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A distributed H ∞ filtering issue is addressed in this paper for discrete-time nonlinear systems in the face of replay attacks over sensor networks, where an indicator variable is introduced to describe whether a replay attack is launched by the adversaries. First, an interesting pattern dependent on three parameters including a time-varying one is established to account for the temporal behavior of malicious attacks. Then, taking advantage of such a model, the resulting filter dynamic is transformed into a switching system with a subsystem with time-varying delays. By means of the famous switching system theory, a sufficient condition guaranteeing the H ∞ performance is derived to disclose the tolerant attack condition, that is, the attack-active duration and proportion. In addition, the applicable filter gains are achieved with the aid of the solutions of matrix inequalities. Finally, an example is purposively given to adequately illustrate the availability of the developed secure filtering strategy. [ABSTRACT FROM AUTHOR]

The paper investigates secure filtering of nonlinear large-scale systems suffering from randomly occurring DoS attacks. By introducing an adjustable parameter, an adaptive event-triggering mechanism is proposed for the sake of decreasing the transmission burden of signals, where the memory is utilized to reflect the influence of past triggered information. The main objective is to design an event-based secure filter to ensure that the dynamics of filtering errors is input-to-state stable in the mean square. Using the constructed Lyapunov function, a sufficient condition is derived where some element matrix inequalities are utilized to handle the inherent coupling of subsystems. Furthermore, the desired filter gains are parameterized by resorting to the feasibility of matrix inequalities. Finally, a numerical simulation about a power system is provided to verify the effectiveness of the developed secure filtering algorithm. • An adaptive event-triggered scheme with memory mechanisms is proposed. • Sufficient conditions are obtained to achieve the input-to-state stability. • The filter parameters are acquired by virtue of the matrix decoupling technique. [ABSTRACT FROM AUTHOR]

The presence of user-generated ratings has dramatically facilitated the development of recommendation systems to aid users in discovering relevant and personalized points of interest (POI). It is worth mentioning that users' choices and preferences are not static but rather dynamic, reflecting the ever-changing nature of human experiences and influences. Furthermore, the utilization of social influence and geographical proximity of users is still insufficient to capture the homophily effect within networks. In this paper, an interesting Hybrid Gate-based Graph Convolutional Network (HyGate-GCN) combining with feature vectors embedding and interaction, where a modified gated-GCN is proposed for personalized recommendations by adequately employing the behavior of users' check-ins, temporal properties of users' decisions, social properties of users, as well as the user/POI profile information data. Specifically, a novel POI graph reflecting the geographical proximity is first established to describe the behavior of users' check-ins and, at the same time, an improved overlap ratio about POIs is employed to effectively describe temporal properties of users' decisions. Then, an attention mechanism is developed to encode feature vectors of both the users and POIs, with the objective of assigning higher importance to features that are deemed relevant. Furthermore, a temporal Kalman filter dynamically estimating ratings is developed to exploit the information about the evolving preferences of users over time. Finally, a modified gated-GCN model with merging and refining gates is constructed to effectively acquire the homophily phenomenon in both trust network graphs and spatial adjacency matrix graphs of users and POIs respectively. Experimental results provide evidence of the effectiveness of our approach in improving accuracy and personalization. • A POI graph is established to describe the behavior of users' check-ins. • An improved overlap ratio reflects the temporal properties of users' decisions. • A Kalman filter estimates ratings to acquire the evolution of users' preferences. • An improved gated-GCN model is proposed to acquire the homophily phenomenon. • A HyGate-GCN model is developed via feature interaction and the improved Gate-GCN. [ABSTRACT FROM AUTHOR]

