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»ç»ç : Grand ICT ¿¬±¸¼¾ÅÍ(ÃæºÏ´ë)
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»ç»ç : Grand ICT ¿¬±¸¼¾ÅÍ(ÃæºÏ´ë)
ÇѱÛÁ¦¸ñ : SOIF-DN: Preserving Small Object Information Flow With Improved Deep Learn...
¿µ¹®Á¦¸ñ : SOIF-DN: Preserving Small Object Information Flow With Improved Deep Learn...
Àú³ÎÁ¤º¸ : IEEE Access   2023 ³â  11 ±Ç  1 È£  1  ~  15
³í¹®±¸ºÐ : SCI(E)
¸ÞÀÎÀúÀÚ : IN JOO
¼­ºêÀúÀÚ : SUNGHOON KIM, GINAM KIM, KWAN-HEE YOO
Å°¿öµå : Defect detection, feature fusion, feature pyramid, printed circuit board (PCB), small object detection, small object information flow (SOIF-DN), deep learning
Defect detection, feature fusion, feature pyramid, printed circuit board (PCB), small object detection, small object information flow (SOIF-DN), deep learning
ȍȍ : This work was supported in part by the Ministry of Science and Information Communication Technology (MSIT), South Korea, through the Grand Information Technology Research Center Support Program, Supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP), under Grant IITP-2023-2020-0-01462.
ÇѱÛÁ¦¸ñ : ¸Ó½Å·¯´×À» »ç¿ëÇÑ À¾¡¤¸é Áö¿ª ÁßÇлýÀÇ °íµîÇб³ ÁøÇÐ ¿¹Ãø
¿µ¹®Á¦¸ñ : Middle School Students in Rural Area Using Machine Learning High School Graduati...
Àú³ÎÁ¤º¸ : Çѱ¹ÄÜÅÙÃ÷ÇÐȸ   2023 ³â  23 ±Ç  10 È£  423  ~  433
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The Characteristic Data of Middle School Students in Eup and Myeon Units, Machine Learning, Predict Students Entrance into High School, Accuracy∣
»ç»ç : º» ¿¬±¸´Â °úÇбâ¼úÁ¤º¸Åë½ÅºÎ ¹× Á¤º¸Åë½Å±âȹÆò°¡¿øÀÇ Áö¿ªÁö´ÉÈ­Çõ½ÅÀÎÀç¾ç¼º(Grand ICT¿¬±¸¼¾ÅÍ) »ç¾÷ÀÇ ¿¬±¸°á°ú·Î ¼ö ÇàµÇ¾úÀ½¡± (IITP-2023-2020-0-01462)
ÇѱÛÁ¦¸ñ : SCE-LSTM: Sparse Critical Event-Driven LSTM Model with Selective Memorization f...
¿µ¹®Á¦¸ñ : SCE-LSTM: Sparse Critical Event-Driven LSTM Model with Selective Memorization f...
Àú³ÎÁ¤º¸ : agriculture   2023 ³â  13 ±Ç  2044 È£  1  ~  22
³í¹®±¸ºÐ : SCI(E)
¸ÞÀÎÀúÀÚ : Ga-Ae Ryu
¼­ºêÀúÀÚ : Tserenpurev Chuluunsaikhan, Aziz Nasridinov, HyungChul Rah and Kwan-Hee Yoo
Å°¿öµå : sparse critical event-driven LSTM (SCE-LSTM); forecasting; pork consumption; unstructured big data
sparse critical event-driven LSTM (SCE-LSTM); forecasting; pork consumption; unstructured big data
ȍȍ : This work has been supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2023-2020-0-01462) su- pervised by the IITP (Institute for Information & communications Technology Planning & Evalua- tion), and the Basic Science Research Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant number:2020R1I1A1A01071884).
ÇѱÛÁ¦¸ñ : Material-centric-strategies of ML and DL for packages programability of develope...
¿µ¹®Á¦¸ñ : Material-centric-strategies of ML and DL for packages programability of develope...
Àú³ÎÁ¤º¸ : European Materials Research Society   2023 ³â  1 ±Ç  1 È£  1  ~  1
³í¹®±¸ºÐ : ±¹¿ÜÇмú´ëȸ¹ßÇ¥³í¹®Áý
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¼­ºêÀúÀÚ : ·ù°üÈñ
Å°¿öµå : packaging method
packaging method
ȍȍ : This work was supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (No. P0022332, Digital data platform for material development)
ÇѱÛÁ¦¸ñ : A versatile strategy for hybridizing small experimental and large simulation da...
¿µ¹®Á¦¸ñ : A versatile strategy for hybridizing small experimental and large simulation da...
Àú³ÎÁ¤º¸ : ELSEVIER   2023 ³â  234 ±Ç  1 È£  1  ~  12
³í¹®±¸ºÐ : SCI(E)
¸ÞÀÎÀúÀÚ : Jeong-Hun Kim, Hyunseok Ko
¼­ºêÀúÀÚ : Dong-Hun Yeo. Zeehoon Park, Upendra Kumar, Kwan-Hee Yoo, Aziz Nasridinov, Sung Beom Cho
Å°¿öµå : Machine learning Simulation Data deficiency Inverse design Tape-casting
Machine learning Simulation Data deficiency Inverse design Tape-casting
ȍȍ : We acknowledge the support from Ministry of Trade, Industry & Energy (20004367) and National Research Foundation (RS-2023- 00209910). This research was also supported by the MSIT(Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program(IITP-2023-2020-0-01462) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation). The computations were carried out using re- sources from Korea Supercomputing Center (KSC-2022-CRE-0348).
ÇѱÛÁ¦¸ñ : The Smart Factory with Variable System Design
¿µ¹®Á¦¸ñ : The Smart Factory with Variable System Design
Àú³ÎÁ¤º¸ : BIGDAS2023   2023 ³â  11 ±Ç  1 È£  85  ~  91
³í¹®±¸ºÐ : ±¹¿ÜÇмú´ëȸ¹ßÇ¥³í¹®Áý
¸ÞÀÎÀúÀÚ : Chae-Hyun Lee
¼­ºêÀúÀÚ : Sung-Jin Im, Ja-Yeon Heo, Jin-Soo Kim and Kwan-Hee Yoo
Å°¿öµå : Smart Factory, MongoDB, Dynamic, Real Time
Smart Factory, MongoDB, Dynamic, Real Time
ȍȍ : This work was partly supported by the Technology development Program of MSS S3290113 and the ICT development R&D program of MSIT S3290113
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