As most of the digital image encryption algorithms are dependent with matrix theory, matrix equations are widely used in digital image encryption algorithms. Matrix inversion is a basic matrix equation operation, and how to effectively solve the matrix inversion problem has drawn considerable attention. However, previous works mainly focused on the static matrix inversion, and they are not suitable for modern time-varying problems solving. Zeroing neural network (ZNN) is a emerging systematic approach for solving time-varying problems, and it has been successfully applied in the dynamic matrix inversion. In order to enhance the performance of the existing models, this paper constructs a novel fuzzy adaptive ZNN (FA-ZNN) model, which incorporates segmented activation functions and an adaptive fuzzy dynamic convergence factor. Based on the constructed FA-ZNN model, a novel time-varying Hill cipher (NTVHC) with chaotic sequences scrambling and encryption for remote sensing images encryption is proposed. The proposed NTVHC algorithm utilizes the tangential-delay elliptic reflection cavity system (TD-ERCS) to generate two chaotic sequences for pixels displacement scrambling while constructing dynamic key matrix for image encryption. The superior performance of the constructed FA-ZNN model for dynamic key matrix inversion are validated by mathematical analysis and comparative simulation results with other models. Additionally, in subsequent practical applications, the model is used during the NTVHC decryption, which further verifies the feasibility of the proposed NTVHC encryption algorithm and the promising application prospects of the FA-ZNN model.

Images captured in hazy environments experience gradient reduction due to the scattering of light by atmospheric particles, yet few existing dehazing methods utilize the gradients of hazy images to restore the visibility of the scene. In this paper, we propose an efficient dehazing method based on gradient line prior. By associating image gradients in atmospheric scattering model, we obtain a stable and simple prior, termed the gradient line prior (GLP), that is, in the corresponding hazy images normalized by atmospheric light, there is a linear relationship between the reciprocal of the gradients distributed across different regions of the hazy image plane and the corresponding ratio of pixel intensity to the gradient. For accurate estimation of the transmission map, its refinement is realized through the analysis of the distribution characteristics of pixels in three channels. Experimental results demonstrate that, compared to different dehazing methods, our approach exhibits good detail enhancement and color restoration performance. The code is available at: https://github.com/heyuanlei/Gradient-Line-Prior.

Cryptography is one of the most important areas in information security. Cryptography ensures secure communication and data privacy, and it is increasingly being applied in healthcare and related fields. As an important classical cryptographic method, the Hill cipher has always been closely studied by experts and scholars. In order to enhance the security of the conventional Hill cipher (CHC), a novel dynamic Hill cipher (NDHC) is proposed in this work. The proposed NDHC not only replaces the static key matrix of the CHC with a time-varying dynamic key matrix (TVDKM) to change the image pixel values over time t, but also uses the Logistic chaos sequence scrambling the image pixel positions, which greatly enhances the security of the CHC. However, how to effectively obtain the dynamic inversion key matrix (DIKM) of the TVDKM becomes an urgent issue in the NDHC decryption. In order to quickly find the DIKM, a fixed-time convergence fuzzy Zeroing neural network (FTCF-ZNN) model is constructed, and the convergence and robustness of the FTCF-ZNN model for solving the DIKM are verified through theoretical analysis and comparative experimental results. Moreover, the effectiveness and security of the proposed NDHC for medical images encryption and decryption are also validated by experiments.

This paper proposes a novel filter-based model-free reinforcement learning (RL) event-triggered control (ETC) method for the fully distributed robust leaderless cooperative output regulation (COR) of unknown heterogeneous multi-agent systems (MASs) with external disturbances over directed graphs. First, the fully distributed event-triggered observers are designed to generate an autonomous system for the robust leaderless COR, in which the frequency of signal transmission and computational burden are significantly reduced, and the Zeno behavior is strictly ruled out. Then, a filter-based model-free RL algorithm without integration operation is developed to obtain the solution of the internal model-based augmented algebraic Riccati equation (AARE) and to release the requirement of recording complete and continuous data. Moreover, with some adaptive parameters, the robust leaderless COR is solved in a fully distributed manner without involving any global information of directed MASs. Finally, simulation results on RLC circuits are illustrated to show the feasibility and effectiveness of the proposed control scheme.

