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ReSLB: Load Balanced Workflow for distributed Deep Learning Mass Spectrometry Database 24

ReSLB: Load Balanced Workflow for Distributed Deep Learning ...

引用

24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Mocheng, Li Yang, Liu Yang, Ou Zhiguang, Chen Nong, Xiao Tao, Chen College of Computer National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Changsha China College of Computer National University of Defense Technology Changsha China School of Computer Sun Yat-sen University Guangzhou China Beijing China

ISBN: (纸本)9798350319934

the proteomics data analysis pipeline based on the shotgun method requires efficient data processing methods. the parallel algorithm of mass spectrometry database search faces the problems of rapidly expanding database size but low scalability. the heterogeneous database search algorithm based on deep learning is an effective way to solve this problem. Still, the deep learning-based distributed parallel database search algorithm is lacking. this paper analyzes the database searching computational load using a neural network and designs ReSLB, a workflow for the restricted search of mass spectrometry data based on GPU cluster and neural network scoring algorithm. this work aims to ensure the high scalability of future deep learning-based mass spectrometry distributed databases. In the performance estimation of 256 GPUs, the load imbalance of less than 30% and the parallel efficiency of 60% are achieved. Compared with state-of-the-art, the time cost is reduced by 75%, and the parallel efficiency from 1 to 256 GPUs is 3.6x higher than that. © 2022 ieee.

关键词： Mass spectrometry

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Aware: Adaptive distributed Training with Computation, Communication and Position Awareness for Deep Learning Model 24

Aware: Adaptive Distributed Training with Computation, Commu...

引用

作者： Zeng, Yan Yi, Guangzheng Yin, Yuyu Wu, Jiyang Xue, Meiting Zhang, Jilin Wan, Jian Zhang, Yunquan School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing China

ISBN: (纸本)9798350319934

the accuracy of the neural networks can usually be improved by increasing the size of the dataset and the layers or operators of the network, as it has strong composability. But, it makes a challenge to train these models efficiently, due to the limited resources of a single device, such as memory, computational resource and so on. For this challenge, it has become an inevitable trend to automatically implement model parallelism across multiple devices. this paper proposes Aware, an adaptive distributed parallel training method, to search distributed parallel strategy automatically for deep learning model. It firstly proposes an operator fusion strategy with computation and communication awareness to simplify the computational graph of deep learning model. And then, it introduces position-aware graph embedding algorithm to extract the structural features of models, which can make the searched parallel strategies to transplant to other networks with similar structures. On this basis, we use reinforcement learning algorithm to search distributed parallel strategy automatically. this paper makes experiments with neural networks such as Inception V3, NMT, GNM, NasNet and ResNet, and uses CIFAR10 and PTB datasets to compare the two methods of Hierarchical and Placeto. the results show that compared with Placeto, Aware achieves up to 5% reductions in runtime. Aware achieves more than 8% reductions in runtime compared with Hierarchical. Moreover, it has better transplantation and generalization capabilities, and supports pre-training for large-scale network parallel strategy search and accelerates convergence. © 2022 ieee.

关键词： Deep learning

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A distributed Photovoltaic Data Synchronous Collection and Recognition Method Based on Improved B-Spline Function 5

A Distributed Photovoltaic Data Synchronous Collection and R...

引用

5th international conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Han, Qi Cai, Yanbin Ren, Zhishuai Wang, Yan Zhang, Dan Che, Wenxue Wang, Hanlin Cui, Xinhui State Grid Shandong Electric Power Company Dongying Power Supply Company Shandong China

ISBN: (纸本)9798350309034

the cost of photovoltaic power generation system equipment is high, and the damage or reduced lifespan of its main equipment such as solar cells and batteries will directly harm the interests of users. Due to the initial promotion and use of photovoltaic power generation systems, there are relatively few relevant testing and monitoring equipment in the market. therefore, it is of great significance to develop a dedicated data synchronization collection and identification system for photovoltaic power generation systems, providing comprehensive characteristic testing for scientific research and ensuring the safe operation of users. Due to the widespread application of distributed photovoltaic power plants in power enterprises, distributed photovoltaic power plants are prone to malfunctions during data collection, which affects the efficiency of data collection in distributed photovoltaic power plants. therefore, in response to the problems in data collection of distributed photovoltaic power plants, this article proposes a distributed photovoltaic power generation data collection method based on time series and uses an improved B-spline function to reduce transmission energy and information transmission congestion probability. this method first removes abnormal data in distributed photovoltaic power plants, and after removing it, missing data is found. After repairing it, a collection model is constructed using a BP neural network (BPNN) to complete the collection of power generation data. the experimental results show that the method proposed in this article has significant help in effectively collecting power generation data, and it will occupy a place in future power generation data collection methods. © 2023 ieee.

关键词： B-spline function Data collection Neural network Photovoltaic power

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Adaptive Robot Control Based on Wireless Undergroud sensor Network in Agriculture 4.0 5

Adaptive Robot Control Based on Wireless Undergroud Sensor N...

