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Edge-assisted indexing for highly dynamic and static data in mixed reality connected autonomous vehicles

Intelligent and Converged Networks 2024年第2期5卷 167-179页

作者： Daniel Mawunyo Doe Dawei Chen Kyungtae Han Haoxin Wang Jiang Xie Zhu Han Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA InfoTech Labs Toyota Motor North America Research and DevelopmentMountain ViewCA 94043USA Department of Computer Science Georgia State UniversityAtlantaGA 30302USA Department of Electrical and Computer Engineering University of North Carolina at CharlotteCharlotteNC 28223USA

The integration of Mixed Reality(MR)technology into Autonomous Vehicles(AVs)has ushered in a new era for the automotive industry,offering heightened safety,convenience,and passenger ***,the substantial and varied data generated by MR-Connected AVs(MR-CAVs),encompassing both highly dynamic and static information,presents formidable challenges for efficient data management and *** this paper,we formulate our indexing problem as a constrained optimization problem,with the aim of maximizing the utility function that represents the overall performance of our indexing *** optimization problem encompasses multiple decision variables and constraints,rendering it mathematically infeasible to solve ***,we propose a heuristic algorithm to address the combinatorial complexity of the *** heuristic indexing algorithm efficiently divides data into highly dynamic and static categories,distributing the index across Roadside Units(RSUs)and optimizing query *** approach takes advantage of the computational capabilities of edge servers or RSUs to perform indexing operations,thereby shifting the burden away from the vehicles *** algorithm strategically places data in the cache,optimizing cache hit rate and space utilization while reducing *** quantitative evaluation demonstrates the superiority of our proposed scheme,with significant reductions in latency(averaging 27%-49.25%),a 30.75%improvement in throughput,a 22.50%enhancement in cache hit rate,and a 32%-50.75%improvement in space utilization compared to baseline schemes.

关键词： mixed reality autonomous vehicles data indexing edge computing query optimization

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Time-delay Correction Control Strategy for HVDC Frequency Regulation Services

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 2027-2037页

作者： Yuqing Dong Kaiqi Sun Jinning Wang Shunliang Wang He Huang Tianqi Liu Yilu Liu School of Electrical Engineering Shandong UniversityJinan 250061China School of Electrical Engineering Shandong UniversityJinan 250061China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxville 37996USA.

With the advancements in voltage source converter(VSC)technology,VSC based high voltage direct current(VSCHVDC)systems provide system operators with a prospective approach to enhance system operating stability and *** addition to long-distance transmission,the VSC-HVDC system can also provide multiple ancillary services,such as frequency regulation,due to its power ***,if a time delay exists in the control signal,the VSC-HVDC system may bring destabilizing influences to the system,which will decrease the system resilience under the *** order to reduce control deviation caused by time delay,in this paper,a small signal model is first conducted to analyze the impact of time delay on system *** a time-delay correction control strategy for HVDC frequency regulation control is developed to reduce the influence of the time *** control performance of the proposed time-delay correction control is verified both in the established small signal model and the runtime simulation in a modified IEEE 39 bus *** results indicate that the proposed time-delay correction control strategy shows significant improvement in system stability.

关键词： Frequency regulation control HVDC small signal analysis time delay time-delay correction control strategy

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Highly Efficient Three-Level AC-AC Converter with Identical In-Phase and Antiphase Buck-Boost Operations

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IEEE Transactions on Power Electronics 2024年第8期39卷 9929-9942页

作者： Ahmed, Hafiz Furqan Khan, Ashraf Ali Al Zaabi, Omar Mousavi, Seyyed Mohammad Javad Babaei, Ebrahim National Sun Yat-Sen University Department of Electrical Engineering Kaohsiung804 Taiwan Memorial University of Newfoundland Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Khalifa University Advanced Power and Energy Center Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates University of Tabriz Faculty of Electrical and Computer Engineering Tabriz51666 Iran Near East University Engineering Faculty Nicosia99138 Turkey

In this article, a new coupled-inductors based three-level bipolar buck-boost ac-ac converter is proposed. The proposed converter can produce highly efficient and symmetric in-phase and antiphase buck and boost modes of operation with much lower component voltage and current stresses. This reduces the operation and design complexity, and helps to achieve a high-efficiency operation. The high-frequency modulating switches of the proposed converter are implemented with coupled-inductor-based dual-buck phase leg, producing three-level input and output voltages, and providing inherent protection from voltage-source short-circuit issue during switch transitions. The other salient features of the proposed converter are no-commutation issue, no need for RC snubbers or dedicated safe-commutation algorithms, provision of high-quality and continuous input/output currents, and support for nonresistive loads. An in-depth circuit analysis of the proposed converter is provided based on the proposed switch modulation strategies. The guidelines for component design/selection are discussed, followed by the comparisons with state-of-the-art three-level ac-ac converters. Finally, practical circuit verifications are performed on a laboratory-assembled prototype. © 1986-2012 IEEE.

关键词： Buck-Boost converter

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Multi-Objective Optimization of Triple Active Bridge DC-DC Converters

Multi-Objective Optimization of Triple Active Bridge DC-DC C...

