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International Conference on Embedded & distributed systems (EDiS)

作者： Leyla Belaiche Laid Kahloul Manel Houimli Selma Sahraoui Saber Benharzallah Maroua Grid Nedjma Abidallah LINFI Laboratory University of Biskra Biskra Algeria University of Biskra Biskra Algeria LAMIE Laboratory University of Biskra Batna Algeria

ISBN: (数字)9798350397543

ISBN: (纸本)9798350397550

Differential evolution (DE) algorithms face performance challenges, which lean on improving solutions quality, speed-up, and exploitation of computational resources. parallelism represents a suitable paradigm for overcoming the DE challenges. The ensemble of differential evolution variants (EDEV) algorithm is a recent DE algorithm. EDEV constitutes three DE variants (JADE, CoDE, and EPSDE), which may decrease its speedup. In this paper, a multi-population parallel ensemble of differential evolution variants (MPPEDEV) is proposed based on the synchronous master/slave parallel model. The performance of the proposed MPPEDEV is tested using a constrained real parameter problem proposed in CEC 2006. Compared to four state-of-the-art DE algorithms, which are JADE, CoDE, EPSDE, and EDEV, the results show that MPPEDEV outperforms EDEV in terms of execution time and solutions quality, depending on the population size as a control parameter. Furthermore, MPPEDEV and EDEV outperform JADE, CoDE, and EPSDE in terms of solutions' quality.

关键词： Codes Sociology parallel processing Main-secondary Statistics Optimization Faces

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Research on Synchronized Data Acquisition System Based on distributed Clock

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MOBILE INFORMATION systems 2022年第1期2022卷

作者： Hao, Yuyu Li, Shugang Zhang, Tianjun Wei, Zongyong Xian Univ Sci & Technol Coll Safety Sci & Engn Xian 710000 Shaanxi Peoples R China

The clock synchronization of mine distributed acquisition system is the key to its normal operation, and the realization of a unified time benchmark is the premise of subsequent data processing. To capture a more accurate gas migration process, in the physical similarity simulation experiment of coal and natural gas, a synchronous data acquisition system based on SQL database and distributed clock is developed to obtain synchronous real-time data. SQL database is used to establish a data collection center to solve the data exchange problem between the three units of storage, collection, and display in the system. The three units are connected in parallel, which simplifies the data flow structure in the system and improves the communication efficiency. In addition, the security and reliability of data storage are greatly improved. Based on the distributed clock, the communication structure of the data acquisition unit is constructed. In a distributed way, the data are aggregated according to the object and finally uploaded to the data acquisition center through TCP/IP (Transmission Control Protocol/Internet Protocol). It meets the requirements of synchronous communication between acquisition boards and provides a reliable data acquisition scheme for the physical simulation system of simultaneous exploitation of coal and natural gas. The physical similarity simulation experiment takes a mine in Shanxi Province as the simulation object. On the basis of this system, the physical similarity simulation experiments of gas emission under different mining conditions are carried out. In this process, the distribution maps of mining stress changes in overlying strata and gas concentration changes in different mining areas are obtained. The results of data acquisition are verified by numerical simulation. The results show that the system can effectively maintain the data acquisition of various areas in the mine.

关键词： Data acquisition

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GAIA: A system for interactive analysis on distributed graphs using a high-level language 18

GAIA: A system for interactive analysis on distributed graph...

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18th USENIX Symposium on Networked systems Design and Implementation, NSDI 2021

作者： Qian, Zhengping Min, Chenqiang Lai, Longbin Fang, Yong Li, Gaofeng Yao, Youyang Lyu, Bingqing Zhou, Xiaoli Chen, Zhimin Zhou, Jingren Group, Alibaba Alibaba Group

ISBN: (纸本)9781939133212

GAIA (GrAph Interactive Analysis) is a distributed system designed specifically to make it easy for a variety of users to interactively analyze big graph data on large clusters at low latency. It adopts a high-level language called Gremlin for graph traversal, and provides automatic parallel execution. In particular, we advocate a powerful new abstraction called Scope that caters to the specific needs in this new computation model to scale graph queries with complex dependencies and runtime dynamics, while at the same time maintaining the simple and concise programming model. GAIA has been deployed in production clusters at Alibaba to support a variety of business-critical scenarios. Extensive evaluations using both benchmarks and real-world applications have validated the effectiveness of the proposed techniques, which enables GAIA to execute complex Gremlin traversal with orders-ofmagnitude better performance than existing high-performance engines, and at much larger scales than recent state-of-the-art Gremlin-enabled systems such as JanusGraph. © 2021 by The USENIX Association.

