This study aimed to integrate Monte Carlo(MC)simulation with deep learning(DL)-based denoising techniques to achieve fast and accurate prediction of high-quality electronic portal imaging device(EPID)transmission dose...This study aimed to integrate Monte Carlo(MC)simulation with deep learning(DL)-based denoising techniques to achieve fast and accurate prediction of high-quality electronic portal imaging device(EPID)transmission dose(TD)for patientspecific quality assurance(PSQA).A total of 100 lung cases were used to obtain the noisy EPID TD by the ARCHER MC code under four kinds of particle numbers(1×10^(6),1×10^(7),1×10^(8)and 1×10^(9)),and the original EPID TD was denoised by the SUNet neural network.The denoised EPID TD was assessed both qualitatively and quantitatively using the structural similarity(SSIM),peak signal-to-noise ratio(PSNR),and gamma passing rate(GPR)with respect to 1×10^(9)as a reference.The computation times for both the MC simulation and DL-based denoising were recorded.As the number of particles increased,both the quality of the noisy EPID TD and computation time increased significantly(1×10^(6):1.12 s,1×10^(7):1.72 s,1×10^(8):8.62 s,and 1×10^(9):73.89 s).In contrast,the DL-based denoising time remained at 0.13-0.16 s.The denoised EPID TD shows a smoother visual appearance and profile curves,but differences between 1×10^(6)and 1×10^(9)still remain.SSIM improves from 0.61 to 0.95 for 1×10^(6),0.70 to 0.96 for 1×10^(7),and 0.90 to 0.97 for 1×10^(8).PSNR increases by>20%for 1×10^(6)and 1×10^(7),and>10%for 1×10^(8).GPR improves from 48.47%to 89.10%for 1×10^(6),61.04%to 94.35%for 1×10^(7),and 91.88%to 99.55%for 1×10^(8).The method that combines MC simulation with DL-based denoising for EPID TD generation can accelerate TD prediction and maintain high accuracy,offering a promising solution for efficient PSQA.展开更多
Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmenta...Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.展开更多
Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)meth...Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.展开更多
Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have becom...Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.展开更多
Gassy soils are distributed in relatively shallow layers the Quaternary deposit in Hangzhou Bay area. The shallow gassy soils significantly affect the construction of underground projects. Proper characterization of s...Gassy soils are distributed in relatively shallow layers the Quaternary deposit in Hangzhou Bay area. The shallow gassy soils significantly affect the construction of underground projects. Proper characterization of spatial distribution of shallow gassy soils is indispensable prior to construction of underground projects in the area. Due to the costly conditions required in the site investigation for gassy soils, only a limited number of gas pressure data can be obtained in engineering practice, which leads to the uncertainty in characterizing spatial distribution of gassy soils. Determining the number of boreholes for investigating gassy soils and their corresponding locations is pivotal to reducing construction risk induced by gassy soils. However, this primarily relies on the engineering experience in the current site investigation practice. This study develops a probabilistic site investigation optimization method for planning investigation schemes (including the number and locations of boreholes) of gassy soils based on the conditional random field and Monte Carlo simulation. The proposed method aims to provide an optimal investigation scheme before the site investigation based on prior knowledge. Finally, the proposed approach is illustrated using a case study.展开更多
The Underwater Communication Link(UCL)is a crucial component of Underwater Wireless Optical Communication(UWOC)systems,requiring optimised design to mitigate the high power attenuation inherent in seawater.To ensure t...The Underwater Communication Link(UCL)is a crucial component of Underwater Wireless Optical Communication(UWOC)systems,requiring optimised design to mitigate the high power attenuation inherent in seawater.To ensure the reliability of an optimal UCL design,it is essential to account for the three primary scattering regimes:forward scattering(FSC),backward scattering(BSC),and isotropic scattering(ISC)in seawater channels.This study introduces a new photon-tracking model based on a discrete equation,facilitating Monte Carlo Simulation(MCS)to evaluate how different scattering