检索结果-内蒙古大学图书馆

38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Wang, Yunong Gage, Alexander Lee, Jaesung Feng, Philip X.-L. University of Florida Herbert Wertheim College of Engineering Department of Electrical and Computer Engineering GainesvilleFL32611 United States University of Central Florida College of Engineering & Computer Science Department of Electrical and Computer Engineering OrlandoFL32816 United States

ISBN: (纸本)9798331508890

We report on studying diamagnetic levitation and rigid body resonances of millimeter- to centimeter-scale trapped graphite mechanical resonators, by combining theoretical analysis with experimental demonstrations. Harnessing the strong diamagnetic susceptibility of pyrolytic graphite, we demonstrate stable levitation of square graphite thin plates with varying dimensions and masses above permanent magnets, without the need of active control. The resonance motions are excited by dielectric gradient forces and detected via an ultrasensitive optical interferometry system at room temperature. We observe rigid body resonance modes with frequencies between 25 Hz and 50 Hz and with their quality (Q) factors from 30 to 70. The Q factors are primarily constrained by air damping and eddy current damping. This work represents an initial step toward developing high-stability, anchor-less levitating resonant systems with large masses, paving the way for high-performance resonant sensors with minimal energy dissipation and exceptional thermal and mechanical isolation with very small power consumption. © 2025 IEEE.

关键词： Damping

来源：评论

学校读者我要写书评

暂无评论

A lightweight identity based generalized signcryption scheme for secure communication in standard model

引用

International Journal of Information Technology (Singapore) 2024年第2期16卷 1221-1229页

作者： Singh, Tej Kumar, Mukesh Gelmecha, Demissie Jabir Thakur, Ganesh Kumar Tyagi, Vikas Sori, Worku Jifara Department of Applied Sciences ABES Engineering College UP Ghaziabad India Institute of Applied Sciences & Humanities GLA University UP Mathura India Department of Electronics and Communication Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of CSE IMS Engineering College UP Ghaziabad India Department of Computer Science School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

The implementation of secure communication in the Industrial Internet of Things (IIoT) has made and it is a significant security concern. Identity-based generalized signcryption scheme is one among the cryptographic tools available that offers a secure way out to meet the security needs of IIoT. Our proposal is a lightweight scheme, an efficient identity-based generalized signcryption that works as an encryption signature, or signcryption scheme using only one algorithm in a standard model. This scheme is appropriate for storage-limited environments like wireless sensor networks and smart cards due to its lightweight nature. This means that it can be implemented in a range of IIoT applications where resources are limited, without compromising on security or efficiency. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bilinear Pairing Generalized signcryption Identity-based cryptography Signcryption

来源：评论

学校读者我要写书评

暂无评论

Optimization of Power Control for Autonomous Hybrid Electric Vehicles With Flexible Power Demand

引用

Journal of Autonomous Vehicles and Systems 2025年第2期5卷 021002页

作者： Kargar, Mohammadali Song, Xingyong Department of Mechanical Engineering College of Engineering Texas A&M University College StationTX77843 United States Department of Engineering Technology and Industrial Distribution The Department of Mechanical Engineering The Department of Electrical and Computer Engineering College of Engineering Texas A&M University College StationTX77843 United States

Technology advancement for on-road vehicles has gained significant momentum in the past decades, particularly in the field of vehicle automation and powertrain electrification. The optimization of powertrain controls for autonomous vehicles typically involves a separated consideration of the vehicle's external dynamics and powertrain dynamics, with one key aspect often overlooked. This aspect, known as flexible power demand, recognizes that the powertrain control system does not necessarily have to precisely match the power requested by the vehicle motion controller at all times. Leveraging this feature can lead to control designs achieving improved fuel economy by adding an extra degrees-of-freedom to the powertrain control while maintaining safety and drive comfort. The present research investigates the use of an approximate dynamic programming (ADP) approach to develop a powertrain controller, which takes into account the flexibility in power demand within the ADP framework. The concept of reachable sets is incorporated into the ADP framework to ensure safety, improve ride comfort, and enhance the accuracy of the optimization solution. The formulation is based on an autonomous hybrid electric vehicle, while the methodology can also be applied to other types of vehicles. It is also found that necessary customization of the ADP algorithm is needed for this particular control problem to prevent convergence issues. Finally, a case study is presented to evaluate the effectiveness of flexible power demand, as addressed by the ADP method. The experiment demonstrates a 14.1% improvement in fuel economy compared to a scenario without flexible power demand. © 2024 by ASME.

