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

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Towards Agent-based Simulation of the parallel Trading Market of Pharmaceuticals

Towards Agent-based Simulation of the Parallel Trading Marke...

引用

IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Ruhollah Jamali Sanja Lazarova-Molnar The Maersk Mc-Kinney Moller Institute University of Southern Denmark Odense Denmark Institute AIFB Karlsruhe Institute of Technology Karlsruhe Germany

Pharmaceutical parallel trade is a legal trade in European countries, where traders can buy medicinal products in one country and sell them in other countries to make a profit. In the pharmaceutical parallel trade market, players such as manufacturers, wholesalers, parallel traders, pharmacies, and hospitals are involved. Studying and analyzing this market is of significant interest to economists and players involved. Agent-based modeling offers a robust algorithmic framework to analyze macroeconomic phenomena through micro-founded models. As an initial step in using agent-based modeling for the parallel trade of pharmaceuticals, we consider a simplified pharmaceutical trading market inspired by available game theory models. In this paper, we developed and elaborated the implementation of an agent-based model for the pharmaceutical trade market and employed it to run multiple scenarios that are impossible to analyze through game-theoretic models. Subsequently, we demonstrated how an agent-based model could be utilized to analyze the market from an economic perspective and how players in this market can recruit this model in their business decisions.

关键词： Analytical models Law Hospitals Biological system modeling Europe Agent-based modeling Macroeconomics

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A Short-Term Electric Load Forecasting Method Based on the Fusion of Global and Local Features

A Short-Term Electric Load Forecasting Method Based on the F...

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IEEE international conference on Data Science in Cyberspace (DSC)

作者： Haozhan Gu Hangwei Tian Jingchun Wang Yumei Li Shandong University Of Arts Academy of Fine Arts China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Accurate short-term electric load forecasting is crucial for ensuring the stability and efficiency of the power grid. This study proposes an innovative dual-branch electric load forecasting model that enhances the accuracy of predictions by processing global and local features in parallel. Global features are extracted through the Savitzky-Golay filter and a linear model to capture the overall trends of the load data. Local features are identified using the Convolutional Self-Attention Module, which integrates the local perception capabilities of Convolutional neural network with the self-attention mechanism of Transformers. By employing a Weighted Feature Fusion Network, we effectively integrate global and local information to enhance the performance of load forecasting. Experimental results demonstrate that the proposed model significantly outperforms existing models in predictive performance. The model presented in this study offers a new perspective on electric load forecasting and has the potential to become an effective tool for power system planning and scheduling.

关键词： Accuracy Load forecasting Electricity Nonlinear filters Predictive models Power system stability Feature extraction Transformers Data models Load modeling

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13th EAI international conference on Simulation Tools and Techniques, SIMUtools 2021

13th EAI International Conference on Simulation Tools and Te...

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13th EAI international conference on Simulation Tools and Techniques, SIMUtools 2021

ISBN: (纸本)9783030971236

The proceedings contain 49 papers. The special focus in this conference is on Simulation Tools and Techniques. The topics include: A Video parallel Retrieval Method Based on Deep Hash;cosmetics Sales Data Classification Method of Japanese Cross-Border E-Commerce Platform Based on Big Data;feature Filtering Spectral Clustering Method Based on High Dimensional Online Clustering Method;Research on CRM Boost PFC Converter Based on GaN Device;Application of Cascode GaN HEMT in LLC Soft Switching Converter;Characteristics and Application of Cascode GaN HEMT;Simulation Research on Modular DC grid-Connected PV System;key Quality Indicators of Social Networking Service;A Dynamic Migration Strategy of SDN Controllers in LEO Networks;discussion on the Project Teaching Method of Investment Banking in Local Ethnic Colleges and Universities;engineering Ecosystem Models: Semantics and Pragmatics;meta-modelling for Ecosystems Security;modelling Organizational Recovery;experimental Comparison of Open Source Discrete-Event Simulation Frameworks;Dynamic and Static Performance Analysis of SiC MOSFET with PWM Control;predictive Current Control of the Three-Level Four-Leg Active Filter Based on In-Phase Carrier Modulation;Harmonic Detection Method of Three-Phase Four-Wire APF Based on p-q-r Without Voltage-Sensor;Effective WiTech Identification Using Deep Transfer Learning with SNR as an Additional Feature;a Neural Network Algorithm of Learning Rate Adaptive Optimization and Its Application in Emitter Recognition;a Network Traffic Measurement Approach for Edge computing Networks;agricultural Hyperspectral Image Classification Based on Deep Separable Convolutional Neural Networks;deep Reinforcement Learning Based Mimicry Defense System for IoT Message Transmission;research on Website Traffic Prediction Method Based on Deep Learning;Prediction Protein-Protein Interactions with LSTM;preface.