regimes influence received photon distribution.Three distinct Scattering Regime Contribution Weight(SRCW)probability sets were employed,each representing different UCL operational configurations dominated by specific scattering regimes.The proposed modeling approach enables a comprehensive assessment of the temporal characteristics of received optical pulses,channel loss,and time spread-ultimately defining the optimal UCL design parameters.The key findings of this study include:(1)Enhancing the FSC regime dominance leads to a quasi-light waveguide effect over link spans and small Fields of View(FOV)<25°,significantly improving channel performance in Harbor seawater compared to Coastal seawater.(2)A well-designed UCL with a small FOV(<25°)can minimise channel loss and time spread,ensuring high capacity and efficient performance in both Coastal and Harbor seawaters.(3)When BSC and ISC contributions exceed FSC dominance,the received optical pulse undergoes significant temporal broadening,particularly for larger FOV angles(>25°)and extended link spans.(4)The developed novel MCS-based discrete equation provides a simple yet robust model for simulating photon propagation in both homogeneous and inhomogeneous underwater channels.These insights contribute to developing more efficient and reliable UCL designs with military standards by enhancing UWOC system performance over a longer linkspan for a given limited optical power across various underwater environments.展开更多
This study presents the results of a Monte Carlo simulation to compare the statistical power of Siegel-Tukey and Savage tests.The main purpose of the study is to evaluate the statistical power of both tests in scenari...This study presents the results of a Monte Carlo simulation to compare the statistical power of Siegel-Tukey and Savage tests.The main purpose of the study is to evaluate the statistical power of both tests in scenarios involving Normal,Platykurtic and Skewed distributions over different sample sizes and standard deviation values.In the study,standard deviation ratios were set as 2,3,4,1/2,1/3 and 1/4 and power comparisons were made between small and large sample sizes.For equal sample sizes,small sample sizes of 5,8,10,12,16 and 20 and large sample sizes of 25,50,75 and 100 were used.For different sample sizes,the combinations of(4,16),(8,16),(10,20),(16,4),(16,8)and(20,10)small sample sizes and(10,30),(30,10),(50,75),(50,100),(75,50),(75,100),(100,50)and(100,75)large sample sizes were examined in detail.According to the findings,the power analysis under variance heterogeneity conditions shows that the Siegel-Tukey test has a higher statistical power than the other nonparametric Savage test at small and large sample sizes.In particular,the Siegel-Tukey test was reported to offer higher precision and power under variance heterogeneity,regardless of having equal or different sample sizes.展开更多
We present an incorporation of the kinetic Monte Carlo(kMC)simulation with the adjacency matrix(AM)method to investigate structural parameters of randomly branched polymers.The AMs of branched polymers are obtained by...We present an incorporation of the kinetic Monte Carlo(kMC)simulation with the adjacency matrix(AM)method to investigate structural parameters of randomly branched polymers.The AMs of branched polymers are obtained by the growth generation model,which is employed in the kMC simulation based on Gillespie’s algorithm.Of particular interest is the effect of the branched units on the relevant structural parameters.As an application in randomly hyperbranched polymers formed by AB_(2)type of monomers,average maximum span length,average length of sub-chains,number distribution of branched units,and meansquare radius of gyration are calculated.It is shown that the branched units can give rise to a significant effect on these physical quantities,whereby such a structure-property relationship can be employed to optimize reaction conditions and implement material designs.展开更多