Accurate and reliable power generation forecasts of offshore wind farm clusters are crucial for the low-carbon operation of multienergy power systems. In practice, measurement data may not always be complete due to various failure issues in data acquisition systems or communication interruptions in harsh marine environments, and missing essential data may significantly reduce the credible prediction accuracy of probabilistic models. To address this problem, this article proposes a novel missing-data tolerant model based on confidence-triggered fuzzy clustering quantile-enhanced transformer (CFCQET). First, a quantile-enhanced transformer-based multistep wind power probabilistic forecasting method is developed, where the predicted values are iteratively updated by conditional confidence expectations. Then, based on the spatio-temporal characteristics of wind farms, a FCM clustering model for offshore wind farms is constructed to divide wind farms with similar power curve attributes for joint modeling. Next, a confidence-triggered strategy is designed for probabilistic power forecasting with missing data under wind farm clusters, where the output interpolated predicted values are used to fill in unobserved input data. Finally, probabilistic prediction tests for twelve offshore wind farms at a time resolution of half an hour. The test results demonstrate that the CFCQET achieves a lower negative form of the continuous ranking probability score (CRPS*), as well as superior sharpness and comparable reliability of the prediction intervals with respect to the benchmarks.

The absorption and scattering of light in different turbid media cause images to suffer from poor visibility and contrast, which severely affects the performance of many computer vision tasks. To address this issue, we propose a fast scene recovery method based on the Ambient light similarity prior (ALSP). In this method, the ambient light similarity metric is designed from both magnitude and orientation, which is embedded into the optical imaging model, and the estimation of scene transmission is derived by simplification and approximation. The estimation of the transmission map is very simple, and its time complexity is O(N), where N is the size of the input image. Moreover, we propose a progressive manner to determine the ambient light for both the near and far regions separately, which can effectively improve the brightness and color saturation of the restored image. Experiments performed in different scenes demonstrate that our method outperforms several state-of-the-art competitors in terms of efficiency and scene recovery performance.

In this article, the global exponential synchronization problem is investigated for a class of delayed nonlinear memristive neural networks (MNNs) with reaction-diffusion items. First, using the Green formula, Lyapunov theory, and proposing a new fuzzy adaptive pinning control scheme, some novel algebraic criteria are obtained to ensure the exponential synchronization of the concerned networks. Furthermore, the corresponding control gains can be promptly adjusted based on the current states of partial nodes of the networks. Besides, a fuzzy adaptive aperiodically intermittent pinning control law is also designed to synchronize the fuzzy MNNs (FMNNs). The controller with intermittent mechanism can obtain appropriate rest time and save energy consumption. Finally, some numerical examples are provided to confirm the effectiveness of the results in this article.

This article studies the stability issue and provides the hierarchical stability conditions for generalized neural networks (GNNs) embedded with interval variant delay (delay’s differential is unidentified). First, by transforming the state vectors with integral in the generalized free-matrix-based integral inequalities (GFIIs) into the multiple integral state vectors, the Lyapunov-Krasovskii functional (LKF) with hierarchy is put up based on these multiple integrals. Then, in the treatment of the LKF derivative, the GFIIs are utilized to estimate the delay related integrals of the quadratic product items. For the LKF differential, it is obtained as the delay function with the$2N-1$degree. Next, to set up the linear matrix inequality (LMI) forms and solve the nonlinear items injected by the GFIIs, the novel matrix-based negative conditions (NCs) for odd degree polynomials are put forward. Finally, the superiority of the proposed stability conditions with hierarchy is illustrated by several numerical examples.

