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IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY 2022年 3卷 496-506页

作者： Feng, Kuo Liu, Chunhua City Univ Hong Kong Sch Energy & Environm Hong Kong Peoples R China

As microgrids develop rapidly, more inverters are adopted to achieve DC/AC or AC/DC/AC conversion of distributed generators (DGs). The virtual synchronous generator (VSG) control has started to replace the traditional droop control for inverters. In order to restore the frequency to its nominal value, most existing secondary frequency control (SFC) methods are based on frequency measurements. However, while reducing the rate of change of frequency (ROCOF), virtual inertia also slows down the convergence of frequency-based SFC. Therefore, this paper proposes a new distributed hierarchical control for fast frequency restoration. Based on the real-time VSG control at the bottom level, a novel frequency restoration control is designed. The power reference values generated by the proposed control can accelerate the frequency restoration with accurate power sharing. Meanwhile, by designing event-triggering conditions, parallel inverter controllers only need to communicate with neighbors at the event-triggered moments. Simulations have been performed in MATLAB/Simulink environment. Furthermore, the proposed control has also been tested on the experiment platform, which contains practical physical circuits and real-time controllers. Both simulation and experiment results verify the effectiveness of the proposed control strategy.

关键词： Frequency control Microgrids Inverters real-time systems Integrated circuit modeling Synchronous generators Industrial electronics Hierarchical control islanded microgrid parallel voltage source inverters (VSIs) real-time experiment virtual synchronous generator (VSG)

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Graph Computing Based distributed parallel Power Flow for AC/DC systems with Improved Initial Estimate

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Journal of Modern Power systems and Clean Energy 2021年第2期9卷 253-263页

作者： Wei Feng Chen Yuan Qingxin Shi Renchang Dai Guangyi Liu Zhiwei Wang Fangxing Li the University of Tennessee 1525 Coleman Road KnoxvilleTennessee 37909USA Global Energy Interconnection Research Institute North America San JoseCalifornia 95134USA

The sequential method is easy to integrate with existing large-scale alternating current(AC)power flow solvers and is therefore a common approach for solving the power flow of AC/direct current(DC)hybrid *** this paper,a highperformance graph computing based distributed parallel implementation of the sequential method with an improved initial estimate approach for hybrid AC/DC systems is *** proposed approach is capable of speeding up the entire computation process without compromising the accuracy of ***,the AC/DC network is intuitively represented by a graph and stored in a graph database(GDB)to expedite data *** the interconnection of AC grids via high-voltage direct current(HVDC)links,the network is subsequently partitioned into independent areas which are naturally fit for distributed power flow *** each area,the fast-decoupled power flow(FDPF)is employed with node-based parallel computing(NPC)and hierarchical parallel computing(HPC)to quickly identify system ***,to reduce the alternate iterations in the sequential method,a new decoupled approach is utilized to achieve a good initial estimate for the Newton-Raphson *** the improved initial estimate,the sequential method can converge in fewer ***,the proposed approach allows for significant reduction in computing time and is able to meet the requirement of the real-time analysis platform for power *** performance is verified on standard IEEE 300-bus system,extended large-scale systems,and a practical 11119-bus system in China.

关键词： AC/DC system distributed parallel computing graph computing initial estimate power flow analysis

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Estimating the Training time in Single- and Multi-Hop Split Federated Learning 8

Estimating the Training Time in Single- and Multi-Hop Split ...

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8th International workshop on Edge systems Analytics and Networking-EDGESYS

作者： Tirana, Joana Lalis, Spyros Chatzopoulos, Dimitris Univ Coll Dublin Dublin Ireland Univ Thessaly Volos Greece

ISBN: (纸本)9798400715594

Split Federated Learning (SFL) is an upcoming and promising approach that balances the two main goals of distributed training, i.e., (i) the data remains at the data owners, and (ii) even devices with resource limitations can participate in the training. This is achieved by splitting the model into multiple parts and offloading them to designated compute nodes. Recent findings show that the number of compute nodes (hops) plays a significant role in the training delay. However, determining the ideal number of hops is not an easy task. Therefore, in this work, we propose a mathematical model that estimates the training delay of single- and multi-hop SFL. This tool not only helps in searching the optimal number of hops before the real deployment happens but also can be used as a lightweight evaluation tool in future research works in SFL. Our numerical evaluations show that the model can make correct estimations with an error smaller than 3.86%. Finally, we have constructed a lightweight optimization problem that finds the optimal cut layers (split points) and model part assignment to minimize training delay.

