Deep echo state networks (Deep-ESNs) play an important role in fault diagnosis. However, due to its limitation in the iterative process of dealing with nonlinear data, the accuracy of fault diagnosis is relatively low...
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Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a ...
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Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a need for a tactile sensor that can be deployed extensively across the robot's body.
Traditional consequent pole motor has the advantages of less magnetic costs, small magnetic resistance and strong inductance. It is widely studied in the design of the electrical propulsion system. However, it has the...
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Negative Bias Temperature Instability (NBTI) is the main reason for the degradation of gate oxide in P-channel power MOSFETs. Affected by long-term negative bias gate voltage and high temperature, the threshold voltag...
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This paper analyzes the stability problem of load frequency control (LFC) for power systems under uncertain transmission delays. First, an argumented LFC system model accounting for uncertainties in transmission delay...
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This work proposes an optimized voltage stabilization approach for electric vehicles (EVs) integrated with PV systems and a hybrid energy storage system (HESS). Specifically, the combination of Adaptive Penalty Model ...
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Reinforcement learning holds promise in enabling robotic tasks as it can learn optimal policies via trial and ***,the practical deployment of reinforcement learning usually requires human intervention to provide episo...
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Reinforcement learning holds promise in enabling robotic tasks as it can learn optimal policies via trial and ***,the practical deployment of reinforcement learning usually requires human intervention to provide episodic resets when a failure *** manual resets are generally unavailable in autonomous robots,we propose a reset-free reinforcement learning algorithm based on multi-state recovery and failure prevention to avoid failure-induced *** multi-state recovery provides robots with the capability of recovering from failures by self-correcting its behavior in the problematic state and,more importantly,deciding which previous state is the best to return to for efficient *** failure prevention reduces potential failures by predicting and excluding possible unsafe actions in specific *** simulations and real-world experiments are used to validate our algorithm with the results showing a significant reduction in the number of resets and failures during the learning.
Under the Bayesian restoration framework, this paper aims at the problem of the inadequate accuracy of the sparse solution under the traditional convex regularization constraint, which leads to the loss of texture det...
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Extracting 3D features from point cloud data is the central component for the automatic repair system of ancient ceramic fragments developed by us. This paper presents a local 3D point clouds descriptor for individual...
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Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method...
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Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.
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