Gaussian Diffusion Model Research Articles

Research on single-point fast three-dimensional concentration reconstruction of a smoke plume based on the static interference infrared imaging spectroscopy system

To meet the requirements of real-time and effectiveness of three-dimensional concentration telemetry of gas clouds and address the difficulty of fast three-dimensional concentration reconstruction of single instrument smoke plumes, the paper proposes a single-point fast three-dimensional concentration reconstruction of the smoke plume based on a static interference infrared Fourier transform imaging spectrometer (ISIFTS) by using the advantages of high throughput, large field of view, high stability, and rapid detection. Based on the scanning field switching characteristics of ISIFTS, a single instrument quickly scans the dual-field map data to obtain the three-dimensional point cloud of the target scene. Combined with the three-dimensional spatial parameters of the scene, a multi-dimensional re-projection algorithm is constructed based on the Gaussian plume continuous diffusion model to simulate the concentration-path-length product (CL) image, and the measured CL image is solved by analyzing the smoke plume image spectra data. The normalized cross-correlation fitting algorithm is used to perform the optimal matching of simulated-measured CL images, and then the three-dimensional concentration distribution of the plume scene is reconstructed based on the dimension mapping relationship. The field smoke plume telemetry experiment was designed and carried out, and the three-dimensional concentration distribution of plume gas at three emission points in the scene was obtained. The CO2 emission rates were calculated to be 13.572 kg / s, 12.225 kg / s, and 14.114 kg / s, respectively. The data are consistent with the emission rate of the coal-fired thermal power plant, which verifies the effectiveness of the single-point fast three-dimensional concentration reconstruction of the smoke plume method based on the ISIFTS system.

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.

Fourier Ptychographic Microscopy Reconstruction Method Based on Residual Local Mixture Network.

Fourier Ptychographic Microscopy (FPM) is a microscopy imaging technique based on optical principles. It employs Fourier optics to separate and combine different optical information from a sample. However, noise introduced during the imaging process often results in poor resolution of the reconstructed image. This article has designed an approach based on a residual local mixture network to improve the quality of Fourier ptychographic reconstruction images. By incorporating channel attention and spatial attention into the FPM reconstruction process, the network enhances the efficiency of the network reconstruction and reduces the reconstruction time. Additionally, the introduction of the Gaussian diffusion model further reduces coherent artifacts and improves image reconstruction quality. Comparative experimental results indicate that this network achieves better reconstruction quality, and outperforming existing methods in both subjective observation and objective quantitative evaluation.

Simulation evaluation of a single-photon laser methane remote sensor for leakage rate monitoring.

We propose a novel methane leakage rate remote sensor that combines a single-photon avalanche diode detector with a near-infrared 1653.7 nm low-power laser. The proposed M sequence and triangle wave signal modulation method simultaneously realizes the detection of methane leakage and target point clouds. Innovatively, the sensor's methane concentration and leakage rate quantification ability were simulated by combining the Gaussian plume diffusion model and the Risley prism. The effects of the prism rotation ratio, wind speed, leakage rate, atmospheric stability (AS), target reflectivity, signal averaging period, and concentration spatial interpolation method on leakage rate are discussed. When plume methane concentrations reduce from 10,000 to 500 ppm·m, the relative concentration bias rise from 1% to 30%, the absolute concentration bias is approximately 100 ppm·m. Two spatial concentration interpolation methods introduced leakage rate bias ranging from 6%-25%. For a low AS, the leakage rate bias under the cubic interpolation method was small (approximately 1.6%). In addition, when the initial leakage rate increased from 100 to 1,000 mg/s, the leakage rate bias was approximately 20% smaller.

24-hour average PM2.5 concentration caused by aircraft in Chinese airports from Jan. 2006 to Dec. 2023

Since 2006, the rapid development of China’s aviation industry has been accompanied by a significant increase in one of its emissions, namely, PM2.5, which poses a substantial threat to human health. However, little data is describing the PM2.5 concentration caused by aircraft activities. This study addresses this gap by initially computing the monthly PM2.5 emissions of the landing-take-off (LTO) stage from Jan. 2006 to Dec. 2023 for 175 Chinese airports, employing the modified BFFM2-FOA-FPM method. Subsequently, the study uses the Gaussian diffusion model to measure the 24-hour average PM2.5 concentration resulting from flight activities at each airport. This study mainly draws the following conclusions: Between 2006 and 2023, the highest recorded PM2.5 concentration data at all airports was observed in 2018, reaching 5.7985 micrograms per cubic meter, while the lowest point was recorded in 2022, at 2.0574 micrograms per cubic meter. Moreover, airports with higher emissions are predominantly located in densely populated and economically vibrant regions such as Beijing, Shanghai, Guangzhou, Chengdu, and Shenzhen.

