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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4331-4340 订阅

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distributed Economic Automatic Generation Control Algorithm for Multi Inverter AC Microgrid

Distributed Economic Automatic Generation Control Algorithm ...

引用

international conference on Cyber Security Intelligence and Analytics, CSIA 2021

作者： Wang, Jing Chaohu University Chaohu Anhui238000 China

ISBN: (纸本)9783030700416

Assigning an agent to each inverter interface power supply in the Microgrid to complete communication and data calculation;designing the communication topology among the agents based on n−1 rule;identifying the Microgrid operation status in a distributed manner;initializing the algorithm based on the identified Microgrid operation state;and allocating an agent to complete the communication and data calculation for each inverter interface power supply in the Microgrid;Through the data interaction between agents, the generation reference value of each power source is distributed;the generation reference value is sent to the bottom droop controller to realize the distributed economic automatic generation control of Microgrid. The invention can combine the large time scale economic dispatch and the small time scale automatic generation control to form an economic automatic generation control, so that the multi inverter micro grid based on droop control can run economically in real time;and the algorithm can be implemented completely distributed and has strong robustness. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Scheduling

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Federated Learning and Crowdsourced Weather Data: Practice and Experience

Federated Learning and Crowdsourced Weather Data: Practice a...

引用

IEEE international conference on e-Science and grid computing

作者： Ciro Giuseppe De Vita Gennaro Mellone Angelo Casolaro Massimiliano Giordano Orsini Jose Luis Gonzalez-Compean Angelo Ciaramella Dep. of Science and Technologies University of Naples "Parthenope" Naples Italy Department of Science and Technology University of Napoli "Parthenope" Napoli Italy Cinvestav Unidad Tamaulipas Research Center Mexico

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

In the era of advanced meteorological data platforms such as Copernicus and Climate Data Store, the frontier of weather forecasting has evolved. The primary challenge is no longer the acquisition of accurate and high-resolution data, but rather the effective integration and utilization of diverse observational datasets to enhance localized weather predictions. Crowd sensed weather data through a network of low-cost, widely distributed weather stations can provide the granular data needed for precise local forecasts. However, this approach introduces challenges such as data integration, consistency, and privacy concerns. Federated Learning (FL) addresses these issues by enabling decentralized data processing while maintaining data *** paper introduces an innovative implementation of a federated learning framework integrated with a cluster of Automated Weather Stations (AWS). The primary objective of this study is to leverage federated learning to enhance the predictive accuracy of the Weather Research and Forecasting (WRF) model by using each weather station not only as a data acquisition point but also as a computational node. This decentralized approach maintains data privacy and security while enabling local training of models, such as Crossformer, Autoformer, and DLinear. These models’ locally trained weights are periodically aggregated on the central server, which updates and redistributes the global *** on data collected over two years from two automated weather stations, the experimental results analyze the possibility of improving WRF model predictions for temperature and humidity. This research highlights the potential of Federated Learning in meteorological applications, offering a robust solution for enhancing weather forecast accuracy while ensuring data privacy and efficient resource utilization.

关键词： Accuracy Federated learning Computational modeling Weather forecasting distributed databases Predictive models Data models

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

On the Effectiveness of Function-Level Vulnerability Detecto...

引用

international conference on Software Engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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22nd Annual international conference on Computational Science, ICCS 2022

22nd Annual International Conference on Computational Scienc...

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22nd Annual international conference on Computational Science, ICCS 2022

ISBN: (纸本)9783031087530

The proceedings contain 253 papers. The special focus in this conference is on Computational Science. The topics include: Practical Aspects of Zero-Shot Learning;a Hypothetical Agent-Based Model Inspired by the Abstraction of Solitary Behavior in Tigers and Its Employment as a Chain Code for Compression;analyzing the Usefulness of Public Web Camera Video Sequences for Calibrating and Validating Pedestrian Dynamics Models;a Highly Customizable Information Visualization Framework;incremental Dynamic Analysis and Fragility Assessment of Buildings with Different Structural Arrangements Experiencing Earthquake-Induced Structural Pounding;PIES with Trimmed Surfaces for Solving Elastoplastic Boundary Problems;linear Computational Cost Implicit Variational Splitting Solver with Non-regular Material Data for Parabolic Problems;a Hadamard Matrix-Based Algorithm to Evaluate the Strength of Binary Sequences;compiling Linear Algebra Expressions into Efficient Code;private and Public Opinions in a Model Based on the Total Dissonance Function: A Simulation Study;analysis of Public Transport (in)accessibility and Land-Use Pattern in Different Areas in Singapore;pseudo-Newton Method with Fractional Order Derivatives;An Energy Aware Clustering Scheme for 5G-Enabled Edge computing Based IoMT Framework;a Framework for Network Self-evolving Based on distributed Swarm Intelligence;investigating an Optimal Computational Strategy to Retrofit Buildings with Implementing Viscous Dampers;ARIMA Feature-Based Approach to Time Series Classification;a Note on Adjoint Linear Algebra;approximate Function Classification;acceleration of Optimized Coarse-grid Operators by Spatial Redistribution for Multigrid Reduction in Time;Interval Modification of the Fast PIES in Solving 2D Potential BVPs with Uncertainly Defined Polygonal Boundary Shape;networks Clustering-Based Approach for Search of Reservoirs-Analogues;KP01 Solved by an n-Dimensional Sampling and Clustering Heuristic;A Deep Neural Network as a

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Two-Stage Optimization for Decentralized P2P Transaction and Ancillary Service in Distribution grid

Two-Stage Optimization for Decentralized P2P Transaction and...

