Ưº°¼¼¼Ç
C9
15:00~16:20
½Å°øÇаü 5143
[»ê¾÷ü Ưº°¼¼¼Ç] P2P(Port to Port) µðÁöÅÐ ¹°·ùÇ÷§Æû ¼³°è
±è´ö¿µ ±³¼ö(Æ÷Ç×°ø°ú´ëÇб³)

  • C 9.1
    µðÁöÅÐ Æ®À© ±â¹Ý µðÁöÅÐ ¹°·ùÇ÷§Æû ÁßÁ¡ °³¹ß ½Ã½ºÅÛ ±¸¼º ¹× ÇÙ½É ±â¼ú
    ¹ÚÁø¿ì, ÀÓÇظ®, ½ÅÁ¤ÇÏ, ¹Î°æȯ, À̸íÈ£, ¹®°æ´ö, ±èº´ÀÎ, ÀÌ°­º¹, ±è´ö¿µ(Æ÷Ç×°ø°ú´ëÇб³ »ê¾÷°æ¿µ°øÇаú)
  • C 9.2
    µðÁöÅÐ ¹°·ùÇ÷§Æû¿¡¼­ ºí·ÏüÀÎ º¸¾È
    ¹®Á¤È¯(Æ柽ÃÅ¥¸®Æ¼½Ã½ºÅÛ(ÁÖ))
  • C 9.3
    ±¹³» ¿¬¾ÈÈ­¹°¿î¼Û ÇÁ·Î¼¼½º ¹× Çö¾ÈÀ̽´
    ¹è½Ã¿õ, Á¤¹ÎÀÇ, ÀÌÀºÁö, ÀÌÁ¤Àº(´õºê¸´ÁöÀü·«ÄÁ¼³ÆÃ(ÁÖ)), ±è´ö¿µ(Æ÷Ç×°ø°ú´ëÇб³(»ê¾÷°æ¿µ°øÇаú))
  • C 9.4
    AAS ±â¹Ý Ç׸¸ µðÁöÅÐ ¹°·ù Ç÷§Æû µ¥ÀÌÅÍ ¼öÁý/ÀúÀåü°è
    ¼Û¿ø¼®(³×½ºÆ®Çʵå(ÁÖ) ±â¾÷ºÎ¼³¿¬±¸¼Ò), ÀÌÁÖ¿¬(¼­¿ï°úÇбâ¼ú´ëÇб³ ±â°è½Ã½ºÅÛµðÀÚÀΰøÇаú), ±èÀ¯Ã¶(³×½ºÆ®Çʵå(ÁÖ) ±â¾÷ºÎ¼³¿¬±¸¼Ò), ¼­¼±¿µ(°æ±âÅ×Å©³ëÆÄÅ© Á¤Ã¥¿¬±¸ÆÀ), ±è³²Çõ(³×½ºÆ®Çʵå(ÁÖ) ±â¾÷ºÎ¼³¿¬±¸¼Ò)
  • C 9.5
    P2P µðÁöÅÐ ¹°·ù Ç÷§Æû ¼³°è
    ±è¿µÀÏ, Ȳ¼öÈ£((ÁÖ)ÆÛÁñµ¥ÀÌÅÍ)
 
C10
15:00~16:20
Online (½Ç½Ã°£ Zoom)
[Ưº°¼¼¼Ç] »ê¾÷ ºòµ¥ÀÌÅÍ ÀÀ¿ë (1)È­»ó°­ÀǽÇ
ÀÌÁöȯ(ºÎ°æ´ëÇб³)

  • C 10.1
    ÇÕ¼º°í¹« È¥·Ã°øÁ¤ °á°ú ¿¹ÃøÀ» À§ÇÑ ¿¹Ãøº¸Çà¸ðµ¨
    ¹Ú±â±º, ¹ÚÇѺ°, ¹èÇý¸²(ºÎ»ê´ëÇб³ »ê¾÷°øÇаú)
  • C 10.2
    ÀÛ¾÷ÀÚ ÃßÁ¾ Ä«Æ® ±â¹Ý µðÁöÅÐ ¾î¼ÒÆà ½Ã½ºÅÛÀÇ?ºÐ·ù ÀÛ¾÷¿¡¼­?Á¦Ç° ¹­À½À» À§ÇÑ ¿¬±¸
    ÀÌÅÂÈÆ, ÀÌÁ¤¸¸(ºÎ»ê´ëÇб³ »ê¾÷°øÇаú), ±è¿µÁÖ(Çѱ¹Ã¶µµ±â¼ú¿¬±¸¿ø), È«¼øµµ(ºÎ»ê´ëÇб³ »ê¾÷°øÇаú)
  • C 10.3
    ABM ±â¹Ý ½Ã¹Ä·¹À̼ÇÀ» ÀÌ¿ëÇÑ ´ë±Ô¸ð Àα¸µ¿ÅÂÀÇ ±¸Çö¿¡ °üÇÑ ¿¬±¸
    ±è¹Î¼ö, ÀÌÁöȯ, ±è¿µÁø(ºÎ°æ´ëÇб³ »ê¾÷¹×µ¥ÀÌÅÍ°øÇаú)
 
C11
15:00~16:20
½Å°øÇаü 5147
[Ưº°¼¼¼Ç] »ý»ê½Ã½ºÅÛ ¼³°è/¿î¿µ ¼Ö·ç¼Ç °³¹ß ¹× Àû¿ë ÇöȲ