An ongoing trend seeking to incorporate forward modeling, which involves the physical laws of wave propagation, into the network architecture to improve the generalization capability of the deep learning (DL) inversion method has showcased promising applications. However, directly embedding the time-consuming 2-D magnetotelluric (MT) forward modeling solved by conventional numerical algorithms to facilitate physics-guided DL MT inversion, which usually necessitates millions of forward operations during a complete training session, is challenging. Hence, in this work, we develop a physics-guided DL inversion method (PGWNet) by constructing a W-shaped DL model and performing a multitask learning strategy. The DL model consists of one encoder and two decoders, where the two decoders are independent of each other and share the encoder. During the training process, two decoders are first optimized independently by minimizing the model misfit, quantifying the discrepancy between the predicted and labeled resistivity models, and the data misfit, quantifying the discrepancy between the predicted and labeled MT responses, respectively. When model and data misfits backpropagate to the encoder, they are combined to jointly optimize the encoder. Moreover, to ensure practical application effect, this work builds a set of random synthetic resistivity models with gradually varying resistivity values to delineate realistic subsurface structures. We substantiate the developed PGWNet inversion method using synthetic and actual MT data and benchmark it against a fully data-driven DL inversion method and the conventional least-squares regularization inversion method. It is anticipated to promote the practicability and applicability of the DL inversion method in practical MT prospecting scenarios.

This article systematically studies the issue of adaptive neural network (NN) output-feedback control for uncertain nonlinear systems using event-triggered output. First, to tackle the problem of unmeasurable states, a compact state observer using event-triggered output is constructed. Then, since the event-triggered output signals are discontinuous, the virtual control laws in backstepping design are no longer differentiable. Hence, the dynamic surface control scheme is introduced to resolve this problem. Unlike existing work requiring system functions to satisfy Lipschitz continuity condition, adaptive NN control is incorporated into the designed algorithm to relax the above constraint. What is more, the event-triggered mechanism is also used for parameter estimation to avoid waste of computing and communication resources. Finally, the results of comparative simulations and the DC brush motor experiment are depicted to demonstrate the practicality and effectiveness of the proposed method.

In this paper, state estimation based on multi-agent theory under intermittent observation is studied. In practice, multi-agent systems often suffer from loss of measurement data. For the case of episodic data loss, the statistical properties of the data before and after the loss are used to detect the loss and reconstruct the measurement function to complete the estimation under intermittent observations; for the case of long-term data loss, the time-varying weighting method is used to reduce the importance of the problem nodes and minimize the global estimation error. The effectiveness of the algorithm is demonstrated by experiments on the dynamic estimation of the target object.

This article addresses the exponential synchronization problem for a class of fuzzy inertial memsirtor-based neural networks with mixed time-varying delays. First, the inertial items are described as second-order systems and transformed into first-order systems by utilizing a appropriate variable substitution. Then, the fuzzy state-feedback control strategy and fuzzy adaptive control strategy are designed to ensure the exponential synchronization under the framework of Filippov solutions. The exponential synchronization algebraic conditions are obtained by choosing a proper Lyapunov–Krasovskii functional. Finally, two numerical simulations are provided to validate the effectiveness and benefit of the proposed results.

The zeroing neural network (ZNN) model, a powerful approach for addressing time-varying problems, has been extensively applied in the calculation and optimization fields. In this article, a new pattern activation function, the power piecewise activation function (PPAF), is proposed to establish a predefined fixed-time convergent ZNN (PFTZNN) for finding solutions to the time-varying quadratic programming problem. In comparison with the traditional activation functions, multisegmentation is a remarkable feature of the PPAF; consequently, the advantage of PPAF is that its parameters can be flexibly adjusted according to actual needs. Specifically, because of the multisegment characteristics of the PPAF, the convergence speed of the PPAF-activated PFTZNN model can be flexibly adjusted based on distinct requirements. The fixed-time convergence property of the PPAF-activated PFTZNN model is validated by detailed mathematical theoretical analysis, and its upper bound convergence time is directly calculated. Then, the comparative simulation results of the PPAF-activated PFTZNN model with other existing ZNN models for time-varying quadratic programming are provided for the further verification of its superior convergence speed and robustness. In addition, the proposed PFTZNN model is applied for dual-arm manipulator cooperative trajectory tracking, and its practical application ability is demonstrated by united simulation experiments of MATLAB and Robot studio. Finally, the PFTZNN model is also applied to control a real dual-arm manipulator to complete the trajectory tracking task, which further validates its superior performance together and widespread applicability.