• A global–local attention module is developed to expand the receptive fields of models while increasing the remote dependence on semantic information. • A reparametrized feedforward network is adopted to relax the coupling between feature maps and also improve the local information extraction. • A dense multiscale module is redesigned to mitigate the semantic bias in the skip connection and decoders. • An FC-UNETTR network with the above modules is proposed for medical image segmentation. The Transformer-based methods are still unable to effectively model local contexts although they make up for the deficiency of remote information dependencies for approaches based on small kernel CNNs. To overcome such a shortage, this paper proposes a novel full-convolution UNet Transformer model, FC-UNETTR, for medical image segmentation. First, a novel global–local attention module is proposed by utilizing multiple small kernels of different sizes for depth-wise convolutions to expand the receptive field of the network model, increase the remote dependence of semantic information in the encoder stage, and also improve the feature extraction capability of the network for fuzzy edges. Then, a reparametrized feedforward network is developed to further improve the local information extraction and mitigate the coupling between feature maps such that the relationship between feature map channels can be better revealed. Furthermore, the skip connection and decoder are redesigned by constructing a dense multiscale module instead of traditional ResNet modules to mitigate semantic bias. Benefiting from the above improvements, the constructed FC-UNETTR without pre-training demonstrates a strong capability to extract local features and capture long-range dependencies of images in medical image segmentation. Experiments show that FC-UNETTR achieves an excellent performance of 85.67% for DSC and 7.82 for HD metrics on the Synapse dataset with fewer model parameters compared with state-of-the-art networks. Furthermore, DSC reaches 92.46% and 94.76% on the ACDC dataset and the private dataset of oral graft bone, respectively, outperforming some of the latest medical image segmentation models.. [ABSTRACT FROM AUTHOR]

This paper is concerned with the neural-network-based (NN-based) output-feedback control issue for a class of nonlinear systems. For the purpose of effectively mitigating the phenomena of data congestion/collision, the stochastic communication protocols are favorably utilized to orchestrate the data transmissions, and the resultant closed-loop plant is represented by a so-called protocol-induced Markovian jump system with uncertain transition probability matrices. Taking such an uncertainty probability into account, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to obtain the desired suboptimal solution with the help of auxiliary quasi-HJB equation, and the algorithm convergence is also investigated via the intensive use of the mathematical analysis. In this ADP framework, an NN-based observer with a novel adaptive tuning law is first adopted to reconstruct the system states. Then, based on the reconfigurable system, an actor–critic NN scheme with online learning is developed to realize the considered control strategy. Furthermore, in light of the Lyapunov theory, some sufficient conditions are derived to guarantee the stability of the zero equilibrium point of the closed-loop system as well as the boundedness of the estimation errors for critic and actor NN weights. Finally, a simulation example is employed to demonstrate the effectiveness of the developed suboptimal control scheme. [ABSTRACT FROM AUTHOR]

State estimation and synchronization of neural networks (NNs) have recently received ever-increasing research interests due to a large number of successful applications in various fields such as repetitive learning, classification of patterns, nonlinear control, adaptive control, image processing, and so forth. Owing to limited communication bandwidth, the network-induced phenomena and the adopted communication scheduling may cause inevitable negative effects or the degradation of dynamical performance of NNs. This paper, from the perspective of dynamical behavior, renders a comprehensive summary on the recent advances of communication-embedded NNs and their application in nonlinear control. First, some common network-induced phenomena and communication protocols are roughly introduced from both models and mechanisms. Then, the state estimation and synchronization analysis of various NNs with network-induced phenomena are systematically reviewed and some interesting results are separately presented. Furthermore, the latest advances for the cases with communication protocols are profoundly surveyed from three aspects: state estimation, synchronization analysis and applications in control realm. Finally, some challenged problems are listed and several potential future research directions are highlighted. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPSs), which are an integration of computation, networking, and physical processes, play an increasingly important role in critical infrastructure, government and everyday life. Due to physical constraints, embedded computers and networks may give rise to some additional security vulnerabilities, which results in losses of enormous economy benefits or disorder of social life. As a result, it is of significant to properly investigate the security issue of CPSs to ensure that such systems are operating in a safe manner. This paper, from a control theory perspective, presents an overview of recent advances on security control and attack detection of industrial CPSs. First, the typical system modeling on CPSs is summarized to cater for the requirement of the performance analysis. Then three typical types of cyber-attacks, i.e. denial-of-service attacks, replay attacks, and deception attacks, are disclosed from an engineering perspective. Moreover, robustness, security and resilience as well as stability are discussed to govern the capability of weakening various attacks. The development on attack detection for industrial CPSs is reviewed according to the categories on detection approaches. Furthermore, the security control and state estimation are discussed in detail. Finally, some challenge issues are raised for the future research. [ABSTRACT FROM AUTHOR]

