Communication Security and Stability in NNCSs: Realistic DoS Attacks Model and ISTA-Supervised Adaptive Event-Triggered Controller Design.

This article addresses the challenge of achieving asymptotic stability in nonlinear networked control systems (NNCSs) amid denial-of-service (DoS) attacks, particularly under constrained communication resources. We begin by establishing a practical DoS attack model using the NSL-KDD dataset, which provides a realistic depiction of DoS attack dynamics based on real-world data. We then introduce the iterative shrinkage-thresholding algorithm (ISTA) to supervise the adaptive event-triggered controller (AETC), ensuring that system parameters are adjusted effectively while conserving communication resources.

Dynamic event-triggered synchronization control for neutral-type SMJ neural networks with additive delays under synchronized attacks.

This paper studies the double event-triggered synchronization (ETS) of neutral-type semi-Markovian jump (SMJ) neural networks under synchronous attacks. Firstly, synchronous attacks are modeled by an independent semi-Markovian jump process. Secondly, the double dynamic event-triggered mechanisms (DDETMs) introduced offer the advantage of conserving communication resources and alleviating the computational burden.

Fuzzy spatiotemporal event-triggered control for the synchronization of IT2 T-S fuzzy CVRDNNs with mini-batch machine learning supervision.

This paper is centered on the development of a fuzzy memory-based spatiotemporal event-triggered mechanism (FMSETM) for the synchronization of the drive-response interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy complex-valued reaction-diffusion neural networks (CVRDNNs). CVRDNNs have a higher processing capability and can perform better than multilayered real-valued RDNNs. Firstly, a general IT2 T-S fuzzy neural network model is constructed by considering complex-valued parameters and the reaction-diffusion terms.

Secure synchronization control for complex dynamic networks with event-triggered communication strategy under multi-channel denial-of-service attacks.

This article addresses the secure synchronization problem for complex dynamical networks (CDNs) with observer-based event-triggered communication strategy (ETCS) under multi-channel denial-of-service attacks (MCDSAs). Due to external environmental interference, the observers are designed to accurately estimate the state of the network systems. Meanwhile, the impact of cyber attacks on system security is considered.

DFA-mode-dependent stability of impulsive switched memristive neural networks under channel-covert aperiodic asynchronous attacks.

This article is concerned with the deterministic finite automaton-mode-dependent (DFAMD) exponential stability problem of impulsive switched memristive neural networks (SMNNs) with aperiodic asynchronous attacks and the network covert channel. First, unlike the existing literature on SMNNs, this article focuses on DFA to drive mode switching, which facilitates precise system behavior modeling based on deterministic rules and input characters. To eliminate the periodicity and consistency constraints of traditional attacks, this article presents the multichannel aperiodic asynchronous denial-of-service (DoS) attacks, allowing for the diversity of attack sequences.

Optimization control for mean square synchronization of stochastic semi-Markov jump neural networks with non-fragile hidden information and actuator saturation.

This paper studies the asynchronous output feedback control and H synchronization problems for a class of continuous-time stochastic hidden semi-Markov jump neural networks (SMJNNs) affected by actuator saturation. Initially, a novel neural networks (NNs) model is constructed, incorporating semi-Markov process (SMP), hidden information, and Brownian motion to accurately simulate the complexity and uncertainty of real-world environments. Secondly, acknowledging system mode mismatches and the need for robust anti-interference capabilities, a non-fragile controller based on hidden information is proposed.

Analysis of medical images super-resolution via a wavelet pyramid recursive neural network constrained by wavelet energy entropy.

Recently, multi-resolution pyramid-based techniques have emerged as the prevailing research approach for image super-resolution. However, these methods typically rely on a single mode of information transmission between levels. In our approach, a wavelet pyramid recursive neural network (WPRNN) based on wavelet energy entropy (WEE) constraint is proposed.

Residual Temporal Convolutional Network With Dual Attention Mechanism for Multilead-Time Interpretable Runoff Forecasting.