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parallel Processing Techniques for Analyzing Large Video Files: a Deep Learning Based Approach

Parallel Processing Techniques for Analyzing Large Video Fil...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Azhar Talha Syed Shikharesh Majumdar Dept. of Systems and Computer Eng. Carleton University Ottawa Canada

Videos are a popular type of media that require analysis to extract the information underlying the data in a timely manner. Often due to the very large size of such data and the involvement of computationally expensive operations, performing the analysis can take a significant amount of time. This paper presents techniques to speed up deep learning-based analysis to perform tasks like tracking objects and filtering video data by applying parallel processing techniques. The proposed approach and techniques leverage parallel processing on two levels: by using GPUs for analyzing individual frames and by distributing the processing load over a fleet of Executor nodes. Experiments with Apache Spark and TensorFlow-based prototypes built for handling various video analysis use cases were conducted on an Amazon EC2 cloud for various combinations of system and workload parameters. Insights into system performance including the reduction in processing time that accrues from applying the proposed parallel processing technique in each scenario are reported in the paper.

关键词： Deep learning Filtering System performance Prototypes Graphics processing units parallel processing Media

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2nd international conference on Power Engineering, computing and Control, PECCON 2019

2nd International Conference on Power Engineering, Computing...

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2nd international conference on Power Engineering, computing and Control, PECCON 2019

ISBN: (纸本)9789811572401

The proceedings contain 80 papers. The special focus in this conference is on Power Engineering, computing and Control. The topics include: Impact of distributed Generation on Distribution System Under Fault and Islanding Condition;enhancement of Power Quality in a 3ph-3bus Distribution System with Unified Power Quality Conditioner;genetic Algorithm and Graph Theory Approach to Select Protection Zone in Distribution System;Analysis on DVR Based on the Classification of Converter Structure and Compensation Schemes;modeling and Simulation Analysis of Shunt Active Filter for Harmonic Mitigation in Islanded Microgrid;fault Diagnosis of Self-aligning Conveyor Idler in Coal Handling Belt Conveyor System by Statistical Features Using Random Forest Algorithm;levy Interior Search Algorithm-Based Multi-objective Optimal Reactive Power Dispatch for Voltage Stability Enhancement;feasible Settlement Process for Primary Market Using distributed Slack Power Flow Strategy;Optimal Placement of DG and Controlled Impedance FCL Sizing Using Salp Swarm Algorithm;soft Switching and Voltage Control for Three Phase Induction Motor;delicate Flower Pollination Algorithm for Optimal Power Flow;optimization of Electric Field Distribution Along a 400-kV Composite Insulator;Vector Control Scheme for the PMSG-Based WPS Under Various grid Disturbances;Phase Balancing of DG-Integrated Smart Secondary Distribution Network;Estimation of Payback Period Incorporating SVC and TCSC in SCUC Problem;investigations on Salp Swarm Algorithm to Solve Combined Heat and Power Economic Dispatch;a Review on Topological Aspects of Transformerless Dynamic Voltage Compensators;a Novel Index Method for distributed Generator Placement in a Radial Distribution System Using Pandapower Python Module;review of Particulate Matter Filters.

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Aleksandr Beznosikov Martin Takáč Alexander Gasnikov Innopolis University Skoltech MIPT Yandex MBZUAI MIPT Skoltech IITP RAS

Variational inequalities are a broad and flexible class of problems that includes minimization, saddle point, and fixed point problems as special cases. Therefore, variational inequalities are used in various applications ranging from equilibrium search to adversarial learning. With the increasing size of data and models, today's instances demand parallel and distributed computing for real-world machine learning problems, most of which can be represented as variational inequalities. Meanwhile, most distributed approaches have a significant bottleneck - the cost of communications. The three main techniques to reduce the total number of communication rounds and the cost of one such round are the similarity of local functions, compression of transmitted information, and local updates. In this paper, we combine all these approaches. Such a triple synergy did not exist before for variational inequalities and saddle problems, nor even for minimization problems. The methods presented in this paper have the best theoretical guarantees of communication complexity and are significantly ahead of other methods for distributed variational inequalities. The theoretical results are confirmed by adversarial learning experiments on synthetic and real datasets.

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An Efficient Block Validation Mechanism for UTXO-based Blockchains

An Efficient Block Validation Mechanism for UTXO-based Block...

