Leakage assessment at the Register Transfer Level (RTL) is essential for identifying vulnerabilities in various designs, including cryptographic systems, AI models, and other applications handling sensitive data durin...
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This paper explores recent innovation in the field of robotic teleoperation, presenting a state-of-the-art system for a robotic arm, configurable as an exoskeleton or prosthetic limb. Based on noninvasive neural heads...
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The role of anomaly detection systems in Critical Infrastructures (CIs) is critical due to the complexity of CIs and their control systems, which are usually implemented by computer-based controllers that constantly p...
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In the process industries, it's hard to control a non-linear process. Nonlinear behavior is frequently seen in real processes. The challenging problem of controlling a spherical tank is result of its nonlinearity ...
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Hydrogen is an energy carrier that can support the development of sustainable and flexible energy systems. However, decarbonization can occur when green sources are used for energy production and appropriate water use...
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Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individua...
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In this letter, we introduce a novel anti-windup design approach for internal model control (IMC) that addresses the issue of asymmetric input saturation. To enhance closed-loop performance during periods of saturatio...
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Since the successful Apollo program, humanity is once again aiming to return to the Moon for scientific discovery, resource mining, and inhabitation. Upcoming decades focus on building a lunar outpost, with robotic sy...
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Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain ***,deep learning techniques have gained prominence as a central fo...
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Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain ***,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis ***,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault ***,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative *** complexity results in high computational costs and limited industrial *** tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault ***,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration ***,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global ***,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and *** study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
To solve the practical engineering problem that the angle of a construction robot with an arbitrary initial value completely and accurately track the desired trajectory, this paper presents a control strategy for exte...
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