Flat-panel X-ray sources(FPXSs)have many advantages in terms of compactness and low-dose imaging,enhancing their capability for novel X-ray applications.Experimental analysis of the X-ray characteristics and optimizin...Flat-panel X-ray sources(FPXSs)have many advantages in terms of compactness and low-dose imaging,enhancing their capability for novel X-ray applications.Experimental analysis of the X-ray characteristics and optimizing the anode panel of an FPXS are time-consuming,expensive,and sometimes impractical.In this study,a FPXS was prepared using a ZnO nanowire cold cathode and a molybdenum film anode target.Monte Carlo(MC)simulations were utilized to optimize the anode panel and obtain the average fluence,average energy,and spatial distribution of the X-rays for the ZnO nanowire FPXS.The accuracy of the MC simulations was verified by comparing the measured and simulated energy spectra.Optimization of the anode target considers the material,thickness,and morphology,whereas optimization of the substrate focuses on the material and thickness.The results show that the difference between the positions of the K-shell peaks in the measured and simulated energy spectra is within 0.26 keV.At the acceleration voltages of 30 kV,60 kV,and 90 kV,the optimal thicknesses of the tungsten array anode were 0.65μm,2.45μm,and 5μm,respectively,while the molybdenum array anode has the optimal thicknesses of 1.45μm,5.25μm,and 24μm,respectively.The microsemi-ellipsoidal anode with a recessed design showed a 5%increase in the transmitted X-ray fluence compared with the film target.The sapphire substrate with a thickness of 0.78 mm exhibits a mechanical strength comparable to that of a glass substrate with a thickness of 3 mm,implying that the former can increase the average X-ray fluence by reducing the filtration of X-rays.The findings of this study provide valuable guidance for the fabrication and optimization of the ZnO nanowire FPXS.展开更多
A microscopic understanding of the complex solute-defect interaction is pivotal for optimizing the alloy’s macroscopic mechanical properties.Simulating solute segregation in a plastically deformed crystalline system ...A microscopic understanding of the complex solute-defect interaction is pivotal for optimizing the alloy’s macroscopic mechanical properties.Simulating solute segregation in a plastically deformed crystalline system at atomic resolution remains challenging.The objective is to efficiently model and predict a phys-ically informed segregated solute distribution rather than simulating a series of diffusion kinetics.To ad-dress this objective,we coupled molecular dynamics(MD)and Monte Carlo(MC)methods using a novel method based on virtual atoms technique.We applied our MD-MC coupling approach to model off-lattice carbon(C)solute segregation in nanoindented Fe-C samples containing complex dislocation networks.Our coupling framework yielded the final configuration through efficient parallelization and localized en-ergy computations,showing C Cottrell atmospheres near dislocations.Different initial C concentrations resulted in a consistent trend of C atoms migrating from less crystalline distortion to high crystalline distortion regions.Besides unraveling the strong spatial correlation between local C concentration and defect regions,our results revealed two crucial aspects of solute segregation preferences:(1)defect ener-getics hierarchy and(2)tensile strain fields near dislocations.The proposed approach is generic and can be applied to other material systems as well.展开更多
Due to uncertainties in seismic pipeline damage and post-earthquake recovery processes,probabilistic characteristics such as mean value,standard deviation,probability density function,and cumulative distribution funct...Due to uncertainties in seismic pipeline damage and post-earthquake recovery processes,probabilistic characteristics such as mean value,standard deviation,probability density function,and cumulative distribution function provide valuable information.In this study,a simulation-based framework to evaluate these probabilistic characteristics in water distribution systems(WDSs)during post-earthquake recovery is developed.The framework first calculates pipeline failure probabilities using seismic fragility models and then generates damage samples through quasi-Monte Carlo simulations with Sobol’s sequence for faster convergence.System performance is assessed using a hydraulic model,and recovery simulations produce time-varying performance curves,where the dynamic importance of unrepaired damage determines repair sequences.Finally,the probabilistic characteristics of seismic performance indicators,resilience index,resilience loss,and recovery time are evaluated.The framework is applied in two benchmark WDSs with different layouts to investigate the probabilistic characteristics of their seismic performance and resilience.Application results show that the cumulative distribution function reveals the variations in resilience indicators for different exceedance probabilities,and there are dramatic differences among the recovery times corresponding to the system performance recovery targets of 80%,90%,and 100%.展开更多
