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中国电机工程学报 2024年第2期44卷 476-488,I0004页

作者：董家伟龚春阳包俊朱国忠王志新上海交通大学电子信息与电气工程学院上海市闵行区200240 上海电力大学电气工程学院上海市浦东新区200090 上海禧龙科技股份有限公司上海市金山区201517 上海正泰电源系统有限公司上海市松江区201614

一致性算法常用于孤岛运行多逆变器并联微电网系统分布式二次调频控制,对逆变器之间通信的实时性要求很高。对此,该文提出基于频率补偿信号的改进一致性算法,基于频率误差越限建立事件触发机制,维持逆变器之间已有的下垂关系不变,实现... 详细信息

一致性算法常用于孤岛运行多逆变器并联微电网系统分布式二次调频控制,对逆变器之间通信的实时性要求很高。对此,该文提出基于频率补偿信号的改进一致性算法,基于频率误差越限建立事件触发机制,维持逆变器之间已有的下垂关系不变,实现孤岛运行多逆变器微电网系统二次调频。同时,基于该事件触发机制和李雅普诺夫函数,获得最短触发时间下界,完全避免了在短时间内无限次事件触发现象的发生。最后,采用MATLAB/Simulink针对包含4台逆变器并联运行的孤岛微电网系统算例进行仿真,结果表明,提出的方法相比文献采用的基本一致性算法通信量减少99%以上,最短触发时间间隔延长至数十ms量级;同时,由于该文方法不使用功率微分信号,微电网系统的动态性能更好。

关键词：孤岛微电网系统分布式二次调频改进一致性算法事件触发李雅普诺夫函数

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A New time-Varying Adaptive Feedforward Sliding Mode Control of PKMs 14th

A New Time-Varying Adaptive Feedforward Sliding Mode Control...

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14th IFAC workshop on Adaptive and Learning Control systems (ALCOS)

作者： Fitas, Y. Chemori, A. Lamaury, J. Pierrot, F. Univ Montpellier CNRS LIRMM Montpellier France SYMETRIE Nimes France

This paper deals with the development of a new time-varying adaptive feedforward sliding mode control (SMC). The proposed controller is designed based on the original SMC by adding a feedforward term, which can be adaptive, in order to estimate the dynamic parameters. The main contribution of this study is a time-varying adaptation gain, which is proposed to efficiently identify these parameters. The objective is then to outperform the original SMC scheme. The proposed controller has been designed and applied to parallel kinematic manipulators (PKMs). It is validated through numerical simulation for different operating conditions on the VELOCE PKM. The obtained simulation results show the efficiency of the proposed controller in terms of tracking performances and robustness, with improvements up to 55% with respect to another adaptive controller (Adaptive Feedforward PD: AFFPD). Copyright (C) 2022 The Authors.

关键词： parallel Kinematic Manipulators Sliding Mode Control Adaptive Control time-Varying Gains Feedforward Dynamic Model

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HGraph: parallel and distributed Tool for Large-Scale Graph Processing 1

HGraph: Parallel and Distributed Tool for Large-Scale Graph ...

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1st International Conference on Artificial Intelligence and Data Analytics, CAIDA 2021

作者： Adoni, Wilfried Yves Hamilton Tarik, Nahhal Krichen, Moez El Byed, Abdeltif International University of Casablanca Hassan II University of Casablanca Casablanca Morocco Hassan II University of Casablanca Faculty of Science Casablanca Morocco University of Sfax Faculty of CSIT Albaha University KSA ReDCAD Laboratory Sfax Tunisia

ISBN: (纸本)9780738131771

Graph are ubiquitous because the fields of application are varied. Well-known examples are social networks, biological networks and path-finding in road networks. real-world graphs processing is very challenging because of 4V characteristics related to big data. They are huge to process them on single-node and the time complexity is exponential. Unfortunately, due to the lack of research, only a few systems are able to ensure the storage and quick processing of large-scale graphs. In this paper, we propose HGraph, a parallel and distributed tool which handles large-scale graphs. HGraph is build on top of Hadoop and Spark frameworks. The proposed tool provides high scalability and is adapted to easily implement algorithms for various graph problems. Experimental tests performed on real-world graphs showed that HGraph is reliable and achieves significant gain time over the state of the art of graph processing systems. © 2021 IEEE.

关键词： Big data

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real-time Scheduling and Analysis of OpenMP Programs with Spin Locks 26

Real-Time Scheduling and Analysis of OpenMP Programs with Sp...