Feasibility of Diffusion-weighted Imaging (DWI) for Assessing Cerebrospinal Fluid Dynamics: DWI-fluidography in the Brains of Healthy Subjects.

The present study aimed to investigate whether diffusion-weighted imaging (DWI) can qualify and quantify cerebrospinal fluid (CSF) dynamics in the brains of healthy subjects. For this purpose, we developed new DWI-based fluidography and compared the CSF dynamics seen on the fluidography with two apparent diffusion coefficients obtained with different DWI signal models at anatomical spaces filled by CSF. DWI with multiple b values was performed for 10 subjects using a 7T MRI scanner. DWI-fluidography based on the DWI signal variations in different motion probing gradient directions was developed for visualizing the CSF dynamics voxel-by-voxel. DWI signals were measured using an ROI in the representative CSF-filled anatomical spaces in the brain. For the multiple DWI signals, the mono-exponential and kurtosis models were fitted and two kinds of apparent diffusion coefficients (ADCC and ADCK) were estimated in each space using the Gaussian and non-Gaussian diffusion models, respectively. DWI-fluidography could qualitatively represent the features of CSF dynamics in each anatomical space. ADCs indicated that the motions at the foramen of Monro, the cistern of the velum interpositum, the quadrigeminal cistern, the Sylvian cisterns, and the fourth ventricle were more drastic than those at the subarachnoid space and anterior horns of the lateral ventricle. Those results seen in ADCs were identical to the findings on DWI-fluidography. DWI-fluidography based on the features of DWI signals could show differences of CSF dynamics among anatomical spaces.

Leakage Diffusion Modeling of Key Nodes of Gas Pipeline Network Based on Leakage Concentration

In order to achieve the prediction and early warning of city gas pipe network leakage accidents, as well as to provide rapid and precise support for emergency response to such accidents, this study focuses on a Gaussian diffusion model applied to a large urban gas pipeline network. Specifically, it investigates the gas gate wells, which are key nodes in the pipeline network, to develop a leakage model. The objective is to analyze the variation in internal gas concentration in the gate wells and determine the range of danger posed by external gas diffusion from the gate wells. In addition, Fluent simulation is utilized to compare the accuracy of the model’s calculations. The findings of this study indicate that the gas concentration inside the gate well, as predicted by the model fitting results and Fluent simulation, exhibit a high level of agreement, with coefficient of determination (R2) values exceeding 0.99. Moreover, when predicting the hazardous distance of gas leakage outside the gate well, the model’s results show an average relative error of 0.15 compared to the Fluent simulation results. This demonstrates that the model is highly accurate and meets the practical application requirements.

Unravelling the impact of CoNiCrAlY coating microstructure and composition on the formation of duplex oxide scales

The formation process of Al2O3-based duplex oxide scales growing on high velocity oxygen fuel (HVOF) CoNiCrAlY alloy was investigated at 1135 °C. The dual-phase nature of the alloy originated a site-specific oxidation behaviour that facilitated the formation of an inhomogeneous duplex oxide scale with widespread embedded mixed oxides. The early-stage formation of these oxides was explained by a modified Gaussian diffusion model where grain boundary proximity is the main variable. The subsequent nucleation/growth of mixed oxides with oxidation time was attributed to continuous cationic diffusion of non-aluminium elements across the scale enabled by the presence of cationic impurities in the scale.

Numerical implementation of level set image recovery based on partial differential equation model

Abstract Image recovery is a class of ill-posed inverse problems, so image recovery has been a challenging and fundamental topic in image processing. In this paper, firstly, to recover blurred images with noise, an adaptive fully variable partial differential image recovery model is proposed based on the fully variable partial differential equation model and Gaussian thermal diffusion model, where the adaptiveness is achieved logarithmically by regularizing the edge detection operator. Secondly, a finite difference method, which covers the gradient and scatters operators in the partial differential equation, is applied to the problem of fixing the global range boundary conditions of the image. Finally, the gradient of the image is restored, and then the gradient domain of the image is solved to obtain the complete recovered image. The results show that in the evaluation of the image restoration effect aspect by two indexes, PSNR and SSIM, the value of the adaptive fully variable partial differential image restoration model increases from 28 to 32, and the value of SSIM increases from 0.70 to 0.88, which has good image restoration performance. The adaptive fully variable partial differential image restoration model proposed in this paper has the characteristics of good edge protection and fast convergence, which is of guiding significance for the numerical implementation of image restoration techniques.

