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3rd International Conference on Blockchain Technology and Information Security, ICBCTIS 2023

作者： Wang, Xu Sun, Shimin Han, Li School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong University Tianjin China School of Computer Science and Engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350340402

The complexity and diversity of smart grid systems increase the likelihood of anomalies in communications between devices in the system, and how these anomalies are detected is critical to the security of the grid system. However, the distribution of traffic types in the grid is often highly unbalanced, with the total amount of normal traffic data often far outweighing the anomalous data. This imbalance compromises the effectiveness of the detection model and poses a significant threat to the overall security of the grid This paper improves on traditional conditional generation adversarial neural networks and designs a new approach to overcome this obstacle and enhance the security of smart grid systems. In the generator part, the spatial features of the data are captured by convolutional neural networks, and the temporal features of the data are captured by gated recurrent units. By integrating these two components into the generative network we can generate synthetic data with both temporal and spatial features, thus enhancing the minority class samples. By incorporating the augmented minority data into the training process, the proposed approach enables the detection model to understand the distribution of features of the minority anomaly samples more accurately. This improved learning accuracy translates into improved model performance, resulting in higher detection rates for Previously under-represented anomalies. In addition, our approach effectively addresses the inherent bias towards traffic types with higher flows typically observed in asymmetric datasets, thus providing a more equitable and comprehensive security solution for smart grid systems. © 2023 IEEE.

关键词： Anomaly detection

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Excellent sensitivity and selectivity of g-C4N3 monolayer based gas senors for efficiently detecting nitrogen oxides: Spin-resolved quantum transport behaviors

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IEEE Sensors Journal 2025年第11期25卷 20687-20694页

作者： Zhou, Wensheng Luo, Cheng Tang, Peng Xiao, Xianbo Xionga, Songbo Chen, Tong Jiangxi University of Science and Technology School of Energy and Mechanical Engineering Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Nanchang330013 China Jiangxi University of Chinese Medicine School of Computer Science Nanchang330004 China

NO and NO2 are important gases that can lead to the formation of photochemical smog and acid rain. Therefore, it is important to develop high-performance nitrogen oxide sensors. Here, the electronic structure, spin transport characteristics, and gas sensitivity of two-dimensional graphitic carbon nitride (2D g-C4N3) monolayers with doped magnetic metal atoms were studied via first-principles methods. A g-C4N3 monolayer exhibited ferromagnetic half-metallic and high-sensitivity properties for NO and NO2 detection. The monolayer nanodevices also exhibited perfect single-spin conduction with a spin-filtering efficiency of up to 100%. A designed g-C4N3 monolayer sensor strongly chemisorbed both NO and NO2, resulting in the formation of electrons in the local scattering region. This directly affected spin transport properties, resulting in NO and NO2 sensitivity up to 100%. A g-C4N3 sensor with transition metal doping (especially Fe) slightly improved NO detection and significantly reduced NO2 detection. Hence, a Fe-doped g-C4N3 sensor in a complex gas environment containing both NO and NO2 largely eliminated NO2 interference and increased NO detection. A g-C4N3 monolayer with excellent half-metal properties and high gas sensitivity will have potential applications in high-performance spin devices and spin-gas sensitive devices. © 2001-2012 IEEE.

关键词： Nitrogen oxides

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Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution Supplementary Material

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IEEE Transactions on Image Processing 2025年 34卷 3013-3024页

作者： Yuan, Jiang Ma, Ji Wang, Bo Hu, Weiming North China Electric Power University School of Control and Computer Engineering Beijing102206 China CASIA State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Implicit degradation modeling-based blind super-resolution (SR) has attracted more increasing attention in the community due to its excellent generalization to complex degradation scenarios and wide application range. How to extract more discriminative degradation representations and fully adapt them to specific image features is the key to this task. In this paper, we propose a new Content-decoupled Contrastive Learning-based blind image super-resolution (CdCL) framework following the typical blind SR pipeline. This framework introduces negative-free contrastive learning technique for the first time to model the implicit degradation representation, in which a new cyclic shift sampling strategy is designed to ensure decoupling between content features and degradation features from the data perspective, thereby improving the purity and discriminability of the learned implicit degradation space. In addition, we propose a detail-aware implicit degradation adapting module that can better adapt degradation representations to specific LR features by enhancing the basic adaptation unit's perception of image details, significantly reducing the overall SR model complexity. Extensive experiments on synthetic and real data show that our method achieves highly competitive quantitative and qualitative results in various degradation settings while obviously reducing parameters and computational costs, validating the feasibility of designing practical and lightweight blind SR tools. Codes and models will be available at https://***/Fieldhunter/CdCL. © 1992-2012 IEEE.