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2024 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2024

作者： Luckett, Benjamin He, JiangBiao University Of Kentucky Department Of Electrical And Computer Engineering LexingtonKY United States University Of Tennessee Department Of Electrical Engineering And Computer Science KnoxvilleTN United States

ISBN: (纸本)9798331529277

The triple active bridge (TAB) is a promising 3-port DC-DC converter technology which performs bi-directional power transfer with galvanic isolation. Due to intensive interactions between parameter selection and a TAB converter's performance in efficiency, specific power, reliability, and cost, many design considerations need to be simultaneously weighed to achieve an optimal converter. In this paper, a genetic algorithm based design optimization framework is developed to enhance the performance of a TAB converter with respect to multiple design objectives. Switching devices, thermal management systems, passive components, and the design of a planar multi-winding transformer are accounted for to ensure multidisciplinary performance benefits. Initial optimization results when aiming to attain the highest specific power and efficiency are presented, and the related design trade-offs are discussed. © 2024 IEEE.

关键词： Transformer windings

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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A versatile acoustically active surface based on piezoelectric microstructures

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Microsystems & Nanoengineering 2022年第3期8卷 51-60页

作者： Jinchi Han Mayuran Saravanapavanantham Matthew R.Chua Jeffrey H.Lang Vadimir Bulovic Department of Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridgeMA 02139USA

We demonstrate a versatile acoustically active surface consisting of an ensemble of piezoelectric microstructures that are capable of radiating and sensing acoustic waves.A freestanding microstructure array embossed in a single step on a flexible piezoelectric sheet of polyvinylidene fluoride(PVDF)leads to high-quality acoustic performance,which can be tuned by the design of the embossed *** high sensitivity and large bandwidth for sound generation demonstrated by this acoustically active surface outperform previously reported thin-flm loudspeakers using PVDF,PVDF copolymers,or voided charged polymers without *** further explore the directivity of this device and its use on a curved *** addition,high-fidelity sound perception is demonstrated by the surface,enabling its microphonic application for voice recording and speaker *** versatility,high-quality acoustic performance,minimal form factor,and scalability of future production of this acoustically active surface can lead to broad industrial and commercial adoption for this technology.

关键词： surface. piezoelectric versatile

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Anastasios N. Angelopoulos’ contribution to the Discussion of ‘Estimating means of bounded random variables by betting’ by Waudby-Smith and Ramdas

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Journal of the Royal Statistical Society. Series B: Statistical Methodology 2024年第1期86卷 33-34页

作者： Angelopoulos, Anastasios N. Department of Electrical Engineering and Computer Science University of California Berkeley CA United States

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Estimating Risk-aware Flexibility Areas for Electric Vehicle Charging Pools via AC Stochastic Optimal Power Flow

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1247-1256页

作者： Juan S.Giraldo Nataly Bañol Arias Pedro P.Vergara Maria Vlasiou Gerwin Hoogsteen Johann L.Hurink Energy Transition Studies Group Netherlands Organisation for Applied Scientific Research(TNO)Amsterdam1043 NTThe Netherlands Department of Electrical Engineering Mathematics and Computer ScienceUniversity of TwenteEnschede7522 NBThe Netherlands Intelligent Electrical Power Grids Group Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2628 CDThe Netherlands

This paper introduces an AC stochastic optimal power flow(SOPF)for the flexibility management of electric vehicle(EV)charging pools in distribution networks under *** AC SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging *** application of the AC SOPF is described where a distribution system operator(DSO)requires flexibility to each charging pool in a day-ahead time frame,minimizing the cost for flexibility while guaranteeing technical *** areas are defined for each charging pool and calculated as a function of a risk parameter involving the uncertainty of the *** show that all players can benefit from this approach,i.e.,the DSO obtains a riskaware solution,while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.

关键词： Electric vehicle flexibility management stochastic optimal power flow(SOPF) risk awareness compensation mechanism

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ElectroBlock: Reinforcement Learning and Blockchain-based Energy Trade to Optimize Tariffs 7

ElectroBlock: Reinforcement Learning and Blockchain-based En...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Islam, Ridwan Arefin Rezwan-Ul-Islam Shatabda, Swakkhar Islam, Salekul McGill University School of Computer Science Montréal Canada United International University Department of Computer Science and Engineering Dhaka Bangladesh Brac University Department of Computer Science and Engineering Dhaka Bangladesh North South University Department of Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798350356717

Energy prices have increased by more than 62% globally on average, while power companies are trying to provide more affordable energy with different options: fixed-rate, slab-based tariffs, and Time-of-Use pricing. This article aims to combine blockchains, smart contracts, smart grids, energy forecasting through reinforcement learning, and energy trading between closed communities, bringing consumer savings. Our proposed model, ElectroBlock, uses the first 25 days of each month's energy usage history along with seasonal and exogenous factors to forecast the final end-of-month usage. Then with the forecast we can predict which customers are the most likely to be pushed to the next energy consumption slab and be charged at a higher rate for the remaining month. This allows these customers to be buyers, and seamlessly buy energy units from users who would sell, the energy units they are predicted to leave unused by the end of the month. Moreover, trading is done with WalletCoins, at a coin per kilowatt hour, but could also be traded directly for fiat currency. Furthermore, the power company makes a profit on every trade through transaction fees. Hence there is great incentive for both customers and power companies to adopt our proposed model. We integrated Hyperledger Fabric into our ElectroBlock prototype to store all customer data and prevent tampering with WalletCoin and consumption records. Finally, we did a performance analysis, on the actual cost savings based on a real-world dataset;and a scalability test for concurrency and customer bases on our prototype. © 2024 IEEE.

关键词： Sales

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