Optimal consumption–investment under partial information in conditionally log-Gaussian models

Certain Merton type consumption−investment problems under partial information are reduced to the ones of full information and within the framework of a complete market model. Then, specializing to conditionally log−Gaussian diffusion models, concrete analysis about the optimal values and optimal strategies is performed by using analytical tools like Feynman−Kac formula, or HJB equations. The explicit solutions to the related forward-backward equations are also given.

Dose Distribution of Radioxenon Due to a Hypothetical Accident of TRIGA Research Reactor in Bangladesh

Radiological dose distribution owing to the deposition of 131m Xe, 133m Xe, 133 Xe, 135m Xe, 135 Xe, and 138 Xe on ground and immersion considering a postulated accident of TRIGA Mark-II research reactor has been assessed. The radiological dose distribution has been carried out in various directions with the help of Gaussian Diffusion Model. Local meteorological data such as average wind speed, frequency, etc. has been collected and evaluated for various directions around the reactor site. For all the dominant directions, the maximum dose values due to 131m Xe, 133m Xe, 133 Xe, 135m Xe, 135 Xe, 138 Xe and the total ( 131m Xe + 133m Xe + 133 Xe + 135m Xe + 135 Xe + 138 Xe) were observed within the limit 3.03E-7–1.23E-4 µSv/h, 1.01E-5–4.09E-3 µSv/h, 0.0003–0.14 µSv/h, 2.29E-5–9.26E-3 µSv/h, 0.002 –1.111 µSv/h, 1.11E-5–4.55E-3 µSv/h, and 0.003–1.269 µSv/h, respectively. Dose distribution was found to be dominant due to immersion and the contribution was 87.55 %. There is shortage of data regarding the release of radioxenon in the atmosphere during nuclear accident especially in the case of TRIGA type research reactor. This paper is the first such detailed study on atmospheric release of radioxenon and its dose distribution for a full power- reactor and the consequences towards the environment and public health. The result can be applied to develop the radiological protective measures and to prepare an emergency response plan for the TRIGA reactor site.

Health Risk Assessment and Projection of Municipal Solid Waste Disposal in China

Background China’s Municipal solid waste (MSW) is growing rapidly. Incineration and landfill are two main approaches of MSW disposal and both of them emit different types of ambient pollutants and threaten human health. There lacks systematic health risk assessment and projections of MSW incineration and landfill in China. Methods We established the historical MSW incineration and landfill emission inventories from 2015 to 2017 and used the Gaussian Diffusion Model to simulate the spatial distribution of ambient pollutants. We then used Risk Quotients Model to assess the non-carcinogenic and carcinogenic health risks through inhalation exposure. Besides, we set seven possible MSW incineration development scenarios to predict health risk levels in 2035, considering the effect of population growth, incineration rate, waste classification and recycling, and technology progress. Results The results show that non-carcinogenic risk caused by MSW incineration from 2015 to 2017 was lower than landfill at the national level. Both of them were meet the maximum acceptable level (HI ≤ 1). From 2015 to 2017, carcinogenic risks caused by landfill were 9.42 × 10-6, 9.40 × 10-6, 9.37 × 10-6, nine times larger than the maximum acceptable level (CR ≤ 1×10-6). Carcinogenic risks caused by incineration were 5.71 × 10-6, 7.92 × 10-6, 9.44 × 10-6, five to nine times larger than the maximum acceptable level. Projection results show that compared to the baseline scenario, through classification and recycling, changing incinerator furnace, and improving, national health risk levels will be decreased by 24%, 94% and 97%. Conclusions We assessed the health risks caused by MSF disposal and found MSF incineration a relatively low risk alternative disposal approach compared to waste landfill. In the future, local government should reduce MSF disposal health risks by optimizing the selection of incinerators locations, improving the garbage sorting recycling system, and strengthening information disclosure and communications.

Tracking and measuring plumes from sailing ships using an unmanned aerial vehicle

Measuring sailing ship plumes provides comprehensive and real-time emission data. However, accurately tracking and measuring ship plumes still requires research. This study describes a tracking and measurement method of ship plumes using an unmanned aerial vehicle (UAV). A UAV ship exhaust measurement system and monitoring process were developed. Data from an automatic identification system (AIS) was used to improve the Gaussian plume diffusion model to facilitate the initial location of the plume on sailing ships. Based on the real-time gas measurement values obtained by the UAV, the UAV flight method was optimized to accurately track and measure the ship's plume. The Pudong Maritime Safety Administration used this system in 2019 to effectively monitor the plumes of 27 sailing ships as part of maritime law enforcement. Consequently, four cases of excessive fuel sulphur content (FSC) were detected in situ for the first time, indicating that China's maritime authorities can use the technology to supervise and control the FSC of sailing ships.