¼ÛÀͼö(LGÀüÀÚ »ý»ê±â¼ú¿ø)

  • C 11.1
    Digital Twin ±â¹Ý »ý»ê½Ã½ºÅÛ ¼³°è / °ËÁõ Ç÷§Æû (PRISM)
    À±Á¤ÀÍ(LGÀüÀÚ »ý»ê±â¼ú¿ø) ÃʷϺ¸±â
    Á¦Á¶¾÷ÀÇ ¹ß´Þ°ú »ý»ê±â¼úÀÇ Çõ½ÅÀ¸·Î LG³»ÀÇ »ý»ê¶óÀÎÀº ¸¹Àº º¯ÇõÀ» °ÅÃÄ¿Ô´Ù. 80~90³â´ë ÁÖÃàÀ» ÀÌ·ð´ø °¡ÀüÁ¦Ç°ÀÇ Á¶¸³»ê¾÷ / È­ÀåÇ°ÀÇ ¿¬¼Ó¶óÀο¡¼­ºÎÅÍ ÇöÀç Çٽɻç¾÷ÀιèÅ͸® / µð½ºÇ÷¹ÀÌÀÇ ÀåÄ¡»ê¾÷±îÁö »ý»ê±â¼ú¿øÀº LG±×·ì Àü¹Ý¿¡ °ÉÄ£ »ý»ê½Ã½ºÅÛ ¼³°è¿Í »ý»ê¶óÀÎ ÄÁ¼Á ±âȹ¿¡¸¹Àº ±â¿©¸¦ °¡Á®¿Ô´Ù. ¶ÇÇÑ, »ý»ê½Ã½ºÅÛ »çÀü °ËÁõ¿¡ ¸ØÃßÁö ¾Ê°í ½ÇÁ¦ ¶óÀÎÀÌ ±¸ÃàµÈ ÀÌÈÄ »çÈÄ °ËÁõ (Real-Time Simulation)±îÁö½º¸¶Æ® Á¦Á¶ ½Ã½ºÅÛ ±¸Ãà¿¡ ÀÖ¾î ÇÊ¿äÇÑ ÃֽŠ±â¼úÀ» ÀÚü ¹ß±¼ / °³¹ß / ±¸Ãà ÁßÀÌ´Ù. ÀÌ·¯ÇÑ R&D ¿ª·®ÀÇ ÀÏȯÀ¸·Î ½Ã¹Ä·¹ÀÌ¼Ç ¿£ÁøÀ» ±â¹ÝÀ¸·Î ½ÇÁ¦ »ý»ê¶óÀÎÀ» °¡»óÀ¸·Î ±¸ÃàÇÑ Virtual Factory¸¦Digital TwinÀ¸·Î ±¸ÃàÇÏ¿© ½Ç½Ã°£À¸·Î µ¥ÀÌÅ͸¦ ¿¬µ¿ÇÏ°í, ÇâÈÄ ¹ß»ýÇÒ ¼ö ÀÖ´Â ½Ã³ª¸®¿À¸¦ ¿¹ÃøÇϴ½ǽ𣠽ùķ¹ÀÌ¼Ç ±¸Ãà »ç·Ê¸¦ Çö ¼¼¼Ç¿¡¼­ ¼Ò°³ÇÏ°íÀÚ ÇÑ´Ù.
  • C 11.2
    Digital Twin ¹× ÀΰøÁö´É ±â¹Ý ½º¸¶Æ®ÆÑÅ丮 ½Ç½Ã°£ ÃÖÀû ¿î¿µ
    ¹Ú»óö(¾ÆÁÖ´ëÇб³) ÃʷϺ¸±â
    ÀüÅëÀûÀÎ ÀÚµ¿È­ °øÀå°ú ½º¸¶Æ®ÆÑÅ丮¸¦ ±¸º°ÇÏ´Â ±âÁØ Áß ÇÑ°¡Áö´ÂÀǵµÇÏÁö ¾ÊÀº µ¹¹ß»óȲ¿¡ ½Ç½Ã°£À¸·Î ´ëóÇÒ ¼ö ÀÖ´Â ÀÚÀ²ÃÖÀû Á¦¾î´É·ÂÀÇ À¯¹«¸¦ µé ¼ö ÀÖ´Ù.ÀüÅëÀûÀÎ ½Ã¹Ä·¹ÀÌ¼Ç ¸ðµ¨Àº »ý»ê½Ã½ºÅÛÀÇ ¼³°è ´Ü°è¿¡ ±¹ÇѵǾî È°¿ëµÇ¾î ¿Ô´Ù.±×·¯³ª ÃÖ±Ù¿¡´Â ÀÌ·¯ÇÑ ¼³°è¿µ¿ªÀ» ¹þ¾î³ª¼­ °íÇØ»óµµÀÇ µðÁöÅÐÆ®À© ¸ðµ¨À» ÀÌ¿ëÇÏ¿© »ý»ê½Ã½ºÅÛÀÇ ¿î¿µÃÖÀûÈ­¸¦ ´Þ¼ºÇÔÀ¸·Î½á ½º¸¶Æ®ÆÑÅ丮¸¦ ±¸ÃàÇÏ´Â ³ë·ÂµéÀÌ À̾îÁö°í ÀÖ´Ù. º» ³í¹®¿¡¼­´Â µðÁöÅÐÆ®À© ¹× ÀΰøÁö´É±â¼úÀ» ÀÌ¿ëÇÏ¿© ½º¸¶Æ®ÆÑÅ丮¸¦ ½Ç½Ã°£ ÃÖÀû ¿î¿µÇÏ´Â °Í¿¡ °üÇÑ ¹æ¹ý·ÐÀ» Á¦½ÃÇÏ°íÀÚ ÇÑ´Ù.