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26th IEEE International Conference on parallel and distributed systems (IEEE ICPADS)

作者： Du, He Jiang, Xu Yang, Tao Lv, Ming Song Yi, Wang Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781728190747

Locking protocol is an essential component in resource management of real-time systems, which coordinates mutually exclusive accesses to shared resources from different tasks. OpenMP is a promising framework for multi-core real-time embedded systems as well as provides spin locks to protect shared resources. In this paper, we propose a resource model for analyzing OpenMP programs with spin locks. Based on our resource model, we also develop a technique for analyzing the blocking time which impacts the total workload. Notably, the resource model provides detailed resource access behavior of the programs, making our blocking analysis more accurate. Further, we derive the schedulability analysis for real-time OpenMP tasks with spin locks protecting shared resources. Experiments with realistic OpenMP programs are conducted to evaluate the performance of our method.

关键词： OpenMP parallel Tasks real-time Scheduling Spin Locks

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distributed and parallel processing for real-time and dynamic spatio-temporal graph

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION systems 2020年第2期23卷 905-926页

作者： Fang, Junhua Ding, Jiafeng Zhao, Pengpeng Xu, Jiajie Liu, An Li, Zhixu Soochow Univ Inst Artificial Intelligence Sch Comp Sci & Technol Suzhou 215006 Peoples R China

As a non-linear data structure consisting of nodes and edges, the graph data span many different domains. In the real world, applications based on such data structures are always time-sensitive, that is, the value of graph data tends to decrease with time. Furthermore, the application based on spatio-temporal graph is one of the typical representatives of time-sensitive, since the time dimension is an inherent feature of spatio-temporal data. The distributed Stream Processing Engine (DSPE) seems an excellent choice for the above requirement, which is commonly partitioned and concurrently processed by a number of threads to maximize the throughput. However, it is not feasible to do such mission directly using the traditional DSPE. In this paper, we propose a computational model suitable for handling the spatio-temporal graph in DSPE, by reconstructing the DSPE's parallel processing slots. Specifically, our proposal includes a general processing framework to deal with the data structure of the spatio-temporal graph, a state information compensation mechanism to ensure the correctness of processing such stateful operation in DSPE, a lightweight summary information calculation method to ensure the performance of the system. Empirical studies on real-world stream applications validate the usefulness of our proposals and prove the considerable advantage of our approaches over state-of-the-art solutions in the literature.

关键词： distributed and parallel computing real-time processing Spatio-temporal graph

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Toward Decentralized and Collaborative Deep Learning Inference for Intelligent IoT Devices

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IEEE NETWORK 2022年第1期36卷 59-68页

作者： Huang, Yakun Qiao, Xiuquan Dustdar, Schahram Zhang, Jianwei Li, Jiulin Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Tech Univ Wien Distributed Syst Grp Comp Sci Vienna Austria Capinfo Co Ltd Beijing Peoples R China Beijing Natl Speed Staking Oval Operat Co Ltd Beijing Peoples R China

Deep learning technologies are empowering IoT devices with an increasing number of intelligent services. However, the contradiction between resource-constrained IoT devices and intensive computing makes it common to transfer data to the cloud center for executing all DNN inference, or dynamically allocate DNN computations between IoT devices and the cloud center. Existing approaches perform a strong dependence on the cloud center, and require the support of a reliable and stable network. Thus, it may directly cause unreliable or even unavailable service in extreme or unstable environments. We propose DeColla, a decentralized and collaborative deep learning inference system for IoT devices, which completely migrates DNN computations from the cloud center to the IoT device side, relying on the collaborative mechanism to accelerate the DNN inference that is difficult for an individual IoT device to accomplish. DeColla uses a parallel acceleration strategy via a DRL-based adaptive allocation for collaborative inference, which aims to improve inference efficiency and robustness. To illustrate the advantages and robustness of DeColla, we built a testbed and employ DeColla to evaluate MobileNet DNN network trained on the ImageNet dataset, and also recognize the object for a mobile web AR application and conduct extensive experiments to analyze the latency, resource usage, and robustness against existing methods. Numerical results show that DeColla outperforms other methods in terms of latency and resource usage, which can especially reduce at least 2.5 times latency than the hierarchical inference method when the collaboration is interrupted abnormally.

关键词： Collaboration Robustness Engines Resource management Cloud computing real-time systems Computational modeling

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A Compensated distributed-Parameter Line Decoupling Approach for real time Applications

A Compensated Distributed-Parameter Line Decoupling Approach...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Karrar, Abdelrahman Ahmed, Babikir Abdelgadir, Amira Saied, Noman Univ Tennessee Elect Engn Chattanooga TN 37403 USA

ISBN: (纸本)9781665405072

This paper proposes a new method called the Compensated distributed-Parameter Line Decoupling (CDLD) method, and used for decoupling distribution networks among parallel processing cores in a real-time multi-core environment. Due to the short length of distribution grid lines, decoupling the distribution network for real-time processing is challenging and approaches such as Stublines and State Space Nodal (SSN) solvers have been proposed. Both approaches have limitations and the method proposed in this work addresses these limitations to implement improvements. Specifically, the CDLD method can be used as an enhanced Stubline decoupling, improving on its accuracy and transient response, or it can be combined with an SSN solver to improve its computational performance and remove bottleneck issues. The CDLD method was tested on three IEEE benchmark systems in a real-time environment, and significant improvement was realized in network response and computational performance compared to the prevailing methods. The combined SSN-CDLD method proved to be the most promising approach for network decoupling.