This paper investigates the design and analysis problem of H ∞ filter for a class of nonlinear systems based on T-S fuzzy models with both multiplicative noises and redundant channels, which are governed by a set of Bernoulli distributed white sequences. The packet dropout’s probability of the i -th channel depends on random Bernoulli variables. The aim of this study is to design and analyze an H ∞ filter that can stabilize the T-S fuzzy filtering error dynamics. By utilizing both Lyapunov functional approach and stochastic analysis technique, we establish some sufficient conditions such that the addressed system is asymptotically stable in the mean square with a given H ∞ performance. The needed filtering parameters are obtained by making use of the matrix inequalities’ solution. In the end, an example is given to demonstrate the effectiveness and usefulness of the proposed filtering approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed recursive filtering problem for a class of discrete time-delayed stochastic systems subject to both uniform quantization and deception attack effects on the measurement outputs. The target plant is disturbed by the multiplicative as well as additive white noises. A novel distributed filter is designed where the available innovations are from not only the individual sensor but also its neighboring ones according to the given topology. Attention is focused on the design of a distributed recursive filter such that, in the simultaneous presence of time-delays, deception attacks and uniform quantization effects, an upper bound for the filtering error covariance is guaranteed and subsequently minimized by properly designing the filter parameters via a gradient-based method at each sampling instant. Furthermore, by utilizing the mathematical induction, a sufficient condition is established to ensure the asymptotic boundedness of the sequence of the error covariance. Finally, a simulation example is utilized to illustrate the usefulness of the proposed design scheme of distributed filters. [ABSTRACT FROM AUTHOR]

In this paper, a new design problem of the H ∞ state estimator is developed for a kind of artificial neural networks (ANNs), where both infinite distributed delays and redundant channels are happening. These adopted redundant channels can effectively improve the reliability of networked systems from the viewpoint of engineering. Two series of stochastic variables satisfying Bernoulli distribution, are introduced to govern the infinite distributed delays and schedule the redundant channels. By utilizing both stochastic analysis and Lyapunov functional approach, we obtain a lot of sufficient conditions to ensure the desired H ∞ performance, while the mean-square stability is also satisfied for this investigated state estimation issues of ANNs. The needed estimator gains are designed making use of the matrix inequalities’ solution. In final, a simulation is showed to demonstrate the effectiveness and usefulness of the developed state estimator in this paper. [ABSTRACT FROM AUTHOR]

This paper is concerned with the attack scheduling of deception attacks for discrete-time systems with attack detection. Specifically, for a class of Kalman filters with χ 2 detectors, an attacker with the given attack task needs to decide how many the maximum number is or how much the attack probability is for different kinds of attack scenarios (i.e. consecutive deception attacks or randomly launched deception attacks). Firstly, in light of the property of χ 2 distribution, the predictions of detection probabilities under attacks are calculated in mean-square sense for these two attack scenarios. Then, in terms of predicted probabilities, the deception attack schemes are designed in the framework of Kalman filtering. For the case of consecutive attacks, the maximum number of attacks is determined via recursive Riccati-like difference equations. For the case of randomly launched deception attacks, the desired attack probability is obtained by solving a Riccati-like equation. Finally, a numerical example on target tracking is presented to demonstrate the effectiveness of the proposed suboptimal attack schemes. [ABSTRACT FROM AUTHOR]

This paper is concerned with the event-triggered consensus control problem for a class of discrete-time stochastic multi-agent systems with state-dependent noises. A novel definition of consensus in probability is proposed to better describe the dynamics of the consensus process of the addressed stochastic multi-agent systems. The measurement output available for the controller is not only from the individual agent but also from its neighboring ones according to the given topology. An event-triggered mechanism is adopted with hope to reduce the communication burden, where the control input on each agent is updated only when a certain triggering condition is violated. The purpose of the problem under consideration is to design both the output feedback controller and the threshold of the triggering condition such that the closed-loop system achieves the desired consensus in probability. First of all, a theoretical framework is established for analyzing the so-called input-to-state stability in probability (ISSiP) for general discrete-time nonlinear stochastic systems. Within such a theoretical framework, some sufficient conditions on event-triggered control protocol are derived under which the consensus in probability is reached. Furthermore, both the controller parameter and the triggering threshold are obtained in terms of the solution to certain matrix inequalities involving the topology information and the desired consensus probability. Finally, a simulation example is utilized to illustrate the usefulness of the proposed control protocol. [ABSTRACT FROM AUTHOR]