Combined Grey Wolf Optimizer Algorithm and Corrected Gaussian Diffusion Model in Source Term Estimation

It is extremely critical for an emergency response to quickly and accurately use source term estimation (STE) in the event of hazardous gas leakage. To determine the appropriate algorithm, four swarm intelligence optimization (SIO) algorithms including Gray Wolf optimizer (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and ant colony optimization (ACO) are selected to be applied in STE. After calculation, all four algorithms can obtain leak source parameters. Among them, GWO and GA have similar computational efficiency, while ACO is computationally inefficient. Compared with GWO, GA and PSO, ACO requires larger population and more iterations to ensure accuracy of source parameters. Most notably, the convergence factor of GWO is self-adaptive, which is in favor of obtaining accurate results with lower population and iterations. On this basis, combination of GWO and a modified Gaussian diffusion model with surface correction factor is used to estimate the emission source term in this work. The calculation results demonstrate that the corrected Gaussian plume model can improve the accuracy of STE, which is promising for application in emergency warning and safety monitoring.

Research on Leakage and Diffusion characteristics of Liquid Hydrogen Based on Gaussian Diffusion Model

In order to analyze the diffusion characteristics of the hydrogen cloud during a liquid hydrogen leakage accident, the Matlab calculation program of liquid hydrogen leakage and diffusion was compiled based on the Gaussian diffusion theory, and analyzed the influence of wind speed, pressure, leakage area and other factors on the size of the dangerous area after liquid hydrogen leakage.The calculation results show that: the wind speed accelerates the diffusion, and the higher the wind speed, the stronger the dilution effect on the hydrogen cloud, and the smaller the dangerous area formed; the increase of pressure and leakage port area will increase the leakage of liquid hydrogen, and as a result, the scope of the dangerous area is increased.

Denoising of Tourist Street Scene Image Based on ROF Model of Second-Order Partial Differential Equation

The noise pollution in tourist street view images is caused by various reasons. A major challenge that researchers have been facing is to find a way to effectively remove noise. Although in the past few decades people have proposed many methods of denoising tourist street scene images, the research on denoising technology of tourist street scene images is still not outdated. There is no doubt that it has become a basic and important research topic in the field of digital image processing. The evolutionary diffusion method based on partial differential equations is helpful to improve the quality of noisy tourist street scene images. This method can process tourist street scene images according to people’s expected diffusion behavior. The adaptive total variation model proposed in this paper is improved on the basis of the total variation model and the Gaussian thermal diffusion model. We analyze the classic variational PDE-based denoising model and get a unified variational PDE energy functional model. We also give a detailed analysis of the diffusion performance of the total variational model and then propose an adaptive total variational diffusion model. By improving the diffusion coefficient and introducing a curvature operator that can distinguish details such as edges, it can effectively denoise the tourist street scene image, and it also has a good effect on avoiding the step effect. Through the improvement of the ROF model, the loyalty term and regular term of the model are parameterized, the adaptive total variation denoising model of this paper is established, and a detailed analysis is carried out. The experimental results show that compared with some traditional denoising models, the model in this paper can effectively suppress the step effect in the denoising process, while protecting the texture details of the edge area of the tourist street scene image. In addition, the model in this paper is superior to traditional denoising models in terms of denoising performance and texture structure protection.

PRELIMINARY DEVELOPMENT OF RADIONUCLIDES RELEASE OF INDIVIDUAL DOSE CODE PROGRAM FOR RADIATION MONITORING PURPOSES

Environmental radiation monitoring is one of the important efforts in protecting society and the environment from radiation hazards, both natural and artificial. The presence of three nuclear research reactors and plans to build a nuclear power plant reactor prompted Indonesia to prepare a radiation monitoring system for safety and security (SPRKK). The goal of the study is to provide an appropriate method for developing radiation monitoring system to support the development of nuclear power plant in the near future. For this preliminary study, the author developed a code program using Gaussian distribution model approach for predicting radionuclide release and individual dose acceptancy by human being within 16 wind directions sectors and up to 50 km distance. The model includes estimation of source term from the nuclear installation, release of radionuclides source into air following Gaussian diffusion model, some of the release deposit to the land and entering human being through inhalation, direct external exposure, and resuspension, and predicted its accepted individual dose. This model has been widely used in various code program such as SimPact and PC-Cosyma. For this study, the model will be validated using SimPact code program. The model has been successfully developed with less than 5% deviation. Further study will be done by evaluating the model with real measuring data from research reactor installation and prepare for interfacing with real time radiation data acquisition and monitoring as part of radiation monitoring system during normal and accident condition.

