Junyong Zhai

1. The Euler–Lagrange (E-L) system denotes a mechanical system that employs the E-L model to describe and analyse the system dynamics, such as robotic manipulator system and unmanned aerial vehicle...

This article investigates the security issue of malicious adversaries undermining the remote estimator over cyber–physical systems (CPSs). A critical-data-oriented (CDO) attack strategy is proposed to enable attackers to utilize essential data judiciously during data transmission, thereby degrading remote state estimation maximally. In addition, the proposed scheme is compared with commonly studied attack strategies such as Bernoulli-distributed and periodic denial-of-service (DoS) attacks within a unified framework. The evaluation of its impact focuses on two factors: the weighting matrix (WM) and the residual information. Furthermore, the consideration of attack energy distribution (AED) and quality of service (QoS) constraints is merged. The theoretical analysis based on stochastic methods reveals the inherent associations among WM, attack energy, and signal-to-interference-plus-noise ratio (SINR). The attack impact on channel throughput is analyzed and formulated as a convex optimization problem. The extended part explores the selection of optimal WM under special scenarios. Finally, two examples are established to validate the correctness and practicality of the concerned CDO attack strategy.

Causal Disentangled Representation Learning(CDRL) aims to learn and disentangle low dimensional representations and their underlying causal structure from observations. However, existing disentanglement methods rely on a standard mean-field approximation with a diagonal posterior covariance, which decorrelates all latent dimensions. Additionally, these methods often assume isotropic Gaussian priors for exogenous noise, failing to capture the complex, non-Gaussian statistical properties prevalent in real-world causal factors. Therefore, we propose FlexCausal, a novel CDRL framework based on a block-diagonal covariance VAE. FlexCausal utilizes a Factorized Flow-based Prior to realistically model the complex densities of exogenous noise, effectively decoupling the learning of causal mechanisms from distributional statistics. By integrating supervised alignment objectives with counterfactual consistency constraints, our framework ensures a precise structural correspondence between the learned latent subspaces and the ground-truth causal relations. Finally, we introduce a manifold-aware relative intervention strategy to ensure high-fidelity generation. Experimental results on both synthetic and real-world datasets demonstrate that FlexCausal significantly outperforms other methods.

This study describes the potential of computer vision (CV) approaches for liver tumor classification. Two-dimensional (2D) computed Tomography (CT) images dataset of benign and malignant liver tumors that were cholangiocarcinoma, focal nodular hyperplasia, hepatic adenoma, hemangioma, hepatoblastoma, and hepatocellular carcinoma was acquired for this study. The CT dataset comprising 150 images, each sized at (512 × 512), encompassing various types of liver tumors. This dataset consisted of a total of 900 (150 × 6) CT images representing six benign and malignant liver tumor types. To enhance data quality, a Mean filter was applied for noise reduction, followed by the selection of two regions of interest (ROIs) from each liver image. Subsequently, the preprocessed data was subjected to feature extraction, resulting in 67 multi-features per image, incorporating histogram, spectral, and texture features. From these features, 21 optimized multi-features were derived through the implementation of a correlation-based feature selection (CFS) algorithm. These optimized multi-features formed the basis for analysis and were fed into six classifiers: multilayer perceptron (MLP), logistic regression, random subspace, decision tree, produce error reduction, and multiclass classifier. The performance evaluation of these classifiers was conducted using 10-fold cross-validation techniques. The MLP showed a better accuracy of 97.67% on the optimized feature dataset among all the deployed CV classifiers. The experimental findings indicated that the suggested approach was systematic and resilient, offering valuable assistance to radiologists in detecting liver tumor diseases through CT Dataset images, even amid differing imaging standards.

In this paper, an exquisite strategic denial-of-service (DoS) attack mechanism is proposed, whose objective is to intercept specific measurements obeying the Round-Robin protocol (RRP) and enlarge the attack destructiveness at the side of the remote estimator. The distinctive contributions of this research can be stressed as follows: firstly, in contrast to the majority of existing DoS attack policies, the property of the considered attack sequence is stochastic, selectively targeting information crucial to the transmission process. Secondly, the concerned attack mechanism yields great disruption compared to the periodic or Bernoulli-distributed DoS attack models. Additionally, the joint consideration of the limited average attack rate (AAR) and energy allocation procedure (EAP) reflecting energy constraints of DoS attacks, are merged into a unified random framework. The relationship between these constraints and attack probability is established by resorting to stochastic analysis methods. Finally, two numerical examples are provided to demonstrate the effectiveness and usefulness of the proposed attack policy.

This article considers the problem of output‐based dual dynamic periodic decentralized event‐triggered control (DPDETC) for nonlinear interconnected cyber‐physical systems (CPSs) subject to deception attacks. Taking into account the conflict between the limited network resources and the growing bandwidth demand, a dual dynamic periodic event‐triggered scheme (DPETS) is designed to reasonably decrease unnecessary data transmission, which includes sensor‐to‐controller (S‐C) and controller‐to‐actuator (C‐A) communication channels. For resisting the impact of attacks, a deception attacks model following Bernoulli distribution is introduced for nonlinear interconnected CPSs. Then, a new decentralized sampled‐data observer is proposed based on output‐dependent periodic event‐triggered conditions, such that the system output under deception attacks can be estimated. By means of decentralized observer and observer‐dependent DPETS, a new decentralized output feedback controller is established. The designed DPDETC strategy can guarantee that the nonlinear interconnected CPSs are mean square stability. Finally, simulation results are performed to validate the superiority of the designed DPDETC scheme.