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
[2024 Mar.] The article “Automated tariff design for energy supply-demand matching based on Bayesian optimization: Technical framework and policy implications” has been accepted in Energy Policy.
[2024 Mar.] The article “Collaborative policy learning for dynamic scheduling tasks in cloud-edge-terminal IoT networks using federated reinforcement learning” has been published in IEEE Internet of Things Journal. [Link]
[2024 Mar.] The article “Universal dynamic pilot allocation for beam alignment based on multi-armed bandits” has been published in IEEE Wireless Communications Letters. [Link]
[2023 Dec.] The article “IoT sensor and modulation scheduling for SWIPT using dual amplitude shift keying with double half-wave rectifier” has been published in IEEE Sensors Journal. [Link]
[2023 Dec.] The article “Universal dynamic pilot allocation for beam alignment based on multi-armed bandits” has been accepted in IEEE Wireless Communications Letters.
[2023 Oct.] The article “IoT sensor and modulation scheduling for SWIPT using dual amplitude shift keying with double half-wave rectifier” has been accepted in IEEE Sensors Journal.
[2023 Oct.] The article “Multi-residential energy scheduling under time-of-use and demand charge tariffs with federated reinforcement learning” has been published in IEEE Transactions on Smart Grid. [Link]
[2023 Oct.] The article “Collaborative policy learning for dynamic scheduling tasks in cloud-edge-terminal IoT networks using federated reinforcement learning” has been accepted in IEEE Internet of Things Journal. [Link]