Time-averaged mean squared displacement ratio test for Gaussian processes with unknown diffusion coefficient.

The time-averaged mean squared displacement (TAMSD) is one of the most common statistics used for the analysis of anomalous diffusion processes. Anomalous diffusion is manifested by non-linear (mostly power-law) characteristics of the process in contrast to normal diffusion where linear characteristics are expected. One can distinguish between sub- and super-diffusive processes. We consider Gaussian anomalous diffusion models and propose a new approach used for their testing. This approach is based on the TAMSD ratio statistic for different time lags. Similar to the TAMSD, this statistic exhibits a specific behavior in the anomalous diffusion regime. Through its structure, it is independent of the diffusion coefficient, which, in general, does not influence anomalous diffusion behavior. Thus, the TAMSD ratio-based approach does not require preliminary knowledge of the diffusion coefficient's value, in contrast to the TAMSD-approach, where this value is crucial in the testing procedure. Based on the quadratic form representation of the TAMSD ratio, we calculate its main characteristics and propose a step-by-step testing procedure that can be applied for any Gaussian process. For the anomalous diffusion model used here, namely, the fractional Brownian motion, we demonstrate the effectiveness of the proposed methodology. We show that the new approach outperforms the TAMSD-based one, especially for small sample sizes. Finally, the methodology is applied to the real data from the financial market.

A deep learning method to repair atmospheric environmental quality data based on Gaussian diffusion

Online monitoring data of atmospheric environmental quality often deviate or are missing, causing a great impact on regional atmospheric quality analysis. In this study, a deep learning method to repair atmospheric environmental quality data based on Gaussian diffusion and gate recurrent unit (GD-GRU) was developed to improve repair accuracy. A multi-source Gaussian diffusion model was developed to estimate PM2.5 based on the pollutant diffusion law and the data of 61 stations in Guilin. The root mean square error (RMSE) of the estimated and observed value was extracted as the error sequence. The error value was regarded as output of gate recurrent unit (GRU) with the inputs of weather and pollutant parameters. Missing data were calculated by Gaussian diffusion estimated value and the error predicted by GRU. The established GD-GRU model was applied to repair the long-sequence missing data. The analytical results indicated that the GD-GRU model had higher prediction accuracy of extreme values than Gaussian diffusion model and GRU model, because GD-GRU based on Gaussian diffusion can calculate the extreme value by simulating the diffusion and transmission mechanism. The established model predicted PM2.5 concentration in the next hours with an RMSE of 12.561, which was approximately 21.02% better, on average, than methods like autoregressive integrated moving average model (ARIMA), support vector regression (SVR), recurrent neural network (RNN), long short-term memory model (LSTM), and GRU. The established GD-GRU model demonstrated good performance on extreme values prediction and air quality data repair, thus providing a new method for air quality long-sequence missing data repair.

A Feature Extraction and Classification Method to Forecast the PM2.5 Variation Trend Using Candlestick and Visual Geometry Group Model

Currently, the continuous change prediction of PM2.5 concentration is an air pollution research hotspot. Combining physical methods and deep learning models to divide the pollution process of PM2.5 into effective multiple types is necessary to achieve a reliable prediction of the PM2.5 value. Therefore, a candlestick chart sample generator was designed to generate the candlestick chart from the online PM2.5 continuous monitoring data of the Guilin monitoring station site. After these generated candlestick charts were analyzed through the Gaussian diffusion model, it was found that the characteristics of the physical transmission process of PM2.5 pollutants can be reflected. Based on a set three-day period, using the time linear convolution method, 2188 sets of candlestick chart data were obtained from the 2013–2018 PM2.5 concentration data. There existed 16 categories generated by unsupervised classification that met the established classification judgment standards. After the statistical analysis, it was found that the accuracy rate of the change trend of these classifications reached 99.68% during the next period. Using the candlestick chart data as the training dataset, the Visual Geometry Group (VGG) model, an improved convolutional neural network model, was used for the classification. The experimental results showed that the overall accuracy (OA) value of the candlestick chart combination classification was 96.19%, and the Kappa coefficient was 0.960. IN the VGG model, the overall accuracy was improved by 1.93%, on average, compared with the support vector machines (SVM), LeNet, and AlexNet models. According to the experimental results, using the VGG classification method to classify continuous pollution data in the form of candlestick charts can more comprehensively retain the characteristics of the physical pollution process and provide a classification basis for accurately predicting PM2.5 values. At the same time, the statistical feasibility of this method has been proved.