In this paper, the envelope-constrained H ∞ filtering problem is investigated for a class of discrete time-varying stochastic systems over a finite horizon. The system under consideration involves fading measurements, randomly occurring nonlinearities (RONs) and mixed (multiplicative and additive) noises. A novel envelope-constrained performance criterion is proposed to better quantify the transient dynamics of the filtering error process over the finite horizon. The purpose of the problem addressed is to design a time-varying filter such that both the H ∞ performance and the desired envelope constraints are achieved at each time step. By utilizing the stochastic analysis techniques combined with the ellipsoid description on the estimation errors, sufficient conditions are established in the form of recursive matrix inequalities (RMIs) reflecting both the envelope information and the desired H ∞ performance index. The filter gain matrix is characterized by means of the solvability of the deduced RMIs. Finally, a simulation example is provided to show the effectiveness of the proposed filtering design scheme. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the consensus control problem for a class of discrete time-varying multi-agent systems with both missing measurements and parameter uncertainties. A directed graph is used to represent the communication topology of the multi-agent network, and a binary switching sequence satisfying a conditional probability distribution is employed to describe the missing measurements. The purpose of the addressed problem is to design a time-varying controller such that, for all probabilistic missing observations and admissible parameter uncertainties, the consensus performance is guaranteed over a given finite horizon for the closed-loop networked multi-agent systems. According to the given topology, the measurement output available for the controller is not only from the individual agent but also from its neighboring agents. By using the completing squares method and stochastic analysis techniques, necessary and sufficient conditions are derived for the consensus to be ensured, and then the time-varying controller parameters are designed by solving coupled backward recursive Riccati difference equations (RDEs). A simulation example is utilized to illustrate the usefulness of the proposed control protocol. [Copyright &y& Elsevier]

Abstract: This paper deals with the distributed state estimation problem for a class of discrete time-varying nonlinear systems with both stochastic parameters and stochastic nonlinearities. The system measurements are collected through sensor networks with sensors distributed according to a given topology. The purpose of the addressed problem is to design a set of time-varying estimators such that the average estimation performance of the networked sensors is guaranteed over a given finite-horizon. Through available output measurements from not only the individual sensor but also its neighboring sensors, a necessary and sufficient condition is established to achieve the performance constraint, and then the estimator design scheme is proposed via a certain -type criterion. The desired estimator parameters can be obtained by solving coupled backward recursive Riccati difference equations (RDEs). A numerical simulation example is provided to demonstrate the effectiveness and applicability of the proposed estimator design approach. [Copyright &y& Elsevier]

This paper is concerned with the distributed filtering issue under the Cauchy-kernel-based maximum correntropy for large-scale systems subject to randomly occurring cyber-attacks in non-Gaussian environments. The considered cyber-attacks are hybrid and consist of both denial-of-service attacks and deception attacks. The weighted Cauchy kernel-based maximum correntropy criterion instead of the traditional minimum variance is put forward to evaluate the filtering performance against non-Gaussian noises as well as cyber-attacks. Based on the matrix decomposition and the fixed-point iterative update rules, the desired filter gain related with a set of Riccati-type equations is obtained to achieve the optimal filtering performance. Then, an improved version only dependent on the local information and neighboring one-step prediction is developed to realize the distributed implementation. Furthermore, the convergence of the developed fixed-point iterative algorithm is addressed via the famous Banach fixed-point theorem. Finally, a standard IEEE 39-bus power system is utilized to show the merit of the proposed distributed filtering algorithm in the presence of cyber-attacks and non-Gaussian noises. [ABSTRACT FROM AUTHOR]

• An improved network architecture of DualGANs for coloring NIR imgae is constructed. • A mixed loss function is designed to enhance the generalization ability. • Comparison analysis demonstrates the superiority over state-of-the-art methods. This paper focuses on the colorization problem of near-infrared (NIR) images. Traditional colorization methods of grayscale images usually depend on users' intervention and cannot be extended to NIR image colorization due to inherent complexity, such as the same near-infrared lights emitted by objects with different colors. Furthermore, a large number of paired and labeled images, which cannot be guaranteed for the addressed problem, need to be provided during the training phase, whether for some traditional reference-based coloring methods or for CNN-based automatic coloring ones. Benefiting from the advantages of deep learning and generative adversarial networks (GANs) in the image-to-image translation, an improved DualGAN architecture is constructed to deal with the investigated problem. The developed architecture contains four blocks and any two adjacent blocks exist a direct connection channel, where convolution layers in each block enclose batch normalization and leaky ReLU nonlinearities. The adoption of dual deep learning networks is to establish the conversion translation relationship between NIR images and RGB images without paired and labeled requirements. Besides, a mixed loss function by integrating generator loss for discriminators' training is designed to decrease the occurrence of incorrect images generated by generators. Finally, an intensive comparison analysis based on common data sets is conducted to verify superiority over leading-edge methods in qualitative and quantitative visual assessments. [ABSTRACT FROM AUTHOR]