关键词： Split Federated Learning distributed Learning Modeling

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Integrating parallel, Tensor-Based Computing in Agent-Based Simulators 27

Integrating Parallel, Tensor-Based Computing in Agent-Based ...

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27th IEEE/ACM International Symposium on distributed Simulation and real time Applications, DS-RT 2023

作者： Sgouros, Nikitas M. University of Piraeus Department of Digital Systems Piraeus Greece

ISBN: (纸本)9798350337846

The rise of deep learning methods has ignited interest in efficient hardware and software systems for tensor-based computing. A question worth investigating is whether other areas in computing can benefit as well from the power and increasing availability of such systems. To this end, we study the integration of tensor-based techniques in agent-based simulators. In particular, we describe methods for the representation as vectors of agent and edge attributes in agent networks and the compilation of rules governing agent behavior and their edge-based interactions into functions that can process and compute such attribute vectors in parallel. We describe a proof of concept implementation of such an idea in Politika, our Elixir-based web framework for concurrent, agent-based simulation. We discuss various simulation scenarios for our implementation and provide directions for future optimizations and integration pathways with concurrent simulation environments. © 2023 IEEE.

关键词： Tensors

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Fault-Tolerant Architectures for distributed Big Data Analytics 1

Fault-Tolerant Architectures for Distributed Big Data Analyt...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sudhakaran, Sunil Shah, Samarth Mishra, Manas Kumar Natarajan, Vignesh Bhatt, Jay Goel, Om Mahatma Gandhi University Kerala Kottayam India University at Albany AlbanyNY12222 United States G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida201306 India Arizona State University AZ United States Northeastem University BostonMA United States ABES Engineering College U.P. Ghaziabad India

ISBN: (纸本)9798331518578

It has created challenges in the processing of data and analysis, which need to go beyond the traditional centralized architecture to solve the current data volume, speed, and types. To address these challenges, fault-tolerant architectures have become one of the keys to meeting the needs of distributed big data analytics. Failure is a normal part of systems;these architectures are meant to accommodate failures of individual components so that the entire system does not need to stop working or, worse, give false results. They make use of several nodes to operate and retain data in a distributed fashion, which supplies excessive scalability and fault tolerance. This means that even if some nodes fail, a fault-tolerant architecture can still provide redundancy by replicating data across many nodes, giving the system redundancy. Fault-tolerant architectures also utilize efficient data processing techniques like partitioning and data parallel processing, to achieve high performance. This allows real-time and near-real-time analysis of streaming data. © 2025 IEEE.

关键词： Data Analytics

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Job Scheduling for HPC Clusters: Constraint Programming vs. Backfilling Approaches 18

Job Scheduling for HPC Clusters: Constraint Programming vs. ...

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18th International Conference on distributed and Event-Based systems (DEBS)

作者： Goponenko, Alexander, V Lamar, Kenneth Allan, Benjamin A. Brandt, James M. Dechev, Damian Univ Cent Florida Orlando FL 32816 USA Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA

ISBN: (纸本)9798400704437

Scheduling problems are naturally formulated in terms of Constraint Programming (CP), yet the application of CP-based approaches for job scheduling on High-Performance Computing (HPC) clusters remains underexplored. This study aims to bridge this gap by analyzing the scheduling of diverse real workload traces using IBM ILOG CP Optimizer. The analysis considers not just the basic metrics Average Bounded Slowdown and Average Response time, but also Area-Weighted Average Response time and Level-2 Priority-Weighted Specific time, which measure packing efficiency and fairness. For each workload trace and metric combination, schedules produced through optimizing the metric with CP Optimizer are compared against schedules generated by the variants of the list scheduling with backfilling that are most suitable for the metric. The CP-based scheduling improves scheduling quality in most of the examined cases. The analysis of the metrics that represent different scheduling goals uncovers several non-trivial insights. Presently, CP Optimizer still encounters scalability issues with large wait queues and non-linear objective functions. Nonetheless, it often demonstrates improvements in packing efficiency, which make CP techniques attractive, particularly in scenarios where high-maintenance clusters run a moderate number of large, rigid, well-characterized jobs.

关键词： high-performance computing parallel job scheduling schedule quality constraint programming CP Optimizer packing efficiency fairness weighted average response time

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A parallel Framework for Efficiently Updating Graph Properties in Large Dynamic Networks 23

A Parallel Framework for Efficiently Updating Graph Properti...

