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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

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Exploration of Coherent Detection Technology for 100G Optical Energy Signal under FPGA Design

Exploration of Coherent Detection Technology for 100G Optica...

引用

2023 international conference on Internet of Things, Robotics and distributed computing, ICIRDC 2023

作者： Chen, Jingjia Zhao, Jinhui Zhang, Zuoxing State Grid Hubei Electric Power Co.Ltd Hubei Province Wuhan430077 China State Grid Information&Communication Branch of Hubei epc. Hubei Province Wuhan430077 China State Grid Hubei Electric Power Co. Ltd. Maintenance Branch Hubei Province Wuhan430070 China

ISBN: (纸本)9798350375275

Major equipment manufacturers are increasing their research and development investment, and have successively launched solutions for 100G (Gigabyte) fiber optic transmission systems. However, the 100G DP-QPSK (Dual-polarization Quadrature Phase Shift Keying) optical transmission system is an ultra long distance transmission project, and its implementation is very difficult. This article provided a detailed explanation of the specific modulation and coherent reception theory of DP-QPSK, and briefly explored its application in the digital domain. This article provided the hardware design of the coherent detection system, including the overall design of the system, power supply design, clock design, monitoring design, etc. It focused on the core power design of DSP (Digital Signal Process), the design of RF (Radio Frequency) analog-to-digital converter clock, and the design of DSP clock. In different rounds of testing, the bit error rate was 0.09% for the 10th round and 0.06% for the 20th round. This article laid a solid foundation for the research on high-precision clock design and FPGA (Field Programmable Gate Array) design of high-speed analog-to-digital converters. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Numerical Diffusion and its Impact on System-Identification for an Industrial Heating Process 11

Numerical Diffusion and its Impact on System-Identification ...

引用

11th Vienna international conference on Mathematical Modelling (MATHMOD)

作者： Weiss, Ruven Diehl, Moritz Reuter, Johannes HTWG Konstanz Inst Syst Dynam Constance Germany Univ Freiburg Dept Microsyst Engn Freiburg Germany

This paper deals with system-identification for a distributed parameter heating process where a solid substrate is moving through a spatially extended heating zone and heated up by applying hot air to its surface. The temperature distribution inside the substrate is modeled in a spatial plane, where heat conduction is considered in the direction, perpendicular to the direction of movement. In contrast to previous work, where scalar model parameters (e.g. the thermal parameters of the substrate) have been identified, here, the quantities for the heat transfer (heat transfer coefficient and air temperature) are identified as functions yielding a significantly improved fit to the measurement data. This improved system-identification is performed for two early-lumping modeling approaches, which differ in the way the advection term in the governing Partial Differential Equation is discretized: one uses Eulerian coordinates, where the computational grid is stationary, whereas the second employs Lagrangian coordinates where the grid is moving with the substrate. The differences of the two approaches are discussed with the main focus on numerical diffusion. Especially its impact on the system-identification is investigated: although the fit to the measurement is comparably good in both cases, very different solutions are obtained for the identified functions which, we argue, is due to the optimizer counteracting the smoothing effect of numerical diffusion. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： System-Identification Numerical Diffusion Thermal Dynamics distributed Parameter Systems Transportation Heat Conduction

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Collaborative, distributed, Scalable and Low-Cost Platform Based on Microservices, Containers, Mobile Devices and Cloud Services to Solve Compute-Intensive Tasks 27th

Collaborative, Distributed, Scalable and Low-Cost Platform B...

引用

27th international European conference on parallel and distributed computing (Euro-Par)

作者： Petrocelli, David De Giusti, Armando Naiouf, Marcelo La Plata Natl Univ Comp Sci Sch La Plata Argentina Lujan Natl Univ Lujan Buenos Aires Argentina La Plata Natl Univ CIC PBA Comp Sci Inst Invest Informat LIDI III LIDI La Plata Argentina CONICET Natl Council Sci & Tech Res Buenos Aires DF Argentina

ISBN: (纸本)9783031061561;9783031061554

When solving compute-intensive tasks, CPU/GPU hardware resources and specialized grid, Custer, Cloud infrastructure are commonly used to achieve high performance. However, this requires a high initial capital expense and ongoing maintenance costs. In contrast, ARM-based mobile devices regularly see improvement in their capacity, stability, and processing power daily while becoming ever more ubiquitous and requiring no massive capital or operating expenditures thanks to their reduced size and energy efficiency. Given this shifting computer paradigm, it is conceivable that a cost- and power-efficient solution for our world's HPC processing tasks would include ARM-based mobile devices, while they are idle during recharging periods. We proposed, developed, deployed and evaluated a distributed, collaborative, elastic and low-cost platform to solve HPC tasks recycling ARM mobile resources based on Cloud, microservices and containers, efficiently orchestrated via Kubernetes. To validate the system scalability, flexibility, and performance a lot of concurrent video transcoding scenarios were run. The results showed the system allows for improvements in terms of scalability, flexibility, stability, efficiency, and cost for HPC workloads.

