the processed video frames per unit time. In the case of burglary, for example, the rate of arrests increases as the processing computer can analyze more video data from the surveillance cameras. As our previous work, the simulation results show that the transaction rates can be improved under our proposed method compared with the conventional approach. The contribution of this paper is an implementation using our proposed method, called PQI-CDI (cycle-based dynamic interval). In the PQI-CDI method, the transaction interval dynamically changes depending on the transaction time. We adopt the PQI approach to further reduce the transaction time. In this paper, we describe an implementation of another PQI-based surveillance system. The experiment results show that the proposed method can improve the transaction rate in our implemented system.

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1F-1 (»þ´Ö: 13:50 - 14:10)
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Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
1F-2 (»þ´Ö: 14:10 - 14:30)
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¥Ú¡¼¥¸pp. 92 - 97
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¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
Í¥½¨ÏÀʸ¾Þ / Paper Awards
1F-3 (»þ´Ö: 14:30 - 14:50)
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Ãø¼Ô*»³Æâ °¦Î¤º» (ÆüËÜÂç³ØʸÍý³ØÉô), ÂçÀ¾ ½Óµ± (ÆüËÜÂç³ØÂç³Ø±¡Áí¹ç´ðÁòʳظ¦µæ²Ê), ÉðÆ£ ͤÂÀ (ÆüËÜÂç³ØʸÍý³ØÉô), Àаæ μ, ÀÄÌî ͵»Ê (ÆüËÜÅÅ¿®ÅÅÏóô¼°²ñ¼Ò NTT¥á¥Ç¥£¥¢¥¤¥ó¥Æ¥ê¥¸¥§¥ó¥¹¸¦µæ½ê), µÜÅÄ ¾Ï͵ (ÆüËÜÂç³ØʸÍý³ØÉô)
¥Ú¡¼¥¸pp. 98 - 106
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1F-4 (»þ´Ö: 14:50 - 15:10)
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1G-1 (»þ´Ö: 13:50 - 14:10)
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1G-2 (»þ´Ö: 14:10 - 14:30)
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1G-3 (»þ´Ö: 14:30 - 14:50)
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1G-4 (»þ´Ö: 14:50 - 15:10)
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Æü»þ: 2020ǯ6·î24Æü(¿å) 13:50 - 15:30
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1H-1 (»þ´Ö: 13:50 - 14:10)
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Ãø¼Ô*³øë ¾°¹¨, ¾¾°æ ²ÃÆ೨ (ÅìµþÅŵ¡Âç³Ø)
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Í¥½¨ÏÀʸ¾Þ / Paper Awards
1H-2 (»þ´Ö: 14:10 - 14:30)
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¥Ú¡¼¥¸pp. 146 - 150
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¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
1H-3 (»þ´Ö: 14:30 - 14:50)
Âê̾ExerSense: Real-time Exercise Segmentation, Classification and Counting using IMU
Ãø¼Ô*Shun Ishii, Anna Yokokubo (Aoyama Gakuin University), Mika Luimura (Turku University of Applied Sciences), Guillaume Lopez (Aoyama Gakuin University)
¥Ú¡¼¥¸pp. 151 - 154
¥­¡¼¥ï¡¼¥ÉHuman Activity Recognition, Sports Sensing, Wearable Devices, Signal Processing
¥¢¥Ö¥¹¥È¥é¥¯¥ÈEven though it is well known that physical exercises have numerous emotional and physical health benefits, maintaining a regular exercise routine is quite challenging. Fortunately, there exist technologies that promote us to do physical activities. Nonetheless, almost all of these technologies only target a narrow set of physical exercises (e.g., either running or physical workouts but not both) and are only applicable either in indoor or in outdoor environments, but do not work well in both environments. This paper introduces ExerSense, a real-time segmentation and classification algorithm that recognizes physical exercises, and that works well in both indoor and outdoor environments. The proposed algorithm achieves a 95% classification accuracy for five indoor and outdoor exercises, including segmentation error. This accuracy is similar or better than previous works that handled only indoor workouts, and those use a vision-based approach. Moreover, while comparable machine learning-based approaches need many training data, the proposed correlation-based method needs only one sample of motion data of each target exercise.

1H-4 (»þ´Ö: 14:50 - 15:10)
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¥Ú¡¼¥¸pp. 155 - 160
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2A-1 (»þ´Ö: 15:50 - 16:30)
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2A-2 (»þ´Ö: 16:30 - 16:50)
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2A-3 (»þ´Ö: 16:50 - 17:10)
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2A-4 (»þ´Ö: 17:10 - 17:30)
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Æü»þ: 2020ǯ6·î24Æü(¿å) 15:50 - 17:30
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2B-1 (»þ´Ö: 15:50 - 16:10)
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2B-2 (»þ´Ö: 16:10 - 16:30)
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¥Ú¡¼¥¸pp. 190 - 198
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2B-3 (»þ´Ö: 16:30 - 16:50)
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2E-1 (»þ´Ö: 15:50 - 16:10)
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2E-4 (»þ´Ö: 16:50 - 17:10)
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2E-5 (»þ´Ö: 17:10 - 17:30)
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Æü»þ: 2020ǯ6·î24Æü(¿å) 15:50 - 17:30
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Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
ºÇÍ¥½¨ÏÀʸ¾Þ / Best Paper Awards
2F-1 (»þ´Ö: 15:50 - 16:10)
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2F-2 (»þ´Ö: 16:10 - 16:30)
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¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
2F-3 (»þ´Ö: 16:30 - 16:50)
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Í¥½¨ÏÀʸ¾Þ / Paper Awards
2F-4 (»þ´Ö: 16:50 - 17:10)
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¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
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3B-1 (»þ´Ö: 17:50 - 18:10)
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3B-2 (»þ´Ö: 18:10 - 18:30)
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3B-3 (»þ´Ö: 18:30 - 18:50)
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3B-4 (»þ´Ö: 18:50 - 19:10)
Âê̾Preliminary Investigation of Unsupervised Factory Activity Recognition with Wearable Sensors via Temporal Structure of Multiple Motifs
Ãø¼Ô*Xia Qingxin, Joseph Korpela (Graduate School of Information Science and Technology, Osaka University), Yasuo Namioka (Corporate Manufacturing Engineering Center, Toshiba Corporation), Takuya Maekawa (Graduate School of Information Science and Technology, Osaka University)
¥Ú¡¼¥¸pp. 374 - 381
¥­¡¼¥ï¡¼¥ÉActivity recognition, Wearable sensor, Factory work
¥¢¥Ö¥¹¥È¥é¥¯¥ÈIn this study, we propose a robust unsupervised learning technique that makes use of two types of sensor data motifs to track the starting time of each operation in every iteration of work periods. A period motif only occurs once in each work period that is used to roughly detect the duration of work periods. An action motif occurs many times in each work period, corresponding to some basic actions in the period. A temporal structure is then constructed based on the temporal distances among motifs in the first period, which is used to improve motif tracking in the following periods as well as roughly detect the location of outliers. We run particle filters to track the starting time of operations and select a best particle series based on the extracted motifs. We evaluate the proposed method using sensor data collected from workers in actual factories and achieved state-of-the-art performance.


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Æü»þ: 2020ǯ6·î24Æü(¿å) 17:50 - 19:10
ºÂĹ: º´Ìî ¾ÄÆó (¶âÂô¹©¶ÈÂç³Ø¹©³ØÉô¾ðÊ󹩳زÊ)

3C-1 (»þ´Ö: 17:50 - 18:10)
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3C-2 (»þ´Ö: 18:10 - 18:30)
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3C-3 (»þ´Ö: 18:30 - 18:50)
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3F-1 (»þ´Ö: 17:50 - 18:10)
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3F-2 (»þ´Ö: 18:10 - 18:30)
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3G-1 (»þ´Ö: 17:50 - 18:10)
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3G-3 (»þ´Ö: 18:30 - 18:50)
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¥Ú¡¼¥¸pp. 480 - 486
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3H-1 (»þ´Ö: 17:50 - 18:10)
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4B-2 (»þ´Ö: 8:50 - 9:10)
Âê̾ŨÂÐŪ·±Îý¤òÍѤ¤¤¿¥µ¥ó¥×¥ê¥ó¥°¼þÇÈ¿ô¤ÎÁê°ã¤Ë´è·ò¤Ê¹Ôưǧ¼±
Ãø¼Ô*ĹëÀî ã¿Í, ÌÚ¼ ÍÎʸ (Ê¡°æÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê ¾ðÊ󡦥á¥Ç¥£¥¢¹©³ØÀ칶)
¥Ú¡¼¥¸pp. 534 - 543
¥­¡¼¥ï¡¼¥É¹Ôưǧ¼±, ŨÂÐŪ·±Îý, ¿¼Áسؽ¬, ²Ã®ÅÙ¥»¥ó¥µ
¥¢¥Ö¥¹¥È¥é¥¯¥È¥¹¥Þ¡¼¥È¥Õ¥©¥óÅù¤òÍѤ¤¤¿¥»¥ó¥·¥ó¥°¤Ë¤è¤ê¥æ¡¼¥¶¤Î¿ÈÂγèÆ°¤Î¼«Æ°Ç§¼±¤ò¹Ô¤¦¹Ôưǧ¼±¤È¤¤¤¦¸¦µæʬÌ¤¢¤ë¡¥Æü¾ï¹ÔÆ°¤ò¥»¥ó¥·¥ó¥°¤¹¤ë¾ì¹ç¡¤¥Ç¥Ð¥¤¥¹¤Î¼ïÎà¤ä½ê»ýÊýË¡¡¤ÁõÃåÊýË¡¡¤·×¬¥¢¥×¥ê¥±¡¼¥·¥ç¥óÅù¤ÎÍÍ¡¹¤Ê·×¬¾ò·ï¤¬¥æ¡¼¥¶¤ä·×¬Æü¤Ë¤è¤Ã¤Æ°Û¤Ê¤ë»ö¤¬Â¿¤¤¡¥¥»¥ó¥µÃͤ«¤é¹ÔÆ°¤òͽ¬¤¹¤ë¥â¥Ç¥ë¤Ïµ¡³£³Ø½¬¤Ç¼Â¸½¤µ¤ì¤ë¤³¤È¤¬Â¿¤¯¡¤·×¬´Ä¶­¤¬ÉÔÅý°ì¤Ê¥Ç¡¼¥¿¤Ïµ¡³£³Ø½¬¤Îͽ¬ÀºÅÙ¤òÄã²¼¤µ¤»¤ë²ÄǽÀ­¤¬¤¢¤ë¡¥Ëܸ¦µæ¤Ç¤Ï¡¤·×¬´Ä¶­¤ÎÃæ¤Ç¤â¥µ¥ó¥×¥ê¥ó¥°¼þÇÈ¿ô¤ÎÁê°ã¤Ë´è·ò¤Ê¹Ôưǧ¼±¼êË¡¤òÄó°Æ¤¹¤ë¡¥Äó°Æ¼êË¡¤ÏÄ̾ï¤Î¹Ôưǧ¼±¥â¥Ç¥ë¤ËÂФ·¤ÆŨÂÐŪ·±Îý¤òŬÍѤ·¡¤¥µ¥ó¥×¥ê¥ó¥°¼þÇÈ¿ô¤òÆÃÄê¤Ç¤­¤Ê¤¯¤¹¤ëÆÃħɽ¸½¤Î³ÍÆÀ¤ò¼Â¸½¤·¤Æ¤¤¤ë¡¥´ðËܹÔưǧ¼±¤ÎHASC¥Ç¡¼¥¿¥»¥Ã¥È¤òÍѤ¤¤Æ¡¤¥µ¥ó¥×¥ê¥ó¥°¼þÇÈ¿ô¤¬º®ºß¤¹¤ë¥Ç¡¼¥¿¤¬·×¬¤µ¤ì¤¿´Ä¶­¤ò¥·¥ß¥å¥ì¡¼¥È¤·É¾²Á¼Â¸³¤ò¹Ô¤Ã¤¿¡¥¤½¤Î·ë²Ì¡¤½¾Íè¼êË¡¤Ç¤Ï¿äÄêÀºÅÙ¤¬Äã²¼¤¹¤ë¤³¤È¡¤Äó°Æ¼êË¡¤Ç¤Ï¤½¤ì¤ò²þÁ±¤Ç¤­¤ë¤³¤È¤òÌÀ¤é¤«¤Ë¤·¤¿¡¥

¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
4B-3 (»þ´Ö: 9:10 - 9:30)
Âê̾Wi-Fi¥Á¥ã¥Í¥ë¾õÂÖ¾ðÊó¤Ë¤è¤ë¹Ôưǧ¼±¤Ë¤ª¤±¤ë¶¯²½³Ø½¬¤òÍѤ¤¤¿¥µ¥ó¥×¥ê¥ó¥°¥ì¡¼¥È¼«Æ°ÁªÂò¤Î¸¡Æ¤
Ãø¼Ô*ðÌî ͧ²Ú, Á°Àî ÂîÌé, ¸¶ δ¹À (ÂçºåÂç³ØÂç³Ø±¡¾ðÊó²Ê³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 544 - 551
¥­¡¼¥ï¡¼¥É¹Ôưǧ¼±, ¶¯²½³Ø½¬, Wi-Fi¥Á¥ã¥Í¥ë¾õÂÖ¾ðÊó
¥¢¥Ö¥¹¥È¥é¥¯¥È¶áǯ¡¤¥Á¥ã¥Í¥ë¾õÂÖ¾ðÊó(Channel State Information: CSI)¤òÍѤ¤¤¿¹Ôưǧ¼±¼êË¡¤¬ÃíÌܤµ¤ì¤Æ¤¤¤ë¡¥CSI¤òÍѤ¤¤¿¼êË¡¤Î¿¤¯¤¬°ì¤Ä¤ÎÉô²°¤ËWi-FiÁ÷¼õ¿®µ¡¤Î¥Ú¥¢¤òÀßÃÖ¤¹¤ë¤³¤È¤òÁÛÄꤷ¤Æ¤ª¤ê¡¤Éô²°¤¬Ê£¿ô¸ºß¤¹¤ë°ìÈÌŪ¤Ê²ÈÄí´Ä¶­¤Ë¼ÂÁõ¤·¤¿¾ì¹ç¡¤¤½¤Î¥Ú¥¢¤Î¿ô¤ËÈæÎ㤷¤¿CSI¤ò½èÍý¤·¤Ê¤±¤ì¤Ð¤Ê¤é¤Ê¤¤¡¥¤³¤ì¤Þ¤Ç¤Î¸¦µæ¤Ç¤Ï¡¤°ìÄê¤Î¹â¤¤¥µ¥ó¥×¥ê¥ó¥°¥ì¡¼¥È¤Ç¼èÆÀ¤µ¤ì¤¿CSI¤òÍøÍѤ·¤Æ¤¤¤ë¤¿¤á¡¤¤½¤ì¤é¤ò¤¹¤Ù¤Æ²òÀϤ¹¤ë¤Ë¤Ï¹â¤¤·×»»¥³¥¹¥È¤¬É¬ÍפȤʤ롥¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¡¤¸½ºß¤Î¹ÔÆ°¤ä¾õÂ֤˱þ¤¸¤ÆŬÀڤ˥µ¥ó¥×¥ê¥ó¥°¥ì¡¼¥È¤òÄ´À°¤·¤Ê¤¬¤é¡¤¤Ê¤ë¤Ù¤¯Ä㤤¥µ¥ó¥×¥ê¥ó¥°¥ì¡¼¥È¤Ç¹Ôưǧ¼±¤ò¹Ô¤¦¼êË¡¤òÄó°Æ¤¹¤ë¡¥Äó°Æ¼êË¡¤Ç¤Ï¡¤¾õ¶·¤Ë±þ¤¸¤¿¹ÔÆ°¤ÎÁªÂò¤ò³Ø½¬²Äǽ¤Ç¤¢¤ë¶¯²½³Ø½¬¤ò¥µ¥ó¥×¥ê¥ó¥°¥ì¡¼¥È¤ÎÁªÂò¤ËÍѤ¤¤ë¡¥¶¯²½³Ø½¬¤Ë¤ª¤±¤ë¡Ö¹ÔÆ°¡×¤ò¥µ¥ó¥×¥ê¥ó¥°¥ì¡¼¥È¤ÎÁªÂò¤È¤·¡¤Ä㤤¥µ¥ó¥×¥ê¥ó¥°¥ì¡¼¥È¤ÇÀµ¤·¤¯¹Ôưǧ¼±½ÐÍ褿ºÝ¤Ë¹â¤¤¡ÖÊ󽷡פòÍ¿¤¨¤ë¡¥¤½¤·¤Æ¡¤¡Ö¾õÂ֡פȤ·¤Æ¡¤¸½ºß¤Î¹ÔÆ°¤Ë´Ø¤¹¤ë¾ðÊó¤òÈ¿±Ç¤¹¤ë¤È¹Í¤¨¤é¤ì¤ë¡¤CSI¤òÍѤ¤¤¿¹Ôưǧ¼±¥â¥Ç¥ë¤ÎÃæ´Ö½ÐÎϤòÍѤ¤¤ë¡¥Äó°Æ¼êË¡¤Ç¤Ï¡¤Ç¤°Õ¤Î¥µ¥ó¥×¥ê¥ó¥°¥ì¡¼¥È¤ÎÆþÎϤò²Äǽ¤È¤¹¤ë¹Ôưǧ¼±¥â¥Ç¥ë¤òÀ߷פ¹¤ë¤³¤È¤Ç¡¤¤½¤ÎÃæ´ÖÁؤǥµ¥ó¥×¥ê¥ó¥°¥ì¡¼¥ÈÈó°Í¸¤ÊÃæ´Öɽ¸½¤ò³ÍÆÀ¤·¡¤¤½¤ì¤ò¡Ö¾õÂ֡פȤ·¤ÆÍѤ¤¤ë¡¥7¼ïÎà¤Î¹ÔÆ°¤ò¹Ô¤Ã¤¿ºÝ¤ÎCSI¤ò¼èÆÀ¤·¡¤É¾²Á¼Â¸³¤ò¹Ô¤Ã¤¿¡¥

4B-4 (»þ´Ö: 9:30 - 9:50)
Âê̾²Ã®ÅÙ¥»¥ó¥µ¤òÍѤ¤¤¿½¸ÃĤˤª¤±¤ë ¥Þ¥¤¥Î¥ê¥Æ¥£¤Ê¾õ¶·¤Î¸¡½Ð¼êË¡
Ãø¼Ô*³á¸¶ ÂçÚö (Ω̿´ÛÂç³ØÂç³Ø±¡¾ðÊóÍý¹©³Ø¸¦µæ²Ê), ¼Èø ÏÂºÈ (Ω̿´ÛÂç³ØÂç³Ø±¡¾ðÊóÍý¹©³Ø¸¦µæ²Ê/JST¤µ¤­¤¬¤±)
¥Ú¡¼¥¸pp. 552 - 556
¥­¡¼¥ï¡¼¥É²Ã®ÅÙ¥»¥ó¥µ, ¹Ôưǧ¼±, ¥Þ¥¤¥Î¥ê¥Æ¥£¸¡½Ð
¥¢¥Ö¥¹¥È¥é¥¯¥ÈËܸ¦µæ¤Ç¤Ï¡¤²Ã®ÅÙ¥»¥ó¥µ¤òÍѤ¤¤Æ¡¤¥À¥ó¥¹¤äÆþ³Ø»î¸³¤Ê¤ÉƱ¼ï¤ÎÆ°ºî¤ò¹Ô¤Ã¤Æ¤¤¤ë½¸ÃĤˤª¤¤¤Æ¡¤¸í¤Ã¤¿¿¶¤êÉÕ¤±¤ä¥«¥ó¥Ë¥ó¥°¤Î¤è¤¦¤ÊÂçÉôʬ¤Î¿Í¤È¤Ï°Û¤Ê¤ë¥Þ¥¤¥Î¥ê¥Æ¥£¤Ê¹ÔÆ°¤ò¤·¤Æ¤¤¤ë¿Í¤ò¶µ»Õ¥Ç¡¼¥¿¤Ê¤·¤Ç¸¡½Ð¤¹¤ë¼êË¡¤òÄó°Æ¤¹¤ë¡¥Äó°Æ¼êË¡¤Ï¥æ¡¼¥¶¤Î¥¹¥Þ¡¼¥È¥Õ¥©¥ó¤ä¥¦¥§¥¢¥é¥Ö¥ë¥»¥ó¥µ¤«¤é3¼´¤Î²Ã®Å٥ǡ¼¥¿¤¬ÆÀ¤é¤ì¤ë´Ä¶­¤òÁÛÄꤹ¤ë¡¥¤Þ¤º¡¤¥»¥ó¥µ¤«¤é3¼´²Ã®ÅÙ¤ò¼èÆÀ¤·¡¤¹çÀ®²Ã®ÅÙ¤òµá¤á¤ë¡¥¼¡¤Ë¡¤¹çÀ®²Ã®Å٤ζá»÷¤ò¹Ô¤¤¡¤¶á»÷¤·¤¿¥Ç¡¼¥¿¤ò¥·¥ó¥Ü¥ëÎó¤ËÊÑ´¹¤¹¤ë¡¥¤½¤·¤Æ¡¤¥·¥ó¥Ü¥ëÎ󤫤é°ìÄê¤ÎŤµ°Ê¾å¤ÎÉôʬʸ»úÎó¤ò¥â¥Á¡¼¥Õ¤È¤·¤ÆÃê½Ð¤·¡¤¥æ¡¼¥¶¤´¤È¤ËBag of Words¡ÊBoW¡Ë¤òºîÀ®¤¹¤ë¡¥¥æ¡¼¥¶´Ö¤ÎBoW¤ÎÎà»÷ÅÙ¤ò»»½Ð¤·¡¤ÂçÉôʬ¤Î¥æ¡¼¥¶¤¤¤º¤ì¤È¤âÎà»÷ÅÙ¤¬Ä㤤¥æ¡¼¥¶¤ò¥Þ¥¤¥Î¥ê¥Æ¥£¤Ê¹ÔÆ°¤ò¹Ô¤Ã¤¿¿Í¤È¤·¤Æ¸¡½Ð¤¹¤ë¡¥É¾²Á¼Â¸³¤Ç¤Ï¡¤14¿Í¤ÎÈ︳¼Ô¤Î¤¦¤Á¤¹¤Ù¤Æ¤Î»þ´Ö¤Ç¥Þ¥¤¥Î¥ê¥Æ¥£¤Ê¹ÔÆ°¤ò¹Ô¤Ã¤¿1¿Í¤Ï¾¤Î¤¤¤º¤ì¤Î13¿Í¤È¤âÎà»÷ÅÙ¤¬Ä㤯¤Ê¤ê¡¤¥Þ¥¤¥Î¥ê¥Æ¥£¤Ê¿Í¤ò¸¡½Ð¤Ç¤­¤ë¤³¤È¤¬Ê¬¤«¤Ã¤¿¡¥¤Þ¤¿¡¤°ìÉô¤Î»þ´Ö¤Î¤ß¥Þ¥¤¥Î¥ê¥Æ¥£¤Ê¹ÔÆ°¤ò¤·¤Æ¤¤¤ë¾ì¹ç¤â¡¤¥¹¥é¥¤¥Ç¥£¥ó¥°¥¦¥£¥ó¥É¥¦¤Ëʬ³ä¤·¤ÆÎà»÷ÅÙ·×»»¤ò¹Ô¤¦¤³¤È¤Ç¸¡½Ð¤Ç¤­¤ë¤³¤È¤¬Ê¬¤«¤Ã¤¿¡¥

Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
4B-5 (»þ´Ö: 9:50 - 10:10)
Âê̾¥»¥ó¥µ¤òÁõÃ夹¤ë¤³¤È¼«ÂΤ¬È︳¼Ô¤Î¥¸¥§¥¹¥Á¥ãµ°ÀפËÍ¿¤¨¤ë±Æ¶Á¤Îɾ²Á
Ãø¼Ô*²Ï¼ Ãε±, ÅÚÅÄ ½¤Ê¿, »ûÅÄ ÅØ, ÄÍËÜ ¾»É§ (¿À¸ÍÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 557 - 566
¥­¡¼¥ï¡¼¥É¥Û¡¼¥½¥ó¸ú²Ì, ¥»¥ó¥µ, ¥¸¥§¥¹¥Á¥ã
¥¢¥Ö¥¹¥È¥é¥¯¥È¾¿Í¤«¤éÃíÌܤòÍá¤Ó¤ë¤ÈÉáÃʰʾå¤ÎÀ®²Ì¤ò½Ð¤½¤¦¤È¤¹¤ë¿´Íý¸ú²Ì¤ò¡Ö¥Û¡¼¥½¥ó¸ú²Ì¡×¤È¸Æ¤Ö¡¥¥Û¡¼¥½¥ó¸ú²Ì¤Ï¿Í¤Î¹ÔÆ°¤ËÎɤ¤±Æ¶Á¤òÍ¿¤¨¤ë¸ú²Ì¤È¤·¤Æ¼ç¤Ëµó¤²¤é¤ì¤Æ¤¤¤ë¡¥Î㤨¤Ð¡¤°åÎŤθ½¾ì¤Ë¤ª¤¤¤ÆµßµÞÂâ°÷¤¬½üºÙÆ°¤ò¹Ô¤¦¤Þ¤Ç¤Î»þ´Ö¤¬Ç¯¡¹Ã»½Ì¤·¤Æ¤¤¤¿¤ê¡¤½¸ÃæŪ¤Ë´µ¼Ô¤ò¼£ÎŤ¹¤ë¤È¾É¾õ¤¬²þÁ±¤µ¤ì¤¿¤ê¤¹¤ë¡¥¤·¤«¤·¡¤¤³¤Î¸ú²Ì¤Ïû´ü´Ö¤Î¼Â¸³´Ä¶­¤Ë¤ª¤¤¤ÆÌäÂê¤Ë¤Ê¤ë²ÄǽÀ­¤¬¤¢¤ë¡¥Î㤨¤Ð¡¤¥»¥ó¥µ¤òÍѤ¤¤ë¹Ôưǧ¼±¤ä¥¹¥Ý¡¼¥Ä¤Î¥¹¥­¥ëɾ²Á¤ò¹Ô¤¦¼Â¸³¤Ë¤ª¤¤¤Æ¤Ï¡¤ÉáÃʤȤϰ㤦¹ÔÆ°¤ò¤·¤Æ¤¤¤ë¤ÈͽÁÛ¤µ¤ì¤ë¡¥¤³¤ì¤Ï¡¤¥»¥ó¥µ¤òÁõÃ夹¤ë¤³¤È¤Ç¡¤ÁõÃåÉô°Ì¤òÆ°¤«¤·¤ÆÍߤ·¤¤¤È¤¤¤Ã¤¿´üÂÔ¤¬¥Û¡¼¥½¥ó¸ú²Ì¤Ë·Ò¤¬¤ê¡¤ÉáÃʰʾå¤ÎÀ®²Ì¤ò½Ð¤½¤¦¤È¤·¤Æ¹ÔÆ°¤¬ÊѲ½¤¹¤ë¤¿¤á¤À¤È¹Í¤¨¤é¤ì¤ë¡¥¤½¤Î¤¿¤á¡¤¤½¤Î¿ÍËÜÍè¤Î¹ÔÆ°¤¬É¾²Á¤µ¤ì¤Ê¤¯¤Ê¤êǧ¼±Î¨¤ÎÄã²¼¤äÎɤ¤¥Õ¥£¡¼¥É¥Ð¥Ã¥¯¤¬¤µ¤ì¤Ê¤¯¤Ê¤ë¤È¹Í¤¨¤é¤ì¤ë¡¥¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¡¤¥»¥ó¥µ¤Î¼è¤êÉÕ¤±°ÌÃÖ¤òÊѤ¨¤ë¤³¤È¤Ç¥»¥ó¥µ¤òÁõÃ夹¤ë¤³¤È¤¬È︳¼Ô¤Î¥¸¥§¥¹¥Á¥ãµ°ÀפËÍ¿¤¨¤ë±Æ¶Á¤Îɾ²Á¤ò¹Ô¤¦¡¥ËܹƤǤϡ¤¥»¥ó¥µ¤Î¼è¤êÉÕ¤±°ÌÃÖ¤òº¸±¦¤Î¼ê¼ó¤Ë¸ÂÄꤷ¡¤Ê£¿ô¤Î¥¸¥§¥¹¥Á¥ã¤ò¹Ô¤Ã¤¿È︳¼Ô¤Î±ÇÁü¤ò²òÀϤ·¤¿¡¥¼Â¸³¤Î·ë²Ì¡¤¥»¥ó¥µ¤ò°Õ¼±¤·¤¿¹ÔÆ°¤ò¤·¤Æ¤¤¤ëÈ︳¼Ô¤¬¸«¤é¤ì¤¿¤³¤È¤«¤é¡¤º£¸å¤Ï¥»¥ó¥µ¤òÁõÃ夷¤Æ¹Ô¤¦¼Â¸³¤Ë¤ª¤¤¤Æ¥»¥ó¥µÁõÃ弫ÂΤ¬È︳¼Ô¤ØÍ¿¤¨¤ë±Æ¶Á¤ò¹Íθ¤¹¤ëɬÍפ¬¤¢¤ë¤È¹Í¤¨¤é¤ì¤ë¡¥