关键词： High level languages

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Study of a Self-Tuning Predictive Voltage and Frequency Controller on a Cyber-Physical Model of a distributed Generation Plant

Study of a Self-Tuning Predictive Voltage and Frequency Cont...

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Ural Smart Energy Conference (USEC)

作者： Yu.N. Bulatov A.V. Kryukov K.V. Suslov Bratsk State University Bratsk Russia Irkutsk State Transport University Irkutsk National Research Technical University Irkutsk Russia National Research University “Moscow Power Engineering Institute” Moscow Russia Transbaikal State University Chita Russia

The development of digital technology for monitoring and controlling physical processes of individual distributed generation plants in the electric power systems and its integration are driving the need to consider cyber-physical systems. Changes in operating conditions of distributed generation plants and in topology of electrical network require tuning the controllers to ensure stability of parallel operation of synchronous generators used. Controllers with a predictive device can be implemented to improve the efficiency of the tuning procedures due to their significant complexity. The paper provides a structure and a description of a digital model of a self-tuning predictive voltage and frequency controller for a synchronous generator. The studies were carried out on a cyber-physical model of a turbine generator set based on the MATLAB/Simulink real time Windows Target software. The purpose of the presented studies is to determine the effectiveness of the proposed self-tuning predictive controller in various transient conditions of the cyber-physical model of a turbine generator set in comparison with typical proportional- integral-derivative (PID) controllers. The experimental results demonstrated the advantages and effectiveness of the proposed predictive controller. In both normal and emergency conditions, it improved the quality indicators of the voltage and frequency control process.

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MTD: Multi-timestep Detector for Delayed Streaming Perception

arXiv

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arXiv 2023年

作者： Huang, Yihui Chen, Ningjiang School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Education Department of Guangxi Zhuang Autonomous Region Nanning China

Autonomous driving systems require real-time environmental perception to ensure user safety and experience. Streaming perception is a task of reporting the current state of the world, which is used to evaluate the delay and accuracy of autonomous driving systems. In real-world applications, factors such as hardware limitations and high temperatures inevitably cause delays in autonomous driving systems, resulting in the offset between the model output and the world state. In order to solve this problem, this paper propose the Multi-timestep Detector (MTD), an end-to-end detector which uses dynamic routing for multi-branch future prediction, giving model the ability to resist delay fluctuations. A Delay Analysis Module (DAM) is proposed to optimize the existing delay sensing method, continuously monitoring the model inference stack and calculating the delay trend. Moreover, a novel timestep Branch Module (TBM) is constructed, which includes static flow and adaptive flow to adaptively predict specific timesteps according to the delay trend. The proposed method has been evaluated on the Argoverse-HD dataset, and the experimental results show that it has achieved state-of-the-art performance across various delay settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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distributed Computation of a Robust Estimator Based on Cauchy Noises 60

Distributed Computation of a Robust Estimator Based on Cauch...

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60th IEEE Conference on Decision and Control (CDC)

作者： Snyder, Nathaniel Idan, Moshe Speyer, Jason L. Univ Calif Los Angeles Dept Mech & Aerosp Engn Los Angeles CA 90024 USA Technion Fac Aerosp Engn Haifa Israel

ISBN: (纸本)9781665436595

A real-time, recursive, multivariate estimation algorithm for time-invariant and time-varying linear systems with modelled Cauchy noises is developed. When previously compared to the Kalman Filter, the Multivariate Cauchy Estimator was shown to be robust against impulsive disturbances in the process or measurement functions, but proved computationally intractable for real-time estimation applications. Two significant insights allow for a reformulation of the Multivariate Cauchy Estimator to possess a streamlined recursive and computationally reduced characteristic function of the conditional probability density function of the system state-vector given the measurement sequence. This characteristic function is represented by a sum of terms, expanding with each measurement. First, we show that a cell-enumeration matrix can be computed for each hyperplane arrangement embedded within each term of the characteristic function of the Cauchy Estimator. We then show that functions used to formulate the terms of this characteristic function can be expressed as a vector of parameters operating on basis functions constructed from this enumeration matrix. This vector is obtained by solving an underdetermined system of equations. We demonstrate that our reformulation allows all terms with equal hyperplane arrangements to be reduced into a unique set. Secondly, we take advantage of advances in parallel processing to exploit the inherent parallelism found in the characteristic function of the Cauchy Estimator. A three state time-invariant system example is used to illustrate the performance of the Cauchy Estimator against the Kalman Filter when subjected to Gaussian and Cauchy noises. We report computational savings of over 99% when compared to the previous formulation. Furthermore, we discuss the real-time architecture of the Cauchy Estimator and report the execution speeds for a three-state system implemented on a single NVIDIA GeForce GTX 1060 graphics processing unit (GPU).