关键词： Kubernetes Containers Video transcoding Microservices Cloud computing Mobile computing distributed and collaborative computing

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Simulation of Distribution Terminal Automation Joint Debugging Model Based on Machine Learning Algorithm

Simulation of Distribution Terminal Automation Joint Debuggi...

引用

2023 international conference on Internet of Things, Robotics and distributed computing, ICIRDC 2023

作者： Huang, Lei Zhou, Zhongqiang Liu, Ying Mei, Jiaxin Wu, Peng Guizhou Power Grid Limited Liability Company Tongren Power Supply Bureau Guizhou Tongren554300 China Guizhou Power Grid Co. Ltd. Guizhou Guiyang550000 China

ISBN: (纸本)9798350375275

In the increasingly advanced environment of technology, the power distribution network is gradually developing towards automation, and the functions of its distribution automation terminal equipment are also becoming more and more complete. Distribution automation, as an important part of China's smart grid construction, plays an important role in improving the production, operation and management level of the distribution network. From the perspective of power construction, the application of distribution automation equipment in the growth of power systems is an inevitable trend in the information society. The research on distribution automation terminal equipment is of crucial significance for the growth of power distribution network automation. At present, the joint debugging between distribution terminals and the main station relies on manual completion. However, with the increasing quantity of distribution terminals, the efficiency of joint debugging is becoming lower and the standardization level is not high. Based on this, this article constructs a distribution terminal automation joint debugging model based on machine learning algorithms to improve work efficiency and facilitate the promotion and application of distribution automation. The simulation results show that the model can achieve important functions such as monitoring the operation status of the distribution network, automatic fault isolation, and fast power restoration, improving the power supply reliability of the distribution network. © 2023 IEEE.

关键词： Electric power distribution

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Graph Computation with Adaptive Granularity 40

Graph Computation with Adaptive Granularity

引用

40th IEEE international conference on Data Engineering, ICDE 2024

作者： Xu, Ruiqi Wang, Yue Xiao, Xiaokui National University of Singapore Singapore Shenzhen Institute of Computing Sciences China

ISBN: (纸本)9798350317152

Despite the development of various distributed graph systems, little attention has been paid to the granularity of computation and communication, which can significantly impact overall efficiency. Moreover, users often struggle to write and optimize new parallel algorithms to fit different programming abstractions, which can be a daunting task. To address these challenges, this paper introduces Argan, a parallel graph system that offers efficient adaptive-grained executions and a user-friendly abstraction. Argan utilizes the adaptive-Grained Asynchronous parallel (GAP) model, which enables runtime adjustments of granularity to enhance performance. Additionally, its programming model allows users to directly derive parallel programs from existing batch sequential algorithms. Our experiments using real-life and synthetic graphs demonstrate that for a variety of graph applications, GAP effectively improves the performance of Argan, which outperforms Grap +, PowerSwitch, and Maiter. © 2024 IEEE.

关键词： Abstracting

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SpDISTAL: Compiling distributed Sparse Tensor Computations

SpDISTAL: Compiling Distributed Sparse Tensor Computations

引用

international conference for High Performance computing, Networking, Storage and Analysis (HPC)

作者： Yadav, Rohan Aiken, Alex Kjolstad, Fredrik Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665454445

We introduce SpDISTAL, a compiler for sparse tensor algebra that targets distributed systems. SpDISTAL combines separate descriptions of tensor algebra expressions, sparse data structures, data distribution, and computation distribution. Thus, it enables distributed execution of sparse tensor algebra expressions with a wide variety of sparse data structures and data distributions. SpDISTAL is implemented as a C++ library that targets a distributed task-based runtime system and can generate code for nodes with both multi-core CPUs and multiple GPUs. SpDISTAL generates distributed code that achieves performance competitive with hand-written distributed functions for specific sparse tensor algebra expressions and that outperforms general interpretation-based systems by one to two orders of magnitude. © 2022 IEEE.

关键词： Computer Science Programming parallel Programming

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Enhancing Trust and Privacy in Edge and Cloud computing Through Blockchain Technology 4

Enhancing Trust and Privacy in Edge and Cloud Computing Thro...

引用

4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Kolpe, Dipti Ravsaheb Lakare, Srushti Vijay Narode, Samiksha Kishor Kolpe, Pooja Shravan Vasant, Mohan Priyanka William, P. Sanjivani College of Engineering Savitribai Phule Pune University Department of Computer Engineering Pune India Sanjivani College of Engineering Savitribai Phule Pune University Department of Information Technology Pune India