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international Symposium on parallel and distributed Processing (IPDPS)

作者： Xiaohai Dai Bin Xiao Jiang Xiao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong

It has been recognized that one of the bottlenecks in the UTXO-based blockchain systems is the slow block validation - the process of validating a newly-received block by a node before locally storing it and further broadcasting it. As a block contains multiple inputs, the block validation mainly involves checking the inputs against the status data, which is also known as the Unspent Transaction Outputs (UTXO) set. As time goes by, the UTXO set becomes more and more expansive, most of which can only be stored on disks. This considerably slows down the input checking and thus block validation, which can potentially compromise system security. To deal with the above problem, we disassemble the function of input checking into three parts: existence validation (EV), unspent validation (UV), and script validation (SV). Based on the disassembly, we propose EBV, an efficient block validation mechanism to speed up EV, UV, and SV individually. First, EBV changes the representation of status data, from UTXO set to a bit-vector set, which drastically reduces its size. The smaller status data can be entirely maintained in memory, thereby accelerating UV and also block validation. Second, EBV requires each transaction to carry the proof data, which enables EV and SV without accessing the disks. Furthermore, we also cope with two challenges in the design of EBV, namely transaction inflation and fake positions. To evaluate the EBV mechanism, we implement a prototype on top of Bitcoin, the most widely known UTXO-based blockchain, and conduct extensive experiments to compare EBV and Bitcoin. The experimental results demonstrate that EBV successfully reduces the memory requirement by 93.1 % and the block validation time by up to 93.5%.

关键词： distributed processing Memory management Prototypes Bitcoin Broadcasting Blockchains Delays

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Proceedings of INDIS 2021: 8th Workshop on Innovating the Network for Data-Intensive Science, Held in conjunction with SC 2021: The international conference for High Performance computing, Networking, Storage and Analysis

Proceedings of INDIS 2021: 8th Workshop on Innovating the Ne...

引用

8th IEEE Workshop on Innovating the Network for Data-Intensive Science, INDIS 2021

ISBN: (纸本)9781665411141

The proceedings contain 6 papers. The topics discussed include: the service analysis and network diagnosis data pipeline;NetGraf: an end-to-end learning network monitoring service;exploring the BBRv2 congestion control algorithm for use on data transfer nodes;learning transfers via transfer learning;deploying per-packet telemetry in a long-haul network: the AmLight use case;and bridging network and parallel I/O research for improving data-intensive distributed applications.

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Optimal Control and Configuration of Energy Storage Devices in Regional Distribution Network with Renewable Energy Sources

Optimal Control and Configuration of Energy Storage Devices ...

引用

Digital Twins and parallel Intelligence (DTPI), IEEE international conference on

作者： Zhou Xiangyu Li Jing Meng Yao Dong Wenxiu Zhang Hedong Marketing Department Jining Power Supply Company of State Grid Shandong Electric Power Company Jining China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

When the distribution network is connected to distributed energy sources (DER) such as large-scale photovoltaics and wind power, the volatility and intermittency of distributed energy sources will adversely affect the power grid. In view of the adverse effects of distributed energy access to the regional distribution network on load characteristics, comprehensively considering the energy storage operation constraints, charge and discharge power constraints and power flow balance constraints of the distribution network, an optimal configuration model of the energy storage system is established, and the control strategy is optimized. Take “peak shaving and valley filling” and “smooth load” as load control objectives respectively. The optimization strategy is used to control the load variance and the square of the load change. Combined with the cost optimization of the energy storage system, the GUROBI algorithm is used to solve the model optimization, and the load characteristics and energy storage optimization trends under different energy storage charge and discharge power constraints are obtained. The changing trend of the optimal configuration of capacity provides an effective reference for the configuration of the energy storage system.

关键词： Distribution networks Fault location Wind power generation Market research Filling Discharges (electric) Resource management

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Energy Loss Minimization with parallel Implementation of Marine Predators Algorithm 13

Energy Loss Minimization with Parallel Implementation of Mar...

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13th international conference on Electrical and Electronics Engineering, ELECO 2021

作者： Younesi, Soheil Ahmadi, Bahman Ceylan, Oguzhan Ozdemir, Aydogan Istanbul Technical University Department of Electrical Engineering Istanbul Turkey Kadir Has University Management and Information Systems Department Istanbul Turkey

ISBN: (纸本)9786050114379

Distribution network (DN) service continuity is one of the significant issues in today's power systems. This paper aims to put a strategy for supplying loads with less discontinuity and affordable energy-consuming. The energy loss in distribution grids causes many problems for the producer and consumer;hence, it needs to be improved to increase supply efficiency accordingly. For this aim a model aiming to minimize power losses by allocating and sizing distributed generators (DGs) is solved using recently developed Marine Predators Algorithm (MPA). Since the proposed method is a time-intensive process due to the vast computations, parallel computation is implemented into MPA to increase computation speed. The proposed formulation and parallel computation are tested for 69-bus radial distribution system. The results are discussed in terms of computational accuracy and solution efficiency. Moreover, the convergence characteristics of MPA are compared with some other heuristic methods. © 2021 Chamber of Turkish Electrical Engineers.

关键词： Smart power grids

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