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3rd International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2020

作者： Ali, Gulsayyar Ilyas Ahmad, Mian Rafi, Arslan Research Center for Modeling and Simulation NUST Islamabad Pakistan

ISBN: (纸本)9781728149707

The ongoing growth in information and technology sector has increased storage requirement in cloud data centers with unprecedented pace. Global storage reached 2.8 trillion GB as per EMC Digital Universe study 2012 [1] and will reach 5247GB per user by 2020. Data redundancy is one of the root factors in storage scarcity because clients upload data without knowing the content available on the server. Ponemon Institute detected 18% redundant data in National Survey on Data centers Outages [15]. To resolve this issue, the concept of data deduplication is used, where each file has a unique hash identifier that changes with the content of the file. If a client tries to save duplicate of an existing file, he/she receives a pointer for retrieving the existing file. In this way, data deduplication helps in storage reduction and identifying redundant copies of the same files stored at data centers. Therefore, many popular cloud storage vendors like Amazon, Google Dropbox, IBM Cloud, Microsoft Azure, Spider Oak, Waula and Mozy adopted data deduplication. In this study, we have made a comparison of commonly used File-level deduplication with our proposed Block-level deduplication for cloud data centers. We implemented the two deduplication approaches on a local dataset and demonstrated that the proposed Block-level deduplication approach shows 5% better results as compared to the File-level deduplication approach. Furthermore, we expect that the performance will further be improved by considering a large dataset with more users working in similar domain. © 2020 IEEE.

关键词： Cloud storage

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Thermofluid topology optimization for cooling channel design

arXiv

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arXiv 2023年

作者： Navah, Farshad Lamarche-Gagnon, Marc-Étienne Ilinca, Florin Simulation and Numerical Modeling Automotive and Surface Transportation Research Center National Research Council BouchervilleQCJ4B 6Y4 Canada

A framework for topology optimization of cooling channels is proposed, which paves the way towards automated design of additively-manufactured cooling channels, required in applications such as the efficient heat management of die casting molds. Combining a selection of pertinent techniques and methods, the proposed density-based approach is strengthened by systematic verification and validation steps, including the body-fitted meshing of an optimized design. Furthermore, this work features applications to simplified, yet industrially-relevant cases, as well as a detailed discussion of the effects of the hyper-parameters of the optimization problem. These enable the reader to acquire a better understanding of the control and regularization mechanisms, which are necessary for a robust development towards complex scenarios. © 2023, CC BY.

关键词： 3D printing

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research on Economic Dispatch Problem Based on Accelerated Dynamic Adjustment Differential Evolution Algorithm 10

Research on Economic Dispatch Problem Based on Accelerated D...

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10th China International Conference on Electricity Distribution, CICED 2022

作者： Li, Anyi Li, Xiaojun Tian, Jiahui Wang, Xinrui Zhang, Rui Qi, Jintao Hebei Key Laboratory of Energy Internet Simulation Modeling and Control State Grid Hebei Electric Power Research Institute Shijiazhuang China Grid Planning and Research Center State Grid Hebei Economic Research Institute Shijiazhuang China State Grid Hebei Electric Power Research Institute Shijiazhuang China

ISBN: (纸本)9781665452687

In order to save energy, reduce carbon and realize sustainable development, the economic dispatch problem with valve-point effects is studied in this paper. Firstly, summarize the solving methods of economic dispatch problem in the previous literature, and put forward the problems of the current solving methods. Then, an accelerated dynamic adjustment differential evolution algorithm is proposed. By introducing chaotic initialization operation, a nonlinear dynamic adjustment strategy of control parameters is formulated, and an accelerated convergence strategy is proposed to comprehensively improve the performance of the algorithm. Finally, the proposed method is verified by numerical simulation, and compared with other methods in terms of result accuracy and robustness. © 2022 IEEE.