As a pivotal subfield within the domain of time series forecasting, runoff forecasting plays a crucial role in water resource management and scheduling. Recent advancements in the application of artificial neural networks (ANNs) and attention mechanisms have markedly enhanced the accuracy of runoff forecasting models. This article introduces an innovative hybrid model, ResTCN-DAM, which synergizes the strengths of deep residual network (ResNet), temporal convolutional networks (TCNs), and dual attention mechanisms (DAMs).

Parametric Stability Criteria for Delayed Recurrent Neural Networks via Flexible Delay-Dividing Method.

This article focuses on investigating the stability issue for recurrent neural networks (RNNs) with interval time-varying delays (TVDs) based on a flexible delay-dividing method with parameters, which are related to the delay derivative. First, an interval of delay is separated into parametric subintervals via the linear combination technique. Then, an establishment of Lyapunov-Krasovskii functional (LKF) is connected to the parameters, and a novel linear technology is suggested to dispose of integral terms in the derivatives of the constructed function.

Novel adaptive synchronization in finite-time and fixed-time for impulsive complex networks with semi-Markovian switching.

This paper intensively studied the finite-time (FNT) and fixed-time (FXT) synchronization issues for complex networks (CNs) with semi-Markovian switching and impulsive effect. The impulses are assumed to be independent of the semi-Markovian switching. Firstly, a unified FNT and FXT stability criterion of impulsive dynamical system with time-varying delays is extended by comparison principle.

Nonfragile power and frequency control in islanded microgrids under abnormal asynchronous stochastic cyber attacks.

In this paper, we focus on the real power sharing and frequency regulation of distributed generators in islanded microgrids with abnormal asynchronous stochastic cyber attacks, which is of great significance to the information security and stable operation of microgrids. Firstly, considering the possible cyber attacks in the communication network, a distributed non-fragile controller with coupled memory delay is proposed according to the nonperiodic sampled-data control. Then, the construction of delay-dependent two-sided looped-functional makes the Lyapunov-Krasovskii functional contain more delay and sampling information and relaxes constraints on free matrices.

Observer-Based Adaptive NN Security Control for Switched Nonlinear Systems Against DoS Attacks: An ADT Approach.

In this article, a novel switched observer-based neural network (NN) adaptive control algorithm is established, which addresses the security control problem of switched nonlinear systems (SNSs) under denial-of-service (DoS) attacks. The considered SNSs are described in lower triangular form with external disturbances and unmodeled dynamics. Note that when an attack is launched in the sensor-controller channel, the controller will not receive any message, which makes the standard backstepping controller not workable.

A hybrid event-triggered stabilization approach for switched systems under asynchronous switching and its application.

This paper investigates the stabilization problem of switched systems with mismatched modes by an event-triggered control approach. A hybrid event-triggered scheme (HETS) with a dynamically adjustable threshold is newly proposed, which combines periodic sampling, continuous event-trigger and slow switching. It is assumed that the modes and states of the controller are updated only at each triggering instant, so the situation of asynchronous switching could arise.

A super-resolution network for medical imaging via transformation analysis of wavelet multi-resolution.

In recent years, deep learning super-resolution models for progressive reconstruction have achieved great success. However, these models which refer to multi-resolution analysis basically ignore the information contained in the lower subspaces and do not explore the correlation between features in the wavelet and spatial domain, resulting in not fully utilizing the auxiliary information brought by multi-resolution analysis with multiple domains. Therefore, we propose a super-resolution network based on the wavelet multi-resolution framework (WMRSR) to capture the auxiliary information contained in multiple subspaces and to be aware of the interdependencies between spatial domain and wavelet domain features.

Performance Degradation Estimation Mechanisms for Networked Control Systems Under DoS Attacks and its Application to Autonomous Ground Vehicle.

This article examines the mechanisms by which aperiodic denial-of-service (DoS) attacks can exploit vulnerabilities in the TCP/IP transport protocol and its three-way handshake during communication data transmission to hack and cause data loss in networked control systems (NCSs). Such data loss caused by DoS attacks can eventually lead to system performance degradation and impose network resource constraints on the system. Therefore, estimating system performance degradation is of practical importance.

Optimal H tracking control of nonlinear systems with zero-equilibrium-free via novel adaptive critic designs.