This paper is concerned with the resilient filtering problem for a class of discrete-time stochastic nonlinear systems with both time-varying delays and probabilistic distributed delays. In order to save the limited communication resource, stochastic communication protocols (SCPs) with multiple rules governed by a switching signal are employed to schedule the data transmission between the sensors and the filter for complying with different network scenarios and preserving the desired performance. An approach based on average dwell time (ADT) is utilized to establish the rule of SCPs for different scenarios. The purpose of the filtering problem is to design a resilient filter such that, under the regulation of SCPs, the dynamics of the filtering error is exponentially mean-square stable, and satisfies a prescribed disturbance attenuation level in a ℓ 2 - ℓ ∞ sense. A sufficient condition for the exponential mean-square stability is first derived and the ℓ 2 - ℓ ∞ performance is then guaranteed. In terms of certain linear matrix inequalities, the solvability of the addressed problem is discussed and the explicit expression of the desired resilient filter is also parameterized. Finally, a numerical example is provided to demonstrate the validity of the proposed design approach. [ABSTRACT FROM AUTHOR]

In this article, the cooperative fault-tolerant tracking control (FTTC) is investigated for discrete-time multi-agent systems (MASs) with time-varying delays (TVDs) under multiple description encoding schemes (MDESs). First, a uniform channel model is proposed to describe the employed MDES subject to the effect of packet dropouts by introducing two independent random variables obeying the Bernoulli distribution and three indicator variables. Subsequently, a novel intermediate estimator is designed to estimate both system states and a fictitious intermediate variable (an integration of faults and leader's inputs) based on relatively measured outputs. In terms of the Lyapunov stability theory, some sufficient conditions are acquired to guarantee that the closed-loop system is exponentially ultimately bounded in the mean-square sense. Furthermore, the desired gain matrices are obtained by resorting to both the graph feature and singular value decomposition. Finally, the effectiveness and superiority are tested by two simulation examples for the proposed tracking protocol. • An intermediate estimator by introducing intermediate variables is designed to estimate system states and all unknown input signals. • Some sufficient conditions are obtained such that tracking errors of MASs with MDESs achieve exponentially ultimate boundedness. • The desired gain matrices are obtained by resorting to the graph feature and singular value decomposition. [ABSTRACT FROM AUTHOR]

This paper addresses the set-membership filtering problem for a class of discrete time-varying nonlinear systems with censored measurements and time-delay under the Round-Robin protocol. The censored measurements resulting from occlusion region, limit-of-detection or sensor faults are modeled by the Tobit Type I model, in which the given threshold governs whether or not the measurement information is directly utilized. A periodic protocol named Round-Robin protocol is employed to reduce the communication burden. Subsequently, a novel periodical model is given to describe censored measurement and the Round-Robin protocol in a uniform framework. In the light of such a model, the existence condition of the set-membership filter is described by a series of periodic threshold-dependent recursive linear matrix inequalities (RLMIs). As a consequence, the desired filter parameters can be obtained by the existence conditions and optimizing the corresponding ellipsoid parameters with the help of the convex optimization approach. Finally, a simulation is provided to illustrate the effectiveness of the proposed estimation approach. [ABSTRACT FROM AUTHOR]

Distributed cooperative control of multi-agent systems has been one of the most active research topics in the fields of automatic control and robotics. This paper provides a survey on recent advances in distributed cooperative control under a sampled-data setting, with special emphasis on the published results since 2011. First, some typical sampling mechanisms related to this topic, such as uniform sampling, nonuniform sampling, random sampling, and event-triggered sampling, are summarized in both asynchronous and synchronous paradigms. Then, based on different coordinated tasks, recent results on distributed sampled-data cooperative control of multi-agent systems are categorized into four classes, i.e., sampled-data leaderless consensus, sampled-data leader-following consensus, sampled-data containment control, and sampled-data formation control. For each class, some explicit research lines are identified according to various sampling mechanisms. In particular, depending on definitions of event triggering conditions, some representative event-triggered sampling mechanisms are sorted out and discussed in detail. Finally, several challenging issues for future research are proposed. [ABSTRACT FROM AUTHOR]

