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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2023年第1期22卷 3-10页

作者： Jin, Yu Zhang, Luping Zhejiang Univ Sch Math Sci Hangzhou 310058 Zhejiang Peoples R China Huazhong Univ Sci & Technol Sch Automat Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Hubei Peoples R China

Asynchronous spiking neural P systems with rules on synapses (ARSSN P systems) are a class of computation models, where spiking rules are placed on synapses. In this work, we investigate the computation power of ARSSN P systems working in the rule synchronization mode, where a family of rule sets are specified, and all the rules in a such set should be synchronously used or not. We prove that ARSSN P systems working in the rule synchronization mode are universal as number generating. Additionally, two universal ARSSN P systems working in the rule synchronization mode are constructed to accept numbers and compute functions, respectively. The results indicate that rule synchronization set is a powerful ingredient for resource-saving, as the constructed universal ARSSN P systems with rule synchronization sets use less neurons than the counterpart universal systems without such sets.

关键词： Neurons Synchronization Synapses Standards Nanobioscience Biomembranes Heating systems Bio-inspired computing membrane computing spiking neural P systems rule synchronization universality

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BPR-Net: Balancing Precision and Recall for Infrared Small Target Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Du, Shuaiyuan Wang, Kewei Cao, Zhiguo Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Wuhan 430074 Peoples R China

Most current infrared small target detection methods attempt to fuse local and global information by using single-scale inputs and creating a multiscale feature pyramid during network feeding forward. Further to this, our research finds that using high-resolution inputs can improve recall, while low-resolution inputs improve precision. Nevertheless, solely focusing on global or local information can result in missing targets and false alarms. To address these issues, we propose the BPR-Net to balance precision and recall via a novel multiscale attention mechanism, which combines semantic and shallow features of multiscale inputs (MS). We first scale the input image into multiple images with varying resolutions and feed them into the network. In the encoder, the scale fusion module (SFM) fuses features from corresponding images of different resolutions. In the decoder, a channel fusion module (CFM) fuses useful information from multiple channels. Furthermore, a wavelet transform cross-layer skip layer (WTL) is employed to enhance the interaction between decoder layers for more effective multiscale feature fusion. Experimental results demonstrate that our approach achieves a balance between recall and precision and yields state-of-the-art performance on challenging benchmarks including Sirst, miss detection versus false alarm (MDvsFA), and small infrared aerial target detection (SIATD). Notably, our approach achieves an F1 score of 0.9409 on the challenging benchmark SIATD, surpassing the state-of-the-art method by 16.7%.

关键词： & nbsp Balancing precision and recall feature fusion infrared small target multiscale attention

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Attack Intention Oriented Dynamic Risk Propagation of Cyberattacks on Cyber-Physical Power Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第3期19卷 2453-2462页

作者： Hu, Bowen Zhou, Chunjie Tian, Yu-Chu Du, Xin Hu, Xiaoya Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Hubei Peoples R China Queensland Univ Technol Sch Comp Sci Brisbane Qld 4001 Australia

Advanced cyber-physical power systems (CPPS) has been put forward by the strong integration of energy networks and communication networks. While CPPS brings a promising solution with high efficiency, strong flexibility, great scalability, and improved reliability, it inevitably poses some security challenges. In order to address these challenges, it is essential to accurately describe the attack behavior and system security situation. In this article, a dynamic risk propagation evaluation approach is proposed for accurately predicting attacks and quantitatively analyzing system risk. It is equipped with a partitioned cellular automata model to deal with spatial heterogeneity in the partitioned system. The intentions of targeted attack are also considered for predicting attacks. Then, the cyber-to-physical risk is quantitatively identified from multiple dimensions. Finally, the verification of attack intention is designed to dynamically update and adjust the predicted result. The presented approach is demonstrated through a case study on a CPPS.

关键词： Computer crime Power systems Principal component analysis Analytical models Predictive models Power system dynamics Informatics Attack intention cyberattacks cyber-physical power systems (CPPS) partitioned cellular automata (PCA) risk propagation

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Synchronization of Delayed Complex Networks on Time Scales via Aperiodically Intermittent control Using Matrix-Based Convex Combination Method

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第6期34卷 2938-2950页

作者： Wan, Peng Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci Ence & Technol Minist China Key Lab Image Proc & Intelligent Control Educ Wuhan 430074 Peoples R China

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.

关键词： Synchronization Complex networks Lyapunov methods Delays Time-varying systems Convergence Technological innovation Complex networks convex combination method intermittent control synchronization time scale

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VSDHS-CIEA: Color image encryption algorithm based on novel variable-structure discrete hyperchaotic system and cross-plane confusion strategy

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INFORMATION SCIENCES 2024年 665卷

作者： Zhang, Hangming Hu, Hanping Ding, Weiping Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

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.

关键词： Color image encryption Chaotic system Fractal theory Cross-plane confusion strategy Synchronous confusion-diffusion

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Microgrid energy management strategy considering microgrid economy and rSOC stability: A model-based study 43

Microgrid energy management strategy considering microgrid e...