关键词： Transient response Multicore processing Distribution networks parallel processing Benchmark testing real-time systems

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What Appears Suboptimal May Surprise You: A Fixed-Rate Scheduling Policy for Geo-distributed CoFlows

What Appears Suboptimal May Surprise You: A Fixed-Rate Sched...

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International Conference on parallel and distributed systems (ICPADS)

作者： Haoyu Liu Feiyan Ding Yao Wang Qiao Xiang Jiwu Shu Haizhou Du Linghe Kong Xue Liu School of Informatics Xiamen University Xiamen China School of Computer Science and Technology University of Electric Power of Shanghai Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science McGill University Montreal Canada

All existing coflow scheduling algorithms compute dynamic-rate schedules that change the rates of flows during transmission. In this paper, we make a crucial finding: although dynamically adjusting the rates of flows could lead to a better coflow completion time (CCT) in theory, it would introduce additional pressures on the congestion control mechanism in the underlying network, which result in poor CCT in practice. This difference between theoretical CCT and practical CCT is further exacerbated in wide-area networks, where the topology does not provide any bisection guarantee as data center networks do. To this end, we designed a fixed-rate coflow scheduling policy called FSCO. Although in theory, the best fixed-rate schedule is usually suboptimal, it keeps the in-flight traffic relatively steady, reducing the risk of triggering congestion control. The core of FSCO is an efficient scheduling algorithm based on the classic network utilization maximization (NUM) framework. We implement a prototype of FSCO and evaluate its performance extensively using real-world topologies and coflow traces. Experimental results show that the total CCT reduces up to 30% compared to baselines while yielding up to 12× speedups compared to the solver.

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Decentralized model predictive control for autonomous robot swarms with restricted communication skills in unknown environments 14

Decentralized model predictive control for autonomous robot ...

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14th International Symposium on Intelligent systems

作者： Puzicha, Alexander Buchholz, Peter Tech Univ Dortmund Emil Figge Str 61 D-44227 Dortmund Germany

The paper presents a new approach and a related real-time parallel simulation tool for modeling and analyzing a swarm of more than 60 distributed autonomous mobile robots communicating over an unreliable and capacity restricted wireless communication network. It includes a physical simulation of static obstacles, dynamic obstacles with scriptable movement, soil condition, active jammers, static and dynamic link obstacles with configurable damping as well as thermal noise. The simulated ground based mobile robots use CPBP [12] as probabilistic model predictive closed loop controller in combination with gradient free cost functions to evaluate complex unsteady goodness aggregations. The goal of this approach is the development of connection aware swarm behavior for complex missions such as terrain exploration, formations, convoy escorting or creation of a mobile ad hoc network in dynamic disaster areas under realistic environmental conditions. The missions can be combined in any manner and the target extraction of the high-level commands for each agent is done implicitly. To validate the developed behavior, the independent software control kernel is able to be used on real robots as well. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： real-time Simulations parallel Simulations Simulation-Based Virtual Environments Swarm Robotics Model Predictive Control Autonomous Robots Swarm Behavior

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Scheduling parallel real-time Tasks on the Minimum Number of Processors

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IEEE TRANSACTIONS ON parallel AND distributed systems 2020年第1期31卷 171-186页

作者： Cho, Hyeonjoong Kim, Chulgoo Sun, Joohyung Easwaran, Arvind Park, Ju-Derk Choi, Byeong-Cheol Korea Univ Dept Comp & Informat Sci Sejong 30019 South Korea Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore ETRI Daejeon 34129 South Korea

Recently, several parallel frameworks have emerged to utilize the increasing computational capacity of multiprocessors. parallel tasks are distinguished from traditional sequential tasks in that the subtasks contained in a single parallel task can simultaneously execute on multiple processors. In this study, we consider the scheduling problem of minimizing the number of processors on which the parallel real-time tasks feasibly run. In particular, we focus on scheduling sporadic parallel real-time tasks, in which precedence constraints between subtasks of each parallel task are expressed using a directed acyclic graph (DAG). To address the problem, we formulate an optimization problem that aims to minimize the maximum processing capacity for executing the given tasks. We then suggest a polynomial solution consisting of three steps: (1) transform each parallel real-time task into a series of multithreaded segments, while respecting the precedence constraints of the DAG;(2) selectively extend the segment lengths;and (3) interpret the problem as a flow network to balance the flows on the terminal edges. We also provide the schedulability bound of the proposed solution: it has a capacity augmentation bound of 2. Our experimental results show that the proposed approach yields higher performance than one developed in a recent study.

关键词： real-time scheduling multicores multiprocessors linear programming flow networks maximum flow problem minimum cost flow problem

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