The attention of this paper is mainly concentrated on the H ∞ state estimator's design problem for a kind of discrete-time memristive neural networks (MNNs) with multiple fading measurements. The phenomenon of multiple fading measurements is represented by a set of individual stochastic variables obeying a predetermined distribution on interval [0,1]. Firstly, the augmented system comprised of MNNs and the dynamics of estimation errors are put forward to implement the performance analysis. Then, under the framework of the difference inclusion theory combined with the Lyapunov function method, several sufficient conditions are established to guarantee the exponential mean-square stability as well as H ∞ performance index. Furthermore, the desired estimator parameter is obtained in view of the solution of a convex optimization problem. In the end, an illustrative numerical example is exploited to check the reliability and usefulness of the design scheme in this paper. [ABSTRACT FROM AUTHOR]

Images captured under low-light conditions usually have poor visual quality, and hence greatly reduce the accuracy of subsequent tasks such as image segmentation and detection. In the low-light image enhancement task, noises in the dark areas are generally amplified while the images' brightness is enhanced. It should be pointed out that many deep learning methods cannot effectively suppress the noise at this stage and capture important feature information. To address the above problem, this paper proposes a Lightweight Attention-guided ConvNeXt Network (LACN) for low-light image enhancement. A novel Attention ConvNeXt Module (ACM) is first proposed by introducing a parameter-free attention module (i.e. SimAM) into the ConvNeXt backbone network. Then, a nontrivial lightweight network LACN based on a multi-attention mechanism is established through stacking two ACMs and fusing their features. In what follows, an improved hybrid attention mechanism, Selective Kernel Attention Module (SKAM), is adopted to effectively extract both global and local information. Such a module realizes the evaluation of lighting conditions for the whole image and the adaptive adjustment of the receptive field. Finally, through the feature fusion module, the features of different stages are aggregated to improve the ability of network to retain color information. Numerous experiments on low-light image enhancement are implemented via comparison with other state-of-the-art methods. Experiments show that the proposed method significantly improves the brightness and contrast of low-illumination images, preserves color information, and suppresses the generation of noises after image brightening. • A novel ACM is proposed by introducing the attention mechanism into the ConvNeXt backbone network. • A novel lightweight network LACN is established through both stacking two ACMs and fusing their features. • The designed SKAM effectively extracts both global information and local information. • A feature fusion module is employed to fuse features from different layers to retain abundant color information. [ABSTRACT FROM AUTHOR]

In this paper, an output-feedback control problem is investigated for a class of stochastic systems with impulsive effects under Round-Robin (RR) protocol. First, the RR protocol is exploited for the purpose of effectively relieving communication burden, and energy-saving. The considered stochastic impulsive system is essentially as a switched impulsive one due mainly to the switching feature of the RR protocol. Next, an extended comparison principle (ECP) of the stochastic impulsive systems is developed to handle the effects from both the switching and impulsive signals. By means of this ECP, sufficient conditions are derived to guarantee the exponential mean-square stability of the stochastic switched impulsive system, and then the output-feedback controller is designed through solving a set of semi-definite programming problems. Finally, an example is given to illustrate the effectiveness and applicability of the proposed control scheme. [ABSTRACT FROM AUTHOR]