The elementary reactions of propylene polymerization catalyzed by conventional Ziegler-Natta catalysts was proposed according to the comprehensive view and without considering the effect of any impurity in the materia...The elementary reactions of propylene polymerization catalyzed by conventional Ziegler-Natta catalysts was proposed according to the comprehensive view and without considering the effect of any impurity in the material on propylene polymerization. The Monte Carlo simulation technique was employed to investigate the kinetics of propylene polymerization in order to determine the validity of the stationary state assumption and the effects of the polymerization temperature on the polymerization. The simulated total amount of active species, which only increases quickly at the beginning of the polymerization, indicates that the stationary state assumption in the studied system is valid. Moreover, significant effects of polymerization temperature on the polymerization conversion, and the molecular weight and its distribution were also analyzed. The simulated results show that the consumption rate of propylene increases with the increase of polymerization temperature; the maximum values of the number-average degree of polymerization are constant at different polymerization temperatures, however, the peak appears earlier with the higher temperature; as the polymerization temperature increases, the average molecular weight decreases and the molecular weight distribution changes greatly.展开更多
A method for designing an X-ray flatness filter for medical electron linac is developed. It is used in the optimization process in the electron beam radiation system. Monte Carlo simulation method is used and two exam...A method for designing an X-ray flatness filter for medical electron linac is developed. It is used in the optimization process in the electron beam radiation system. Monte Carlo simulation method is used and two examples of real radiation system optimization processes for China-made medical electron linac are provided： 15 MV X- ray system of BJ-20 linac, and 12 MeV electron system of BJ-14. Results are verified by using the traditional method.展开更多
The Monte Carlo simulators with the three valley model and the full band Monte Carlo model are used to explore electron transport in bulk wurtzite gallium nitride (GaN).Comparison of the results based on the two mode...The Monte Carlo simulators with the three valley model and the full band Monte Carlo model are used to explore electron transport in bulk wurtzite gallium nitride (GaN).Comparison of the results based on the two models is made.The results based on both models are basically the same at the lower field region,but exhibit some differences at the higher field region.The electron average energy exhibits obvious difference at the high field region between the two models.This difference further causes several other differences of GaN properties,such as the drift velocity versus field characteristics,the repopulation.Because of the complicated energy band structures at the high energy region for wurtzite GaN,the analytical band structures in the three valley model can not cover all properties of the band structures of wurtzite GaN,so the results based on the full band Monte Carlo model should be more exact.展开更多
To get the probability of long span bridges under the influence of external random factors, the Monte Carlo method using Latin hypercube sampling is applied. Combined with the condition assessment system on Runyang Su...To get the probability of long span bridges under the influence of external random factors, the Monte Carlo method using Latin hypercube sampling is applied. Combined with the condition assessment system on Runyang Suspension Bridge, which is the longest suspension bridge in China, the structural probabilities in normal and damaged situations are calculated with the external random factors considered including environmental temperature, wind load, load of vehicles, etc. The main assessment items contain the maximal vertical displacement of girder, the maximal stress of cables, the maximal horizontal displacement of towers etc. Finally, the probabilities and their cumulative distribution functions are provided. The analysis results can be plotted on line in a clear and vivid way, so the efficiency of assessment is increased and the decision-making of maintenance is more objective and accurate.展开更多