关键词： Depression Anxiety disorders Face recognition Schizophrenia Visualization Training Data collection Uncertainty Facial features Emotion recognition

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Learning Communities from Equilibria of Nonlinear Opinion Dynamics 63

Learning Communities from Equilibria of Nonlinear Opinion Dy...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Xing, Yu Bizyaeva, Anastasia Johansson, Karl H. Kth Royal Institute of Technology and with Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm Sweden Cornell University Sibley School of Mechanical and Aerospace Engineering IthacaNY United States

ISBN: (纸本)9798350316339

This paper studies community detection for a nonlinear opinion dynamics model from its equilibria. It is assumed that the underlying network is generated from a stochastic block model with two communities, where agents are assigned with community labels and edges are added independently based on these labels. Agents update their opinions following a nonlinear rule that incorporates saturation effects on interactions. It is shown that clustering based on a single equilibrium can detect most community labels (i.e., achieving almost exact recovery), if the two communities differ in size and link probabilities. When the two communities are identical in size and link probabilities, and the intercommunity connections are denser than intra-community ones, the algorithm can achieve almost exact recovery under negative influence weights but fails under positive influence weights. Utilizing fixed point equations and spectral methods, we also propose a detection algorithm based on multiple equilibria, which can detect communities with positive influence weights. Numerical experiments demonstrate the performance of the proposed algorithms. © 2024 IEEE.

关键词： Nash equilibrium

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Biomedically Informed ECG Synthesis: Customizing Cardiac Cycle Phases with Diffusion Model

Biomedically Informed ECG Synthesis: Customizing Cardiac Cyc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Yuxin Ma, Jing Wang, Wei Wu, Zhihao Dong, Suyu Luo, Gongning Wang, Kuanquan Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology Faculty of Computing Harbin China

ISBN: (纸本)9798350386226

Cardiovascular diseases are a major global health challenge, with electrocardiography (ECG) being critical for diagnosis and monitoring. As artificial intelligence and automated ECG diagnostic technologies rapidly advance, the demand for large-scale ECG databases continues to grow. Generative ECG has become a mainstream method to enhance database size and diversity. However, existing methods typically generate ECG randomly or focus on limited physiological categories, lacking the ability to synthesize ECG with varying physiological features and cardiac cycles, which is crucial for various practical applications. In response to this need, we propose a novel approach introducing a diffusion model called DIFF-ECG to generate precisely customized ECG that accurately reflect diverse cardiac conditions. Segmentation-based quality assessments confirmed that the synthesized ECG accurately followed the specified cardiac cycle information, with our model significantly outperforming baseline diffusion and GAN-based methods. Therefore, our approach addresses the critical need for generating clinically relevant and customizable ECG, contributing significantly to the field of automated cardiac disease diagnosis. By enabling fine-tuning of cardiac cycle phases, our method significantly expands the application range of generative ECG, potentially improving the diagnostic accuracy for rare diseases and advancing personalized medicine. © 2024 IEEE.

关键词： Electrocardiography

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Achieving Fine-Grained and Flexible Access control on Blockchain-Based Data Sharing for the Internet of Things

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China Communications 2022年第6期19卷 22-34页

作者： Ruimiao Wang Xiaodong Wang Wenti Yang Shuai Yuan Zhitao Guan School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Finance Operationsand Information SystemsBrock UniversityCanada

The traditional centralized data sharing systems have potential risks such as single point of failures and excessive working load on the central *** a distributed and collaborative alternative,approaches based upon blockchain have been explored recently for Internet of Things(IoTs).However,the access from a legitimate user may be denied without the pre-defined policy and data update on the blockchain could be costly to the *** this paper,we first address these issues by incorporating the Accountable Subgroup Multi-Signature(ASM)algorithm into the Attribute-based Access control(ABAC)method with Policy Smart Contract,to provide a finegrained and flexible ***,we propose a policy-based Chameleon Hash algorithm that allows the data to be updated in a reliable and convenient way by the authorized ***,we evaluate our work by comparing its performance with the *** results demonstrate significant improvement on the effectiveness and efficiency.

关键词： blockchain access control smart contract multi-signature chameleon-hash data sharing Internet of Things

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Intelligent Proximate Analysis of Coal Based on Near-Infrared Spectroscopy and Multioutput Deep Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1398-1410页

作者： Zou, Liang Qiao, Jiahui Yu, Xinhui Chen, Xun Lei, Meng School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

Proximate analysis of coal indicates the moisture, ash, volatile content, and calorific value, which has been widely utilized as the basis for coal characterization. It involves heating the coal under various conditions until a constant weight is obtained. Although it is a relatively simple process that does not require expensive analytical equipment, determining these characteristics is time consuming. An alternative way for proximate analysis is spectral analysis in combination with various machine learning methods. However, most previous works analyze individual characteristics and fail to explore the relationship among them. In this study, we propose a method for proximate analysis based on near-infrared spectroscopy and a multioutput attention Unet (MOA-Unet), which can predict multiple characteristics simultaneously. First, an attention-based Unet is designed as the shared feature extraction subnetwork, including an encoder, a decoder, convolutional block attention modules, and multiscale feature fusion modules, which can improve the representation power of the U-shape network through aggregating features of shallower layers and concatenating features of deeper layers. Second, four individual subnetworks with fully connected layers, designed for four outputs, are utilized for regressing those four characteristics. We employ the gradient normalization algorithm to alleviate the gradient magnitude masking effect caused by training imbalance among different tasks. The proposedMOA-Unet is compared with classical chemometric methods on 670 coal samples from on-site *** experimental results demonstrate that the proposedmodel achieves state-of-the-art performance with correlation coefficients of 0.9015, 0.9538, 0.8986, and 0.8884, corresponding to moisture, ash, volatile content, and calorific value, respectively. Impact Statement-The proximate analysis of coal has been widely utilized as the basis for determining the rank of coal which is in connection with coa

