Multi-axis robotic arms are extensively utilized in intelligent manufacturing scenarios, with trajectory control in flexible scenarios constituting a primary challenge. Physics-Informed Neural Networks (PINNs) represe...
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Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical information. However, for a classical P...
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Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical information. However, for a classical PGNN, the penalization of the physics-guided part is at the output level, which leads to a conservative result as systems with highly similar state-transition functions, i.e. only slight differences in parameters, can have significantly different time-series outputs. Furthermore, the classical PGNN cost function regularizes the model estimate over the entire state space with a constant trade-of hyperparameter. In this paper, we introduce a novel model augmentation strategy for nonlinear state-space model identification based on PGNN, using a weighted function regularization (W-PGNN). The proposed approach can efficiently augment the prior physics-based state-space models based on measurement data. A new weighted regularization term is added to the cost function to penalize the difference between the state and output function of the baseline physics-based and final identified model. This ensures the estimated model follows the baseline physics model functions in regions where the data has low information content, while placing greater trust in the data when a high informativity is present. The effectiveness of the proposed strategy over the current PGNN method is demonstrated on a benchmark example.
This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models...
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This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE
This paper,from the view of a defender,addresses the security problem of cyber-physical systems(CPSs)subject to stealthy false data injection(FDI)attacks that cannot be detected by a residual-based anomaly detector wi...
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This paper,from the view of a defender,addresses the security problem of cyber-physical systems(CPSs)subject to stealthy false data injection(FDI)attacks that cannot be detected by a residual-based anomaly detector without other defensive *** detect such a class of FDI attacks,a stochastic coding scheme,which codes the sensor measurement with a Gaussian stochastic signal at the sensor side,is proposed to assist an anomaly detector to expose the FDI *** order to ensure the system performance in the normal operational context,a decoder is adopted to decode the coded sensor measurement when received at the controller *** this detection scheme,the residual under the attack can be significantly different from that in the normal situation,and thus trigger an *** design condition of the coding signal covariance is derived to meet the constraints of false alarm rate and attack detection *** minimize the trace of the coding signal covariance,the design problem of the coding signal is converted into a constraint non-convex optimization problem,and an estimation-optimization iteration algorithm is presented to obtain a numerical solution of the coding signal covariance.A numerical example is given to verify the effectiveness of the proposed scheme.
Dear Editor,In this letter,in order to deal with random network delays and packet losses in a class of networked nonlinear systems,three data-driven networked predictive control methods are *** closed-loop systems and...
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Dear Editor,In this letter,in order to deal with random network delays and packet losses in a class of networked nonlinear systems,three data-driven networked predictive control methods are *** closed-loop systems and control increments are derived,respectively.
Electronic medical records and doctor-patient conversations contain a wealth of useful information, such as disease symptoms, drug names, and cure cycles. Traditional deep learning approaches utilize bidirectional rec...
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Path planning is one of the most critical links in mobile robots. Its timeliness, security and accessibility are crucial to the development and wide application of mobile robots. However, in solving the problem of pat...
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The problem of solving discrete-Time Lyapunov equations (DTLEs) is investigated over multiagent network systems, where each agent has access to its local information and communicates with its neighbors. To obtain a so...
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Stereo vision plays a crucial role in scientific expeditions and facility maintenance. However, fixed-baseline stereo vision lacks accuracy for long-distance measurements, while wide-baseline stereo vision encounters ...
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The paper investigates a microoptoelectromechanical accelerometer that implements optical measuring transducer built on the optical tunneling effect. The article discusses the issues of micromechanical sensing element...
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