Parallel algorithm of enhanced historical data integration using neural networks

V. Turchenko, C. Triki, L. Grandinetti, A. Sachenko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The main feature of neural network using for accuracy improvement of physical quantities (for example, temperature, humidity, pressure etc.) measurement by data acquisition systems is insufficient volume of input data for predicting neural network training at an initial exploitation period of sensors. The authors have proposed the technique of data volume increasing for predicting neural network training using integration of historical data method. In this paper we have proposed enhanced integration historical data method with its simulation results on mathematical models of sensor drift using single-layer and multi-layer perceptrons. We also considered a parallelization technique of enhanced integration historical data method in order to decrease its working time. A modified coarse-grain parallel algorithm with dynamic mapping on processors of parallel computing system using neural network training time as mapping criterion is considered. Fulfilled experiments have showed that modified parallel algorithm is more efficient than basic parallel algorithm with dynamic mapping, which does not use any mapping criterion.

Original languageEnglish
Title of host publicationProceedings of the Third Workshop - 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2005
Pages66-73
Number of pages8
DOIs
Publication statusPublished - 2007
Event3rd IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2005 - Sofia, Bulgaria
Duration: Sep 5 2005Sep 7 2005

Other

Other3rd IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2005
CountryBulgaria
CitySofia
Period9/5/059/7/05

Fingerprint

Data integration
Historical Data
Data Integration
Parallel algorithms
Parallel Algorithms
Neural Networks
Neural networks
Sensor
Sensors
Humidity
Multilayer neural networks
Parallel processing systems
Parallel Computing
Perceptron
Data Acquisition
Parallelization
Exploitation
Multilayer
Data acquisition
Atmospheric humidity

Keywords

  • Coarse-grain parallel algorithm
  • Computational grids
  • Dynamic mapping
  • Integration historical data
  • Neural networks
  • Sensor drift

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Theoretical Computer Science

Cite this

Turchenko, V., Triki, C., Grandinetti, L., & Sachenko, A. (2007). Parallel algorithm of enhanced historical data integration using neural networks. In Proceedings of the Third Workshop - 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2005 (pp. 66-73). [4062094] https://doi.org/10.1109/IDAACS.2005.282943

Parallel algorithm of enhanced historical data integration using neural networks. / Turchenko, V.; Triki, C.; Grandinetti, L.; Sachenko, A.

Proceedings of the Third Workshop - 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2005. 2007. p. 66-73 4062094.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Turchenko, V, Triki, C, Grandinetti, L & Sachenko, A 2007, Parallel algorithm of enhanced historical data integration using neural networks. in Proceedings of the Third Workshop - 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2005., 4062094, pp. 66-73, 3rd IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2005, Sofia, Bulgaria, 9/5/05. https://doi.org/10.1109/IDAACS.2005.282943
Turchenko V, Triki C, Grandinetti L, Sachenko A. Parallel algorithm of enhanced historical data integration using neural networks. In Proceedings of the Third Workshop - 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2005. 2007. p. 66-73. 4062094 https://doi.org/10.1109/IDAACS.2005.282943
Turchenko, V. ; Triki, C. ; Grandinetti, L. ; Sachenko, A. / Parallel algorithm of enhanced historical data integration using neural networks. Proceedings of the Third Workshop - 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2005. 2007. pp. 66-73
@inproceedings{3d98151191e8400a951686227c3990e3,
title = "Parallel algorithm of enhanced historical data integration using neural networks",
abstract = "The main feature of neural network using for accuracy improvement of physical quantities (for example, temperature, humidity, pressure etc.) measurement by data acquisition systems is insufficient volume of input data for predicting neural network training at an initial exploitation period of sensors. The authors have proposed the technique of data volume increasing for predicting neural network training using integration of historical data method. In this paper we have proposed enhanced integration historical data method with its simulation results on mathematical models of sensor drift using single-layer and multi-layer perceptrons. We also considered a parallelization technique of enhanced integration historical data method in order to decrease its working time. A modified coarse-grain parallel algorithm with dynamic mapping on processors of parallel computing system using neural network training time as mapping criterion is considered. Fulfilled experiments have showed that modified parallel algorithm is more efficient than basic parallel algorithm with dynamic mapping, which does not use any mapping criterion.",
keywords = "Coarse-grain parallel algorithm, Computational grids, Dynamic mapping, Integration historical data, Neural networks, Sensor drift",
author = "V. Turchenko and C. Triki and L. Grandinetti and A. Sachenko",
year = "2007",
doi = "10.1109/IDAACS.2005.282943",
language = "English",
isbn = "0780394461",
pages = "66--73",
booktitle = "Proceedings of the Third Workshop - 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2005",

}

TY - GEN

T1 - Parallel algorithm of enhanced historical data integration using neural networks

AU - Turchenko, V.

AU - Triki, C.

AU - Grandinetti, L.

AU - Sachenko, A.

PY - 2007

Y1 - 2007

N2 - The main feature of neural network using for accuracy improvement of physical quantities (for example, temperature, humidity, pressure etc.) measurement by data acquisition systems is insufficient volume of input data for predicting neural network training at an initial exploitation period of sensors. The authors have proposed the technique of data volume increasing for predicting neural network training using integration of historical data method. In this paper we have proposed enhanced integration historical data method with its simulation results on mathematical models of sensor drift using single-layer and multi-layer perceptrons. We also considered a parallelization technique of enhanced integration historical data method in order to decrease its working time. A modified coarse-grain parallel algorithm with dynamic mapping on processors of parallel computing system using neural network training time as mapping criterion is considered. Fulfilled experiments have showed that modified parallel algorithm is more efficient than basic parallel algorithm with dynamic mapping, which does not use any mapping criterion.

AB - The main feature of neural network using for accuracy improvement of physical quantities (for example, temperature, humidity, pressure etc.) measurement by data acquisition systems is insufficient volume of input data for predicting neural network training at an initial exploitation period of sensors. The authors have proposed the technique of data volume increasing for predicting neural network training using integration of historical data method. In this paper we have proposed enhanced integration historical data method with its simulation results on mathematical models of sensor drift using single-layer and multi-layer perceptrons. We also considered a parallelization technique of enhanced integration historical data method in order to decrease its working time. A modified coarse-grain parallel algorithm with dynamic mapping on processors of parallel computing system using neural network training time as mapping criterion is considered. Fulfilled experiments have showed that modified parallel algorithm is more efficient than basic parallel algorithm with dynamic mapping, which does not use any mapping criterion.

KW - Coarse-grain parallel algorithm

KW - Computational grids

KW - Dynamic mapping

KW - Integration historical data

KW - Neural networks

KW - Sensor drift

UR - http://www.scopus.com/inward/record.url?scp=43549086684&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=43549086684&partnerID=8YFLogxK

U2 - 10.1109/IDAACS.2005.282943

DO - 10.1109/IDAACS.2005.282943

M3 - Conference contribution

SN - 0780394461

SN - 9780780394469

SP - 66

EP - 73

BT - Proceedings of the Third Workshop - 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2005

ER -