检索结果-内蒙古大学图书馆

Analysis of Moving Target Defense in Unbalanced and Multiphase Distribution Systems Considering Voltage Stability

Analysis of Moving Target Defense in Unbalanced and Multipha...

2021 IEEE International Conference on Communications, control, and Computing Technologies for Smart Grids, SmartGridComm 2021

作者： Liu, Mengxiang Zhao, Chengcheng Zhang, Zhenyong Deng, Ruilong Cheng, Peng College of Control Science and Engineering Zhejiang University State Key Lab. of Industrial Control Technology Hangzhou China College of Computer Science and Technology Guizhou University State Key Lab. of Public Big Data Guiyang China

ISBN: (纸本)9781665415026

Moving Target Defense (MTD) is a new technology to defend against the false data injection attack (FDIA) on distribution system state estimation (DSSE). It works by proactively perturbing the branch reactance. However, due to the challenges induced by the nonlinear dynamics and the coupling phases in the three-phase AC DSSE model, the analysis on the effectiveness and hiddenness of MTD, which are two essential performance metrics, has not yet been conducted. In this paper, we attempt to optimize the effectiveness and hiddenness of MTD while considering voltage stability. Firstly, we quantify the two metrics with approximated measurement residuals. Based on the quantified metrics, we formulate an optimization problem to maximize the effectiveness with guaranteed hiddenness and ensure voltage stability by minimizing the voltage variation induced by MTD. The original problem is transformed to a polynomial optimization problem based on the observation that the alteration of the projection matrix caused by reactance perturbation is neglectable, such that the near-optimal result can be obtained. Finally, extensive simulations are conducted on the IEEE 13-bus test feeder to evaluate the performance of the proposed MTD. © 2021 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Contrast enhancement of optical videoscope images

引用

Iran Journal of computer science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

来源：评论

学校读者我要写书评

暂无评论

Multimodal Trustworthy Semantic Communication for Audio-Visual Event Localization

arXiv

引用

arXiv 2024年

作者： Li, Yuandi Xiang, Zhe Yu, Fei Zhang, Zhuoran Guan, Zhangshuang Ji, Hui Wan, Zhiguo Feng, Cheng Jiangsu University Zhenjiang212013 China Zhejiang Lab Hangzhou311121 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China School of Computer and Control Engineering Northeast Forestry University Harbin150040 China

The exponential growth in wireless data traffic, driven by the proliferation of mobile devices and smart applications, poses significant challenges for modern communication systems. Ensuring the secure and reliable transmission of multimodal semantic information is increasingly critical, particularly for tasks like Audio-Visual Event (AVE) localization. This letter introduces MMTrustSC, a novel framework designed to address these challenges by enhancing the security and reliability of multimodal communication. MMTrustSC incorporates advanced semantic encoding techniques to safeguard data integrity and privacy. It features a two-level coding scheme that combines error-correcting codes with conventional encoders to improve the accuracy and reliability of multimodal data transmission. Additionally, MMTrustSC employs hybrid encryption, integrating both asymmetric and symmetric encryption methods, to secure semantic information and ensure its confidentiality and integrity across potentially hostile networks. Simulation results validate MMTrustSC’s effectiveness, demonstrating substantial improvements in data transmission accuracy and reliability for AVE localization tasks. This framework represents a significant advancement in managing intermodal information complementarity and mitigating physical noise, thus enhancing overall system performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Audiovisual

来源：评论

学校读者我要写书评

暂无评论

Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

引用

IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

Neural-Network-Based Recursive State Estimation for Nonlinear Networked Systems With Binary-Encoding Mechanisms

引用

IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10072-10083页

作者： Yuhan Zhang Zidong Wang Lei Zou Wei Qian Shuxin Du College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Computer Science Brunel University of London Middlesex Uxbridge U.K. College of Information Science and Technology Donghua University Shanghai China Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou China

This work addresses the problem of recursive state estimation for networked control systems with unknown nonlinearities and binary-encoding mechanisms (BEMs). To enhance transmission reliability and reduce network resource consumption, BEMs are used to convert measurement signals into binary bit strings (BBSs) of limited length, which are then transmitted to the estimator through noisy communication channels. During transmission, random bit errors may occur in the BBSs due to channel noise. For the considered nonlinear networked control systems affected by random bit errors, a neural-network (NN)-based recursive estimation strategy is proposed, where an NN with a time-varying tuning scalar is employed to approximate the unknown nonlinearity of the networked control systems. By using the proposed strategy, the upper bounds of the estimation error of the system state and the trace of the estimation error of the NN weight (NNW) are first derived. These bounds are then minimized by recursively designing both the estimator gain matrix and the tuning scalar of the NNW. Finally, the effectiveness of the proposed estimation strategy is demonstrated through a numerical example.

关键词： Artificial neural networks State estimation Encoding Noise Networked control systems Electronic mail Symmetric matrices Estimation error Tuning Recursive estimation

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

Testing and Development of a Multi-Object Tracker Based on Deep Learning Techniques 1st

Testing and Development of a Multi-Object Tracker Based on D...

