检索结果-内蒙古大学图书馆

Enabling Quadruple-D Compensation Coil Integration for Efficient Power Transfer to Receiver in Wireless Power Transfer systems for EV Charging

引用

IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Erel, Mehmet Zahid Imtiaz, Tabish Elsanabary, Ahmed Mubin, Marizan Soon, Tey Kok Mekhilef, Saad Ankara Yildirim Beyazit University Department of Energy Systems Engineering Ankara Turkey Universiti Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Port Said University Faculty of Engineering Department of Electrical Engineering Port Said Egypt Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies Hawthorn Australia Presidency University Department of Electrical and Electronics Engineering Karnataka Bengaluru India

Wireless power transfer (WPT) presents a promising approach for charging electric vehicles (EVs), offering increased convenience, reliability, and safety for EV customers. Recently, the incorporation of multiple coil structures in wireless charging systems has addressed various operational challenges, enhanced power transfer, and provided a scalable solution for EVs. However, cross-coupling issues and space limitation problems arise when multiple coils are introduced to the WPT system. In this paper, a novel integrated compact coupler is proposed, including two naturally decoupled coils on the transmitter side, while both are coupled with the receiver coil to improve the power transfer capability of the system. ANSYS Maxwell is employed to simulate the proposed coupler and evaluate its performance under misalignment operations. The proposed wireless charging system inherently detects misalignment direction based on the variation of output voltage with respect to x-misalignment. As a result, the experimental prototype of the designed wireless WPT system presents a peak efficiency of 97.08% at 1.147-kW output power for an air gap of 120 mm under fully-aligned condition. Additionally, the designed compensation coil can transfer power of 243.76-W under fully-aligned condition and increases reliability in power transfer during misalignment operations. © 2013 IEEE.

关键词： Inductive power transmission

来源：评论

学校读者我要写书评

暂无评论

IOT based wearable sensor system architecture for classifying human activity

引用

Measurement: Sensors 2025年 39卷

作者： Mahalakshmi, V. Kumar, Pramod Bhende, Manisha Keshta, Ismail Rathod, Swatiben Yashvantbhai Ramesh, Janjhyam Venkata Naga Department of Computer Science College of Engineering and computer science Jazan University Jazan45142 Saudi Arabia Computer science and Technology Ganga Institute of Technology and Management Kablana Haryana Jhajjar124104 India Dr. D. Y. Patil Vidyapeeth Pune India Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Instrumentation And Control Department Vishwakrma Government Engineer College Chandkheda Ahmedabad382424 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist. Andhra Pradesh 522302 India

Human Activity Recognition (HAR) has applications in diverse fields, including sports management and behavior classification. Existing HAR methods can be categorized into three main approaches: camera-based, wearable sensor-based, and Wi-Fi sensing-based. Camera-based methods suffer from privacy concerns, while wearable sensor-based methods face limitations in battery longevity and continuous monitoring. Wi-Fi sensing methods mitigate privacy and battery issues but rely on costly Intel 5300 network cards or software-defined radio (SDR) platforms, limiting scalability. This paper presents a cost-effective IoT-based human activity recognition system using ESP32, leveraging its Wi-Fi sensing capabilities. The proposed system follows a structured workflow: (i) channel state information (CSI) is extracted from ESP32 modules, (ii) data preprocessing is performed using Hampel and Gaussian filters for noise and outlier removal, (iii) dimensionality reduction is achieved through Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT), and (iv) activity classification is conducted using Dynamic Time Warping (DTW) and the K-Nearest Neighbors (KNN) algorithm. Experimental evaluations demonstrate that the proposed system achieves an average recognition accuracy of 98.6 % across six human activities, comparable to high-end Intel 5300-based HAR systems, while significantly reducing hardware costs and improving ease of deployment. © 2025

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Sensing With Random Communication Signals

引用

IEEE Network 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing210044 China Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security China Southeast University MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025 IEEE.

