Combining SVD and Co-occurrence Matrix Information to Recognize Organic Solar Cells Defects with a Elliptical Basis Function Network Classifier

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

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

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…Complex algorithms for domain transfer from device-dependent systems to device-independent systems are progressively being used for various important applications, including medical imaging; however, these conversions may cause serious errors and outliers when generalized. Sciuto et al (2017) [14] and Lo Sciuto et al (2021) [15] reported some improved network classifiers and feature extraction algorithms for better results to recognize organic solar cells defects. The typical RGB values of standard illuminants in different device-dependent RGB spaces are given in Table 2.…”

Section: Color Difference For Human Perceptionmentioning

confidence: 99%

“…Further, we conducted ground truth experiments with three dyes using an equal proportion of their primary, secondary, and ternary mixtures (Table 4 and Figure 8). Various color space RGB representations of the dye mixtures (BC at 1, 2, 3, and 4%) were computed [14] and represented in Figure 9. A high coefficient of correlation was observed for all the cases (R 2 > 0.99), confirming a good prediction probability from calibrated imaging.…”

Section: Color Combinations and Verification Of Various Color Space A...mentioning

confidence: 99%

Section: Color Combinations and Verification Of Various Color Space A...mentioning

confidence: 99%

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Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-II

Rout

Baciu

et al. 2023

Electronics

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The extended application of device-dependent systems’ vision is growing exponentially, but these systems face challenges in precisely imitating the human perception models established by the device-independent systems of the Commission internationale de l’éclairage (CIE). We previously discussed the theoretical treatment and experimental validation of developing a calibrated integrated sphere imaging system to imitate the visible spectroscopy environment. The RGB polynomial function was derived to obtain a meaningful interpretation of color features. In this study, we dyed three different types of textured materials in the same bath with a yellow reactive dye at incremental concentrations to see how their color difference profiles tested. Three typical cotton textures were dyed with three ultra-RGB remozol reactive dyes and their combinations. The color concentration ranges of 1%, 2%, 3%, and 4% were chosen for each dye, followed by their binary and ternary mixtures. The aim was to verify the fundamental spectral feature mapping in various imaging color spaces and spectral domains. The findings are quite interesting and help us to understand the ground truth behind working in two domains. In addition, the trends of color mixing, CIE color difference, CIExy (chromaticity) color gamut, and RGB gamut and their distinguishing features were verified. Human perception accuracy was also compared in both domains to clarify the influence of texture. These fundamental experiments and observations on human perception and calibrated imaging color space could clarify the expected precision in both domains.

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Section: Color Difference For Human Perceptionmentioning

confidence: 99%

Section: Color Combinations and Verification Of Various Color Space A...mentioning

confidence: 99%

Section: Color Combinations and Verification Of Various Color Space A...mentioning

confidence: 99%

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Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-II

Rout

Baciu

et al. 2023

Electronics

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“…The latter is paramount for the prototype testing phase when the electrical performances are determined. Finally, once the data has been collected, the prototype is evaluated in order to decide whether it can be produced or not [22]. In the latter case, the prototype is rejected, and the production workflow starts again until a suitable solution is found for the mass production of a finalized device.…”

Section: Device Fabrication Workflowmentioning

confidence: 99%

Optimizing the Organic Solar Cell Manufacturing Process by Means of AFM Measurements and Neural Networks

et al. 2018

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Abstract:In this paper we devise a neural-network-based model to improve the production workflow of organic solar cells (OSCs). The investigated neural model is used to reckon the relation between the OSC's generated power and several device's properties such as the geometrical parameters and the active layers thicknesses. Such measurements were collected during an experimental campaign conducted on 80 devices. The collected data suggest that the maximum generated power depends on the active layer thickness. The mathematical model of such a relation has been determined by using a feedforward neural network (FFNN) architecture as a universal function approximator. The performed simulations show good agreement between simulated and experimental data with an overall error of about 9%. The obtained results demonstrate that the use of a neural model can be useful to improve the OSC manufacturing processes.

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Exploiting OSC Models by Using Neural Networks with an Innovative Pruning Algorithm

Sciuto

Capizzi

Napoli

et al. 2018

Artificial Intelligence and Soft Computing

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Combining SVD and Co-occurrence Matrix Information to Recognize Organic Solar Cells Defects with a Elliptical Basis Function Network Classifier

Cited by 3 publications

References 15 publications

Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-II

Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-II

Optimizing the Organic Solar Cell Manufacturing Process by Means of AFM Measurements and Neural Networks

Exploiting OSC Models by Using Neural Networks with an Innovative Pruning Algorithm

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