To achieve high-quality 3D imaging for stereoscopic displays, addressing challenges related to the detection of key parameters and crosstalk between adjacent sub-pixels is essential. In this study, we propose an automatic stereoscopic display screen mask detection method based on slanted grating technology and image masking techniques. Our approach diverges from traditional methods that involve filling sub-pixel components of all-black and all-white images into multi-view stereoscopic images for grating detection. Instead, we establish an image mask grating detection model, which applies a mask to multi-viewpoint images to extract specific sub-pixel regions. This allows us to synthesize multi-viewpoint images and acquire crucial parameters for column lens gratings. Experimental results indicate that, with an inclination angle accuracy of 99.9%, our method achieves accuracy rates of 94.55%, 93.78%, and 86.31% at distances of 350 mm, 750 mm, and 1150 mm from the LCD screen, respectively. The experimental outcomes unequivocally demonstrate that our method significantly enhances the performance of automatic stereoscopic displays, offering outstanding accuracy and efficiency in high-quality 3D imaging for stereoscopic displays.
In recent years, organic solar cells (OSCs) have gradually become the focus of renewable energy research. In order to predict the photovoltaic characteristics of OSCs more accurately and efficiently, researchers have incorporated numerous machine learning models into their studies. In this research, we designed and implemented a neural network model based on molecular fingerprints and applied it to the study of the power conversion efficiency (PCE) prediction of OSCs. We explored the impact of different organic photovoltaic material structures and feature extraction methods on the prediction of the PCE. Through experimental evaluations, the model not only achieved good experimental results in terms of material structure and PCE prediction but also compared the feature extraction methods of different molecular fingerprints. It was found that both Morgan and Circular fingerprints performed excellently in multiple scenarios.
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