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 Nov.] The article “Beam alignment in non-stationary environments using a novel time-varying structured bandit” has been accepted in IEEE Transactions on Vehicular Technology.
[2024 Sept.] The article “A review on label cleaning techniques for learning with noisy labels” has been accepted in ICT Express.
[2024 Jul.] The article “Time series explanatory fault prediction framework for marine main engine using explainable artificial intelligence” has been published in Journal of Marine Science and Engineering. [Link]
[2024 Jul.] The article “Early exiting-aware joint resource allocation and DNN splitting for multi-sensor digital twin in edge-cloud collaborative systems” has been accepted in IEEE Internet of Things Journal. [Link]
[2024 Jul.] The article “To exit or not to exit: Cost-effective early exit architecture based on Markov decision process” has been published in Mathematics. [Link]
[2024 May] The article “Automated tariff design for energy supply-demand matching based on Bayesian optimization: Technical framework and policy implications” has been published in Energy Policy. [Link]
[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]