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

共 34256 条记录，以下是4541-4550 订阅

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Decoding the Granular Puzzle of Macromolecules: Efficient 3D Protein Structure Alignment in the Age of Big Data with Apache Spark

Decoding the Granular Puzzle of Macromolecules: Efficient 3D...

引用

IEEE international conference on Big Data

作者： Bożena Małysiak-Mrozek Paulina Pawłowicz Vaidy Sunderam Che-Lun Hung Andrzej Kwiecień Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Computer Science Emory University Atlanta GA USA Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Proteins are complex biological information granules that play a crucial role in various cellular processes within living organisms. Processing 3D protein structures, which are the most informative from the biological point of view, is both intricate and time-consuming. In particular, performing 3D protein structure searches against large protein datasets involves identifying similarities and conducting structural alignments across numerous molecules (granules). This task demands advanced methods for matching identical and similar regions within protein structures and substantial computational resources to handle large collections of macromolecular data efficiently. In this paper, we present our parallel implementation of scalable 3D structural alignment on the Apache Spark big data platform. We describe a customized approach that leverages Spark data transformations within the data processing pipeline for the alignment process. Our experimental results demonstrate that this solution, tightly integrated with the Spark processing model, is both efficient and scalable, even with the increasing volume of protein structure data.

关键词： Proteins Solid modeling Three-dimensional displays Pipelines Cluster computing Big Data Biology Organisms Decoding Sparks

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A Novel MPPT Approach for a grid Tied Wind Energy System

A Novel MPPT Approach for a Grid Tied Wind Energy System

引用

Automation, computing and Renewable Systems (ICACRS), international conference on

作者： D. RaviKishore B. Kavya Santhoshi K. Sravani Kalpaguri Maruthi Department of Electrical and Electronics Engineering Affiliated to JNTUK Godavari Institute of Engineering and Technology (Autonomous) Kakinada Rajahmundry A.P India Dr. Samuel George Institute of Engineering (JNTUK) AP India

ISBN: (纸本)9781665460859

As the number of distributed power systems that use non-linear loads has increased, improving power quality has become a top priority for academics. In this work, we look at how the harmonics in a distributed power system may be reduced with the use of a device called a Distribution Static Synchronous Compensator (D-STATCOM). When compared to other FACTS devices, D- STATCOM has more reliable voltage stability because to its built-in voltage regulator. As a result of its high voltage-gain ratio, LUO converter is employed. Using an ANFIS -based MPPT (Maximum Power Point Tracking) guarantees regulated output and facilitates the extraction of maximum power from the PV panel. The DFIG-based WECS utilises a PWM rectifier, which is in turn controlled by a PI controller, to complete the AC-DC conversion. Reference current generation is essential for harmonic removal, and this is achieved by the Hysteresis Current Controller. MATLAB is used to simulate the complete regulated process, and the results show that the suggested method provides higher power quality with less distortion than other methods.

关键词： Maximum power point trackers Renewable energy sources Power quality Power system stability Power system harmonics Harmonic analysis Stability analysis

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High Impedance Fault Detection of Looped Microgrid based on Estimated Differential Energy using EMD-TKEO

High Impedance Fault Detection of Looped Microgrid based on ...

引用

Electrical Power Engineering, Communication and computing Technology(ODICON), Odisha international conference on

作者： Ankan Chandra G K Singh Vinay Pant Electrical Engineering IIT Roorkee Roorkee India

ISBN: (纸本)9781665478403

The potential benefits of distributed generation (DG) integration into the distribution system (DS) are substantially undermined due to serious issues embarked with its protection during the unusual event of high impedance fault (HIF). Nonlinearly deterministic fault current characteristics with low fault current magnitude, nearly to the full load current, have thrown a challenge, while detecting HIF. This has further been augmented due to dissimilar contributions of fault current by different types of DGs, creating ambiguity for the overcurrent-based protection. In order to address this, in this work, a fast and simple HIF detection algorithm, based on Empirical Mode Decomposition (EMD) in conjugation with Teager-Kaiser Energy Operator (TKEO) is proposed for the looped microgrid system operating in grid-connected as well as in autonomous mode. Principally, it is established based on the extracted energy difference of current signals, which are obtained from successive processing of the retrieved current signals through EMD and TKEO. As this, does not rely on fault current magnitude; therefore, this method has essentially suppressed the difficulties that are associated with dynamic fault current of the microgrid.

关键词： Power engineering Power measurement Power system dynamics Signal processing algorithms Microgrids Real-time systems distributed power generation

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Adaptive Neuro-Fuzzy based Forward Kinematics Analysis of a three DoF Delta Robot

Adaptive Neuro-Fuzzy based Forward Kinematics Analysis of a ...