关键词： Mobile telecommunication systems

来源：评论

学校读者我要写书评

暂无评论

Unknown-Aware Bilateral Dependency Optimization for Defending Against Model Inversion Attacks

引用

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Peng, Xiong Liu, Feng Wang, Nannan Lan, Long Liu, Tongliang Cheung, Yiu-Ming Han, Bo Hong Kong Baptist University Department of Computer Science Hong Kong The University of Melbourne School of Computing and Information Systems Australia Xidian University State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Shaanxi Xi'an710071 China National University of Defense Technology State Key Laboratory of High Performance Computing School of Computer Hunan Changsha410000 China The University of Sydney Sydney AI Centre School of Computer Science Faculty of Engineering Australia

By abusing access to a well-trained classifier, model inversion (MI) attacks pose a significant threat as they can recover the original training data, leading to privacy leakage. Previous studies mitigated MI attacks by imposing regularization to reduce the dependency between input features and outputs during classifier training, a strategy known as unilateral dependency optimization. However, this strategy contradicts the objective of minimizing the supervised classification loss, which inherently seeks to maximize the dependency between input features and outputs. Consequently, there is a trade-off between improving the model's robustness against MI attacks and maintaining its classification performance. To address this issue, we propose the bilateral dependency optimization strategy (BiDO), a dual-objective approach that minimizes the dependency between input features and latent representations, while simultaneously maximizing the dependency between latent representations and labels. BiDO is remarkable for its privacy-preserving capabilities. However, models trained with BiDO exhibit diminished capabilities in out-of-distribution (OOD) detection compared to models trained with standard classification supervision. Given the open-world nature of deep learning systems, this limitation could lead to significant security risks, as encountering OOD inputs—whose label spaces do not overlap with the in-distribution (ID) data used during training-is inevitable. To address this, we leverage readily available auxiliary OOD data to enhance the OOD detection performance of models trained with BiDO. This leads to the introduction of an upgraded framework, unknown-aware BiDO (BiDO+), which mitigates both privacy and security concerns. As a highlight, with comparable model utility, BiDO-HSIC+ reduces the FPR95 by 55.02% and enhances the AUCROC by 9.52% compared to BiDO-HSIC, while also providing superior MI robustness. © 1979-2012 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

A Multi-Carrier Deep Learning CSK Communication System

引用

IEEE Communications Letters 2025年

作者： Mobini, Majid Kaddoum, Georges Herceg, Marijan Babol Noshirvani University of Technology Department of ECE Babol Iran University of Québec École de technologie supérieure Département de génie électrique MontréalQC Canada Lebanese American University Cyber Security Systems and Applied AI Research Center Beirut Lebanon University of Osijek Faculty of Electrical EngineeringComputer Science and Information Technology Department of Communications Croatia

A new Multi-Carrier Deep Learning Chaos Shift Keying (MC-DLCSK) modulation is designed in this paper. The proposed receiver includes a Neural Network (NN)-based classifier that recovers MC-DLCSK signals. The results demonstrate that the MC-DLCSK system achieves a considerable Bit Error Rate (BER) gain compared to all existing similar systems under both Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. © 1997-2012 IEEE.

关键词： White noise

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Optimization Algorithms for Energy Management System in Microgrids Including Control Strategy

Multi-Objective Optimization Algorithms for Energy Managemen...

引用

Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES Companion), IEEE Symposia on

作者： Saiful Islam Sanaz Mostaghim Michael Hartmann Research Associate Otto von Guericke University Magdeburg SRH University of Applied Sciences Germany Chair of Computational Intelligence Faculty of Computer Science Otto von Guericke University Magdeburg Director of the Fraunhofer Institute for Transportation and Infrastructure Systems IVI Germany Dean of Berlin School of Technology SRH University of Applied Sciences Germany

ISBN: (数字)9798331519681

ISBN: (纸本)9798331519698

This paper focuses on multi-objective optimization methods to address control strategies in microgrid systems. We introduce an unique problem formulation capable of processing real-time data and propose a new methodology that integrates multiple energy management constraints within a multi-objective optimization framework. Additionally, we propose a set of objective functions tailored for this optimization approach. The goal of this paper is to implement several multi-objective optimization algorithms within this strategy and use a dataset to evaluate them. This approach is applied to optimize microgrid design based on energy requirements, while conducting optimal sizing analysis accompanied by sensitivity analysis. An Energy Management System has been developed with technical functionalities that incorporate various constraints. Our findings demonstrate that the NSGA-II algorithm delivers highly promising results, achieving significant reductions in energy costs and total net present value, while maximizing energy production from sustainable sources.

