Abstract: The latest evolution in power systems, is ‘smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the ...
Abstract: By using the nonvolatile and stochastic properties of memristor, memristors crossbar arrays can efficiently accelerate Bayesian neural networks (BNNs). However, Bayesian convolutional neural ...
Abstract: Electric vehicle (EV) users’ behaviors are influenced by users’ willingness, which is not directly observable. Emerging large language models (LLMs) have advantages in handling this problem.
Abstract: Background: Adaptive gait trajectory prediction is essential to achieve natural and stable locomotion in prosthetic limbs and legged robots, particularly under varied conditions such as ...
Abstract: Traffic accidents are recurring ordinary disruptive events in an urban road network. To measure the performance and recovery of an urban road network, it is necessary to identify and predict ...
Abstract: For Bayesian network structure learning with continuous data, traditional methods typically require data discretization or assume that the data follows a Gaussian distribution. However, the ...
Abstract: Compressive sensing (CS) algorithms have demonstrated superior direction-of-arrival (DoA) estimation accuracy in the low signal-to-noise ratio (SNR) regime by exploiting inherent angular ...
Abstract: To address the problem of sea ice collisions threatening offshore drilling operations in polar regions, this paper proposes a Bayesian network-based collision risk assessment model for ...
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