ISBN: (纸本)9798350393248

Decentralized character, solid database, security features, and, to a significant part, anonymity, block chain provides a novel method for the storing, exchange, and preservation of private data. This is one of the reasons why it has become so popular. This research combines the benefits of blockchain technology and edge computing by developing core technological solutions for edge computing that are based on block chain. In addition to this, it makes use of the Parlier cryptosystem, which is what enables additive homomorphism to function. This is done to guarantee that the block chain operates as effectively as possible and to reduce the amount of computational strain that is placed on the client. The edge node is able to process the data it has received in cypher text, extract the outcome, and then deliver it to the client in cypher text. All of the data is encrypted on the side where the task is being performed. The results of the experiment with the computer simulation demonstrate that the method that was presented is practical and effective. The term 'edge computing' today refers to a network of different computer resources that are all linked to one another via a broad variety of communication mechanisms. The operation of the energy grid is dependent on distributed energy systems that are connected to the edge of the internet. Assume that every edge device is a client-owned distributed energy resource (DER) that is securely linked to a grid service provider. On the internet, information is safeguarded by SSL standards, but confidence must always be earned. In order to demonstrate how a model for representing and evaluating trust in a more comprehensive sense might be put into effect, the key example that is used in this study is the energy grid. Actors at the edge of the network may engage with one another in addition to interacting with the datacentre in the centre of the network. © 2023 IEEE.

关键词： Blockchain

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High-performance distributed Feature Storage and Consumption Services in Power grid Scenarios 8

High-performance Distributed Feature Storage and Consumption...

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8th IEEE international conference on Big Data Security on Cloud, 8th IEEE international conference on High Performance and Smart computing, and 8th IEEE international conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

作者： Wang, Guoqing Lu, Yiming Liu, Peng Zhou, Wang State Grid Shanghai Energy Interconnection Research Institute Beijing China Peking University Beijing China

ISBN: (纸本)9781665480697

As more and more services in the power grid introduce machine learning and deep learning technologies, feature engineering has become more and more complex. In order to improve the efficiency of feature engineering, this paper proposes a high-performance distributed feature storage and consumption service based on grid scenarios, referred to as HFSA (High-performance feature storage and access). HFSA designed a feature data storage and consumption platform with the help of the idea of message middleware. HFSA services include HFSA Cluster, Producer and Consumer. This article verifies whether the HFSA service meets the design requirements from three aspects: functional testing, accuracy testing and performance testing. The experimental results show that the HFSA service has complete functions and good performance, which basically solves the problem of characteristic storage and consumption of the power grid. © 2022 IEEE.

关键词： Middleware

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A Load-Balancing Strategy Based on Multi-Task Learning in a distributed Training Environment 4

A Load-Balancing Strategy Based on Multi-Task Learning in a ...

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4th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

作者： Huang, Yinqi Zhou, Wenjiang Li, Liang Lyu, Ting Xu, Haitao Univerisity of Science and Technology Beijing School of Computer and Communication Engineering Beijing China China Academy of Information and Communications Technology Beijing China Shunde Innovation School Univerisity of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350308082

With the development of machine learning and big data technologies, distributed training has become an important way to improve computational efficiency. However, in the distributed training environment, the performance difference between workers and the interference of unrelated tasks may lead to the uneven load of the system and cause the 'straggler phenomenon'. Therefore, how to solve the straggler phenomenon and achieve load balancing of the system is a pressing problem of distributed training. To address the shortcomings of the existing parallel computing model DSSP, this paper proposes a load-balancing strategy for DDP, which effectively reduces the load difference by adjusting the batch size and data size of workers in the training process. Then, to explore the correlation between data size in DDP and synchronization threshold in DSSP in dynamic adjustment, we perform multi-task learning for the two dynamic adjustment strategies and integrate the proposed Joint Multi-Task Prediction scheme on DSSP to implement a new parallel computing model ESP. extensive experiments The results show that ESP can not only guarantee the model accuracy but also effectively improve the training speed in distributed training. © 2023 IEEE.

关键词： Computational efficiency

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From Agents to Robots: A Training and Evaluation Platform for Multi-robot Reinforcement Learning 30

From Agents to Robots: A Training and Evaluation Platform fo...

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30th IEEE international conference on parallel and distributed Systems, ICPADS 2024

作者： Liang, Zhiuxan Cao, Jiannong Jiang, Shan Saxena, Divya Cao, Rui Xu, Huafeng The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798331515966

Multi-robot reinforcement learning (MRRL) is a promising approach to solving cooperation problems and has been widely adopted in many applications. In the past decades, researchers have proposed various approaches to improve the efficiency of MRRL. However, most of them are trained and evaluated only in simulated environments with simple interaction scenarios. The problem of how these methods perform in the real-world environment with complex interaction scenarios remains unsolved. To meet this emergent need, we introduce a scalable multi-robot reinforcement learning platform (SMART) for training and evaluation. Specifically, SMART consists of two components: 1) a simulation environment with an uncertainty-aware social agent model that provides a variety of complex interaction scenarios for training and 2) a real-world multi-robot system for realistic performance evaluation. To evaluate the generalizability of MRRL baselines, we introduce a novel generalization metric that takes into account their performance across changes in the environment as well as the policies of other agents. Furthermore, we conduct a case study on the multi-vehicle cooperative lane change and summarize the unique challenges of MRRL, which are rarely considered previously. Finally, we open-source the simulation environments, associated benchmark tasks, and state-of-the-art baselines to encourage and empower MRRL research. Our code is available at https://***/Blackmamba-xuan/MRST. © 2024 IEEE.

关键词： Adversarial machine learning

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