关键词： Renewable energy sources Costs Sensitivity analysis Heuristic algorithms Microgrids Production Reliability engineering Real-time systems Energy management systems Optimization

来源：评论

学校读者我要写书评

暂无评论

MVis4SID: Multimodal Visual Interactive System for Suicide Ideation Detection 25

MVis4SID: Multimodal Visual Interactive System for Suicide I...

引用

Companion Proceedings of the ACM on Web Conference 2025

作者： Md Kowsar Hossain Sakib Md Rafiqul Islam Shanjita Akter Prome Sadia Jahan David Asirvatham Neethiahnanthan Ari Ragavan Cesar Sanin Weidong Huang Department of Computer Science Taylor's University Subang Jaya Selangor Malaysia Faculty of Science and Technology Charles Darwin University Darwin NT Australia Department of Computer Science and Engineering University of Information Technology & Sciences Dhaka Dhaka Bangladesh Asia School of Business Kuala Lumpur Selangor Malaysia Faculty of Social Sciences & Leisure Management Taylor's University College Subang Jaya Selangor Malaysia Information Systems Australian Institute of Higher Education Sydney NSW Australia Transdisciplinary School University of Technology Sydney Sydney NSW Australia

ISBN: (纸本)9798400713316

Suicide has become a global health crisis driven by a complex mix of social, environmental, and psychological factors. The prevalence of suicide has emerged as a worldwide health crisis, presenting a multitude of intricate issues. The correlation between increased concerns about sustainability, climate change, and ecological degradation and the subsequent burden on psychological health is an increasing concern. Although Suicidal Ideation Detection (SID) has been effectively addressed by prior research using Machine Learning (ML) and Deep Learning (DL) models, many studies fail to handle the complexities of real-time detection. To overcome these flaws, we developed an novel system called Multimodal Visual interactive system for Suicide Ideation Detection (MVis4SID), which utilizes visual insights and real-time data to provide a more comprehensive exploration of suicidal thoughts. MVis4SID employs multimodal aspects, including click or touch, text, and voice, to visualize the performance metrics for suicidal ideation using ML and DL models; thus, offering a more holistic solution to this escalating problem. The rigorous evaluation of the user study shows that MVis4SID not only outperforms existing methods in accuracy, but also explains the responsible use of the underlying AI approach, demonstrating its potential to improve SID and intervention strategies. In addition, it provides a user-friendly interface for decision-makers, allowing them to represent the model performance and the efficient visual evaluation.

关键词： deep learning

来源：评论

学校读者我要写书评

暂无评论

HeX: Encrypted Rich Queries with Forward and Backward Privacy Using Trusted Hardware

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wu, Haotian Peng, Zhe Xiao, Jiang Xue, Lei Lin, Chenhao Chung, Sai-Ho The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hung Hom Hong Kong The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong Huazhong University of Science and Technology School of Computer Science and Technology China Sun Yat-sen University School of Cyber Science and Technology China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China

Dynamic searchable symmetric encryption (DSSE) schemes empower data owners to outsource their encrypted data to clouds while retaining the ability to update or search on it. Despite a lot of efforts devoted in recent years, there are still several challenges that have not been well addressed. Firstly, the confidentiality of data might be compromised if forward privacy and backward privacy cannot be ensured. Secondly, only the traditional single keyword-file search has attracted tremendous attention, while other popular queries like Boolean queries and range queries are not fully investigated. Lastly, how to solve these problems on untrusted servers that may deviate from pre-defined protocols is also challenging. In this paper, aiming to tackle the above problems, we propose a novel DSSE scheme named HeX based on Trusted Execution Environment (TEE) that supports rich queries on untrusted servers while guaranteeing forward and backward privacy. We achieve strong forward and backward security by designing a deferred obfuscating read-write technique atop the bitmap index. We further extend the basic scheme to realize Boolean queries and range queries by reducing them to basic keyword queries. Strict theoretical analysis is conducted to prove the security of HeX, and extensive evaluations illustrate its efficiency and practicality. © 2004-2012 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Optimizing vibratory energy harvesters with differentiable data-driven control

