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arXiv 2023年

作者： Ma, Jie Wang, Pinghui Wang, Zewei Kong, Dechen Hu, Min Han, Ting Liu, Jun The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The China Mobile Research Institute China The Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favorable out-of-distribution generalization ability, while non-debiasing methods sacrifice a considerable amount of out-of-distribution performance in order to obtain high in-distribution performance. Therefore, it is challenging for them to deal with the complicated changing real-world situations. In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and the current optimization state on mini-batch training data. This loose optimization can be used to prevent non-debiasing methods from overlearning data bias while enabling debiasing methods to maintain slight bias learning. Experiments on the visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2, GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases. The source code has been released publicly in https://***/reml-group/ALO. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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A Fault Diagnostic Method for Rolling Bearings Based on CAR-SNN Model

A Fault Diagnostic Method for Rolling Bearings Based on CAR-...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Qiuting Li Xiuqing Wang Wenxia Du College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China College of China Combustion Engineering Hebei Normal University Shijiazhuang China

In the era of big data, high-performance rolling bearing fault diagnostic methods are of great significance to ensure manufacturing smoothly and avoid mass economic losses. A rolling bearing fault diagnostic CAR-SNN model based on Convolutional Block Attention Module (CBAM), Residual Network (ResNet), and Spiking Neural Networks (SNNs) is proposed based on deep learning (DL). The CAR-SNN model combines CBAM, ResNet, and SNNs to extract critical features while avoiding gradient vanishing or explosion phenomena. The ability of the CAR-SNN model to process spatio-temporal information is improved by using SNNs that incorporate spatio-temporal information simultaneously. The proposed model is used for the rolling bearing fault diagnosis, which datasets are from the experimental platforms of CWRU and Jiangnan University, with the highest diagnostic accuracy 99.995% and 99.18%, respectively. The experimental results validate the effectiveness and stability of the proposed model. The results of comparison experiments show that the CAR-SNN model has better fault diagnostic accuracy and stability than that of the CAR model and other comparison methods. The proposed data-driven rolling bearing fault diagnostic method based on the CAR-SNN model is easy to be implemented, and meeting the needs of high fault diagnostic accuracy in practical applications, and provides valuable input for fault diagnosis of rolling bearings.

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Diffusion-Based mmWave Radar Point Cloud Enhancement Driven by Range Images

arXiv

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arXiv 2025年

作者： Wu, Ruixin Li, Zihan Wang, Jin Xu, Xiangyu Yu, Huan Zheng, Zhi Huang, Kaixiang Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China State Key Laboratory of Robotics and Systems Department of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China

Millimeter-wave (mmWave) radar has attracted significant attention in robotics and autonomous driving. However, despite the perception stability in harsh environments, the point cloud generated by mmWave radar is relatively sparse while containing significant noise, which limits its further development. Traditional mmWave radar enhancement approaches often struggle to leverage the effectiveness of diffusion models in super-resolution, largely due to the unnatural range-azimuth heatmap (RAH) or bird’s eye view (BEV) representation. To overcome this limitation, we propose a novel method that pioneers the application of fusing range images with image diffusion models, achieving accurate and dense mmWave radar point clouds that are similar to LiDAR. Benefitting from the projection that aligns with human observation, the range image representation of mmWave radar is close to natural images, allowing the knowledge from pre-trained image diffusion models to be effectively transferred, significantly improving the overall performance. Extensive evaluations on both public datasets and self-constructed datasets demonstrate that our approach provides substantial improvements, establishing a new state-of-the-art performance in generating truly three-dimensional LiDAR-like point clouds via mmWave radar. Copyright © 2025, The Authors. All rights reserved.

关键词： Radar imaging

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Stealthy False data Injection Attacks in Multi-Channel Vehicular Communication: White-Box and Gray-Box Strategies

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IEEE Transactions on Vehicular Technology 2025年

作者： Xu, Chongcong Zheng, Fangfang Guo, Ge Southwest Jiaotong University School of Transportation and Logistics Chengdu611756 China Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Chengdu611756 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China

Understanding the vulnerabilities of vehicular networks from an attacker's perspective is crucial for identifying stealthy false data injection (FDI) attacks and developing robust defense methods. This paper investigates stealthy FDI attacks in multi-channel vehicular communication systems. We construct a multi-source state estimation system using a sequential Kalman filter to integrate and estimate redundant state information transmitted by preceding vehicles across multiple channels. To enhance the system's resilience against FDI attacks, we incorporate a Sage-Husa adaptive filter and a chi-squared detector, which dynamically estimate and detect channel noise based on estimation errors. Within this framework, we utilize real-time state information and channel transmission residual to determine the probability of an attack window occurring. The attack process is divided into three phases, during which critical noise disturbances are introduced to create attack windows, allowing the attack to progressively align with the target state. Based on this analysis, we develop an optimization model for attack generation with complete channel information (AG-with-CCI) to facilitate a white-box attack. Additionally, acknowledging that attackers may not have access to real-time information from all channels, we analyze the iterative equations of state estimation errors and residuals to establish the conditions under which stealthy FDI attacks can be successfully executed, enabling the attack generation with attacked channel information (AG-with-ACI) to achieve a gray-box attack. Numerical simulations demonstrate that both attack methods can evade current detection mechanisms and achieve their intended objectives. © 2025 IEEE.

