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检索条件"任意字段=Proceedings - 3rd International Conference on Distributed Computing Systems."

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On a class of distributed Storage systems.with Prioritized Data Sources 2019

On a class of Distributed Storage Systems with Prioritized D...

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proceedings of the 2019 3rd international conference on Computer Science and Artificial Intelligence

作者： Congduan Li Sun Yat-sen University Guangdong China

ISBN: (纸本)9781450376273

The rate regions and sufficiency of simple linear codes for a class of distributed storage systems.with prioritized data sources are investigated in this paper. The exact repair problem is considered. Different from conventional setups, the scenario considered in this paper has prioritized data sources, where the hot data has higher priority than cold data in the decoding process. It is assumed that a user demanding cold data demands hot data as well. Instead of using same capacity for all storage nodes, the rate regions of interest are all feasible different storage sizes versus different tuples of source entropies, with assumption of sufficient large repair bandwidth. Both symmetric and asymmetric repairs are discussed in the paper. Linear network codes over some finite field are said to be sufficient for such a distributed storage system if and only if for every point in the rate region, there exists a code over that finite field to achieve it. As a multi-source multi-sink network coding problem, the rate regions are obtained from computer-aided approaches via bounding the region of entropic vectors. Experimental results on the rate regions of hundreds of non-isomorphic distributed storage systems.are presented for demonstration. In addition, it is shown that binary linear codes suffice for most of them.

关键词： multilevel diversity computer aided distributed storage rate region

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Research on power big data storage platform based on distributed file system 3rd

Research on power big data storage platform based on distrib...

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3rd international conference on Intelligent, Interactive systems.and Applications, IISA2018

作者： Fei, Liu Hao-Yuan, Pang Yi-Ying, Zhang Kun, Liang Ye-Shen, He Xiang-Zhen, Li Zhu, Liu College of Computer Science and Information Engineering Tianjin University of Science and Technology Tianjin300457 China China Gridcom Co. LTD. ShenzhenGuangdong China Jiaxing University JiaxingZhejiang China State Grid Information and Telecommunication Group Co. LTD. Beijing China

ISBN: (纸本)9783030028039

With the development of science and technology, the power industry has rapidly transformed into informationization, automation, and intelligent. Information flows, business flows, and power flows are intertwined. Concomitantly, the amount of data generated by each link of the power grid has grown exponentially, and the data types have also shifted from relatively simple structured data to unstructured data. A large variety of structured data, semi-structured data, and unstructured data in the power grid constitute power big data. This paper is aimed at power big data, in-depth analysis of a variety of power data characteristics, based on distributed file systems. the development of power big data storage platform. According to different data types, corresponding storage strategies are formulated, and a power large data storage platform based on distributed file systems.is developed to provide powerful technical support for the storage of power big data. © Springer Nature Switzerland AG 2019.

关键词： Big data

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An Improved Version of Water-Filling Algorithm for Underground Tunnel systems. A Comprehensive Literature Review 3

An Improved Version of Water-Filling Algorithm for Undergrou...

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3rd international conference on Inventive Computation Technologies (ICICT)

作者： Rana, Md Masud Hasan, Md Sohel Khulna Univ Engn & Technol Dept Elect & Commun Engn Khulna Bangladesh

ISBN: (数字)9781538649855

ISBN: (纸本)9781538649855

This paper provides a comprehensive literature review for water-filling algorithms and underground tunnel systems. Afterwards, an improved version of water-filling algorithm is proposed for distributed multi input multi output systems.in underground tunnels. The proposed algorithm is based on the conventional water-filling and Nash equilibrium approaches. It is believed that the designed scheme can enhance both the channel capacity and system performance for distributed multi input multi output in underground tunnel systems.

关键词： distributed multi input multi output systems improved water-filling algorithm Nash equilibrium underground tunnels

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Fuzzy TOPSIS approach based on axiomatic fuzzy set for assessing thermal-energy storage in concentrated solar power (CSP) systems. CPS systems.assessment 3

Fuzzy TOPSIS approach based on axiomatic fuzzy set for asses...

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3rd Annual international conference on Network and Information systems.for Computers, ICNISC 2017

作者： Tao, Li-Li Dong, Yun-Ying Qi, Li-Yan Lv, Si-Yang College of Science Dalian Ocean University Dalian China Dalian Shiningway International Trade Co. Ltd Dalian China

ISBN: (纸本)9781538616185

In classical TOPSIS method, the criteria values and criteria weights are known precisely. However, the values of some criteria are often vague in real-life situation, and it expressed only by exact numerical values is inadequate, the application of linguistic variables or fuzzy terms in decision making is deemed to be relevant. Therefore, the aim of this paper is to develop a fuzzy TOPSIS method to deal with above multiple criteria decision making(MCDM) problems. Here, the linguistic variables are represented by membership degrees applying the axiomatic fuzzy set (AFS) theory, then the application of TOPSIS approach is extended. In addition, the semantic interpretations are provided for the decision results by AFS theory. The proposed method is used to assess thermal-energy storage in concentrated solar power (CSP) systems. © 2019 IEEE.

