Object's details: Application of electrical capacitance tomography and artificial neural networks to rapid estimation of cylindrical shape parameters of industrial flow structure

Provider:Czasopisma PAN

Description

Title:
Application of electrical capacitance tomography and artificial neural networks to rapid estimation of cylindrical shape parameters of industrial flow structure
Creator:
Garbaa, Hela;
Romanowski, Andrzej;
Jackowska-Strumiłło, Lidia;
Grudzień, Krzysztof
Description:
ARCHIVES OF ELECTRICAL ENGINEERING (AEE) (previously Archiwum Elektrotechniki), quarterly journal of the Polish Academy of Sciences is OpenAccess (PAN Electronic Library, publishing original scientific articles and short communiques from all branches of Electrical Power Engineering exclusively in English. The main fields of interest are related to the theory & engineering of the components of an electrical power system: switching devices, arresters, reactors, conductors, etc. together with basic questions of their insulation, ampacity, switching capability etc.; electrical machines and transformers; modelling & calculation of circuits; electrical & magnetic fields problems; optimization methods; electromagnetic compatibility; control problems; power electronics; electrical power engineering;renewable energy; nondestructive testing & nondestructive evaluation. Journal Impact Factor: 2022 – 1.3, Five Year – 0.9 CiteScore metrics from Scopus, CiteScore 2022: 2.4 SCImago Journal Rank (SJR) 2022: 0.325 Source Normalized Impact per Paper (SNIP) 2022: 0.743 ICI Journal Master List 2021, Index Copernicus Value: 128.77 Scoring assigned by the Polish Ministry of Science and Higher Education: 100 points Submit your article;
Editor-in-Chief Professor Andrzej Demenko, Poznan University of Technology, Poland Deputy/ Managing Editor Mariusz Barański, Ph.D., Poznan University of Technology, Poland Łukasz Knypiński, Ph.D., Poznan University of Technology, Poland Editorial Advisory Board Chairman: Marian P. Kaźmierkowski, Warsaw, Poland Secretary: Mariusz Jagieła, Opole, Poland Members professors: Anouar Belahcen, Espoo, Finland Frede Blaabjerg,Aalborg, Denmark Ion Boldea,Timisoara, Romania Herbert De Gersem, Darmstadt, Germany Paolo Di Barba, Pavia, Italy Jacek Gieras, Rockford, USA Kay Hameyer, Aachen, Germany Marian K. Kazimierczuk, Dayton, USA Stefan Kulig, Dortmund, Germany David A. Lowther, Montreal, Canada Jacek Marecki, Gdańsk, Poland Sohrab Mirsaeidi, Beijing, China Fausto Pedro García Márquez, Ciudad Real, Spain Zhuoxiang Ren, Paris, France José Rodríguez Pérez, Santiago, Chile Ruth V. Sabariego, Leuven, Belgium Ryszard Sikora, Szczecin, Poland Zbigniew Styczyński, Magdeburg, Germany Jan Sykulski, Southampton, UK Sławomir Wiak, Łódź, Poland Language Editor Krystyna Guzek Statistical Editor Mariusz Barański, Poznan, Poland Poznan University of Technology Theme Editors Jerzy Barglik, Gliwice, Poland Professor at Silesian University of Technology Zbigniew Lubosny, Gdansk, Poland Professor at Gdańnk University of Technology Marian Łukaniszyn, Opole, Poland Professor at Opole University of Technology Kazimierz Jakubiuk, Gdansk, Poland Professor at Gdansk University of Technology Lech Grzesiak, Warsaw, Poland Professor at Warsaw University of Technology Henryka Danuta Stryczewska, Lublin, Poland Professor at Lublin University of Technology Jan Sykulski, Southampton, UK Professor at University of Southampton Adam Szelag, Warsaw, Poland Professor at Warsaw University of Technology Romulad Włodek, Krakow, Poland Professor at AGH University of Science and Technology Technical Editor : Typesetting in LATEX: Drukarnia Braci Grodzickich Sp.j., 05-500 Piaseczno, ul. Geodetów 47a, Poland
Object availability:
free access
Rights:
Archives of Electrical Engineering jest czasopismem wydawanym w wolnym dostępie na licencji CC BY-NC-ND 4.0. </span></p>
Date:
2016
Type:
Artykuły / Articles
Source:
Archives of Electrical Engineering; 2016; vol. 65; No 4 December; 657-669
Coverage:
657-669
Publisher:
Polish Academy of Sciences
Subject:
artificial neural networks;
electrical capacitance tomography;
flow parameters estimation;
inverse problem;
Nauki Techniczne
Identifier:
ISSN: 1427-4221;
eISSN: 2300-2506
Data provider:
Czasopisma PAN

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Object's details: Application of electrical capacitance tomography and artificial neural networks to rapid estimation of cylindrical shape parameters of industrial flow structure

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