引用

RSI/ISM international conference on Robotics and Mechatronics (ICROM)

作者： Masoud Chatavi Rasul FesharakiFard Mohammad A. Khosravi Dept. of Mechanical Engineering Amirkabir University of Technology Tehran Iran Faculty of Textile Engineering Amirkabir University of Technology Tehran Iran Faculty of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (纸本)9781665454537

It is always very challenging to obtain a unique solution for the forward kinematics of parallel robots due to nonlinear and coupled equations. Soft computing is one of the most widely used methods in identifying systems. In the present paper, some different supervised clustering methods are applied to obtain a model for the forward kinematics of the 3-DoF delta parallel robot. In the proposed process, a hybrid of fuzzy logic and neural networks is used for modeling. In this paper, the Subtractive Clustering Method (SCM), Fuzzy C-means (FCM), and grid Partitioning (GP) techniques are described, and the simulation results obtained from the explained approaches are examined in detail. Finally, it concluded that the (GP) method for this application provides better performance than (FCM) and (SCM) techniques.

关键词： Training parallel robots Adaptation models Clustering methods Simulation 3-DOF Neural networks

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Task Allocation in distributed Real Time Database Systems in IoT 1

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4th international conference on Internet of Things and Connected Technologies (ICIoTCT)

作者： Choudhary, Shetan Ram Jha, C. K. Banasthali Vidyapith Bioinformat Ctr PO Banasthali Vidyapith Banasthali 304022 Rajasthan India Banasthali Vidyapith Dept Comp Sci PO Banasthali Vidyapith Vanasthali 304022 Rajasthan India

ISBN: (数字)9783030398750

ISBN: (纸本)9783030398750;9783030398743

In present scenario, distributed and parallel systems in the form of grid, cloud and even cloud based Internet of things (IoT) are cater the needs of demand for computing capacity. Internet of Things (IoT) is a new come up to connect objects/things and therefore transmit information between a variety of entities of the corporeal world or to the control centers where interpret this information. By use of available resources are play very crucial role to ensure systems schedule. In distributed (Real time) database system, data allocation is one of the major problems. It affects the efficiency of the access to the requested data and thereby has large impact on the performance of the whole system. The data allocation involves data splitting, fragment replication, allocation choice to name a few issues. The distributed database system design putting all these factors together into consideration is complex and a Non-deterministic Polynomial (NP) hard. By applying Genetic Algorithm (GA), this work presents a virtual machine (VM) scheduling model to address the job allocation problem aiming to minimize the turnaround time. GA helps to attain a reasonable time for the query execution. The results of experiments have been examined to appraise the efficiency of our approach by comparing with best fit VM scheduling approach.

关键词： Task allocation distributed (real time) database systems Genetic Algorithm (GA) Task allocation model Task scheduling Turnaround Time (TAT)

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Simulation of Nuclear Reactor Accident Scenarios using Physics-Informed Neural Networks and Transfer-Learning

Simulation of Nuclear Reactor Accident Scenarios using Physi...

引用

Electronic Information Engineering and Computer Science (EIECS), 2021 international conference on

作者： Yang Yu Yufei Xie Wenlin Wang Guohua Wu Haitao Lan Enbo Lin Ping An Zibin Sun Haichuan Zhang Yixian Wu National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R 2 : 0.735).

关键词： Radioactive materials Accuracy Monte Carlo methods Transfer learning Neural networks Robustness Data models Fuels Inductors Accidents

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PrioRAT: Criticality-Driven Prioritization Inside the On-Chip Memory Hierarchy 27th

PrioRAT: Criticality-Driven Prioritization Inside the On-Chi...

引用

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

作者： Dimic, Vladimir Moreto, Miquel Casas, Marc Valero, Mateo Barcelona Supercomp Ctr BSC Barcelona Spain Univ Politecn Catalunya UPC Barcelona Spain

ISBN: (纸本)9783030856656;9783030856649

The ever-increasing gap between the processor and main memory speeds requires careful utilization of the limited memory link. This is additionally emphasized for the case of memory-bound applications. Prioritization of memory requests in the memory controller is one of the approaches to improve performance of such codes. However, current designs do not consider high-level information about parallel applications. In this paper, we propose a holistic approach to this problem, where the runtime system-level knowledge is made available in hardware. Processor exploits this information to better prioritize memory requests, while introducing negligible hardware cost. Our design is based on the notion of critical path in the execution of a parallel code. The critical tasks are accelerated by prioritizing their memory requests within the on-chip memory hierarchy. As a result, we reduce the critical path and improve the overall performance up to 1.19 x compared to the baseline systems.

