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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Nigam, Ritika Kundu, Arpan Yu, Xinrui Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (数字)9781665480093

ISBN: (纸本)9781665480109

This paper presents the design and implementation of the Machine Vision Surveillance System Artificial Intelligence (MaViSS-AI) for Covid-19 Norms using jetson nano. This system is designed to be cost-effective, accurate, efficient, and secure. The proposed system tracks and counts humans for monitoring social distancing and detects face masks using object detection methods. We used YOLO as an object detection method and neural network to detect a person and count them. And for social distancing monitoring the concept of the centroid is based on calculating the distance between pairs of centroids, and thus checking whether there are any violations of the threshold or not. To detect the face mask, a YOLO V4 deep learning model is used as the mask detection algorithm. The system also raises alerts when any suspicious event occurs. Given this alert, security personnel can take relevant actions. This research aims to provide a holistic approach to overcoming the real-time challenges encountered during the monitoring of Covid-19 norms. © 2022 IEEE.

关键词： Object detection

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An Efficient Data Collection using Whale Optimization Algorithm (WOA) in Wireless Sensor Network 2

An Efficient Data Collection using Whale Optimization Algori...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Pasupathi, Udhayakumar Gopi Saminathan, A. Manikandan, A. Subhashini, M. Gowrishankar, C. Poornappriya, T.S. Department of Computer Science Tamilnadu India Npr College of Engineering and Technology Dindigul India Srm Institute of Science and Technology Chennai India Department of Computer Science Trichy India Kongunadu College of Engineering and Technology Department of Electrical and Electronics Engineering Tiruchirappalli621215 India Aspire Systems Pvt Ltd Karnataka 560037 India

ISBN: (纸本)9798350388916

Wireless sensor networks (WSNs) are an innovative technology that can be used in critical situations such as battlefields as well as commercial applications including buildings, traffic monitoring, smart homes, habitat monitoring, and more. A wireless sensor network (WSN) comprises small nodes capable of wireless sensing, processing, and communication. One of the most pressing concerns plaguing WSNs today is security. This paper suggests and investigates an energy-efficient cluster head (CH) selection approach using the Whale Optimization Algorithm (WOA) (called WOA-Clustering (WOA-C)) and the Geographic and Energy Aware Routing Algorithm (GEAR). As a result, the suggested method simplifies the decision-making process of energy-aware CHs by utilizing the fitness function that assesses node residual energy and the total energy of neighbouring nodes. Compared to the GWO-C, GABEEC, and PSOMSB protocols, the proposed WOA-GEAR protocol consumes 28%, 22%, and 7.5% less power. The suggested algorithm's network lifetime, power efficiency, buffer occupancy, throughput, and end-to-end latency were evaluated. © 2024 IEEE.

关键词： Decision making

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Design Flow of mmWave Radar and Machine Vision Fusion for Pedestrian Collision Warning

Design Flow of mmWave Radar and Machine Vision Fusion for Pe...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Graves, Kaitlyn Kanwal, Madiha Yu, Xinrui Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

This paper presents a millimeter wave (mmWave) radar and Machine Vision fusion system to alert drivers of potential pedestrian collisions. The system is composed of two subsystems, the mmWave Pedestrian Localization subsystem and the Machine Vision Pedestrian Classification subsystem. The mmWave Pedestrian Localization subsystem obtains the relative location of the pedestrians using a mmWave radar sensor while the Machine Vision Pedestrian Classification subsystem uses Histogram of Oriented Gradients and Support Vector Machine algorithms to classify pedestrians in a camera's field-of-view. The two-layer pedestrian detection design protects the system from any miss detections within a single subsystem. By utilizing mmWave technology with Machine Vision, the safety operation of cars and pedestrian safety can be increased. The proposed system utilizes Texas Instrument's AWR1642BOOST mmWave Radar with the high computing power of NVIDIA's Jetson Nano © 2022 IEEE.

关键词： computer vision

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Experimental Evaluation of the Impact of Using Oriented Steel in Proportion to Segmentation on Core Loss of AC Electric Machine Stator

Experimental Evaluation of the Impact of Using Oriented Stee...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Khoshoo, Bhuvan Aggarwal, Anmol Agapiou, John Foster, Shanelle N. Michigan State University Department of Electrical & Computer Engineering East LansingMI United States General Motors PontiacMI United States General Motors Manufacturing Systems Research Laboratory WarrenMI United States

ISBN: (纸本)9798350376067

The strategic incorporation of oriented steel in a segmented stator AC electric machine has the potential to enhance machine performance by reducing core losses compared to a machine designed with non-oriented steel. However, the introduction of cut-edges and the stress on individual segments resulting from segmented stator construction may have unintended consequences. Despite several attempts to assess the impact of cut-edges or oriented steel on core losses in segmented stators, the literature lacks an analysis of the loss separation between tooth and back-iron components using manufactured stators. This gap in understanding prevents a comprehensive assessment of the true impact of using oriented steel in segmented stator construction for core loss improvement. In this work, the loss of each tooth and back-iron segment is calculated by conducting experiments with three different electromagnetic devices, specifically H-shaped steel laminations. By comparing the results from three manufactured stators, we elucidate the influence of oriented steel in proportion to the segmented stator construction. © 2024 IEEE.