关键词： Numerical methods

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FedECCR: Federated Learning Method with Encoding Comparison and Classification Rectification 19th

FedECCR: Federated Learning Method with Encoding Comparison...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yan Zheng, Hui Wang, Xin Zhang, Beibei Zhou, Mingyao Zhang, Jilin Ren, YongJian School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Zhejiang Lab Hangzhou311100 China Hangzhou Huawei Communication Technology Co. Ltd. Hangzhou310052 China

ISBN: (纸本)9783031545306

Federated learning is a distributed training method that integrates multi-party data information using privacy-preserving technologies through dispersed client data sets to jointly construct a global model under the coordination of a central server. However, in practical applications, there is a high degree of data distribution skewness among clients, which causes the optimization direction of the client models to diverge, resulting in model bias and reducing the accuracy of the global model. Existing methods require the calculation and transmission of much information to correct the optimization direction of the client models, or only roughly limit the deviation of the client models end-to-end, ignoring targeted processing of the internal structure of the model, resulting in unclear improvement effects. To address these problems, we propose a federated optimization algorithm FedECCR based on encoding contrast and classification correction. This algorithm divides the model into an encoder and a classifier. It utilizes prototype contrastive training of the model encoder and unbiased classification correction of the classifier. This approach notably improves the accuracy of the global model while maintaining low communication costs. We conducted experiments on multiple data sets to evaluate the validity of our method, and the quantified results showed that FedECCR can improve the global model classification accuracy by approximately 1% to 6% compared to FedAvg, FedProx, and MOON. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Classification (of information)

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research on HRI Evaluation System and Diver Safety Equipment

Research on HRI Evaluation System and Diver Safety Equipment

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Lee, Ye Jun Kim, Min-Gyu Shin, Myung-Jun Park, Tae Sung Kim, Jinhyung Kim, Sunghun Lee, Kichoon Lee, Jihun Min, Jeongtack Research Center Pohang Korea Republic of Pusan National University Hospital Pusan National University School of Medicine Department of Rehabilitation Medicine Busan Korea Republic of Institute of Technology Pusan National University Hospital Department of Convergence Medical Busan Korea Republic of Technical Research Center RNWare Co. Ltd Daegu Korea Republic of Gleam-Systems System Development Division Daejeon Korea Republic of Gleam-Systems Modeling and Simulation Division Daejeon Korea Republic of Marine Leisure Equipment R&d Center Pohang Korea Republic of

ISBN: (纸本)9798331540081

Scuba diving is a common aquatic leisure sport that many individuals enjoy in the sea. With the increasing number of people participating, there is a corresponding rise in related incidents and accidents. Typically, such events are caused by inadequate user proficiency with equipment, sudden onset of physical symptoms, failure to respond to external obstacles, and equipment malfunctions. As interest in scuba diving grows, the development of diver safety equipment that enhances convenience and usability through the interaction between divers and equipment is crucial, not only to assist underwater activities but also to preemptively prevent accidents. This paper explores the analysis of divers' physiological conditions and diver equipment evaluation criteria. Building upon this, it proposes a HRI(Human-Robot Interaction) evaluation system to facilitate the research and development of safety equipment for secure scuba diving operations. © 2024 IEEE.

关键词： Human robot interaction

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A Provably Secure and Lightweight Access Control Protocol for EI-Based Vehicle to Grid Environment

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IEEE Internet of Things Journal 2023年第18期10卷 16650-16657页

作者： Shamshad, Salman Mahmood, Khalid Shamshad, Usman Hussain, Ibrar Hussain, Shafiq Das, Ashok Kumar The University of Lahore Department of Software Engineering Lahore54590 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan Riphah International University Riphah School of Computing and Innovation Lahore54000 Pakistan University of Sahiwal Department of Computer Science Sahiwal57000 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Virginia Modeling Analysis and Simulation Center SuffolkVA23435 United States

