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Sensor Array and Multichannel Signal Processing workshop

作者： Debesh Jha Ashish Rauniyar Hå vard D. Johansen Dag Johansen Michael A. Riegler På l Halvorsen Ulas Bagci Northwestern University USA SINTEF Digital Norway UiT The Arctic University of Norway Norway SimulaMet Norway Oslo Metropolitan University Norway

Ubiquitous sensors and Internet of Things (IoT) technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post game. New methods, including machine learning, image and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. Following FIFA's 2015 approval of electronics performance and tracking system during games, performance data of a single player or the entire team is allowed to be collected using GPS-based wearables. Data from practice sessions outside the sporting arena is being collected in greater numbers than ever before. realizing the significance of data in professional soccer, this paper presents video analytics, examines recent state-of-the-art literature in elite soccer, and summarizes existing real-time video analytics algorithms. We also discuss real-time crowdsourcing of the obtained data, tactical and technical performance, distributed computing and its importance in video analytics and propose a future research perspective.

关键词： Training Visual analytics Wearable computers Signal processing algorithms Games Streaming media real-time systems

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SPEAR: Dynamic Spatio-Temporal Query Processing over High Velocity Data Streams 37

SPEAR: Dynamic Spatio-Temporal Query Processing over High Ve...

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37th IEEE International Conference on Data Engineering (IEEE ICDE)

作者： Baig, Furqan Teng, Dejun Kong, Jun Wang, Fusheng SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA Georgia State Univ Dept Comp Sci Dept Math & Stat Atlanta GA 30303 USA

ISBN: (纸本)9781728191843

With the advent of IoT and emerging 5G technology, real-time streaming data are being generated at unprecedented speed and volume having both temporal and spatial dimensions. Effective analysis at such scale and speed require support for dynamically adjusting querying capabilities in real-time. In the spatio-temporal domain, this warrants data as well as query optimization strategies especially for objects with changing motion states. Contemporary spatio-temporal data stream management systems in the distributed domain are mostly dominated by specify-once-apply-continuously query model. Any modification in query state requires query restart limiting system responsiveness and producing outdated or in worst case erroneous results. In this paper, we propose adaptations of principles from streaming databases, spatial data management, and distributed computing to support dynamic spatio-temporal query processing over high-velocity big data streams. We first formulate a set of spatio-temporal data types and functions to seamlessly handle changes in distributed query states. We develop a comprehensive set of streaming spatio-temporal querying methods and propose geohash based dynamic spatial partitioning for effective parallel processing. We implement a prototype on top of Apache Flink, where the in-memory stream processing fits nicely with our spatio-temporal models. Comparative evaluation of our prototype demonstrates the effectiveness of our strategy by maintaining high consistent processing rates for both stationary as well as moving queries over high velocity spatio-temporal big data streams.

关键词： spatio-temporal stream-processing spatiotemporal-stream real-time-spatio-temporal spatial-stream distributed-stream

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CHIL Testbed of Consensus Control-Based Battery Energy Storage systems 52

CHIL Testbed of Consensus Control-Based Battery Energy Stora...

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52nd North American Power Symposium (NAPS)

作者： Li, Yin Fan, Lingling Bao, Li Miao, Zhixin Univ S Florida Dept Elect Engn Tampa FL 33620 USA

ISBN: (纸本)9781728181929

Consensus control has been proposed by our prior research to operate multiple battery energy storage systems (BESS). The objective of this paper is to validate the performance of consensus control using a controller-hardware-in-the-loop (CHIL) testbed. This paper describes the system topology and the setup process of this testbed. The testbed emulates a microgrid with three parallel BESS connected to the IEEE-9 bus system. The circuit part is simulated in real-time simulator while the control systems for BESS are implemented in three FPGA-based controllers. Using this testbed, the consensus control was tested under both of grid-connected mode and autonomous mode.