关键词： Thermal energy

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Big data scalability based on Spark Machine Learning Libraries 19

Big data scalability based on Spark Machine Learning Librari...

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proceedings of the 3rd international conference on Big Data Research

作者： Anna Karen Garate-Escamilla Amir Hajjam El Hassani Emmanuel Andres Nanomedicine Lab Univ. Bourgogne Franche-Comte UTBM Rue Thierry Mieg Rue Edouard Branly Belfort Service de Médecine Interne Diabète et Maladies métaboliques de la Clinique Médicale B CHRU de Strasbourg Molière Strasbourg

ISBN: (纸本)9781450372015

The paper introduces the challenge of scalability in machine learning algorithms suitable for massive datasets. Today, big data has relevant applications in the industry due to improvements in the system performance and by turning information into knowledge. Big data challenges include the lack of strategies to process computational cost and the large amount of data when computing machine learning predictions. To overcome these scalability issues, it is convenient to work with distributed and parallelized architecture across multiple nodes. The approach is based on Apache Spark, an in-memory distributed application that offers extensive machine learning libraries. The main contribution of the study is to measure the scalability by calculating the execution time that a classifier achieves with larger workloads. We validate our classifier models with experiments on logistic regression and random forest by studying their adaptability to the Apache Spark framework. The present work expects to combine the areas of big data and machine learning on scalability, and the use of optimization methods, cache and persist. In addition, a comparison between the classifiers is provided. The evaluation experiments show that logistic regression performed the shortest execution time and best scalability.

关键词： scalability Machine Learning distributed systems Execution time prediction Apache Spark

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A distributed computing platform for conventional hybrid simulation 7

A distributed computing platform for conventional hybrid sim...

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7th international conference on Computational Methods in Structural Dynamics and Earthquake Engineering, COMPDYN 2019

作者： Wang, Kung-Juin Chuang, Ming-Chieh Li, Chao-Hsien Tsai, Keh-Chyuan National Center for Research on Earthquake Engineering No. 200 Sec. 3 Xinhan Rd. Taipei Taiwan Department of Structural Engineering University of California San Diego SME bldg 341 Matthews Lane San DiegoCA92161 United States Department of Civil Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan

ISBN: (纸本)9786188284463

This paper describes the development of a distributed computing platform that supports general-purpose quasi-static structural testing, including conventional hybrid simulation. It depicts the design of this platform, including the assignment of the responsibilities of the participating software components, the communication protocols by which the software components communicate across the networks. In this study, the finite element analysis program "Platform of Inelastic Structural Analysis for 3D systems. (PISA3D) was chosen as the analysis kernel. The required programming augmentation of PISA3D to support geographically distributed hybrid simulation in a general-purpose manner is given. Using the proposed platform, a series of conventional hybrid tests was conducted on a specimen of steel panel damper (SPD) by employing a multi-axial testing system. The prototype building is a three-dimensional six-story moment resisting frame with four SPDs incorporated in each story as the main seismic resisting system. During the tests an online model updating (OMU) technique was employed to dynamically update the material properties of other relevant numerical SPD elements to enhance the fidelity of the structural model. Test results proved the validity and the high flexibility of the proposed platform. Copyright © 2019 COMPDYN proceedings. All rights reserved.

关键词： Simulation platform

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3rd EAI international conference on Advanced Hybrid Information Processing, ADHIP 2019

3rd EAI International Conference on Advanced Hybrid Informat...

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3rd EAI international conference on Advanced Hybrid Information Processing, ADHIP 2019

ISBN: (纸本)9783030364014

The proceedings contain 101 papers. The special focus in this conference is on Advanced Hybrid Information Processing. The topics include: Multi-source Heterogeneous Data Acquisition Algorithm Design Different Time Periods;research on Data Security Acquisition System Based on Artificial Intelligence;weak Coverage Area Detection Algorithms for Intelligent Networks Based on Large Data;Research on RF Fingerprinting Extraction of Power Amplifier Based on Multi-domain RF-DNA Fingerprint;fault Feature Analysis of Power Network Based on Big Data;design of All-Pass Filter System for Power Communication with High Anti-harmonic Interference;improved Design of Classification Algorithm in Cloud computing and Big Data Environment;research on Dynamic Access Control Model of distributed Network Under Big Data Technology;intelligent Data Acquisition Method for Cross-border E-commerce Guidance and Purchase Considering User Demand;Research on Communication Individual Identification Method Based on PCA-NCA and CV-SVM;Optimization Design of Cross-Border E-commerce Shopping Guide System Combining Big Data and AI Technology;research on Balanced Scheduling Algorithm of Big Data in Network Under Cloud computing;optimization of Rational Scheduling Method for Cloud computing Resources Under Abnormal Network;design of Agricultural Product Quality and Safety Big Data Fusion Model Based on Blockchain Technology;artificial Intelligence Integration Method for Agricultural Product Supply Chain Quality Data Based on Block Chain;research on Adaptive Scheduling Method of Communication Resource Information in Internet of Things Environment;research on Dynamic Network Load Evaluation Algorithm Based on Throughput Monitoring;research on the Classification Method of Network Abnormal Data;optimized PointNet for 3D Object Classification;deep Learning Based Adversarial Images Detection.