Matrix completion-assisted channel estimation is considered one of promising techniques in millimeter wave (mmWave) massive multiple input multiple output (MIMO) system by exploiting the low-rank property of channel matrix in the angle domain. However, existing channel estimation approaches are hard to achieve high accuracy due to the inevitable bias solution caused by nuclear norm based minimization (NNM). To address this problem, this paper proposes a novel matrix completion-assisted mmWave massive MIMO channel estimation method. We employ an effective and flexible rank function named joint weighted and truncated nuclear norm as relaxation of nuclear norm, and then construct an novel matrix completion model for channel estimation problem. Moreover, a popular framework of alternating direction method of multipliers (ADMM) is derived for minimization of the resulting optimization problem. Simulation results are provided to verify the proposed method that can flexibly and effectively improve the channel estimation accuracy with reliable convergence.

As an extension of cloud computing, edge computing has attracted the attention of academia and industry because of its characteristics of low latency, high bandwidth, and low energy consumption. However, due to limited terminal resources and insufficient security design, the edge computing environment still faces many challenges in terms of data security and privacy protection. Among them, how to effectively control access to outsourced data is one of the main issues. In this article, we propose a lightweight and verifiable ciphertext-policy attribute-based encryption (CP-ABE)-based multiauthority access control scheme for edge computing-assisted Internet of Things (IoT), which adopts the method of outsourcing decryption to mitigate the computational cost of data users with limited resources. In addition, our scheme realizes the feature of attribute revocation, and the design of the multiauthority mechanism enables our scheme to avoid the problem of key escrow. Therefore, our proposed scheme not only ensures data confidentiality but also can resist the collusion attack. Besides, our scheme is secure against the chosen plaintext attack in the random oracle model under the decision$q$-BDHE assumption. Finally, we compared our scheme with some related work in performance, and the results demonstrate that our scheme is efficient in computation and communication. Because our scheme greatly mitigates the overhead of data users, it is very suitable for edge computing supported IoT applications with restricted computation resources.

The traditional malicious uniform resource locator (URL) detection method excessively relies on the matching rules formulated by the network security personnel, which is hard to fully express the text information of the URL. Thus, an improved multilayer recurrent convolutional neural network model based on the YOLO algorithm is proposed to detect malicious URL in this paper. First, single characters are mapped to dense vectors using word embedding, and the dense vectors are participated in the training process of the whole model according to the structural characteristics of the URL in the method. Then, the CSPDarknet neural network model based on the improved YOLO algorithm is proposed to extract features of the URL. Finally, the extracted features are used to evaluate malicious URL by the bidirectional LSTM recurrent neural network algorithm. In order to verify the validity of the algorithm, a total of 200,000 URLs are collected, including 100,000 normal URLs labeled “good” and 100,000 malicious URLs labeled “bad”. The experimental results show that the method detects malicious URLs more quickly and effectively and has high accuracy, high recall rate, and high accuracy compared with Text-RCNN, BRNN, and other models.

Disturbances, uncertainties, noises and device faults commonly exist in microgrids and often undermine the system stability. To meet these challenges, robust control methods have been recently employed in microgrids systems. In this paper, an active fault-tolerant control scheme is proposed for DC islanded microgrids subjected to sensor fault and external disturbance. Firstly, a distributed H∞observer is designed to estimate the uncorrupted voltage and current with high accuracy. Then, an observer based state feedback controller is proposed to ensure stability of voltage regulation for the system. A consensus based control layer in the secondary level is provided to realize current sharing. For the plug-and-play operation of DC microgrids, decentralized parameters design approaches for the observer and controller are both discussed. Finally, simulation studies and experiments are carried out on a DC microgrid system to evaluate the effectiveness of the proposed fault-tolerant control scheme. The simulation and experiment results show that compared with previous works, the proposed observer based control strategy can significantly improve the reliability and resilience of DC microgrid systems.