  • C 11.3
    »ý»ê-ÀÚÀç µ¿±âÈ­ ¿î¿µÀ» À§ÇÑ ½Ã¹Ä·¹ÀÌ¼Ç ±â¹Ý ½Ç½Ã°£ °èȹÀ̽´ °¨Áöü°è °³¹ß
    ¹®»óÀÎ(LGÀüÀÚ »ý»ê±â¼ú¿ø) ÃʷϺ¸±â
    »ý»êÇöÀå¿¡¼­´Â »ý»ê¿î¿µ¾ÈÁ¤È­¸¦ À§ÇØ »ý»ê°èȹ ¼ö¸³ ½Ã ¼³ºñÀÇ °¡¿ëCapa.¸¦ ¸íÈ®ÇÏ°Ô ¹Ý¿µÇÏ´Â ³ë·Â°ú ´õºÒ¾î ÀÚÀ纰 Á¶´Þ ¸®µåŸÀÓ, Çù·Âȸ»çº° Capa µî ÀÚÀçÁ¶´Þ °¡´É¼ºÀ» Æò°¡ÇÏ¿© »ý»ê-ÀÚÀç °èȹÀÇ µ¿±âÈ­¸¦ È®º¸Çϱâ À§Çѳë·ÂÀ» ÇÏ°í ÀÖÀ¸³ª Çù·Â¾÷üÀÇ »ý»êÀ̽´, Ç°Áú°Ë»ç ½Ã ºÒÇÕ°Ý, Á¶´Þ°úÁ¤¿¡¼­ÀÇ ¿î¼Û Æ®·¯ºí µîÀ¸·Î ÀÎÇØÀÚÀç³³ÀÔ ÀÏÁ¤ÀÌ º¯°æµÇ´Â °æ¿ì°¡ ºó¹øÇÏ¿© ÃÖÃÊ ¼ö¸³ÇÑ »ý»ê°èȹÀ» ÁöÅ°Áö ¸øÇÏ°íºÒ°¡ÇÇÇÏ°Ô º¯°æÇؾßÇÏ´Â °æ¿ì°¡ ¹ß»ýÇÑ´Ù.
 
D9
16:30~17:50
Online (½Ç½Ã°£ Zoom),½Å°øÇаü 4147
[Ưº°¼¼¼Ç] ½ÅÀÓ±³¿ø Ưº°¼¼¼ÇÈ­»ó°­ÀǽÇ
¹ÚöÁø (ÇѾç´ëÇб³)

  • D 9.1
    ¿îÇà µ¥ÀÌÅÍ¿Í ºñÀ½¼ö Çà·Ä ºÐÇØ (NMF)¸¦ È°¿ëÇÑ ¹ö½º À§Çè¿îÀü Á¡¼öÈ­ ¹æ¹ý
    ¼­Çö¿ì(UNIST »ê¾÷°øÇаú), ½ÅÁ¾°æ(UNIST ÀΰøÁö´É ´ëÇпø), ±è±âÈÆ(ºÎ»ê´ëÇб³ »ê¾÷°øÇаú), ÀÓÄ¡Çö(UNIST »ê¾÷°øÇаú), ¹èÁßö(Çѱ¹±³Åë¾ÈÀü°ø´Ü) ÃʷϺ¸±â
    ±âÁ¸ À§Çè¿îÀü Á¡¼öÈ­ ¹æ¹ýµéÀº ¿îÇà µ¥ÀÌÅÍ¿Í?»ç°í ±â·Ï °£ÀÇ °ü°è¸¦ ÃßÁ¤ÇÏ°í, ±× °ü°è¿¡ ±Ù°ÅÇØ À§Çè¿îÀü Á¡¼ö¸¦ »êÃâÇÑ´Ù. ¿©±â¼­ »ç°í ±â·ÏÀº ¿îÀü À§Çèµµ¸¦ ´ëº¯ÇÏ´Â µ¥ÀÌÅÍ·Î È°¿ëµÈ´Ù. ±×·¯³ª »ç°í ±â·ÏÀº À§Çè¿îÀü°ú ¹«°üÇÏ°Ô ¹ß»ýÇÑ »ç°í Á¤º¸ ¶Ç´Â ºÎÁ¤È®ÇÑ »ç°í Á¤º¸¸¦ Æ÷ÇÔÇÑ´Ù. ÀÌ·Î ÀÎÇØ ±âÁ¸ ¹æ¹ýµéÀº À§Çèµµ Á¡¼öÈ­ÀÇ Á¤È®¼º¿¡ ÇÑ°è°¡ ÀÖ´Ù. º» ¿¬±¸´Â NMF¸¦ È°¿ëÇØ »ç°í ±â·Ï ¾øÀÌ ¿îÇà µ¥ÀÌÅ͸¸À¸·Î ¹ö½º À§Çè¿îÀüÀ» Á¡¼öÈ­ÇÏ´Â ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. º» ¹æ¹ýÀº ¹ö½º ¿îÀüÀÚÀÇ À§Çè¿îÀü ¸ð´ÏÅ͸µ ¹× ±³À°¿¡ È°¿ëµÉ °ÍÀ¸·Î ±â´ëµÈ´Ù.