• A novel structure of the distributed interval estimator is designed over sensor networks under the aperiodic DoS attack and AETP. • The system real state is contained within an estimation interval by employing the interval analysis method and the stability analysis method. • A distributed interval estimation algorithm is proposed such that the error dynamics is exponentially stable and the disturbance rejection attenuation contains a satisfactory H ∞ performance. The problem of the distributed interval estimation is studied for sensor networks under the denial-of-service (DoS) attack and the adaptive event-triggered protocol (AETP). The DoS attacks are described by the attack frequency and occurred duration in the channels between the local estimator and its neighbor nodes. Furthermore, AETP is employed to reduce the communication burden resulting from the limited bandwidth of communication networks. The dynamic threshold parameters of the proposed AETP are governed by an adaptive law, which is directly related with the error between the innovation at the current instant and the broadcast innovation at the latest trigger instant. The purpose of this article is to design a distributed interval estimator by utilizing the local information and the neighboring information such that, in the simultaneous presence of AETP, the bounded noises, and the aperiodic DoS attacks, system real states are involved in an interval. Then, some sufficient conditions are gained by employing the stability analysis theory and positive system theory, and the desired estimator gains are obtained by solving a set of linear matrix inequalities. Finally, a simulation example is proposed to show the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the consensusability problem for a class of discrete-time multi-agent systems (DT-MASs). A binary encoding scheme (BES) is employed during the data transmission, and only a finite-length binary bit string is transmitted due primarily to the limited network bandwidth in practice. Meanwhile, the binary bit string, transmitted via memoryless binary symmetric channels, might suffer from random bit errors with a certain probability. The purpose of this paper is to derive some consensusability conditions, under which there must exist a distributed controller such that the mean-square bounded consensus is achieved by the considered DT-MASs with BESs subject to random bit errors. To this end, the statistical properties are first revealed for the BES-induced quantization errors and the random bit errors. Then, by resorting to the solvability analysis of a modified Riccati inequality, some sufficient conditions are, respectively, derived to ensure the mean-square bounded consensus under the undirected communication topology in two scenarios: (1) identical bit-error rates (BERs); and (2) non-identical BERs. In particular, a necessary and sufficient condition is established for a single input case with identical BERs. Furthermore, the ultimate upper bound of the consensus errors in the mean-square sense is established to examine the effects of the length of bit string and the BERs. Finally, an illustrative simulation example is provided to validate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Global asymptotic stability is an important issue for wide applications of recurrent neural networks with time-varying delays. The Lyapunov–Krasovskii functional method is a powerful tool to check the global asymptotic stability of a delayed recurrent neural network. When the Lyapunov–Krasovskii functional method is employed, three steps are necessary in order to derive a global asymptotic stability criterion: (i) constructing a Lyapunov–Krasovskii functional, (ii) estimating the derivative of the Lyapunov–Krasovskii functional, and (iii) formulating a global asymptotic stability criterion. This paper provides an overview of recent developments in each step with insightful understanding. In the first step, some existing Lyapunov–Krasovskii functionals for stability of delayed recurrent neural networks are anatomized. In the second step, a free-weighting matrix approach, an integral inequality approach and its recent developments, reciprocally convex inequalities and S -procedure are analyzed in detail. In the third step, linear convex and quadratic convex approaches, together with the refinement of allowable delay sets are reviewed. Finally, some challenging issues are presented to guide the future research. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to detect crack in metals by utilizing the magnetic optical image (MOI) method. MOI detection is one of a nondestructive testing method, which is based on the Faraday magneto-optical effect. The artificial neural network (ANN) approach is applied to identify the crack, according to the dynamically threshold selection. There are two kinds of threshold vectors that are proposed by the ANN algorithm. A good threshold can be obtained from the vectors using a law. The new image acquired is processed by a magnetic domain spots filter. This filtering method is based on the connection law who holds good ability to process the spots in the MOI image. Based on these methods, the detecting system can recognize the crack clearly and the results would confirm it. [ABSTRACT FROM AUTHOR]

In this paper, a general event-triggered framework is set up to deal with the variance-constrained H ∞ control problem for a class of discrete time-varying systems with randomly occurring saturations, stochastic nonlinearities and state-multiplicative noises. Based on the relative error with respect to the measurement signal, an event indicator variable is introduced and the corresponding event-triggered scheme is proposed in order to determine whether the measurement output is transmitted to the controller or not. The stochastic nonlinearities under consideration are characterized by statistical means which can cover several classes of well-studied nonlinearities. A set of unrelated random variables is exploited to govern the phenomena of randomly occurring saturations, stochastic nonlinearities and state-dependent noises. The purpose of the addressed multiobjective control problem is to design a set of time-varying output feedback controller such that, over a finite horizon, the closed-loop system achieves both the prescribed H ∞ noise attenuation level and the state covariance constraints. A recursive matrix inequality approach is developed to derive the sufficient conditions for the existence of the desired finite-horizon controllers, and the analytical characterization of such controllers is also given. Simulation studies are conducted to demonstrate the effectiveness of the developed controller design scheme. [ABSTRACT FROM AUTHOR]