引用

Chaos, Solitons and Fractals 2025年 198卷

作者： Al-zahrani, Mohammed S. Alsaade, Fawaz W. Alsaadi, Fuad E. Department of Computer Networks and Communications College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Department of Computer Science College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Communication Systems and Networks Research Group Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

Nonlinear vibratory energy harvesters often exhibit multiple coexisting attractors, making their control challenging and energy-intensive. Ensuring an effective transition between these attractors while minimizing control effort is crucial for maximizing power generation and maintaining system stability. This paper introduces a differentiable data-driven control strategy that optimizes energy harvester performance by leveraging a neural network (NN)-based control framework. Unlike traditional fixed-gain or surrogate modeling approaches, the proposed method directly integrates system dynamics and control power consumption into the gain adaptation process, ensuring real-time adaptability. By exploiting the differentiability of neural networks, the controller employs gradient-based optimization to continuously refine control parameters in response to changes in operating conditions. The control framework consists of an offline training phase, where the neural network learns an energy-efficient control strategy through differentiable simulations. During this phase, the neural network is trained using a large dataset of system responses to different control inputs, allowing it to learn the most energy-efficient control strategy. This trained network is then used in the online deployment phase, where the trained controller dynamically adjusts control parameters in real-time. The proposed approach minimizes control energy consumption and efficiently guides the system toward the high-energy attractor, avoiding unnecessary control effort. Our approach significantly outperforms conventional PID-based sliding mode controllers. Simulation results confirm that the differentiable controller considerably enhances energy harvesting efficiency and reduces chattering, ensuring a smooth transition to the high-energy orbit while maintaining robust system stability. The study also highlights the necessity of an adaptive control strategy, stressing the urgency of its implementation for o

关键词： Lyapunov methods

来源：评论

学校读者我要写书评

暂无评论

Synthesizing realistic images for improved early detection of novel pandemics: A GAN enabled approach

引用

Heliyon 2025年第6期11卷

作者： Rounaq, Sumera Shah, Shahid Munir Aljawarneh, Mahmoud Mohammad Khan, Sarah Muhammad, Ghulam Department of Computer Science DHA Suffa University Karachi Pakistan Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman Jordan System Architect Low Code Solutions London United Kingdom Department of Computer Science Bahria University Karachi Pakistan

Infectious diseases like the novel Coronavirus (COVID-19) affect millions of individuals if not managed well in time. Thus, to reduce the transmission rate, effective diagnostic techniques must be identified. Early detection and diagnosis may be aided by Artificial Intelligence (AI) based automated diagnostics;however, these algorithms need a lot of pertinent data to be trained. In the early stages of a pandemic, gathering a lot of data is never an easy task. This presents challenges for accurate automatic diagnosis of such infectious diseases. To address this issue, a customized Generative Adversarial Networks (GAN) based architecture i. e., CoroWGAN-GP is proposed to synthesize images and to expand datasets. A dataset of 588 images with COVID-19, Normal, and Viral Pneumonia categories (taken from Cohen's repository [1]) was expanded using the proposed CoroWGAN-GP approach and used to train a proposed customized deep CNN architecture i. e., DeepCNN-CovNet19. Experimental results demonstrate that the proposed model achieved a considerable detection accuracy. i. e., 98.0 % in detecting the disease. The effectiveness of the proposed method was further evaluated by comparing its results with the state-of-the-art methods in the literature. Comparative analyses indicate that the proposed approach offers a promising solution for the accurate diagnosis of COVID-19-like pandemics. © 2025 The Authors

关键词： Chest X-ray Convolutional neural networks COVID-19 CXR GAN Pandemic Wasserstein GAN

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：