  • D 9.2
    Clinical and Operational Decision Making in Healthcare
    ÀÌÈ¿°æ(°í·Á´ëÇб³ »ê¾÷°æ¿µ°øÇкÎ) ÃʷϺ¸±â
    ÇコÄÉ¾î »ê¾÷¿¡¼­ »ý¼ºµÇ´Â ¹æ´ëÇÑ ¾çÀÇ ÀÇ·á µ¥ÀÌÅ͸¦ È°¿ëÇÏ¿© µ¥ÀÌÅÍ¿¡ ±â¹ÝÇÑ È¯ÀÚÁß½ÉÀÇ ÀÇ»ç°áÁ¤À» Áö¿øÇØÁÖ´Â ÀÇ»ç°áÁ¤Áö¿ø½Ã½ºÅÛÀÇ Á߿伺ÀÌ ³ô¾ÆÁö°í ÀÖ´Ù. º» ¹ßÇ¥¿¡¼­´Â ÇコÄɾî Á¶Á÷ÀÇ °ü¸® ¹× ¿î¿µÀ» Áö¿øÇØÁÖ´Â ÀÇ»ç°áÁ¤Áö¿ø½Ã½ºÅÛ°ú ȯÀڷκÎÅÍ ¾ò¾îÁø ÀÓ»óÁ¤º¸¸¦ ¹ÙÅÁÀ¸·Î Áúº´ÀÇ Áø´Ü°ú Ä¡·á¸¦ µµ¿ÍÁÖ´Â ÀÓ»óÀÇ»ç°áÁ¤Áö¿ø½Ã½ºÅÛÀÇ µÎ°¡Áö ºÐ¾ßÀ» ´ÙÀ½ÀÇ ¿¬±¸ ÁÖÁ¦µéÀ» ÅëÇÏ¿© »ìÆ캻´Ù: º´¿ø º´»ó °ü¸® ½Ã½ºÅÛ, ¼ö¼ú ÈÄ ÀçÈ° ÇÁ·Î¼¼½º ÃÖÀûÈ­ ½Ã½ºÅÛ, ÁßȯÀÚ½Ç È¯ÀÚµéÀÇ °³ÀÎ ¸ÂÃãÇü ¾à¹°¿ä¹ý ½Ã½ºÅÛ, ¿Ü»ó¼º³ú¼Õ»ó ȯÀÚµéÀÇ ³ú¾Ð°ü¸®¸¦ À§ÇÑ Áö¿ø½Ã½ºÅÛ
  • D 9.3
    An improved approximation scheme?for the stochastic shortest path problem
    Seulgi Joung(Chonnam National University, Department of Industrial Engineering), Jisun Lee(University of California, Berkeley, Department of Industrial Engineering and Operations Research), Kyungsik Lee(Seoul National University, Department of Industrial Engineering) ÃʷϺ¸±â
    In this talk, we propose an improved fully polynomial time approximation scheme for the stochastic shortest path problem. For each arc, the travel time follows an independent normal distribution. The problem finds a path that?maximizes?the probability of arriving at the destination within a given threshold. The proposed scheme iteratively solves deterministic shortest path problems. The proposed approximation scheme can be applied to other combinatorial optimization problems such as the minimum spanning tree problem.
  • D 9.4
    Áö½ÄÀ¶ÇÕ ¸Ó½Å·¯´× ¹æ¹ý·Ð
    À±Çö¼ö(¿¬¼¼´ëÇб³ »ê¾÷°øÇаú) ÃʷϺ¸±â
    µ¥ÀÌÅÍ ºÐ¼®¿¡ ¸Ó½Å·¯´×À» Àû¿ëÇÒ ¶§, ¶óº§¸µÀÇ À¯¹«, ºñ´ëĪ¼º, ÀϹÝÈ­ ¼º´É, Ãʱ⿡ ƯÁ¤ ¹®Á¦¿¡ Á¦ÇÑµÈ »ùÇà ¼ö µîÀÌ ¹®Á¦°¡ µÇ´Â °æ¿ì°¡ ÀÚÁÖ ¹ß»ýÇÕ´Ï´Ù. Á¦¾ÈÇÏ´Â Áö½ÄÀ¶ÇÕ ¸Ó½Å·¯´× ¹æ¹ý·ÐÀº ´Ù¾çÇÑ À¯ÇüÀÇ Á¤º¸ ±â¼úÀ»(µµ¸ÞÀÎ Áö½Ä, ¹°¸®ÇÐ ¶Ç´Â »ý¹°ÇÐÀûÀÎ ¿ø¸®, ¸ðµ¨ ³»¿¡¼­ Á¤º¸, ÇнÀµÇ´Â ƯÁú µî) È°¿ëÇÏ¿© ¼º´ÉÀ» °³¼±ÇÏ°í °ß°í¼ºÀ» ³ôÀÌ´Â µ¥ È°¿ëµÉ ¼ö ÀÖ½À´Ï´Ù. µµ¸ÞÀÎ °£ÀÇ Áö½Ä ÀüÀÌ, ±â°èÇнÀ ¸ðµ¨°ú ¿ø¸®¿¡ ¹ÙÅÁÀ» µÐ ¼öÇÐ ¸ðÇü °£ÀÇ ¸ðµ¨ À¶ÇÕ, »ý¼º ¸ðµ¨À» È°¿ëÇÑ µµ¸ÞÀÎ ÀûÀÀ ¹æ½ÄÀ» È°¿ëÇϸé ÀǷ῵»óÁø´Ü, ¼¾¼­ µ¥ÀÌÅÍ, ¿­¿ªÇÐ ¿µ»ó ºÐ¼® µî¿¡¼­ ¼º´ÉÀ» °³¼±ÇÏ´Â °ÍÀ» È®ÀÎÇÏ¿´½À´Ï´Ù.