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¥»¥Ã¥·¥ç¥ó 4C  ¥¹¥Ý¡¼¥Ä
Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 8:30 - 10:10
ºÂĹ: À¶ÅÄ ÍÛ»Ê (³ô¼°²ñ¼ÒLIFULL)

4C-1 (»þ´Ö: 8:30 - 8:50)
Âê̾¥Õ¥£¡¼¥ë¥É¥Û¥Ã¥±¡¼¤Ë¤ª¤±¤ë°µÎÏ¥»¥ó¥µÉÕ¥¹¥Æ¥£¥Ã¥¯¤òÍѤ¤¤¿µ»½Ñ¸þ¾å»Ù±ç¥·¥¹¥Æ¥à¤ÎÀ߷פȼÂÁõ
Ãø¼Ô*´äËÜ ½¡Âç, ÂçÀ¾ °¾Èþ, »ûÅÄ ÅØ, ÄÍËÜ ¾»É§ (¿À¸ÍÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 567 - 576
¥­¡¼¥ï¡¼¥É¥Õ¥£¡¼¥ë¥É¥Û¥Ã¥±¡¼, ¥¹¥Ý¡¼¥Ä¥»¥ó¥·¥ó¥°, ²Ä»ë²½, Îý½¬»Ù±ç
¥¢¥Ö¥¹¥È¥é¥¯¥È¥Õ¥£¡¼¥ë¥É¥Û¥Ã¥±¡¼¤Ï¥¤¥ó¥°¥é¥ó¥Éȯ¾Í¤Î¥¹¥Æ¥£¥Ã¥¯¤È¹Åµå¤òÍѤ¤¤Æ¹Ô¤ï¤ì¤ëµåµ»¤Ç¤¢¤ê¡¤¥¹¥Æ¥£¥Ã¥¯Áàºî¤¬Æñ¤·¤¤¡¥¤Þ¤¿¡¤Íľ¯´ü¤Î¶¥µ»¿Í¸ý¤¬¾¯¤Ê¤¤¤¿¤á¡¤½é¿´¼Ô¤ËÂФ·¤ÆŬÀڤʥ¢¥É¥Ð¥¤¥¹¤¬¤Ç¤­¤ë»ØƳ¼Ô¤¬¾¯¤Ê¤¤¡¥¤½¤³¤ÇËÜÏÀʸ¤Ç¤Ï¡¤¥Õ¥£¡¼¥ë¥É¥Û¥Ã¥±¡¼½é¿´¼Ô¤Î¥Ò¥Ã¥È¡¤¥×¥Ã¥·¥åµ»½Ñ¸þ¾å¤Î¤¿¤á¤Ë°µÎÏ¥»¥ó¥µ¤Ë¤è¤ë2¼ïÎà¤ÎÂÇÅÀ²Ä»ë²½¥·¥¹¥Æ¥à¤òÄó°Æ¤·¤¿¡¥¥Ò¥Ã¥Èµ»½Ñ¤ò½¬ÆÀ¤·¤Æ¤¤¤Ê¤¤¶¥µ»·Ð¸³¼Ô¤È̤·Ð¸³¼Ô¤½¤ì¤¾¤ì1 ̾¤º¤Ä¤òÂоݤȤ·¤¿¥Ò¥Ã¥Èµ»½Ñ¸þ¾å¤Îɾ²Á¼Â¸³¤Ç¤Ï¡¤Ì¤·Ð¸³¼Ô¤ÎÂÇÅÀ¤¬·Ð¸³¼Ô¤ÈÈæ³Ó¤·¤Æ¥Õ¥£¡¼¥É¥Ð¥Ã¥¯»þ¤Ë¿Ä¤Ë¶á¤Å¤¤¤¿¤³¤È¤«¤é¡¤Ì¤·Ð¸³¼Ô¤ËÂФ·¤Æ¥Õ¥£¡¼¥É¥Ð¥Ã¥¯¤¬Í­¸ú¤Ç¤¢¤ë²ÄǽÀ­¤ò¼¨¤·¤¿¡¥¤Þ¤¿¡¤¶¥µ»Ì¤·Ð¸³¼Ô5̾¤òÂоݤȤ·¤¿¥×¥Ã¥·¥åµ»½Ñ¸þ¾å¤Îɾ²Á¼Â¸³¤Ç¤Ï¡¤5̾Á´°÷¤¬¥Õ¥£¡¼¥É¥Ð¥Ã¥¯¤ò¹Ô¤Ã¤¿¤È¤­¤ÎÊý¤¬¹â¤¤°µÎÏÃͤòÆÀ¤é¤ì¤¿¤³¤È¤«¤é¡¤¥Õ¥£¡¼¥É¥Ð¥Ã¥¯¤Ë¤è¤Ã¤Æ¶¯¤¤¥×¥Ã¥·¥å¤òÂǤÁ½Ð¤¹¤³¤È¤¬¤Ç¤­¤¿¤³¤È¤ò¼¨¤·¤¿¡¥¤Þ¤¿¡¤°ìÉô¤ÎÈ︳¼Ô¤ËÂФ·¤Æ¥Õ¥£¡¼¥É¥Ð¥Ã¥¯¤Ë¤è¤ê¥×¥Ã¥·¥åÆ°ºîÃæ¤Î¥¹¥Æ¥£¥Ã¥¯¾å¤Î¥Ü¡¼¥ë¤ÎÀÜ¿¨°ÌÃ֤ΰÜÆ°·ÐÏ©¤Ë¤ª¤¤¤Æ²þÁ±¤¬¤ß¤é¤ì¤¿¡¥

4C-2 (»þ´Ö: 8:50 - 9:10)
Âê̾Accurate Boomerang Rotation Speed Estimation from Low-Cost IMU
Ãø¼Ô*Takumi Kondo (Aoyama Gakuin University), Yvan Madec (DARWIN BOOMERANGS), Pierre Maret (University Jean Monnet), Guillaume Lopez (Aoyama Gakuin University)
¥Ú¡¼¥¸pp. 577 - 580
¥­¡¼¥ï¡¼¥É¥¹¥­¥ë¸þ¾å»Ù±ç, ¥¹¥Ý¡¼¥Ä, ¥Ö¡¼¥á¥é¥ó
¥¢¥Ö¥¹¥È¥é¥¯¥ÈIn recent years, some research and techniques have shown many approaches for analyzing the athlete movement with sensor data. A smartphone's application is already available to provide feedback without an instructor by analyzing the user movement with device mounted sensors. To ease boomerang learning, we are developing a smart boomerang mounted a low-cost IMU sensor and a high-cost gyroscope. In the future, to be able to sell smart boomerangs as off the shelf, it will be necessary to remove the high-cost gyroscope. However, the gyroscope value of the IMU sensor saturates while the boomerang is flying. On the other hand, the high-cost one has enough performance. In this study, we estimated the high-cost gyroscope value in the range of low-cost gyroscope saturation using other sensor values. As a result, the polynomial estimated formula of the second degree using x-axis acceleration has enough good accuracy to make up for the high-cost gyroscope.

¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
4C-3 (»þ´Ö: 9:10 - 9:30)
Âê̾²áµî¤ÎÁö¹Ô¤òºÆ¸½¤¹¤ë¤è¤¦¤Ë¸÷¤ëLED¥Æ¡¼¥×¤òÍøÍѤ·¤¿Ã»µ÷Î¥ÁöÎý½¬ÊýË¡
Ãø¼Ô*ËþÃç ˾, ÅÚÅÄ ½¤Ê¿, »ûÅÄ ÅØ, ÄÍËÜ ¾»É§ (¿À¸ÍÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê), ÌøÂô Ë­ (¥¨¥à¥×¥é¥¹¥×¥é¥¹³ô¼°²ñ¼Ò)
¥Ú¡¼¥¸pp. 581 - 587
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4C-5 (»þ´Ö: 9:50 - 10:10)
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4D-1 (»þ´Ö: 8:30 - 8:50)
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4D-4 (»þ´Ö: 9:30 - 9:50)
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Í¥½¨ÏÀʸ¾Þ / Paper Awards
4E-3 (»þ´Ö: 9:30 - 9:50)
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¥»¥Ã¥·¥ç¥ó 4F  ¥³¥ß¥å¥Ë¥±¡¼¥·¥ç¥ó
Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 8:50 - 10:10
ºÂĹ: Ê¡Åç Âó (Âçºå¹©¶ÈÂç³Ø)

4F-1 (»þ´Ö: 8:50 - 9:10)
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Ãø¼Ô*ÉðÆ£ ͤÂÀ (ÆüËÜÂç³ØʸÍý³ØÉô), ¸â ·òϯ, ÉÙ±Ê »í²» (ÆüËÜÂç³ØÂç³Ø±¡Áí¹ç´ðÁòʳظ¦µæ²Ê), »³Æâ °¦Î¤º», µÜÅÄ ¾Ï͵ (ÆüËÜÂç³ØʸÍý³ØÉô)
¥Ú¡¼¥¸pp. 663 - 668
¥­¡¼¥ï¡¼¥ÉÂÐÏ÷¿¥¨¡¼¥¸¥§¥ó¥È, ¥æ¡¼¥â¥¢, Word2Vec
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4F-2 (»þ´Ö: 9:10 - 9:30)
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Ãø¼Ô*¾È°æ ͤµ¨, º£Â¼ ÎÜ°ìϺ, ¹¾ÌÚ ·¼·± (Åŵ¤ÄÌ¿®Âç³Ø Âç³Ø±¡¾ðÊóÍý¹©³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 669 - 672
¥­¡¼¥ï¡¼¥É¼«¸Ê¸úÎÏ´¶, TA, ¿¶¤êÊÖ¤ê, ¥¦¥§¥¢¥é¥Ö¥ë¥Ç¥Ð¥¤¥¹, ¹Ôưǧ¼±
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4F-3 (»þ´Ö: 9:30 - 9:50)
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Ãø¼ÔĹÅÄ ñ¥ÅÍ, *µÈÌî ¹§ (ϲλ³Âç³Ø)
¥Ú¡¼¥¸pp. 673 - 682
¥­¡¼¥ï¡¼¥É´Ñ¸÷»Ù±ç, ¾ðÊóÄó¼¨, PMV
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4F-4 (»þ´Ö: 9:50 - 10:10)
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Ãø¼Ô*±üÀî Ï´õ (ÆüËÜÂç³ØʸÍý³ØÉô), ÂçÏ ͤµ± (ÆüËÜÂç³ØÂç³Ø±¡Áí¹ç´ðÁòʳظ¦µæ²Ê), Âç²Ï¸¶ ¹ª (ÆüËÜÂç³ØʸÍý³ØÉô), ¼»³ Í¥»Ò (ÄÅÅĽÎÂç³Ø¿ô³Ø¡¦·×»»µ¡²Ê³Ø¸¦µæ½ê), µÜÅÄ ¾Ï͵ (ÆüËÜÂç³ØʸÍý³ØÉô)
¥Ú¡¼¥¸pp. 683 - 687
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Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 8:30 - 10:10
ºÂĹ: ÂçÅì ½ÓÇî (Å쳤Âç³Ø)

4G-1 (»þ´Ö: 8:30 - 8:50)
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¥Ú¡¼¥¸pp. 688 - 692
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4G-2 (»þ´Ö: 8:50 - 9:10)
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¥Ú¡¼¥¸pp. 693 - 697
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Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
4G-3 (»þ´Ö: 9:10 - 9:30)
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¥Ú¡¼¥¸pp. 698 - 705
¥­¡¼¥ï¡¼¥É¥¢¥Î¥Þ¥ê¸¡ÃÎ, µ¡³£³Ø½¬, ¼«Á³¸À¸ì½èÍý, ¿¯Æþ¸¡ÃÎ, IDS
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4G-4 (»þ´Ö: 9:30 - 9:50)
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Ãø¼Ô*¼ï¼ ¿¿Í³»Ò, ¾®¸ý Àµ¿Í (¤ªÃã¤Î¿å½÷»ÒÂç³Ø)
¥Ú¡¼¥¸pp. 706 - 711
¥­¡¼¥ï¡¼¥É´°Á´½àƱ·¿°Å¹æ, FP-growth, °ÑÂ÷½èÍý
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Í¥½¨ÏÀʸ¾Þ / Paper Awards
4G-5 (»þ´Ö: 9:50 - 10:10)
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¥Ú¡¼¥¸pp. 712 - 719
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4H-1 (»þ´Ö: 8:30 - 8:50)
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¥Ú¡¼¥¸pp. 720 - 725
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¥Ú¡¼¥¸pp. 726 - 734
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4H-3 (»þ´Ö: 9:10 - 9:30)
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¥Ú¡¼¥¸pp. 735 - 739
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4H-4 (»þ´Ö: 9:30 - 9:50)
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¥Ú¡¼¥¸pp. 740 - 747
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4H-5 (»þ´Ö: 9:50 - 10:10)
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¥Ú¡¼¥¸pp. 748 - 754
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Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 10:30 - 12:10
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5A-1 (»þ´Ö: 10:30 - 11:10)
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¥Ú¡¼¥¸p. 755
¥­¡¼¥ï¡¼¥ÉSociety5.0, ¥¢¥Õ¥¿¡¼¥Ç¥¸¥¿¥ë, ¥¢¥Õ¥¿¡¼¥³¥í¥Ê, ¥»¥­¥å¥ê¥Æ¥£


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Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 10:30 - 12:10
ºÂĹ: °ë»³ ľÌé (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø)

¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
5B-1 (»þ´Ö: 10:30 - 10:50)
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¥Ú¡¼¥¸pp. 756 - 765
¥­¡¼¥ï¡¼¥ÉRSSI, ²°Æâ°ÌÃÖ¿äÄê, ¥Î¥¤¥º½üµî, ·ç»ÃÍÊä´Ö, Wi-Fi¥Õ¥£¥ó¥¬¡¼¥×¥ê¥ó¥Æ¥£¥ó¥°
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5B-2 (»þ´Ö: 10:50 - 11:10)
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¥Ú¡¼¥¸pp. 766 - 771
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Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
5B-3 (»þ´Ö: 11:10 - 11:30)
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Ãø¼Ô*Ê¿Ìî ή, ×¢°æ ·Å, ÊÆß· ÂóϺ (̾¸Å²°Âç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê), ²Ï¸ý ¿®É× (̾¸Å²°Âç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê/̾¸Å²°Âç³Ø̤Íè¼Ò²ñÁϤµ¡¹½)
¥Ú¡¼¥¸pp. 772 - 777
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5B-4 (»þ´Ö: 11:30 - 11:50)
Âê̾ÈïºÒ¼Ô¹ÔÆ°¤ÎÉԳμÂÀ­¤ò¹Íθ¤·¤¿ºÒ³²¼å¼Ô¤Î¤¿¤á¤ÎÈòÆñ½ê·èÄê¼êË¡¤ÎÄó°Æ
Ãø¼Ô*ÅÄÃæ ÃÒ´ð, ¾¾ÅÄ Íµµ®, Æ£ËÜ ¤Þ¤Ê¤È, ¿Ûˬ Çîɧ, °ÂËÜ ·Ä°ì (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø)
¥Ú¡¼¥¸pp. 778 - 784
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5B-5 (»þ´Ö: 11:50 - 12:10)
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¥Ú¡¼¥¸pp. 785 - 793
¥­¡¼¥ï¡¼¥É°ÌÃÖ¾ðÊó¥·¥¹¥Æ¥à, ¸«¼é¤ê, ¹ÔÆ°ÈÏ°Ï, À¸³è¥Ñ¥¿¡¼¥ó
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¥»¥Ã¥·¥ç¥ó 5C  ¥Ø¥ë¥¹¥±¥¢
Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 10:30 - 12:10
ºÂĹ: ¿åËÜ °°ÍÎ (ÂçºåÂç³ØÂç³Ø±¡¾ðÊó²Ê³Ø¸¦µæ²Ê)

5C-1 (»þ´Ö: 10:30 - 10:50)
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Ãø¼Ô*¶â»Ò À², °æ¾å ÁϤ (¶å½£¹©¶ÈÂç³Ø)
¥Ú¡¼¥¸pp. 794 - 800
¥­¡¼¥ï¡¼¥Éµ¡³£³Ø½¬, ²ð¸îµ­Ï¿, ¥¢¥×¥ê¥±¡¼¥·¥ç¥óÀß·×
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5C-2 (»þ´Ö: 10:50 - 11:10)
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Ãø¼Ô*¿ÅÄ ¹ä»Ë, Âç¼ Î÷ (Ë­¶¶µ»½Ñ²Ê³ØÂç³Ø)
¥Ú¡¼¥¸pp. 801 - 806
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5C-3 (»þ´Ö: 11:10 - 11:30)
Âê̾»É·ã¤Ë¤è¤êÂñÕʬÈçÎÌÁý²Ã¤òÂ¥¤¹³Ü²¼ÉôÁõÃå·¿¥¦¥§¥¢¥é¥Ö¥ë¥Ç¥Ð¥¤¥¹¤ÎÄó°Æ
Ãø¼Ô*ÏÉÌî ³¤, ÂçÀ¾ °¾Èþ, »ûÅÄ ÅØ, ÄÍËÜ ¾»É§ (¿À¸ÍÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²ÊÅŵ¤ÅŻҹ©³ØÀ칶)
¥Ú¡¼¥¸pp. 807 - 814
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5C-4 (»þ´Ö: 11:30 - 11:50)
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Ãø¼Ô*Haomin Mao, ÅÚÅÄ ½¤Ê¿ (¿À¸ÍÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²ÊÅŵ¤ÅŻҹ©³ØÀ칶), Yongbeom Kim, ¶âÅÄ ÎÛÂÀϺ, Ë٠δǷ (¥½¥Õ¥È¥Ð¥ó¥¯), »ûÅÄ ÅØ, ÄÍËÜ ¾»É§ (¿À¸ÍÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²ÊÅŵ¤ÅŻҹ©³ØÀ칶)
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5C-5 (»þ´Ö: 11:50 - 12:10)
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¥Ú¡¼¥¸pp. 823 - 831
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5D-1 (»þ´Ö: 10:30 - 10:50)
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¥Ú¡¼¥¸pp. 832 - 837
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5D-2 (»þ´Ö: 10:50 - 11:10)
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¥Ú¡¼¥¸pp. 838 - 843
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5D-3 (»þ´Ö: 11:10 - 11:30)
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¥Ú¡¼¥¸pp. 844 - 851
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¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
5D-4 (»þ´Ö: 11:30 - 11:50)
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¥Ú¡¼¥¸pp. 852 - 858
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Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 10:30 - 12:10
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5E-1 (»þ´Ö: 10:30 - 10:50)
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Ãø¼Ô*²¬ÅÄ ¸÷Âå (·ÄØæµÁ½ÎÂç³Ø), »³Æâ Àµ¿Í (·ÄØæµÁ½ÎÂç³Ø/¾ðÊó·Ð±Ä¥¤¥Î¥Ù¡¼¥·¥ç¥óÀìÌ翦Âç³Ø), Ì ¾¿ (·ÄØæµÁ½ÎÂç³Ø/DSInnovation³ô¼°²ñ¼Ò), º½¸¶ ½¨¼ù (·ÄØæµÁ½ÎÂç³Ø)
¥Ú¡¼¥¸pp. 859 - 865
¥­¡¼¥ï¡¼¥É¥×¥é¥¤¥Ð¥·¡¼·üÇ°, ¥Ñ¡¼¥½¥Ê¥ë¥Ç¡¼¥¿, ¥é¥¤¥Õ¥í¥°, ¾ðÊó¶ä¹Ô
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Í¥½¨ÏÀʸ¾Þ / Paper Awards
5E-2 (»þ´Ö: 10:50 - 11:10)
Âê̾·ò¹¯¿ÇÃǥǡ¼¥¿¤òÍѤ¤¤¿À¸³è½¬´·²þÁ±¹ÔÆ°¤Ë¤ª¤±¤ë²ÁÃÍ¿äÄê¼êË¡¤ÎÄó°Æ
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¥Ú¡¼¥¸pp. 866 - 874
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5E-3 (»þ´Ö: 11:10 - 11:30)
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¥Ú¡¼¥¸pp. 875 - 881
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5E-4 (»þ´Ö: 11:30 - 11:50)
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¥Ú¡¼¥¸pp. 882 - 891
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5E-5 (»þ´Ö: 11:50 - 12:10)
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¥Ú¡¼¥¸pp. 892 - 899
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5F-1 (»þ´Ö: 10:30 - 10:50)
Âê̾³èȯ¤Ê±ó³ÖµÄÏÀ¤Î¼Â¸½¤òÌÜŪ¤È¤·¤¿¡¤¼«¿È¤ÎÆ°¤­¤Î³ÈÄ¥¤Ë¤è¤ê¡¤±ó³Ö¼Ô¤ÎÆ°¤­¤ÎÍý²ò¤òÍưפˤ¹¤ë¼êË¡¤ÎÄó°Æ
Ãø¼Ô*»³ÅÄ ÆÆ»Ö (ÌÀ¼£Âç³ØÂç³Ø±¡Àèü¿ôÍý²Ê³Ø¸¦µæ²Ê), ¾®ÎÓ Ì­ (ÌÀ¼£Âç³ØÁí¹ç¿ôÍý³ØÉô)
¥Ú¡¼¥¸pp. 900 - 903
¥­¡¼¥ï¡¼¥É±ó³Ö²ñµÄ, ¥³¥ß¥å¥Ë¥±¡¼¥·¥ç¥ó»Ù±ç, ȯÏûٱç
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5F-2 (»þ´Ö: 10:50 - 11:10)
Âê̾VectionͶȯ±ÇÁü¤òÍѤ¤¤Æ·¹¤­¤òÄ󼨤¹¤ë¼Ö°Ø»Ò¥·¥ß¥å¥ì¡¼¥¿¤Î¹Í»¡
Ãø¼Ô*Âç²Ï¸¶ ¹ª (ÆüËÜÂç³ØʸÍý³ØÉô), Ëܲ¬ ¹¨Õò, ÂçÏ ͤµ± (ÆüËÜÂç³ØÂç³Ø±¡Áí¹ç´ðÁòʳظ¦µæ²Ê), ±üÀî Ï´õ, µÜÅÄ ¾Ï͵ (ÆüËÜÂç³ØʸÍý³ØÉô)
¥Ú¡¼¥¸pp. 904 - 908
¥­¡¼¥ï¡¼¥ÉVection, ¥¢¥¯¥»¥·¥Ó¥ê¥Æ¥£, ¥·¥ß¥å¥ì¡¼¥¿
¥¢¥Ö¥¹¥È¥é¥¯¥È¼Ö°Ø»Ò¤ËÉÔ´·¤ì¤Ê¼Ö°Ø»Ò¥æ¡¼¥¶¤Ï¿¤¯¤Î¾ì¹ç²°³°¤ò°ÜÆ°¤¹¤ë¤³¤È¤ËÉ԰¤ò´¶¤¸¡¤Æä˺äÆ»¤Ê¤É¤Î¥Ð¥ê¥¢¤òÄ̲᤹¤ëºÝ¤Ë¤Ï¶²Éݤò´¶¤¸¤ë¤³¤È¤â¤¢¤ë¡¥¤³¤Î¤¿¤á¡¤Èà¤é¤¬¼Ö°Ø»Ò¤Ë¾è¤ë´¶³Ð¤Ë´·¤ì¤ë¤³¤È¤Ê¤É¤òÌÜŪ¤È¤·¤Æ¡¤Virtual Reality¡Ê°Ê¹ß VR¡Ë¤òÍѤ¤¤¿¼Ö°Ø»Ò¥·¥ß¥å¥ì¡¼¥¿¤¬¿ô¿¤¯³«È¯¤µ¤ì¤Æ¤­¤¿¡¥¤·¤«¤·¡¤VR¤òÍѤ¤¤¿½¾Íè¤Î¼Ö°Ø»Ò¥·¥ß¥å¥ì¡¼¥¿¤Ï¡¤¶âÁ¬Åª¤Ê¥³¥¹¥È¤È¸½¼Â´¶¤¬¥È¥ì¡¼¥É¥ª¥Õ¤Î´Ø·¸¤Ë¤¢¤Ã¤¿¡¥¤³¤ÎÌäÂê¤ò²ò·è¤¹¤ë¤¿¤á¤Ë¡¤²æ¡¹¤ÏVectionͶȯ±ÇÁü¤ÈÅÅÆ°¼Ö°Ø»Ò¤ÎÄ㼫ͳÅÙÆ°ºî¤òÁȤ߹ç¤ï¤»¤¿¼Ö°Ø»Ò¥·¥ß¥å¥ì¡¼¥¿¤òÄó°Æ¤·¤Æ¤­¤¿¡¥²æ¡¹¤Ï¤³¤Î¥·¥ß¥å¥ì¡¼¥¿¤òÍѤ¤¤Æ¥æ¡¼¥¶¤ËºäÆ»¤òÄ̲᤹¤ë´¶³Ð¤òÍ¿¤¨¤ë¤¿¤á¤ÎÄó°Æ¤ò¹Ô¤Ã¤Æ¤­¤¿¤¬¡¤¥æ¡¼¥¶¤Ë²£ÃǸûÇۤΤ¢¤ëÆ»¤òÄ̲᤹¤ë´¶³Ð¤òÍ¿¤¨¤ë¤¿¤á¤ÎÄó°Æ¤Ï¹Ô¤ï¤ì¤Æ¤¤¤Ê¤¤¡¥¤½¤³¤Ç¡¤ËܹƤǤϤ³¤Î¥·¥ß¥å¥ì¡¼¥¿¤òÍѤ¤¤Æ¥æ¡¼¥¶¤Ë²£ÃǸûÇۤΤ¢¤ëÆ»¤òÄ̲᤹¤ë´¶³Ð¤òÍ¿¤¨¤ëÊýË¡¤òÄó°Æ¤¹¤ë¡¥

