Combining SVD and co-occurrence matrix information to recognize organic solar cells defects with a elliptical basis function network classifier

Grazia Lo Sciuto; Giacomo Capizzi; Dor Gotleyb; Sivan Linde; Rafi Shikler; Marcin Woźniak; Dawid Połap

doi:10.1007/978-3-319-59060-8_47

Combining SVD and co-occurrence matrix information to recognize organic solar cells defects with a elliptical basis function network classifier

Grazia Lo Sciuto
, Giacomo Capizzi
, Dor Gotleyb
, Sivan Linde
, Rafi Shikler
, Marcin Woźniak
, Dawid Połap

Katedra Modelowania Sztucznej Inteligencji (RMS/RMS2)

University of Catania
Ben-Gurion University of the Negev

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

4 Cytowania z bazy Scopus

Abstrakt

This paper presents a new methodology based on elliptical basis function (EBF) networks and an innovative feature extraction technique which makes use of the co-occurrence matrices and the SVD decomposition in order to recognize organic solar cells defects. The experimental results show that our algorithm achieves an high accuracy of recognition of 96% and that the feature extraction technique proposed is very effective in the pattern recognition problems that involving the texture’s analysis. The proposed methodology can be used as a tool to optimize the fabrication process of the organic solar cells. All the tests carried out for this work were made by using the organic solar cells realized in the Optoelectronic Organic Semiconductor Devices Laboratory at Ben Gurion University of the Negev.

Język oryginału	angielski
Tytuł publikacji goszczącej	Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings
Redaktorzy	Jacek M. Zurada, Lotfi A. Zadeh, Ryszard Tadeusiewicz, Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer
Wydawca	Springer Verlag
Strony	518-532
Liczba stron	15
ISBN (drukowany)	9783319590592
Identyfikatory DOI	https://doi.org/10.1007/978-3-319-59060-8_47
Status publikacji	Opublikowano - 2017
Wydarzenie	16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017 - Zakopane, Polska Czas trwania: 11 cze 2017 → 15 cze 2017

Seria publikacji

Nazwa	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Tom	10246 LNAI
ISSN (drukowany)	0302-9743
ISSN (elektroniczny)	1611-3349

Konferencja

Konferencja	16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017
Kraj/Terytorium	Polska
Miejscowość	Zakopane
Okres	11/06/17 → 15/06/17

Cele SDG ONZ

Ten wynik przyczynia się do realizacji następujących celów zrównoważonego rozwoju

Cel 7 Przystępna i czysta energia

Obszary tematyczne ASJC Scopus

Informatyka teoretyczna
Informatyka ogólna

Uzyskaj dostęp do dokumentu

10.1007/978-3-319-59060-8_47

Inne pliki i linki

Link do publikacji w Scopus

Cytowanie

Lo Sciuto, G., Capizzi, G., Gotleyb, D., Linde, S., Shikler, R., Woźniak, M., & Połap, D. (2017). Combining SVD and co-occurrence matrix information to recognize organic solar cells defects with a elliptical basis function network classifier. W J. M. Zurada, L. A. Zadeh, R. Tadeusiewicz, L. Rutkowski, M. Korytkowski, & R. Scherer (Red.), Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings (str. 518-532). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Tom 10246 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-59060-8_47

Lo Sciuto, Grazia ; Capizzi, Giacomo ; Gotleyb, Dor i in. / Combining SVD and co-occurrence matrix information to recognize organic solar cells defects with a elliptical basis function network classifier. Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. redaktor / Jacek M. Zurada ; Lotfi A. Zadeh ; Ryszard Tadeusiewicz ; Leszek Rutkowski ; Marcin Korytkowski ; Rafal Scherer. Springer Verlag, 2017. str. 518-532 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e62941ae6eaa453d9ef4c63e67caa970,

title = "Combining SVD and co-occurrence matrix information to recognize organic solar cells defects with a elliptical basis function network classifier",

abstract = "This paper presents a new methodology based on elliptical basis function (EBF) networks and an innovative feature extraction technique which makes use of the co-occurrence matrices and the SVD decomposition in order to recognize organic solar cells defects. The experimental results show that our algorithm achieves an high accuracy of recognition of 96\% and that the feature extraction technique proposed is very effective in the pattern recognition problems that involving the texture{\textquoteright}s analysis. The proposed methodology can be used as a tool to optimize the fabrication process of the organic solar cells. All the tests carried out for this work were made by using the organic solar cells realized in the Optoelectronic Organic Semiconductor Devices Laboratory at Ben Gurion University of the Negev.",

keywords = "Co-occurrence matrix, EBFs neural networks, Organic solar cells, Singular Value Decomposition",