 
D10
16:30~17:50
Online (½Ç½Ã°£ Zoom)
[Ưº°¼¼¼Ç] »ê¾÷ ºòµ¥ÀÌÅÍ ÀÀ¿ë (2)È­»ó°­ÀǽÇ
±è¹Î¼ö(ºÎ°æ´ëÇб³)

  • D 10.1
    The Implementation of Spending and Operational Analytics Indirect Procurement (STEALTH) for the Digital Healthcare Companies
    Nicholas, À¯Å¼±(ºÎ°æ´ëÇб³ »ê¾÷¹×µ¥ÀÌÅÍ°øÇаú »ê¾÷µ¥ÀÌÅÍ°øÇÐÀ¶ÇÕÀü°ø) ÃʷϺ¸±â
    In this research, we defined some problems related to the procurement data analytics for digital healthcare companies. As a supporting function, Procurement Department plays significant role to generate cost saving hence monitoring the spending and operation are crucial. However, most of the procurement professionals face challenges in terms of the required lead time to collect and prepare for the spending and operational data, not to mention analyze the data yet. Therefore, we proposed the implementation of tableau for the spending and operational analytics (STEALTH) at Procurement Department of the digital healthcare companies. STEALTH was a collaborative project to collect the procurement data then present them in a more user-friendly way to support the data analytics.
  • D 10.2
    Deep Learning Time Series for Well logging data Imputation
    ANTARIKSA GIAN(Pukyong National University, Department of Industrial Data Engineering), RADHI MUAMMAR(Conrad Petroleum, Co. Ltd), AGUNG NUGRAHA, JIHWAN LEE(Pukyong National University, Department of Industrial Data Engineering)
  • D 10.3
    Decay Assesement for Propeller of the Frigate Naval Propulsion System with Explainable Artificial Intelligence
    HANDAYANI MELIA, ANTARIKSA GIAN, JIHWAN LEE(Pukyong National University, Department of Industrial and Data Engineering) ÃʷϺ¸±â
    A type of warship called ¡®Frigate¡¯ is designed mainly for quick maneuverability and operated for escorting larger ship and manage the protection against any kind of threat. Thus, the maintenance of its engine is a core process to make sure that the ship can always operate well. In this study, the object of research is Frigate that built with CODLAG-type of propulsion system, designed to make the ship cruise silently. From one important component of the system, which is the propeller, this study aims to conduct the decay classification of gas turbine based on simulated operational data of the component and added explainability to the results of machine learning classification. The classification is done using explainability approach utilizing SHap Additive exPlanations (SHAP) methodologies for machine learning interpretabity. The results not only able to segment the type of risks given the coefficient of decay state in the propeller, but also explain the factors that mostly affected the occurrence of decay.
 
D11
16:30~17:50
½Å°øÇаü 5143
[Ưº°¼¼¼Ç] 4Â÷»ê¾÷Çõ¸í½Ã´ëÀÇ Ç°Áú°øÇÐ
ÀåÁß¼ø(¾ÆÁÖ´ëÇб³)

  • D 11.1
    AI ±â¹Ý Ç°Áú°ü¸®¿¡¼­ÀÇ À̽´(Ç°Áú°Ë»çÀÇ ¹ßÀü¹æÇâ)
    ¹Ú»óö(¾ÆÁÖ´ëÇб³) ÃʷϺ¸±â
    ±âÁ¸ÀÇ ½Å·Ú¼º ȤÀº ¼ö¸í¿¹Ãø ¹æ¹ýÀº ÀüÅëÀû Åë°è ¹× ¼öÇÐÀû ±â¹ý¿¡ ±â¹ÝÇÏ¿©,ºÎÇ° ȤÀº ½Ã½ºÅÛÀÇ ¼º´ÉÀ» ¿¹ÃøÇÏ´Â °ÍÀ̾ú´Ù. ±âÁ¸ ¹æ¹ýµéÀº ¿©·¯ °¡Áö ÀåÁ¡ÀÌ ÀÖÀ½¿¡µµ ºÒ±¸ÇÏ°í, ´ëºÎºÐ Çö½Ç µ¥ÀÌÅÍÀÇ ºÎÀç, ´Ù¾çÇÑ »óÈ£ÀÛ¿ë, »ç¿ë ȯ°æÀÇ ´Ù¾ç¼º ±×¸®°í Á¤È®ÇÑ ¹°¸®Àû ¸ðµ¨ ±¸ÃàÀÇ ¾î·Á¿ò µîÀ¸·Î ÀÎÇØ ÇöÀå Àû¿ë¿¡ ¾î·Á¿òÀ» °Þ°í ÀÖ´Ù. ÀΰøÁö´É ±â¼úÀº Á¡Â÷ ´Ù¾çÇÑ °÷¿¡ Àû¿ëµÇ°í ÀÖ´Â Ãß¼¼ À̸ç, ÀÌ¹Ì Ç°Áú, ½Å·Ú¼º ºÐ¾ß¿¡µµ Àû¿ëµÇ°í ÀÖ´Ù. º» ±Û ¿¡¼­´Â ÀΰøÁö´É ±â¼úÀ» Ç°Áú ½Å·Ú¼º ºÐ¾ß¿¡ Àû¿ëÇÒ ¶§ °í·ÁÇØ¾ß ÇÒ À̽´µéÀ» ´Ù·é´Ù.