The conformational and dynamic properties of polypropylene （PP） for both pure melts and blends with different chain tacticity were investigated by Monte Carlo simulation of isotactic （iPP）, atactic （aPP） and syn...The conformational and dynamic properties of polypropylene （PP） for both pure melts and blends with different chain tacticity were investigated by Monte Carlo simulation of isotactic （iPP）, atactic （aPP） and syndiotactic （sPP） polypropylenes. The simulation of coarse-grained PP models was performed on a high coordination lattice incorporating short- and long-range intramolecular interactions from the rotational isomeric state （RIS） model and Lennard-Jones （LJ） potential function of propane pairs, respectively. The dynamics of chains in binary PP/PP mixture were investigated with the composition of C150H302 with different chain taciticity. The diffusion rates of PP with different stereochemistry are generally in the order as： iPP 〉 aPP 〉〉 sPP. For PP/PP blends with 50：50 wt% binary mixtures, immiscibility was observed when sPP was introduced into the mixtures. The diffusion rate of iPP and aPP became slower after mixing, while sPP diffuses significantly faster in the binary mixtures. The mobility of PP chains depends on both intramolecular （molecular size and chain stiffness） and intermolecular （chain packing） interactions. The effect of intramolecular contribution is greater than that of intermolecular contribution for iPP and aPP chains in binary mixtures. For sPP chain, intermolecular interaction has greater influence on the dynamics than intramolecular contribution.展开更多
基金supported by National Key R&D Program of China(No.2022YFC2404604)Chongqing Research Institution Performance Incentive Guidance Special Project(No.CSTB2023JXJL-YFX0080)Chongqing Medical Scientific Research Project(Joint project of Chongqing Health Commission and Science and Technology Bureau)(No.2022DBXM005)。
文摘This study aimed to integrate Monte Carlo(MC)simulation with deep learning(DL)-based denoising techniques to achieve fast and accurate prediction of high-quality electronic portal imaging device(EPID)transmission dose(TD)for patientspecific quality assurance(PSQA).A total of 100 lung cases were used to obtain the noisy EPID TD by the ARCHER MC code under four kinds of particle numbers(1×10^(6),1×10^(7),1×10^(8)and 1×10^(9)),and the original EPID TD was denoised by the SUNet neural network.The denoised EPID TD was assessed both qualitatively and quantitatively using the structural similarity(SSIM),peak signal-to-noise ratio(PSNR),and gamma passing rate(GPR)with respect to 1×10^(9)as a reference.The computation times for both the MC simulation and DL-based denoising were recorded.As the number of particles increased,both the quality of the noisy EPID TD and computation time increased significantly(1×10^(6):1.12 s,1×10^(7):1.72 s,1×10^(8):8.62 s,and 1×10^(9):73.89 s).In contrast,the DL-based denoising time remained at 0.13-0.16 s.The denoised EPID TD shows a smoother visual appearance and profile curves,but differences between 1×10^(6)and 1×10^(9)still remain.SSIM improves from 0.61 to 0.95 for 1×10^(6),0.70 to 0.96 for 1×10^(7),and 0.90 to 0.97 for 1×10^(8).PSNR increases by>20%for 1×10^(6)and 1×10^(7),and>10%for 1×10^(8).GPR improves from 48.47%to 89.10%for 1×10^(6),61.04%to 94.35%for 1×10^(7),and 91.88%to 99.55%for 1×10^(8).The method that combines MC simulation with DL-based denoising for EPID TD generation can accelerate TD prediction and maintain high accuracy,offering a promising solution for efficient PSQA.
基金Postgraduate Innovation Top notch Talent Training Project of Hunan Province,Grant/Award Number:CX20220045Scientific Research Project of National University of Defense Technology,Grant/Award Number:22-ZZCX-07+2 种基金New Era Education Quality Project of Anhui Province,Grant/Award Number:2023cxcysj194National Natural Science Foundation of China,Grant/Award Numbers:62201597,62205372,1210456foundation of Hefei Comprehensive National Science Center,Grant/Award Number:KY23C502。
文摘Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.
基金supported by theHubei Provincial Technology Innovation Special Project and the Natural Science Foundation of Hubei Province under Grants 2023BEB024,2024AFC066,respectively.
文摘Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.
基金Under the auspices of the Natural Science Foundation of China(No.32371875,32001249)。
文摘Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.