author = "\{Lo Sciuto\}, Grazia and Giacomo Capizzi and Dor Gotleyb and Sivan Linde and Rafi Shikler and Marcin Wo{\'z}niak and Dawid Po{\l}ap",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017 ; Conference date: 11-06-2017 Through 15-06-2017",

year = "2017",

doi = "10.1007/978-3-319-59060-8\_47",

language = "English",

isbn = "9783319590592",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "518--532",

editor = "Zurada, \{Jacek M.\} and Zadeh, \{Lotfi A.\} and Ryszard Tadeusiewicz and Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer",

booktitle = "Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings",

address = "Germany",

}

Lo Sciuto, G, Capizzi, G, Gotleyb, D, Linde, S, Shikler, R, Woźniak, M & Połap, D 2017, Combining SVD and co-occurrence matrix information to recognize organic solar cells defects with a elliptical basis function network classifier. w JM Zurada, LA Zadeh, R Tadeusiewicz, L Rutkowski, M Korytkowski & R Scherer (red), Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), tom 10246 LNAI, Springer Verlag, str. 518-532, 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, Zakopane, Polska, 11/06/17. https://doi.org/10.1007/978-3-319-59060-8_47

Combining SVD and co-occurrence matrix information to recognize organic solar cells defects with a elliptical basis function network classifier. / Lo Sciuto, Grazia; Capizzi, Giacomo; Gotleyb, Dor i in.
Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. red. / Jacek M. Zurada; Lotfi A. Zadeh; Ryszard Tadeusiewicz; Leszek Rutkowski; Marcin Korytkowski; Rafal Scherer. Springer Verlag, 2017. str. 518-532 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Tom 10246 LNAI).

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

TY - GEN

T1 - Combining SVD and co-occurrence matrix information to recognize organic solar cells defects with a elliptical basis function network classifier

AU - Lo Sciuto, Grazia

AU - Capizzi, Giacomo

AU - Gotleyb, Dor

AU - Linde, Sivan

AU - Shikler, Rafi

AU - Woźniak, Marcin

AU - Połap, Dawid

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - This paper presents a new methodology based on elliptical basis function (EBF) networks and an innovative feature extraction technique which makes use of the co-occurrence matrices and the SVD decomposition in order to recognize organic solar cells defects. The experimental results show that our algorithm achieves an high accuracy of recognition of 96% and that the feature extraction technique proposed is very effective in the pattern recognition problems that involving the texture’s analysis. The proposed methodology can be used as a tool to optimize the fabrication process of the organic solar cells. All the tests carried out for this work were made by using the organic solar cells realized in the Optoelectronic Organic Semiconductor Devices Laboratory at Ben Gurion University of the Negev.

AB - This paper presents a new methodology based on elliptical basis function (EBF) networks and an innovative feature extraction technique which makes use of the co-occurrence matrices and the SVD decomposition in order to recognize organic solar cells defects. The experimental results show that our algorithm achieves an high accuracy of recognition of 96% and that the feature extraction technique proposed is very effective in the pattern recognition problems that involving the texture’s analysis. The proposed methodology can be used as a tool to optimize the fabrication process of the organic solar cells. All the tests carried out for this work were made by using the organic solar cells realized in the Optoelectronic Organic Semiconductor Devices Laboratory at Ben Gurion University of the Negev.

KW - Co-occurrence matrix

KW - EBFs neural networks

KW - Organic solar cells

KW - Singular Value Decomposition

UR - https://www.scopus.com/pages/publications/85020888955

U2 - 10.1007/978-3-319-59060-8_47

DO - 10.1007/978-3-319-59060-8_47

M3 - Conference contribution

AN - SCOPUS:85020888955

SN - 9783319590592

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 518

EP - 532

BT - Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings

A2 - Zurada, Jacek M.

A2 - Zadeh, Lotfi A.

A2 - Tadeusiewicz, Ryszard

A2 - Rutkowski, Leszek

A2 - Korytkowski, Marcin

A2 - Scherer, Rafal

PB - Springer Verlag

T2 - 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017

Y2 - 11 June 2017 through 15 June 2017

ER -

Lo Sciuto G, Capizzi G, Gotleyb D, Linde S, Shikler R, Woźniak M i in. Combining SVD and co-occurrence matrix information to recognize organic solar cells defects with a elliptical basis function network classifier. W Zurada JM, Zadeh LA, Tadeusiewicz R, Rutkowski L, Korytkowski M, Scherer R, redaktorzy, Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. Springer Verlag. 2017. str. 518-532. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59060-8_47

Combining SVD and co-occurrence matrix information to recognize organic solar cells defects with a elliptical basis function network classifier

Abstrakt

Seria publikacji

Konferencja

Cele SDG ONZ

Obszary tematyczne ASJC Scopus

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Inne pliki i linki

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