检索结果-内蒙古大学图书馆

18th International Learning and technology Conference, L and T 2021

作者： Junejo, Muhammad Haleem Ab Rahman, Ab Al-Hadi Shaikh, Riaz Ahmed Yusof, Kamaludin Mohamad Kumar, Dileep Memon, Imran Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81310 Malaysia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Department of Computer Science Bahria University Karachi campus sindh Pakistan

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET. © 2021 The Authors. Published by Elsevier B.V.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods

arXiv

引用

arXiv 2023年

作者： Li, Shilei Li, Lijing Shi, Dawei Lou, Yunjiang Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Information and Control Engineering China University of Mining and Technology China The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China

This paper presents two computationally efficient algorithms for the orientation estimation of inertial measurement units (IMUs): the multi-kernel correntropy-based gradient descent (CGD) and the multi-kernel correntropy-based decoupled orientation estimation (CDOE). Traditional methods, such as gradient descent (GD) and decoupled orientation estimation (DOE), rely on the least square (LS) criterion in algorithm derivation, making them vulnerable to external acceleration and magnetic interference. To address this issue, we first demonstrate that the multi-kernel correntropy loss (MKCL) is an optimal objective function under the maximum likelihood estimation (MLE) framework when the noise follows a specific type of heavy-tailed distribution. Then, we provide some important properties of the MKCL as a cost function. By replacing the LS cost with the MKCL, we develop the CGD and CDOE algorithms. We evaluate the effectiveness of our proposed methods by comparing them with existing algorithms in various situations. Experimental results indicate that our proposed methods (CGD and CDOE) outperform their conventional counterparts (GD and DOE), especially when faced with external acceleration and magnetic disturbances. Furthermore, the new algorithms demonstrate significantly lower computational complexity than Kalman filter-based approaches, making them suitable for applications with low-cost microprocessors. Copyright © 2023, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

Exploiting Mode-Switching between Aerial-RIS and Active Radio in UAV-Assisted Wireless Networks

Exploiting Mode-Switching between Aerial-RIS and Active Radi...

引用

IEEE Conference on Wireless Communications and Networking

作者： Songhan Zhao Yusi Long Bo Gu Nguyen Cong Luong Bin Lyu Shimin Gong School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China Faculty of Computer Science Phenikaa University Vietnam School of Communications and Information Engineering Nanjing University of Posts and Telecommunications China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, we employ dual-mode unmanned aerial vehicles (UAVs) equipped with both the active radio frequency (RF) module and aerial reconfigurable intelligent surface (ARIS) to assist ground users (GUs) for both the downlink energy transfer and uplink data transmission in a wireless-powered network. To maximize the GUs' minimum throughput, we propose a collaborative mode switching scheme for the dual-mode UAVs to dynamically switch between the active RF and passive ARIS modes according to the time-varying channel conditions. Besides, we jointly optimize the GUs' transmission control, the UAVs' beamforming, and the trajectory planning strategies. This optimization problem is intractable directly due to the non-convexity in both the objective and constraints. We design an iterative algorithm to first decompose the original problem into several subproblems, and then solve each subproblem individually by approximate optimization methods. Numerical results verify that the UAVs' collaborative mode switching along with their trajectories efficiently improves the transmission performance compared to the benchmarks in which both UAVs are operating in one fixed mode.

关键词： Radio frequency Trajectory planning Wireless networks Collaboration Switches Time-varying channels Throughput

来源：评论

学校读者我要写书评

暂无评论

BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer's Disease

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China Stevens Institute of Technology Department of Systems and Enterprises United States Hong Kong Metropolitan University School of Science and Technology Hong Kong Hong Kong VinUniversity College of Engineering and Computer Science VinUni-Illinois Smart Health Center Viet Nam King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Biosystems and Machines Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Dhahran Saudi Arabia Uppsala University Department of Game Design Faculty of Arts Sweden Tiangong University School of Software Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer's disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. © 2025 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

On the Complexity of Computing the Maximal Positive Invariant Set

On the Complexity of Computing the Maximal Positive Invarian...