引用

1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Rajput, Shaurya Verma, Sourabh Verma, Om Prakash Gupta, Himanshu Sharma, Tarun Ateya, Abdelhamied A. Centre for Artificial Intelligence Dr. B.R. Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar India Department of Instrumentation and Control Engineering Dr. B.R. Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar India Department of Computer Science-Internet of Things ABES Institute of Technology Ghaziabad201009 India University Meerut India Department of Electronics and Communications Engineering Zagazig University Sharqia Zagazig44519 Egypt EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia

ISBN: (纸本)9789819981342

In today’s scenario, computer vision is one of the fundamental research areas of artificial intelligence including object detection and object tracking which are the upcoming trends. In the present work, the TransTrack model has been reimplemented which includes ResNet50 as a backbone network. Further, to improve the overall performance of the TransTrack model, some recently developed backbones such as ResNet152 and SwinT have been incorporated. Moreover, to test the optimum training of the existing model, it has been retrained for higher epochs (such as 160 epochs). However, the accuracy of the existing tracker has improved significantly as the number of epochs increases but, at the same time, it increases the computational complexity. On the contrary, when the existing tracker (yields 68.4% accuracy with ResNet50) is incorporated with ResNet152 and SwinT, the performance has significantly improved (71% for ResNet152 and 71.6% for SwinT) by 1.7% and 2.1%, respectively. The mentioned analysis is enough to prove that the incorporation of a more efficient backbone has the potential to improve the performance of trackers. In the future, the newly introduced backbone may open the window to improve the performance of multi-object tracking (MOT). © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Build a Cross-modality Bridge for Image-to-Point Cloud Registration

Build a Cross-modality Bridge for Image-to-Point Cloud Regis...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Lin Bie Shouan Pan Kai Cheng Li Han School of Software Tsinghua University Beijing China Command Control College Army Engineering University Nanjing China School of Computer Science and Artificial Intelligence Liaoning Normal University Dalian China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Although the fusion of images and LiDAR point clouds is crucial to many applications in computer vision, the relative poses of cameras and LiDAR scanners often need to be discovered. The general 2D-3D registration pipeline establishes correspondences and performs pose estimation based on the generated matches. However, 2D-3D correspondences are inherently challenging to establish due to the large gap between images and LiDAR point clouds. To this end, we build a bridge to alleviate the 2D-3D gap and propose a practical framework to align LiDAR point clouds to the virtual points generated by images. In this way, the modality gap is converted to the domain gap of point clouds. Furthermore, we propose a registration method with cross-domain feature extraction, frustum classification, and domain-agnostic correspondence pruning to narrow the domain gap to establish 3D-3D correspondences between real and virtual point clouds. Experimental results demonstrate that our image-to-point cloud registration method achieves state-of-theart performance on the KITTI Odometry and Oxford RobotCar datasets.

关键词： Point cloud compression Bridges Cloud computing Laser radar Three-dimensional displays Pipelines Pose estimation Feature extraction Cameras Odometry

来源：评论

学校读者我要写书评

暂无评论

Improved Res2Net Based on Multi-scale Attention Mechanism for Breast Cancer Image Classification 12th

Improved Res2Net Based on Multi-scale Attention Mechanism fo...

引用

12th International Conference on computer engineering and Networks, CENet 2022

作者： Lei, Suqin Deng, Xing Shao, Haijian Xia, Wang Department of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China College of Automation Key Laboratory of Ministry of Education of Complex Engineering System Measurement and Control Southeast University Jiangsu Nanjing210009 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Breast cancer is one of the most common cancers among female diseases. Since the classification accuracy of pathological images is crucial to the diagnosis of breast cancer, in order to reduce the error of manual diagnosis and improve the automatic classification accuracy of pathological images, this paper proposes a high-accuracy breast cancer classification method based on multi-scale attention mechanism enhanced Res2Net. By constructing a multi-channel residual connection in the residual block, and introducing a convolutional attention mechanism to enhance the feature extraction ability of Res2Net, and using group convolution to replace part of the ordinary convolution. In addition, since the channel information interaction across groups cannot be realized in the group convolution, which reduces the feature extraction ability. In this paper, the channel shuffling operation is used to reorganize the feature maps after the group convolution to achieve the information interaction between different groups. The evaluation results on the BreaKHis dataset show that, the accuracy and precision of the method in this paper have reached 98.5% and 98.9% respectively, in binary classification of histopathological images compared with the benchmark method, and the advantages of recall rate and F1 value are also more obvious. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Advancing Virtual Reality Interaction: A Ring-Shaped controller and Pose Tracking

Advancing Virtual Reality Interaction: A Ring-Shaped Control...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhuqing Zhang Dongxuan Li Jiayao Ma Yijia He Pan Ji Rong Xiong Hongdong Li Yue Wang State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control Zhejiang University Zhejiang China Tencent Holdings Tencent XR Vision Labs Shenzhen China College of Engineering and Computer Science Australian National University Canberra

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Ensuring robust tracking of controllers’ movement is critical for human-robot interaction in virtual reality (VR) scenarios. This paper proposes a robust tracking algorithm based on a novel wearable ring-shaped controller equipped with an inertial measurement unit (IMU) and a light-emitting diode (LED). This novel controller design allows users to free up their hands for more immersive experiences. To track the controller’s motion accurately and robustly, we resort to various forms of visual measurements, including 6 DoF and 5 DoF pose measurements from hand gesture detection, as well as 3 DoF position measurement and 2 DoF image measurement derived from the LED. We theoretically analyze the performances of these observation models and propose an optimal observation model combination scheme. Moreover, the necessity and rationale of online estimating system gravity are illustrated. The effectiveness of our tracking method is validated through extensive experiments.

关键词： Solid modeling Analytical models Visualization Measurement units Tracking Virtual reality Inertial navigation

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：