Zhiyuan Zhang¹, Meng Shuai², Bin Wang¹, Ying He³, Fan Yang¹, Liyan Ren^4,*, Yuyuan Zhang⁴, Ziren Wang⁴

Energy Engineering, Vol.122, No.10, pp. 4169-4194, 2025, DOI:10.32604/ee.2025.066793 - 30 September 2025

Abstract With the rapid development of renewable energy, the Microgrid Coalition (MGC) has become an important approach to improving energy utilization efficiency and economic performance. To address the operational optimization problem in multi-microgrid cooperation, a cooperative game strategy based on the Nash bargaining model is proposed, aiming to enable collaboration among microgrids to maximize overall benefits while considering energy trading and cost optimization. First, each microgrid is regarded as a game participant, and a multi-microgrid cooperative game model based on Nash bargaining theory is constructed, targeting the minimization of total operational cost under constraints such as More >

Taiyu Yan¹, Yuxin Cao¹, Guoxia Xu¹, Xiaoran Zhao², Hu Zhu¹, Lizhen Deng^3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3741-3764, 2023, DOI:10.32604/cmc.2023.039828 - 26 December 2023

Abstract Unmanned Aerial Vehicle (UAV) tracking has been possible because of the growth of intelligent information technology in smart cities, making it simple to gather data at any time by dynamically monitoring events, people, the environment, and other aspects in the city. The traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking operations. But these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization terms. In light of the aforementioned issues, this work suggests a dual-domain Jensen-Shannon divergence… More >

Yangyang Li¹, Xue Wang², Weiwei Fang^2,*, Feng Xue², Hao Jin¹, Yi Zhang¹, Xianwei Li³

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 493-508, 2019, DOI:10.32604/cmc.2019.05178

Abstract With the recent proliferation of Internet-of-Things (IoT), enormous amount of data are produced by wireless sensors and connected devices at the edge of network. Conventional cloud computing raises serious concerns on communication latency, bandwidth cost, and data privacy. To address these issues, edge computing has been introduced as a new paradigm that allows computation and analysis to be performed in close proximity with data sources. In this paper, we study how to conduct regression analysis when the training samples are kept private at source devices. Specifically, we consider the lasso regression model that has been More >