引用

European control Conference (ECC)

作者： Bogdan Gheorghe Florin Stoican Ionela Prodan Faculty of Automation Control and Computer Science National University of Science and Technology Politehnica Bucharest Romania Grenoble INP LCIS Univ. Grenoble Alpes Institute of Engineering and Management Univ. Grenoble Alpes Valence France

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Computing the maximal (robust) positive invari-ant (M(R)PI) set for linear dynamics and a polyhedral constraint set is well-known in the literature but, the effects and limitations of the different methods employed are not sufficiently clear, especially for high dimensional systems. In this paper we propose a systematic analysis of the existing techniques as well as the application of new ideas to accelerate the computation of the MPI set. This includes new stop conditions for the set recurrence that spans it. We analyze and compare these variations over a dynamical system whose dimension can be arbitrarily increased to draw conclusions about their relative strengths and weaknesses.

关键词： Systematics Europe Complexity theory Dynamical systems Predictive control

来源：评论

学校读者我要写书评

暂无评论

Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis

引用

computer Modeling in engineering & sciences 2023年第12期137卷 2129-2147页

作者： Qiankun Zuo Junhua Hu Yudong Zhang Junren Pan Changhong Jing Xuhang Chen Xiaobo Meng Jin Hong School of Information Engineering Hubei University of EconomicsWuhan430205China State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijing100038China School of Computing and Mathematic Sciences University of LeicesterLeicesterLE17RHUK Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518055China Faculty of Science and Technology University of MacaoMacao999078China School of Geophysics Chengdu University of TechnologyChengdu610059China Laboratory of Artificial Intelligence and 3D Technologies for Cardiovascular Diseases Guangdong Provincial Key Laboratory of South China Structural Heart DiseaseGuangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhou519041China Medical Research Institute Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhou519041China

The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia *** brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia ***,it is challenging to access considerable amounts of brain functional network data,which hinders the widespread application of data-driven models in dementia *** this study,a novel distribution-regularized adversarial graph auto-Encoder(DAGAE)with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset,improving the dementia diagnosis accuracy of data-driven ***,the label distribution is estimated to regularize the latent space learned by the graph encoder,which canmake the learning process stable and the learned representation ***,the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain *** typical topological properties and discriminative features can be preserved ***,the generated brain functional networks improve the prediction performance using different classifiers,which can be applied to analyze other cognitive *** on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that the proposed model can generate good brain functional *** classification results show adding generated data can achieve the best accuracy value of 85.33%,sensitivity value of 84.00%,specificity value of 86.67%.The proposed model also achieves superior performance compared with other related ***,the proposedmodel effectively improves cognitive disease diagnosis by generating diverse brain functional networks.

关键词： Adversarial graph encoder label distribution generative transformer functional brain connectivity graph convolutional network dementia

来源：评论

学校读者我要写书评

暂无评论

Sub-Messages Extraction for Industrial control Protocol Reverse engineering

SSRN

引用

SSRN 2022年

作者： Liu, Yuhuan Zhang, Fengyun Ding, Yulong Jiang, Jie Yang, Shuang-Hua Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

The Industrial Internet of Things (IIoT) connects various industrial devices and processes for smart manufacturing purposes. The industrial devices and processes may employ standard or private communication protocols. Protocol Reverse engineering (PRE) can infer the format of the unknown protocol by analyzing traffic traces. Existing work in the field mainly focuses on Internet protocol only, handling text messages. PRE for industrial control protocols is difficult and particularly designed for IIoT for real-time interconnection among industrial devices. Given the phenomenon that many consecutive sub-messages are often embedded in a lengthy message payload and have a similar format, a novel sub-messages extraction algorithm is proposed in this work by using template iteration as an intermediate step to form a full message format inference framework. An improved evaluation criterion is also proposed to evaluate the sub-messages extraction results. We carry out our algorithm on three standard industrial control protocols and two unknown protocols. Experiments show that adding our sub-messages extraction in PRE for IIoT can greatly improve the accuracy of the overall protocol format inference compared with the existing work. © 2022, The Authors. All rights reserved.

关键词： Reverse engineering

来源：评论

学校读者我要写书评

暂无评论

Design of control System for Lower Limb Rehabilitation Robot on the Healthy Side sEMG Signal 20

Design of Control System for Lower Limb Rehabilitation Robot...

