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Park Sangjun
Department
Energy Intelligence Research Section
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Type Year Title Cited Download
Journal
2024 A Training/Inference Strategy based on Restart: Improvement of Performance of Koopman Autoencoder for Future Estimation   박상준   전기학회논문지, v.73, no.8, pp.1376-1383 0
Conference
2024 Development of Large Language Model for field of electricity based on Retrieval-Augmented Generation   이준기   대한전기학회 학술 대회 (하계) 2024, pp.2063-2064
Conference
2024 Physics Prediction with Koopman Autoencoder based on Multi-Scale Time Resolution   황유민   통신 정보 합동 학술 대회 (JCCI) 2024, pp.517-517
Journal
2024 Technical Trends in Artificial Intelligence for Robotics Based on Large Language Models   이준기   전자통신동향분석, v.39, no.1, pp.95-105
Conference
2023 An Introduction to the Method of Energy System Modeling using the Koopman Operator   박상준   한국 에너지 학술 대회 2023, pp.45-46
Journal
2023 Enhanced Prediction Model for Human Activity Using an End-to-End Approach   Sangjun Park   IEEE Internet of Things Journal, v.10, no.7, pp.6031-6041 0
Conference
2022 A study on the sleep quality analysis of single-person households based on life pattern data using ambient sensors   Jungi Lee   International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1879-1881 1
Journal
2021 Deep Learning for Human Activity Recognition Based on Causality Feature Extraction   Yu Min Hwang   IEEE Access, v.9, pp.112257-112275 10
Conference
2021 Performance Comparison of Abnormality Diagnosis Model for Complex Sensor Data   김낙우   한국통신학회 종합 학술 발표회 (동계) 2021, pp.819-820
Conference
2020 Machine Learning-based Analysis of Correlation between Energy Consumption Data of the Company and its Sales   Jungi Lee   International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1258-1260 0
Conference
2020 Acoustic Feature based Abnormal Diagnosis Techniques with Capsule Networks   NacWoo Kim   International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1147-1149 1
Conference
2020 Introduction to an Adaptive-Activity Recognition Method using a Probabilistic Finite State Machine   박상준   대한전자공학회 학술 대회 (하계) 2020, pp.2484-2487