Improved Gaussian Model Research Articles

Pollutant Dispersion of Aircraft Exhaust Gas during the Landing and Takeoff Cycle with Improved Gaussian Diffusion Model

Evaluating aviation emissions and examining the dispersion properties of contaminants are crucial for understanding atmospheric pollution. To assess the pollutant emissions and dispersion of aircraft during the landing and takeoff (LTO) cycle, and address air pollution surrounding the airport resulting from flight operations, this study evaluated emissions throughout the LTO phase based on Quick Access Recorder (QAR) data in conjunction with the first-order approximation method. An improved Gaussian diffusion model for mobile point sources was employed to examine the diffusion characteristics of contaminants. Additionally, CFD calculation outcomes for various exhaust velocities and wind speeds were utilized to validate the trustworthiness of the improved Gaussian model. The discussion also encompasses the influence of diffusion time, wind direction, wind speed, temperature gradient, and particle deposition on the concentration distribution of contaminants. The findings indicated that the Gaussian diffusion model aligned with the results of the CFD calculations. The diffusion distribution of contaminants around airports varies over time and is significantly influenced by atmospheric environmental factors, including wind direction, wind speed, and atmospheric stability. Specifically, a change in wind direction from 0° to 45° caused a shift of approximately 1000 m in the contaminant’s center. An increase in wind speed from 3 m/s to 5 m/s led to a decrease in concentration by about 15%. Furthermore, a transition in atmospheric stability from category ‘a’ (very unstable) to ‘f’ (very stable) resulted in a two-order-of-magnitude increase in contaminant concentrations.

Research on a Human Moving Object Detection Method Based on Gaussian Model and Deep Learning

In order to understand human motion object detection methods, the author proposes a research on human motion object detection method based on Gaussian model. Firstly, traditional Gaussian models are unable to detect complex scenes or slow moving targets. Therefore, an improved Gaussian model based moving object detection algorithm is proposed. Secondly, multiple Gaussian models are used to represent the features of each pixel in the moving target image, and based on the matching of each pixel in the image with the Gaussian model, it is considered as a background point. Conversely, it is based on the principle of the foreground, and the Gaussian model is updated. Finally, by updating the foreground model and calculating short-term stability indicators, the detection effect of moving targets is improved. By determining the Gaussian distribution and pixel relationship, new parameters are set to construct the background model and eliminate the impact caused by sudden changes in lighting. The experimental analysis results show that this method can effectively detect and track moving targets, with good noise resistance, high clarity, and an accuracy rate of up to 99%. Compared with traditional Gaussian model methods, the improved method can more effectively detect moving targets and has better robustness.

Continuously Adaptive Mean-shift Tracking Algorithm Based on Improved Gaussian Model

In complex scenes with light changes, deformations, and occlusions, target tracking easily contains a large amount of background color information when building a target color model. Thus, the tracking effect is reduced. To improve the accuracy of the traditional continuously adaptive mean-shift algorithm (CAMShift) in complex scenarios, a target tracking algorithm based on an improved Gaussian mixture model was proposed. Using the Gaussian mixture model, the tracking image was divided into the foreground and background superposition. The histograms of the hue component were respectively established in the foreground and background of the target area. By suppressing the same hue as the background color in the tracking image, the target color model was established. The target position was iteratively obtained by implementing the CAMShift algorithm using the enhanced target color model. The Bhattacharyya distance between the candidate target and the target template was used as basis for updating the target model. Simulation analysis under benchmark data sets and actual monitoring scenarios verified the accuracy of the proposed algorithm. Results show that the distance precision and overlap success rate of the proposed algorithm are 0.88 and 0.625, respectively. The proposed algorithm effectively solves long-term target tracking problems with complex scenes, such as occlusion, background clutters, and illumination variation. This study eliminates the problem of target recognition caused by environmental changes and provides references for real-time monitoring of abnormal traffic conditions.

Self-consistent Gaussian model of nonperturbative QCD vacuum

We show that the minimal Gaussian model of nonlocal vacuum quark and quark-gluon condensates in QCD violates the transverse character of the correlator of two vector currents. We suggest the improved Gaussian model of the nonperturbative QCD vacuum, which respects QCD equations of motion and minimizes the revealed gauge-invariance breakdown. We obtain the refined values of pion distribution amplitude (DA) conformal moments 〈ξ2N〉π (N = 1, ..., 5) using the improved QCD vacuum model, including the inverse moment 〈x −1〉π, being inaccessible if one uses the standard QCD SR. We construct the allowed region for Gegenbauer coefficients a 2 and a 4 of the pion DA for two values of the QCD vacuum nonlocality parameter, λ 2 q = 0.4 and 0.5 GeV2.

New formulations of eddy diffusivity for solution of diffusion equation in a convective boundary layer

In this paper, new formulations of eddy diffusivity have been derived in order to be used in atmospheric dispersion models. The obtained formulae depend on source distance for inhomogeneous turbulence. To evaluate these parameters, the classical statistical diffusion theory, the observed spectral properties, and observed characteristics of energy-containing eddies have been considered. A vertical eddy diffusivity was used in air pollution model validated with data of Copenhagen experiments, and compared with an improved Gaussian model. The results obtained are shown to agree with the measurements of Copenhagen and also with those of the Gaussian model. The current study suggests the inclusion of the memory effect is important in regions near an elevated continuous point source and improves the description of the turbulent transport process of atmospheric contaminants. The major advance shown in this paper is the necessity of including the downwind distance-dependent eddy diffusivity in air quality modeling, and it is demonstrated that such theory would suggest that it should yield better results than the asymptotic solution alone. It, in fact, does yield better results in the near and intermediate fields.

Derivation of an Eddy Diffusivity Depending on Source Distance for Vertically Inhomogeneous Turbulence in a Convective Boundary Layer

Abstract Accounting for the current knowledge of the convective boundary layer structure and characteristics, a new formulation for the eddy diffusivities to be used in atmospheric dispersion models has been derived. That is, expressions for the eddy diffusivities, depending on source distance, for inhomogeneous turbulence are proposed. The classical statistical diffusion theory, the observed spectral properties, and observed characteristics of energy-containing eddies are used to estimate these parameters. In addition, a vertical eddy diffusivity was introduced into an air pollution model, validated with the data of Copenhagen experiments, and compared with an improved Gaussian model. The results of this new method are shown to agree with the measurements of Copenhagen and also with those of the Gaussian model. Furthermore, the current study suggests that the inclusion of the memory effect, important in regions near an elevated continuous point source, improves the description of the turbulent transport ...

An improved gaussian model for long-term average air concentration estimates

A long-term sector average Gaussian dispersion model that incorporates temporal vertical stability variations, estimated wind speed at release height, a method to account for calm winds, and day-night mixing depths, is tested against 2 years of weekly average 85Kr air concentrations collected at 13 locations from 30 to 150 km from a quasi-continuous source. The root mean square error (RMSE) of ratios of observed to calculated concentrations was determined for the weekly to annual average values with the RMSE decreasing from 5 down to 2, respectively. A sensitivity study of these four improvements indicated that the first three were the most significant.