关键词： Adaptive filtering

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A Multi-direction Prediction Multi-objective Hybrid Chemical Reaction Optimization Algorithm for Dynamic Multi-objective Optimization 16th

A Multi-direction Prediction Multi-objective Hybrid Chemical...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Li, Hongye Pan, Xiaoying Gan, Wei Wang, Lei School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China Xi’an Shiyou University Shaanxi Xi’an710065 China School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi University of Technology Shaanxi Han Zhong723001 China

ISBN: (纸本)9789811912559

The challenge of solving dynamic multi-objective optimization problems is to trace the varying Pareto optimal front and/or Pareto optimal set quickly and efficiently. This paper proposes a multi-direction prediction strategy using a hybrid chemical reaction optimization, aimed at finding the dynamic Pareto optimal front and/or Pareto optimal set as quickly and accurately as possible before the next environmental change occurs. The proposed method, multi-direction prediction multi-objective hybrid chemical reaction optimization algorithm which mainly includes a hybrid chemical reaction optimization algorithm is proposed for solving dynamic multi-objective problems, which can guide population trace the optimum. When the environment has changed, the population is divided into several subpopulations. In subpopulation, a center point was found to construct multi-direction prediction model. As a result, this approach enhances the diversity of algorithm. While the environment has not changed, the hybrid chemical reaction algorithm with particle swarm optimization algorithm can efficiency find the optimal solution, it can achieve good diversity as well as guarantee the avoidance of local optimal solutions. The proposed algorithm is measured on several benchmark test suites with various dynamic characteristics and different difficulties. Experimental results show that this algorithm is very competitive in dealing with dynamic multi-objective optimization problems when compared with four state-of-the-art approaches. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Relep: Region and Edge Synergetic Level Set Model Based on Edge-Enhanced Entropy and Variable Exponential P-Laplace Equation

SSRN

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SSRN 2023年

作者： Wu, Jiangfeng Zhou, Zhiheng Zhou, Wenlue Tao, Xiyuan Deng, Ming School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou China

Image segmentation under conditions such as noise and intensity inhomogeneity poses a significant challenge for models. Hybrid level set approaches integrating area and edge information show promise in addressing these challenges. In this study, we propose a novel hybrid level set model, called region and edge synergetic level set model based on edge-enhanced entropy and variable exponential p-Laplace equation (RELEP), which is based on an edge-enhanced entropy and a variable exponential p-Laplace equation. Firstly, a new probability distribution is defined to enhance the edge extraction ability of image entropy in the presence of image intensity inhomogeneity and noise, termed as edge-enhanced entropy. The feature map obtained after edge-enhanced entropy processing is then used to co-fit the energy function with the original map to form the region term. Furthermore, the incorporation of edge-enhanced entropy into the p-Laplace equation leads to the formation of a variable exponential p-Laplace equation edge term. This integration facilitates the acquisition of precise and smooth boundaries, while simultaneously bolstering the robustness of the model. Finally, by incorporating the region term and edge term into the variational level set framework, the RELEP model is proposed. Experimental results on both synthetic and natural images demonstrate that the proposed RELEP model outperforms several representative and state-of-the-art models in terms of segmentation performance. © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Graph Structure Prefix Injection Transformer for Multi-Modal Entity Alignment

SSRN

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SSRN 2024年

作者： Li, Zhifei Luo, Xiangyu Zhang, Miao Zhang, Yan Xiao, Kui School of Computer Science and Information Engineering Hubei University Wuhan430062 China School of Cyber Science and Technology Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan430062 China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University Wuhan430062 China