5F-3 (»þ´Ö: 11:10 - 11:30)
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Ãø¼Ô*º´¡¹ÌÚ ¹§Êå, êÏ »ÒÛ¿ (ÃÞÇÈÂç³Ø Âç³Ø±¡¿Þ½ñ´Û¾ðÊó¥á¥Ç¥£¥¢¸¦µæ²Ê), °æ¾å ÃÒͺ (ÃÞÇÈÂç³Ø ¿Þ½ñ´Û¾ðÊó¥á¥Ç¥£¥¢·Ï)
¥Ú¡¼¥¸pp. 909 - 918
¥­¡¼¥ï¡¼¥É±ó³Ö¶¨Ä´ºî¶È, ¥Æ¥ì¥×¥ì¥¼¥ó¥¹¥í¥Ü¥Ã¥È, °ÌÃÖƱ´ü, ¥Î¥ó¥Ð¡¼¥Ð¥ë¾ðÊó
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5F-4 (»þ´Ö: 11:30 - 11:50)
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¥Ú¡¼¥¸pp. 919 - 926
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Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 10:30 - 12:10
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5G-1 (»þ´Ö: 10:30 - 10:50)
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¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
5G-3 (»þ´Ö: 11:10 - 11:30)
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Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 10:30 - 12:10
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5H-1 (»þ´Ö: 10:30 - 10:50)
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¥Ú¡¼¥¸pp. 955 - 962
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5H-2 (»þ´Ö: 10:50 - 11:10)
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5H-3 (»þ´Ö: 11:10 - 11:30)
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¥Ú¡¼¥¸pp. 970 - 975
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Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 14:10 - 15:30
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6A-1 (»þ´Ö: 14:10 - 14:50)
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¥Ú¡¼¥¸pp. 977 - 982
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6A-3 (»þ´Ö: 15:10 - 15:30)
Âê̾¿á¤­½Ð¤·É½¼¨¤Ë¤è¤ë¥×¥í¥°¥é¥à¼Â¹ÔÉôʬ¤Î²Ä»ë²½µ¡Ç½
Ãø¼Ô*¾®ÎÓ ´§ÂÀ, °ËÆ£ â«´ð, »ûÅÄ Ïµ­ (Ω̿´ÛÂç³Ø±¡ ¾ðÊóÍý¹©³Ø¸¦µæ²Ê), ¹âÅÄ ½¨»Ö (Ω̿´ÛÂç³Ø ¾ðÊóÍý¹©³ØÉô)
¥Ú¡¼¥¸pp. 983 - 989
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¥»¥Ã¥·¥ç¥ó 6B  Ä´Íý¡¦¿©»ö¹ÔÆ°
Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 14:10 - 15:30
ºÂĹ: ĹëÀî ã¿Í (Ê¡°æÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²ÊÃμ±¼Ò²ñ´ðÁù©³ØÀ칶¾ðÊ󹩳إ³¡¼¥¹)

ºÇÍ¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Best Presentation Awards
ºÇÍ¥½¨ÏÀʸ¾Þ / Best Paper Awards
6B-1 (»þ´Ö: 14:10 - 14:30)
Âê̾ʣ¿ô¥ì¥·¥Ô¤ÇʹÔÄ´Íý¤¹¤ëºÝ¤ÎÄ´Íý´Ä¶­¤Ë±þ¤¸¤¿ºÇŬĴÍý¼ê½çºîÀ®Ë¡¤Èɾ²Á
Ãø¼Ô*ÃæÉô ¿Î (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø), ¿åËÜ °°ÍÎ (ÂçºåÂç³ØÂç³Ø±¡¾ðÊó²Ê³Ø¸¦µæ²Ê), ¿Ûˬ Çîɧ (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø/Íý²½³Ø¸¦µæ½ê ³×¿·ÃÎǽÅý¹ç¸¦µæ¥»¥ó¥¿¡¼(AIP)), °ÂËÜ ·Ä°ì (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø)
¥Ú¡¼¥¸pp. 990 - 999
¥­¡¼¥ï¡¼¥ÉÄ´Íý¼ê½ç, ºÇŬ²½, ¥¹¥±¥¸¥å¡¼¥ê¥ó¥°, ¥¢¥ë¥´¥ê¥º¥à
¥¢¥Ö¥¹¥È¥é¥¯¥È¶áǯ¡¤¼«Ê¬¤ÇÎÁÍý¤òºî¤ë¿Í¤¬Áý²Ã¤·¤Æ¤ª¤ê¡¤Ê£¿ô¤ÎÎÁÍý¤ò´Þ¤à¸¥Î©¤ò¸úΨ¤è¤¯Ä´Íý¤Ç¤­¤ë¤è¤¦¤ÊºÇŬĴÍý¼ê½ç¤òÄ󼨤¹¤ë¤³¤È¤¬µá¤á¤é¤ì¤Æ¤¤¤ë¡¥°ìÊý¤Ç¡¤Ä´Íý¼Ô¤´¤È¤ËÄ´Íý´Ä¶­¤¬°Û¤Ê¤ê¡¤ºÇŬĴÍý¼ê½ç¤ÏÄ´Íý¼Ô¤Ë¤è¤Ã¤Æ¿¼ï¿Íͤˤʤ뤿¤á¡¤ºÇŬĴÍý¼ê½ç¤òÄ´Íý¼Ô¼«¿È¤¬¹Í¤¨¤ë¤³¤È¤Ïº¤Æñ¤Ç¤¢¤ë¡¥¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¡¤³Æ¥ì¥·¥Ô¤Î¥¿¥¹¥¯¥°¥é¥Õ¤òºîÀ®¤·¡¤Ê£¿ô¥ì¥·¥Ô¤ÎʹÔÄ´Íý¤ò¥¿¥¹¥¯¥¹¥±¥¸¥å¡¼¥ê¥ó¥°ÌäÂê¤È¤·¤Æª¤¨¤¿ºÇŬĴÍý¼ê½ç¤ÎºîÀ®Ë¡¤òÄó°Æ¤¹¤ë¡¥¥¿¥¹¥¯¥¹¥±¥¸¥å¡¼¥ê¥ó¥°ÌäÂê¤ÎºÇŬ²½¥¢¥ë¥´¥ê¥º¥àDF/IHSË¡¤ò¥Ù¡¼¥¹¤È¤·¡¤Á°½èÍýÉô¤È»Þ´¢¤êÉô¤ËÎÁÍýÆÃÍ­¤ÎÀö¤¤Êª¤ò¹Íθ¤·¤¿³ÈÄ¥¤ò¹Ô¤¦¤³¤È¤Ç¡¤¸úΨ¤è¤¯ºÇŬĴÍý¼ê½ç¤òõº÷¤¹¤ë¡¥É¾²Á¼Â¸³¤Ë¤ª¤¤¤Æ¡¤Äó°Æ¥¢¥ë¥´¥ê¥º¥à¤ò¡¤¼ÂºÝ¤Î¥ì¥·¥Ô¤«¤éºîÀ®¤·¤¿¸¥Î©¤ËÂФ·¤Æ¼Â¹Ô¤·¤¿¤È¤³¤í¡¤¼êÆ°¤ÇºîÀ®¤·¤¿Ä´Íý¼ê½ç¤ÈÈæ³Ó¤·¡¤Àö¤¤Êª¤â´Þ¤á¹ç·×Ä´Íý»þ´Ö¤ò15%û½Ì¤Ç¤­¤ëºÇŬĴÍý¼ê½ç¤òõº÷¤Ç¤­¤Æ¤¤¤ë¤³¤È¤ò³Îǧ¤·¤¿¡¥

¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
6B-2 (»þ´Ö: 14:30 - 14:50)
Âê̾²Ã®ÅÙ¥»¥ó¥µ¤òÍѤ¤¤¿ÊñÃúµ»½Ñ¸þ¾å»Ù±ç¥·¥¹¥Æ¥à¤ÎÄó°Æ
Ãø¼Ô¾®ÎÓ ²ÖºÚǵ, *²ÃÆ£ ³ÙÂç, ²£·¦ °ÂÆà, ¥í¥Ú¥º ¥®¥è¡¼¥à (ÀÄ»³³Ø±¡Âç³Ø)
¥Ú¡¼¥¸pp. 1000 - 1003
¥­¡¼¥ï¡¼¥ÉÎÁÍý, ÊñÃú, ²Ã®ÅÙ¥»¥ó¥µ, µ»Ç½»Ù±ç
¥¢¥Ö¥¹¥È¥é¥¯¥È¿Í´Ö¤¬·ò¹¯¤ËÀ¸¤­¤ë¤¿¤á¤Ë¿©À¸³è½¬´·¤Ï½ÅÍפǤ¢¤ê¡¤¿©À¸³è¤ò¹¥¤Þ¤·¤¤Êý¸þ¤ËƳ¤¯¤¿¤á¤Ë¤Ï¡¤ÎÁÍý¤Î³Ø½¬¤È¼ÂÁ©¤ò¤·¡¤Ä´Íýµ»½Ñ¤ò¸þ¾å¤µ¤»¤ë¤³¤È¤¬É¬Í×ÉԲķç¤Ç¤¢¤ë¡¥¤·¤«¤·¡¤ÎÁÍý½é¿´¼Ô¤Ë¤È¤Ã¤Æ¡¤ÊñÃú¤Ï²ø²æ¤ò¤¹¤ë¶²¤ì¤¬¤¢¤ëÆ»¶ñ¤Ç¤¢¤ê¡¤ÊñÃú¤Î´ðËÜŪ¤Ê»È¤¤Êý¤ò½¬ÆÀ¤·¤Æ¤¤¤Ê¤¤¿Í¤¬Â¿¤¤¡¥Ëܸ¦µæ¤Ç¤Ï¡¤ÎÁÍý¤Î´ðËܤǤ¢¤ëÊñÃúµ»½Ñ¤ò¸þ¾å¤µ¤»¤ë¤¿¤á¤Ë¡¤ÊñÃú¤ÎÊÁÀè¤ËÁõÃå²Äǽ¤Ê²Ã®ÅÙ¥»¥ó¥µ¤òÍѤ¤¤Æ¡¤½ÏÎý¼Ô¤ÎÊñÃúµ»½Ñ¤òȽÄꤹ¤ëÊñÃúµ»½Ñ¸þ¾å»Ù±ç¥·¥¹¥Æ¥à¤òÄó°Æ¤¹¤ë¡¥ÎÁÍý½é¿´¼Ô¤ÈÎÁÍý½ÏÎý¼Ô¤Ë¤è¤ëÊñÃúµ»½Ñ¤ÎÆÃħÎ̤òÌÀ³Î¤Ë¤¹¤ë¤¿¤á¤Ë¡¤Äó°Æ¥·¥¹¥Æ¥à¤òÍѤ¤¤ÆÊñÃúµ»½Ñ¤òȽÄꤹ¤ë¼Â¸³¤ò¹Ô¤Ã¤¿¡¥¤½¤Î·ë²Ì¡¤½é¿´¼Ô¤ÎÊñÃúµ»½Ñ¤ò¸þ¾å¤µ¤»¤ë»Øɸ¤È¤·¤Æº¸±¦Êý¸þ²Ã®ÅÙ¤ÎÊ¿¶ÑÃÍ¡¤Á°¸åÊý¸þ²Ã®ÅÙ¤Îɸ½àÊк¹¡¤¾å²¼Êý¸þ²Ã®ÅÙ¤Îɸ½àÊк¹¤Î3»Øɸ¤¬Í­¸ú¤Ç¤¢¤ë¤³¤È¤¬³Îǧ¤Ç¤­¤¿¡¥

6B-3 (»þ´Ö: 14:50 - 15:10)
Âê̾¼«Á³¤Ê¿©»ö´Ä¶­²¼¤Ç¼ý½¸¤·¤¿¿©»ö²»À¼¥Ç¡¼¥¿¤Ë¤è¤ë ¿©»ö¾ÜºÙ¹ÔưʬÎà¼êË¡¤ÎÄó°Æ
Ãø¼Ô*³÷ÃÏ ÍÚ, ¶áÆ£ ¾¢³¤, ²£·¦ °ÂÆà, Guillaume Lopez (ÀÄ»³³Ø±¡Âç³Ø)
¥Ú¡¼¥¸pp. 1004 - 1007
¥­¡¼¥ï¡¼¥É¹Ôưǧ¼±, ²»À¼²òÀÏ, ¿©»ö¹ÔÆ°, ¥¦¥§¥¢¥é¥Ö¥ë
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¥»¥Ã¥·¥ç¥ó 6C  »þ¶õ´Ö
Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 14:10 - 15:30
ºÂĹ: Á°Àî ÂîÌé (ÂçºåÂç³ØÂç³Ø±¡¾ðÊó²Ê³Ø¸¦µæ²Ê)

6C-1 (»þ´Ö: 14:10 - 14:30)
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Ãø¼Ô*¾±»Ò ÏÂÇ· (̾¸Å²°Âç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê), ×¢°æ ·Å (µþÅÔÂç³ØËɺҸ¦µæ½ê), ÊÆß· ÂóϺ (̾¸Å²°Âç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê), ¼òÅÄ Íý¿Í (³ô¼°²ñ¼Ò¥Ö¥í¥°¥¦¥©¥Ã¥Á¥ã¡¼), ²Ï¸ý ¿®É× (̾¸Å²°Âç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê/̾¸Å²°Âç³Ø̤Íè¼Ò²ñÁϤµ¡¹½)
¥Ú¡¼¥¸pp. 1008 - 1013
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6C-2 (»þ´Ö: 14:30 - 14:50)
Âê̾γÅ٤ΰۤʤë¿Íͤʻþ¶õ´Ö¥Ç¡¼¥¿¤ÎÊݸ¡¦Ãê½Ð¥·¥¹¥Æ¥à¤ÎÀ߷פȼÂÁõ
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¥Ú¡¼¥¸pp. 1014 - 1019
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Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
6C-3 (»þ´Ö: 14:50 - 15:10)
Âê̾ʣ¿ô¤Î3DÄ̲᥻¥ó¥µ¤Ë¤è¤ë¥×¥é¥¤¥Ð¥·¡¼¤ò¹Íθ¤·¤¿¿Íή¿äÄê¼êË¡
Ãø¼Ô*±ÊÅÄ µÈµ± (̾¸Å²°Âç³Ø ¹©³ØÉô), ×¢°æ ·Å (µþÅÔÂç³Ø ËɺҸ¦µæ½ê), ÊÆß· ÂóϺ, ²Ï¸ý ¿®É× (̾¸Å²°Âç³ØÂç³Ø±¡ ¹©³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1020 - 1028
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6C-4 (»þ´Ö: 15:10 - 15:30)
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Ãø¼Ô*Õú ÂÙÌÀ (ÆüËÜÂç³ØʸÍý³ØÉô), ÉÙ±Ê »í²» (ÆüËÜÂç³ØÂç³Ø±¡Áí¹ç´ðÁòʳظ¦µæ²Ê), Ω²Ö ¹ª¼ù (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³ØÀèü²Ê³Øµ»½Ñ¸¦µæ²Ê), ÎëÌÚ ñ¥ÇÏ (ÌÀ¼£Âç³ØÂç³Ø±¡Àèü¿ôÍý²Ê³Ø¸¦µæ²Ê), ½©»³ ÏÂδ, µÜÅÄ ¾Ï͵ (ÆüËÜÂç³ØʸÍý³ØÉô)
¥Ú¡¼¥¸pp. 1029 - 1032
¥­¡¼¥ï¡¼¥É¶¦Í­¶õ´Ö, ÀìÍ­´¶, ¿§, IoT
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Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 14:10 - 15:30
ºÂĹ: ¾®¸ý Àµ¿Í (¤ªÃã¤Î¿å½÷»ÒÂç³Ø)

Í¥½¨ÏÀʸ¾Þ / Paper Awards
6D-1 (»þ´Ö: 14:10 - 14:30)
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¥Ú¡¼¥¸pp. 1033 - 1040
¥­¡¼¥ï¡¼¥É¥¨¥Ã¥¸¥³¥ó¥Ô¥å¡¼¥Æ¥£¥ó¥°, SRv6, ·ÐÏ©À©¸æ, 5G
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6D-2 (»þ´Ö: 14:30 - 14:50)
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Ãø¼Ô*µÜ±Û °ìµ© (ÂçºåÂç³Ø), »ûÀ¾ ͵°ì (¾ðÊóÄÌ¿®¸¦µæµ¡¹½/ÂçºåÂç³Ø), Àî¾å ÊþÌé (Ê¡°æÂç³Ø/ÂçºåÂç³Ø), µÁµ× ÃÒ¼ù, ²¼Ûê ¿¿»Ê (ÂçºåÂç³Ø)
¥Ú¡¼¥¸pp. 1041 - 1047
¥­¡¼¥ï¡¼¥ÉWeb of Things, ʬ»¶¥Ç¡¼¥¿¥Õ¥í¡¼½èÍý, ¥Þ¥¤¥¯¥í¥µ¡¼¥Ó¥¹
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¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
6D-3 (»þ´Ö: 14:50 - 15:10)
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¥Ú¡¼¥¸pp. 1048 - 1053
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Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 14:10 - 15:30
ºÂĹ: Åò¼ Íã (¾ðÊóÄÌ¿®¸¦µæµ¡¹½)

6E-1 (»þ´Ö: 14:10 - 14:30)
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¥Ú¡¼¥¸pp. 1054 - 1060
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6E-2 (»þ´Ö: 14:30 - 14:50)
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Ãø¼Ô*Âç¾â ͦµ±, ¿åÌî Îòí (°¦Ã驶ÈÂç³Ø¾ðÊó²Ê³ØÉô¾ðÊó²Ê³Ø²Ê), ±ÝË٠ͥ (̾¸Å²°Âç³ØÂç³Ø±¡¾ðÊó³Ø¸¦µæ²Ê), ³á ¹îɧ (°¦Ã驶ÈÂç³Ø¾ðÊó²Ê³ØÉô¾ðÊó²Ê³Ø²Ê)
¥Ú¡¼¥¸pp. 1061 - 1070
¥­¡¼¥ï¡¼¥ÉBLE¥Ó¡¼¥³¥ó, °Üưǧ¼±, ¼Ö°Ø»Ò, ¥»¥ó¥µ¿®¹æ½èÍý, ¥Ç¥¸¥¿¥ë¥Õ¥£¥ë¥¿
¥¢¥Ö¥¹¥È¥é¥¯¥È°åÎŵ»½Ñ¤Îȯã¤Ë¤è¤ê¿Í¤Î¼÷Ì¿¤Ïǯ¡¹¿­¤Ó¡¤WHO¡ÊÀ¤³¦ÊÝ·òµ¡´Ø¡Ë¤Ï¤³¤ì¤«¤é¤Ïñ¤Ë¼÷Ì¿¤ò±ä¤Ð¤¹¤Î¤Ç¤Ï¤Ê¤¯¡¤·ò¹¯¼÷Ì¿¤ò±ä¤Ð¤·¤Æ¤¤¤¯¤Î¤¬ÂçÀÚ¤À¤ÈÄ󾧤·¤Æ¤¤¤ë¡¥·ò¹¯¼÷Ì¿¤ò±ä¤Ð¤¹½ÅÍפÊÍ×ÁǤÎ1¤Ä¤È¤·¤Æ±¿Æ°¤¬¤¢¤ê¡¤¤½¤ì¤òÃΤë»Øɸ¤È¤·¤ÆÊâ¿ô¤ò³Îǧ¤¹¤ëÊýË¡¤¬µó¤²¤é¤ì¤ë¡¥¤·¤«¤·¤Ê¤¬¤é¡¤Êâ¿ô¤Ï¼«Î©¤·¤ÆÊ⤱¤ë¿Í¤Î¤ß¤ËŬÍѤǤ­¤ë»Øɸ¤Ç¤¢¤ê¡¤¼Ö°Ø»Ò»ÈÍѼԤǤϤ³¤Î»Øɸ¤òÍѤ¤¤¿±¿Æ°Î̤οäÄê¤Ï¹Ô¤¨¤Ê¤¤¡¥¤³¤ì¤Þ¤Ç¼Ö°Ø»Ò¤Ë¤ª¤±¤ë°Üưǧ¼±¤ÎÀè¹Ô¸¦µæ¤È¤·¤Æ¡¤GPS¤òÍѤ¤¤¿¼êË¡¤ä²Ã®ÅÙ¡¤³Ñ®ÅÙ¡¤Ãϼ§µ¤¤òÍѤ¤¤¿¼êË¡¤Ê¤ÉÍÍ¡¹¤Ê¼êË¡¤¬Äó°Æ¤µ¤ì¤Æ¤­¤¿¡¥¤·¤«¤·¡¤¤³¤ì¤é¤Î¼êË¡¤Ï¹âÀºÅ٤ʰÌÃÖ¿äÄê¤Ë¾ÇÅÀ¤òÅö¤Æ¤Æ¤¤¤ë¤¿¤áƳÆþ¥³¥¹¥È¤¬¹â¤¯¡¤°ìÈ̤οͤǤϱ¿ÍѤ¬Æñ¤·¤¤¡¥¤½¤³¤ÇËܸ¦µæ¤Ç¤ÏBLE¥Ó¡¼¥³¥ó¤òÍѤ¤¤Æ¡¤Ä㥳¥¹¥È¤ÇƳÆþ¡¦±¿ÍѤ¬¹Ô¤¨¤ë°Üưǧ¼±¼êË¡¤òÄó°Æ¤¹¤ë¡¥ËܼêË¡¤Ï¼Ö°Ø»Ò¤ËBLE¥Ó¡¼¥³¥ó¤ò¼è¤êÉÕ¤±¡¤¾õÂ֤ˤè¤Ã¤ÆÊѲ½¤¹¤ëBLE¥Ó¡¼¥³¥ó¤ÎÅÅÇȶ¯ÅÙ¤ò¤â¤È¤Ë°Üưǧ¼±¤ò¹Ô¤¦¡¥¤³¤Î»þ¡¤¼èÆÀ¤·¤¿¥Ç¡¼¥¿¤Ë¤ÏÍÍ¡¹¤Ê¥Î¥¤¥º¤¬ºÜ¤Ã¤Æ¤¤¤ë¤¿¤á¡¤Ê£¿ô¤Î¥Ç¥¸¥¿¥ë¥Õ¥£¥ë¥¿¤òŬÍѤ·¥Î¥¤¥º¤Î·Ú¸º¤ò»î¤ß¤ë¡¥¤½¤·¤Æ¡¤¥Î¥¤¥º¤ò·Ú¸º¤·¤¿¥Ç¡¼¥¿¤«¤é¼ÖÎؤβóž¿ô¤ä²óžÊý¸þ¤ò¿äÄꤷ¡¤°ÜÆ°¤ò¿äÄꤷ¤Æ¤¤¤¯¡¥¾åµ­¤Î¼êË¡¤òÍѤ¤¤Æ¥Æ¥¹¥È´Ä¶­¤Ç¼Ö°Ø»Ò¤Î°Üưǧ¼±ÀºÅÙ¤ò³Î¤«¤á¤¿¡¥¤½¤Î·ë²Ì¡¤¼ÖÎؤβóž¿ô¿äÄê¤Ï100¡ó¡¤Á°¿Ê¡¦¸å¿Ê¿äÄê¤Ï100¡ó¡¦98.5¡ó¤ÎÀºÅ٤ǿäÄê¤Ç¤­¤¿¡¥¤Þ¤¿°ÜÆ°·ÐÏ©¿äÄê¤Ï¡¤Ä¹Êý·Á¤Î¼þ¤ê¤ò1¼þ¤¹¤ë°ÜÆ°¡¤¥¹¥é¥í¡¼¥à¤ÎÆ°¤­¡¤±ß¤Î¼þ¤ê¤ò1¼þ¤¹¤ë°ÜÆ°¤Î3¼ïÎà¤Ç¼Â°ÌÃÖ¤È¤Î¸íº¹¤òÈæ³Ó¤·É¾²Á¤ò¹Ô¤Ê¤Ã¤¿¡¥¤½¤Î·ë²Ì¡¤¤½¤ì¤¾¤ì¤Î°ÜÆ°¤Ë¤ª¤±¤ëÊ¿¶Ñ¿äÄê¸íº¹¤Ï13.95m¡¤6.75m¡¤6.51m¤Ç¤¢¤Ã¤¿¡¥

