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arXiv 2023年

作者： Zhang, Tian Guan, Wenshan Miao, Hao Huang, Xiujie Liu, Zhiquan Wang, Chaonan Guan, Quanlong Fang, Liangda Duan, Zhifei Department of Cyberspace Security College of Information Science and Technology Jinan University Guangzhou511486 China Department of Computer Science College of Information Science and Technology Jinan University Guangzhou510632 China College of Information Science and Technology Jinan University Guangdong Institution of Smart Education Jinan University Guangzhou510632 China College of Cyber Security Jinan University Guangzhou510632 China Guangdong Gene Data Processing and Analysis Engineering Research Center China Pazhou Laboratory Guangzhou510330 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Guangzhou XPeng Motors Technology Co. Ltd No.8 Songgang Road Changxing Street Cencun Tianhe District Guangzhou China

The field of intelligent connected in modern vehicles continues to expand, and the functions of vehicles become more and more complex with the development of the times. This has also led to an increasing number of vehicle vulnerabilities and many safety issues. Therefore, it is particularly important to identify high-risk vehicle intelligent connected systems, because it can inform security personnel which systems are most vulnerable to attacks, allowing them to conduct more thorough inspections and tests. In this paper, we develop a new model for vehicle risk assessment by combining I-FAHP with FCA clustering: VSRQ model. We extract important indicators related to vehicle safety, use fuzzy cluster analys (FCA) combined with fuzzy analytic hierarchy process (FAHP) to mine the vulnerable components of the vehicle intelligent connected system, and conduct priority testing on vulnerable components to reduce risks and ensure vehicle safety. We evaluate the model on OpenPilot and experimentally demonstrate the effectiveness of the VSRQ model in identifying the safety of vehicle intelligent connected systems. The experiment fully complies with ISO 26262 and ISO/SAE 21434 standards, and our model has a higher accuracy rate than other models. These results provide a promising new research direction for predicting the security risks of vehicle intelligent connected systems and provide typical application tasks for VSRQ. The experimental results show that the accuracy rate is 94.36%, and the recall rate is 73.43%, which is at least 14.63% higher than all other known indicators. Copyright © 2023, The Authors. All rights reserved.

关键词： Vehicle safety

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TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

arXiv

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arXiv 2023年

作者： Yang, Xinquan Xie, Jinheng Li, Xuguang Li, Xuechen Li, Xin Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China National University of Singapore Singapore

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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A Machine learning based intrusion detection approach for industrial networks

A Machine learning based intrusion detection approach for in...

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IEEE International Conference on Industrial technology (ICIT)

作者： Hanli Qiao Jan Olaf Blech Huazhou Chen School of Science Center for Data Analysis and Algorithm Technology Guilin University of Technology China School of Electrical Engineering Aalto University Finland

ISBN: (数字)9781728157542

ISBN: (纸本)9781728157559

Industry 4.0 and Industrial Internet of Things (IIoT) are current trends in the industrial automation world. They require connections of factory networks to the internet. This trend increases the vulnerability of factory networks to attacks. Here, we present an approach that monitors the activities of factory network traffic based on two linear feature extraction algorithms, i.e. LDA and PCA. A Machine-Learning-based approach is used to analyze the records of network connections from the UNSW-NB15 database and to detect and report anomalies such as malicious attacks. The experimental results show the feasibility of the provided method in accuracy, detection rate, and false alarm rate.

关键词： Feature extraction Principal component analysis Training Testing Machine learning databases Computer security

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Dynamic Viscosity of the ABC-stacked Multilayer Graphene in the Collisionless Regime

arXiv

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arXiv 2024年

作者： Chen, Weiwei Shen, Yedi Zhan, Tianle Zhu, W. Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of Science China Jiliang University Hangzhou310018 China School of Science Westlake University 18 Shilongshan Road Hangzhou310024 China Hefei National Laboratory for Physical Sciences at the Microscale and Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Anhui Hefei230026 China Institute of Natural Sciences Westlake Institute for Advanced Study 18 Shilongshan Road Hangzhou310024 China

We explore the dynamic shear viscosity of the undoped ABC-stacked multilayer graphene based on the chiral-N effective Hamiltonian, where the chirality N is equivalent to the layer number. We investigate the dependence of the dynamic shear viscosity on the frequency in the collisionless regime and calculate Coulomb interaction corrections by three leading order Feynman diagrams: self-energy diagram, vertex diagram, and honey diagram. We propose that the dynamic shear viscosity is generated by the relaxation of momentum flux polarization through electron-hole excitations, and that the interaction can amplify this effect. Furthermore, our research indicates that the dynamic shear viscosity exhibits a robust linear positive dependence on N. This finding suggests that by making modifications to the number of layers in graphene, it is possible to finely tune the electron viscous effects. © 2024, CC0.