  • D 11.2
    Ç°Áú°øÇÐ °ü·Ã ±â¾÷³» ±³À°°ú º¯È­(Á¦Á¶ºÎ¹® Ç°Áú°ü¸®ÀÇ ¹ßÀü¹æÇâ)
    µ¿½ÂÈÆ(»ï¼ºÀüÀÚ DSºÎ¹® SSIT(Samsung Institute of Technology)) ÃʷϺ¸±â
    ´õ¿í º¹ÀâÇØÁö°í ¹Ì¼¼È­ µÇ´Â ¹ÝµµÃ¼ Á¦Ç°ÀÇ ¼³°è ¹× Á¦Á¶°úÁ¤¿¡ ÀÖ¾î ¾ç»ê¼º È®º¸¸¦ ÀüÁ¦·Î ¿ä±¸µÇ´Â Ç°Áú¼öÁØÀ» ¸¸Á·½ÃÅ°±â À§Çؼ­´Â °íÀ¯ ±â¼úÀû Çõ½Å»Ó¸¸ ¾Æ´Ï¶ó Ç°Áú°øÇÐ µî »ê¾÷°øÇÐÀû ¹ü¿ë±â¼ú°úÀÇ »óÈ£º¸¿ÏÀû °áÇÕÀÌ ´õ¿í ÇÊ¿äÇÏ´Ù. ÇÑÆí, Ç°Áú°øÇÐÀû ¹æ¹ý·ÐÀ» ¹ÝµµÃ¼ ÇöÀå¿¡ Àû¿ë Çϴµ¥ À־´Â ½ÇÇèÀÇ È®·üÈ­ µî ¹æ¹ý·Ð Ãø¸éÀÇ ÁÖ¿ä Á¦¾à»çÇ×À» ÀÌÇØÇÏ´Â °Í ÀÌ¿Ü¿¡µµ ´Ù¾çÇÑ ±â¾÷³» ºñÀü°ø ¿£Áö´Ï¾îµé¿¡ ´ëÇÑ ¹ü¿ë Ç°Áú±â¼ú½Ç¹« ±³À°ÇÁ·Î±×·¥À» Çаè¿Í ¿¬°èÇÏ¿© °®Ãß°í Áö¼ÓÀûÀ¸·Î Àü°³ÇÏ´Â °Í ¶ÇÇÑ ¸Å¿ì Áß¿äÇÏ´Ù. ÀÌ·¯ÇÑ °üÁ¡¿¡¼­ ¹ÝµµÃ¼ ´ë±â¾÷ ÇöÀå¿¡¼­ 30¿©³â°£ÀÇ Ç°Áú±³À° °ü·Ã °æÇè°ú º¯È­ °úÁ¤À» °øÀ¯ÇÏ°íÀÚ ÇÑ´Ù. ¶ÇÇÑ, °èÃø, °¢Á¾ ½Ã¹Ä·¹ÀÌÅÍ »Ó¸¸ ¾Æ´Ï¶ó °¢Á¾ ¼³ºñ µî¿¡¼­ ¹ß»ýµÇ´Â ¼ö ¸¹Àº ¼¾¼­ µ¥ÀÌÅÍÀÇ Æø¹ßÀû Áõ°¡¿Í À̸¦ ½Å¼ÓÇÏ°Ô Ã³¸®ÇÏ´Â ÄÄÇ»ÆÃ, ³×Æ®¿öÅ© ±â¼úÀÇ ¹ßÀüÀÌ ÀÌ·ç¾îÁö°í ÀÖ´Â 4Â÷ »ê¾÷Çõ¸í ½Ã´ëÀÇ ¹ÝµµÃ¼ Á¦Á¶ ȯ°æ¿¡ ÇÊ¿äÇÑ Ç°Áú°øÇÐ °ü·Ã ¿¬±¸°úÁ¦, ¹æÇâ µîÀ» »ìÆ캸°íÀÚ ÇÑ´Ù.
  • D 11.3
    4Â÷ »ê¾÷Çõ¸í ½º¸¶Æ®ÆÑÅ丮 È¿¿ë¼º Á¦°í¸¦ À§ÇÑ ¿ø·ù´Ü°è¿¡¼­ÀÇ Á¦Ç° ¹× Ç°Áú °³¹ß(Ç°Áú¼³°èÀÇ ¹ßÀü¹æÇâ)
    Áö¼öÀ±(¼­°æ´ëÇб³)
  • D 11.4
    ±â¾÷°æÀï·Â Çâ»ó°ú Ç°ÁúÇõ½Å-Ç°ÁúÁ¤º¸½Ã½ºÅÛÀ» Áß½ÉÀ¸·Î(Ç°ÁúÁ¤º¸½Ã½ºÅÛÀÇ ¹ßÀü¹æÇâ)
    ±èÁ¾¸¸(¸íÁö´ëÇб³) ÃʷϺ¸±â