This article is concerned with the event-triggered guaranteed cost control for a class of Markovian jump systems with time-varying delays and partly unknown transition probabilities, which is described by the Takagi-Sugeno fuzzy model. For the sake of saving network bandwidth, an event-triggered mechanism related to system modes is developed in the feedback channel, in which network-induced delay randomly occurs. The stability criterion for the system with a guaranteed cost index is derived by the Lyapunov-Krasovskii functional. Moreover, sufficient conditions that ensure the existence of the admissible fuzzy controllers are given. By means of the proposed approach, the fuzzy control gains and event-triggered parameters can be codesigned. Finally, some simulation results are presented to demonstrate the superiority and effectiveness of the developed method.

The investigation of risk factors associated with hypertension patients has been extensively studied in the past decades. However, the pattern of natural progressive trajectories to hypertension from nonhypertensive states was rarely explored. In this study, we are interested in discovering the underlying transition patterns between different blood pressure states, namely normal state, elevated state, and hypertensive state among the working population in the United States. A multi-state Markov model was built based on 88,966 clinical records from 34,719 participants we collected during the worksite preventive screening from 2012 to 2018. We first investigated the various risk factors, and we found that body mass index (BMI) is the most critical factor for developing new-onset hypertension. The transition probabilities, survival probabilities, and sojourn time of each state were derived given different levels of BMI, age groups, and gender categories. We found the underweight participants are more likely to remain in the current nonhypertensive states within 3 years, while extremely obese participants have a higher probability of developing hypertension. We discovered the distinct transition patterns among male and female participants. On average, the sojourn time in the normal state for normal-weight participants is 4.33 years for females and 2.18 years for their male counterparts. For the extremely obese participants, the average sojourn time in the normal state is 1.38 years for females and 0.71 years for males. In the end, a web-based graphical user interface (GUI) application was developed for clinicians to visualize the impact of behavioral interventions on delaying the progression of hypertension. Our analysis can provide a unique insight into hypertension research and proactive interventions.

In this paper, the tracking performance limitation of networked control systems (NCSs) is studied. The NCSs are considered as continuous-time linear multi-input multioutput (MIMO) systems with random reference noises. The controlled plants include unstable poles and nonminimum phase (NMP) zeros. The output feedback path is affected by multiple communication constraints. We focus on some basic communication constraints, including additive white noise (AWN), quantization noise, bandwidth, as well as encoder-decoder. The system performance is evaluated with the tracking error energy, and used a two-degree-of-freedom (2DOF) controller. The explicit representation of the tracking performance is given in this paper. The results indicate the tracking performance limitations rely to internal characteristics of the plant (unstable poles and NMP zeros), reference noises [the reference noise power distribution (RNPD) and its directions], and the characteristics of communication constraints. The characteristics of communication constraints include communication noise power distribution (CNPD); quantization noise power distribution (QNPD), and their distribution directions; transform bandwidth allocation (TBA); transform encoder-decoder allocation (TEA), and their allocation directions; and NMP zeros and MP part of bandwidth. Moreover, the tracking performance limitations are also affected by the angles between the each transform NMP zero direction and RNPD direction, and these angles between each transform unstable poles direction and the direction of communication constraint distribution/allocation. In addition, for MIMO NCSs, bandwidth (there are not identical two channels) can always affect the direction of unstable poles, and the channel allocation of bandwidth and encode-decode may be used for a feasible method for the performance allocation of each channel. Finally, an instance is given for verifying the effectiveness of the theoretical outcomes.