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Comparison of manual assembly training possibilities in various virtual reality systems 3rd

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3rd conference on Innovation, Engineering and Entrepreneurship, Regional HELIX 2018

作者： Buń, Pawel Górski, Filip Lisek, Nikola Chair of Management and Production Engineering Poznań University of Technology Piotrowo 3 STR Poznan60-965 Poland

ISBN: (纸本)9783319913339

The paper presents results of preliminary studies on possibilities of using low – cost devices to train operators of production line on how to perform a simple assembly task in Virtual Reality. Two different approaches to manipulate virtual objects without haptic feedback were compared and tested. The overall time and successful rate was measured for all users in both systems. The results lead to conclusion that smartphone based training applications in VR have a long way to go, but with increasing computing power seems like a promising direction. © 2019, Springer international Publishing AG, part of Springer Nature.

关键词： Virtual reality

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Continuous power flow of islanded microgrids with droop-controlled distributed generations 3

Continuous power flow of islanded microgrids with droop-cont...

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3rd IEEE international conference on Energy Internet, ICEI 2019

作者： Zhongmei, Pan Huanhuan, Fu Peijia, Hou Xiangqian, Tong Automation and Information Engineering Xi'an University of Technology Xi'an China

ISBN: (纸本)9781728114934

Due to the small scale and the randomness of the power of intermittent distributed generations (DG) and loads, islanded microgrids (IMGs) are vulnerable to voltage instability problems. IMGs with droop-controlled DGs are even more weak because no master DGs are responsible for the regulation of frequency and voltage. Continuous power flow (CPF) is an effective tool for static voltage stability analysis. However, a droop-regulated has its own unique features. The steady-state model of IMGs with droop-controlled DGs are different from those of traditional power systems. Moreover, the convergence of Newton-Raphson method for the load flow equations of IMGs with droop-controlled DGs is not satisfactory. Thus, a CPF calculation method based on Newton-Raphson with optimal multipliers for droop-regulated IMGs is proposed in this paper. The Newton-Raphson method with optimal multipliers is adopted to solve the initial point and the steady state solution of the correction step. The tangent prediction method and the local parameterization method is employed in the prediction step and the correction step respectively. Vertical correction is adopted in the initial steps, and then horizontal correction is adopted when approaching the critical point. Newton-Raphson method with optimal multipliers allow to track the PV curves with a larger step in the whole process. Finally, the proposed method is applied to an IMG system modified from 33-bus test system, verifying the correctness and effectiveness of the proposed algorithm. © 2019 IEEE.

关键词： Newton-Raphson method

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3rd EAI international conference on Advanced Hybrid Information Processing, ADHIP 2019

3rd EAI International Conference on Advanced Hybrid Informat...

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3rd EAI international conference on Advanced Hybrid Information Processing, ADHIP 2019

ISBN: (纸本)9783030364045

The proceedings contain 101 papers. The special focus in this conference is on Advanced Hybrid Information Processing. The topics include: Research on Delay Control Method of Ultra-Wideband Wireless Communication Based on Artificial Intelligence;research on Anomaly Monitoring Algorithm of Uncertain Large Data Flow Based on Artificial Intelligence;Research on Parallel Mining Method of Massive Image Data Based on AI;Floating Small Target Detection in Sea Clutter Based on Jointed Features in FRFT Domain;research on Fatigue Life Prediction Method of Ballastless Track Based on Big Data;design of High Speed Railway Turnout Structural Damage Identification System Based on Machine Learning;research on Data Integrity Encryption Method of Cloud Storage Users Based on Big Data Analysis;intelligent Detection Method for Maximum Color Difference of Image Based on Machine Learning;automatic Color Control Method of Low Contrast Image Based on Big Data Analysis;construction Quality Inspection Method of Building Concrete Based on Big Data;research on Reduced Dimension Classification Algorithm of Complex Attribute Big Data in Cloud computing;research on Hierarchical Mining Algorithm of Spatial Big Data Set Association Rules;uniform Acceleration Motion Target Location and Tracking Based on Time-Frequency Difference;Variable Scale Iterative SAR Imaging Algorithm Based on Sparse Representation;ioT Security Access Authentication Method Based on Blockchain;continuous Predictive Model for Quality of Experience in Wireless Video Streaming;Knowledge-Aided Group GLRT for Range distributed Target Detection in Partially Homogeneous Environment;Asynchronous distributed ADMM for Learning with Large-Scale and High-Dimensional Sparse Data Set;smart Phone Aided Intelligent Invoice Reimbursement System.

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