This paper examines fault-tolerant quantized control for neural networks under persistent dwell-time switching, considering the presence of actuator faults and dynamic output quantization. The dynamic scaling factor (...
详细信息
Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical...
详细信息
Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.
This article tackles the boundary event-based bipartite consensus tracking control problem for the flexible manipulator multi-agent network over a signed diagraph. Each follower agent is the flexible manipulator with ...
详细信息
This article tackles the boundary event-based bipartite consensus tracking control problem for the flexible manipulator multi-agent network over a signed diagraph. Each follower agent is the flexible manipulator with unknown disturbances,modeling uncertainties, input saturations and backlashes, and asymmetric output constraints. To reduce the continuous updating of control inputs, a new dynamic event-triggering mechanism is used. Under multiple constraints, achieving the asymptotic convergence point by point in space of the manipulator's vibration state is a control challenge. To solve this issue, we propose a new asymptotic convergence lemma. In control design, radial basis neural networks are employed to estimate nonlinear uncertain terms and the barrier Lyapunov function is used to accomplish the output constraints. Based on the Lyapunov direct method, a novel distributed boundary event-based control algorithm is designed to guarantee that the closed-loop network can reach the asymptotical bipartite consensus tracking and vibration suppression. Moreover, Zeno behaviors can be excluded for each agent. Finally, some numerical results are presented to demonstrate the validity and superiority of the designed control algorithm.
This paper considers the asynchronous control for discrete-time Markov jump systems (MJSs) using a multi-node round-robin protocol (MNRRP). Compared to the traditional round-robin protocol, MNRRP increases the number ...
详细信息
Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inher...
详细信息
Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.
Recently,many researches have created adversarial samples to enrich the diversity of training data for improving the text classification performance via reducing the loss incurred in the neural network ***,existing st...
详细信息
Recently,many researches have created adversarial samples to enrich the diversity of training data for improving the text classification performance via reducing the loss incurred in the neural network ***,existing studies have focused solely on adding perturbations to the input,such as text sentences and embedded representations,resulting in adversarial samples that are very similar to the original *** adversarial samples can not significantly improve the diversity of training data,which restricts the potential for improved classification *** alleviate the problem,in this paper,we extend the diversity of generated adversarial samples based on the fact that adding different disturbances between different layers of neural network has different *** propose a novel neural network with perturbation strategy(PTNet),which generates adversarial samples by adding perturbation to the intrinsic representation of each hidden layer of the neural ***,we design two different perturbation ways to perturb each hidden layer:1)directly adding a certain threshold perturbation;2)adding the perturbation in the way of adversarial *** above settings,we can get more perturbed intrinsic representations of hidden layers and use them as new adversarial samples,thus improving the diversity of the augmented training *** validate the effectiveness of our approach on six text classification datasets and demonstrate that it improves the classification ability of the *** particular,the classification accuracy on the sentiment analysis task improved by an average of 1.79%and on question classification task improved by 3.2%compared to the BERT baseline,respectively.
Thanks to its ubiquity,using radio frequency (RF) signals for sensing has found widespread *** traditional integrated sensing and communication systems,such as joint radar-communication systems,common sensing tasks in...
Thanks to its ubiquity,using radio frequency (RF) signals for sensing has found widespread *** traditional integrated sensing and communication systems,such as joint radar-communication systems,common sensing tasks include target localization and ***,increasingly intelligent systems,such as smart agriculture,lowaltitude economy,and smart healthcare,have demanded more comprehensive and continuous information sensing capabilities to support higher-level *** sensing has the potential to offer both spatial and temporal continuity,meeting the multi-dimensional sensing needs of these intelligent ***,numerous advanced systems have been proposed,expanding the application scope of RF sensing to be more pervasive,including discrete state ubiquitous sensing tasks (such as material identification [1]),and continuous state ubiquitous sensing tasks (such as health monitoring [2]).With the advent of the 6G era,it is anticipated that the sensing potential of RF systems will be further unleashed.
Currently,the main idea of iterative rendering methods is to allocate a fixed number of samples to pixels that have not been fully rendered by calculating the completion *** is obvious that this strategy ignores the c...
详细信息
Currently,the main idea of iterative rendering methods is to allocate a fixed number of samples to pixels that have not been fully rendered by calculating the completion *** is obvious that this strategy ignores the changes in pixel values during the previous rendering process,which may result in additional iterative operations.
Visible and infrared image fusion(VIF)aims to combine information from visible and infrared images into a single fused *** VIF methods usually employ a color space transformation to keep the hue and saturation from th...
详细信息
Visible and infrared image fusion(VIF)aims to combine information from visible and infrared images into a single fused *** VIF methods usually employ a color space transformation to keep the hue and saturation from the original visible ***,for fast VIF methods,this operation accounts for the majority of the calculation and is the bottleneck preventing faster *** this paper,we propose a fast fusion method,FCDFusion,with little color *** preserves color information without color space transformations,by directly operating in RGB color *** incorporates gamma correction at little extra cost,allowing color and contrast to be rapidly *** regard the fusion process as a scaling operation on 3D color vectors,greatly simplifying the calculations.A theoretical analysis and experiments show that our method can achieve satisfactory results in only 7 FLOPs per *** to state-of-theart fast,color-preserving methods using HSV color space,our method provides higher contrast at only half of the computational *** further propose a new metric,color deviation,to measure the ability of a VIF method to preserve *** is specifically designed for VIF tasks with color visible-light images,and overcomes deficiencies of existing VIF metrics used for this *** code is available at https://***/HeasonLee/FCDFusion.
Bluetooth-low-energy (BLE)-based low-power cameras have expanded the applications of battery-powered low-power Internet of Things (IoT) vision systems. The adaptive tuning of connection and image encoding settings pla...
详细信息
暂无评论