关键词： Dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

引用

IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Inferring Electrocardiography from Optical Sensing Using Lightweight Neural Network

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3535-3550页

作者： Li, Yuenan Tian, Xin Zhu, Qiang Wu, Min University of Maryland College ParkMD20742 United States Tianjin University School of Electrical and Information Engineering Tianjin300072 China University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States Meta Inc. Menlo ParkCA94025 United States University of Maryland Department of Electrical and Computer Engineering Institute for Advanced Computer Studies College ParkMD20742 United States

This article presents a computational solution that enables continuous cardiac monitoring through cross-modality inference of electrocardiogram (ECG). While some smartwatches now allow users to obtain a 30-s ECG test by tapping a built-in bio-sensor, these short-term ECG tests often miss intermittent and asymptomatic abnormalities of cardiac functions. It is also infeasible to expect persistently active user participation for long-term continuous cardiac monitoring in order to capture these and other types of cardiac abnormalities. To alleviate the need for continuous user attention and active participation, we design a lightweight neural network that infers ECG from the photoplethysmogram (PPG) signal sensed at the skin surface by a wearable optical sensor. We also develop a diagnosis-oriented training strategy to enable the neural network to capture the pathological features of ECG, aiming to increase the utility of reconstructed ECG signals for screening cardiovascular diseases (CVDs). We also leverage model interpretation to obtain insights from data-driven models, for example, to reveal some associations between CVDs and ECG/PPG and to demonstrate how the neural network copes with motion artifacts in the ambulatory application. The experimental results on three datasets demonstrate the feasibility of inferring ECG from PPG, achieving a high fidelity of ECG reconstruction with only about 40 000 parameters. © 2020 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Optimal Inference of Hidden Markov Models Through Expert-Acquired Data

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第8期5卷 3985-4000页

作者： Ravari, Amirhossein Ghoreishi, Seyede Fatemeh Imani, Mahdi Northeastern University Department of Electrical and Computer Engineering BostonMA02115 United States Northeastern University Department of Civil and Environmental Engineering Khoury College of Computer Sciences BostonMA02115 United States

This article focuses on inferring a general class of hidden Markov models (HMMs) using data acquired from experts. Expert-acquired data contain decisions/actions made by humans/users for various objectives, such as navigation data reflecting drivers' behavior, cybersecurity data carrying defender decisions, and biological data containing the biologist's actions (e.g., interventions and experiments). Conventional inference methods rely on temporal changes in data without accounting for expert knowledge. This article incorporates expert knowledge into the inference of HMMs by modeling expert behavior as an imperfect reinforcement learning agent. The proposed method optimally quantifies experts' perceptions about the system model, which, alongside the temporal changes in data, contributes to the inference process. The proposed inference method is derived through a combination of dynamic programming and optimal recursive Bayesian estimation. The applicability of this method is demonstrated to a wide range of inference criteria, such as maximum likelihood and maximum a posteriori. The performance of the proposed method is investigated through a comprehensive numerical experiment using a benchmark problem and biological networks. © 2020 IEEE.

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

Quantum Guard: Pioneering Quantum-Based Malware Defense for IoT Devices 7

Quantum Guard: Pioneering Quantum-Based Malware Defense for ...

引用

7th IEEE Conference on Dependable and Secure computing, DSC 2024

作者： Khan, Mansoor Ali Aman, Muhammad Naveed Sikdar, Biplab College of Design and Engineering National University of Singapore Department of Electrical and Computer Engineering 117575 Singapore School of Computing College of Engineering University of Nebraska-Lincoln LincolnNE68588-0115 United States