关键词： control-systems stochastic estimation bayesian estimation CUDA-C programming GPU linear programming cell enumeration Cauchy pdfs

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Local-Adaptive Transformer for Multivariate time Series Anomaly Detection and Diagnosis

Local-Adaptive Transformer for Multivariate Time Series Anom...

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IEEE International Conference on systems, Man and Cybernetics

作者： Xiaohui Zhou Yijie Wang Hongzuo Xu Mingyu Liu Ruyi Zhang National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China

time series data are pervasive in varied real-world applications, and accurately identifying anomalies in time series is of great importance. Many current methods are insufficient to model long-term dependence, whereas some anomalies can be only identified through long temporal contextual information. This may finally lead to disastrous outcomes due to false negatives of these anomalies. Prior arts employ Transformers (i.e., a neural network architecture that has powerful capability in modeling long-term dependence and global association) to alleviate this problem; however, Transformers are insensitive in sensing local context, which may neglect subtle anomalies. Therefore, in this paper, we propose a local-adaptive Transformer based on cross-correlation for time series anomaly detection, which unifies both global and local information to capture comprehensive time series patterns. Specifically, we devise a cross-correlation mechanism by employing causal convolution to adaptively capture local pattern variation, offering diverse local information into the long-term temporal learning process. Furthermore, a novel optimization objective is utilized to jointly optimize reconstruction of the entire time series and matrix derived from cross-correlation mechanism, which prevents the cross-correlation from becoming trivial in the training phase. The generated cross-correlation matrix reveals underlying interactions between dimensions of multivariate time series, which provides valuable insights into anomaly diagnosis. Extensive experiments on six real-world datasets demonstrate that our model outperforms state-of-the-art competing methods and achieves 6.8%-27.5% $F_real$ score improvement. Our method also has good anomaly interpretability and is effective for anomaly diagnosis.

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A novel parallel accelerated CRPF algorithm

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APPLIED INTELLIGENCE 2020年第3期50卷 849-859页

作者： Wang, Jinhua Cao, Jie Li, Wei Yu, Ping Huang, Kaijie Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Gansu Peoples R China Lanzhou Univ Technol Key Lab Gansu Adv Control Ind Proc Lanzhou 730050 Gansu Peoples R China Lanzhou Univ Technol Natl Expt Teaching Ctr Elect & Control Engn Lanzhou 730050 Gansu Peoples R China

Particle filtering is one of the most important algorithms for solving state estimation of nonlinear systems and has been widely studied in many fields. However, due to the unknown complex noise in the actual system, its estimation performance is degraded. Moreover, when the number of particles increase, the real-time performance of the algorithm is poor. For these two problems above, this paper proposed a parallel acceleration CRPF (cost-reference particle filter) algorithm based on CUDA (Compute Unified Device Architecture). CRPF does not need known noise statistics in nonlinear system state estimation, which can reduce the influence of unknown noise on state estimation accuracy. Combined with GPU's (Graphics Processing Unit) multi-thread parallel computing capability, CRPF parallel acceleration can be realized. Since the data association can't be parallel resampled, all the particles are evenly distributed to multiple blocks, and resampling process can be parallelized by block parallel computing, so as to improve the speed of the algorithm. At the same time, in order to reduce the global particle performance degradation caused by block resampling, the particles with low probability mass in each block are optimized by using a portion of global high-quality particles. Through two sets of simulation experiments, it is proved that the proposed method has improved in estimation accuracy and the real-time performance has been improved significantly, which can provide a new idea for the practical application of nonlinear filtering method.

关键词： Particle filter CRPF GPU Accelerated parallel processing CUDA

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3D electrical impedance tomography system for real-time annular multiphase flow metering

3D electrical impedance tomography system for real-time annu...