关键词： Consensus control battery energy storage system controller hardware-in-the-loop microgrid distributed control FPGA-based controller

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A distributed WND-LSTM model on MapReduce for short-term traffic flow prediction

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NEURAL COMPUTING & APPLICATIONS 2021年第7期33卷 2393-2410页

作者： Xia, Dawen Zhang, Maoting Yan, Xiaobo Bai, Yu Zheng, Yongling Li, Yantao Li, Huaqing Guizhou Minzu Univ Coll Data Sci & Informat Engn Guiyang 550025 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

Building data-driven intelligent transportation is a significant task for establishing data-centric smart cities, and exceptionally efficient and accurate traffic flow prediction (TFP) is a crucial technology in constructing intelligent transportation systems (ITSs). To address the computation and storage problems of processing traffic flow big data with the centralized model on a traditional mining platform, we propose a distributed long short-term memory weighted model combined with a time window and normal distribution based on a MapReduce parallel processing framework in this paper, named as WND-LSTM. More specifically, under the Hadoop distributed computing platform, a distributed modeling framework of forecasting traffic flow on MapReduce is developed to solve the existing issues of storage and calculation in handling large-scale traffic flow data with the stand-alone learning model. Moreover, a distributed WND-LSTM model is presented on the MapReduce-based distributed modeling framework to enhance the accuracy, efficiency, and scalability of short-term TFP. Finally, we forecast the traffic flow on the Sanlihe East Road of Beijing in China using the proposed WND-LSTM model with the real-world taxi trajectory big data. In particular, the extensively experimental results from a case study demonstrate that the MAPE value of WND-LSTM is 88.48%, 65.79%, 70.46%, 68.21%, 66.95%, 68.43%, and 70.41% lower than that of the autoregressive integrated moving average (ARIMA), logistical regression (LR), support vector regression (SVR),k-nearest neighbor (KNN), stacked autoencoders (SAEs), gated recurrent unit (GRU), and long short-term memory (LSTM), respectively, and achieves 71.25% accuracy improvement on average.

关键词： Big data analytics Deep learning LSTM neural network Traffic flow prediction MapReduce

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Demeter: Resource-Efficient distributed Stream Processing under Dynamic Loads with Multi-Configuration Optimization

arXiv

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arXiv 2024年

作者： Geldenhuys, Morgan K. Scheinert, Dominik Kao, Odej Thamsen, Lauritz Technische Universität Berlin Berlin Germany University of Glasgow Glasgow United Kingdom

distributed Stream Processing (DSP) focuses on the near real-time processing of large streams of unbounded data. To increase processing capacities, DSP systems are able to dynamically scale across a cluster of commodity nodes, ensuring a good Quality of Service despite variable workloads. However, selecting scaleout configurations which maximize resource utilization remains a challenge. This is especially true in environments where workloads change over time and node failures are all but inevitable. Furthermore, configuration parameters such as memory allocation and checkpointing intervals impact performance and resource usage as well. Sub-optimal configurations easily lead to high operational costs, poor performance, or unacceptable loss of service. In this paper, we present Demeter, a method for dynamically optimizing key DSP system configuration parameters for resource efficiency. Demeter uses time Series Forecasting to predict future workloads and Multi-Objective Bayesian Optimization to model runtime behaviors in relation to parameter settings and workload rates. Together, these techniques allow us to determine whether or not enough is known about the predicted workload rate to proactively initiate short-lived parallel profiling runs for data gathering. Once trained, the models guide the adjustment of multiple, potentially dependent system configuration parameters ensuring optimized performance and resource usage in response to changing workload rates. Our experiments on a commodity cluster using Apache Flink demonstrate that Demeter significantly improves the operational efficiency of long-running benchmark jobs. Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamic loads

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distributed Model Predictive Control for Multiple Trains with Coupled Constraints

Distributed Model Predictive Control for Multiple Trains wit...

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Chinese Control Conference (CCC)

作者： Zixuan Zhang Haifeng Song Weiqi Bai Hongwei Wang Hairong Dong State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Electronic Information Engineering Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

This paper presents a distributed model predictive control of multiple trains, aiming at overcoming coupled constraints and achieving cooperative operation while reducing the computational burden. According to the coupled constraints associated with maintaining safety distance between adjacent trains, trains are divided into sub-systems and distributed optimal control problem is then formulated. To solve the problem, an alternating direction method of multipliers (ADMM) is employed with a fully distributed scheme, decomposing the original optimal control problem into smaller sub-problems that can be computed in parallel and meet the real-time control requirements. The proposed method effectively guarantees the cooperative operation of trains under local and coupled safety constraints. Numerical simulations are provided to demonstrate the feasibility and effectiveness of the proposed train control model and methods.

