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

Department of Electrical & Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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’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.

Original language	English
Title of host publication	Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings
Editors	Jacek M. Zurada, Lotfi A. Zadeh, Ryszard Tadeusiewicz, Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer
Publisher	Springer Verlag
Pages	518-532
Number of pages	15
ISBN (Print)	9783319590592
DOIs	https://doi.org/10.1007/978-3-319-59060-8_47
State	Published - 1 Jan 2017
Event	16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017 - Zakopane, Poland Duration: 11 Jun 2017 → 15 Jun 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10246 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017
Country/Territory	Poland
City	Zakopane
Period	11/06/17 → 15/06/17

Keywords

Co-occurrence matrix
EBFs neural networks
Organic solar cells
Singular Value Decomposition

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science (all)

Access to Document

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

Cite this

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. In J. M. Zurada, L. A. Zadeh, R. Tadeusiewicz, L. Rutkowski, M. Korytkowski, & R. Scherer (Eds.), Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings (pp. 518-532). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10246 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-59060-8_47

Lo Sciuto, Grazia ; Capizzi, Giacomo ; Gotleyb, Dor et al. / 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. editor / Jacek M. Zurada ; Lotfi A. Zadeh ; Ryszard Tadeusiewicz ; Leszek Rutkowski ; Marcin Korytkowski ; Rafal Scherer. Springer Verlag, 2017. pp. 518-532 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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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 = "Funding Information: Authors acknowledge contribution to this project from the “Diamond Grant 2016” No. 0080/DIA/2016/45 funded by the Polish Ministry of Science and Higher Education. 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",

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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. in JM Zurada, LA Zadeh, R Tadeusiewicz, L Rutkowski, M Korytkowski & R Scherer (eds), 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), vol. 10246 LNAI, Springer Verlag, pp. 518-532, 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, Zakopane, Poland, 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 et al.
Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. ed. / Jacek M. Zurada; Lotfi A. Zadeh; Ryszard Tadeusiewicz; Leszek Rutkowski; Marcin Korytkowski; Rafal Scherer. Springer Verlag, 2017. p. 518-532 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10246 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 - Funding Information: Authors acknowledge contribution to this project from the “Diamond Grant 2016” No. 0080/DIA/2016/45 funded by the Polish Ministry of Science and Higher Education. Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017/1/1

Y1 - 2017/1/1

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

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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 et al. Combining SVD and co-occurrence matrix information to recognize organic solar cells defects with a elliptical basis function network classifier. In Zurada JM, Zadeh LA, Tadeusiewicz R, Rutkowski L, Korytkowski M, Scherer R, editors, Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings. Springer Verlag. 2017. p. 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

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