引用

5th international conference on Computer Communication and the Internet, ICCCI 2023

作者： Cariou, Christophe Moiroux-Arvis, Laure Pinet, François Chanet, Jean-Pierre University Clermont Auvergne Inrae Ur Tscf AubièreF-63178 France

ISBN: (纸本)9798350326956

From sensor nodes fully buried at a few dozens of centimeters deep in Wireless Underground sensor Networks (WUSN), the soil moisture information can be measured anywhere, including in the passage of vehicles. this opens new opportunities, in particular in Agriculture 4.0, to adapt the speed and work of agricultural mobile robots to the ground conditions. By integrating a collector node in a robot, connected to its CAN bus, this paper experimentally demonstrates the possibility to adapt in real time the speed of the robot with respect to the soil moisture information transmitted in LoRa by a buried sensor node. the hardware and software developements are presented, and the first experimental results discussed. Future research directions are given to extend the capabilities of the system proposed. the innovative approach presented in this paper enables to envision new possibilities in the combination of WUSN and robotics technologies. © 2023 ieee.

关键词： Soil moisture

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Performance Analysis of distributed Antenna Array Synthesis for Geostationary Earth Orbit distributed Satellite Cluster 5

Performance Analysis of Distributed Antenna Array Synthesis ...

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5th international Academic Exchange conference on Science and Technology Innovation, IAECST 2023

作者： Wang, Changxu He, Yuanzhi Institute of Systems Engineering Academy of Military Sciences Beijing China

ISBN: (纸本)9798350357738

the maturity of rocket reusability and multiple satellite deployment technologies has presented new opportunities for the development of distributed satellite cluster (DSC). As an emerging satellite communication technology, DSC offer a solution to challenges such as limited capacity, inadequate antenna performance, and insufficient interference mitigation faced by single satellite communication. distributed antenna arrays, utilizing spatial dimension resources and diversity gain, offer higher antenna gain, achieving greater composite field strength and power compared to single satellite antennas, thereby exhibiting immense research potential. this paper first presents the scenario model and theoretical foundations of field strength and power synthesis for DSC distributed antenna arrays. Subsequently, simulations and analyses of the far-field patterns of the DSC distributed antenna arrays are conducted by varying the relative positions, orbit heights, and antenna carrier phases of single, two, and four DSC satellites in Geostationary Earth Orbit (GEO). the simulation results demonstrate that the relative positions of the satellites, orbit heights, and antenna carrier phases all exert an influence on the far-field patterns of the DSC distributed antenna arrays. Additionally, it is observed that the DSC distributed antenna arrays achieve electric field strength that is N times higher and equivalent isotropically radiated power (EIRP) values that are N2 times greater than those of single satellite antennas, thus confirming the feasibility of DSC distributed antenna arrays. Finally, we summarize this paper and identify the challenges faced by this research. © 2023 ieee.

关键词： Antenna arrays

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Networked Multi-Agent distributed Control Framework for Demand Response 5

Networked Multi-Agent Distributed Control Framework for Dema...

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5th international conference on Industrial Artificial Intelligence, IAI 2023

作者： Yu, Jing Yu, Meng Nari Group Corporation State Grid Electric Power Research Institute Nanjing210000 China State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Company Limited Wuhan430074 China

ISBN: (纸本)9798350325294

this paper addresses the challenges of massive and dispersed controllable electricity loads and vulnerability to time delay and packet loss for demand response in power systems. To improve the control efficiency of massive and dispersed electricity loads, a novel framework that contains an aggregation layer between the main site and dispersed resources is proposed. this framework enables dispersed electricity loads to follow the global objective in different geographical conditions. However, the increasing number of massive resources can lead to a communication explosion, especially when communication delay and packet loss are considered. To mitigate this issue, a distributed algorithm is introduced to reduce the communication volume, improving the convergence rate. We show that the proposed algorithm achieves consensus. Numerical examples demonstrate the effectiveness of the proposed algorithm on big-scale systems, showing a faster convergence rate than centralized methods. © 2023 ieee.

关键词： Multi agent systems

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2023 5th international conference on Electronic Engineering and Informatics, EEI 2023

2023 5th International Conference on Electronic Engineering ...

引用

5th international conference on Electronic Engineering and Informatics, EEI 2023

ISBN: (纸本)9798350327076

the proceedings contain 138 papers. the topics discussed include: a temperature and voltage-insensitive ring oscillator;a galvanic isolated full bridge rectifier with adaptive zero current control;active quenching and reset circuit for Geiger mode avalanche photodiodes;design of strip-loaded directional coupler;substrate integrated waveguide wideband bandpass filter based on spoof surface plasmon polariton;realization of parallel distributed temperature sensor network based on field programmable gate array;data-efficient pin defect detection with transformer in transmission lines;research on video surveillance security system based on domestic password;sliding mode speed control strategy of permanent magnet synchronous motor with disturbance compensation;research on improvement of low voltage crossing of isolated island permanent magnet wind turbine;design of a compact calibration platform for characterization of tunneling magnetoresistive sensors under varying temperatures;and study on influence of impedance parameters of multiplier element in equivalent model of wind farm.