Multi-modal entity alignment aims to integrate equivalent entities in diverse multi-modal knowledge graphs. However, previous studies have primarily focused on multi-modal fusion methods without considering the impact of graph structure heterogeneity on entity alignment tasks due to the distinct structural features of different knowledge graphs. Moreover, the multi-modal information that enriches entity representations, including image, textual content, and numerical attributes, frequently incorporates noise due to modal discrepancies. To tackle these challenges, we introduce the Graph Structural Prefix Injection transformer (GSIEA) for multi-modal entity alignment. This novel approach minimizes structural heterogeneity’s impact and optimizes structural data utilization by integrating a structural prefix inject interaction module within the multi-head attention mechanism. Then, we adopt a convolutional enhancement module to extract local features of multi-modal information and complete feature fusion by cross-modal weights. Extensive experiments on two datasets demonstrate that GSIEA outperforms previous baseline models, showcasing an average performance improvement of 3.26% on MRR, with a maximum enhancement of 12.5%. Additionally, the average performance improvement on Hits@1 is 4.96%, with a maximum enhancement of 16.92%. Our code is available at https://***/HubuKG/GSIEA. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Analysis of Interpretable Spiking Neural Networks for Motor Imagery Recognition Tasks

SSRN

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SSRN 2023年

作者： Wang, Xiuqing Yang, Yunpeng Li, Qingru Li, Qiuting An, Yang Ye, Xiaoya Key Laboratory of Network & Information Security College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Hebei Normal University Shijiazhuang China

Brain-computer interface (BCI) is an important aspect of human-computer interaction (HCI). Motor imagery (MI) EEG-based BCI has great application potential for assisting disabled people in motor function reconstruction and restoration. To fulfill the given tasks based on MI-EEG signals successfully, decoding MI-EEG signals accurately is important. Although deep learning (DL) can be used to analyze EEG signals, it still lacks interpretability. Compared with traditional artificial neural networks (ANNs), spiking neural networks (SNNs) can use spiking neurons which are bio-plausible for the neurons in the brain to communicate with each other, and analyze EEG signals by spike trains with better bio-interpretability. We propose a deep spiking convolutional neural network based on a self-attention (DSCNN-SA) mechanism for EEG-based MI recognition. The DSCNN-SA model is first used to extract the high-level spatio-temporal features of the EEG signal and transform the extracted features into spike trains as input to the deep spiking convolutional neural networks (DSCNNs), and analyze the EEG signal for MI according to the firing patterns of spiking neurons. The classification of 2-class motor imagery tasks is evaluated by the BCI Competition IV-2a and IV-2b datasets, and the average accuracy of the DSCNN-SA model is 85.53% and 81.52% respectively, which is better than that of the comparison models, such as KNN, MLP, DCNN-SA, ECCSP-TB2B, CNN-SAE, EEGNet, KLD and STNN, etc. The experimental results show that the DSCNN-SA model is effective for EEG-based MI recognition. © 2023, The Authors. All rights reserved.

关键词： Brain computer interface

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Multi-Hop Attention Diffusion Graph Neural Networks For Multimodal Fake News Detection

Multi-Hop Attention Diffusion Graph Neural Networks For Mult...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhongqiang Huang Dongli Lu Ying Sha Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China College of Informatics Huazhong Agricultural University Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Fake news often spreads rapidly on social media in various forms, exposing users to a large amount of misinformation and disinformation. Recently, promising results have been achieved in fake news detection. However, most of the approaches only consider one-hop neighborhood information in cross-modal mapping relationships or focus mainly on pairwise interactions of multimodal fake news. However, the fine-grained feature mapping of multimodal fake news may go beyond binary relations. To address these issues, we propose a Multi-Hop Attention Diffusion Graph Neural Networks in this work to capture and complement the multivariate mapping relationships between fine-grained features, while extracting cross-modal higher-order alignment representations for multimodal fake news detection via cross-modal contrastive learning and modal matching. Extensive experiments on three public datasets demonstrate the effectiveness of the proposed approach in the multimodal fake news detection task.

关键词： Visualization Social networking (online) Fuses Spread spectrum communication Contrastive learning Signal processing Feature extraction Graph neural networks Fake news Speech processing

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Event-Triggered single-network Control for Nonlinear Continuous-Time Zero-Sum Games with Partially Unknown Dynamics

Event-Triggered single-network Control for Nonlinear Continu...

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Cyber-Energy Systems and intelligent Energy (ICCSIE), International Conference on

作者： Binbin Peng Xiaohong Cui Zimeng Wan Haijiao Yu The College of Mechanical and Electrical Engineering China Jiliang University Hangzhou China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou China

This paper proposes an event-triggered integral reinforcement learning algorithm to solve the Hamilton-JacobiIsaacs (HJI) equation related to the zero-sum game problem. Then, we design a triggering rule to constrain the control updates. Secondly, the HJI partially differential equation is approximately solved using a single-critic neural network structure. The control law and disturbance law are modified when an event occurs. The system state and the network weights error are uniformly ultimately bounded (UUB) by Lyapunov theory. Finally, an engineering example is utilized to confirm the performance of the algorithm.

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