关键词： Feature extraction

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Novel Cooperative Particle Swarm Optimization with Interactive Learning Strategy 1

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Qiliang Liu, Yiheng Xu, Hao Zhang, Pinggai Wu, Qinlin Wei, Lisheng Anhui Key Laboratory of Electric Drive and Control School of Electrical Engineering Anhui Polytechnic University Anhui Wuhu241000 China Industrial Process Control Optimization and Automation Engineering Research Center School of Electronic Engineering Chaohu University Anhui Chaohu238024 China School of Computer Science and Artificial Intelligence Chaohu University Anhui Chaohu238024 China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

Particle Swarm Optimization (PSO) is an optimization method grounded in swarm intelligence, designed to mimic the social behaviors of bird flocks during hunting. By sharing information about their positions, the entire swarm can explore and search diverse areas within a given space. This method features rapid convergence, minimal parameters, and straightforward implementation, making it a potent and efficient metaheuristic technique. Nevertheless, conventional PSO algorithms, despite their simplicity and efficiency, often become trapped in local optima when tackling complex multimodal problems, which limits their global search capability. To mitigate these limitations, a new Cooperative Particle Swarm Optimization algorithm with Interactive Learning (CPSOIL) is introduced. This approach incorporates an interactive learning strategy, allowing particles to learn from their neighbors by perceiving the surrounding particles during updates. Moreover, a mechanism has been added to modify the particle velocity update when the algorithm encounters local optima, thereby preserving population diversity, enhancing the likelihood of escaping local optima, and improving algorithm accuracy. Comparisons with other leading algorithms on the CEC2005 benchmark functions indicate that, in most cases, the enhanced algorithm outperforms its counterparts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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Disentangled Feature Distillation for Light Field Super-Resolution with Degradations

Disentangled Feature Distillation for Light Field Super-Reso...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Zhou, Linjie Gao, Wei Li, Ge Yuan, Hui Zhao, Tiesong Yue, Guanghui School of Electronic and Computer Engineering Peking University Shenzhen China School of Control Science and Engineering Shandong University Jinan China Fuzhou University Department of Communication Engineering Fuzhou China School of Biomedical Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350313154

Light field (LF) super-resolution has achieved remarkable results with the assumption of only downsampling. However, real-world LF scenes contain multiple degradation effects, which makes it difficult for existing methods to deal with hybrid distortions. In this paper, we propose a disentangled feature distillation framework for LF super-resolution with degradations. To reduce the learning difficulty, we propose a feature disentanglement mechanism to split the mixed reconstruction for both super-resolution and denoising into two single task learning processes. We also propose a feature enhancement strategy via knowledge distillation to transfer prior feature of each single reconstruction to our task of mixed reconstruction. Finally, the separate restored representations are fused to reconstruct a clean high-resolution LF. Experiments demonstrate the superior performance of our framework for different scale factors and noise levels. Additionally, our approach can also obtain excellent performance for joint super-resolution and deblurring, showing its gencralization for practical LF super-resolution applications. © 2023 IEEE.

关键词： Distillation

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Ante-Hoc Generation of Task-Agnostic Interpretation Maps

Ante-Hoc Generation of Task-Agnostic Interpretation Maps

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Akash Guna, R.T. Benitez, Raul Sikha, O.K. Amrita School of Engineering Department of Computer Science and Engineering Coimbatore India Universitat Politècnica de Catalunya BarcelonaTech Departament of Automatic Control Spain

ISBN: (纸本)9798350302493

Existing explainability approaches for convolutional neural networks (CNNs) are mainly applied after training (post-hoc) which is generally unreliable. Ante-hoc explainers trained simultaneously with the CNN are more reliable. However, current ante-hoc explanation methods mainly generate inexplicit concept-based explanations tailored to specific tasks. To address these limitations, we propose a task-agnostic ante-hoc framework that can generate interpretation maps to visually explain any CNN. Our framework simultaneously trains the CNN and a weighting network - an explanation generation module. The generated maps are self-explanatory, eliminating the need for manual identification of concepts. We demonstrate that our method can interpret classification, facial landmark detection, and image captioning tasks. We show that our framework is explicit, faithful, and stable through experiments. To the best of our knowledge, this is the first ante-hoc CNN explanation strategy that produces visual explanations generic to CNNs for different tasks. © 2023 IEEE.

关键词： computer vision

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