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P-ckpt: Coordinated Prioritized Checkpointing

P-ckpt: Coordinated Prioritized Checkpointing

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International Symposium on parallel and distributed Processing (IPDPS)

作者： Subhendu Behera Lipeng Wan Frank Mueller Matthew Wolf Scott Klasky North Carolina State University Oak Ridge National Laboratory

Good prediction accuracy and adequate lead time to failure are key to the success of failure-aware Check-point/Restart (C/R) models on current and future large-scale High-Performance Computing (HPC) systems. This paper develops a novel checkpointing technique, called p-ckpt, that aims to maintain the performance efficiency of failure-aware C/R models even when failures are predicted with a small lead time. The p-ckpt technique is developed for HPC systems with multi-level memory systems to prioritize checkpoints from vulnerable nodes (nodes with predicted failure) in the event of failure prediction. It applies coordination among the nodes within an application so that vulnerable nodes' checkpoint data is stored to the parallel File System (PFS) first by assigning priorities based on the lead time to failure. Vulnerable nodes thus have low-latency access on the critical path to the PFS before any failure happens. Further, we create the hybrid p-ckpt model by integrating Live Migration (LM) because of its cost-effectiveness and to reduce checkpoint frequency. Our hybrid p-ckpt C/R model considers prediction lead time and checkpoint latency to the PFS to decide on a feasible proactive action such as p-ckpt and LM. Simulations of six real-world applications for the Summit supercomputer show a ≈53-65% reduction in overhead due to the hybrid p-ckpt model compared to a ≈31-61% reduction in a state-of-the-art solution. We assess our C/R models against multiple failure distributions and consider lead time variability and failure prediction accuracy. Based on this evaluation and assessment, we discuss the trade-offs of using these models and their impact on application overhead.

关键词： Checkpointing Fault tolerance distributed processing File systems Computational modeling High performance computing Fault tolerant systems

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G-thinker: a general distributed framework for finding qualified subgraphs in a big graph with load balancing

G-thinker: a general distributed framework for finding quali...

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作者： Yan, Da Guo, Guimu Khalil, Jalal Özsu, M. Tamer Ku, Wei-Shinn Lui, John C. S. Department of Computer Science The University of Alabama at Birmingham BirminghamAL United States David R. Cheriton School of Computer Science University of Waterloo WaterlooON Canada Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Computer Science and Engineering The Chinese University of Hong Kong N.T Shatin Hong Kong

Finding from a big graph those subgraphs that satisfy certain conditions is useful in many applications such as community detection and subgraph matching. These problems have a high time complexity, but existing systems that attempt to scale them are all IO-bound in execution. We propose the first truly CPU-bound distributed framework called G-thinker for subgraph finding algorithms, which adopts a task-based computation model, and which also provides a user-friendly subgraph-centric vertex-pulling API for writing distributed subgraph finding algorithms that can be easily adapted from existing serial algorithms. To utilize all CPU cores of a cluster, G-thinker features (1) a highly concurrent vertex cache for parallel task access and (2) a lightweight task scheduling approach that ensures high task throughput. These designs well overlap communication with computation to minimize the idle time of CPU cores. To further improve load balancing on graphs where the workloads of individual tasks can be drastically different due to biased graph density distribution, we propose to prioritize the scheduling of those tasks that tend to be long running for processing and decomposition, plus a timeout mechanism for task decomposition to prevent long-running straggler tasks. The idea has been integrated into a novelty algorithm for maximum clique finding (MCF) that adopts a hybrid task decomposition strategy, which significantly improves the running time of MCF on dense and large graphs: The algorithm finds a maximum clique of size 1,109 on a large and dense WikiLinks graph dataset in 70 minutes. Extensive experiments demonstrate that G-thinker achieves orders of magnitude speedup compared even with the fastest existing subgraph-centric system, and it scales well to much larger and denser real network data. G-thinker is open-sourced at http://***/gthinker with detailed documentation. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Spring

关键词： Large dataset

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Impact of VR Technology on a Human in Semi-autonomous Multi-robot Navigation: Control-Theoretic Perspective 4

Impact of VR Technology on a Human in Semi-autonomous Multi-...

