Using the sun-sky radiometer CE318 data in 2013~2019 observed in typical dust regions including Kashgar, Zhangye and Minqin, the variation characteristics of Aerosol Optical Depth(AOD), Angstrom exponent, single scattering albedo and asymmetry factor were analyzed. The results show as follows. There are significant differences in the variability characteristics of AOD at three typical dust-type stations. The seasonal variation of AOD in Kashgar is the most obvious. The highest AOD is in spring with more dispersed KDE, and the lowest in winter with more concentrated KDE. In Zhangye and Minqin, the seasonal difference of AOD is small, with monthly means below 0.9 and more concentrated distribution in the range of 0.5~0.8. The value of Angstrom exponent in Kashgar is higher in autumn and winter than spring and summer. In spring, summer and autumn, Zhangye and Minqin have significantly higher Angstrom exponent than Kashgar. The single scattering albedo in all three stations is above 0.85, and the asymmetry factor ranges from 0.63~0.8, indicating that the aerosol scattering effects are evident throughout the year.
High power fiber lasers can be incoherently combined to form the basis for high energy laser applications. Incoherent combining of fiber lasers has a number of advantages over other laser beam combining methods. However, the far-field beam quality of the incoherently combined laser array can still be significantly degraded by atmospheric optical turbulence. In this article, a general scaling law for propagation of incoherently combined laser array through atmosphere is developed by employing theoretical analysis and the common stochastic wave optics technique, and mainly focus on the effects of diffraction and atmospheric optical turbulence. The scaling law developed in the present work differs from standard scaling laws in its definition of irradiance. We show that the far-field irradiance and beam dispersion of any incoherently combined laser array, regardless of near-field beamlets geometry, can be obtained in terms of four basic parameters: laser power, field effective area, pupil field factor, and the Fried parameter.The results show that the formula is simple but predicts peak irradiance and beam dispersion accurately in the far field with varying levels of atmospheric turbulence, regardless of the near-field beamlets geometry.
At present, apple disease control mainly relies on manual recognition, which causes a waste of human resources. In order to improve the efficiency of disease detection, this study focuses on apple leaf disease images in natural scenes. A lightweight network RepSSD consisting of feature extraction module, multi-scale feature recognition, and structural reparameterization is designed to detect apple leaf diseases. Replace the backbone part of the network with RepVGG-A0 without identity branch, and the auxiliary convolutional layer is replaced with a re-parameterized RepConv module without identity branch on top of the SSD. The CBAM attention mechanism module is also added to ensure adequate extraction of global features of apple leaf disease images in natural scenes. The experimental results show that the improved SSD has better detection accuracy and real-time performance in apple leaf disease recognition with an accuracy of 93.05%, which is 2.56% better than the original SSD, and the overall number of parameters decreases by 47.8%, the computation volume reduces by 86.7%, so the overall effect is better than the current common object detection models. Therefore, the algorithm can be better applied to apple disease control. Therefore, the model can be better used for apple disease control.
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