Existing deep-learning models for transformer fault diagnosis often struggle with feature decoupling, limiting their accuracy under multidimensional, nonlinear, and strongly coupled operating conditions. To address this, we propose an optimized Deep Belief Network (DBN) architecture. Unlike conventional DBNs, our model...
Yong-Hao Zhang, Gengxin Ding, Fei-Ran Sun et al.· Journal of Physics, Conferen...· 0 citations
Smart charging stations require mobile charging robots to respond to dynamically arriving charging requests with heterogeneous priorities, varying travel costs, and uneven workloads while maintaining online scheduling feasibility. Conventional single-layer approaches often optimize task assignment or route ordering sep...
Meiyu Chang, Zhaoyu Ku, Xuan-Yu Xing et al.· Machines· 0 citations
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