关键词： parallel processing systems

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DEAR: distributed Evaluation of Alerting Rules 13

DEAR: Distributed Evaluation of Alerting Rules

引用

IEEE 13th international conference on Cloud computing (CLOUD)

作者： Mormul, Mathias Hirmer, Pascal Stach, Christoph Mitschang, Bernhard Univ Stuttgart Inst Parallel & Distributed Syst Univ Str 38 D-70569 Stuttgart Germany

ISBN: (纸本)9781728187808

Cloud computing passed the hype cycle long ago and firmly established itself as a future technology since then. However, to utilize the cloud as cost-efficiently as possible, a continuous monitoring is key to prevent an over- or under-commissioning of resources. In large-scaled scenarios, several challenges for cloud monitoring, such as high network traffic volume, low accuracy of monitoring data, and high time-to-insight, require new approaches in IT Operations while considering administrative complexity. To handle these challenges, we present DEAR, the distributed Evaluation of Alerting Rules. DEAR is a plugin for monitoring systems which automatically distributes alerting rules to the monitored resources to solve the trade-off between high accuracy and low network traffic volume without administrative overhead. We evaluate our approach against requirements of today's IT monitoring and compare it to conventional agent-based monitoring approaches.

关键词： cloud monitoring agent-based alerting

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D-SmartML: A distributed Automated Machine Learning Framework 40

D-SmartML: A Distributed Automated Machine Learning Framewor...

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40th IEEE international conference on distributed computing Systems (ICDCS)

作者： Abd Elrahman, Ahmed El Helw, Mohamed Elshawi, Radwa Sakr, Sherif Nile Univ Giza Egypt Univ Tartu Tartu Estonia

ISBN: (纸本)9781728170022

Nowadays, machine learning is playing a crucial role in harnessing the value of massive data amount currently produced every day. The process of building a high-quality machine learning model is an iterative, complex and time-consuming process that requires solid knowledge about the various machine learning algorithms in addition to having a good experience with effectively tuning their hyper-parameters. With the booming demand for machine learning applications, it has been recognized that the number of knowledgeable data scientists can not scale with the growing data volumes and application needs in our digital world. Therefore, recently, several automated machine learning (AutoML) frameworks have been developed by automating the process of Combined Algorithm Selection and Hyper-parameter tuning (CASH). However, a main limitation of these frameworks is that they have been built on top of centralized machine learning libraries (e.g. scikit-learn) that can only work on a single node and thus they are not scalable to process and handle large data volumes. To tackle this challenge, we demonstrate D-SmartML, a distributed AutoML framework on top of Apache Spark, a distributed data processing framework. Our framework is equipped with a meta learning mechanism for automated algorithm selection and supports three different automated hyper-parameter tuning techniques: distributed grid search, distributed random search and distributed hyperband optimization. We will demonstrate the scalability of our framework on handling large datasets. In addition, we will show how our framework outperforms the-state-of-the-art framework for distributed AutoML optimization, TransmogrifAI.

关键词： Large dataset

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Design and Construction of a Low Cost All-Digital Phase Locked Loop Based on Field Programmable Gate Array

Design and Construction of a Low Cost All-Digital Phase Lock...

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2021 international conference on Internet of Things and Smart City, IoTSC 2021

作者： Zhang, Chunyu Wang, Shouxiang He, Ruxun Zhao, Qianyu Wang, Kai School of Electrical Automation and Information Engineering Tianjin University Tianjin300072 China State Grid Tianjin Information and Telecommunication Company Tianjin300072 China

The phase-locked loop (PLL) is a key element to capture the voltage phase of the grid in power systems with high permeability of new energy sources. An accurate phase information catcher which can be applied in the field can provide a strong support for the control process of the system. This paper constructs a low-cost all-digital PLL (ADPLL) on an embedded field-programmable gate array (FPGA) for real-time (RT) digital simulation. Compared with offline simulation, RT digital simulation can realize hardware-in-loop (HIL) testing of utility connected devices to validate their performance in every working condition, which would not be possible using only experimental tests. Meanwhile, an improved simulation architecture construction method based on Algorithm Architecture Adequation (A-A-A) is presented in this paper. This method further saves the internal hardware resources on the basis of making full use of the parallel computing capability of FPGA. Analysis and experimental results made based on an FPGA ALTERA Stratix V show that the proposed modeling algorithm can achieve both efficiency and accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Phase locked loops

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