Terahertz Amplitude Polynomial Principle Component Regression for Aramid–Basalt Hybrid Composite Laminate Inspection
| Parent link: | IEEE Transactions on Industrial Informatics Vol. 14, iss. 12.— 2018.— [P. 5601-5609] |
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| Yhteisötekijä: | |
| Muut tekijät: | , , , , , , , |
| Yhteenveto: | Title screen As an emerging nondestructive diagnostic and monitoring technique, terahertz time-domain spectroscopy (THz-TDS) imagery is attracting more attention. In this regard, new THz image processing algorithms based on infrared thermography (IRT) concepts are greatly needed, since most IRT imagery modalities are fast for in-line industrial inspection. However, this scenario is difficult due to some physical constraints to be reached, although this idea should be followed to avoid the loss of useful information during image processing. In this paper, a novel THz amplitude polynomial principle component regression (APPCR) algorithm is proposed for the inspection of aramid-basalt hybrid composite laminates. This algorithm segments THz amplitude-frequency curves to simulate heating-up and cooling-down behaviors as in IRT; in addition, it uses an empirical orthogonal functions-based principle component regression modality to simplify the THz image analysis procedure. This experimental and analytical study shows that APPCR can, first, simplify the THz image analysis procedure, and second, enhance image contrast and spatial resolution. A theoretical analysis was conducted as experimental explanation, while the IRT imagery results were used for comparative purposes. In addition, signal-to-noise ratio analysis was used to evaluate quantitatively the image enhancement. Finally, it is possible to conclude that THz is more suitable to inspect transparent or semitransparent materials. Advantages and disadvantages of THz-TDS and IRT are summarized in the text. Режим доступа: по договору с организацией-держателем ресурса |
| Kieli: | englanti |
| Julkaistu: |
2018
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| Aiheet: | |
| Linkit: | https://doi.org/10.1109/TII.2018.2870670 |
| Aineistotyyppi: | Elektroninen Kirjan osa |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=659688 |
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| 200 | 1 | |a Terahertz Amplitude Polynomial Principle Component Regression for Aramid–Basalt Hybrid Composite Laminate Inspection |f Zhang Hai [et al.] | |
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| 300 | |a Title screen | ||
| 320 | |a [References: 29 tit.] | ||
| 330 | |a As an emerging nondestructive diagnostic and monitoring technique, terahertz time-domain spectroscopy (THz-TDS) imagery is attracting more attention. In this regard, new THz image processing algorithms based on infrared thermography (IRT) concepts are greatly needed, since most IRT imagery modalities are fast for in-line industrial inspection. However, this scenario is difficult due to some physical constraints to be reached, although this idea should be followed to avoid the loss of useful information during image processing. In this paper, a novel THz amplitude polynomial principle component regression (APPCR) algorithm is proposed for the inspection of aramid-basalt hybrid composite laminates. This algorithm segments THz amplitude-frequency curves to simulate heating-up and cooling-down behaviors as in IRT; in addition, it uses an empirical orthogonal functions-based principle component regression modality to simplify the THz image analysis procedure. This experimental and analytical study shows that APPCR can, first, simplify the THz image analysis procedure, and second, enhance image contrast and spatial resolution. A theoretical analysis was conducted as experimental explanation, while the IRT imagery results were used for comparative purposes. In addition, signal-to-noise ratio analysis was used to evaluate quantitatively the image enhancement. Finally, it is possible to conclude that THz is more suitable to inspect transparent or semitransparent materials. Advantages and disadvantages of THz-TDS and IRT are summarized in the text. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t IEEE Transactions on Industrial Informatics | ||
| 463 | |t Vol. 14, iss. 12 |v [P. 5601-5609] |d 2018 | ||
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