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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)

Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) ½Ã¸Çƽ ³×Æ®¿öÅ© ±¸Á¶¸¦ ±â¹ÝÀ¸·Î ÇÑ ¶óÀÌÇÁ·Î±×·ÎºÎÅÍ ÆäÆ®¸®³ÝÀ» ÀÌ¿ëÇÑ ÆÐÅÏÃßÃâ
¿µ¹®Á¦¸ñ(English Title) Pattern Extraction from Lifelog Based on Semantic Network Structure Using Petri-Net
ÀúÀÚ(Author) ±èÅ¿µ   Á¶¼º¹è   Tae-Young Kim   Sung-Bae Cho  
¿ø¹®¼ö·Ïó(Citation) VOL 47 NO. 06 PP. 0553 ~ 0558 (2020. 06)
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(Korean Abstract)
ÃÖ±Ù ´Ù¾çÇÑ ½º¸¶Æ® ±â±âÀÇ È®»êÀ¸·Î ¿©·¯ Á¾·ùÀÇ ¼¾¼­¸¦ ÅëÇØ »ç¿ëÀÚÀÇ ¶óÀÌÇÁ·Î±× µ¥ÀÌÅÍ°¡ ÀÚµ¿À¸·Î ÀúÀåµÇ°í ÀÖ´Ù. ±×·¯³ª ½º¸¶Æ® ±â±â·ÎºÎÅÍ ¼öÁýµÈ ¶óÀÌÇÁ·Î±×´Â ¼­·Î ´Ù¸¥ ¼¾¼­·ÎºÎÅÍ ÀÌÁúÀûÀÎ Á¤º¸¸¦ ÀÚµ¿À¸·Î ±â·ÏÇÑ´Ù. ¶ÇÇÑ »ç¿ëÀÚÀÇ »ýÈ° ÆÐÅÏÀÌ ¶óÀÌÇÁ·Î±×ÀÇ ¼­·Î ´Ù¸¥ ÆÇÁ¤ Áֱ⿡ ÀÇÇØ °áÁ¤µÇ±â ¶§¹®¿¡ ´Ü¼øÇÑ ±ÔÄ¢ ±â¹Ý ½Ã½ºÅÛÀ¸·Î Á¤ÀÇÇϱ⠾î·Æ´Ù. µû¶ó¼­ ¶óÀÌÇÁ·Î±×·ÎºÎÅÍ À¯¿ëÇÑ »ýÈ° ÆÐÅÏÀ» ÃßÃâÇÏ¿© »ç¿ëÀÚ¿¡°Ô Á¦°øÇϱâ À§Çؼ­´Â ¼ö¸¹Àº µ¿Àû ¿ä¼ÒµéÀÇ °ü°è¸¦ Ç¥ÇöÇؾßÇÑ´Ù. º» ³í¹®¿¡¼­´Â ½Ã¸Çƽ ³×Æ®¿öÅ© ±¸Á¶·Î Ç¥ÇöµÈ ¶óÀÌÇÁ·Î±×·ÎºÎÅÍ ÆäÆ®¸®³ÝÀ» ÀÌ¿ëÇÏ¿© »ç¿ëÀÚ »ýÈ° ÆÐÅÏÀ» ÀÚµ¿À¸·Î ÃßÃâÇÏ´Â ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. Á¦¾ÈÇÏ´Â »ýÈ° ÆÐÅÏ ÃßÃâ ¹æ¹ýÀº ÀÌÁúÀûÀ¸·Î ¼öÁýµÈ »ç¿ëÀÚ ¶óÀÌÇÁ·Î±×ÀÇ Àǹ̰ü°è¸¦ ³ªÅ¸³»±â À§ÇØ Àṉ̀¸Á¶¸¦ Á¤ÀÇÇÏ°í ½Ã¸Çƽ ³×Æ®¿öÅ©·Î ±¸Á¶È­ÇÑ´Ù. ¶ÇÇÑ ÆäÆ®¸®³Ý ±×·¡ÇÁ¸¦ ÀÌ¿ëÇÏ¿© ½Ã°£È帧¿¡ µû¶ó ºÒ±ÔÄ¢ÀûÀ¸·Î º¯È­ÇÏ´Â ¶óÀÌÇÁ·Î±×¸¦ ÀÇÇÐÀû Áø´Ü Ç׸ñÀ» ±â¹ÝÀ¸·Î ÀÚµ¿À¸·Î ÆÇ´ÜÇÏ¿© °³ÀÎÀÇ ¼ö¸é, ½Ä»ç »ýÈ°ÆÐÅÏÀ» ÃßÃâÇÑ´Ù. ÆäÆ®¸®³ÝÀº ½º¸¶Æ® ±â±â ¼¾¼­ µ¥ÀÌÅÍÀÇ ºÒÈ®½Ç¼ºÀ» ÁÙÀÌ°í »ýÈ° ÆÐÅÏÀÇ ´Ù¾ç¼ºÀ» Áõ°¡½ÃŲ´Ù. Á¦¾ÈÇÏ´Â ¹æ¹ýÀÇ À¯¿ë¼ºÀ» È®ÀÎÇϱâ À§ÇØ ¾Èµå·ÎÀÌµå ¾ÛÀ¸·Î ¼öÁýÇÑ 65¸íÀÇ ¶óÀÌÇÁ·Î±× µ¥ÀÌÅ͸¦ »ç¿ëÇÏ¿© ½ÇÇèÀ» ÁøÇàÇÏ°í »ç¿ëÀÚÀÇ ¼ö¸é, ½Ä»ç ÆÐÅÏ¿¡ µû¸¥ ÆäÆ®¸®³ÝÀÇ ÆÐÅÏÃßÃâ Á¤È®µµ¸¦ È®ÀÎÇÑ´Ù.
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(English Abstract)
Recently, with the spread of smart devices, the user¡¯s lifelog data is automatically stored through various types of sensors. But the lifelog collected from smart devices records heterogeneous information from different sensors. In addition, since the user's life patterns are determined by different judgment cycles, it is difficult to express them in a simple rule-based system. Therefore, in order to extract and provide useful life patterns for users from the lifelog, it is necessary to express the relationship of numerous dynamic elements. In this paper, we propose a method to automatically extract user life patterns using Petri-nets from the lifelog represented by the semantic network. Petri-net reduces the uncertainty in smart device sensor data and increases the diversity of life patterns. The proposed life pattern extraction method is structured by the semantic network to represent the semantic relationship of heterogeneously collected user lifelog. Also, the Petri-net graph automatically determines the lifelog and then extracts individual sleep and eating patterns.
Å°¿öµå(Keyword) ¶óÀÌÇÁ·Î±×   ºòµ¥ÀÌÅÍ   ÆÐÅÏ ¸¶ÀÌ´×   ½Ã¸Çƽ ³×Æ®¿öÅ©   ÆäÆ®¸®³Ý   life log   big data   pattern mining   semantic network   petri-net  
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