ISBN: (纸本)9798331540289

In the burgeoning landscape of the Internet of Things (IoT), ensuring the integrity and security of embedded devices is paramount. With the increasing frequency of cyberattacks targeting these typically low-powered devices, there is an urgent need for improved defensive infrastructure. Traditional malware detection methods such as signature-based, heuristic-based, and specification-based techniques are hampered by limitations in scanning speed, microprocessor capabilities, and energy efficiency. These classical approaches encompassing static, dynamic, and hybrid analyses struggle to keep pace with the demands of modern resource-constrained IoT devices. Addressing these challenges, this article introduces 'Quantum Guard,' a trans-formative quantum-based malware detection framework that utilizes a hybrid approach. Here, the IoT device handles initial data probing by inspecting potential threats, while the quantum processor runs Grover's algorithm with a quantum oracle for intensive search tasks to accurately identify and mark specific malware classes. This innovative approach significantly enhances the speed and efficiency of malware detection across diverse types by employing quantum superposition to enable simultaneous scanning of multiple memory locations. The integration of a quantum oracle facilitates precise targeting of malware signatures, while the amplitude amplification algorithm improves the probability of detecting and accurately classifying signatures within unstructured datasets. Performance analysis shows that 'Quantum Guard' significantly outperforms traditional methods, offering a scalable and efficient solution with a space complexity of O(log N) and a time complexity of O(VN). This prototype not only allows for effective detection and protection against various malware types, including elusive polymorphic and metamorphic variants, but also integrates quantum cryptographic techniques to introduce a unique layer of security inherently resistant to eme

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Research on position correction strategy for unmanned aerial vehicle formation based on pure azimuth passive positioning 3

Research on position correction strategy for unmanned aerial...

引用

3rd International Conference on Electronics Technology and Artificial Intelligence, ETAI 2024

作者： Zhang, Wei Gao, Minyi Lin, Kai Zhang, Huichun Zeng, Guangpeng Department of Electrical and Computer Engineering Nanfang College Guangzhou510900 China

ISBN: (纸本)9781510683143

In modern drone applications, precise positioning in formation flying is one of the primary issues that needs to be addressed. Among these, passive positioning technology is a commonly used and effective method for precise formation flying. However, most such methods require the transmission and reception of radio signals, which is inconvenient in scenarios requiring electromagnetic silence. This study proposes a drone formation positioning correction strategy based solely on azimuth passive positioning. This strategy, through geometric analysis and mathematical visualization techniques combined with polar coordinate positioning and a 0-1 planning model, corrects the positions of drones with deviations, optimizes flight efficiency, and enhances safety. Experimental results show that this method can effectively improve the positioning accuracy of drone formations under conditions of electromagnetic silence, demonstrating substantial practical value and prospects for wider application. © 2024 SPIE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

引用

Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

来源：评论

学校读者我要写书评

暂无评论

SEF:A Smart and Energy-Aware Forwarding Strategy for NDN-Based Internet of Healthcare

引用

computers, Materials & Continua 2024年第12期81卷 4625-4658页

作者： Naeem Ali Askar Adib Habbal Hassen Hamouda Abdullah Mohammad Alnajim Sheroz Khan Department of Computer Engineering Faculty of EngineeringKarabük UniversityKarabük78050Türkiye Department ofManagement Information Systems College of Business AdministrationMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesQassim51911Saudi Arabia

Named Data Networking(NDN)has emerged as a promising communication paradigm,emphasizing content-centric access rather than location-based *** model offers several advantages for Internet of Healthcare Things(IoHT)environments,including efficient content distribution,built-in security,and natural support for mobility and ***,existing NDN-based IoHT systems face inefficiencies in their forwarding strategy,where identical Interest packets are forwarded across multiple nodes,causing broadcast storms,increased collisions,higher energy consumption,and *** issues negatively impact healthcare system performance,particularly for individuals with disabilities and chronic diseases requiring continuous *** address these challenges,we propose a Smart and Energy-Aware Forwarding(SEF)strategy based on reinforcement learning for NDN-based *** SEF strategy leverages the geographical distance and energy levels of neighboring nodes,enabling devices to make more informed forwarding decisions and optimize next-hop *** approach reduces broadcast storms,optimizes overall energy consumption,and extends network *** system model,which targets smart hospitals and monitoring systems for individuals with disabilities,was examined in relation to the proposed *** SEF strategy was then implemented in the NS-3 simulation environment to assess its performance in healthcare *** demonstrated that SEF significantly enhanced NDN-based IoHT ***,it reduced energy consumption by up to 27.11%,82.23%,and 84.44%,decreased retrieval time by 20.23%,48.12%,and 51.65%,and achieved satisfaction rates that were approximately 0.69 higher than those of other strategies,even in more densely populated *** forwarding strategy is anticipated to substantially improve the quality and efficiency of NDN-based IoHT systems.

关键词： Energy efficient forwarding strategy health information system internet technologies IoHT people with disabilities reinforcement learning Q-learning

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：