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43rd Chinese control Conference, CCC 2024

作者： Huang, Haizhou Xia, Zhiping Shan, Xinkai Wang, Jie Li, Xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

ISBN: (纸本)9789887581581

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) is currently a new type of energy conversion and storage. Combining with microgrid systems is a great way to benefit from its high capacity, high efficiency, and cleanliness. However, rSOC's complex system of coupled heat, electricity, and gas presents a significant challenge in finding energy management strategies for microgrid applications containing rSOC. In this study, for a microgrid system combining rSOC, battery energy storage system (BESS), solar photovoltaic (PV), and wind turbine generator (WTG), we propose a model-based energy management strategy that simultaneously takes into account the economy of the microgrid system and the stability of the rSOC system. First of all, the microgrid is modeled with great detail, including the wind turbine, solar panels, battery storage system, and rSOC. In addition, the microgrid energy management problem is framed as a mathematical programming problem, with the attention focusing on the costs of BESS aging and operational energy loss of rSOC. In particular, this study also proposes a penalty strategy to reduce the power fluctuation amplitude of the rSOC system and reduce the mode switching frequency of the rSOC system. Using the solver, solving for each device's target power in a microgrid. The actual power operating curve is output by tracking the target power under slope constraints using the Simulink model. We then conduct a case study using data from Wuhan, China. Our research shows that compared with the control strategy based on rule control, the control strategy proposed in this study can reduce the power variation amplitude of the rSOC system by 79%, reduce the mode switching frequency by 85%, reduce the system operating cost by 4.5% and increase the energy self-sufficiency rate by 9%. © 2024 Techn

关键词： Wind turbines

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A Security and Trust Protection Framework for Open CNC Production Line

A Security and Trust Protection Framework for Open CNC Produ...

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2022 Chinese Automation Congress, CAC 2022

作者： Dong, Zeyang Qin, Yuanqing Li, Zewei Wang, Hanwen Huazhong University of Science and Technology School of Artificial Intelligence and Automation MOE Lab of Image Processing and Intelligent Control Wuhan China

ISBN: (纸本)9781665465335

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 only internal threats, but also threats from the Internet. Starting from the characteristics and cybersecurity problem analysis of open CNC production lines, this paper proposes a security and trust protection framework based on trusted computing 3.0 and the concept of defense in depth, where a tri-element peer architecture is introduced to realize two-way authentication between the access device and the platform and an active immunity scheme for open CNC system is designed to ensure the trust from the source, and the trusted NC-Link agent/adaper are utilized to connect the heterogeneous equipments trustedly in the production line. The proposed framework can improve the information security level of the open CNC production line while maintaining its openness. © 2022 IEEE.

关键词： Trusted computing

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Fabric Defect Defection based on Lightweight Convolutional Denoising Auto-Encoder 22

Fabric Defect Defection based on Lightweight Convolutional D...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Jin, Dong Li, Wenzheng Sun, Zhigang Li, Xiao School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450396110

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 using deep learning models usually requires high computation cost and advanced hardware platform, which is not suitable for running on embedded platforms. To solve this problem, this paper proposes an unsupervised fabric defect detection method based on lightweight convolutional denoising auto-encoder(LCDAE). The LCDAE model is constructed by the bottleneck layer in MobileNetV3. Experimental results show that the method can effectively detect and localize defects with less computation and has the potential to perform defect detection on edge-embedded platforms. © 2022 ACM.

关键词： Defects

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SymmNeRF: Learning to Explore Symmetry Prior for Single-View View Synthesis 16th

SymmNeRF: Learning to Explore Symmetry Prior for Single-View...

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16th Asian Conference on Computer Vision (ACCV)

作者： Li, Xingyi Hong, Chaoyi Wang, Yiran Cao, Zhiguo Xian, Ke Lin, Guosheng Huazhong Univ Sci & Technol Sch AIA Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

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.

关键词： Novel view synthesis NeRF Symmetry HyperNetwork

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Adaptive Homophily Clustering: Structure Homophily Graph Learning With Adaptive Filter for Hyperspectral image

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Ding, Yao Zhang, Zhili Kang, Weijie Yang, Aitao Zhao, Junyang Feng, Jie Hong, Danfeng Zheng, Qinghe Xian Res Inst High Technol Key Lab Intelligent Control Xian 710025 Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Shandong Management Univ Sch Intelligent Engn Jinan 250357 Peoples R China

Hyperspectral image (HSI) clustering is a fundamental yet challenging task that typically operates without training labels. Recent advancements in deep graph clustering methods have shown promise for HSI due to their ability to effectively encode spatial structural information. However, limitations such as inadequate utilization of structural information, poor feature representation, and weak graph update capabilities hinder their performance. In this article, we propose an adaptive homophily structure graph clustering (AHSGC) method for HSI. Our approach begins with the generation of homogeneous regions to process HSI and construct the initial graph. Next, we design an adaptive filter graph encoder that captures both high and low-frequency features for subsequent processing. We then develop a graph embedding clustering self-training decoder using KL Divergence to generate pseudo-labels for network training. To enhance graph learning, we introduce homophily-enhanced structure learning, which updates the graph based on the clustering task. This involves estimating node connections through orient correlation estimation and dynamically adjusting graph edges via graph edge sparsification. Finally, we implement joint network optimization to facilitate self-training and graph updates, with K-means used to express latent features. The clustering accuracy on three datasets is 83.60%, 63.65%, and 86.03%, the FLOPs are 3.57G, 30.62G, and 2.95G. The source code will be available at https://***/DY-HYX.

关键词： Training Correlation Clustering methods Source coding Adaptive filters Estimation Reinforcement learning Decoding Optimization Hyperspectral imaging Adaptive filter graph encoder homophily-enhanced structure learning hyperspectral image (HSI) clustering joint network optimization

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