Aiming at the problem that the arbitrary emission of wastewater from industrial enterprises, which has caused serious pollution of soil and groundwater in China, a set of intelligent control system for groundwater in-situ remediation equipment is designed in this paper. The system uses a programmable logic controller (PLC) as the main control unit, combined with pressure sensors, flow meters, ozone concentration detectors and other equipment, and uses injection well technology to achieve intelligent control of groundwater in-situ remediation equipment. In order to improve the performance of groundwater remediation, the injection module of the system can set a specific injection sequence and injection time for injection wells according to requirements. The system is of some significance for improving the automatic control level of groundwater treatment equipment.
Turbulence profile is an important parameter for characterization of atmospheric turbulence intensity at different altitudes. Based on generalized Hufnagel-Valley atmospheric turbulence model and measurement data of DCIM lidar, we first analyzed the difficulties encountered by analytical methods, then a numerical method based on particle swarm optimization algorithm was proposed to retrieval the unknown parameters of generalized Hufnagel-Valley model. To enhance the accuracy of high altitudes, whole layer isoplanatic angle was also as a constraint in the inversion. Moreover,we compared the particle swarm optimization algorithm and Levenberg-Marquardt algorithm in term of inversion accuracy.
This paper uses the measured atmospheric coherence length profile data of DCIM lidar to analyze the effect of different regularization parameter selection strategies on the inversion of atmospheric turbulence profile. The criterions of L-curve, generalized cross-validation(GCV), quasi-optimal are used respectively, The inversion results is evaluated by signal-tonoise ratio(SNR) and root mean square error(RMSE). The results show that the GCV criterion perform more stable for various measurements than L-curve and quasi-optimal criterion.
With the rapid development of artificial intelligence, computer vision systems based on image recognition, object detection, target tracking and other technologies are widely used in aviation, military industry, agriculture and other fields. However, due to bad weather conditions, such as haze and rain, the quality of the images collected by this type of system is impaired, which directly causes its performance to decline, causing serious losses to related fields. Therefore, the research on removing rain and fog on images has attracted the attention of many scholars. In recent years, deep learning has shined in the field of computer vision. Hence, many scholars have combined deep learning methods to remove rain and fog on degraded images and have achieved certain results. In order to gain a deeper understanding of the research progress of the single image rain and fog removal algorithm based on deep learning, this paper collates and analyzes some related literature, introduces and summarizes the algorithm research of the two application scenarios of rain and fog removal in detail. Finally, this paper will briefly summarize these rain and fog removal algorithms and puts forward a prospect for the research of deep learning in single image rain and fog removal.
We develop differential column image motion (DCIM) lidar for monitoring atmosphere refractive structure constant Cn2 profile. It is important to use an appropriate regularization method for DCIM lidar since the ill-posedness of the integral equation between the Cn2 profile and the measured r0 profile. In this paper, three typical regularization methods are studied to retrieve the Cn2 profiles from r0 profiles.The experiments illustrate that the Tikhonov method and truncated SVD method perform good performance, while damped SVD shows poorer inversion accuracy.
The image captured in foggy weather is often degraded, traditional defogging algorithms take a long time.Concerning this issue, in this paper, we propose a fast defogging method based on the quickly edge-preserving filtering algorithm, and apply it into image defogging.First, the atmospheric veil is estimated by taking use of the properties that the quickly edge-preserving filtering is available to preserve edge and smooth noise, so as we can solve the atmospheric transmittance distribution. Then, estimating the atmospheric light by quadtree search algorithm .Finally, the haze-free image is recovered by transforming the atmospheric scattering model. The experimental results show that the algorithm can effectively restore the fog image,and has a small time complexity, which is beneficial to the realization of real-time defogging.
Differential column image motion lidar (DCIM lidar) can obtain the Fried’s transverse coherence length (r0) of different altitudes with a high spatial and temporal resolution. According to the integral equation of atmospheric coherence length (r0) of spherical wave, the refractive structure constant(C2n) profile can be retrieved from r0 profile. Aiming at improving the retrieval accuracy of atmospheric turbulence profile, noise reduction on r0 profile is implemented before inversion. Two methods of wavelet threshold and complementary ensemble empirical mode decomposition (CEEMD) are used to denoise r0 profile. The effects of denoised methods on r0 profile and C2n profile are investigated. The numeric simulations and experiments are both carried out to validate the two denoised methods. The results show that both the two methods can improve the signal-to-noise ratio (SNR) of atmospheric coherent length profile and reduce the recovered error of the atmospheric turbulence profile, and wavelet threshold method is superior to CEEMD method under different noise conditions.
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