Publications

A lossy source coding system based on the protograph low-density parity-check (P-LDPC) code is proposed for Gaussian source compression. In the proposed system, the conventional belief propagation (BP) algorithm is modified to be a concatenated BP-inverse BP (BP- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> BP) for encoding and decoding, where the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> BP is constructed by a fully-connected layer of a neural network. Compared to the existing approximate message passing algorithm, the proposed BP- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> BP realizes a float-to-bit compression with low complexity for arbitrary Gaussian sources. The BP- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> BP is implemented based on the linking relation of the protograph; therefore, it is necessary to optimally design the protograph to obtain better rate-distortion function (RDF) performance. Regarding the coding optimal procedure, a mutual information iteration convergence (MIIC) algorithm is designed as the optimal criterion to determine the source P-LDPC code with minimum distortion. Inspired by the plane construction of quantum stabilizer code, a lattice topological splicing (LTS) algorithm is proposed for regularly building the protograph to reduce the code searching complexity. By using the MIIC and the LTS algorithms, the BP- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> BP based on the designed P-LDPC code maintains good distortion performance close to the RDF limit.

The article deals with the security control stabilization problem of uncertain Markov jump power systems with input dead zone under stochastic denial‐of‐service (DoS) attack. DoS attack is modeled as a discrete‐time Markov process. Dual hidden Markov models are respectively used to detect the modes of the original power systems and the one under DoS attack. Based on the detected modes and neural networks (NNs), adaptive NN‐based security asynchronous control strategies are proposed, where both state feedback and output feedback are studied simultaneously. With the developed control laws, all trajectories of the closed‐loop systems are bounded stable in the stochastic setting. Simulation results demonstrate the correctness and usefulness of the proposed techniques.

No abstract is provided for this article.

This paper presents a stochastic feedback coupling strategy for almost sure exponential synchronization of networked harmonic oscillators. Differing from most of existing publications, in which noise is not beneficial to synchronization in complex dynamical networks, a stochastic coupling strategy is designed here by using discrete-time noisy sampled-data to achieve the almost sure exponential synchronization of networked harmonic oscillators. Both leaderless synchronization and leader-following synchronization of networked harmonic oscillators are considered. Some sufficient conditions are established to reach two types of synchronization by employing algebraic graph theory, Itô stochastic integral, stochastic control technique and mathematical analysis. Two numerical simulations are provided to illustrate the effectiveness of the theoretical results.

No abstract is provided for this article.

Network robustness is vital in both engineering and social systems to maintain reliability, resilience, and security against various disruptions such as malicious attacks, random failures, and cascading failures. This paper presents a framework called the robustness potential explorer (RPE) to facilitate the study of network robustness. The RPE framework comprises three components: RPE-F, which represents networks using extracted features; RPE-V, which provides visualization; RPE-P, which predicts network robustness enhancement potential. As a case study, the RPE is evaluated by integrating a set of 20 graph features as RPE-F, employing t-distributed stochastic neighbor embedding (t-SNE) as RPE-V, and utilizing three machine learning algorithms as RPE-P. In particular, RPE-V enables meaningful visualization for observing the robustness enhancement process, while RPE-P quantifies the robustness enhancement potential for the given network. Extensive experimental studies demonstrate that the proposed RPE outperforms two state-of-the-art CNN- and GNN-based schemes, with acceptably low prediction errors. These findings highlight the effectiveness of the RPE as a versatile tool for understanding, analyzing, and improving network robustness.

The sampled-data-based consensus control problem of multi-agent systems (MASs) with multiplicative noise and time-delays is studied in this paper. The MASs are given in a continuous-time setting while information exchange occurs only at the sampling instants. Therefore, the traditional methods for continuous-time or discrete-time models are not applicable. To resolve the problem, a discretization-reconnection method is developed to investigate the consensus control of first-order and second-order MASs, respectively. Some sufficient conditions, explicitly related to the control gains and the sampling step size, are established for both mean-square (m.s.) and almost sure (a.s.) consensus. Under these conditions, consensus can be achieved by designing appropriate control gains and the sampling step size for MASs with given noise intensity and time-delays. Numerical simulations verify the effectiveness of the theoretical results.

This paper proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances. After presetting multiple nominal process noise covariances and an initial measurement noise covariance, a variational Bayesian method and a fixed-point iteration method are utilized to jointly estimate the posterior state vector and the unknown noise covariances under a stochastic event-triggered mechanism. The proposed algorithm ensures low communication loads and excellent estimation performances for a wide range of unknown noise covariances. Finally, the performance of the proposed algorithm is demonstrated by tracking simulations of a vehicle.