文摘Gassy soils are distributed in relatively shallow layers the Quaternary deposit in Hangzhou Bay area. The shallow gassy soils significantly affect the construction of underground projects. Proper characterization of spatial distribution of shallow gassy soils is indispensable prior to construction of underground projects in the area. Due to the costly conditions required in the site investigation for gassy soils, only a limited number of gas pressure data can be obtained in engineering practice, which leads to the uncertainty in characterizing spatial distribution of gassy soils. Determining the number of boreholes for investigating gassy soils and their corresponding locations is pivotal to reducing construction risk induced by gassy soils. However, this primarily relies on the engineering experience in the current site investigation practice. This study develops a probabilistic site investigation optimization method for planning investigation schemes (including the number and locations of boreholes) of gassy soils based on the conditional random field and Monte Carlo simulation. The proposed method aims to provide an optimal investigation scheme before the site investigation based on prior knowledge. Finally, the proposed approach is illustrated using a case study.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia,has funded this project under Grant No.(KEP-PhD:72-130-1443).
文摘The Underwater Communication Link(UCL)is a crucial component of Underwater Wireless Optical Communication(UWOC)systems,requiring optimised design to mitigate the high power attenuation inherent in seawater.To ensure the reliability of an optimal UCL design,it is essential to account for the three primary scattering regimes:forward scattering(FSC),backward scattering(BSC),and isotropic scattering(ISC)in seawater channels.This study introduces a new photon-tracking model based on a discrete equation,facilitating Monte Carlo Simulation(MCS)to evaluate how different scattering regimes influence received photon distribution.Three distinct Scattering Regime Contribution Weight(SRCW)probability sets were employed,each representing different UCL operational configurations dominated by specific scattering regimes.The proposed modeling approach enables a comprehensive assessment of the temporal characteristics of received optical pulses,channel loss,and time spread-ultimately defining the optimal UCL design parameters.The key findings of this study include:(1)Enhancing the FSC regime dominance leads to a quasi-light waveguide effect over link spans and small Fields of View(FOV)<25°,significantly improving channel performance in Harbor seawater compared to Coastal seawater.(2)A well-designed UCL with a small FOV(<25°)can minimise channel loss and time spread,ensuring high capacity and efficient performance in both Coastal and Harbor seawaters.(3)When BSC and ISC contributions exceed FSC dominance,the received optical pulse undergoes significant temporal broadening,particularly for larger FOV angles(>25°)and extended link spans.(4)The developed novel MCS-based discrete equation provides a simple yet robust model for simulating photon propagation in both homogeneous and inhomogeneous underwater channels.These insights contribute to developing more efficient and reliable UCL designs with military standards by enhancing UWOC system performance over a longer linkspan for a given limited optical power across various underwater environments.
文摘This study presents the results of a Monte Carlo simulation to compare the statistical power of Siegel-Tukey and Savage tests.The main purpose of the study is to evaluate the statistical power of both tests in scenarios involving Normal,Platykurtic and Skewed distributions over different sample sizes and standard deviation values.In the study,standard deviation ratios were set as 2,3,4,1/2,1/3 and 1/4 and power comparisons were made between small and large sample sizes.For equal sample sizes,small sample sizes of 5,8,10,12,16 and 20 and large sample sizes of 25,50,75 and 100 were used.For different sample sizes,the combinations of(4,16),(8,16),(10,20),(16,4),(16,8)and(20,10)small sample sizes and(10,30),(30,10),(50,75),(50,100),(75,50),(75,100),(100,50)and(100,75)large sample sizes were examined in detail.According to the findings,the power analysis under variance heterogeneity conditions shows that the Siegel-Tukey test has a higher statistical power than the other nonparametric Savage test at small and large sample sizes.In particular,the Siegel-Tukey test was reported to offer higher precision and power under variance heterogeneity,regardless of having equal or different sample sizes.