6E-3 (»þ´Ö: 14:50 - 15:10)
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Ãø¼Ô*¿åÌî Îòí, Âç¾â ͦµ± (°¦Ã驶ÈÂç³Ø ¾ðÊó²Ê³ØÉô), ±ÝË٠ͥ (̾¸Å²°Âç³ØÂç³Ø±¡ ¾ðÊó²Ê³Ø¸¦µæ²Ê), ³á ¹îɧ (°¦Ã驶ÈÂç³Ø ¾ðÊó²Ê³ØÉô)
¥Ú¡¼¥¸pp. 1071 - 1078
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Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 14:10 - 15:30
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Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
Í¥½¨ÏÀʸ¾Þ / Paper Awards
6F-1 (»þ´Ö: 14:10 - 14:30)
Âê̾Cellular-V2X¤òÍѤ¤¤¿¥ê¥¢¥ë¥¿¥¤¥à¾ðÊó¶¦Í­¤ÈÇÛ¿®¤Î¤¿¤á¤Î̵Àþ¥ê¥½¡¼¥¹¤Î¸úΨŪ¤Ê¥¹¥±¥¸¥å¡¼¥ê¥ó¥°Ë¡¤ÎÄó°Æ
Ãø¼Ô*À¥Àî Íβð (Åŵ¤ÄÌ¿®Âç³Ø), ¾åÌî ¹âÌÀ, Âç´ß ÃÒɧ (KDDIÁí¹ç¸¦µæ½ê), ¾®²Ö ÄçÉ×, Åò ÁÇ²Ú (Åŵ¤ÄÌ¿®Âç³Ø)
¥Ú¡¼¥¸pp. 1079 - 1087
¥­¡¼¥ï¡¼¥ÉITS, ¼Ö¼Ö´ÖÄÌ¿®, Cellular-V2X, ¥µ¥¤¥É¥ê¥ó¥¯, ´ðÃ϶ɷÐͳÄÌ¿®
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Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
6F-2 (»þ´Ö: 14:30 - 14:50)
Âê̾¼«Æ°¼Ö¥Í¥Ã¥È¥ï¡¼¥¯¤Ë¤ª¤±¤ëÄÌ¿®ÃÙ±ä¤ò¹Íθ¤·¤¿Ì©Å٥١¼¥¹Æ°Åª²¾Ì¾Êѹ¹¼êË¡¤Î¸¡Æ¤
Ãø¼Ô*»³ºê Îè, Ô¥ ³§¿Í, µÈÅÄ ¶©»Ö, ½ÅÌî ´² (·ÄØæµÁ½ÎÂç³ØÂç³Ø±¡Íý¹©³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1088 - 1093
¥­¡¼¥ï¡¼¥ÉITS, ¼«Æ°¼Ö¥Í¥Ã¥È¥ï¡¼¥¯, °ÌÃ֥ץ饤¥Ð¥·Êݸî, ²¾Ì¾Êѹ¹, ¥ß¥Ã¥¯¥¹¥¾¡¼¥ó
¥¢¥Ö¥¹¥È¥é¥¯¥ÈITS¡ÊIntelligent Transport Systems¡Ë¤Î¿¤¯¤Î¥¢¥×¥ê¥±¡¼¥·¥ç¥ó¤Ç¤Ï¡¤¼«Æ°¼Ö¥Í¥Ã¥È¥ï¡¼¥¯¤Ë¤Æ³Æ¼Öξ¤Î°ÌÃÖ¾ðÊó¤Î¸ò´¹¤¬É¬ÍפǤ¢¤ë¡¥°ìÊý¤Ç¡¤³Æ¼Öξ¤Î°ÌÃÖ¾ðÊó¤òÄÉÀפ¹¤ë¤³¤È¤Ç¡¤°ÌÃ֥ץ饤¥Ð¥·¤ÏÍưפ˿¯³²¤µ¤ì¤Æ¤·¤Þ¤¦¡¥°ÌÃ֥ץ饤¥Ð¥·¤òÊݸ¤ë¼êÃʤȤ·¤Æ¡¤¼«Æ°¼Ö¥Í¥Ã¥È¥ï¡¼¥¯¤Ë¤ª¤±¤ë²¾Ì¾¤Î»ÈÍѤ¬ÃíÌܤµ¤ì¤Æ¤¤¤ë¡¥Àè¹Ô¸¦µæ¤Ç¤Ï¡¤¼Ö¤é¤ÎÍ×µá¤È¼þÊÕ¼Öξ¿ô¤Ë±þ¤¸¤Æ²¾Ì¾¤òưŪ¤ËÊѹ¹¤¹¤ë¤³¤È¤Ë¤è¤Ã¤Æ¡¤°ÌÃ֥ץ饤¥Ð¥·¤òÊݸ¤Æ¤¤¤ë¡¥¤·¤«¤·¡¤ÌµÀþϩ¦µ¡¤Î¿ô¤¬¾¯¤Ê¤¤¾ì¹ç¤äÄÌ¿®Ãٱ䤬Â礭¤¤¾ì¹ç¡¤²¾Ì¾Êѹ¹¤ËɬÍפÊÄÌ¿®¤Ë¼ºÇÔ¤·¡¤Å¬Àڤʲ¾Ì¾Êѹ¹¤ò¹Ô¤¦¤³¤È¤¬¤Ç¤­¤Ê¤¤¡¥¤½¤³¤Ç¡¤ËܹƤǤÏVDMC¡ÊVehicle-based Dynamic Mix-zone Considering Communication Delay for Location Privacy in Vehicular Networks¡Ë¤òÄó°Æ¤¹¤ë¡¥VDMC¤Ï¡¤Mod-IBS¼êË¡¤òŬÍѤ·¤¿Ç§¾ÚÉÕ¤­²¾Ì¾ID¸°ÄÌ¿®¤ò³èÍѤ¹¤ë¤³¤È¤Ç¡¤²¾Ì¾¤òÊѹ¹¤¹¤ë¤¿¤á¤Ëϩ¦µ¡¤ò»ÈÍѤ¹¤ë¤³¤È¤¬ÉÔÍפˤʤ롥¤µ¤é¤Ë¡¤²¾Ì¾Êѹ¹¤Î¤¿¤á¤Î´ð½à¤È¤·¤ÆÊ£¿ô¤ÎÎΰè¤òÍѤ¤¤ë¤³¤È¤Ç¡¤²¾Ì¾Êѹ¹¤Î¼ºÇÔ¤òËɤ°¡¥¥·¥ß¥å¥ì¡¼¥·¥ç¥óɾ²Á¤è¤ê¡¤Äó°Æ¼êË¡¤¬Àè¹Ô¸¦µæ¤ÈÈæ³Ó¤·¤Æ¡¤ÄÌ¿®´°Î»Î¨¤È²¾Ì¾Êѹ¹²ó¿ô¤ò²þÁ±¤Ç¤­¤ë¤³¤È¤ò¼¨¤¹¡¥

6F-3 (»þ´Ö: 14:50 - 15:10)
Âê̾Ʊ°ì¼Öξ¤Ë´Ø¤¹¤ë¶¦Í­¥Ç¡¼¥¿¤È¥í¡¼¥«¥ë¥Ç¡¼¥¿¤ò¥Ê¥ó¥Ð¡¼¥×¥ì¡¼¥È¤òÍøÍѤ·¤ÆÅý¹ç¤¹¤ë¤¿¤á¤Î¥Þ¥Ã¥Á¥ó¥°¼êË¡¤ÎÄó°Æ
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¥Ú¡¼¥¸pp. 1094 - 1101
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Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
ºÇÍ¥½¨ÏÀʸ¾Þ / Best Paper Awards
6F-4 (»þ´Ö: 15:10 - 15:30)
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Ãø¼Ô*¾¾ÅÄ ÌÀÂç, ¾¾°æ ÃÒ°ì (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø), ¾¾ÅÄ Íµµ®, ¿Ûˬ Çîɧ, °ÂËÜ ·Ä°ì (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø/Íý²½³Ø¸¦µæ½ê)
¥Ú¡¼¥¸pp. 1102 - 1110
¥­¡¼¥ï¡¼¥ÉÏ©¾åÃóÄä¼Ö, ¥É¥é¥¤¥Ö¥ì¥³¡¼¥À, ʪÂÎǧ¼±, µ¡³£³Ø½¬
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6F-5 (»þ´Ö: 15:30 - 15:50)
Âê̾SAX¤òÍѤ¤¤¿¥Ö¥ì¡¼¥­Áàºî»þ¤Î±¿Å¾ÆÃÀ­¤ÎÃê½Ð
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¥Ú¡¼¥¸pp. 1111 - 1119
¥­¡¼¥ï¡¼¥É¼«Æ°±¿Å¾, ¼«Æ°±¿Å¾ÉáµÚ´ü, ¼ÖξµóÆ°, SAX, ±¿Å¾ÆÃÀ­
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¥»¥Ã¥·¥ç¥ó 6G  ¥¨¥Ã¥¸¡¦AI
Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 14:10 - 15:30
ºÂĹ: »³°æ À®ÎÉ (ÅìµþÇÀ¹©Âç³Ø)

¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
6G-1 (»þ´Ö: 14:10 - 14:30)
Âê̾A Method of Gas Source Localization from Sensor Network using Machine Learning
Ãø¼Ô*Worachat Arunothaikrit, Ayumi Ohnishi, Shuhei Tsuchida, Tsutomu Terada, Masahiko Tsukamoto (Graduated School of Engineering, Kobe University)
¥Ú¡¼¥¸pp. 1120 - 1125
¥­¡¼¥ï¡¼¥ÉGas source localization, Internet of things, Machine learning, Sensors, AI
¥¢¥Ö¥¹¥È¥é¥¯¥ÈGas source localization (GSL) is one of the most important tasks to find the origin of the gas source to avoid potential danger. GSL in natural conditions is a big challenge because it has many complex conditions, especially when the wind blows in an unpredictable direction. Mobile robots use a lot of energy to work and still have short working time. That makes using an immobile sensor is better, just placing it in the right place will lead to a longer lifetime and use less energy. However, finding the location of a gas leak is difficult both on relevant and irrelevant factors. Here we show that the location and distance of the gas emission source between the gas source to the station gas sensor array in the indoor environment. We found that the machine learning algorithm is applicable to localize our experiment gas source using standard performance metrics for the regression problem in machine learning: Mean absolute error (MAE) metric. Currently, an estimated position of the source with a deviation of 3.90 cm (93.1% using R-Squared) by using Random Forests Regression (RF regression). Our results show how the stationary sensor network tends to work in finding GSLs in an indoor environment using machine learning to find the distance between the gas and the sensor in natural wind conditions. We expect our experiment to be the starting point for bringing GSL to more complex forms, for example finding distance in multiple wind direction conditions and using it on a daily basis.

Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
6G-2 (»þ´Ö: 14:30 - 14:50)
Âê̾¼Ò²ñŪ¸ÉΩ¤Î¬Äê¤È¥±¥¢¤òÌÜŪ¤È¤·¤¿²ð¸î»ÜÀ߸þ¤±¸«¼é¤ê¥·¥¹¥Æ¥à¥Õ¥ì¡¼¥à¥ï¡¼¥¯¤ÎÄó°Æ
Ãø¼Ô*±Ê´Ö ¿µÂÀϺ, ¾ÂÈø ²íÇ· (Åŵ¤ÄÌ¿®Âç³Ø Âç³Ø±¡ ¾ðÊóÍý¹©³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1126 - 1132
¥­¡¼¥ï¡¼¥É¸«¼é¤ê¥·¥¹¥Æ¥à, ¥·¥¹¥Æ¥à¥Õ¥ì¡¼¥à¥ï¡¼¥¯, ʬ»¶½èÍý, ¹Ôưǧ¼±
¥¢¥Ö¥¹¥È¥é¥¯¥ÈÀ¤³¦Ãæ¤Ç¹âÎ𲽤Ͽʤó¤Ç¤ª¤ê¡¤²ð¸îÉéô¤Î·Ú¸º¤Î¤¿¤á¤Ë¸«¼é¤ê¥·¥¹¥Æ¥à¤¬Æ³Æþ¤µ¤ì»Ï¤á¤Æ¤¤¤ë¡¥´û¸¤Î¸«¼é¤ê¥·¥¹¥Æ¥à¤Ï¡¤Ã±µ¡Ç½¤Î¤â¤Î¤¬¤Û¤È¤ó¤É¤Ç¤¢¤ê¡¤Î㤨¤Ð¡¤¾²¥»¥ó¥µ¤Ë¤è¤Ã¤Æµ¯¾²¤ò¸¡ÃΤ¹¤ë¡¥¤·¤«¤·¸«¼é¤ê¥·¥¹¥Æ¥à¤Ëµá¤á¤é¤ì¤ëµ¡Ç½¤ÏÈó¾ï¤Ë¿¤¯¡¤³ÈÄ¥À­¤È½ÀÆðÀ­¤¬µá¤á¤é¤ì¤ë¡¥Æä˶áǯ¡¤¹âÎð¼Ô¤Î·ò¹¯¼÷Ì¿¤ò¿­¤Ð¤¹¤³¤È¤Ë´Ø¿´¤¬¹â¤Þ¤Ã¤Æ¤ª¤ê¡¤ÆüÃæ¤ÎÆü¾ïÀ¸³èÆ°ºî¤ò¥â¥Ë¥¿¥ê¥ó¥°¤·¡¤¹âÎð¼Ô¤Î·ò¹¯¾õÂÖ¤òÇÄ°®¤·¡¤°Ý»ý¡¦¸þ¾å¤·¤Æ¤¤¤¯¤¿¤á¤Î¥±¥¢¤Ë·Ò¤¬¤ë¤è¤¦¤Ê¸«¼é¤ê¥·¥¹¥Æ¥à¤Ë¤·¤Æ¤¤¤¯É¬Íפ¬¤¢¤ë¡¥¤½¤Î¤¿¤á¤ËɬÍפʵ¡Ç½¤òʬÀϤ·¤¿¤È¤³¤í¡¤Ê£¿ô¤Î¥»¥ó¥µ¡¼¡¦Ê£¿ô¥·¥Ê¥ê¥ª¤òÀ©¸æ¤Ç¤­¤ë»ÅÁȤߤ¬É¬ÍפǤ¢¤ë¤³¤È¤¬¤ï¤«¤Ã¤¿¡¥¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï²ð¸î»ÜÀ߸þ¤±¤ËÊ£¿ô¿Í¤Î¥â¥Ë¥¿¥ê¥ó¥°¤¬²Äǽ¤Ç¡¤Â¿µ¡Ç½¤«¤Äµ¡Ç½¤ÎÄɲá¦Êѹ¹¤¬²Äǽ¤Ê¡¤¸«¼é¤ê¥·¥¹¥Æ¥à¤ò¹½ÃÛ¤¹¤ë¤¿¤á¤Î¥Õ¥ì¡¼¥à¥ï¡¼¥¯¤òÄó°Æ¤¹¤ë¡¥¸«¼é¤ê¥·¥¹¥Æ¥à¥Õ¥ì¡¼¥à¥ï¡¼¥¯¤Ç¤Ï¡¤¥·¥¹¥Æ¥à¤Ë»ÈÍѤ¹¤ë¥»¥ó¥µ¡¼¤ä¥¢¥¯¥Á¥å¥¨¡¼¥¿¡¼¤ÎÄɲá¦Êѹ¹¤¬ÍưפǤ¢¤ê¡¤¥·¥¹¥Æ¥à¤ÎµóÆ°¤â¼«Í³¤ËÊѹ¹²Äǽ¤Ê¤¿¤á¡¤»ÜÀߤǵá¤á¤é¤ì¤ë¥·¥Ê¥ê¥ª¤Ë¤â½ÀÆð¤ËÂбþ¤¹¤ë¤³¤È¤¬¤Ç¤­¤ë¡¥¥Õ¥ì¡¼¥à¥ï¡¼¥¯¤òÍѤ¤¤Æ¼ÂÁõ¤·¤¿¸«¼é¤ê¤Õ¤¯¤í¤¦¤òÍѤ¤¤Æ¥Õ¥ì¡¼¥à¥ï¡¼¥¯¤ÎÆ°ºî¸¡¾Ú¤ò¹Ô¤¤¡¤¿·¤¿¤Ê¥»¥ó¥µ¡¼¤ÎÄɲᤥ·¥Ê¥ê¥ª¤òµ­½Ò¤¹¤ë¤³¤È¤ÇÂÐÏýèÍý¤ò¹Ô¤¨¤ë¤³¤È¤ò³Îǧ¤·¤¿¡¥

6G-3 (»þ´Ö: 14:50 - 15:10)
Âê̾¥¢¥Ë¡¼¥ê¥ó¥°¥Þ¥·¥ó¤ò³èÍѤ·¤¿¥¨¥Ã¥¸AI¤Ë¤ª¤±¤ëÀ¸À®¥â¥Ç¥ë¤Î³Ø½¬¸úΨ²½¤Î¤¿¤á¤Î¥¢¡¼¥­¥Æ¥¯¥Á¥ã
Ãø¼Ô*ÄáÅÄ Çîʸ, ¾¾ËÜ Î¼²ð (¤µ¤¯¤é¥¤¥ó¥¿¡¼¥Í¥Ã¥È³ô¼°²ñ¼Ò ¤µ¤¯¤é¥¤¥ó¥¿¡¼¥Í¥Ã¥È¸¦µæ½ê)
¥Ú¡¼¥¸pp. 1133 - 1140
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¥»¥Ã¥·¥ç¥ó 7A  ¥Í¥Ã¥È¥ï¡¼¥¯¥µ¡¼¥Ó¥¹
Æü»þ: 2020ǯ6·î26Æü(¶â) 8:30 - 10:10
ºÂĹ: ÀÆÆ£ ŵÌÀ (ÅìµþÄÌ¿®Âç³Ø)

7A-1 (»þ´Ö: 8:30 - 9:10)
Âê̾(¾·ÂÔ¹Ö±é) ¥Ó¥Ç¥ª²ñµÄ¤Î¥¢¥¦¥§¥¢¥Í¥¹
Ãø¼ÔÉßÅÄ ´´Ê¸ (¹âÃι©²ÊÂç³Ø)
¥Ú¡¼¥¸p. 1141

7A-2 (»þ´Ö: 9:10 - 9:30)
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Ãø¼Ô*À¾Â¼ ½Ù¿Í, Ìî¼ ²Â½¨ (ÉÙ»ÎÄ̸¦µæ½ê)
¥Ú¡¼¥¸pp. 1142 - 1146
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7A-3 (»þ´Ö: 9:30 - 9:50)
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Ãø¼Ô*ÅòÀõ ÈþÌî, µÈÌî ¹§ (ϲλ³Âç³Ø), ÀÄÌÚ ²ÃÉÄ (Ï²λ³¸©Î©¶áÂåÈþ½Ñ´Û)
¥Ú¡¼¥¸pp. 1147 - 1153
¥­¡¼¥ï¡¼¥ÉÂÐÏÃ¥·¥¹¥Æ¥à, Èþ½Ñ´Õ¾Þ
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7A-4 (»þ´Ö: 9:50 - 10:10)
Âê̾Asynchronous Multi-Party Video Chat System with Reaction
Ãø¼Ô*Ari Nugraha, °æ¾å ÃÒͺ, Izhar Wahono, Jianpeng Zhanghe, ¸¶ÅÄ ÎÑ¹Ô (ÃÞÇÈÂç³Ø)
¥Ú¡¼¥¸pp. 1154 - 1160
¥­¡¼¥ï¡¼¥É¥Ó¥Ç¥ª²ñµÄ¥·¥¹¥Æ¥à, ÈóƱ´ü¥³¥ß¥å¥Ë¥±¡¼¥·¥ç¥ó, ¥Ó¥Ç¥ª²ñÏÃ, ¿¿Í¿ô²ñÏÃ, ±ó³Ö¥³¥ß¥å¥Ë¥±¡¼¥·¥ç¥ó
¥¢¥Ö¥¹¥È¥é¥¯¥ÈIn this study, we developed an asynchronous multi-party video communication system which allows three persons to communicate using video messages. Reaction of the message recipients when they are viewing the video message is recorded and then automatically sent back to the message sender for review so they can sense an emotion response to their message. Our main objective was to evaluates how our system could produce better engagement between conversation party when reaction of recipient shown between message exchange. Our system evaluation using within-subject experiment showed that our proposed multi-party video messaging system with reaction performs better than the non-reaction system as it provides better user experience.


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Æü»þ: 2020ǯ6·î26Æü(¶â) 8:30 - 10:10
ºÂĹ: Âç¼ Î÷ (Ë­¶¶µ»½Ñ²Ê³ØÂç³Ø)

Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
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7B-1 (»þ´Ö: 8:30 - 8:50)
Âê̾¥¦¥§¥¢¥é¥Ö¥ë¤À¤ë¤Þ¤µ¤ó¤¬¤³¤í¤ó¤À: ¥×¥ì¡¼¥äµÚ¤Ó´ÑµÒ¤ÎǼÆÀ´¶¤ò¹Íθ¤·¤¿µ¡³£È½Äꥷ¥¹¥Æ¥à¤òÍѤ¤¤¿¥²¡¼¥à¤Î¼ÂÁõ¤È±¿ÍÑ
Ãø¼Ô*ÌøÀ¸ ÍÚ, »°ÌÚ Î´Í´, ÂçÀ¾ °¾Èþ, ÅÚÅÄ ½¤Ê¿, »ûÅÄ ÅØ, ÄÍËÜ ¾»É§ (¿À¸ÍÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1161 - 1171
¥­¡¼¥ï¡¼¥Éµ¡³£È½Äê, ¤À¤ë¤Þ¤µ¤ó¤¬¤³¤í¤ó¤À, Ëþ­´¶, ²Ã®ÅÙ¥»¥ó¥µ, ¼ÂÀ¤³¦¥²¡¼¥à
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7B-2 (»þ´Ö: 8:50 - 9:10)
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Ãø¼Ô*Âçüâ ¿¿Í³, ²ÃÆ£ ϯ (·ÄØæµÁ½ÎÂç³ØÂç³Ø±¡ ¥á¥Ç¥£¥¢¥Ç¥¶¥¤¥ó¸¦µæ²Ê), »³Æâ Àµ¿Í (·ÄØæµÁ½ÎÂç³ØÂç³Ø±¡¥á¥Ç¥£¥¢¥Ç¥¶¥¤¥ó¸¦µæ²Ê/¾ðÊó·Ð±Ä¥¤¥Î¥Ù¡¼¥·¥ç¥óÀìÌ翦Âç³Ø), º½¸¶ ½¨¼ù (·ÄØæµÁ½ÎÂç³ØÂç³Ø±¡ ¥á¥Ç¥£¥¢¥Ç¥¶¥¤¥ó¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1172 - 1177
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7B-3 (»þ´Ö: 9:10 - 9:30)
Âê̾VR±ÇÁü¤Ë¤è¤ë¿ÈÂΰÜÆ°´¶³Ð¤ÎÉÕÍ¿¤¬²Î¾§»þ¤ÎȯÀ¼¤ËÍ¿¤¨¤ë±Æ¶Á¤Îɾ²Á
Ãø¼Ô*ºä̾ ½ãÂÀ, ÅÚÅÄ ½¤Ê¿, »ûÅÄ ÅØ, ÄÍËÜ ¾»É§ (¿À¸ÍÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1178 - 1183
¥­¡¼¥ï¡¼¥ÉVirtual Reality, ²¾ÁÛ¸½¼Â, ȯÀ¼»Ù±ç
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7B-4 (»þ´Ö: 9:30 - 9:50)
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¥Ú¡¼¥¸pp. 1184 - 1193
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Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
7B-5 (»þ´Ö: 9:50 - 10:10)
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¥Ú¡¼¥¸pp. 1194 - 1203
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Æü»þ: 2020ǯ6·î26Æü(¶â) 8:30 - 10:10
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7C-1 (»þ´Ö: 8:30 - 8:50)
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¥Ú¡¼¥¸pp. 1204 - 1207
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Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
7C-2 (»þ´Ö: 8:50 - 9:10)
Âê̾¿ÈÂΤò°µÇ÷¤¹¤ë¤³¤È¤Ë¤è¤ëÌ®ÇÈÀ©¸æ¤òÍѤ¤¤¿¥¦¥§¥¢¥é¥Ö¥ë¥Ç¥Ð¥¤¥¹¥¤¥ó¥¿¥é¥¯¥·¥ç¥ó¼êË¡¤ÎÄó°Æ
Ãø¼Ô*½©¸µ Í¥Ëà (Ω̿´ÛÂç³ØÂç³Ø±¡¾ðÊóÍý¹©³Ø¸¦µæ²Ê), ¼Èø ÏÂºÈ (Ω̿´ÛÂç³ØÂç³Ø±¡¾ðÊóÍý¹©³Ø¸¦µæ²Ê/JST¤µ¤­¤¬¤±)
¥Ú¡¼¥¸pp. 1208 - 1214
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ºÇÍ¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Best Presentation Awards
Í¥½¨ÏÀʸ¾Þ / Paper Awards
7C-3 (»þ´Ö: 9:10 - 9:30)
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¥Ú¡¼¥¸pp. 1215 - 1222
¥­¡¼¥ï¡¼¥É¥Ø¥ë¥á¥Ã¥È, °µÎÏ¥»¥ó¥µ, ƬÉô·Á¾õ, ¸Ä¿Í¼±ÊÌ, ËÜ¿Íǧ¾Ú
¥¢¥Ö¥¹¥È¥é¥¯¥È¥Ø¥ë¥á¥Ã¥È¤Ï¼Ò²ñÀ¸³è¤Ë¤ª¤¤¤Æ¹­¤¯ÍøÍѤµ¤ì¤Æ¤¤¤ë¡¥Ëܸ¦µæ¤Ç¤Ï32¸Ä¤Î°µÎÏ¥»¥ó¥µ¤òÅëºÜ¤·¤¿¥Ø¥ë¥á¥Ã¥È¤òÁõÃ夹¤ë¤³¤È¤Ç¡¤Æ¬Éô·Á¾õ¤«¤é¸Ä¿Í¤ò¼±Ê̤¹¤ë¼êË¡¤òÄó°Æ¤¹¤ë¡¥Äó°Æ¼êË¡¤Ë¤è¤Ã¤Æ¡¤¥Ø¥ë¥á¥Ã¥È¾åÉô¤Ë¼è¤êÉÕ¤±¤¿¥Ç¥£¥¹¥×¥ì¥¤¤Ë̾Á°¤òɽ¼¨¤·¤¿¤ê¡¤»ëÀþ¾ðÊó¤Ê¤É¤Î¥Ç¡¼¥¿¤òµ­Ï¿¤¹¤ëºÝ¤Ë¼ê´Ö¤Ê¤¯ºî¶È¼Ô¤Î¥é¥Ù¥ë¤òÉÕÍ¿¤Ç¤­¤ë¡¥¤Þ¤¿¡¤¹©¾ì¤Ê¤É¤ÇÌò¿¦¤Ê¤É¤Ë¤è¤êÆþ¼¼¤Ç¤­¤ëÉô²°¤¬À©¸Â¤µ¤ì¤Æ¤¤¤ë¾ì¹ç¤ËÈâ¤Î¸°¤È¤·¤Æ¤â»ÈÍѤǤ­¤ë¡¥Äó°Æ¼êË¡¤Ï¡¤¤¢¤é¤«¤¸¤á¥Ç¡¼¥¿¤¬ÅÐÏ¿¤µ¤ì¤¿Ê£¿ô¤Î¿Íʪ¤Î¤¦¤Á¤Î1¿Í¤¬¥Ø¥ë¥á¥Ã¥È¤òÁõÃ夷¤¿¤È¤­¤Ë¤½¤Î¿Íʪ¤ò¼±Ê̤¹¤ë¸Ä¿Í¼±Ê̤ȡ¤¥Ø¥ë¥á¥Ã¥È¤òÁõÃ夷¤¿¿Íʪ¤¬ÅÐÏ¿¼Ô¤Ç¤¢¤ì¤Ðǧ¾Ú¤·¡¤ÅÐÏ¿¼Ô¤Ç¤Ê¤±¤ì¤ÐµñÈݤ¹¤ëËÜ¿Íǧ¾Ú¤Î2¤Ä¤Îµ¡¹½¤òÈ÷¤¨¤ë¡¥¥×¥í¥È¥¿¥¤¥×¥Ç¥Ð¥¤¥¹¤È²òÀÏÍѤΥ½¥Õ¥È¥¦¥§¥¢¤ò¼ÂÁõ¤·¤¿¸å¡¤È︳¼Ô9¿Í¥Ç¡¼¥¿¤òºÎ¼è¤·¡¤¸Ä¿Í¼±Ê̤ǤÏÀºÅÙ¤¬100%¡¤ËÜ¿Íǧ¾Ú¤Ç¤ÏÈ︳¼ÔÁ´°÷¤ÎÊ¿¶ÑEER¤¬Ìó7.6%¤È¤¤¤¦·ë²Ì¤òÆÀ¤¿¡¥

