To minimize the effect of thermally-induced distortion and avoid the reabsorption phenomenon caused by the atomic alkali inside an alkali vapor cell (generally several or several ten millimeters long) in a diode-pumped alkali laser (DPAL) system, a novel concept of thin-disk DPALs in which alkali is sealed in a symmetric thin-disk cell has recently been proposed by referring a solid-state thin-disk laser. In this paper, we construct a theoretical model to study a V-pumped thin-disk DPAL system where the pump beam propagates along a V-shaped path. The influence of the thickness and the radius of a thin-disk cell, the incident angle of a pump beam, and the cell temperature on the output features of a thin-disk DPAL is studied by employing this model. In addition, we also investigate the effects of the profile of a pump beam such as a flat-top beam or a Gaussian beam on the uniformity of the temperature distribution and output power of a thin-disk DPAL. It has been demonstrated that a V-pumped DPAL might be better than an end-pumped DPAL. With respect to the uniformity of temperature distribution at the end-windows of a cell, the results reveal that a flat-top beam holds out a considerable merit compared with a Gaussian beam.
This paper uses the BP neural network and grey algorithm to forecast and study radar wind field. In order to reduce the residual error in the wind field prediction which uses BP neural network and grey algorithm, calculating the minimum value of residual error function, adopting the residuals of the gray algorithm trained by BP neural network, using the trained network model to forecast the residual sequence, using the predicted residual error sequence to modify the forecast sequence of the grey algorithm. The test data show that using the grey algorithm modified by BP neural network can effectively reduce the residual value and improve the prediction precision.
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