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The 3rd International Conference on distributed Sensing and Intelligent systems (ICDSIS 2022)

作者： M. Tarek M. Meribout V. K. Tiwari Department of Electrical and Computer Engineering Khalifa University Abu Dhabi UAE

With the rapid progress of tomography systems, a need has emerged for innovation that can provide high accuracy measurements, fast acquisition rate, and low-cost robust systems for multi-phase flow and medical instruments. The main objective of this work is to design and implement a 3D Electrical Impedance Tomography (EIT) system to effectively monitor a multi-phase flow which consists of an annular flow comprising of an air-core of unknown diameter, surrounded by a liquid phase, such as oil and water mixture of high water-cut value. The system consists of an array of electrode sensors evenly distributed around the pipe confining the three-phase flow. Subsequently, a dedicated hardware module (GPU) is used to reconstruct these signals, captured from the sensors, into a 3D image of the flow by solving the forward-inverse problem. The annular flow is generated using a flow conditioner consisting of a swirl cage followed by a bluff body that generates vortices that push the high dense liquid phase to the outer side of the pipeline. The 3D EIT system is designed to accurately measure the gas liquid fraction and the liquid-liquid fraction and provide the 3D volume image of the flow. Hence, it can be utilized for pipeline and reservoirs characterization to achieve high efficiency, cost reduction, and safer production. The approach is to design a dedicated, highly parallel hardware architecture that can sustain the high computation complexity of the 3D EIT algorithm. Coincidentally, advanced software development tools are available to help effectively develop these parallel hardware algorithms. The main novelty is utilizing GPUs to outperform the existing real-time tomography hardware platforms, mainly based on Field Programmable Gate Array (FPGAs), which have the disadvantage of being less dense and feature lower precise arithmetic operations. Matrix multiplication and matrix inversion are considered the most intensive computation in Electric Impedance Tomography (EIT). T

关键词： computational complexity computerised instrumentation electric impedance imaging flow measurement flowmeters image reconstruction inverse problems matrix multiplication multi-threading parallel processing

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Secondary-Frequency and Voltage-Regulation Control of Multi-parallel Inverter Microgrid System

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ENERGIES 2022年第22期15卷 8533-8533页

作者： Dong, Jiawei Gong, Chunyang Bao, Jun Zhu, Lihua Hou, Yuanjun Wang, Zhixin Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shanghai Univ Elect Power Coll Elect Engn Shanghai 200090 Peoples R China Shanghai Xilong Technol Co Ltd Shanghai 201109 Peoples R China Shanghai Chint Power Syst Co Ltd Shanghai 201614 Peoples R China Shanghai Da Yu Informat Tech Co Ltd Shanghai 200000 Peoples R China

As an important form of distributed renewable energy utilization and consumption, the multi-parallel inverter microgrid system works in both an isolated and grid-connected operation mode. Secondary-frequency and voltage-regulation control are very important in solving problems that appears in these systems, such as the distributed secondary-frequency regulation real-time scheme, voltage and reactive power balancing, and the secondary-frequency regulation control under the disturbances and unbalanced conditions of a microgrid system. This paper introduces key technologies related to these issues, such as the consensus algorithm and event-triggered technique, the dynamic and adaptive virtual impedance technique, and the robust and self-anti-disturbance control technique. Research and design methods such as small-signal state-space analysis, the Lyapunov function design method, the impedance analysis method, mu-synthesis design, and the LMI matrix design method are adopted to solve the issues in secondary-frequency regulation and voltage regulation. As the number of inverters increases, the structure of the microgrid becomes more and more complex. Suggestions and prospects for future research are provided to realize control with low-communication technology and a distributed scheme. Finally, for the case study, the droop-control model and primary frequency/voltage deviation of a multi-parallel inverter microgrid system is analyzed, and a state-space model of a multi-parallel inverter microgrid system with a droop-control loop is established. Then, the quantitative relationship between the primary frequency/voltage deviation and the active and reactive power output in the system is discussed. The methods and problems of centralized and decentralized secondary-frequency regulation methods, secondary-frequency regulation methods based on a consensus algorithm and an event-triggered mechanism, reactive power and voltage equalization, power distribution, and small-signal st

关键词： multi-parallel inverter microgrid secondary frequency and voltage regulation consensus algorithm event-triggered mechanism virtual impedance robust design

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