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24th International Conference on distributed Computing and Networking (ICDCN)

作者： Khanda, Arindam Das, Sajal K. Missouri Univ Sci & Technol Rolla MO 65409 USA

ISBN: (纸本)9781450397964

Graph queries on large networks leverage the stored graph properties to provide faster results. Since real-world graphs are mostly dynamic, i.e., the graph topology changes over time, the corresponding graph attributes also change over time. In certain situations, recompiling or updating earlier properties is necessary to maintain the accuracy of a response to a graph query. Here, we first propose a generic framework for developing parallel algorithms to update graph properties on large dynamic networks. We use our framework to develop algorithms for updating Single Source Shortest Path (SSSP) and Vertex Color. Then we propose applications of the developed algorithms in Unmanned Aerial Vehicle (UAV) based delivery systems under time-varying dynamics. Finally, we implement our SSSP and vertex color update algorithms for Nvidia GPU architecture and show empirically that the developed algorithms can update properties in large dynamic networks faster than the state-of-the-art techniques.

关键词： datasets neural networks gaze detection text tagging

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FRAME 2022: The 2nd workshop on Flexible Resource and Application Management on the Edge 31

FRAME 2022: The 2nd Workshop on Flexible Resource and Applic...

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31st International Symposium on High-Performance parallel and distributed Computing, HPDC 2022

作者： Babuji, Yadu Chard, Kyle Foster, Ian Li, Zhuozhao Argonne National Lab University of Chicago ChicagoIL United States Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9781450391993

Serverless computing presents an attractive model for general distributed computing as it focuses on abstracting the infrastructure required to execute an application. This workshop investigates the intersection between high performance computing and serverless computing, looking both at the high performance and distributed systems used to deliver serverless platforms and also at the use of serverless models for high performance and distributed systems. © 2022 Owner/Author.

关键词： Computer programming

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A GPU-Oriented Application Programming Interface for Digital Audio Workstations

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IEEE TRANSACTIONS ON parallel AND distributed systems 2022年第8期33卷 1924-1938页

作者： Bianchi, Daniele Avanzini, Federico Barate, Adriano Ludovico, Luca A. Presti, Giorgio Univ Milan Comp Sci Dept I-20133 Milan Italy

A Digital Audio Workstation (DAW) is a hardware and/or software device aiming to ease those operations required for music production, such as arranging, recording, editing, mixing, and, more in general, modifying sounds creatively. A peculiarity of a DAW environment is that most of the work is highly parallelizable, since the basic architecture of a DAW consists in the simultaneous processing of different audio tracks, mainly independent from each other. In order to exploit such a feature, this paper proposes an interface that lets the DAW interact with the Graphics Processing Unit (GPU) in a standardized way. Despite some academic research and experimentation, the professional audio software industry almost never exploited GPUs when implementing entire DAWs, but only when realising very specific tools or third party extensions (plugins). This work also presents and discusses the outcomes of a number of tests conducted in order to choose the optimal architecture. As a result, a GPU-based approach turned to be a valid alternative to the use of CPUs in the computation of audio effects, such as the rendering of audio tracks after mixing and mastering operations, both in real time and offline.

关键词： Graphics processing units Task analysis real-time systems Software Rendering (computer graphics) parallel processing Central Processing Unit Graphic processing unit (GPU) audio digital audio workstation (DAW) heterogeneous computing

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A Multi-Modal distributed real-time IoT System for Urban Traffic Control 5

A Multi-Modal Distributed Real-Time IoT System for Urban Tra...

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5th workshop on Next Generation real-time Embedded systems, NG-RES 2024

作者： Khanam, Zeba Achari, Vejey Pradeep Suresh Boukhennoufa, Issam Jindal, Anish Singh, Amit Kumar BT Security Research Adastral Park United Kingdom Keele University Keele United Kingdom University of Essex Colchester United Kingdom Durham University Durham United Kingdom

ISBN: (纸本)9783959773133

Traffic congestion is one of the growing urban problem with associated problems like fuel wastage, loss of lives, and slow productivity. The existing traffic system uses programming logic control (PLC) with round-robin scheduling algorithm. Recent works have proposed IoT-based frameworks that use traffic density of each lane to control traffic movement, but they suffer from low accuracy due to lack of emergency vehicle image datasets for training deep neural networks. In this paper, we propose a novel distributed IoT framework that is based on two observations. The first observation is major structural changes to road are rare. This observation is exploited by proposing a novel two stage vehicle detector that is able to achieve 77% vehicle detection accuracy on UA-DETRAC dataset. The second observation is emergency vehicle have distinct siren sound that is detected using a novel acoustic detection algorithm on an edge device. The proposed system is able to detect emergency vehicles with an average accuracy of 99.4%. © 2024 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Deep neural networks

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