引用

20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Yu, Shiqin Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Beijing Institute of Technology Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science No.5 Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

With the number of stroke patients increasing year by year, rehabilitation exoskeleton robot has been paid more and more attention. For the rehabilitation exoskeleton robot, human-computer interaction ability is an important index, which affects the effect of rehabilitation therapy to a great extent. Surface Electromyography (sEMG) signals are the combined effect of sEMG signals and electrical activities on nerve stem on the skin surface, which can reflect neuromuscular activities in advance and can be used to predict movement intention by sEMG signals. Therefore, this article proposes to use sEMG signals to monitor the motion information of the healthy leg in real-time, extract the characteristics of the electromyography signals, use the sEMG of the healthy leg as the control signal, reflect the motion patterns reflected by the sEMG collected from the healthy side, and then use the motion intention recognition method of Long Short Term Memory(LSTM) neural network to identify the motion intention of the prosthetic limb by identifying the motion patterns of the swing phase of the healthy side. The results indicate that the predicted maximum Root Mean Squared Error(RMSE) is 5.3729, which proves the feasibility of using LSTM model for motion intention recognition and contributes to the real-time and accuracy of lower limb exoskeleton rehabilitation. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

ArmorCLIP: A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models

arXiv

引用

arXiv 2024年

作者： Li, Yijun Liang, Yuhan Niu, Yumeng Shen, Qianhe Liu, Hangyu Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Faculty of Engineering University of Bristol BristolBS8 1TH United Kingdom Computer Science and Technology Beijing University of Civil Engineering and Architecture Beijing102612 China Computer Science Mathematics & Statistics University of Toronto Mississauga MississaugaL5L 1C6 Canada Xi'an Tieyi ICC Shanxi Xian710054 China

The robustness of Vision-Language Models (VLMs) such as CLIP is critical for their deployment in safety-critical applications like autonomous driving, healthcare diagnostics, and security systems, where accurate interpretation of visual and textual data is essential. However, these models are highly susceptible to adversarial attacks, which can severely compromise their performance and reliability in real-world scenarios. Previous methods have primarily focused on improving robustness through adversarial training and generating adversarial examples using models like FGSM, AutoAttack, and DeepFool. However, these approaches often rely on strong assumptions, such as fixed perturbation norms or predefined attack patterns, and involve high computational complexity, making them challenging to implement in practical settings. In this paper, we propose a novel adversarial training framework that integrates multiple attack strategies and advanced machine learning techniques to significantly enhance the robustness of VLMs against a broad range of adversarial attacks. Experiments conducted on real-world datasets, including CIFAR-10 and CIFAR-100, demonstrate that the proposed method significantly enhances model robustness. The fine-tuned CLIP model achieved an accuracy of 43.5% on adversarially perturbed images, compared to only 4% for the baseline model. The neural network model achieved a high accuracy of 98% in these challenging classification tasks, while the XGBoost model reached a success rate of 85.26% in prediction tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Multi-kernel Correntropy Regression: Robustness, Optimality, and Application on Magnetometer Calibration

arXiv

引用

arXiv 2023年

作者： Li, Shilei Chen, Yihan Lou, Yunjiang Shi, Dawei Li, Lijing Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China The School of Automation Beijing Institute of Technology China The School of Information and Control Engineering China University of Mining and Technology China

This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers and state that the kernel bandwidth plays an important role in minimizing the lowest upper error bound. Then, we find that the proposed MKC is related to a specific heavy-tail distribution, where its head shape is consistent with the Gaussian distribution but its tail shape is heavy-tailed, and the extent of heavy-tail is controlled by the kernel bandwidth. It becomes a Gaussian distribution when the bandwidth is infinite, which allows one to tackle both Gaussian and non-Gaussian problems without explicitly investigating the noise distributions. To explore the optimal underlying distribution parameters, an expectation-maximization-like (EM) algorithm is developed to estimate the parameter vectors and the distribution parameters in an alternating manner. The results show that our algorithm can achieve equivalent performance compared with the traditional linear regression under Gaussian noise, and it significantly outperforms the conventional method under heavy-tailed noise. Both numerical simulations and experiments on a magnetometer calibration application verify the effectiveness of the proposed method. Note to Practitioners-The goal of this paper is to enhance the accuracy of conventional linear regression in handling outliers while maintaining its optimality under Gaussian assumptions. Our algorithm is formulated under the maximum likelihood estimation (MLE) framework, assuming the regression residuals follow a type of heavy-tailed noise distribution with an extreme case of Gaussian. The degree of the heavy tail is explored alternatingly using an Expectation-Maximization (EM) algorithm which converges very quickly. The robustness and optimality of the proposed approach are investigated and compared with the traditional approaches. Both th

关键词： Linear regression

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：