    1930³â´ë °ü¸®µµ¿Í »ùÇøµ ±â¹ýÀÌ °³¹ßµÈ ÀÌÈÄ, Áö³­ ÇѼ¼±â µ¿¾È Ç°Áú°ü¸® ±³°ú¸ñÀÇ ÁÖ¿ä ³»¿ëÀº ÀÌ µÎ°¡Áö ±â¹ý¿¡ ÃÊÁ¡ÀÌ ¸ÂÃçÁ® ÀÖ´Ù. ±×·¯³ª ½ÇÁ¦ ±â¾÷¿¡¼­ ¹ß»ýÇÏ´Â Ç°Áú¹®Á¦´Â ÀÌ µÎ°¡Áö ±â¹ýÀ¸·Î¸¸ ÇØ°áÇϱ⿡´Â ÇÑ°è°¡ ÀÖ´Ù. Áï, ÃÖ±Ù µé¾î¼­µµ ´ë±Ô¸ð ¸®ÄÝ »çÅ°¡ ºó¹øÈ÷ ¹ß»ýÇÏ°í, ºÒ·®·üÀÌ ¿©ÀüÈ÷ ³ôÀº °æ¿ì°¡ ¸¹±â ¶§¹®¿¡ ÀÌ¿¡ ´ëÇÑ ÇØ°á¹æ¹ýÀÌ ¿ä±¸µÈ´Ù. Ç°Áú¹®Á¦´Â ÇØ°áÇϱâ À§Çؼ­´Â ±â¾÷ ³»ºÎÀÇ ¼³°è, »ý»ê ¹× ½ÃÇè´Ü°è¿¡¼­ ºÒ·®À» ÃÖ¼ÒÈ­ÇÏ¿©¾ß ÇÏ°í, ÆǸÅÀÌÈÄ ±â¾÷ ¿ÜºÎ¿¡ ³ª°£ Á¦Ç°¿¡ ´ëÇÑ °íÀå ¸ÞÄ¿´ÏÁòÀ» ºÐ¼®ÇÏ¿©¾ß ÇÑ´Ù. ±×¸®°í ÀÌ¿Í °°Àº ±â¾÷ ³»ºÎ¿Í ¿ÜºÎÀÇ µ¥ÀÌÅ͸¦ »óÈ£ ¿¬°áÇÏ¿© ºÐ¼®ÇÒ ¼ö ÀÖ´Â Çǵå¹é ü°è°¡ °®ÃçÁ®¾ß ÇÑ´Ù. À̸¦ À§ÇØ ±â¾÷¿¡¼­´Â Ç°ÁúÁ¤º¸½Ã½ºÅÛÀ» È°¿ëÇÏ¿© ±â¾÷ ³»,¿ÜºÎÀÇ µ¥ÀÌÅ͸¦ ÀÚµ¿À¸·Î ¼öÁý, ºÐ¼®ÇÏ°í, ±âÁ¸ ·¹°Å½Ã ½Ã½ºÅÛ°úÀÇ ¿¬°è¸¦ ÅëÇØ ºÒ·®ÀÇ ¿øÀÎÀ» ¼¼¹ÐÇÏ°Ô ºÐ¼®ÇÏ´Â È°µ¿¿¡ ÁýÁßÇÏ°í ÀÖ´Ù. ¶ÇÇÑ, IoT, ºòµ¥ÀÌÅÍ µîÀÇ ¹ß´Þ·Î ÀÎÇÏ¿© ½Ç½Ã°£À¸·Î ¿©·¯°¡Áö ÆĶó¸ÞÅ͸¦ ½Ç½Ã°£À¸·Î ºÐ¼®ÇÏ¿© °íÀåÀ» ¿¹ÃøÇÏ°í ¹Ì¸® ¿¹¹æÇÏ´Â ½Ã½ºÅÛÀ» ±¸ÃàÇÏ°íÀÚ ÇÑ´Ù. º» ¹ßÇ¥¿¡¼­´Â ÀÌ·¯ÇÑ Ç°ÁúÁ¤º¸½Ã½ºÅÛ¿¡ ´ëÇؼ­ ¾Ë¾Æº¸°í ÇâÈÄ Ç°Áú ½Ã½ºÅÛÀÌ ³ª¾Æ°¡¾ß ÇÒ ¹æÇâ°ú ±×¿¡ µû¸¥ ±³À°ÀÇ ¹ßÀü ¹æÇâ¿¡ ´ëÇؼ­ »ìÆ캸°íÀÚ ÇÑ´Ù.
  • D 11.5
    Ç°Áú°øÇÐÀÇ ¿ª»ç¿Í ¹ßÀü¹æÇâ(Ç°Áú°øÇÐÀÇ ¹ßÀü¹æÇâ)
    ÀåÁß¼ø(¾ÆÁÖ´ëÇб³) ÃʷϺ¸±â
    Ç°Áú°øÇÐÀº Ç°Áú°Ë»ç¸¦ Æ÷ÇÔÇÑ Ç°Áú°ü¸®·ÎºÎÅÍ ºñ·ÔµÇ¾î TQC, Ç°Áúº¸Áõ½Ã½ºÅÛ, Ç°Áú°æ¿µ, 6½Ã±×¸¶ µîÀ¸·Î ¹ßÀüÇÏ¿© ¿Ô´Ù. º» ¿¬±¸¿¡¼­´Â ÀÌ·¯ÇÑ ¹æ¹ýµéÀÇ Æ¯Â¡À» ¾Ë¾Æº¸°í, 4Â÷ »ê¾÷Çõ¸í½Ã´ë¿¡¼­ÀÇ ¹ßÀü¹æÇâ¿¡ ´ëÇÏ¿© Àü¸ÁÇÑ´Ù. ƯÈ÷ Ç°Áú°øÇп¡¼­ ´Ù·ç¾î¾ß ÇÒ ±â¼úÀ» Áß½ÉÀ¸·Î ³íÀÇÇÑ´Ù.