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4th IFAC workshop on Cyber-Physical and Human systems (CPHS)

作者： Hatanaka, Takeshi Mocizuki, Takahiro Torreblanca, Jose Maria Maestre Chopra, Nikhil Tokyo Inst Technol Sch Engn Tokyo 1528552 Japan Univ Seville Syst & Automat Engn Dept Seville Spain Univ Maryland Dept Mech Engn College Pk MD 20742 USA

In this paper, we investigate a scenario of one-human-multiple-robot navigation in three dimensions, and examine the impacts of the VR (Virtual reality) technology on human properties from a control-theoretic perspective. We start by reviewing a passivity-based distributed control architecture that takes complementary interactions such that motion synchronization is autonomously completed by a distributed robot controller while the operator is dedicated to robot navigation. Due to the limited human capability of 3-D recognition and limited dimensionality on the real-time manipulability, 3-D navigation is completely different from that of the one- or two-dimensional case and we need to carefully design both feedback and command interfaces between the operator and robots. Specifically, we employ two different pairs of the interfaces, traditional joystick controller with 2-D display monitor and HMD (Head Mounted Display) with VR controller. We then build human models from the operation data with these interfaces on a human-in-the-loop simulator. Through the human modeling, we present two novel findings: (i) VR interfaces improve the accuracy of the human model with about 25 similar to 45% of fitting ratio, which must drastically eases the design of human-robot collaboration systems, (ii) VR interfaces enhance human passivity, which is a key to ensuring closed-loop stability for the human-in-the-loop system. Copyright (c) 2022 The Author. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.00

关键词： distributed control Mobile robots Human-machine interface Synchronization System identification Virtual reality Passivity

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A Modulized Three-Port Interlinking Converter for Hybrid AC/DC/DS Microgrids Featured With a Decentralized Power Management Strategy

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第12期68卷 12430-12440页

作者： Zhang, Zhe Jin, Chi Tang, Yi Dong, Chaoyu Lin, Pengfeng Mi, Yang Wang, Peng Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore V Flow Tech Pte Ltd Singapore 637141 Singapore Nanyang Technol Univ Energy Res Inst Singapore 637141 Singapore Shanghai Univ Elect Power Sch Elect Engn Shanghai 200090 Peoples R China

A compact interlinking converter modular (ICM) for hybrid ac/dc microgrids with a distributed storage (DS) bus for storage integration is proposed in this article. The advanced ICM for hybrid ac/dc/ds microgrids involves the nine-switch topology with compact configuration and decreased number of active operating switches, which structurally equivalents to afford a dc interconnection port for dc subgrid, an ac interconnection port for ac subgrid, a ds interconnection port for ds subgrid, and two dc-dc converters for the interconnection ports for parallel DSs. In addition to the popular advantage of component elements saving, the suggested ICM, aggregating various interlinking converter cells (ICCs) into an ICM, is also proficient of participating computations from all ICC sensors to enhance the overall modular performance without building extra communication connection links between ICCs. With this in mind, a decentralized power management control strategy is realized by employing a coordination power control methodology to systematically handle various distributed generations (DGs), DSs, and ICM in a decentralized manner with improved communication fault ride-through capability. The controller hardware-in-the-loop experiments on a real-time digital simulator platform validates the achievement of the advanced ICM application in hybrid ac/dc/ds microgrids and the effectiveness of the associated decentralized power management scheme.

关键词： Microgrids Switches Power system management Hybrid power systems Topology Logic gates Decentralized control distributed generation (DG) droop control energy storage system (ESS) pulsewidth modulation (PWM) smart grid and hybrid microgrid

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