检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Big Data, BigData 2024

作者： Mai, Huy Wu, Xintao University of Arkansas Department of Electrical Engineering and Computer Science FayettevilleAR United States

ISBN: (纸本)9798350362480

Despite having benefits of privacy preservation and secure computation, federated learning (FL) faces the issue of data heterogeneity. Specifically, the performance of FL systems can be degraded due to sample selection heterogeneity. We define the sample selection heterogeneity scenario with two points. First, each local training set is subject to missing-not-at-random (MNAR) sample selection bias where some labels are non-randomly missing. This requires incorporating a sample selection model that utilizes two equations to account for the prediction and selection of samples. Choosing selection features is a challenging task, especially when the number of observed features is large. Second, the sample selection mechanism is not the same across all clients. This implies that clients do not share the same set of selection features. In this work, we propose FL-MNAR to address FL under sample selection heterogeneity. The framework integrates an existing sample selection model that robustly handles sample selection bias for each client. FL-MNAR also trains an assignment function that gives a set of selection features to each client based on how well the features fit selection. Experimental results show that FL-MNAR achieves state-of-the-art performance under sample selection heterogeneity. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A Reduced State-Space Generation Method for Concurrent Systems Based on CPN-PR Model

引用

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia University College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model-checking method traverses the full state space, which suffers from the state-space explosion problem. A reduced state-space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state-space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model-checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

来源：评论

学校读者我要写书评

暂无评论

Fortifying Multi-Agent Architectures in Smart Manufacturing: Leveraging Federated Learning and Blockchain for Security and Resilience 22

Fortifying Multi-Agent Architectures in Smart Manufacturing:...

引用

22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Tshakwanda, Petro M. Tsegaye, Henok B. Almaayn, Raddad Devetsikiotis, Michael University of New Mexico Department of Electrical and Computer Engineering United States University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798331508050

This work presents a blockchain-based communication architecture for multi-agent smart manufacturing systems, enhanced with federated learning to improve security, data privacy, and scalability. Comparative evaluation with traditional centralized systems across various configurations reveals that our approach achieves up to 12 % higher model accuracy and 15 % greater security, particularly in large-scale deployments, with scalability scores surpassing those of centralized systems by 18 % as agent numbers increase. Although initial latency and energy consumption are higher, these factors improve significantly as the system scales, indicating suitability for complex manufacturing networks. This research underscores the potential of combining federated learning and blockchain to enhance resilience, security, and efficiency in Industry 4.0 smart manufacturing systems. © 2025 IEEE.

关键词： Sensitive data

来源：评论

学校读者我要写书评

暂无评论

ACDD: Automated COVID Detection using Deep Neural Networks

引用

IEIE Transactions on Smart Processing and Computing 2023年第6期12卷 518-525页

作者： Raza, Ghulam Musa Shoaib, Muhammad Kim, Byung-Seo Department of Software and Communications Engineering Hongik University Sejong Korea Republic of School of Electrical Engineering and Computer Science National University of Science and Technology Islamabad Pakistan

December 2019 witnessed the outbreak of a novel coronavirus, thought to have started in the Chinese city of Wuhan. The situation worsened owing to its quick spread across the globe, leading to a worldwide pandemic that became known as COVID-19. To suppress the pandemic, early detection of positive COVID-19 patients has become highly important. There is a lack of precise automated tool kits available for use in diagnosing medical conditions, so auxiliary diagnostic tools are in high demand. Important information about this virus can be extracted from X-ray images, which can be used in conjunction with advanced artificial intelligence. This study addresses the unavailability of physicians in remote areas, and the complex algorithm proposed in this study can find potential matches for patients in rural areas who need care. This could help to improve access to care for those who need it most. The purpose of this study is to develop a novel model that can automatically detect COVID-19 by utilizing chest X-ray images. The proposed model incorporates binary and multi-class classification and can be employed by radiologists for timely detection of the COVID-19 virus in an effective manner. © 2023 The Institute of Electronics and Information Engineers.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

An 840 V-to-120 V Radiation-Tolerant Flying Capacitor Multilevel Converter for Space Robotics

An 840 V-to-120 V Radiation-Tolerant Flying Capacitor Multil...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Krause, Elisa Coday, Samantha Horowitz, Logan Pilawa-Podgurski, Robert C.N. University of California Department of Electrical Engineering and Computer Sciences BerkeleyCA United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA United States

ISBN: (纸本)9798350376067

High-performance, high conversion ratio power electronics are necessary to enable robotics in future space exploration. This work utilizes the flying capacitor multilevel topology to build a lightweight, efficient converter for these applications. The design is optimized to minimize the mass of the passive components, and a stacked printed circuit board layout is used to further reduce the size. Radiation-tolerant part equivalents are utilized to emulate the performance of a space-rated prototype, and GaN HEMTs are used to further improve the converter's resiliency towards radiation effects. The design approach is verified through an 840 V, 600 W, 8-level FCML hardware prototype. The design achieves a peak efficiency of 95.4% and a power density of 4.92 kW/kg. © 2024 IEEE.