7C-4 (»þ´Ö: 9:30 - 9:50)
Âê̾¼ùÌÚ¤ÎΩÂι½Â¤ÇÄ°®¤Î¤¿¤á¤Î¼ù·Á¤Ë´ð¤Å¤¯ÅÀ·²¤Î¸ÄÂÎʬ³ä¼êË¡
Ãø¼Ô*ÀçÅÄ ·°, ÈËÅÄ Î¼ (ÅìµþÂç³ØÂç³Ø±¡¹©³Ø·Ï¸¦µæ²Ê), ³Ô °Ò (ÅìµþÂç³ØÂç³Ø±¡ÇÀ³ØÀ¸Ì¿²Ê³Ø¸¦µæ²Ê), À ·½Çî (ÅìµþÂç³ØÂç³Ø±¡¹©³Ø·Ï¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1223 - 1229
¥­¡¼¥ï¡¼¥ÉÅÀ·², ¥É¥í¡¼¥ó, ¸ÄÂÎʬ³ä, ²Ì¼ù, ¥Ü¥ê¥å¡¼¥à¿äÄê
¥¢¥Ö¥¹¥È¥é¥¯¥È¶áǯ¡¤¥É¥í¡¼¥ó¤òÍѤ¤¤¿¥ê¥â¡¼¥È¥»¥ó¥·¥ó¥°¤ä¥ì¡¼¥¶¥¹¥­¥ã¥Ê¡ÊLiDAR¤Ê¤É¡Ë¤òÍѤ¤¤Æ¿¢Êª¤Î»°¼¡¸µ¹½Â¤¤òÅÀ·²¥Ç¡¼¥¿¤È¤·¤ÆÇÄ°®¤·¡¤ÇÀ¶ÈÀ¸»º¤Ë³èÍѤ·¤è¤¦¤È¤¤¤¦¼è¤êÁȤߤ¬¹Ô¤ï¤ì¤Æ¤¤¤ë¡¥Î㤨¤Ð¡¤²Ì¼ù¤òÂÐ¾Ý¤Ë¥É¥í¡¼¥ó¤Ç¶õ»£¤·¤¿Æ󼡸µ²èÁü¤«¤é»°¼¡¸µºÆ¹½À®¤ò¹Ô¤¤¡¤ÆÀ¤é¤ì¤¿»°¼¡¸µÅÀ·²¥Ç¡¼¥¿¤ò¤â¤È¤ËÌڤΥܥê¥å¡¼¥à¤ò¿äÄꤷ¼ý³ÏÎÌͽ¬¤Ë³èÍѤ¹¤ë¼êË¡¤¬¸¡Æ¤¤µ¤ì¤Æ¤¤¤ë¡¥¤½¤ÎºÝ¡¤¥É¥í¡¼¥ó¤Ë¤è¤ë¬Äê¤Ç¤ÏÊà¾ìÁ´ÂΤò»£±Æ¤¹¤ë¤¿¤á¡¤ÅÀ·²¥Ç¡¼¥¿¤Ë¤Ï´Ä¶­Ãæ¤ÎÉÔɬÍפÊʪÂΤäÊ£¿ô¤Î¸ÄÂΤ¬´Þ¤Þ¤ì¤ë¡¥½¾¤Ã¤Æ½é¤á¤ËÅÀ·²¥Ç¡¼¥¿¤ò¸ÄÂΤ´¤È¤Ëʬ³ä¤¹¤ëÁ°½èÍý¤¬É¬ÍפȤʤ롥¤³¤Îʬ³ä½èÍý¤ÎÀºÅ٤ϼý³ÏÎÌͽÁۤʤɸå¤Îºî¶È¤ÎÆþÎϥǡ¼¥¿¤È¤·¤ÆľÀܱƶÁ¤¹¤ë¤¿¤á¡¤¤è¤ê¹âÀºÅÙ¤Êʬ³ä¥¢¥ë¥´¥ê¥º¥à¤¬µá¤á¤é¤ì¤Æ¤¤¤ë¡¥´û¸¤Î¼è¤êÁȤߤȤ·¤Æ¡¤»°¼¡¸µ¶õ´ÖÃæ¤Î¥æ¡¼¥¯¥ê¥Ã¥Éµ÷Î¥¤Ë´ð¤Å¤¯°ìÈÌŪ¤Ê¥¯¥é¥¹¥¿¥ê¥ó¥°¤¬¹­¤¯¸¦µæ¤µ¤ì¤Æ¤¤¤ë¡¥¤·¤«¤·Êà¾ìÃæ¤Î¸ÄÂΤɤ¦¤·¤¬¸ß¤¤¤ËÀܶᤷ¤Æ¤¤¤ë¾ì¹ç¡¤»Þ¤É¤¦¤·¤¬½Å¤Ê¤ê¹ç¤¤¤¦¤Þ¤¯Ê¬³ä¤Ç¤­¤Ê¤¤ÌäÂ꤬¤¢¤ë¡¥¤½¤³¤ÇËܹƤǤϡ¤¼ù·Á¤ò¹Íθ¤·¤Æ»°¼¡¸µ¶õ´ÖÃæ¤ÎÅÀ·²¤òõº÷¤·¸ÄÂΤ´¤È¤Ëʬ³ä¤¹¤ë¼êË¡¤òÄó°Æ¤¹¤ë¡¥¼ÂºÝ¤Î¬Äê¥Ç¡¼¥¿¤ËÄó°Æ¼êË¡¤òŬÍѤ·¤Æʬ³äÀ­Ç½¤òɾ²Á¤·¡¤²Ì¼ù¤Î¥Ü¥ê¥å¡¼¥à¿äÄ꤫¤é¼ýÎ̤òͽ¬¤¹¤ë¼êË¡¤ò¸¡Æ¤¤¹¤ë¡¥


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¥»¥Ã¥·¥ç¥ó 7D  ʬ»¶½èÍý2
Æü»þ: 2020ǯ6·î26Æü(¶â) 8:30 - 10:10
ºÂĹ: ¶úÅÄ ¹â¹¬ (Åìµþ¹©²ÊÂç³Ø)

7D-1 (»þ´Ö: 8:30 - 8:50)
Âê̾ÄÌ¿®ÂÓ°è¤Ë´ð¤Å¤¯¾õÂÖʬ³ä¤òÍѤ¤¤¿ ¹­°èState Machine Replication¤Ë¤ª¤±¤ë¾õÂÖžÁ÷¼êË¡
Ãø¼Ô*ÀéÍÕ ÂÙÍý, Âç¼ Î÷ (Ë­¶¶µ»½Ñ²Ê³ØÂç³Ø ¾ðÊó¡¦ÃÎǽ¹©³Ø·Ï), Ãæ¼ ½ãºÈ (Ë­¶¶µ»½Ñ²Ê³ØÂç³Ø ¾ðÊó¥á¥Ç¥£¥¢´ðÈ×¥»¥ó¥¿¡¼)
¥Ú¡¼¥¸pp. 1230 - 1237
¥­¡¼¥ï¡¼¥ÉState Machine Replication, ¹­°èSMR, ¾õÂÖžÁ÷
¥¢¥Ö¥¹¥È¥é¥¯¥ÈËܸ¦µæ¤Ç¤Ï¡¤ÄÌ¿®ÂÓ°è¤Ë´ð¤Å¤¯¾õÂÖʬ³ä¤òÍѤ¤¤¿¹­°è State Machine Replication (SMR) ¤Ë¤ª¤±¤ë¾õÂÖžÁ÷¼ê Ë¡¤òÄó°Æ¤¹¤ë¡¥SMR ¤Ï¥µ¡¼¥Ó¥¹¤òÊ£¿ô¤Î¥ì¥×¥ê¥«¤ËÊ£À½¤¹¤ë¤³¤È¤ÇÂѾ㳲À­¤ò¸þ¾å¤µ¤»¤ë»ÅÁȤߤǤ¢¤ê¡¤¥ì¥×¥ê ¥«¤ÎÄɲᦲóÉü»þ¤Ë¥ì¥×¥ê¥«´Ö¤Ç¥µ¡¼¥Ó¥¹¤Î¾õÂÖ¤òžÁ÷¤¹¤ë¡¥ËܼêË¡¤Ç¤Ï¡¤Å¾Á÷¤¹¤ë¾õÂÖ¤ò¥Á¥ã¥ó¥¯¤Î½¸¹ç¤È¤·¤Æ °·¤¦¡¥¼õ¿®¥ì¥×¥ê¥«¤Ï¡¤³Æ¥ì¥×¥ê¥«¤È¤ÎÄÌ¿®ÂÓ°è¤Ë±þ¤¸¤¿¿ô¤Î¥Á¥ã¥ó¥¯¤òÁ÷¿®Â¦¤ËÍ׵᤹¤ë¡¥Á÷¿®¥ì¥×¥ê¥«¤Ï¡¤Í× µá¤µ¤ì¤¿¿ô¤Î¥Á¥ã¥ó¥¯¤òžÁ÷¤¹¤ë¡¥Á÷¿®¥ì¥×¥ê¥«¤´¤È¤ËÄÌ¿®ÂÓ°è¤Ëº¹¤¬¤¢¤ë¾ì¹ç¤ÏÍ׵᤹¤ëžÁ÷¥Á¥ã¥ó¥¯¿ô¤òÁý¸º ¤¹¤ë¡¥¤³¤ì¤Ë¤è¤ê´û¸¼êË¡¤Ç¸«¤é¤ì¤¿¡¤¹­°è SMR ¤ÎÉԶѰì¤ÊÄÌ¿®ÂÓ°è¤Ë¤è¤ëÀ­Ç½Äã²¼¤ò²þÁ±¤¹¤ë¡¥Amazon EC2 ¤Ë ¹½ÃÛ¤·¤¿¹­°è SMR ¾å¤Ç¼Â»Ü¤·¤¿É¾²Á¼Â¸³¤«¤é¡¤Äó°Æ¼êË¡¤Ï Bessani ¤é¤Î¼êË¡¤ËÂФ·¤ÆºÇÂç 42%¤Î¾õÂÖžÁ÷®Å٤Π¸þ¾å¤¬¤ß¤é¤ì¤¿¤¬¡¤¾ò·ï¤Ë¤è¤Ã¤Æ¤Ï Bessani ¤é¤Î¼êË¡¤ËÈæ¤Ù¤Æ¾õÂÖžÁ÷®ÅÙ¤¬Äã²¼¤¹¤ë¤³¤È¤¬¤¢¤Ã¤¿¡¥¤³¤ÎžÁ÷® ÅÙÄã²¼¤ËÂФ·¤Æ¸¡¾Ú¤ò¹Ô¤Ã¤¿·ë²Ì¡¤Å¾Á÷®ÅÙ¤ÎÄã²¼¤¬µ¯¤³¤ë¾ì¹ç¡¤»öÁ°¤Ë·×¬¤ò¹Ô¤Ã¤¿ÄÌ¿®ÂÓ°è¤ÈÈæ¤Ù¤Æ¼ÂºÝ¤ÎÄÌ ¿®ÂӰ褬Ä㤯¤Ê¤Ã¤Æ¤¤¤ë¤³¤È¤ò³Î¤«¤á¤¿¡¥

7D-2 (»þ´Ö: 8:50 - 9:10)
Âê̾Â絬ÌÏ¥»¥ó¥µ¥Ç¡¼¥¿¥¹¥È¥ê¡¼¥à¼ý½¸¤Ë¤ª¤±¤ë¥Ç¡¼¥¿½¸Ìó¤Î¤¿¤á¤ÎÂÔ¤Á»þ´ÖÀßÄêÊý¼°¤Î¸¡Æ¤
Ãø¼Ô*Àî¾å ÊþÌé (Ê¡°æÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê), µÁµ× ÃÒ¼ù, ¾¾ËÜ Å¯ (ÂçºåÂç³Ø¥µ¥¤¥Ð¡¼¥á¥Ç¥£¥¢¥»¥ó¥¿¡¼), »ûÀ¾ ͵°ì (¹ñΩ¸¦µæ³«È¯Ë¡¿Í¾ðÊóÄÌ¿®¸¦µæµ¡¹½/ÂçºåÂç³Ø¥µ¥¤¥Ð¡¼¥á¥Ç¥£¥¢¥»¥ó¥¿¡¼)
¥Ú¡¼¥¸pp. 1238 - 1244
¥­¡¼¥ï¡¼¥É¥¹¥È¥ê¡¼¥à¥Ç¡¼¥¿½èÍý, ¥ª¡¼¥Ð¥ì¥¤¥Í¥Ã¥È¥ï¡¼¥¯, Éé²Ùʬ»¶, µöÍÆÃÙ±ä, °ÌÁêÄ´À°
¥¢¥Ö¥¹¥È¥é¥¯¥ÈIoT¤¬¼Â¸½¤µ¤ì¤Ä¤Ä¤¢¤ê¡¤Â¿¼ï¿Íͤʥ»¥ó¥µ¤ò¤â¤Äµ¡´ï¡Ê¥Î¡¼¥É¡Ë¤¬¥¤¥ó¥¿¡¼¥Í¥Ã¥È·Ðͳ¤Ç¥Ç¡¼¥¿¤òϢ³Ū¤Ëȯ¿®¤·¤Æ¤¤¤ë¡¥¤³¤Î¤È¤­¡¤½èÍýǽÎϤä¥Í¥Ã¥È¥ï¡¼¥¯´Ä¶­¤Ï¥Î¡¼¥É¤´¤È¤Ë°Û¤Ê¤ë¤¿¤á¡¤¥Î¡¼¥É¦¤¬µá¤á¤ë¥Ç¡¼¥¿¤ÎÀ¸À®¤äȯ¿®¤Î´Ö³Ö¤â°Û¤Ê¤ë¡¥Ëܸ¦µæ¤Ç¤Ï³Æ¥Î¡¼¥É¤¬Ï¢Â³Åª¤«¤ÄÄê´üŪ¤Ëȯ¿®¤¹¤ë¥Ç¡¼¥¿¤ò¡Ö¥»¥ó¥µ¥Ç¡¼¥¿¥¹¥È¥ê¡¼¥à¡×¡¤È¯¿®¤Î´Ö³Ö¤ò¡Ö¼þ´ü¡×¤È¸Æ¤Ö¡¥²æ¡¹¤ÏÊ£¿ô¤Î¥Î¡¼¥É¤¬È¯¿®¤¹¤ë¥»¥ó¥µ¥Ç¡¼¥¿¥¹¥È¥ê¡¼¥à¤ò¼ý½¸¤¹¤ë»ÅÁȤߤȤ·¤Æ¡¤¥ª¡¼¥Ð¥ì¥¤¥Í¥Ã¥È¥ï¡¼¥¯¤Î°ì¤Ä¤Ç¤¢¤ë¥¹¥­¥Ã¥×¥°¥é¥Õ¤òÍѤ¤¤ÆÊ£¿ô¤Î¥¹¥È¥ê¡¼¥à¤ò½¸Ì󤹤뤳¤È¤Ç¡¤³Æ¥Î¡¼¥É¤Î¥Ç¡¼¥¿Á÷¼õ¿®¤Ë¤è¤ëÉé²Ù¤ò·Ú¸º¤¹¤ë¼êË¡¤òÄó°Æ¤·¤¿¡¥¤·¤«¤·¡¤Â¿¤¯¤Î¥Ç¡¼¥¿¤ò½¸Ì󤹤뤿¤á¤Ë¤Ï³ÆÃæ·Ñ¥Î¡¼¥É¾å¤ÇÂÔ¤Á»þ´Ö¤¬É¬ÍפȤʤꡤÉÔŬÀÚ¤ÊÂÔ¤Á»þ´Ö¤ÎÀßÄê¤Ë¤è¤Ã¤Æ¡¤½¸ÌóÁ°¤Ë¼¡¤Î¥Î¡¼¥É¤Ø¥Ç¡¼¥¿¤¬Å¾Á÷¤µ¤ì¤¿¤ê¡¤ºÇ½ªÅª¤Ê°¸Àè¥Î¡¼¥É¤Þ¤Ç¤ÎÃÙ±ä¤Ê¤É¤¬À¸¤¸¤ë¡¥¤½¤³¤ÇËÜÏÀʸ¤Ç¤Ï¡¤³Æ¥Î¡¼¥É¤ÎºÇÂçÂÔ¤Á»þ´Ö¤¬Ìܰ¤Ȥ·¤ÆÍ¿¤¨¤é¤ì¤ë¤â¤Î¤È¤·¡¤¤½¤ÎºÇÂçÂÔ¤Á»þ´Ö°ÊÆâ¤Ç¡¤³Æ¥Î¡¼¥É¤¬¥Ç¡¼¥¿¤ò¸úΨŪ¤Ë½¸Ìó²Äǽ¤ÊÂÔ¤Á»þ´ÖÀßÄêÊý¼°¤òÄó°Æ¤¹¤ë¡¥Äó°Æ¼êË¡¤Ï¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¤è¤êɾ²Á¤·¡¤³Æ¥Î¡¼¥É¤ØÀßÄꤷ¤¿ÂÔ¤Á»þ´Ö¤Ë¤è¤Ã¤Æ¥Ç¡¼¥¿¼ý½¸»þ¤ÎÉé²Ù¤ä°¸Àè¥Î¡¼¥É¤Þ¤Ç¤ÎÃٱ䤬ÊѲ½¤¹¤ë¤³¤È¤ò³Îǧ¤·¤¿¡¥

7D-3 (»þ´Ö: 9:10 - 9:30)
Âê̾ʬ»¶¥¹¥È¥ê¡¼¥à½èÍý¥Õ¥ì¡¼¥à¥ï¡¼¥¯¤òÍѤ¤¤¿Æ°ºî¼±Ê̼êË¡¤Î¸¡Æ¤
Ãø¼Ô*¹âºê ÃÒ¹á»Ò (¤ªÃã¤Î¿å½÷»ÒÂç³Ø), ÃÝ˼ ¤¢¤Ä»Ò (¹ñΩ¾ðÊó³Ø¸¦µæ½ê), ÃæÅÄ ½¨´ð (»º¶Èµ»½ÑÁí¹ç¸¦µæ½ê), ¾®¸ý Àµ¿Í (¤ªÃã¤Î¿å½÷»ÒÂç³Ø)
¥Ú¡¼¥¸pp. 1245 - 1250
¥­¡¼¥ï¡¼¥Éʬ»¶½èÍý, ¿¼Áسؽ¬, Apache Kafka, OpenPose, Apache Flink
¥¢¥Ö¥¹¥È¥é¥¯¥È¥»¥ó¥µµ¡´ï¤ä¥¯¥é¥¦¥É¥³¥ó¥Ô¥å¡¼¥Æ¥£¥ó¥°¤ÎÉáµÚ¤Ë¤è¤ê¡¤°ìÈ̲ÈÄí¤Ç¼èÆÀ¡¤ÃßÀѤ·¤¿Æ°²èÁü¤¬»Ò¶¡¤ä¤ªÇ¯´ó¤ê¤Î¸«¼é¤ê¥µ¡¼¥Ó¥¹¤äËÉÈÈÂкö¡¤¥»¥­¥å¥ê¥Æ¥£¤Ë³èÍѤµ¤ì¤ë¤è¤¦¤Ë¤Ê¤Ã¤Æ¤­¤¿¡¥²ÈÄí¤Î¥»¥ó¥µ¤Ç¼èÆÀ¤·¤¿Æ°²èÁü¤ò¥ê¥¢¥ë¥¿¥¤¥à¤Ëµ¡³£³Ø½¬¤òÍѤ¤¤Æ²òÀϤ¹¤ë¤Ë¤Ï¡¤¥Ç¡¼¥¿Å¾Á÷Î̤ȲòÀÏ·×»»Î̤¬²ÝÂê¤È¤ë¡¥²æ¡¹¤Ï¡¤¥»¥ó¥µÂ¦¤Ç»ÑÀª¿äÄê¥é¥¤¥Ö¥é¥ê OpenPose ¤ò»ÈÍѤ·¤ÆÆ°²èÁü¤«¤é´ØÀá¤ÎÆÃħÎ̥ǡ¼¥¿¤òÃê½Ð¤·¤ÆžÁ÷¤·¡¤¥¯¥é¥¦¥É¤Ç¤½¤ÎÆÃħÎ̥ǡ¼¥¿¤Î¤ß¤òÍѤ¤¤Æµ¡³£³Ø½¬¤Ë¤è¤ëÆ°ºî¼±Ê̤ò¹Ô¤¦¤³¤È¤Ç¡¤½èÍýÃÙ±ä¤ä¥×¥é¥¤¥Ð¥·¤ÎÌäÂê¤ËÂн褹¤ë¥»¥ó¥µ¤È¥¯¥é¥¦¥É¤Ç¤Îʬ»¶½èÍý¼êË¡¤òÄó°Æ¤·¤Æ¤¤¤ë¡¥¤·¤«¤·¡¤Ê£¿ô²ÈÄí¤Î¥»¥ó¥µ¤«¤éϢ³Ū¤ËÁ÷¤é¤ì¤ëÂçÎ̤Υǡ¼¥¿¤ò¥¯¥é¥¦¥É¤Ç½èÍý¤¹¤ë¤Ë¤Ï¡¤µÞ·ã¤Ê¥Ç¡¼¥¿¤ÎÁý²Ã¤Ë¤è¤ë¥·¥¹¥Æ¥àÉé²Ù¾å¾º¤ËÂѤ¨¤¦¤ë½èÍý´ðÈפ¬É¬ÍפǤ¢¤ë.Ëܸ¦µæ¤Ç¤Ï¡¤ÂçÎ̤Υǡ¼¥¿¤ò¸úΨ¤è¤¯½èÍý²Äǽ¤Êʬ»¶¥¹¥È¥ê¡¼¥à½èÍý´ðÈפι½ÃÛ¤òÌܻؤ·¤Æ¡¤¥¨¥Ã¥¸¤ÇÃê½Ð¤·¤¿´ØÀá¤ÎÆÃħÎ̥ǡ¼¥¿¤òApache Kafka¤òÍѤ¤¤Æ¼ý½¸¤·¡¤¥¯¥é¥¦¥É¤Ë¤ª¤¤¤ÆApache Flink¤Îʬ»¶¥¹¥È¥ê¡¼¥à½èÍýµ¡Ç½¤òÍѤ¤¤Æµ¡³£³Ø½¬½èÍý¤ò¹Ô¤¦¥·¥¹¥Æ¥à¤ò¹½ÃÛ¤·¡¤²òÀÏ¥¹¥ë¡¼¥×¥Ã¥È¤òÄ´ºº¤·¤¿¡¥¼Â¸³¤«¤é¡¤²òÀÏ¥¹¥ë¡¼¥×¥Ã¥È¤ÏFlink¤ÎÊÂÎóÅÙ¤ËÈæÎ㤷¤ÆÁý²Ã¤¹¤ë¤³¤È¤¬³Îǧ¤Ç¤­¤¿¡¥