The energy Internet (EI) presents a novel paradigm for renewable energy distribution that utilizes communication and computing technologies to revolutionize the conventional intelligent transportation systems (ITSs) and power grid into a structure that provides assistance to open innovation. The EI offers sustainable and bidirectional transmission for the improvement and analysis of energy convention among electric vehicles (EVs) and service providers. To ensure efficient, reliable, and secure operation, EI must be safe from security attacks. Therefore, efficient and secure key negotiation is a significant issue for the EI-based Vehicle-to-Grid (V2G) architecture. Thus, to ensure the system's security, we have devised a robust scheme to facilitate a secure key agreement between the entities involved for the secure and efficient renewable energy distribution for the EI-based energy vehicles in V2G. The devised scheme's robustness is solicited through the widely accepted formal random or real (RoR) model. In addition, the informal security analysis is conducted on the devised scheme, which is evident that the designed scheme achieves all the required security features of EI-based ITS. Moreover, the performance evaluation results endorse that the designed scheme achieves the desired security and minimizes the communication, computation, and energy overhead by 29.33%, 27.94%, and 28.08% in comparison to the existing competitive schemes. © 2014 IEEE.

关键词： Vehicle-to-grid

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Multimodal Fluorescence Lifetime Imaging and Optical Coherence Elastography for Mesoscopic Structural, Biomechanical, and Molecular Imaging

Multimodal Fluorescence Lifetime Imaging and Optical Coheren...

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2024 Clinical and Translational Biophotonics, Translational 2024 - Part of Optica Biophotonics Congress: Biomedical Optic

作者： Chavez, Luis Gao, Shan Pandey, Vikas Yuan, Nanxue Li, Jiayue Hepburn, Matt S. Smith, Percy Edelheit, Caroline Corr, David T. Kennedy, Brendan F. Intes, Xavier Center for Modeling Simulation and Imaging for Medicine Rensselaer Polytechnic Institute TroyNY12180 United States BRITElab Harry Perkins Institute of Medical Research QEII Medical Centre Nedlands and Centre for Medical Research The University of Western Australia PerthWA6009 Australia Department of Electrical Electronic & Computer Engineering School of Engineering The University of Western Australia PerthWA6009 Australia Institute of Physics Nicolaus Copernicus University Torun Poland

We report on a methodology to manufacture multimodal phantoms with fluorescence lifetime imaging and optical coherence elastography contrasts. Phantom validation demonstrates the ability to tune fluorescence and strai... 详细信息

关键词： Phantoms

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A Computational Pipeline for Patient-Specific Prediction of the Post-operative Mitral Valve Functional State 12th

A Computational Pipeline for Patient-Specific Prediction of...

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Functional Imaging and modeling of the Heart - 12th International Conference, FIMH 2023, Proceedings

作者： Liu, Hao Simonian, Natalie T. Pouch, Alison M. Gorman, III, Joseph H. Gorman, Robert C. Sacks, Michael S. James T. Willerson Center for Cardiovascular Modeling and Simulation The Oden Institute for Computational Engineering and Sciences and the Department of Biomedical Engineering The University of Texas at Austin AustinTX78712 United States Departments of Radiology and Bioengineering University of Pennsylvania PhiladelphiaPA19104 United States Gorman Cardiovascular Research Group Department of Surgery Smilow Center for Translational Research Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States