关键词： Graphene

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Power Grid Critical State Search Based on Improved Particle Swarm Optimization 6th

Power Grid Critical State Search Based on Improved Particle ...

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6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Luo, Jie Deng, Hui-Qiong Li, Qin-Bin Zheng, Rong-Jin Li, Pei-Qiang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou350108 China Fujian Provincial University Engineering Research Center of Smart Grid Simulation Analysis and Integrated Control Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

This article proposes a method to find the closest critical initial running state of power grid, aiming at the possible chain reaction failure caused by branch fault in power system. First of all, assuming the initial failure of power grid, considering the specific performance of relay protection, analyze the critical state of the branch in cascading trip in detail, the electrical distance between the current running state and the interlocking fault expressed in the form of nodal injection power is given. Secondly, on the basic of the relationship between the nodal injections power and the system safety, a safety level model that the system will not trigger cascading failures is established. Finally, the improved simplified mean particle swarm optimization algorithm is used to solve the model, and an example is analyzed on IEEE-14 node system. An example shows the effectiveness and feasibility of the proposed method, which provides a reference for further prevention of cascading trip accidents. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Outages

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A Causal Adversarial Graph Neural Network for Multi-center Autism Spectrum Disorder Identification

SSRN

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SSRN 2025年

作者： Zhang, Zhuan Wang, Lihui Chen, Qijian Wang, Li Jian, Caiqing Zhu, Yue-Min Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Due to the heterogeneous distributions of multi-center rs-fMRI data, currently it is still challenging to identify accurately autism spectrum disorder (ASD) patients from these heterogenous data. To deal with the influence of heterogeneous distributions on multi-center ASD diagnosis, a novel causal adversarial graph neural network (CAG) for multi-center ASD prediction is proposed in this work. CAG mainly consists of a graph filtering (GF) module and a learnable causal graph predictor (CGP). Specifically, the brain connection graph is first constructed from rs-fMRI data, after passing a GF module, the filtered graph is further divided by a causal subgraph and causally irrelevant subgraph. The ground-truth environmental labels and ASD labels are predicted from causal subgraph by a clustering method and a classification block, respectively. In addition, from the filtered graph and causally irrelevant subgraph, the environmental labels are predicted, while from the causally irrelevant subgraph the ASD labels are predicted. Assuming that the predicted environmental labels are independent to causal subgraph, while the predicted ASD labels are independent to causally irrelevant subgraph, accordingly, several adversarial losses are implemented by reversing the gradient. Through this adversarial learning strategy, the derived causal graph can predict multi-center ASD. The comparison experiments on both ABIDE-I and ABIDE-II datasets demonstrate that, the ACC and AUC of CAG has been improved by 3.78\% and 7.38\% at least comparing against the existing methods. Moreover, some abnormal functional connectivities in ASD group are identified from the causal subgraph which may provide useful imaging biomarkers for the early diagnosis of ASD. © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Cognitive protection and brain entropy changes from omega-3 polyunsaturated fatty acids supplement in late-life depression: A 52-week randomized controlled trial

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Journal of Affective Disorders 2024年 351卷 15-23页

作者： Lin, Chemin Lee, Shwu-Hua Huang, Chih-Mao Wu, Yu-Wen Chang, You-Xun Liu, Ho-Ling Ng, Shu-Hang Cheng, Ying-Chih Chiu, Chih-Chiang Wu, Shun-Chi Department of Psychiatry Keelung Chang Gung Memorial Hospital Keelung City Taiwan College of Medicine Chang Gung University Taoyuan County Taiwan Community Medicine Research Center Chang Gung Memorial Hospital Keelung Keelung Taiwan Department of Psychiatry Linkou Chang Gung Memorial Hospital Taoyuan County Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu Taiwan Department of Imaging Physics University of Texas MD Anderson Cancer Center Houston TX United States Department of Head and Neck Oncology Group Linkou Chang Gung Memorial Hospital and Chang Gung University Taoyuan Taiwan Department of Diagnostic Radiology Linkou Chang Gung Memorial Hospital and Chang Gung University Taoyuan Taiwan Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan Department of Psychiatry China Medical University Hsinchu Hospital China Medical University Hsinchu Taiwan Research Center of Big Data and Meta-analysis Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Psychiatry Taipei City Psychiatric Center Taipei City Hospital Taipei Taiwan Department of Psychiatry Taipei Medical University Taipei Taiwan

