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This article investigates a robust guaranteed cost finite-time control for coupled neural networks with parametric uncertainties. The parameter uncertainties are assumed to be time-varying norm bounded, which appears on the system state and input matrices. The robust guaranteed cost control laws presented in this article include both continuous feedback controllers and intermittent feedback controllers, which were rarely found in the literature. The proposed guaranteed cost finite-time control is designed in terms of a set of linear-matrix inequalities (LMIs) to steer the coupled neural networks to achieve finite-time synchronization with an upper bound of a guaranteed cost function. Furthermore, open-loop optimization problems are formulated to minimize the upper bound of the quadratic cost function and convergence time, it can obtain the optimal guaranteed cost periodically intermittent and continuous feedback control parameters. Finally, the proposed guaranteed cost periodically intermittent and continuous feedback control schemes are verified by simulations.

In the literature, a lot of work focused on studying intermittent control problems via feedback control strategy. No study on the intermittent control problems via sliding mode control method has been reported so far. This paper studies the problem of fast synchronization between two complex dynamical networks via aperiodically intermittent control and sliding mode control. In order to achieve fast synchronization of complex dynamical networks by using aperiodically intermittent sliding mode controller, new differential inequalities are derived firstly. After that, some sufficient finite-time synchronization criteria and finite-time achieving slide mode surface are obtained based on finite-time stability theory, aperiodically intermittent sliding mode control technique and constructing Lyapunov function. Finally, an example is provided to verify the effectiveness of the proposed theoretical methods.

Network security has become an important issue in our work and life. Hackers' attack mode has been upgraded from normal attack to APT( Advanced Persistent Threat, APT) attack. The key of APT attack chain is the penetration and intrusion of active directory, which can not be completely detected via the traditional IDS and antivirus software. Further more, lack of security protection of existing solutions for domain control aggravates this problem. Although researchers have proposed methods for domain attack detection, many of them have not yet been converted into effective market-oriented products. In this paper, we analyzes the common domain intrusion methods, various domain related attack behavior characteristics were extracted from ATT&CK matrix (Advanced tactics, techniques, and common knowledge) for analysis and simulation test. Based on analyzing the log file generated by the attack, the domain attack detection rules are established and input into the analysis engine. Finally, the available domain intrusion detection system is designed and implemented. Experimental results show that the network attack detection method based on the analysis of domain attack behavior can analyze the log file in real time and effectively detect the malicious intrusion behavior of hackers , which could facilitate managers find and eliminate network security threats immediately.

Nowadays, multi-label learning is ubiquitous in practical applications, in which multi-label data is always confronted with the curse of high-dimensional features. Feature selection has been shown to effectively improve learning performance by selecting discriminative features. Conventional multi-label feature selection only focuses on associating input features with corresponding labels while neglecting the potential structural information, i.e., instance correlations and label correlations. To tackle this problem, we propose manifold learning with structured subspace for multi-label feature selection. Specifically, we first uncover a latent subspace for a more compact and accurate data representation, and take advantage of the subspace to explore the correlations among instances. Then, we explore label correlations in manifold learning to guarantee the global and local structural consistency of labels. Besides, l 2 , 1 -norm is introduced into loss function and sparse regularization to facilitate feature selection process. A detail optimization algorithm is presented to solve the objective function of the proposed method. Extensive experiments on real-world data show the superiority of the proposed method under various metrics.

The segmentation of a point cloud is one of the key technologies for three-dimensional reconstruction, and the segmentation from three-dimensional views can facilitate reverse engineering. In this paper, we propose a self-adaptive segmentation algorithm, which can address challenges related to the region-growing algorithm, such as inconsistent or excessive segmentation. Our algorithm consists of two main steps: automatic selection of seed points according to extracted features and segmentation of the points using an improved region-growing algorithm. The benefits of our approach are the ability to select seed points without user intervention and the reduction of the influence of noise. We demonstrate the robustness and effectiveness of our algorithm on different point cloud models and the results show that the segmentation accuracy rate achieves 96%.

Like single-label learning, multi-label learning also suffers from the curse of dimensionality. Due to the existence of high-dimensional data, feature selection as a preprocessing tool always plays a key position in the multi-label learning. Although a variety of multi-label feature selection methods have been developed, they neglect to consider the redundancy of features, thereby degrading learning performance. To address this problem, we present a novel multi-label feature selection approach with uncorrelated regression and adaptive spectral graph. Specifically, we first construct a manifold framework with uncorrelated regression model to hunt for uncorrelated yet discriminative features, which also utilize the low-dimensional representation based on feature space to fit the label distribution. Then, a spectral graph term based on information entropy is incorporated into the manifold framework, so as to ensure the local geometric structure of data in subsequent learning process. Following the above principles, we design an objective function to achieve multi-label feature selection, and propose a detailed optimization method. Comprehensive experiments are conducted on multiple public multi-label data sets, the results show that our method is superior to other compared methods.

This paper presents a novel approach for bas-relief generation and synthesis. In contrast to previous methods, we divide this problem into two parts: the selection of the best view and arrangement of the relief layout. Taking these into account, we incorporate the visual saliency and photographic composition rules into the bas-relief generation. Additionally, a nonlinear compression function is used to compress the models, and finally, we implement surface parameterization by directly manipulating the mesh triangles to generate curved surface bas-relief. We validate our approach through a variety of models. The results indicate that the proposed approach is effective to adapt different types of target surface with topology unchanged. Comparing with conventional methods, our approach is able to effectively produce bas-relief with a reasonable layout and distinct details.

This paper focuses on using a TI's DSP to design an AC electricity acquisition module, which can accomplish the function of distributed synchronous AC electrical parameters sampling. The hardware diagram of the design used a DSP and other auxiliary devices to set up an AC electricity acquisition module with the ability of high frequency distributed simultaneous electrical parameters sampling. Specifically, a GPS was mounted on the module to synchronize sampling between different modules and a SD card was employed to store the collected data. Regarding the software design, a PC control/monitor program was developed. The electricity acquisition terminal is capable of 200KHz simultaneous sampling when it works without SD card while it is verified that the highest sample frequency is 10KHz when it works with a SD card.