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°Ë»ö°á°ú : 652
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
Ű¿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
ÇѱÛÁ¦¸ñ : Enhancing IoT Network Intrusion Detection Model through Over-sampling Technique...
¿µ¹®Á¦¸ñ : Enhancing IoT Network Intrusion Detection Model through Over-sampling Technique...
Àú³ÎÁ¤º¸ : BIGDAS2023   2023 ³â  11 ±Ç  1 È£  79  ~  84
³í¹®±¸ºÐ : ±¹¿ÜÇмú´ëȸ¹ßÇ¥³í¹®Áý
¸ÞÀÎÀúÀÚ : Eun-Beom Sung
¼­ºêÀúÀÚ : Sung-Jin Im, Jin-Soo Kim, and Kwan-Hee Yoo
Ű¿öµå : IoT, Machine Learning, SMOTE, ADASYN, Borderline-SMOTE
IoT, Machine Learning, SMOTE, ADASYN, Borderline-SMOTE
ȍȍ : This work was partly supported by the Technology development Program of MSS S3290113 and the ICT development R&D program of MSIT S3290113
ÇѱÛÁ¦¸ñ : Cloud and Edge Computing Model for Smart IoT Systems
¿µ¹®Á¦¸ñ : Cloud and Edge Computing Model for Smart IoT Systems
Àú³ÎÁ¤º¸ : BIGDAS2023   2023 ³â  11 ±Ç  1 È£  5  ~  9
³í¹®±¸ºÐ : ±¹¿ÜÇмú´ëȸ¹ßÇ¥³í¹®Áý
¸ÞÀÎÀúÀÚ : In Joo
¼­ºêÀúÀÚ : Kwan-Hee Yoo
Ű¿öµå : Smart IoT; Platform approach; Interoperability; Offloading; Cloud computing; Edge computing;
Smart IoT; Platform approach; Interoperability; Offloading; Cloud computing; Edge computing;
ȍȍ : This research was 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)This work was partly supported by the Technology development Program of MSS S3290113 and the ICT development R&D program of MSIT S3290113
ÇѱÛÁ¦¸ñ : Classification of tomato leaf diseases using various CNN models
¿µ¹®Á¦¸ñ : Classification of tomato leaf diseases using various CNN models
Àú³ÎÁ¤º¸ : BIGDAS2023   2023 ³â  11 ±Ç  1 È£  39  ~  44
³í¹®±¸ºÐ : ±¹¿ÜÇмú´ëȸ¹ßÇ¥³í¹®Áý
¸ÞÀÎÀúÀÚ : JuHan-Song
¼­ºêÀúÀÚ : Sunghoon Kim, Kwan Hee-Yoo
Ű¿öµå : Plant Disease, DeepLearning, ResNet, DenseNet, GoogLeNet, U- Net
Plant Disease, DeepLearning, ResNet, DenseNet, GoogLeNet, U- Net
ȍȍ : This research was 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)
ÇѱÛÁ¦¸ñ : Alarm Signal based Machine status estimation using Deep Learning
¿µ¹®Á¦¸ñ : Alarm Signal based Machine status estimation using Deep Learning
Àú³ÎÁ¤º¸ : BIGDAS2023   2023 ³â  11 ±Ç  1 È£  10  ~  13
³í¹®±¸ºÐ : ±¹¿ÜÇмú´ëȸ¹ßÇ¥³í¹®Áý
¸ÞÀÎÀúÀÚ : Dimang Chhol
¼­ºêÀúÀÚ : Kwan-Hee Yoo
Ű¿öµå : Machine Health Stability, Alarm Signals, Deep Learning, Machine Learning, LSTM
Machine Health Stability, Alarm Signals, Deep Learning, Machine Learning, LSTM
ȍȍ : This research 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 : Unsupervise transfer learning using DANN in Color contact lenses
¿µ¹®Á¦¸ñ : Unsupervise transfer learning using DANN in Color contact lenses
Àú³ÎÁ¤º¸ : BIGDAS2023   2023 ³â  11 ±Ç  1 È£  26  ~  29
³í¹®±¸ºÐ : ±¹¿ÜÇмú´ëȸ¹ßÇ¥³í¹®Áý
¸ÞÀÎÀúÀÚ : Ginam-Kim
¼­ºêÀúÀÚ : Kwan Hee-Yoo
Ű¿öµå : Domain adaptation, DANN, Color contact lens, Unsupervise Learning
Domain adaptation, DANN, Color contact lens, Unsupervise Learning
ȍȍ : This research was 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)
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¿µ¹®Á¦¸ñ : Design of Evacuation Direction Guidance System using Fire Location and Dijkstra...
Àú³ÎÁ¤º¸ : ºòµ¥ÀÌÅͼ­ºñ½ºÇÐȸ ³í¹®Áý   2023 ³â  7 ±Ç  1 È£  49  ~  57
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¼­ºêÀúÀÚ : ·ù°üÈñ
Ű¿öµå : IoT, Àç³­°¨Áö ¼¾¼­, ¾Ë°í¸®Áò
ȍȍ : The Ministry of Science and ICT (MSIT), Korea, under the Grand Information Technology Research Center support program (IITP-2023-2020-0-01462), supervised by the Institute for In-formation & Communications Technology Planning & Evaluation (IITP).
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¿µ¹®Á¦¸ñ : Deep Learning-based Vehicle Detection to Improve Intersection Queue Length Calcu...
Àú³ÎÁ¤º¸ : RICIC ÃæºÏ´ëÇб³ ÄÄÇ»ÅÍÁ¤º¸Åë½Å¿¬±¸¼Ò   2023 ³â  31 ±Ç  1 È£  25  ~  31
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¼­ºêÀúÀÚ : À±¼ºÃ¶, ·ù°üÈñ
Ű¿öµå : deep-learning, YOLOv5, data augmentation
deep-learning, YOLOv5, data augmentation
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ÇѱÛÁ¦¸ñ : Hybrid deep learning for efficient detection of recalled products
¿µ¹®Á¦¸ñ : Hybrid deep learning for efficient detection of recalled products
Àú³ÎÁ¤º¸ : BigDataService 2023   2023 ³â  1 ±Ç  1 È£  151  ~  152
³í¹®±¸ºÐ : ±¹¿ÜÇмú´ëȸ¹ßÇ¥³í¹®Áý
¸ÞÀÎÀúÀÚ : Hyo-Jae Yoo
¼­ºêÀúÀÚ : Kwan-Hee Yoo
Ű¿öµå : Recalled Product, E-Commerce, Object Detection, Image Feature, Measure Similarity
Recalled Product, E-Commerce, Object Detection, Image Feature, Measure Similarity
ȍȍ : This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Grand Information Technology Research Center support program (IITP-2023- 2020-0-01462), supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP).
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¿µ¹®Á¦¸ñ : Method for detecting small object using deep learning, and recording medium ther...
Àú³ÎÁ¤º¸ : ƯÇãû   2023 ³â  1 ±Ç  1 È£  1  ~  1
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¼­ºêÀúÀÚ : ·ù°üÈñ
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ȍȍ : GrandICT
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