MAchine Intelligence and Networking (MAIN) lab에서는 지능형 의사결정, 설명 가능한 인공지능 등의 기계학습/인공지능 관련 분야에 관한 연구와 함께, 인공지능 기술을 무선 통신 네트워크, 디지털트윈/메타버스, 스마트그리드 등 다양한 분야에 응용하는 연구를 수행하고 있습니다.
MAchine Intelligence and Networking (MAIN) lab develops machine learning (ML) methods for intelligent decision-making and explainable AI and applies AI/ML for various domains such as wireless networks, digital twins/metaverse, and smart grids.
Recent News
[2023 Aug.] The article “A novel time-frequency feature fusion approach for robust fault detection of marine main engine” has been accepted and published in Journal of Marine Science and Engineering. [Link]
[2023 June] The article “Packet-based fronthauling in 5G networks: Network slicing-aware packetization” has been published in IEEE Communications Standards Magazine. [Link]
[2023 June] The article “Meta-scheduling framework with cooperative learning towards beyond 5G” has been published in IEEE Journal on Selected Areas in Communications. [Link]
[2023 Apr.] The article “Robust energy management system with safe reinforcement learning using short-horizon forecasts” has been published in IEEE Transactions on Smart Grid. [Link]
[2023 Apr.] The article “Meta-scheduling framework with cooperative learning towards beyond 5G” has been accepted in IEEE Journal on Selected Areas in Communications. [Link]
[2023 Feb.] The article “Multi-residential energy scheduling under time-of-use and demand charge tariffs with federated reinforcement learning” has been accepted in IEEE Transactions on Smart Grid.
[2023 Jan.] The article “Robust energy management system with safe reinforcement learning using short-horizon forecasts” has been accepted in IEEE PES letters (to be published in IEEE Transactions on Smart Grid).
[2022 Dec.] The paper “Device selection and resource allocation for layerwise federated learning in wireless networks” has been published in IEEE Systems Journal.