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An evaporation duct is a type of atmospheric stratification that affects radio systems. Atmospheric duct prediction is helpful for radar detection. In this article, we used the deep forest, which is different from a deep learning framework, to predict the atmospheric duct height. At the same time, the long short-term memory (LSTM) neural network and other machine learning algorithms, such as the logistic regression (LR), random forest (RF), Bayes, and support vector regression (SVR) algorithms, were adopted to predict the evaporation duct height (EDH). The predicted results with filled and unfilled missing data show that an accurate prediction of the EDH can be achieved using the deep forest.

A multihole probe is a very effective sensor for measuring the velocity and direction of a flow field. Compared with other sensors, it has a highly reliable and wide range of application prospects. A comparative analysis of probes of different shapes is carried out, where it is found that a hemispherical seven-hole probe with a vertical surface structure opening is the upgrade structure. Based on the spherical structure, a theoretical calibration method for the probe is proposed, and the correlation between the pressure in the hole and the velocity of the flow field is established by a pressure-velocity parameter equation. By calibrating the hemispherical seven-hole probe, it is found that the speed error is less than 5%, and the angle error is less than 2°. Compared with other calibration methods, the proposed calibration process is greatly shortened. At the same time, the hemispherical seven-hole probe is used to measure the flow field around a cylinder, where frequency changes of 3.497, 2.083, and 1.657 Hz are observed in 3, 5, and 9 cm cylindrical vortexes, respectively. It is proved that the multihole probe also has the ability to detect complex flow fields.

ABSTRACTSingular-value decomposition (SVD) can be used to estimate the point spread function (PSF) of a blurred image to denoise the image, but it does not consider the local difference of the image; moreover, the selection of recombination order or rank is related to the estimation of the PSF and the recovery effect. An optical image restoration and denoising method with white Gaussian noise using the block-based singular-value derivative is proposed in this paper. First, PSF is estimated using the average energy spectrum of the singular-value vector of the ideal image, which is based on SVD properties, starting from the discrete degenerate model. Next, the inverse filtering is used to restore the image. Finally, the denoised image is obtained using block-based SVD filtering and combination, in which the singular-value recombination order is determined by the singular-value derivative. The experimental results demonstrate that the proposed method has a higher PSNR (peak signal-to-noise ratio) than other methods, and it can get a more clear image.