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A Node-Level distributed Power Flow Based on Newton-Raphson Method 5

A Node-Level Distributed Power Flow Based on Newton-Raphson ...

引用

5th international conference on Renewable Energy and Power Engineering

作者： Tan, Sikai Wu, Minghuan Peng, Jianchun Jiang, Hui Shenzhen Univ Coll Mech & Control Engn Shenzhen Peoples R China Shenzhen Univ Coll Optoelectron Engn Shenzhen Peoples R China

ISBN: (数字)9781665482066

ISBN: (纸本)9781665482073;9781665482066

In the future power system, it is necessary to develop a power flow method that can protect the privacy information of generators and electricity purchasers, and alleviate the calculation pressure of the central calculator. In this paper, a node-level distributed power flow method based on the Newton-Raphson method is established. First, the method regards a single node in the power network as an agent (capable of independent computing devices), and then constructs a single node power balance equation. Taking the voltage of the neighbor node as a known invariant and using the first-order Taylor approximation, the iterative formula of the node voltage is derived. this method disperses the computational load confronted in the central processor by constructing an iterative formula for a single node. In addition, each node only needs the voltage value of its neighboring nodes for computation. Hence, this method can well protect the privacy information of generators and electricity purchasers. By simulating several ieee systems, the results show that the distributed method and the centralized method get the same answer, which means that the distributed method is effective.

关键词： power flow distributed computing node-level distribution privacy protection

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Automatic target detection with monostatic-bistatic miniSAR and infrared sensor data fusion 5

Automatic target detection with monostatic-bistatic miniSAR ...

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5th international conference on Geology, Mapping and Remote Sensing (ICGMRS)

作者： Da Vida, Bilendo Delvis Geng, Zhe Xu, Chongqi Zhang, Shiyu Yu, Xiang Zhu, Daiyin Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Nanjing 210016 Peoples R China Nanjing Inst Technol Sch Comp Engn Nanjing 211167 Peoples R China

ISBN: (纸本)9798350365719;9798350365726

Teamwork between monostatic-bistatic miniature synthetic aperture radar (MiniSAR) systems and infrared (IR) sensors mounted on spatially separated drones would bring enhanced trajectory flexibility and anti-stealth/anti-jamming capability. Leveraging the monostatic-bistatic SAR images and the IR images over the same scene of interest, this paper presents a novel framework for multimodal data fusion in automatic target detection (ATD). For monostatic SAR ATD, the YOLOv5 is used as the base structure for the multiview SAR DNN (MSAR-DNN), and the optimum hyperparameters are obtained via evolution. For the more challenging bistatic SAR ATD, a two-stage procedure is followed. In Stage-1, a YOLO variant is employed for target detection. In stage-2, target classification is carried out with an 8-key-sector-view feature fusion mechanism. the multi-view IR DNN (MIR-DNN) features confidence score ensemble based on the unbalanced information provided in diverse-aspect images, for which greater weights are assigned to the images with finer ground resolution. Simulation results show that the proposed method is effective in combining the key multiview target features reflected by the SAR and the IR imagery.

关键词： SAR automatic target detection DNN multimodal data fusion

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Joint Energy and Computation Workload Management for Geo-distributed Data Centers in Smart Grid 23

Joint Energy and Computation Workload Management for Geo-Dis...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Zizhen Wang, Ran Zhu, Kun Yi, Changyan Liu, Linfeng Niyato, Dusit Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University of Posts and Telecommunications Nanjing China Nanyang Technological University Singapore

ISBN: (纸本)9781665494571

With the increasing demands of data computation and storage for cloud-based services, the energy demand of data centers (DCs) is rising rapidly and becoming a noticeable challenge for current power networks. the smart grid (SG) is deemed as the future power system paradigm enabling more affordable and sustainable energy supply, which can effectively relieve the power load pressure of DCs. Moreover, with growing concerns regarding harmful emissions due to combustion of fossil fuels, the exploitation of renewable energy sources (RES) has attracted extensive attention, benefiting both SGs and DCs at large. However, the geo-distributed property of DCs and SGs, as well as the uncertain nature of RES production pose severe challenges to the optimal management of computation and energy resources in such coupling system. Focusing on these issues, a joint energy and computation workload management framework is proposed for enabling a sustainable DC paradigm with distributed RES. Specifically, to model the interactions between data center operator (DCO) and SGs, a Stackelberg game is formulated. the SG offers the DCO an electricity selling price to maximize the benefit. the DCO allocates the computation workload to geo-distributed DCs, aiming to minimize the costs of energy consumption and carbon emissions. We obtain the equilibrium state of the game and prove its uniqueness and optimality. Simulation experiments are conducted to evaluate the performance of the proposed scheme, showing that it can achieve considerable cost savings over counterparts. the proposed approach and obtained results provide useful insights for helping the DCO developing rational management strategies. © 2021 ieee.

关键词： Data centers Costs Smart cities Computational modeling Games Carbon dioxide Data models

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