 
D12
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  • D 12.1
    ¹°·ù ÀÚµ¿È­ ½Ã½ºÅÛÀÇ µðÁöÅÐ Æ®À©
    À念Àç(Ä«À̽ºÆ®, »ê¾÷ ¹× ½Ã½ºÅÛ °øÇаú/´ÙÀÓ¸®¼­Ä¡) ÃʷϺ¸±â
    º» ¹ßÇ¥¿¡¼­´Â Á¦Á¶ ¹× ¹°·ù ½Ã½ºÅÛ¿¡¼­ÀÇ ·Îº¿ ±â¹Ý ÀÚµ¿È­ ½Ã½ºÅÛÀÇ µðÁöÅÐ Æ®À© °³³äÀ» ¼³¸íÇÑ´Ù. µðÁöÅÐÆ®À©ÀÇ °¡»óȯ°æ¿¡¼­ ÀΰøÁö´É °­È­ÇнÀÀÇ ÇнÀÀ» ÁøÇàÇÏ¸ç µ¿½Ã¿¡ ½ÇÁ¦ ÇöÀå¿¡¼­ÀÇ IoT±â¹Ý ÇнÀÀ» ÁøÇàÇÏ´Â ¿À-¿ÀÇÁ µ¿½Ã ÇнÀÀ» ÅëÇÑ ÀÚµ¿È­ ½Ã½ºÅÛ °³³äÀ» ¼Ò°³ÇÑ´Ù.
  • D 12.2
    ÆòÆÇ µð½ºÇ÷¹ÀÌ ¹× 2Â÷ÀüÁö ¹°·ù ¹Ý¼Û ½Ã½ºÅÛ µðÁöÅÐ Æ®À© È°¿ë
    Ȳ ¼³(´ÙÀÓ¸®¼­Ä¡) ÃʷϺ¸±â
    °­È­ÇнÀ°ú µðÁöÅÐ Æ®À©À» È°¿ëÇÏ¿© ÆòÆÇ µð½ºÇ÷¹ÀÌ ¹°·ù ¹Ý¼Û ½Ã½ºÅÛ È°¿ë Àû¿ë »ç·Ê¸¦ ¼Ò°³ÇÑ´Ù. ½ÉÃþ°­È­ÇнÀ (Deep Q-learning)À» È°¿ëÇÏ¿© µÎ´ëÀÇ Å©·¹ÀÎ ·Îº¿À» ¿¬µ¿ÇÏ¸ç µðÁöÅÐ Æ®À©À» È°¿ëÇÑ ÇнÀ Áõ°­ °³³äÀ» ¼³¸íÇÑ´Ù. ¶ÇÇÑ 2Â÷ÀüÁö ¹°·ù ¹Ý¼Û ½Ã½ºÅÛ µðÁöÅÐÆ®À© È°¿ë °¡´É¼º¿¡ ´ëÇØ ³íÀÇÇÑ´Ù.
  • D 12.3
    Smart Factory AGV ¿î¿µ ½Ã½ºÅÛ °³¹ß ½ÇÁõ »ç·Ê
    ÀÌÀç¿õ, À念Àç(Ä«À̽ºÆ®, »ê¾÷ ¹× ½Ã½ºÅÛ °øÇаú) ÃʷϺ¸±â
    Automated Guided Vehicle (AGV) ½Ã½ºÅÛÀº Á¦Á¶ °øÀå ³» °øÁ¤ °£ ¹ÝÁ¦Ç° ¿î¹ÝÀ» ´ã´çÇÏ´Â ´ëÇ¥ÀûÀÎ ¹°·ù ÀÚµ¿È­ ½Ã½ºÅÛÀÌ´Ù. AGV Á¦¾î ½Ã½ºÅÛ(AGV Control System; ACS)´Â °øÀå ³» AGV¸¦ Á¦¾îÇϱâ À§ÇÑ ½Ã½ºÅÛÀ¸·Î, ÀÛ¾÷ ÇÒ´ç°ú °æ·Î ÇÒ´ç µîÀÇ ÀÇ»ç°áÁ¤À» ¼öÇàÇÑ´Ù. ÇÏÁö¸¸, ÃÖ´Ü °æ·Î »êÃâ ¹æ¹ýÀ» Æ÷ÇÔÇÑ ±âÁ¸ ÀÇ»ç°áÁ¤ ¹æ¹ýÀº ¼ö½Ê~¼ö¹é ´ë ±Ô¸ðÀÇ AGV ½Ã½ºÅÛ ¿î¿µ¿¡´Â ÇÑ°è°¡ ÀÖ´Ù. º» ¹ßÇ¥¿¡¼­´Â ´ë±Ô¸ð AGV ½Ã½ºÅÛ ¿î¿µÀ» À§ÇØ »ê¾÷°è°¡ Á÷¸éÇÑ À̽´¿Í Çö¾÷ÀÇ ´ëÀÀ ÇöȲÀ» ¼Ò°³ÇÑ´Ù. ¶ÇÇÑ, ´ë±Ô¸ð AGV ½Ã½ºÅÛ¿¡¼­ Dijkstra ¾Ë°í¸®Áò ÀÇ»ç°áÁ¤À» È¿À²ÀûÀ¸·Î ¼öÇàÇÒ ¼ö ÀÖ´Â Node Reduction ¹æ¹ýÀ» ¼Ò°³ÇÏ°í, ½ÇÁ¦ Àû¿ë »ç·Ê ¹× °³¼± °á°ú¸¦ ¼Ò°³ÇÑ´Ù.
  • D 12.4
    ±ºÁý ·Îº¿ µðÁöÅÐ Æ®À© ½Ã½ºÅÛ »ê¾÷ Àû¿ë »ç·Ê - ¹ÝµµÃ¼/ÆòÆǵð½ºÇ÷¹ÀÌ/2Â÷ÀüÁö
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