关键词： Gallium nitride

来源：评论

学校读者我要写书评

暂无评论

Effectiveness of Almost Sub Frame Approach as Inter Cell Interference Management in LTE-A Macro - Pico Scenario 9

Effectiveness of Almost Sub Frame Approach as Inter Cell Int...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Jember, Kehali A. Abose, Tadele A. Kassahun, Ermias Madhav, T. Venu Adama Science And Technology University Department Electronics And Communication Engineering Adama Ethiopia Mettu University Department Electrical And Computer Engineering Ethiopia Gondar University Department Electrical And Computer Engineering Gondar Ethiopia

ISBN: (纸本)9798350394474

Heterogeneous networks are promising solutions for enhancing network performance of LTE-A mobile networks by deploying small cells within the area of the serving macro cells. The goal of deploying such networks is to offload data from the congested macro cells towards the small cells. However, cross tier interference is a serious issue, which is introduced due to the transmitted power difference between Macro cell node and small cell node. Hence, cell age users are mainly affected by high power interference originated from Macro node. The paper develops enhanced inter-cell interference coordination using Almost Blank Sub-Frame (ABS) to improve user throughput. This can be achieved by stopping data transmission on in certain sub frame of macro cell while Pico users are scheduled at the same frame. computer simulation results depict that the performance of Pico cell users is highly degraded, especially for cell edge users. The ABS technique improves cell edge user throughput of Pico cell by 95% and center cell user throughput by 75.25%. © 2024 IEEE.

关键词： Heterogeneous networks

来源：评论

学校读者我要写书评

暂无评论

Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix

Arrhythmia Classification Using Graph Neural Networks Based ...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Han, Seungwoo Tokyo University of Agriculture and Technology Dept. of Electrical Engineering and Computer Science Japan

ISBN: (纸本)9798350386226

With the advancements in graph neural network (GNN), there has been increasing interest in applying GNN to electrocardiogram (ECG) analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted ECG features and applied a GNN to classify arrhythmias. Our work was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that proposed method can be considered an approach for arrhythmia classification. © 2024 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

A Parallel Color Image Encryption Algorithm Based on a 2-D Logistic-Rulkov Neuron Map

引用

IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Iu, Herbert Ho-Ching Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia Dalian University of Technology School of Mathematical Sciences Dalian116024 China Karamanoglu Mehmetbey University Faculty of Engineering Department of Computer Engineering Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

来源：评论

学校读者我要写书评

暂无评论

Image-Based Automatic Energy Meter Reading Using Deep Learning

引用

computers, Materials & Continua 2023年第1期74卷 203-216页

作者： Muhammad Imran Hafeez Anwar Muhammad Tufail Abdullah Khan Murad Khan Dzati Athiar Ramli Institute of Computer Sciences and Information Technology Faculty of Management and Computer SciencesThe University of AgriculturePeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadAttock CampusAttock CityPakistan Kuwait College of Science and Technology Kuwait CityKuwait School of Electrical&Electronic Engineering USM Engineering CampusUniversiti Sains MalaysiaPenang14300Malaysia

We propose to perform an image-based framework for electrical energy meter *** aim is to extract the image region that depicts the digits and then recognize them to record the consumed *** the readings of serial numbers and energy meter units,an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time ***,such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display,orientation and scale variations caused by camera positioning,and non-uniform illumination caused by *** this end,our work evaluates and compares the stateof-the art deep learning algorithm You Only Look Once(YOLO)along with traditional handcrafted features for text extraction and *** image dataset contains 10,000 images of electrical energymeters and is further expanded by data augmentation such as in-plane rotation and scaling tomake the deep learning algorithms robust to these image *** training and evaluation,the image dataset is annotated to produce the ground truth of all the ***,YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98%for all the *** proves to be robust against the mentioned image variations compared with the traditional handcrafted *** proposed method can be highly instrumental in reducing the time and effort involved in the currentmeter reading,where workers visit door to door,take images ofmeters and manually extract readings from these images.

关键词： Convolutional neural network object localization machine learning

来源：评论

学校读者我要写书评

暂无评论

Lower Bounds and Accelerated Algorithms for Bilevel Optimization

引用

Journal of Machine Learning Research 2023年第1期24卷 1-56页

作者： Ji, Kaiyi Liang, Yingbin Department of Computer Science and Engineering University at Buffalo BuffaloNY14260-2500 United States Department of Electrical and Computer Engineering The Ohio State University ColumbusOH98195-4322 United States

Bilevel optimization has recently attracted growing interests due to its wide applications in modern machine learning problems. Although recent studies have characterized the convergence rate for several such popular algorithms, it is still unclear how much further these convergence rates can be improved. In this paper, we address this fundamental question from two perspectives. First, we provide the first-known lower complexity bounds of (Formula presented) respectively for strongly-convex-strongly-convex and convex-strongly-convex bilevel optimizations. Second, we propose an accelerated bilevel optimizer named AccBiO, for which we provide the first-known complexity bounds without the gradient boundedness assumption (which was made in existing analyses) under the two aforementioned geometries. We also provide significantly tighter upper bounds than the existing complexity when the bounded gradient assumption does hold. We show that AccBiO achieves the optimal results (i.e., the upper and lower bounds match up to logarithmic factors) when the inner-level problem takes a quadratic form with a constant-level condition number. Interestingly, our lower bounds under both geometries are larger than the corresponding optimal complexities of minimax optimization, establishing that bilevel optimization is provably more challenging than minimax optimization. Our theoretical results are validated by numerical experiments. ©2022 Kaiyi Ji and Yingbin Liang.

关键词： Optimization algorithms

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：