ISBN: (纸本)9783031353017

Mitral valve (MV) repair is safer than replacement for mitral regurgitation (MR) treatment, but long-term outcomes remain suboptimal and poorly understood. Moreover, preoperative optimization is complicated due to the heterogeneity of MR presentations and potential repair configurations. We thus developed a patient-specific MV computational pipeline to quantitatively predict the post-repair MV functional state using standard-of-care preoperative imaging data alone. First, we built a finite-element model of the full patient-specific MV apparatus by quantifying the MV chordae tendinae (MVCT) distributions from 5 CT-imaged excised human hearts and incorporating this data with patient-specific MV leaflet geometries and and MVCT origin displacements from preoperative 3D echocardiography. We then calibrated the leaflet and MVCT pre-strains by simulating preoperative MV closure in order to tune the functionally equivalent, patient-specific mechanical behavior. With this fully calibrated MV model, we simulated undersized ring annuloplasty (URA) by modifying the annular displacement to match the applied ring size. In all patient cases, the postoperative geometries were predicted to within 1 mm of the target, and the MV leaflet strain fields demonstrated very good global and local correspondence to results from a previous heavily validated pipeline. Additionally, our model predicted increased postoperative posterior leaflet tethering in a recurrent patient, which is the likely driver of long-term MV repair failure. This pipeline allows us to predict postoperative outcomes using strictly preoperative clinical data, which lays the foundation for quantitative surgical planning, personalized patient selection, and ultimately, more durable MV repairs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Echocardiography

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FedAG:A Federated Learning Method Based on Data Importance Weighted Aggregation

FedAG:A Federated Learning Method Based on Data Importance W...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Xu, Mengchu Zeng, Yan Xue, Meiting Zhang, Ji-Lin Wan, Jian Zhou, Mingyao Wen, Yilin Shi, Yukun Hangzhou Dianzi University School of Computer Science and Technology Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China Zhejiang Engineering Research Center of Data Security Governance Hangzhou China Hangzhou Dianzi University School of Cyberspace Hangzhou China Huawei Enterprise Communication Technology Co Hangzhou China

ISBN: (纸本)9798350345384

Federated learning is a distributed machine learning approach that trains models with multiple clients and data locally. However, the existing methods ignore the differences between local models caused by the data heterogeneity, and give a high aggregation weight for the low-quality local model, which leads to low accuracy of the global model. For this problem, we propose a federated learning method based on data importance weighted aggregation, FedAG. It constructs an evaluation model of data importance based on the data distribution skewness, data volume, and data quality of clients, and aggregates local models with different weights computed by the evaluation model of data importance into a global model. We make experiments on five datasets including EMNIST, MNIST, and CIFAR-10. The results show that the accuracy of FedAG is respectively improved by 6.5%, 1.8%, 1.3% and 2.1%, compared with FedAvg, FedProx, FedVar, and Scaffold algorithms. © 2023 IEEE.

关键词： Scaffolds

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Adaptive Statistical Spatial Downscaling of Precipitation Supported by High-Resolution Atmospheric simulation Data for Mountainous Areas of Nepal

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Journal of Meteorological research 2023年第4期37卷 508-520页

作者： Hua YANG Kun YANG Jun QIN Baohong DING Yaozhi JIANG Yingying CHEN Xu ZHOU Yan WANG Shankar SHARMA National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth SystemEnvironment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101 University of Chinese Academy of Sciences Beijing 100049 Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceInstitute for Global Change StudiesTsinghua UniversityBeijing 100084 State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101 Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101

In complex terrain regions, it is very challenging to obtain high accuracy and resolution precipitation data that are required in land hydrological studies. In this study, an adaptive precipitation downscaling method is proposed based on the statistical downscaling model MicroMet. A key input parameter in the MicroMet is the precipitation adjustment factor(PAF) that shows the elevation dependence of precipitation. Its value is estimated conventionally based on station observations and suffers sparse stations in high altitudes. This study proposes to estimate the PAF value and its spatial variability with precipitation data from high-resolution atmospheric simulations and tests the idea in Nepal of South Himalayas, where rainfall stations are relatively dense. The result shows that MicroMet performs the best with the PAF value estimated from the simulation data at the scale of approximately 1.5 degrees. Not only the value at this scale is qualitatively consistent with early knowledge obtained from intensive observations, but also the downscaling performance with this value is better than or comparable to that with the PAF estimated from dense station data. Finally, it is shown that the PAF estimation, although critical, cannot replace the importance of increasing input station density for downscaling.

关键词： precipitation statistical downscaling precipitation adjustment factor adaptive estimation high resolution dynamical downscaling

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