关键词： Stators

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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multiphysical analysis and motor design optimization. This article proposes a new method for thermal steady state modeling and analysis based on transfer learning (TL), which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on TL is developed: the model is pretrained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then, the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R^{2} and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Empowering Smart Homes with Fog Computing for IoT Connectivity

Empowering Smart Homes with Fog Computing for IoT Connectivi...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Mahdi, Ali Jafer Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq AI-Zahraa University for Women Department of Scientific Affairs Bangi43600 Malaysia College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Innovative grid technology leverages Information and Communication Technology (ICT) to enhance energy efficiency and mitigate losses. This paper introduces a 'novel three-tier hierarchical framework for smart homes within the Internet of Things (IoT)' ecosystem, addressing the current shortage of intelligent solutions that underutilize IoT advantages. Our framework's primary objective is to elevate smart homes to the microgrid level, enabling the seamless integration from the microgrid's renewable distributed energy sources and attaining maximum energy efficiency. In addition to traditional data processing, we propose the application of fog computing in smart homes. Through a rigorous simulation using real smart meter datasets, our study demonstrates that fog computing, mainly when using predictive filtering, may drastically cut down on the amount of data being sent and the load on the home's network. This innovation holds immense potential for revolutionizing energy management and resource allocation in the context of smart homes, thus contributing to a sustainable and efficient future. This research represents a critical step towards harnessing the power of IoT and fog computing in smart grids and homes, offering a promising path towards more intelligent, eco-friendly, and energy-efficient living spaces. © 2024 IEEE.

关键词： Smart homes

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Tackling Multiple Security Threats in an IoT Environment

Tackling Multiple Security Threats in an IoT Environment

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Gromov, Mikhail Arnold, David Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

While in traditional computing there are many cybersecurity flaws that have been solved, in IoT the computing model differs from regular computing, and can be changed based on the application. Due to this, some new flaws are introduced in IoT, and some old flaws need new solutions. While there have been investigations into individual flaws, sometimes proposed solutions can also create new flaws or a new attack vector for another attack. Therefore, it is important to consider the totality of an application in order to decide on what security measures need to be taken, and how they can be implemented in the design. In this paper, a few common IoT system styles including medical devices based on IoT, IoT devices in home networks, and wide area IoT networks are analyzed. This analysis uses a simple method to consider the vulnerabilities of each system individually and determine how to best respond to these weaknesses. The weaknesses analyzed include Distributed Denial of Service, public access, device identification, and variability of hardware. © 2022 IEEE.

关键词： Cybersecurity

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ISAS: AAA Protocol-based Handover and Improved Security Methodology through the Integration Security Authentication System Constitute

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IEIE Transactions on Smart Processing and Computing 2023年第4期12卷 358-367页

作者： Park, Byungjoo Kim, Jaehwan McNair, Janise Department of Multimedia Engineering Hannam University Daeduk-gu Daejeon Korea Republic of Mobile and Multimedia Communication Laboratory Hannam University Daeduk-gu Daejeon Korea Republic of Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States

Security risks were raised during the handover of Mobile Nodes (MNs) in the Mobile Internet Protocol version 6 (MIPv6), which were demonstrated in the instigation of route optimization between the MN and its Correspondent Node (CN). The return routability procedure provides a basic security measure for the communication between the MN and CN, but this security scheme still suffers from various weaknesses and loopholes. Thus, various methodologies in securing the handovers for MIPv6 were incorporated in line with the security agreement with authentication, authorization, and accounting (AAA) infrastructure. This paper proposes an establishment of the Integration Security Authentication System based on the AAA infrastructure to maintain the security level of the fast handover mobility management to provide an improved Quality of Service. Copyrights © 2023 The Institute of Electronics and Information Engineers.

关键词： Quality of service

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Feature Model Configuration Reuse Scheme for Self-Adaptive systems

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computers, Materials & Continua 2022年第4期71卷 1249-1262页

作者： Sumaya Alkubaisi Said Ghoul Oguz Ata Electrical and Computer Engineering Department Altinbas UniversityIstanbul34218Turkey Faculty of Information Technology Research Laboratory on Bio-Inspired Software EngineeringPhiladelphia UniversityAmman26160Jordan

Most large-scale systems including self-adaptive systems utilize feature models(FMs)to represent their complex architectures and benefit from the reuse of commonalities and variability ***-adaptive systems(SASs)are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite ***,reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and *** process of configuration reuse can be a better alternative to some contexts to reduce computational time,effort and ***,systems’complexity can be reduced while the development process of systems by reusing elements or *** are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system *** current FM-based modelling techniques represent,manage,and reuse elementary features to model SASs concepts,modeling and reusing configurations have not yet been *** this context,this study presents an extension to FMs by introducing and managing configuration features and their reuse *** results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.

关键词： Self-adaptive system feature model system reuse configuration management variability modeling

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PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy 40

PrivShape: Extracting Shapes in Time Series under User-Level...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Mao, Yulian Ye, Qingqing Hu, Haibo Wang, Qi Huang, Kai Southern University of Science and Technology Department of Computer Science and Engineering China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China School of Computer Science and Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350317152

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ?-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes. © 2024 IEEE.

关键词： Time series

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