In this paper, a novel adaptive critic control method is designed to solve an optimal H tracking control problem for continuous nonlinear systems with nonzero equilibrium based on adaptive dynamic programming (ADP). To guarantee the finiteness of a cost function, traditional methods generally assume that the controlled system has a zero equilibrium point, which is not true in practical systems. In order to overcome such obstacle and realize H optimal tracking control, this paper proposes a novel cost function design with respect to disturbance, tracking error and the derivative of tracking error.

AutoGMap: Learning to Map Large-Scale Sparse Graphs on Memristive Crossbars.

The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., social networks and knowledge graphs) on traditional computing architectures (CPU, GPU, or TPU).

Adaptive fixed-time output synchronization for complex dynamical networks with multi-weights.

This paper addresses fixed-time output synchronization problems for two types of complex dynamical networks with multi-weights (CDNMWs) by using two types of adaptive control methods. Firstly, complex dynamical networks with multiple state and output couplings are respectively presented. Secondly, several fixed-time output synchronization criteria for these two networks are formulated based on Lyapunov functional and inequality techniques.

Asynchronous control for semi-Markov switching systems with singular perturbation and an improved protocol.

This study addresses the asynchronous control problem for a semi-Markov switching system in the presence of singular perturbation and an improved triggering protocol. To decrease the occupation of network resources, an improved protocol is skillfully established by adopting two auxiliary offset variables. Unlike the existing protocols, the established improved protocol is capable of arranging information transmission with more degrees of freedom, thereby reducing the communication frequency and maintaining control performance.

Sedimentary structures of microbial carbonates in the fourth member of the Middle Triassic Leikoupo Formation, Western Sichuan Basin, China.

In recent years, abundant natural gas has been found in microbial carbonates in the fourth member of the Leikoupo Formation in Western Sichuan Basin. In this study, from the observation of 626 microbial thin sections, four types of microbial carbonates are classified based on the differences of mesostructures. Among them, thrombolites and stromatolites are subdivided into eight types based on the differences of microstructures.

Dual attentive fusion for EEG-based brain-computer interfaces.

The classification based on Electroencephalogram (EEG) is a challenging task in the brain-computer interface (BCI) field due to data with a low signal-to-noise ratio. Most current deep learning based studies in this challenge focus on designing a desired convolutional neural network (CNN) to learn and classify the raw EEG signals. However, only CNN itself may not capture the highly discriminative patterns of EEG due to a lack of exploration of attentive spatial and temporal dynamics.

Mode-Mixed Effects Based Intralayer-Dependent Impulsive Synchronization for Multiple Mismatched Multilayer Neural Networks.

This article focuses on the intralayer-dependent impulsive synchronization of multiple mismatched multilayer neural networks (NNs) with mode-mixed effects. Initially, a novel multilayer NN model that removes the one-to-one interlayer coupling constraint and introduces nonidentical model parameters is first established to meet diverse modeling requirements in complex applications. To help the multilayer target NNs with mismatched connection coefficients and time delays achieve synchronization, the hybrid controller is designed using intralayer-dependent impulsive control and switched feedback control approaches.

Practical synchronization of neural networks with delayed impulses and external disturbance via hybrid control.

This paper studies the problem of practical synchronization for delayed neural networks via hybrid-driven impulsive control in which delayed impulses and external disturbance are taken into account. Firstly, a switching method which establishes the relationship between error signals and a threshold function is introduced, which determines whether time-driven control or event-driven control is activated. Secondly, the effects of delayed impulses and external disturbance on impulsive systems are considered, and the corresponding comparison lemma is proposed.

Sampled-Data Control for Exponential Synchronization of Delayed Inertial Neural Networks With Aperiodic Sampling and State Quantization.

This article is devoted to dealing with exponential synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) under the framework of aperiodic sampling and state quantization. First, by taking the effect of aperiodic sampling and state quantization into consideration, a novel quantized sampled-data (QSD) controller with time-varying control gain is designed to tackle the exponential synchronization of INNs. Second, considering the available information of the lower and upper bounds of each HTVD, a refined Lyapunov-Krasovskii functional (LKF) is proposed.