Optimizing input data for training an artificial neural network used for evaluating defect depth in infrared thermographic nondestructive testing; Infrared Physics and Technology; Vol. 102
| Parent link: | Infrared Physics and Technology Vol. 102.— 2019.— [103047, 7 p.] |
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| Corporate Authors: | , |
| Andre forfattere: | , , , , , |
| Summary: | Title screen Ten different sets of input data have been used for training and verification of the neural network intended for determining defect depth in infrared thermographic nondestructive testing. The input data sets included raw temperature data, polynomial fitting, principle component analysis, Fourier transform and others. A minimum error (up 0.02 mm for defects in CFRP at depths from 0.5 to 2.5 mm) has been achieved by using polynomial fitting in logarithmic coordinates with further computation of the first temperature derivatives (the TSR technique), and close results have been obtained by processing raw data with the PCA technique. Both techniques require no use of reference points. Режим доступа: по договору с организацией-держателем ресурса |
| Sprog: | engelsk |
| Udgivet: |
2019
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| Fag: | |
| Online adgang: | https://doi.org/10.1016/j.infrared.2020.103289 |
| Format: | MixedMaterials Electronisk Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663088 |
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| 200 | 1 | |a Optimizing input data for training an artificial neural network used for evaluating defect depth in infrared thermographic nondestructive testing |f A. O. Chulkov, D. A. Nesteruk, V. P. Vavilov [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 18 tit.] | ||
| 330 | |a Ten different sets of input data have been used for training and verification of the neural network intended for determining defect depth in infrared thermographic nondestructive testing. The input data sets included raw temperature data, polynomial fitting, principle component analysis, Fourier transform and others. A minimum error (up 0.02 mm for defects in CFRP at depths from 0.5 to 2.5 mm) has been achieved by using polynomial fitting in logarithmic coordinates with further computation of the first temperature derivatives (the TSR technique), and close results have been obtained by processing raw data with the PCA technique. Both techniques require no use of reference points. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Infrared Physics and Technology | ||
| 463 | |t Vol. 102 |v [103047, 7 p.] |d 2019 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a infrared thermographic testing | |
| 610 | 1 | |a neural network | |
| 610 | 1 | |a data processing | |
| 610 | 1 | |a defect depth | |
| 610 | 1 | |a composite material | |
| 610 | 1 | |a инфракрасный контроль | |
| 610 | 1 | |a нейронные сети | |
| 610 | 1 | |a обработка данных | |
| 610 | 1 | |a глубина | |
| 610 | 1 | |a дефекты | |
| 610 | 1 | |a композитные материалы | |
| 701 | 1 | |a Chulkov |b A. O. |c specialist in the field of non-destructive testing |c Deputy Director for Scientific and Educational Activities; acting manager; Senior Researcher, Tomsk Polytechnic University, Candidate of Technical Sciences |f 1989- |g Arseniy Olegovich |3 (RuTPU)RU\TPU\pers\32220 |9 16220 | |
| 701 | 1 | |a Nesteruk |b D. A. |c specialist in the field of descriptive geometry |c Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences |f 1979- |g Denis Alekseevich |3 (RuTPU)RU\TPU\pers\31502 |9 15663 | |
| 701 | 1 | |a Vavilov |b V. P. |c Specialist in the field of dosimetry and methodology of nondestructive testing (NDT) |c Doctor of technical sciences (DSc), Professor of Tomsk Polytechnic University (TPU) |f 1949- |g Vladimir Platonovich |3 (RuTPU)RU\TPU\pers\32161 |9 16163 | |
| 701 | 1 | |a Moskovchenko |b A. I. |g Aleksey Igorevich | |
| 701 | 1 | |a Saeed |b N. |g Numan | |
| 701 | 1 | |a Omar |b M. A. | |
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