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Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment

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computer Modeling in engineering & Sciences 2023年第6期135卷 2243-2266页

作者： Zhengtao Xi Chaofan Song Jiahui Zheng Haifeng Shi Zhuqing Jiao School of Microelectronics and Control Engineering Changzhou UniversityChangzhou213164China School of Computer Science and Artificial Intelligence Changzhou UniversityChangzhou213164China Department of Radiology Changzhou Second People’s Hospital Affiliated to Nanjing Medical UniversityChangzhou213003China

The structure and function of brain networks have been altered in patients with end-stage renal disease(ESRD).Manifold regularization(MR)only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain *** solve this issue,we developed a method to construct a dynamic brain functional network(DBFN)based on dynamic hypergraph MR(DHMR)and applied it to the classification of ESRD associated with mild cognitive impairment(ESRDaMCI).The construction of DBFN with Pearson’s correlation(PC)was transformed into an optimization *** convolution and hyperedge convolution superposition were adopted to dynamically modify the hypergraph structure,and then got the dynamic hypergraph to form the manifold regular terms of the dynamic *** DHMR and L_(1) norm regularization were introduced into the PC-based optimization model to obtain the final DHMR-based DBFN(DDBFN).Experiment results demonstrated the validity of the DDBFN method by comparing the classification results with several related brain functional network construction *** work not only improves better classification performance but also reveals the discriminative regions of ESRDaMCI,providing a reference for clinical research and auxiliary diagnosis of concomitant cognitive impairments.

关键词： End-stage renal disease mild cognitive impairment brain functional network dynamic hypergraph manifold regularization classification

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A Method for Abnormal Detection and Poisoned Data Recovery in Clustered Federated Learning 18th

A Method for Abnormal Detection and Poisoned Data Recovery...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Chen, Yingying Shi, Lei Xu, Hao Ye, Junyu Xu, Juan School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei230009 China

ISBN: (纸本)9783031714665

Clustered Federated Learning (CFL), as a type of Federated Learning (FL) paradigm, divides the clients into multiple clusters through the clustering process and trains them within the clusters, thus improving the overall model training accuracy. But the clustering process is also the weak link, which can lead to the failure of the whole model training if it is interfered or destroyed by the attacker. In this paper, we study the attack methods and defense strategies for attackers to implement data poisoning by tampering with client data, resulting in overall clustering failure. Our defense approach is designed to identify anomalous clients and recover their poisoned data. In order to resist the malicious behavior of attackers, we propose a dual-threshold pixel difference and watermark authentication defense method for detecting the presence of anomalous clients. The method can accurately identify the abnormal fluctuations in the data and effectively screen out the abnormal clients. Meanwhile, we propose a self-embedding watermarking defense algorithm based on shuffling idea for recovering poisoned data. Among them, our self-embedding watermarking algorithm not only accurately locates and recovers the tampered region of the image, but also improves the watermark security to prevent attackers from cracking it easily. Simulation results show that our proposed algorithm can accurately identify the abnormal client and recover the data. Even if the client’s data is massively tampered with, we can recover the tampered data images with high quality and ensure that the CFL clustering is not corrupted. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Named Entity Recognition for Safety Risks in Electrical Work

Named Entity Recognition for Safety Risks in Electrical Work

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Liu, Peng Fan, Shanshan Cao, Xiaofan Jia, Yujiao Yang, Jianing Institute of Energy and Electrical Engineering Changchun University of Science and Technology Changchun China School of Control and Computer Engineering North China Electric Power University Beijing China School of Electrical Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798350378214

The texts of safety risks in eletrical work describe the safety risks involved and management measures. The text records information on potential risks, risk levels, and preventive measures within the electrical work, which is crucial for extracting and mining safety risks. Named Entity Recognition for Safety Risks in Electrical Work (EWSR-NER) is a key technique for information extraction and knowledge discovery. It identifies objects related to safety risks in electrical work, such as risk level, risk factors, risk categories and protective measures, which convey critical meanings within the text. Current Named Entity Recognition (NER) is used to identify generic entities. However, their accuracy is limited by inherent linguistic irregularities in natural language texts, such as nested entities, lengthy entities, and the scarcity of domain-specific annotated corpora. To address this issue, the established standards and principles of safety risk texts in electrical work were followed to analyze the structure and characteristics of the text, developing a dataset of safety risk in electrical work. A new model, RoBERTa-BiLSTM-Efficient GlobalPointer (RoBEGP), was proposed in this paper. It uses the Robertly Optimized Bidirectional Encoder Representation from Transformers Pretraining Approach(RoBERTa) pretraining model to learn the characteristics of electrical work content and generate representation vectors. These representation vectors are input into a dynamic fusion layer, which adjusts the weights of each layer's representation vectors based on the specific requirements of the task, reducing the dimensionality from 1024 to 512. The fused vectors are input into a Bidirectional Long Short-Term Memory(BiLSTM) network to generate feature representations. Efficient GlobalPointer decodes these representations to identify nested entities within the text. Experimental results show that the proposed model achieves an F1 score of 92.38% on a self-constructed dataset of safe

关键词： Risk assessment

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Hierarchical Energy Management Strategy of Hybrid Electric Truck Fleet under Connected Environment 43

Hierarchical Energy Management Strategy of Hybrid Electric T...

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43rd Chinese control Conference, CCC 2024

作者： Lu, Qizhe Tang, Wenbin Pang, Zitong Zhang, Yahui Jiao, Xiaohong Yanshan University School of Electrical Engineering Qinhuangdao066004 China Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Hebei Province Qinhuangdao066004 China Yanshan University School of Mechanical Engineering Qinhuangdao066004 China

ISBN: (纸本)9789887581581

Considering the slow-moving and energy-consuming issues that tend to occur when truck fleets drive, this paper proposes a hierarchical energy management strategy (EMS) for the hybrid electrical truck (HET) fleet, including speed planning and energy management. The speed threshold range that allows the whole fleet to pass through the traffic light is computed in the upper-layer speed planning. Within the model predictive control framework, a multi-objective optimization function is constructed for speed planning to find the optimum solution. In the lower-layer energy management, an offline model is established for power allocation between the Integrated Starter Generator and the Electric Motor and gear shift selection of automated manual transmission to reduce the dimensionality of the control variables. Then, an adaptive equivalent consumption minimum strategy (A-ECMS) is designed based on the fleet's optimal speed provided. In the offline part, the transformation relationship between the co-state and equivalence factor is established based on Pontryagin's minimum principle (PMP), and PMP obtains the globally optimal control sequence in the historical driving cycle to train a back-propagation neural network (BPNN). The BPNN model predicts the current optimal co-state in the online part, transformed into a real-time optimal equivalence factor to guide the A-ECMS online application. The simulation results indicate that the fuel economy of the proposed EMS is comparable to that of the global optimization method PMP and superior to that of Fixed EF-ECMS and PI-ECMS. Additionally, it enables the HET fleet to pass through traffic lights without stopping, thereby ensuring queue continuity and improving the passage efficiency of the fleet. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Predictive control systems

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A Study of Data Augmentation for Imbalance Problem in Network Traffic 3

A Study of Data Augmentation for Imbalance Problem in Networ...

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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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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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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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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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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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