This article reconsiders synchronization problem of linear complex networks with time-varying delay on time scales. For different types of time scales, aperiodically intermittent control scheme is established by using...
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This article reconsiders synchronization problem of linear complex networks with time-varying delay on time scales. For different types of time scales, aperiodically intermittent control scheme is established by using a matrix-based convex combination method, which has great potential in reducing control consumption and saving communication bandwidth. By employing a common Lyapunov function, aperiodically intermittent controllers are utilized successfully to achieve synchronization of linear delayed complex networks on special time scales onto an isolated node. Next, by constructing a special Lyapunov function with time-varying coefficients, sufficient criteria that consist of two linear matrix inequalities are demonstrated to make linear delayed complex networks on general time scales synchronized onto an isolated system with an exponential convergence rate given in advance. Due to delayed complex networks in this article defined on time scales, the proposed control schemes are applicable to continuous-time networks, their discrete-time forms, and any combination of them. Four numerical examples are offered to highlight the effectiveness and superiority of the proposed aperiodically intermittent control schemes at last.
Recently, the color image encryption algorithm based on chaos theory has become the focus of current research. When encrypting color images, the common practice is to treat color images as different gray components an...
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Recently, the color image encryption algorithm based on chaos theory has become the focus of current research. When encrypting color images, the common practice is to treat color images as different gray components and process them severally, which results in more redundancy and low efficiency. The security of chaotic cryptosystems also depends on the performance of the chaotic systems adopted. The structures of many current chaotic systems are fixed, making their behaviors highly predictable. Additionally, the range of chaotic region parameters is limited and discontinuous. To solve the above-mentioned problems, a new color image encryption algorithm (CIEA) using fractal and chaos theory is presented, which fully considers the inherent connection among the RGB components of color images. First, we propose a variable-structure discrete hyperchaotic system (VSDHS) to solve the dilemma encountered by existing chaotic systems. The excellent dynamic properties of VSDHS are verified by rigorous mathematical proof and simulation performance analyses. Then, VSDHS-CIEA is designed using fractal theory and VSDHS. The algorithm makes full use of the inherent connection among the different components of color images and performs cross-plane confusion. Simulations and performance analyses prove that VSDHS-CIEA has higher security and better performance than some representative image encryption algorithms.
The research of fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission. However, the lower efficiency of energy management has been a critical fac...
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Microgrids possess the ability to use renewable energy efficiently and play an increasingly significant role in environmental protection and sustainable development. Meanwhile, reversible solid oxide fuel cell (rSOC) ...
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With the further integration of information technology and industry, the computer numerical control (CNC) production line has gradually changed from the original isolated and closed mode to an open one. It faces not o...
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We consider the problem of maximizing a monotone nondecreasing set function under multiple constraints, where the constraints are also characterized by monotone nondecreasing set functions. We propose two greedy algor...
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We consider the problem of maximizing a monotone nondecreasing set function under multiple constraints, where the constraints are also characterized by monotone nondecreasing set functions. We propose two greedy algorithms to solve the problem with provable approximation guarantees. The first algorithm exploits the structure of a special class of the general problem instance to obtain a better time complexity. The second algorithm is suitable for the general problem. We characterize the approximation guarantees of the two algorithms, leveraging the notions of submodularity ratio and curvature introduced for set functions. We then discuss particular applications of the general problem formulation to problems that have been considered in the literature. We validate our theoretical results using numerical examples.& COPY;2023 Elsevier Ltd. All rights reserved.
In the industrial production environment, the scarcity of defect samples and the high labor cost of labeling defect samples make supervised machine learning models difficult to implement. In addition, defect detection...
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作者：
Zhang, LupingXu, FeiHuazhong Univ Sci & Technol
Sch Artificial Intelligence & Automation Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Hubei Peoples R China
The asynchronous spiking neural P system with rules on synapses (ASNPR system) is a type of distributive and non-deterministic computing model inspired by neural activities in biology. In this work, the synchronous ex...
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The asynchronous spiking neural P system with rules on synapses (ASNPR system) is a type of distributive and non-deterministic computing model inspired by neural activities in biology. In this work, the synchronous excitation/inhibition phenomenon of coupled neurons is abstracted as the all -or-none behavior of coupled neurons: spikes in the coupled neurons are supplemented/consumed if and only if all the coupled neurons in the same set synchronously receive/consume spikes. Based on all-or-none behaviors, the ASNPR system with coupled neurons (ASNPRC system) is introduced, where coupled neurons are grouped into several sets, and the changes in the content of coupled neurons in the same set are positively correlated. The computational power of the ASNPRC systems is investigated. It has been proven that ASNPRC systems using standard rules are Turing universal function-computing devices. Moreover, a universal ASNPRC system consisting of four neurons is constructed to compute functions. The results show that "coupled neurons"is an efficient ingredient for the computation power of ASNPR systems in the sense that ASNPR systems using relatively few neurons achieve their universality with the help of "couple neurons".(c) 2022 Elsevier B.V. All rights reserved.
We study the problem of novel view synthesis of objects from a single image. Existing methods have demonstrated the potential in single-view view synthesis. However, they still fail to recover the fine appearance deta...
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ISBN:
(纸本)9783031263187;9783031263194
We study the problem of novel view synthesis of objects from a single image. Existing methods have demonstrated the potential in single-view view synthesis. However, they still fail to recover the fine appearance details, especially in self-occluded areas. This is because a single view only provides limited information. We observe that man-made objects usually exhibit symmetric appearances, which introduce additional prior knowledge. Motivated by this, we investigate the potential performance gains of explicitly embedding symmetry into the scene representation. In this paper, we propose SymmNeRF, a neural radiance field (NeRF) based framework that combines local and global conditioning under the introduction of symmetry priors. In particular, SymmNeRF takes the pixel-aligned image features and the corresponding symmetric features as extra inputs to the NeRF, whose parameters are generated by a hypernetwork. As the parameters are conditioned on the image-encoded latent codes, SymmNeRF is thus scene-independent and can generalize to new scenes. Experiments on synthetic and real-world datasets show that SymmNeRF synthesizes novel views with more details regardless of the pose transformation, and demonstrates good generalization when applied to unseen objects. Code is available at: https://***/xingyi-li/SymmNeRF.
Herein, an accurate and efficient algorithm for digital-display instrument positioning and recognition is proposed. The isolated forest algorithm and Otsu watershed threshold algorithm were used to distinguish digital...
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Herein, an accurate and efficient algorithm for digital-display instrument positioning and recognition is proposed. The isolated forest algorithm and Otsu watershed threshold algorithm were used to distinguish digital-display instruments from nondigital-display instrument areas and separate the foreground from the background, respectively. The histogram of oriented gradient??? support vector machine classification algorithm was used to distinguish instrument and non-instrument regions, which considerably improved the accuracy of digital-display instrument region positioning, avoided the interference of non-digital tube character regions, and reduced the search time of the digital tube region. A convolutional neural network was used for character recognition. Global characteristics of the character region were fully utilized, and partial digital character issues and scenarios where the decimal point is not obvious were mitigated. The proposed method can adapt to angle deviation, partial character missing, and image noise and exhibits excellent robustness and adaptability to the location and recognition of the digital tube.
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