Optimum display luminance under a wide range of ambient light for cockpit displays.

The self-luminous cockpit displays need to be adaptive to a wide range of ambient light levels, which changes from very low illuminance to very high levels. Yet, current studies on evaluation and luminance setting of displays in bright surroundings are still limited. In this study, a three-dimensional visual ergonomic experiment was carried out to investigate how bright a cockpit display should be to meet aircrew operational requirements under different illuminance. A lab study with a within-subjects (N = 12) design was conducted in a simulated cockpit. According to the Weber-Fechner's Law, human observers evaluated five display luminance conditions (101, 101.5, 102, 102.5, 103 cd/m2) under five ambient illuminance conditions (10°, 101, 102, 103, 104 lx). Visual performance, visual fatigue and visual comfort were used as evaluation bases, which were measured by d2 task, subjective fatigue questionnaire and visual perception semantic scales. Nonlinear function fitting was used to calculate the optimal luminance under a certain illuminance. Finally, curvilinear regression was used to analyze the illuminance and its corresponding optimal luminance. Based on Silverstein luminance power function, a luminance adjustment model with the form of power function was obtained. The proposed three-dimensional model fits the experimental data well and is consistent with the existing studies. It can be regarded as a supplement and optimization of the previous model under high ambient illuminance. This study can contribute not only to the pleasing luminance setting of panel displays in aircraft cockpits but also to other self-luminous devices, such as tablet devices, outdoor monitoring equipment and advertising screens.

Non-destructive assessment of soluble solids content in kiwifruit using hyperspectral imaging coupled with feature engineering.

The maturity of kiwifruit is widely gauged by its soluble solids content (SSC), with accurate assessment being essential to guarantee the fruit's quality. Hyperspectral imaging offers a non-destructive alternative to traditional destructive methods for SSC evaluation, though its efficacy is often hindered by the redundancy and external disturbances of spectral images. This study aims to enhance the accuracy of SSC predictions by employing feature engineering to meticulously select optimal spectral features and mitigate disturbance effects. We conducted a comprehensive investigation of four spectral pre-processing and nine spectral feature selection methods, as components of feature engineering, to determine their influence on the performance of a linear regression model based on ordinary least squares (OLS). Additionally, the stacking generalization technique was employed to amalgamate the strengths of the two most effective models derived from feature engineering. Our findings demonstrate a considerable improvement in SSC prediction accuracy post feature engineering. The most effective model, when considering both feature engineering and stacking generalization, achieved an RMSEp of 0.721, a MAPEp of 0.046, and an RPDp of 1.394 in the prediction set. The study confirms that feature engineering, especially the careful selection of spectral features, and the stacking generalization technique are instrumental in bolstering SSC prediction in kiwifruit. This advancement enhances the application of hyperspectral imaging for quality assessment, offering benefits that extend across the agricultural industry.