7D-4 (»þ´Ö: 9:30 - 9:50)
Âê̾¥Ç¡¼¥¿¥»¥Ã¥ÈºÙʬ²½¤òÍѤ¤¤¿»þ·ÏÎó¥Ç¡¼¥¿²óµ¢¥â¥Ç¥ë²½¼êË¡¤Î¸¡Æ¤
Ãø¼Ô*¹â¶¶ ͤΤ»Ò (¤ªÃã¤Î¿å½÷»ÒÂç³Ø), ÎëÌÚ À®¿Í, »³ËÜ Âó»Ê, Ê¡ÅÄ Íµ¹¬ (³ô¼°²ñ¼ÒÉÙ»ÎÄ̸¦µæ½ê), ¾®¸ý Àµ¿Í (¤ªÃã¤Î¿å½÷»ÒÂç³Ø)
¥Ú¡¼¥¸pp. 1251 - 1256
¥­¡¼¥ï¡¼¥É»þ·ÏÎó¥Ç¡¼¥¿, µ¡³£³Ø½¬, ¿¼Áسؽ¬, TFLearn, fine tuning
¥¢¥Ö¥¹¥È¥é¥¯¥È¶áǯ¤Î¥¯¥é¥¦¥É¥µ¡¼¥Ó¥¹¤Ë¤ª¤¤¤Æ¡¤¥µ¡¼¥Ð¤ò²¾ÁÛ²½¤¹¤ë¤³¤È¤Ç»ÈÍÑΨ¤ò¸þ¾å¤µ¤»¡¤¥µ¡¼¥Ð¿ô¤òºï¸º¤¹¤ë¼è¤êÁȤߤ¬¹Ô¤ï¤ì¤Æ¤¤¤ë¡¥¤³¤Î¼è¤êÁȤߤǤϡ¤¥µ¡¼¥Ð¤¬¼«¿È¤ÎCPU»ñ¸»¤òĶ¤¨¤¿CPU¤ò³ä¤êÅö¤Æ¤é¤ì¤ë¥ª¡¼¥Ð¡¼¥³¥ß¥Ã¥È¾õÂ֤˴٤뤳¤È¤Ç¡¤²¾ÁÛ¥µ¡¼¥Ð¤ÎÀ­Ç½¤¬Äã²¼¤¹¤ë²ÄǽÀ­¤¬¤¢¤ë¤¿¤á¡¤À©¸æÂоݤΤ¹¤Ù¤Æ¤Î²¾ÁÛ¥µ¡¼¥Ð¤ÎCPU»ÈÍÑΨ¤òͽ¬¤·À©¸æ¤ò¹Ô¤¦É¬Íפ¬¤¢¤ë¡¥ Ëܸ¦µæ¤Ç¤Ï¡¤²¾ÁÛ¥µ¡¼¥Ð¤ÎCPU»ÈÍÑΨ¤ÎÈÆÍÑŪ¤Ê¿¼Áسؽ¬Í½Â¬¥â¥Ç¥ë¤ÎÀ¸À®¤Ë¸þ¤±¤Æ¡¤»þ·ÏÎó¥Ç¡¼¥¿¤Î²óµ¢¥â¥Ç¥ë²½¼êË¡¤Ë¤Ä¤¤¤Æ¤Î¸¡Æ¤¤ò¹Ô¤¦¡¥ÊýË¡¤òÌϺ÷¤·¤¿·ë²Ì¡¤»þ·ÏÎó¥Ç¡¼¥¿¤ò³Ø½¬¤ËɬÍפÊŤµ¤´¤È¤ËÃê½Ð¤ò¹Ô¤¤¡¤ºÙʬ²½¤·¤¿¸å¤Î¥Ç¡¼¥¿¤ò¥é¥ó¥À¥à¤Ë»ÈÍѤ¹¤ë¤³¤È¤Ç¡¤ºÆ³Ø½¬»þ¤Ë»ÈÍѤ¹¤ë¥Ç¡¼¥¿¿ô¤Îºï¸º¤¬²Äǽ¤Ç¤¢¤ë¤³¤È¤ò³Îǧ¤·¤¿¡¥


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¥»¥Ã¥·¥ç¥ó 7E  ¹Ôưǧ¼±£³
Æü»þ: 2020ǯ6·î26Æü(¶â) 8:30 - 10:10
ºÂĹ: ÃæÀî °¦Íü (»°É©Åŵ¡³ô¼°²ñ¼Ò)

7E-1 (»þ´Ö: 8:50 - 9:10)
Âê̾¸ÞËÜ»ØÁõÃå·¿ÆþÎϥǥХ¤¥¹¤Ç¤Î¥¿¥Ã¥×Æ°ºî¤Ë¤ª¤±¤ëÊ¿²¾Ì¾ÆþÎϵ¬Â§¤Îɾ²Á¼êË¡
Ãø¼ÔÅÄÃæ ½ãÇ·²ð, *¾¡´Ö μ (ÂçºåÉÜΩÂç³Ø)
¥Ú¡¼¥¸pp. 1257 - 1263
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7E-2 (»þ´Ö: 9:10 - 9:30)
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¥Ú¡¼¥¸pp. 1264 - 1270
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7E-3 (»þ´Ö: 9:30 - 9:50)
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¥Ú¡¼¥¸pp. 1271 - 1279
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7E-4 (»þ´Ö: 9:50 - 10:10)
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¥Ú¡¼¥¸pp. 1280 - 1289
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Æü»þ: 2020ǯ6·î26Æü(¶â) 8:30 - 10:10
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7F-1 (»þ´Ö: 8:30 - 8:50)
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¥Ú¡¼¥¸pp. 1290 - 1302
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7F-2 (»þ´Ö: 8:50 - 9:10)
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¥Ú¡¼¥¸pp. 1303 - 1310
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7F-3 (»þ´Ö: 9:10 - 9:30)
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¥Ú¡¼¥¸pp. 1311 - 1318
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7F-4 (»þ´Ö: 9:30 - 9:50)
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¥Ú¡¼¥¸pp. 1319 - 1328
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Æü»þ: 2020ǯ6·î26Æü(¶â) 8:30 - 10:10
ºÂĹ: ¾®Àî δ°ì (IPA)

7G-1 (»þ´Ö: 8:30 - 8:50)
Âê̾¥Þ¥ë¥Á¥Ù¥ó¥À³«È¯Ê¸½ñήÄ̤ˤª¤±¤ëP2P·¿¥»¥­¥å¥ê¥Æ¥£´ÉÍý¼êË¡¤ÎÀß·×
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¥Ú¡¼¥¸pp. 1329 - 1334
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Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
7G-2 (»þ´Ö: 8:50 - 9:10)
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¥Ú¡¼¥¸pp. 1335 - 1340
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Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
7G-3 (»þ´Ö: 9:10 - 9:30)
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¥Ú¡¼¥¸pp. 1341 - 1350
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Æü»þ: 2020ǯ6·î26Æü(¶â) 8:30 - 10:10
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7H-1 (»þ´Ö: 8:30 - 8:50)
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7H-2 (»þ´Ö: 8:50 - 9:10)
Âê̾co-Sound: Web AR¤òÍѤ¤¤¿¥¤¥ó¥¿¥é¥¯¥Æ¥£¥Ö¥á¥Ç¥£¥¢´ðÈפÎÀ߷פȼÂÁõ
Ãø¼Ô*°æ¸ý Ï¿¿ (ÅìµþÂç³Ø¹©³ØÉôÅŵ¤ÅŻҹ©³Ø²Ê), ÄÍÅÄ ³Ø, ¹¾ºê ¹À (ÅìµþÂç³ØÂç³Ø±¡¾ðÊóÍý¹©³Ø·Ï¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1356 - 1365
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7H-3 (»þ´Ö: 9:10 - 9:30)
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Ãø¼Ô³¤Ìî μ, *ÄÍÅÄ ³Ø, Íî¹ç ½¨Ìé, ¹¾ºê ¹À (ÅìµþÂç³Ø)
¥Ú¡¼¥¸pp. 1366 - 1372
¥­¡¼¥ï¡¼¥É¥Ñ¥é¥á¥È¥ê¥Ã¥¯¥¹¥Ô¡¼¥«, Augmented Reality, ²Ä»ë²½
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7H-4 (»þ´Ö: 9:30 - 9:50)
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¥Ú¡¼¥¸pp. 1373 - 1380
¥­¡¼¥ï¡¼¥É¥³¥ó¥Æ¥ó¥Ä´ÉÍý, Ontology, RDF, ºÆµ¢Åªµ­½Ò, Software Defined Media
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Æü»þ: 2020ǯ6·î26Æü(¶â) 10:30 - 12:10
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8A-1 (»þ´Ö: 10:30 - 11:10)
Âê̾(¾·ÂÔ¹Ö±é) ¥¯¥é¥¦¥É¤ò¥¨¥Ã¥¸¤Ë±ä¿­¤»¤è - ¥¨¥Ã¥¸¥³¥ó¥Ô¥å¡¼¥Æ¥£¥ó¥°¼Â¸½¤Ë¤à¤±¤¿¤µ¤¯¤é¥¤¥ó¥¿¡¼¥Í¥Ã¥È¤Ç¤Î¼è¤êÁÈ¤ß -
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¥Ú¡¼¥¸p. 1381
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Æü»þ: 2020ǯ6·î26Æü(¶â) 10:30 - 12:10
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8B-1 (»þ´Ö: 10:30 - 10:50)
Âê̾¿Í¤È¶¨Ä´¤¹¤ëÁÒ¸ËÆâÇÛÁ÷¥í¥Ü¥Ã¥È¤Î ¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤òÍѤ¤¤¿ºÇŬ²½¤Î¸¡Æ¤
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¥Ú¡¼¥¸pp. 1382 - 1387
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8B-2 (»þ´Ö: 10:50 - 11:10)
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¥Ú¡¼¥¸pp. 1388 - 1393
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¥¢¥Ö¥¹¥È¥é¥¯¥È¶áǯ¡¤¶ÚÅŵÁ¼ê¤ä¥Ñ¥ï¡¼¥¢¥·¥¹¥È¥¹¡¼¥Ä¤ò¤Ï¤¸¤á¤È¤¹¤ë¥í¥Ü¥Æ¥£¥¯¥¹µ»½Ñ¤Î¼ÂÀ¸³è¤Ø¤ÎƳÆþ¤¬¿Ê¤á¤é¤ì¡¤¿Í´Ö¤È¥í¥Ü¥Ã¥È¤Î¶¦À¸¡¦Í»¹ç¤Î·Á¤Ë¤Ä¤¤¤Æ¤µ¤Þ¤¶¤Þ¤Ê»ëÅÀ¤«¤é¸¡Æ¤¤µ¤ì¤Ï¤¸¤á¤Æ¤¤¤ë¡¥Æäˡ¤À½Â¤¶È¤Ç¤Ï¥í¥Ü¥Ã¥È¥¢¡¼¥à¤Îºî¶È¶õ´Ö¤Ø¤ÎƳÆþ¤¬¿Ê¤á¤é¤ì¤Æ¤¤¤ë¤¬¡¤½¾Íè¼êË¡¤Ç¤Ï¥í¥Ü¥Ã¥È¥¢¡¼¥à¤òÆ°ºî¤µ¤»¤ëºÝ¤Ë¥æ¡¼¥¶¤¬Äê´üŪ¤Ë´ØÏ¢µ¡´ï¤ÎÁàºî¤ò¹Ô¤¦É¬Íפ¬¤¢¤ë¤¿¤á¡¤ºî¶È»þ´Ö¤ÎϲÈñ¤äÀ¸»º¸úΨ¤ÎÄã²¼¤Ë¤Ä¤Ê¤¬¤Ã¤Æ¤¤¤ë¡¥¤³¤ì¤Ë²Ã¤¨¡¤¥æ¡¼¥¶¤Ïξ¼ê¤Çºî¶È¤¹¤ë¤³¤È¤â¤¢¤ë¤¿¤á¡¤¥í¥Ü¥Ã¥È¥¢¡¼¥à¤ò¥Ï¥ó¥º¥Õ¥ê¡¼¤ÇÆ°ºî¤µ¤»¤ë¼êË¡¤¬Å¬¤·¤Æ¤¤¤ë¤È¹Í¤¨¤é¤ì¡¤¤³¤ì¤Þ¤Ç¶ÚÅṲ̋ˤè¤ëÀ©¸æ¼êË¡¤äÂè3¼Ô¤Ë¤è¤ë±ó³ÖÀ©¸æ¼êË¡¤Ê¤É¤¬Äó°Æ¤µ¤ì¤Æ¤­¤¿¡¥¤·¤«¤·¡¤¤³¤ì¤é¤Î¼êË¡¤Ç¤Ï¥æ¡¼¥¶¤Î°Õ¿Þ¤·¤Ê¤¤¶ÚÅÅ°Ì¿®¹æ¤ÎȯÀ¸¤äÁõÃå¼Ô¤ª¤è¤Ó±ó³ÖÁàºî¼Ô¤Î2¼Ô´Ö¤Ë¤ª¤±¤ë°Õ»×ÁÂÄ̤κ¤ÆñÀ­¤Ëµ¯°ø¤¹¤ë¡¤°Õ¿Þ¤·¤Ê¤¤¥í¥Ü¥Ã¥È¥¢¡¼¥àÆ°ºî¤ÎȯÀ¸¤Ê¤É¤¬²ÝÂê¤È¤·¤Æµó¤²¤é¤ì¤ë¡¥¤½¤³¤Ç¡¤ËܹƤǤϥ¦¥§¥Ö¥«¥á¥é¤ÈQR¥³¡¼¥É¤òÍѤ¤¤Æ¥í¥Ü¥Ã¥È¥¢¡¼¥à¤Î»ÈÍÑ¥¿¥¤¥ß¥ó¥°¤ò»ØÄꤷ¡¤Æ¬ÉôÆ°ºî¤òÍѤ¤¤Æ¥í¥Ü¥Ã¥È¥¢¡¼¥à¤ò¥Ï¥ó¥º¥Õ¥ê¡¼¤ÇÀ©¸æ¤¹¤ë¼êË¡¤òÄó°Æ¤¹¤ë¡¥

¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
8B-3 (»þ´Ö: 11:10 - 11:30)
Âê̾¼ê½Ñ¥í¥Ü¥Ã¥ÈÁàºî»þ¤Ë¤ª¤±¤ë¥¦¥§¥¢¥é¥Ö¥ë¥»¥ó¥µ¤òÍѤ¤¤¿ÈèÏ«ÅÙ¿äÄê¼êË¡
Ãø¼Ô*ÂçÀ¾ °¾Èþ, ÅìÆî ñ¥, »ûÅÄ ÅØ (¿À¸ÍÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê), ÉþÉô Ì­, ¹¥Ãæ µ×¾½, ¼÷Èþ ͵²ð, ·ÃÌÚ ¹ÀÇ· (¹­ÅçÂç³ØÂç³Ø±¡°å·Ï²Ê³Ø¸¦µæ²Ê), ÄÍËÜ ¾»É§ (¿À¸ÍÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1394 - 1402
¥­¡¼¥ï¡¼¥É¥í¥Ü¥Ã¥È¼ê½Ñ, ÈèÏ«ÅÙ¿äÄê, ¥¦¥§¥¢¥é¥Ö¥ë¥³¥ó¥Ô¥å¡¼¥Æ¥£¥ó¥°
¥¢¥Ö¥¹¥È¥é¥¯¥È¥À¥ô¥£¥ó¥Á¥µ¡¼¥¸¥«¥ë¥·¥¹¥Æ¥à¤ËÂåɽ¤µ¤ì¤ë¥í¥Ü¥Ã¥È»Ù±ç¤Ë¤è¤ëÊ¢¹Ð¶À²¼¼ê½Ñ¤Ï¡¤½Ñ¼Ô¤¬Ä¾ÀÜ´µ¼Ô¤ÎÊ¢¹ÐÆâ¤ËÆþ¤ì¤¿îé»Ò¤òÁàºî¤¹¤ë¼êË¡¤ËÈæ¤Ù¤Æ¡¤ÀºÅ٤ι⤤¼ê½Ñ¤ò²Äǽ¤Ë¤·¤¿¤À¤±¤Ç¤Ï¤Ê¤¯¡¤½Ñ¼Ô¤ÎÀº¿ÀŪ¡¦ÆùÂÎŪ¤Ê¥¹¥È¥ì¥¹¤â·Ú¸º¤·¤¿¤È¤¤¤ï¤ì¤Æ¤¤¤ë¤¬¡¤¤½¤ì¤Ç¤â½Ñ¼Ô¤Ï¼«¿È¤ÎÈèÏ«¤ä½¸ÃæÎϤÎÄã²¼¤Ëµ¤ÉÕ¤«¤Ê¤¤¤Þ¤Þ¼ê½Ñ¤ò³¤±¤Æ¤·¤Þ¤Ã¤¿¤ê¡¤ÈèÏ«¤ò´¶¤¸¤Æ¤¤¤Æ¤â̵»ë¤·¤Æ³¤±¤Æ¤·¤Þ¤Ã¤¿¤ê¤¹¤ëÌäÂ꤬¤¢¤ë¡¥¤³¤ÎÈèÏ«¤ÎÃßÀѤ佸ÃæÎϤÎÄã²¼¤ò¥ê¥¢¥ë¥¿¥¤¥à¤Ç¿ôÃÍŪ¤Ë»»½Ð¤·¡¤¼ê½ÑÃæ¤Î½Ñ¼Ô¤ËÃΤ餻¤ë¤³¤È¤¬¤Ç¤­¤ì¤Ð¡¤Å¬Àڤʥ¿¥¤¥ß¥ó¥°¤ÇµÙ·Æ¤òÂ¥¤¹¤Ê¤É¡¤¼ê½Ñ¸úΨ¤ä¼ê½Ñ¥ß¥¹¤Î¥ê¥¹¥¯¤ò²þÁ±¤Ç¤­¤ë¡¥ ¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¡¤Àè¹Ô¸¦µæ¤«¤é¼ê½Ñ¥í¥Ü¥Ã¥È»ÈÍÑÃæ¤Î½Ñ¼Ô¤¬ÈèÏ«¤ò´¶¤¸¤ä¤¹¤¤ÂΤÎÉô°ÌÅù¤Ë¾®·¿¤Î¥¦¥§¥¢¥é¥Ö¥ë¥»¥ó¥µ¤ò¼è¤êÉÕ¤±¤ë¤³¤È¤Ç¡¤¥í¥Ü¥Ã¥ÈÁàºî¤Î˸¤²¤Ë¤Ê¤ë¤³¤È¤Ê¤¯½Ñ¼Ô¤ÎÂΤξõÂÖ¤ò¥»¥ó¥·¥ó¥°¤·¡¤ÄêÎÌŪ¤ËÈèÏ«ÅÙ¤ò¿äÄꤹ¤ë¼êË¡¤òÄó°Æ¤¹¤ë¡¥Ëܸ¦µæ¤Ç¤Ï¡¤¼ê½Ñ¥í¥Ü¥Ã¥È¤ÎÎý½¬ÍÑ¥·¥ß¥å¥ì¡¼¥¿¤Î¥¿¥¹¥¯¤òĹ»þ´Ö¹Ô¤¤¡¤¥·¥ß¥å¥ì¡¼¥¿¤¬»»½Ð¤¹¤ë¥¹¥³¥¢¤ò½Ñ¼Ô¤ÎÈèÏ«ÅÙ¤Îɽ½Ð¤È¤·¤Æ¿äÄꤹ¤ë¡¥¥¦¥§¥¢¥é¥Ö¥ë¥»¥ó¥µ¤«¤éÆÀ¤é¤ì¤¿¥Ç¡¼¥¿¤È¡¤±ÇÁü¤«¤éµ­Ï¿¤·¤¿½Ö¤­²ó¿ô¤«¤é¤½¤ì¤¾¤ìÊ¿¶ÑÃÍ¡¤Ê¬»¶ÃͤòÆÃħÎÌ¥»¥Ã¥È¤È¤·¤Æ»»½Ð¤·¡¤¤½¤ì¤¾¤ì¤ÎÆÃħÎ̤´¤È¤ËRondom Forest¤òÍѤ¤¤¿¸òº¹¸¡¾Ú¤ò¹Ô¤¤¡¤1¡Á3²óÌܤΥ¿¥¹¥¯¤Î¥¹¥³¥¢¤ò³Ø½¬¥Ç¡¼¥¿¤È¤·¤ÆÍѤ¤¤Æ4²óÌܤΥ¹¥³¥¢(59.3)¤ò¿äÄꤷ¤¿¡¥·ë²Ì¤Ï¡¤¼ó¤Èº¸ÏӤζÚÅÅ°Ì¥»¥ó¥µ¥Ç¡¼¥¿¤ò¹ç¤ï¤»¤¿ÆÃħÎÌ¥»¥Ã¥È¤ÎÊ¿¶ÑÀäÂÐ¸íº¹¤¬1.67¡¤»ëÀþ¡¦Æ·¹¦·Â¥»¥ó¥µ¥Ç¡¼¥¿¤ÎÆÃħÎÌ¥»¥Ã¥È¤ÎÊ¿¶ÑÀäÂÐ¸íº¹¤¬2.19¤Ç¤¢¤ê¡¤Â¾¤Î¥»¥ó¥µ¤ÈÈæ¤Ù¤Æ¾®¤µ¤¤¤³¤È¤¬Ê¬¤«¤Ã¤¿¡¥

8B-4 (»þ´Ö: 11:30 - 11:50)
Âê̾¥í¥Ü¥Ã¥È¥Ï¥ó¥É¥Þ¥Ã¥µ¡¼¥¸µ¡³«È¯¤Ë¸þ¤±¤¿Èï»Ü½Ñ¼Ô¤Î´¶¾ð¿äÄê¼êË¡¤Î¸¡Æ¤
Ãø¼Ô*±§ÅÔ Ï¿¿ (Ë­¶¶µ»½Ñ²Ê³ØÂç³ØÂç³Ø±¡), Âç¼ Î÷ (Ë­¶¶µ»½Ñ²Ê³ØÂç³Ø)
¥Ú¡¼¥¸pp. 1403 - 1408
¥­¡¼¥ï¡¼¥É´¶¾ð¿äÄê, À¸Âο®¹æ, µ¡³£³Ø½¬
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8B-5 (»þ´Ö: 11:50 - 12:10)
Âê̾¶¨Ä´¥í¥Ü¥Ã¥È¤Î¼Â¸½¤Ë¸þ¤±¤¿¿Í´Ö¹ÔÆ°¤Îµ¡³£³Ø½¬¤Ë¤è¤ë¶á̤Íèͽ¬¤Ë¤à¤±¤Æ
Ãø¼Ô*µÈ±Ê ¾ÏÂÀϯ, ÅÚ°æ Í´²ð, °æ¾å ÁϤ (¶å½£¹©¶ÈÂç³Ø)
¥Ú¡¼¥¸pp. 1409 - 1415
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Æü»þ: 2020ǯ6·î26Æü(¶â) 10:30 - 12:10
ºÂĹ: Guillaume Lopez (ÀÄ»³³Ø±¡Âç³Ø)

8C-1 (»þ´Ö: 10:30 - 10:50)
Âê̾³Ø½¬¸úΨ¸þ¾å¤Ë¸þ¤±¤¿Ç¾ÇȤ˴ð¤Å¤¯ VR-HMD ¥æ¡¼¥¶¤ÎÓϹ¥À­¿äÄê
Ãø¼Ô*º£ÊÌÉÜ ËüÂç, ĹëÀî ã¿Í (Ê¡°æÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²ÊÃμ±¼Ò²ñ´ðÁù©³ØÀ칶¾ðÊ󹩳إ³¡¼¥¹)
¥Ú¡¼¥¸pp. 1416 - 1421
¥­¡¼¥ï¡¼¥É¥¦¥§¥¢¥é¥Ö¥ë¥Ç¥Ð¥¤¥¹, µ¡³£³Ø½¬, ǾÇÈ, ÓϹ¥À­¿äÄê, ³Ø½¬»Ù±ç
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¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
8C-2 (»þ´Ö: 10:50 - 11:10)
Âê̾±ÇÁü¤È²»À¼¤òÍѤ¤¤¿µÄÏÀ¤Ø¤Î´ØÍ¿»ÑÀª¤ä¹ÎÄêŪ¡¦ÈÝÄêŪÂÖÅ٤οäÄêÊý¼°¤Î¸¡Æ¤
Ãø¼Ô*¶â²¬ Íã, ¾å¸¶ ͤÂÀϺ, ¸¶ ľ, °¤Éô ¶©¿­ (²¬»³Âç³Ø Âç³Ø±¡¥Ø¥ë¥¹¥·¥¹¥Æ¥àÅý¹ç²Ê³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1422 - 1429
¥­¡¼¥ï¡¼¥ÉµÄÏÀʬÀÏ, µ¡³£³Ø½¬, SVM, ²»¶ÁÆÃħÎÌ, ¥é¥ó¥À¥à¥Õ¥©¥ì¥¹¥È
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¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
8C-3 (»þ´Ö: 11:10 - 11:30)
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Ãø¼Ô*ĹëÀî ±Í°ì, °ë»³ ľÌé, ¼òÅÄ ¿®¿Æ, À¶Àî À¶ (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø)
¥Ú¡¼¥¸pp. 1430 - 1436
¥­¡¼¥ï¡¼¥É¥¹¥Þ¡¼¥È¥°¥é¥¹, ¼ç´Ñ»þ´Ö, ºÆÀ¸Â®ÅÙ, ºî¶È®ÅÙ, ¥¦¥§¥¢¥é¥Ö¥ë
¥¢¥Ö¥¹¥È¥é¥¯¥È¥¹¥Þ¡¼¥È¥°¥é¥¹¾å¤ËÆÃÄê¤Î¾ðÊó¤òÄ󼨤¹¤ë¤³¤È¤Ç¥æ¡¼¥¶¤Î¿´Íý¡¦¹ÔÆ°¤òÀ©¸æ¤¹¤ë¥·¥¹¥Æ¥à¤¬Äó°Æ¤µ¤ì¤Æ¤­¤Æ¤¤¤ë¡¥¤·¤«¤·¡¤½¾Íè¤Î¥·¥¹¥Æ¥à¤Ç¤Ï¡¤¥æ¡¼¥¶¤¬¥·¥¹¥Æ¥à¤ÎÂоݤȤ¹¤ë¹ÔÆ°Ãæ¤Ë»ë³Ð¾ðÊó¤ò±ÜÍ÷¤¹¤ë¤³¤È¤Ç¼õ¤±¤ë±Æ¶Á¤Ë¤Ä¤¤¤Æ¹Í¤¨¤é¤ì¤Æ¤ª¤ê¡¤±ÜÍ÷¸å¤Î¥æ¡¼¥¶¤Î¹ÔÆ°¤Ø¤Î±Æ¶Á¤Ë¤Ä¤¤¤Æ¤Ï¹Í褵¤ì¤Æ¤¤¤Ê¤¤¾ì¹ç¤¬Â¿¤¤¡¥¥¹¥Þ¡¼¥È¥°¥é¥¹¾å¤ËÄ󼨤¹¤ë»ë³Ð¾ðÊ󤬡¤±ÜÍ÷¸å¤Î¥æ¡¼¥¶¤Î¹ÔÆ°¤ËÍ¿¤¨¤ë±Æ¶Á¤òÌÀ¤é¤«¤Ë¤·±þÍѤ¹¤ë¤³¤È¤Ç¡¤ÉáÃÊÍøÍѤ¹¤ëSNS¤ä¥Ë¥å¡¼¥¹¡¤Æ°²è¤Ê¤É¤Î¥³¥ó¥Æ¥ó¥Ä¤ò¥æ¡¼¥¶¤¬¤¿¤À±ÜÍ÷¤¹¤ë¤À¤±¤Ç¡¤¸å¤Î¹ÔÆ°¤¬µ¤ÉÕ¤«¤Ê¤¤¤¦¤Á¤Ë³èÀ­²½¤µ¤ì¤ë¤È¤¤¤Ã¤¿¸ú²Ì¤¬´üÂԤǤ­¤ë¡¥¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¡¤¥¹¥Þ¡¼¥È¥°¥é¥¹¾å¤Ç»ëÄ°¤¹¤ëÆ°²è¤ÎºÆÀ¸Â®ÅÙ¤¬¡¤¸å¤Î¥Ç¥¹¥¯¥ï¡¼¥¯¤Ê¤É¤Ë¤ª¤±¤ë¥¿¥¹¥¯¥Ñ¥Õ¥©¡¼¥Þ¥ó¥¹¤Ë±Æ¶Á¤òÍ¿¤¨¤é¤ì¤ë¤«¤Ë¤Ä¤¤¤Æ¸¡¾Ú¤ò¹Ô¤¦¡¥ºÆÀ¸Â®Å٤ή¤¤Æ°²è¤¬¡¤¸å¤Î»ëÄ°¼Ô¤Îºî¶È®ÅÙ¤ò¸þ¾å¤µ¤»¤ë¤È¤¤¤¦²¾Àâ¤òΩ¤Æ¼Â¸³¤ò¹Ô¤Ã¤¿¡¥·ë²Ì¡¤Åý·×Ū¸¡Äê¤Ë¤è¤ê¡¤Ä󼨤¹¤ëÆ°²è¤ÎºÆÀ¸Â®ÅÙ¤ò®¤¯¤¹¤ë¤Û¤É¡¤»ëÄ°¼Ô¤Î¸å¤Îºî¶È®ÅÙ¤¬Â®¤¯¤Ê¤ë·¹¸þ¤ò³Îǧ¤·¤¿¡¥

¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
8C-4 (»þ´Ö: 11:30 - 11:50)
Âê̾À¸ÂΥꥺ¥à¤È¾ðÊóÄ󼨥¿¥¤¥ß¥ó¥°¤ÎƱ´ü¤Ë¤è¤ë¾ðÊóÁªÂò¤Ø¤Î±Æ¶Áɾ²Á
Ãø¼Ô*Àж¶ ľ¿Í, »ûÅÄ ÅØ, ÄÍËÜ ¾»É§ (¿À¸ÍÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1437 - 1442
¥­¡¼¥ï¡¼¥É¾ðÊóÄó¼¨, À¸ÂΥꥺ¥à
¥¢¥Ö¥¹¥È¥é¥¯¥È¿´Íý³Ø¤ÎʬÌî¤Ë¤ª¤¤¤Æ¤Ï¡¤¿Í¤Î´¶¾ð¤ä°Õ»×·èÄê¤Ë±Æ¶Á¤òÍ¿¤¨¤ë¿´Íý¸ú²Ì¤¬Â¿¿ô¸ºß¤¹¤ë¤³¤È¤¬¤ï¤«¤Ã¤Æ¤¤¤ë¡¥ Î㤨¤Ð¡¤¼«¿È¤ÈƱĴ¤·¤¿¹ÔÆ°¤ò¤È¤ë¿Íʪ¤ËÂФ·¤Æ¹¥´¶¤òÊú¤¯¡¤¥ß¥é¡¼¸ú²Ì¤È¸Æ¤Ð¤ì¤ë¿´Íý¸ú²Ì¤¬Â¸ºß¤¹¤ë¡¥ ¤³¤Î¤è¤¦¤Ê¿´Íý¸ú²Ì¤ò³èÍѤ¹¤ë¤³¤È¤Ç¡¤¤¢¤ë¿Í¤äʪ¤ËÂФ¹¤ë°õ¾Ý¤òÁàºî¤·¤¿¤ê¡¤ÆÃÄê¤Î¤â¤Î¤ò¶¯Ä´¤·¤ÆÅÁ¤¨¤¿¤ê¤Ç¤­¤ë¡¥ ¤Þ¤¿¡¤¥¦¥§¥¢¥é¥Ö¥ë¥»¥ó¥·¥ó¥°µ»½Ñ¤Îȯã¤Ë¤è¤ê¿´Çï¤Ê¤É¤ÎÀ¸ÂξðÊó¤Î¾ï»þ¬Ä꤬²Äǽ¤È¤Ê¤ê¡¤À¸ÂξðÊó¤Î¾ðÊóÄ󼨤ؤγèÍѤ¬´üÂÔ¤µ¤ì¤Æ¤¤¤ë¡¥ ¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¡¤¥æ¡¼¥¶¼«¿È¤ÎÀ¸ÂΥꥺ¥à¤È¾ðÊóÄ󼨥¿¥¤¥ß¥ó¥°¤ÎƱ´ü¤Ë¤è¤ë¡¤¥æ¡¼¥¶¤ÎÁªÂò·¹¸þ¤Ø¤Î±Æ¶Á¤òɾ²Á¤¹¤ë¡¥ ÆäËËÜÏÀʸ¤Ç¤Ï¡¤ÍøÍѤ¹¤ëÀ¸ÂΥꥺ¥à¤ò¿´Çɽ¼¨¤¹¤ë¾ðÊó¤òPC²èÌ̾å¤Î¥Ü¥¿¥ó¤È¤·¡¤¿´Çï¥ê¥º¥à¤ÈƱ´ü¤·¤ÆÅÀÌǤ¹¤ë¥Ü¥¿¥ó¤Ø¤ÎÁªÂò·¹¸þ¤òÄ´ºº¤¹¤ë¡¥ PC²èÌ̾å¤Î3¤Ä¤Î¥Ü¥¿¥ó¤«¤é1¤Ä¤òÁªÂò¤¹¤ë¼Â¸³¤ò¹Ô¤¤¡¤È︳¼Ô¤ÎÁªÂò·ë²Ì¤òµ­Ï¿¤·¡¤È︳¼Ô13̾¤ÎÆâ8̾¤¬¼«¿È¤Î¿´Çï¤Ë¶á¤¤Â®ÅÙ¤ÎÅÀÌǤò¤¹¤ë¥Ü¥¿¥ó¤ò¿¤¯ÁªÂò¤·¤¿¤³¤È¤«¤é¡¤¿´Çï¤È¾ðÊóÄ󼨥¿¥¤¥ß¥ó¥°¤òƱ´ü¤·¤¿¤³¤È¤Ë¤è¤ë¾ðÊóÁªÂò¤Ø¤Î±Æ¶Á¤¬³Îǧ¤Ç¤­¤¿¡¥


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Æü»þ: 2020ǯ6·î26Æü(¶â) 10:30 - 12:10
ºÂĹ: ¿¢ÅÄ Ï·û (¹âÃι©²ÊÂç³Ø)

¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
Í¥½¨ÏÀʸ¾Þ / Paper Awards
8D-1 (»þ´Ö: 10:30 - 10:50)
Âê̾°Û¼ï̵Àþº®¹çDTN¤òÍѤ¤¤¿Æ»Ï©À£ÃǾðÊó¶¦Í­¤Ë¤è¤ëÈòÆñ»þ´Öû½Ì¤Î¸¡Æ¤
Ãø¼Ô*Ì𸶠͵Âç (ÀŲ¬Âç³ØÂç³Ø±¡Áí¹ç²Ê³Øµ»½Ñ¸¦µæ²Ê¹©³ØÀ칶), ²ÃÆ£ ¿·ÎÉÂÀ (ÀŲ¬Âç³ØÁϤ²Ê³Øµ»½ÑÂç³Ø±¡¼«Á³²Ê³Ø·Ï¶µ°éÉô), üâ°æ ÊöÀ¸ (ÂçºåÂç³ØÂç³Ø±¡¾ðÊó²Ê³Ø¸¦µæ²Ê/¥«¥ê¥Õ¥©¥ë¥Ë¥¢Âç³Ø¥í¥µ¥ó¥¼¥ë¥¹¹»), Àи¶ ¿Ê (ÀŲ¬Âç³Ø³Ø½Ñ±¡¹©³ØÎΰè)
¥Ú¡¼¥¸pp. 1443 - 1451
¥­¡¼¥ï¡¼¥ÉÈòÆñ»Ù±ç, DTN, °Û¼ï̵Àþ¥Í¥Ã¥È¥ï¡¼¥¯, ºÒ³², ¥·¥ß¥å¥ì¡¼¥·¥ç¥ó
¥¢¥Ö¥¹¥È¥é¥¯¥ÈºÒ³²È¯À¸»þ¤Ë¤ª¤¤¤Æ¡¤ÈòÆñ¼Ô´Ö¤Çƻϩ¤ÎÉÔÄ̲սê¤äÈïºÒ¼Ô¤Î°ÌÃÖÅù¤ÎÈòÆñ»Ù±ç¾ðÊó¤Î¶¦Í­¤Ï¡¤Í­ÍÑ ¤Ç¤¢¤ë¡¥¤·¤«¤·¤Ê¤¬¤é¡¤ºÒ³²»þ¤Ë¤ÏÄÌ¿®¥¤¥ó¥Õ¥é»²õ¤Î²ÄǽÀ­¤¬¤¢¤ë¤¿¤á¡¤ÆÃÄê¤ÎÄÌ¿®¼êÃʤ˰͸¤·¤Ê¤¤¾ðÊó¶¦Í­¼êË¡¤¬µá¤á¤é¤ì¤ë¡¥É®¼Ô¤é¤Ï¡¤ºÒ³²»þ¤Ë¤¢¤ëÄÌ¿®¼êÃʤ¬ÍøÍÑÉÔǽ¤È¤Ê¤Ã¤¿¾ì¹ç¤Ç¤â¾ðÊó¶¦Í­¤ò²Äǽ¤È¤¹¤ë¤¿¤á¤Ë¡¤¥»¥ë¥éÄÌ¿®¡¤Wi-Fi¡¤LPWA(Low Power Wide Area) ÄÌ¿®Åù¤ÎÊ£¿ô¤Î°Û¼ï̵ÀþÄÌ¿®¼êÃʤÈDTN(Delay/Disruption Tolerant Network) ¤òÍѤ¤¤¿ÈòÆñ»Ù±ç¾ðÊó¶¦Í­¥·¥¹¥Æ¥à¤Î³«È¯¤òÌܻؤ·¤Æ¤¤¤ë¡¥ °Û¼ï̵Àþº®¹çDTN¤òÍѤ¤¤¿ÈòÆñ»Ù±ç¾ðÊó¶¦Í­¥·¥¹¥Æ¥à¤Î¼Â¸½¤Ë¤Ï¡¤ºÒ³²»þ¤Î¸Â¤é¤ì¤¿ÄÌ¿®µ¡²ñ¤Ç¾ðÊó¶¦Í­¤ò¤¹¤ë¤¿¤á¤Ë¾ðÊóÁ÷¿®Í¥ÀèÀ©¸æ¡¤³ÆÄÌ¿®¼êÃʤÎÆÃÀ­¤Ë±þ¤¸¤¿¾ðÊóÇÛ¿®¼êË¡¤¬É¬ÍפȤʤ롥ËܹƤǤϡ¤Á÷¿®¤¹¤ëÈòÆñ»Ù±ç¾ðÊó¤Î¼ïÊÌ¡¤¥Ç¡¼¥¿¥µ¥¤¥º¡¤°¸À衤½ÅÍ×Å٤˱þ¤¸¤¿Á÷¿®Í¥ÀèÅ٤ˤĤ¤¤Æ¸¡Æ¤¡¤Ê£¿ô¤ÎÄÌ¿®¼êÃʤòÁȤ߹ç¤ï¤»¤¿ÈòÆñ»Ù±ç¾ðÊó¤ÎÇÛ¿®¼êË¡¤òÄó°Æ¤¹¤ë¡¥ÈòÆñ¼Ô¡¤¸ÇÄêÃæ·Ñ¥Î¡¼¥É¡¤ºÒÂÐËÜÉô´Ö¤Ç°Û¼ï̵Àþº®¹çDTN¤òÍѤ¤¤ÆÈòÆñ»Ù±ç¾ðÊó¤ò¶¦Í­¤·¤¿¤È¤­¤ËÈòÆñ¼Ô¤Î¹ÔÆ°¤ËÍ¿¤¨¤ë±Æ¶Á¤ò³Î¤«¤á¤ë¤¿¤á¤Ë¡¤ÈòÆñ ¹ÔÆ°¤È̵ÀþÄÌ¿®¤Î´Êά¥â¥Ç¥ë¤ò¹½ÃÛ¤·¡¤¥·¥ß¥å¥ì¡¼¥·¥ç¥óɾ²Á¤Ë¤·¤¿¡¥¤³¤Î·ë²Ì¡¤»ÈÍѤ¹¤ëÄÌ¿®¼êÃʤ˱þ ¤¸¤¿ÈòÆñ»Ù±ç¾ðÊó¤ÎÇÛ¿®¼êË¡¤Ï¡¤ÈòÆñ»þ´Öû½Ì¤ËÍ­¸ú¤Ç¤¢¤ë¤³¤È¤¬³Îǧ¤Ç¤­¤¿¤â¤Î¤Î¡¤ÈòÆñ»Ù±ç¾ðÊó¤òÊÝ»ý¤¹¤ëÈòÆñ¼Ô¤¬¡¤¾ðÊó¤òÄ󶡤¹¤Ù¤­ÈòÆñ¼ÔÁ´°÷¤ËÂФ·¤Æ¾ðÊóÄ󶡤ò¹Ô¤¨¤º¡¤ÃÖ¤­µî¤ê¤Ë¤¹¤ë²ÝÂ꤬¸ºß¤¹ ¤ë¤³¤È¤¬³Îǧ¤µ¤ì¤¿¡¥

8D-2 (»þ´Ö: 10:50 - 11:10)
Âê̾iBeacon¤òÍѤ¤¤¿°ÌÃÖ¿äÄê¤Ë¤ª¤±¤ë¿ÍÂΤαƶÁ¤Ë¤è¤ë¸íº¹¤Î·Ú¸º
Ãø¼Ô*µÜºê ¶¬¹Ô, ËÒÅÄ ³ÙÂç (Ä»¼èÂç³ØÂç³Ø±¡ »ý³À­¼Ò²ñÁÏÀ¸²Ê³Ø¸¦µæ²Ê), ¹â¶¶ ·ò°ì, Àî¼ ¾°À¸, ¿û¸¶ °ì¹¦ (Ä»¼èÂç³ØÂç³Ø±¡ ¹©³Ø¸¦µæ²Ê/Ä»¼èÂç³Ø ¹©³ØÉôÉÕ°¥¯¥í¥¹¾ðÊó²Ê³Ø¸¦µæ¥»¥ó¥¿¡¼)
¥Ú¡¼¥¸pp. 1452 - 1457
¥­¡¼¥ï¡¼¥ÉiBeacon, °ÌÃÖ¿äÄê, ¿ÍÂΤαƶÁ, ¸íº¹, ·Ú¸º
¥¢¥Ö¥¹¥È¥é¥¯¥È¶áǯ¡¤²°Æâ³°¤Î°ÌÃÖ¾ðÊó¤¬ÍÍ¡¹¤Ê¥µ¡¼¥Ó¥¹¤ÇÍøÍѤµ¤ì¤Æ¤¤¤ë¡¥²°³°¤Î°ÌÃÖ¾ðÊó¤òÍøÍѤ·¤¿¥µ¡¼¥Ó¥¹¤Î¿¤¯¤ÏGPS(Global Positioning System)¤òÍøÍѤ·¤Æ¤¤¤ë¡¥¤·¤«¤·¡¤GPS¤Ï¿Í¹©±ÒÀ±¤«¤é¤ÎÅÅÇȤ¬ÆϤ­¤Ë¤¯¤¤²°Æâ¤Ç¤ÏÀµ³Î¤Ë°ÌÃÖ¤ò¿äÄꤹ¤ë¤³¤È¤¬¤Ç¤­¤Ê¤¤¡¥¤½¤³¤Ç¡¤ÍÍ¡¹¤Ê²°Æâ°ÌÃÖ¿äÄê¼êË¡¤¬Äó°Æ¤µ¤ì¤Æ¤¤¤ë¡¥¤½¤ÎÃæ¤Î£±¤Ä¤ËiBeaconµ¬³Ê¤Î¥Ó¡¼¥³¥ó¤ÎÅÅÇȶ¯ÅÙ¤òÍøÍѤ·¤¿²°Æâ°ÌÃÖ¿äÄê¼êË¡¤¬Â¸ºß¤¹¤ë¡¥iBeacon¤Ï¾®·¿¡¤Äã¾ÃÈñÅÅÎϤÇĹ»þ´Ö¤Î²ÔƯ¤¬²Äǽ¤Ç¤¢¤ê¡¤Æ³Æþ¥³¥¹¥È¤¬Ä㤤¤È¤¤¤¦ÆÃħ¤¬¤¢¤ë¡¥¤·¤«¤·¡¤ÍÍ¡¹¤Ê´Ä¶­¾ò·ï¤Î±Æ¶Á¤ò¼õ¤±¿äÄê°ÌÃÖ¤Î¸íº¹¤¬Â礭¤¤¤È¤¤¤¦ÌäÂ꤬¤¢¤ë¡¥¥Ó¡¼¥³¥ó¤ÎÅÅÇȤÏ2.4GhzÂÓ¤ò»ÈÍѤ·¤Æ¤ª¤ê¡¤¸íº¹¤Î¸¶°ø¤Î£±¤Ä¤È¤·¤Æ¿ÍÂΤαƶÁ¤ò¼õ¤±¤ë¤³¤È¤¬Ê¬¤«¤Ã¤Æ¤¤¤ë¡¥¿ÍÂΤòÄ̲ᤷ¤¿¥Ó¡¼¥³¥ó¤ÎÅÅÇȤÏÂ礭¤¯¸º¿ê¤·¡¤¿äÄê°ÌÃÖ¤Ë¸íº¹¤¬À¸¤¸¤ë¡¥¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¡¤Ä¾Á°¤Î¿äÄê°ÌÃÖ¤«¤é¤Î°ÜÆ°µ÷Î¥¤È°ÜÆ°Êý¸þ¤ò¤â¤È¤Ë°ÌÃÖ¤ò¿äÄꤹ¤ë¤³¤È¤Ç¿ÍÂΤαƶÁ¤Ë¤è¤ë¿äÄê°ÌÃÖ¤Î¸íº¹¤ò·Ú¸º¤¹¤ë¡¥

8D-3 (»þ´Ö: 11:10 - 11:30)
Âê̾¥×¥ì¥¤¥ä¥Ñ¥Õ¥©¡¼¥Þ¥ó¥¹Äã²¼¤òÍÞÀ©¤¹¤ë¥ª¥ó¥é¥¤¥ó¥²¡¼¥à¸þ¤±ÃÙ±äÊä½þ¤Ë´Ø¤¹¤ë°ì¸¦µæ
Ãø¼Ô*ËÜÀ¸ ¿ò¿Í, Àîºê »ü±Ñ, Æ£¶¶ ÂîÌé, ±îÅÏ ½Ó²ð, ÅÏÊÕ ¾° (ÂçºåÂç³ØÂç³Ø±¡¾ðÊó²Ê³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1458 - 1467
¥­¡¼¥ï¡¼¥É¥ª¥ó¥é¥¤¥ó¥²¡¼¥à, ¥Í¥Ã¥È¥ï¡¼¥¯ÃÙ±ä
¥¢¥Ö¥¹¥È¥é¥¯¥ÈÍ­Àþ¥Í¥Ã¥È¥ï¡¼¥¯¤ª¤è¤Ó̵Àþ¥Í¥Ã¥È¥ï¡¼¥¯¤Î¥Ö¥í¡¼¥É¥Ð¥ó¥É²½¤Ë¤È¤â¤Ê¤Ã¤Æ¡¤¥Í¥Ã¥È¥ï¡¼¥¯¤ò²ð¤·¤ÆÊ£¿ô¤Î¥×¥ì¥¤¥ä¤È¶¦Ä̤Υª¥ó¥é¥¤¥ó¥²¡¼¥à¤ò¶¨Îϥץ쥤¡¦ÂÐÀï¥×¥ì¥¤¤¹¤ë¼ûÍפ¬¹â¤Þ¤Ã¤Æ¤¤¤ë¡¥ °ìÊý¤Ç¡¤Ê£¿ô¥×¥ì¥¤¥ä¤¬¶¦Ä̤Υª¥ó¥é¥¤¥ó¥²¡¼¥à¤ò¶¨Îϥץ쥤¡¦ÂÐÀï¥×¥ì¥¤¤¹¤ë¾ì¹ç¡¤¥Ñ¥±¥Ã¥È»¼º¤äíÕíԤʤɤ˵¯°ø¤¹¤ë¥Í¥Ã¥È¥ï¡¼¥¯ÃÙ±ä¤ÎÁý²Ã¤¬³Æ¥×¥ì¥¤¥ä¤Î¥Ñ¥Õ¥©¡¼¥Þ¥ó¥¹¤Ë°­±Æ¶Á¤ò¤â¤¿¤é¤¹¡¥ Æäˡ¤¥·¥å¡¼¥Æ¥£¥ó¥°¥²¡¼¥à¤ËÂåɽ¤µ¤ì¤ëÈóƱ´ü·¿¥²¡¼¥à¤Ç¤Ï¡¤¥Í¥Ã¥È¥ï¡¼¥¯ÃÙ±ä¤Ëµ¯°ø¤·¤Æ¥×¥ì¥¤¥äüËö´Ö¤ÇȯÀ¸¤¹¤ë¥²¡¼¥à¾ðÊó¤Îº¹°Û¤¬¥×¥ì¥¤¥ä¤Ë¤è¤ë¹¶·â¹ÔÆ°¡¦²óÈò¹ÔÆ°¤Ë±Æ¶Á¤òµÚ¤Ü¤¹¡¥ ¥Í¥Ã¥È¥ï¡¼¥¯ÃÙ±ä¤Ëµ¯°ø¤¹¤ë¥×¥ì¥¤¥ä¥Ñ¥Õ¥©¡¼¥Þ¥ó¥¹¤Ø¤Î°­±Æ¶Á¤ò·Ú¸º¤¹¤ë¤³¤È¤òÌÜŪ¤È¤·¤Æ¡¤ËܹƤǤϳƥץ쥤¥äüËö¾å¤ÇÆ°ºî¤¹¤ë¥ª¥ó¥é¥¤¥ó¥·¥å¡¼¥Æ¥£¥ó¥°¥²¡¼¥à¸þ¤±ÃÙ±äÊä½þ¼êË¡¤òÄó°Æ¤¹¤ë¡¥Äó°Æ¼êË¡¤Ç¤Ï²áµî¤Î¥²¡¼¥à¾ðÊó¤òÍѤ¤¤Æ¥×¥ì¥¤¥äüËö´Ö¤ÇÀ¸¤¸¤ë¥²¡¼¥à¾ðÊó¤Îº¹°Û¤òÊä½þ¤¹¤ë¡¥ ¶ñÂÎŪ¤Ë¤Ï¡¤²áµî¤Î¥­¥ã¥é¥¯¥¿°ÌÃÖ¾ðÊó¤ò¸µ¤Ë¤·¤¿Àþ·ÁÊä´Ö¤Ë´ð¤Å¤¯ÃÙ±äÊä½þ¼êË¡¡¤²áµî¤Î¥²¡¼¥à²èÌ̤ò¸µ¤Ë¤·¤¿¿¼Áض¯²½³Ø½¬¤Ë´ð¤Å¤¯ÃÙ±äÊä½þ¼êË¡¤ò¤½¤ì¤¾¤ìÄó°Æ¤·¤Æ¥×¥ì¥¤¥äüËö¾å¤Ç·çÍ¤¿Áê¼ê¥×¥ì¥¤¥ä¤Î°ÌÃÖ¾ðÊó¡¦Áàºî¾ðÊó¤òÊä½þ¤¹¤ë¡¥ ¥Í¥Ã¥È¥ï¡¼¥¯Ãٱ䡤¿Í´Ö¤Ë¤è¤ëÁàºî¤ò¹Íθ¤·¤¿¼Â¸³É¾²Á¤«¤é¡¤Àþ·ÁÊä´Ö¤ª¤è¤Ó¿¼Áض¯²½³Ø½¬¤òÍѤ¤¤¿Äó°Æ¼êË¡¤½¤ì¤¾¤ì¤¬¥Í¥Ã¥È¥ï¡¼¥¯ÃÙ±ä¤Ëµ¯°ø¤¹¤ë¥×¥ì¥¤¥ä¥Ñ¥Õ¥©¡¼¥Þ¥ó¥¹¤Ø¤Î°­±Æ¶Á¤òÄ㸺¤Ç¤­¤ë¤³¤È¤ò³Îǧ¤·¤¿¡¥

¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
8D-4 (»þ´Ö: 11:30 - 11:50)
Âê̩̾½¸ÌµÀþLAN´Ä¶­¤Ë¤ª¤±¤ëºÆÁ÷¤ò¹Íθ¤·¤¿Á÷¿®ÅÅÎÏ¡¦¿®¹æ¸¡ÃÎïçÃÍÀ©¸æ¤Î¸¡Æ¤
Ãø¼Ô*ºä°æ ¾ÄÂÀ, Zhao Wen Chow, ½ÅÌî ´² (·ÄØæµÁ½ÎÂç³ØÂç³Ø±¡Íý¹©³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1468 - 1473
¥­¡¼¥ï¡¼¥ÉÌ©½¸ÌµÀþLAN, ±£¤ìüËöÌäÂê, ¤µ¤é¤·Ã¼ËöÌäÂê, TPC, DSC
¥¢¥Ö¥¹¥È¥é¥¯¥ÈÌ©½¸ÌµÀþLAN ´Ä¶­¤Ç¤Ï¡¤¤½¤ì¤¾¤ì¥Î¡¼¥É¤ÎÄÌ¿®ÈϰϤ¬½ÅÊ£¤·¡¤½ÅÊ£¤·¤¿²Õ½ê¤Ç¤Ï±£¤ì¡¦¤µ¤é¤·Ã¼ËöÌäÂ꤬ȯÀ¸¤¹¤ë¡¥¤Þ¤¿¡¤±£¤ì¡¦¤µ¤é¤·Ã¼ËöÌäÂê¤Ï¥Î¡¼¥É¤ÎÌ©½¸ÅÙ¤ÎÁý²Ã¤Ë¤·¤¿¤¬¤Ã¤Æ¿¼¹ï²½¤·¡¤¥¹¥ë¡¼¥×¥Ã¥ÈÀ­Ç½¤ÎÄã²¼¤¬Ãø¤·¤¯¤Ê¤ë¡¥¤³¤Î¤è¤¦¤ÊÌ©½¸´Ä¶­¤ÇȯÀ¸¤¹¤ëÌäÂê¤ËÂФ·¤Æ¡¤Á÷¿®ÅÅÎÏ¡¤¿®¹æ¸¡ÃÎïçÃͤòÀ©¸æ¤·¡¤ÄÌ¿®ÈϰϤò½Ì¾®¤¹¤ë¤³¤È¤Ç¼þÇÈ¿ô»ñ¸»¤ò¸úΨŪ¤ËÍøÍѤ¹¤ë¤³¤È¤Ç¡¤¥¹¥ë¡¼¥×¥Ã¥ÈÀ­Ç½¤ò²þÁ±¤¹¤ë¤³¤È¤¬¤Ç¤­¤ë¡¥´û¸¸¦µæ¤Ç¤Ï¡¤ÄÌ¿®ÈϰϤòºÇ¾®²½¤¹¤ë¤³¤È¤Ç¥¹¥ë¡¼¥×¥Ã¥ÈÀ­Ç½¤ò¸þ¾å¤µ¤»¤¿¤¬¡¤ÎÙÀܤ¹¤ë¥Î¡¼¥É´Ö¤Î´³¾Ä¤¬¹Í褵¤ì¤Æ¤ª¤é¤º¥¹¥ë¡¼¥×¥Ã¥ÈÀ­Ç½¤¬°­²½¤¹¤ëüËö¤¬È¯À¸¤·¤Æ¤¤¤¿¡¥ËܹƤǤϡ¤ºÆÁ÷¤ò¹Íθ¤·Æ°Åª¤ËÁ÷¿®ÅÅÎÏ¡¤¿®¹æ¸¡ÃÎïçÃͤòÀ©¸æ¤¹¤ë¡¥ËÜÀ©¸æ¤Ç¤Ï¡¤ºÆÁ÷Ψ¤ò¼õ¿®¥Î¡¼¥É¦¤Î¾BSS¥Î¡¼¥É¤Ë¤è¤ë´³¾Ä¤ÎÂ礭¤µ¤Î»Øɸ¤È¤·¡¤Â¾BSS¥Î¡¼¥É¤Ë¤è¤ëºÆÁ÷Ψ¤¬°­²½¤·¤Ê¤¤ÈϰϤÇÄÌ¿®ÈϰϤò½Ì¾®¤ò¤¹¤ë¤è¤¦¤ËÀ©¸æ¤¹¤ë¤³¤È¤Ç¡¤³Æ¥Î¡¼¥É¤Î¥¹¥ë¡¼¥×¥Ã¥ÈÀ­Ç½¤Î¸þ¾å¤µ¤»¤ë¡¥

¥ä¥ó¥°¥ê¥µ¡¼¥Á¥ã¾Þ / Young Researcher Awards
8D-5 (»þ´Ö: 11:50 - 12:10)
Âê̾Learning based Spatial Reuse with Adaptive Timestep and Action Space for Dense WLANs
Ãø¼Ô*Zhao Wen Chow, ºä°æ ¾ÄÂÀ, ½ÅÌî ´² (·ÄØæµÁ½ÎÂç³ØÂç³Ø±¡Íý¹©³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1474 - 1479
¥­¡¼¥ï¡¼¥Édense WLAN, spatial reuse, reinforcement learning, TPC, DSC
¥¢¥Ö¥¹¥È¥é¥¯¥ÈThe rapid densification of IEEE 802.11 Wireless Local Area Networks (WLANs) has lead to higher interferences among Basic Service Sets (BSSs) and has negatively impacted their performance. Spatial reuse methods such as Dynamic Sensitivity Control (DSC) or Transmit Power Control (TPC) help mitigate the hidden and exposed terminals issues in these dense deployments. In this work, a Reinforcement Learning (RL) based method with adaptive timestep and action space is proposed to enhance the spatial reuse in dense WLANs. In particular, the problem is modeled through Multi-Armed Bandits (MABs) and the Thompson Sampling strategy is employed. In this scheme, a learner first observes the Received Signal Strengths (RSSs) it can sense and derives a set of Carrier Sense Thresholds (CSTs) from these. It then applies Thompson Sampling with the computed set and updates the model after a specified number of transmissions or a predefined timeout. Simulation results show that the proposed scheme is able to improve the fairness compared to a previous RL scheme while providing a considerable aggregate throughput.


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¥»¥Ã¥·¥ç¥ó 8E  ´Ñ¸÷¤È°ÜÆ°
Æü»þ: 2020ǯ6·î26Æü(¶â) 10:30 - 12:10
ºÂĹ: °ð¼ ¹À (¸øΩ¤Ï¤³¤À¤Æ̤ÍèÂç³Ø)

Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
8E-1 (»þ´Ö: 10:50 - 11:10)
Âê̾¿Í¤Î°ÜÆ°¥Ç¡¼¥¿¤Ë´ð¤Å¤¯ÃÏ°è¤Î¥¯¥é¥¹¥¿¥ê¥ó¥°¤È¤½¤Î±þÍÑ
Ãø¼Ô*Íî¹ç ·Ë°ì, »ûÅÄ ²íÇ· (NTT¥É¥³¥â)
¥Ú¡¼¥¸pp. 1480 - 1486
¥­¡¼¥ï¡¼¥É°ÜÆ°¥Ç¡¼¥¿, °ÌÃÖ¾ðÊó, ¥¯¥é¥¹¥¿¥ê¥ó¥°
¥¢¥Ö¥¹¥È¥é¥¯¥È¿Í¡¹¤Î¼Ò²ñÀ¸³è¤Ë¤ª¤¤¤Æ°ÜÆ°¤Ï´ðËÜŪ¤Ê¹ÔÆ°¤Ç¤¢¤ê¡¤¿Í¡¹¤Î°ÜÆ°¤Ï·ÐºÑ³èÆ°¤ä¸òÄÌ¡¤¸ø½°±ÒÀ¸¤Ê¤ÉÍÍ¡¹¤ÊʬÌî¤È´Ø¤ï¤ê¤¬¿¼¤¤¡¥ ¿Í¡¹¤Î°ÜÆ°¥Ç¡¼¥¿¤È±þÍÑÀè¤ÎʬÌî¤Î¥Ç¡¼¥¿¤òÁȤ߹ç¤ï¤»¤Æ²òÀϤ¹¤ë¤³¤È¤Ç¡¤ ·ÐºÑ³èÆ°¤Îͽ¬¤ä¸òÄ̤κÇŬ²½¤Ê¤É³ÆʬÌî¤Ç¤ÎʬÀÏÀºÅ٤θþ¾å¤ä¡¤¤è¤ê¾ÜºÙ¤ÊʬÀϤ¬´üÂÔ¤µ¤ì¤ë¡¥ ¤½¤³¤ÇËܸ¦µæ¤Ç¤Ï¡¤¿Í¤Î°ÜÆ°¥Ç¡¼¥¿¤Ë´ð¤Å¤¤¤ÆÃÏ°è¤ò¥¯¥é¥¹¥¿¥ê¥ó¥°¤¹¤ë¤³¤È¤ÎÍ­ÍÑÀ­¤ò¼Â¥Ç¡¼¥¿¤ò»È¤Ã¤Æɾ²Á¤¹¤ë¡¥ ¤Þ¤º¡¤Ìó11Ëü¥æ¡¼¥¶¤ÎȾǯ´Ö¤Î°ÜÆ°¥Ç¡¼¥¿¤«¤éÃÏ°è¤ò¥¯¥é¥¹¥¿¥ê¥ó¥°¤¹¤ë¡¥ ¼¡¤Ë¡¤¥¯¥é¥¹¥¿¥ê¥ó¥°¤ÎÍ­ÍÑÀ­¤ò¡¤¡Ê1¡Ë»ÔĮ¼Ê̴Ѹ÷¾ÃÈñ³Û¤Îǯ´Ö¿ä°Ü¤ÎÎà»÷À­¡¤ ¡Ê2¡ËÆþ¹þ´Ñ¸÷µÒ¿ô¤Îͽ¬ÌäÂê¤È¤¤¤¦2¤Ä¤Îɾ²Á¼Â¸³¤Ë¤è¤ê¸¡¾Ú¤¹¤ë¡¥ ͽ¬ÌäÂê¤Ç¤Ï¡¤¹ÔÀ¯¤¬·è¤á¤¿ÃÏ°è¶èʬ¤ÈÈæ³Ó¤·¤Æͽ¬¸íº¹¤òºï¸º¤Ç¤­¤ë¤³¤È¤ò³Îǧ¤·¤¿¡¥

Í¥½¨ÏÀʸ¾Þ / Paper Awards
8E-2 (»þ´Ö: 11:10 - 11:30)
Âê̾´Ñ¸÷¥Ê¥Ó¤Ë¤ª¤¤¤Æɬ¸«¥¹¥Ý¥Ã¥È¤ò¤è¤ê¤è¤¤»þ´ÖÂÓ¤ËˬÌä²Äǽ¤Ë¤¹¤ë¥ª¥ó¥µ¥¤¥È¥×¥é¥ó¥Ë¥ó¥°¼êË¡
Ãø¼Ô*°ëÅÄ ¾Í¸ã (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø/Íý²½³Ø¸¦µæ½ê ³×¿·ÃÎǽÅý¹ç¸¦µæ¥»¥ó¥¿¡¼), Æü¹â ¿¿¿Í (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø), ¾¾ÅÄ Íµµ®, ¿Ûˬ Çîɧ, °ÂËÜ ·Ä°ì (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø/Íý²½³Ø¸¦µæ½ê ³×¿·ÃÎǽÅý¹ç¸¦µæ¥»¥ó¥¿¡¼)
¥Ú¡¼¥¸pp. 1487 - 1498
¥­¡¼¥ï¡¼¥É´Ñ¸÷¿äÁ¦, ¥¢¥ë¥´¥ê¥º¥à, ¥¹¥Þ¡¼¥È¥·¥Æ¥£, ¥³¥ó¥Æ¥­¥È¥¢¥¦¥§¥¢¥Í¥¹, ¥Ê¥Ó¥²¡¼¥·¥ç¥ó
¥¢¥Ö¥¹¥È¥é¥¯¥È´Ñ¸÷·ÐÏ©¿äÁ¦¤Ë´Ø¤·¤Æ¡¤´û¸¸¦µæ¤Î¿¤¯¤Ï¡¤¼¡Ë¬Ì䥹¥Ý¥Ã¥È¤Î¤ß¤ÎËþ­Å٤˴ð¤Å¤¤¤Æ¿äÁ¦¤·¤Æ¤¤¤ë¤¬¡¤¼¡°Ê¹ß¤Ëˬ¤ì¤ë¥¹¥Ý¥Ã¥È¤ò¹Íθ¤ËÆþ¤ì¤Æ¤¤¤Ê¤¤¤¿¤á¡¤¼¡¥¹¥Ý¥Ã¥È°Ê¹ß¤Î´Ñ¸÷¤¬À©¸Â¤µ¤ì¤ë¡¥¼¡Ë¬Ì䥹¥Ý¥Ã¥È¤ÎËþ­Å٤ȡ¤¼¡Ë¬Ì䥹¥Ý¥Ã¥È°Ê¹ß¤Î´üÂÔËþ­ÅÙ¤ò¹Í¤¨¤¿¾ì¹ç¡¤¤³¤ÎÆó¤Ä¤Ë¥È¥ì¡¼¥É¥ª¥Õ´Ø·¸¤¬È¯À¸¤¹¤ë²ÄǽÀ­¤¬¤¢¤ë¡¥¤Þ¤¿Æ±°ì¥¹¥Ý¥Ã¥È¤òˬÌ䤹¤ëºÝ¤Ë¡¤Ë¬¤ì¤ë»þ´ÖÂӤˤè¤Ã¤ÆËþ­ÅÙ¤¬°Û¤Ê¤ë¤¿¤á¡¤¤è¤êÎɤ¤»þ´ÖÂÓ¤ËˬÌ䤹¤ë¤³¤È¤¬´Ñ¸÷Á´ÂΤÎËþ­ÅÙ¸þ¾å¤Ë¤Ä¤Ê¤¬¤ë¡¥¤³¤ì¤é2ÅÀ¤ò¹Íθ¤·¤¿¾å¤Ç¡¤¥æ¡¼¥¶¤Ë´Ñ¸÷¥ë¡¼¥È¤òÄ󼨤¹¤ë¤Î¤¬Ë¾¤Þ¤·¤¤¤¬¡¤²æ¡¹¤¬ÃΤë¸Â¤ê¡¤¤½¤Î¤è¤¦¤Ê¥·¥¹¥Æ¥à¤Ï¸ºß¤·¤Ê¤¤¡¥ ËܹƤǤϡ¤¾åµ­¤ò¹Íθ¤¹¤ë¤¿¤á¡¤¤Þ¤º¡¤¼¡¥¹¥Ý¥Ã¥È¤ÎÀÅŪ´Ñ¸÷ÃÏ¥³¥ó¥Æ¥­¥¹¥È¤ÈưŪ´Ñ¸÷ÃÏ¥³¥ó¥Æ¥­¥¹¥È¡¤¼¡¥¹¥Ý¥Ã¥È°Ê¹ß¤ËˬÌ䤹¤ë¥¹¥Ý¥Ã¥È¤«¤éÆÀ¤é¤ì¤ë´üÂÔËþ­ÅÙ¤Î3¤Ä¤ÎÍ×ÁǤ«¤éÀ®¤ë¥Ä¥¢¡¼¥¹¥³¥¢¤òÄê¼°²½¤¹¤ë¡¥¥Ä¥¢¡¼¥¹¥³¥¢¤¬ºÇÂç¤È¤Ê¤ë´Ñ¸÷¥ë¡¼¥È¡Ê¼¡Ë¬Ì䥹¥Ý¥Ã¥È¤È¤½¤ì°Ê¹ß¤ËˬÌ䤹¤ë¥¹¥Ý¥Ã¥È·²¤ÎÎó¡Ë¤ò»»½Ð¤¹¤ëÌäÂê¤ÏNPº¤Æñ¤Ç¤¢¤ê¡¤¥ª¥ó¥µ¥¤¥È¤Ç½àºÇŬ²ò¤òµá¤á¤ë¤¿¤á¡¤ìÅÍßË¡¤ò¥Ù¡¼¥¹¤È¤·¤¿3¤Ä¤Î¥¢¥ë¥´¥ê¥º¥à¡§(1) ¼¡¥¹¥Ý¥Ã¥È¤Î¤ß¤ò¹Íθ¤·¤¿ìÅÍßË¡¡¤(2) ´Ñ¸÷»þ´ÖÁ´ÂΤò¹Íθ¤·¤¿ìÅÍßË¡¡¤(3) ´Ñ¸÷»þ´ÖÁ´ÂΤª¤è¤Óõº÷Éý¤ò¹­¤²¤¿ìÅÍßË¡¤ÎÄó°Æ¤ò¹Ô¤¦¡¥ Äó°Æ¥¢¥ë¥´¥ê¥º¥à¤ÎÍ­ÍÑÀ­¤òÄ´ºº¤¹¤ë¤¿¤á¡¤µþÅÔ»ÔÅ컳¶è¤Ë¤¢¤ë20²Õ½ê¤ÎˬÌ䥹¥Ý¥Ã¥È¤òÂоݤË3¤Ä¤Î¥¢¥ë¥´¥ê¥º¥à¤òŬÍѤ¹¤ë¤³¤È¤Ç¡¤½ÐÎϲò¤¬µþÅԤΥâ¥Ç¥ë¥ë¡¼¥È¤ÈÈæ³Ó¤·¤ÆÍ¥°Ì¤Ç¤¢¤ê¡¤¤½¤ì¤¾¤ì1.9¡Þ0.1 (s)¡¤15.9¡Þ0.5 (s)¡¤7766.6¡Þ8.9 (s)¤Î·×»»»þ´Ö¤Ç»»½Ð¤Ç¤­¤ë¤³¤È¤ò³Îǧ¤·¤¿¡¥

Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
8E-3 (»þ´Ö: 11:30 - 11:50)
Âê̾´Ñ¸÷¥á¥â¥ê¥¢¥ëÆ°²è¤Î¤¿¤á¤Î¼ÖºÜÆ°²è¥­¥å¥ì¡¼¥·¥ç¥ó¥¢¥ë¥´¥ê¥º¥à¤Î¸¡Æ¤
Ãø¼Ô*ÊÒ»³ ÍÎÊ¿, Ê¿Ìî ÍÛÂç, ¿Ûˬ Çîɧ (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø/Íý²½³Ø¸¦µæ½ê), ¸à ÍÎ (µþÅÔÂç³Ø), °ÂËÜ ·Ä°ì (ÆàÎÉÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø/Íý²½³Ø¸¦µæ½ê)
¥Ú¡¼¥¸pp. 1499 - 1506
¥­¡¼¥ï¡¼¥ÉÆ°²è¥­¥å¥ì¡¼¥·¥ç¥ó, ´Ñ¸÷¥á¥â¥ê¥¢¥ëÆ°²è, Æ°²èÍ×Ìó, ¼ÖºÜÆ°²è, ´Ñ¸÷
¥¢¥Ö¥¹¥È¥é¥¯¥È¶áǯ¡¤Æ°²è¤ò´Ñ¸÷¤Ë³èÍѤ¹¤ë¥±¡¼¥¹¤¬Áý¤¨¤Æ¤¤¤ë¡¥´Ñ¸÷¸å¤Ë´Ñ¸÷ÃϤβèÁü¤ò¥­¥å¥ì¡¼¥·¥ç¥ó¤·¥á¥â¥ê¥¢¥ëÆ°²è¤È¤¹¤ë¤è¤¦¤Ê¥·¥¹¥Æ¥à¤Ï¿¿ô¸ºß¤·¤Æ¤¤¤ë¤¬¡¤´Ñ¸÷·ÐÏ©Æ°²è¤ò¥­¥å¥ì¡¼¥·¥ç¥ó¤¹¤ë¥·¥¹¥Æ¥à¤Ï¤Þ¤À¸ºß¤·¤Æ¤¤¤Ê¤¤¡¥ËܹƤǤϡ¤´Ñ¸÷ÃϤǤΥì¥ó¥¿¥«¡¼ÍøÍѸå¤Î´Ñ¸÷µÒ¤Ë¸þ¤±¤¿¡¤¥á¥â¥ê¥¢¥ëÆ°²è¤Î¤¿¤á¤Î¥É¥é¥¤¥Ö¥ì¥³¡¼¥ÀÆ°²è¥­¥å¥ì¡¼¥·¥ç¥ó¥¢¥ë¥´¥ê¥º¥à¤òÄó°Æ¤¹¤ë¡¥Äó°Æ¼êË¡¤Ç¤Ï¥¯¥é¥¦¥É¥½¡¼¥·¥ó¥°¤Ë¤è¤ê½ÅÍ×ÅÙ¥¹¥³¥¢¤ò¼èÆÀ¤·¡¤²èÁü¤Ë¼Ì¤ë¥«¥Æ¥´¥ê¤ÈÃÏ°è¤Î¥é¥ó¥É¥Þ¡¼¥¯¤òÍøÍѤ·¤¿ÆÃħÎ̤òÍѤ¤¤Æµ¡³£³Ø½¬¤Ë¤è¤ë½ÅÍ×ÅÙ¥¹¥³¥¢¤Î¿äÄê¤òÄó°Æ¤·¤¿¡¥ËܹƤǤϴѸ÷·ÐÏ©Á´ÂΤνÅÍ×ÅÙ¥¹¥³¥¢¼èÆÀ¤ò¹Ô¤¦Ëܼ¸³¤Ë¸þ¤±¤Æ¡¤²­Æì¤Ç»£±Æ¤µ¤ì¤¿¥É¥é¥¤¥Ö¥ì¥³¡¼¥À²èÁü¤ò50ËçÍøÍѤ·¤¿Í½È÷¼Â¸³¤Ë¤è¤êÄó°Æ¥¢¥ë¥´¥ê¥º¥à¤ÎÀ­Ç½É¾²Á¤ò¹Ô¤Ã¤¿¡¥Í½È÷¼Â¸³¤Î·ë²Ì¤«¤é¡Ö²­Æì¤é¤·¤µ¡×¤¬¡Ö¥á¥â¥ê¥¢¥ëÆ°²è¤Ø¤ÎɬÍ×Å١פÈÁê´Ø·¸¿ô0.92¤ò¼¨¤·Í­°Õ¤Ë¶¯¤¤Àµ¤ÎÁê´Ø¤ò»ý¤Ä¤³¤È¡¤Äó°Æ¼êË¡¤«¤é¡Ö²­Æì¤é¤·¤µ¡×¤Î½ÅÍ×ÅÙ¥¹¥³¥¢¤Î¿äÄ꤬²Äǽ¤Ç¤¢¤ë¤³¤È¤ò¼¨º¶¤·¤¿¡¥


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¥»¥Ã¥·¥ç¥ó 8F  ¼«Æ°±¿Å¾
Æü»þ: 2020ǯ6·î26Æü(¶â) 10:30 - 12:10
ºÂĹ: ²°Âå ÃÒÇ· (ÀéÍÕ¹©¶ÈÂç³Ø)

Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
8F-1 (»þ´Ö: 10:30 - 10:50)
Âê̾Investigation of a Scheduling Scheme for Cooperative Merging at a Highway On-Ramp with Maximizing Average Speed of Automated Vehicles
Ãø¼Ô*µÈÅÄ ¶©»Ö, Ä«ÈæÆà ·¼, ½ÅÌî ´², ºûÀ¥ ´à (·ÄØæµÁ½ÎÂç³ØÂç³Ø±¡ Íý¹©³Ø¸¦µæ²Ê)
¥Ú¡¼¥¸pp. 1507 - 1511
¥­¡¼¥ï¡¼¥ÉITS, ¼«Æ°±¿Å¾, ¹çή, ¸òÄÌÎÌ, ¥¹¥±¥¸¥å¡¼¥ê¥ó¥°
¥¢¥Ö¥¹¥È¥é¥¯¥ÈRecently, connected and automated vehicles have potential to mitigate traffic congestion and improve traffic efficiency at a highway on-ramp. The state-of-the-art scheme improves traffic efficiency by assigning timing of merging to automated vehicles on roads by minimizing total travel time of them. The scheme, however, causes traffic congestion when inflow traffic becomes high because of the characteristic that the scheme makes groups of vehicles on the same road. Since length of the groups of vehicles becomes too long as inflow traffic becomes high, this length should be short by decreasing density of vehicles on roads in order to mitigate traffic congestion. In this paper, we show investigation of a scheduling scheme for cooper- ative merging at a highway on-ramp with maximizing average speed of automated vehicles. Based on traffic model that density and speed of vehicles have a negative correlation, we maximize speed of vehicles in order to decrease density of vehicles. Furthermore, we consider fairness between both the main and ramp road in terms of traffic efficiency by utilizing difference of average speed of vehicles on both the main and ramp road. Simulation result shows that the proposed scheme increases outflow traffic from a highway on-ramp by 400 (veh/h) at most in comparison with related schemes when inflow traffic becomes 2000 (veh/h). Furthermore, simulation result shows that the proposed scheme decreases density of vehicles.

8F-2 (»þ´Ö: 10:50 - 11:10)
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¥Ú¡¼¥¸pp. 1512 - 1519
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8F-3 (»þ´Ö: 11:10 - 11:30)
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¥Ú¡¼¥¸pp. 1520 - 1527
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8F-4 (»þ´Ö: 11:30 - 11:50)
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¥Ú¡¼¥¸pp. 1528 - 1537
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Í¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Presentation Awards
8G-1 (»þ´Ö: 10:30 - 10:50)
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¥Ú¡¼¥¸pp. 1538 - 1546
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ºÇÍ¥½¨¥×¥ì¥¼¥ó¥Æ¡¼¥·¥ç¥ó¾Þ / Best Presentation Awards
ºÇÍ¥½¨ÏÀʸ¾Þ / Best Paper Awards
8G-2 (»þ´Ö: 10:50 - 11:10)
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¥Ú¡¼¥¸pp. 1547 - 1553
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8G-3 (»þ´Ö: 11:10 - 11:30)
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Ãø¼Ô*¿û ͧÍü¹á, µÈ¼ ¹¯É§, »³²¼ ¹âÀ¸, Â翹 ˧ɧ (ÆüËÜÅÅ¿®ÅÅÏóô¼°²ñ¼Ò NTT¥Í¥Ã¥È¥ï¡¼¥¯¥µ¡¼¥Ó¥¹¥·¥¹¥Æ¥à¸¦µæ½ê)
¥Ú¡¼¥¸pp. 1554 - 1560
¥­¡¼¥ï¡¼¥É¸Ä¿Í¾ðÊó, Self-Sovereign Identity, Decentralized Identity
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Æü»þ: 2020ǯ6·î26Æü(¶â) 10:30 - 12:10
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8H-1 (»þ´Ö: 10:30 - 10:50)
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¥Ú¡¼¥¸pp. 1561 - 1568
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8H-2 (»þ´Ö: 10:50 - 11:10)
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Ãø¼Ô*¾®³Þ¸¶ Àé¹É, ¿åÌî ¿µ»Î (°¦Ã驶ÈÂç³ØÂç³Ø±¡)
¥Ú¡¼¥¸pp. 1569 - 1575
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8H-3 (»þ´Ö: 11:10 - 11:30)
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¥Ú¡¼¥¸pp. 1576 - 1583
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Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 17:30 - 19:00
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DS-1
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¥Ú¡¼¥¸pp. 1584 - 1593
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DS-2
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¥Ú¡¼¥¸pp. 1594 - 1600
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DS-3
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¥Ú¡¼¥¸pp. 1601 - 1605
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Ãø¼Ô*Ãݸ¶ °ì½Ù, ÛسÀ ζùþ, ËÜÉô ·úÂç, ÆïÌÜ ´´, À¾²¬ Âç½õ (¹áÀîÂç³Ø), À¾»³ ¸­, ¹çÅÄ æÆ (STNet), ºÇ½ê ·½»°, ´îÅÄ ¹°»Ê (¹áÀîÂç³Ø)
¥Ú¡¼¥¸pp. 1606 - 1615
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DS-5
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¥Ú¡¼¥¸pp. 1616 - 1622
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Æü»þ: 2020ǯ6·î25Æü(ÌÚ) 15:50 - 16:50
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SP-1 (»þ´Ö: 15:50 - 16:50)
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