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This article addresses the state estimator design issue of interval type-2 (IT2) fuzzy systems under deception attacks. The permission of sensors to access the network is determined by a stochastic protocol obeying Markov scheduling rules. An attack-resilient IT2 fuzzy state estimator is proposed to mitigate the adverse effect of deception attacks on estimation performance. By using the known bounds of system membership functions and introducing two new weighting functions, the MFs of fuzzy state estimator are reconstructed. A hidden Markov model is employed to describe the asynchronous behaviour of scheduling rules between the sensor and the estimator. Sufficient conditions are derived to ensure the ultimate boundedness of estimation error. Finally, a numerical example demonstrates the applicability of the proposed attack-resilient asynchronous state estimation method. [ABSTRACT FROM AUTHOR]

A higher-order iterative learning control (ILC) is developed for nonlinear continuous-time systems with input saturation, the process of which consists of input-driven part and the free part. In the time interval of the input-driven part, the controlled system operates according to the imposed control input. Then, it executes autonomously in the rest of the time interval, which is regarded as the free part. Thus, the developed higher-order ILC law pursues the desired trajectory tracking within the time interval affected by control input. Based on the assumption that the initial state is variable around the desired one within a bound, the ILC tracking errors can be driven into a range whose bound is proportional to the initial state vibration. As a special case, when the initial state equals the desired initial state, the ILC tracking errors are controlled to zero as the iteration number tends to infinity. Robustness and convergence analysis of the proposed higher-order ILC law are provided against the initial states vibration. Simulation results are given to illustrate the effectiveness of the presented higher-order ILC scheme. [ABSTRACT FROM AUTHOR]

Industrial control systems (ICSs), as critical national infrastructures, are increasingly susceptible to sophisticated security threats. To address this challenge, our study introduces the CAE-T, a deep convolutional autoencoding transformer network designed for efficient anomaly detection and real-time fault monitoring in ICS. The CAE-T utilizes unsupervised deep learning, employing a convolutional autoencoder for spatial feature extraction from multidimensional time-series data, and combines this with a transformer architecture to capture long-term temporal dependencies. The design of the model facilitates rapid training and inference, while its dual-component approach, utilizing an optimization function based on support vector data description (SVDD), enhances detection accuracy. This integration synergistically combines spatiotemporal feature extraction, significantly improving the robustness and precision of anomaly detection in ICS environments. The CAE-T model demonstrated notable performance enhancements across three industrial control system datasets. Notably, the CAE-T model achieved approximately a 70.8% increase in F1 score and a 9.2% rise in AUC on the WADI dataset. On the SWaT dataset, the model showed improvements of approximately 2.8% in F1 score and 5% in AUC. The power system dataset saw more modest gains, with an approximately 0.1% uptick in F1 score and a 1% increase in AUC. These improvements validate the CAE-T model's efficacy and robustness in anomaly detection across various scenarios. [ABSTRACT FROM AUTHOR]

In recent decades, THz metamaterials have emerged as a promising technology for biosensing by extracting useful information (composition, structure and dynamics) of biological samples from the interaction between the THz wave and the biological samples. Advantages of biosensing with THz metamaterials include label-free and non-invasive detection with high sensitivity. In this review, we first summarize different THz sensing principles modulated by the metamaterial for bio-analyte detection. Then, we compare various resonance modes induced in the THz range for biosensing enhancement. In addition, non-conventional materials used in the THz metamaterial to improve the biosensing performance are evaluated. We categorize and review different types of bio-analyte detection using THz metamaterials. Finally, we discuss the future perspective of THz metamaterial in biosensing. [ABSTRACT FROM AUTHOR]