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Reinforcement Learning-based Optimal Design of Cooling Channels for batteries in Electric Vechcles
Á¤À̱³, ÀÌÀ±±¸, °­³²¿ì(Çѱ¹°úÇбâ¼ú¿ø)
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±èÀÎÅÂ(¾ÆÁÖ´ëÇб³), ¸¶Æò½Ä, ÀÌÇõ(Çѱ¹±â°è¿¬±¸¿ø), Á¤ÁØÇÏ(¾ÆÁÖ´ëÇб³)
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µ¥ÀÌÅÍ ±â¹Ý Àü±âÀÚµ¿Â÷ ¸®Æ¬ÀÌ¿Â ¹èÅ͸® ¿­È­ ¿¹ÃøÀ» À§ÇÑ ¸é¾Ð Ư¼º °³¹ß
À̽ÂÀ±, ÃÖÇöÈñ(¼­¿ï´ëÇб³), À̱Լ®(Çѱ¹ÀüÀÚ±â¼ú¿¬±¸¿ø), À±º´µ¿(¼­¿ï´ëÇб³)
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¿¬·áÀüÁö ¾ç±ØÆÇÀÇ ¿øÇÏ´Â Çü»ó Á¦ÀÛÀ» À§ÇÑ ¿ª¸ðµ¨ ¼³°è
±èÇü¹Î(¼­¿ï´ëÇб³), ÀÌ»ó¿í(Fraunhofer Institute for Production Technology), À̵¿ÈÞ, À±º´µ¿(¼­¿ï´ëÇб³)
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Optimizing Deposition Chamber Systems in the Semiconductor Industry Using Deep Learning: Addressing Challenges with Limited Simulation Data
¹Úµ¿±Ù, º¯Áø¾ð, ÀÌÀÎÈ¿, À¯½ÂÈ­(Çѱ¹°úÇбâ¼ú¿ø (KAIST))

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