Background: Late-life depression (LLD) is associated with risk of dementia, yet intervention of LLD provides an opportunity to attenuate subsequent cognitive decline. Omega-3 polyunsaturated fatty acids (PUFAs) supplement is a potential intervention due to their beneficial effect in depressive symptoms and cognitive function. To explore the underlying neural mechanism, we used resting-state functional MRI (rs-fMRI) before and after omega-3 PUFAs supplement in older adults with LLD. Methods: A 52-week double-blind randomized controlled trial was conducted. We used multi-scale sample entropy to analyze rs-fMRI data. Comprehensive cognitive tests and inflammatory markers were collected to correlate with brain entropy changes. Results: A total of 20 patients completed the trial with 11 under omega-3 PUFAs and nine under placebo. While no significant global cognitive improvement was observed, a marginal enhancement in processing speed was noted in the omega-3 PUFAs group. Importantly, participants receiving omega-3 PUFAs exhibited decreased brain entropy in left posterior cingulate gyrus (PCG), multiple visual areas, the orbital part of the right middle frontal gyrus, and the left Rolandic operculum. The brain entropy changes of the PCG in the omega-3 PUFAs group correlated with improvement of language function and attenuation of interleukin-6 levels. Limitations: Sample size is small with only marginal clinical effect. Conclusion: These findings suggest that omega-3 PUFAs supplement may mitigate cognitive decline in LLD through anti-inflammatory mechanisms and modulation of brain entropy. Larger clinical trials are warranted to validate the potential therapeutic implications of omega-3 PUFAs for deterring cognitive decline in patients with late-life depression. © 2024

关键词： Cognitive decline Fish oil Late-life depression Multi-scale entropy Omega-3 PUFAs Resting-state fMRI

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EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced Graph Sample and Aggregate Inductive Learning algorithm

EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuejia Wu Jian-Tao Zhou Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

关键词： Training Knowledge engineering Convolution Aggregates Optimization methods Knowledge graphs Learning (artificial intelligence)

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A Fixed-Time Distributed algorithm for Least Square Solutions of Linear Equations 31

A Fixed-Time Distributed Algorithm for Least Square Solution...

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第31届中国控制与决策会议

作者： Wu Ai Shuling Li Jian Wu Quanxi Feng College of Science Guilin University of Technology Center for Data analysis and Algorithm Technology Guilin University of Technology School of Computer and Information Anqing Normal University University Key Laboratory of Intelligent Perception and Computing of Anhui Province

ISBN: (纸本)9781728101071;9781728101064

In this paper, we presents a fixed-time convergent distributed algorithm to achieve least square solutions of networked linear equations. Each agent in the network only knows a subset of the equations and can only exchange messages with its nearest neighbors. Compared with the finite-time algorithm, it is shown that the settling time is independent to the initial states of the algorithm and can be preassign according to requirements of the task. A numerical example is provided to illustrate the effectiveness of the theoretical result.

关键词： Fixed-time stability Distributed algorithms Least Squares Linear Equations

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Design of Leakage Current Monitoring System for Low Voltage Power Grid Based on NB-IoT 6th

Design of Leakage Current Monitoring System for Low Voltage ...

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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Xu, GuanWei Zheng, RongJin Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Institute of Engineering Smart Grid Simulation Analysis and Integrated Control of Fujian University Engineering Research Center Fuzhou Fujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9783030697167

Leakage current of low-voltage power grid lines will cause major safety hazards such as personal electric shock and electrical fire. Traditional leakage current protectors can not play their due protection role due to their quality problems and aging, and other reasons. When leakage current accidents occur, traditional manual troubleshooting is inefficient. Aiming at the safety of leakage current in a low-voltage power grid, a new leakage current monitoring system is designed based on NB-IoT technology. The system includes the detection and transmission of leakage current and the construction of the cloud platform, which can realize the real-time monitoring of leakage current in low-voltage power grid and prevent and solve the leakage current safety problems. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Leakage currents

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