引用

Power Energy Systems and Applications (ICoPESA), international conference on

作者： Daoxin Han Hao Xiao Wei Pei Tianjiao Pu Ji Qiao Zibo Wang Institute of Electrical Engineer Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China China Electric Power Research Institute Beijing China

ISBN: (数字)9798350351668

ISBN: (纸本)9798350351675

With the high penetration of renewable energy sources, traditional voltage regulation ancillary services provided by conventional generation may face challenges of scarcity. Microgrids (MGs), leveraging flexible resources such as energy storage and generation units, inherently possess voltage support capabilities. Therefore, we propose a two-stage optimization approach to explore the potential of MGs in providing voltage regulation ancillary services. In stage 1, decentralized peer-to-peer (P2P) transactions are conducted, and profit allocation is performed using the supply-demand ratio (SDR) method. In stage 2, optimization of ancillary service trading strategies is carried out, and a secondary distribution of ancillary service subsidies is conducted using a network loss sharing method based on the Shapley value. This aims to reduce the operational costs of the distribution system operator (DSO). In addition, an improved alternating direction method of multipliers (ADMM) algorithm is proposed for the distributed solution of the two stages. Through simulation using a modified IEEE 33-node distribution system, the proposed method is validated to incentivize MGs to provide voltage regulation ancillary services at lower costs for the DSO, thereby preventing voltage violations.

关键词： Electric potential Renewable energy sources Costs Optimization methods Microgrids Peer-to-peer computing Resource management Voltage control Faces Energy storage

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T-COFFEE Multiple Sequence Aligner on Hadoop Spark Cluster 9

T-COFFEE Multiple Sequence Aligner on Hadoop Spark Cluster

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9th international conference on Information and Communication Technology, ICoICT 2021

作者： Prihatiningrum, Viebiyanty Setyorini Karimah, Siti Amatullah Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9781665404471

DNA (Deoxyribose Nucleid Acid) is a series of nucleotide acid proteins that exist in the organism body where DNA will be identical with inheritance. The sequence alignment mechanism is one of the most important methods in finding a match between DNA sequences. This mechanism is also used in the mechanism for making vaccines, where the process takes a large portion of the time. parallel and distributed systems exist to solve this problem. One of them is using the Hadoop platform, which is currently being used for processing biological data. The making of the COVID-19 vaccine is one example of the implementation of using a distributed and parallel system model, so that the manufacturing process can be done in a fairly short time. In this study, we used MSA (Multiple Sequence Alignment) where one of the algorithms which has a high accuracy value is T-COFFEE (Tree Based Consistency Objective Function for Alignment Evaluation) algorithm. T-COFFEE is an algorithm for multiple sequences which is very suitable for finding similarities in DNA data by focusing on very high accuracy values. Besides having a high accuracy value, T-COFFEE requires a very long time to process. So this research did implementation of T-COFFEE on hadoop parallelization using Spark which has been proven to reduce the execution time. © 2021 IEEE.

关键词： Big data

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22nd Annual international conference on Computational Science, ICCS 2022

22nd Annual International Conference on Computational Scienc...

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22nd Annual international conference on Computational Science, ICCS 2022

ISBN: (纸本)9783031087561

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Model-based Self-adaptive Management in a Smart grid Substation

Model-based Self-adaptive Management in a Smart Grid Substat...

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international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Salim Chehida Karim Fellah Eric Rutten Guillaume Giraud Stéphane Mocanu Univ. Grenoble Alpes Inria CNRS Grenoble INP LIG Grenoble France RTE Paris France

The design of Cyber Physical Systems (CPS) is becoming increasingly complex due to the dynamic changes in their environments and infrastructures, requiring them to be self-adaptive. An important class of CPS is Industrial Control Systems (ICS), where a major trend is to upgrade from historically specific hardware and technologies towards more software-defined, virtual approaches involving the Could-Fog-Edge continuum. In this work, we propose a model-based approach for the design of the self-adaptation in ICS, inspired by, and applied to an industrial case study in Smart grids, more particularly an electrical substation from RTE (the French Energy Transmission company). The problem is to allocate and reallocate dynamically a set of control functions upon a distributed computing infrastructure, with self-adaptation to variations and perturbations. We define and implement the model-based autonomic management feedback loop using constraint programming, to describe the space of possible configurations, as well as the constraints and objectives formalizing the operators strategies. This model is used in simulation, calling the constraints solver at each cycle of the loop.

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Optimizing trace tool-overhead for lock-intensive multi-threaded parallel applications 6

Optimizing trace tool-overhead for lock-intensive multi-thre...

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6th international conference on parallel, distributed and grid computing, PDGC 2020

作者： Singh, Ajit Chakraborty, Pavan Indian Institute of Information Technology Dept. Name of IT Allahabad India

ISBN: (纸本)9781728171326

Often a tool collecting traces for lock-intensive applications adds overheads of its own and distorts the lock-related measurements. We highlight why tool-overhead is particularly problematic for lock-intensive applications. Tool-overhead has received limited attention in existing research. The primary reason for high tool-overhead, as per our analysis is cache- coherence related overheads for tracing tool data structure. Using the insight, we develop Mutexis, an optimized user-level dynamic binary instrumentation (DBI) tracing PIN tool. To show tool effectiveness, we use lock-intensive applications from PARSEC and Splash3X benchmarks. We compare the proposed tool's overhead with tool-overhead of other researchers. The tool-overhead of mutexis is minimal, growing up to 2.1X for lock- intensive applications (4X to lOOX for others) and is negligible in most cases. This is so, even when our tool captures detail cycle- stamped traces of POSIX lock function compared to limited aggregate statistics collected by other researchers tools. © 2020 IEEE.

关键词： Locks (fasteners)

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22nd Annual international conference on Computational Science, ICCS 2022

22nd Annual International Conference on Computational Scienc...

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22nd Annual international conference on Computational Science, ICCS 2022

ISBN: (纸本)9783031087592

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