基金supported by the Central Guidance on Local Science and Technology Development Fund of Hebei Province (No.236Z7601G)the Interdisciplinary Research Program of Natural Science of Hebei University, China (No.DXK202112)。
文摘We present an incorporation of the kinetic Monte Carlo(kMC)simulation with the adjacency matrix(AM)method to investigate structural parameters of randomly branched polymers.The AMs of branched polymers are obtained by the growth generation model,which is employed in the kMC simulation based on Gillespie’s algorithm.Of particular interest is the effect of the branched units on the relevant structural parameters.As an application in randomly hyperbranched polymers formed by AB_(2)type of monomers,average maximum span length,average length of sub-chains,number distribution of branched units,and meansquare radius of gyration are calculated.It is shown that the branched units can give rise to a significant effect on these physical quantities,whereby such a structure-property relationship can be employed to optimize reaction conditions and implement material designs.
基金supported by the National Key Research and Development Program of China(Nos.2022YFA1204203 and 2022YFA1204201)Opening Fund of the State Key Laboratory of Optoelectronic Materials and Technologies at Sun Yat-sen University(No.OEMT-2023-KF-01)+1 种基金National Natural Science Foundation of China(Nos.61971463,82272131,and 82202960)the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515010537).
文摘Flat-panel X-ray sources(FPXSs)have many advantages in terms of compactness and low-dose imaging,enhancing their capability for novel X-ray applications.Experimental analysis of the X-ray characteristics and optimizing the anode panel of an FPXS are time-consuming,expensive,and sometimes impractical.In this study,a FPXS was prepared using a ZnO nanowire cold cathode and a molybdenum film anode target.Monte Carlo(MC)simulations were utilized to optimize the anode panel and obtain the average fluence,average energy,and spatial distribution of the X-rays for the ZnO nanowire FPXS.The accuracy of the MC simulations was verified by comparing the measured and simulated energy spectra.Optimization of the anode target considers the material,thickness,and morphology,whereas optimization of the substrate focuses on the material and thickness.The results show that the difference between the positions of the K-shell peaks in the measured and simulated energy spectra is within 0.26 keV.At the acceleration voltages of 30 kV,60 kV,and 90 kV,the optimal thicknesses of the tungsten array anode were 0.65μm,2.45μm,and 5μm,respectively,while the molybdenum array anode has the optimal thicknesses of 1.45μm,5.25μm,and 24μm,respectively.The microsemi-ellipsoidal anode with a recessed design showed a 5%increase in the transmitted X-ray fluence compared with the film target.The sapphire substrate with a thickness of 0.78 mm exhibits a mechanical strength comparable to that of a glass substrate with a thickness of 3 mm,implying that the former can increase the average X-ray fluence by reducing the filtration of X-rays.The findings of this study provide valuable guidance for the fabrication and optimization of the ZnO nanowire FPXS.
基金the funding from the Ger-man Research Foundation(DFG)-BE 5360/1-1 and ThyssenKrupp Europe.
文摘A microscopic understanding of the complex solute-defect interaction is pivotal for optimizing the alloy’s macroscopic mechanical properties.Simulating solute segregation in a plastically deformed crystalline system at atomic resolution remains challenging.The objective is to efficiently model and predict a phys-ically informed segregated solute distribution rather than simulating a series of diffusion kinetics.To ad-dress this objective,we coupled molecular dynamics(MD)and Monte Carlo(MC)methods using a novel method based on virtual atoms technique.We applied our MD-MC coupling approach to model off-lattice carbon(C)solute segregation in nanoindented Fe-C samples containing complex dislocation networks.Our coupling framework yielded the final configuration through efficient parallelization and localized en-ergy computations,showing C Cottrell atmospheres near dislocations.Different initial C concentrations resulted in a consistent trend of C atoms migrating from less crystalline distortion to high crystalline distortion regions.Besides unraveling the strong spatial correlation between local C concentration and defect regions,our results revealed two crucial aspects of solute segregation preferences:(1)defect ener-getics hierarchy and(2)tensile strain fields near dislocations.The proposed approach is generic and can be applied to other material systems as well.
基金National Key R&D Program of China under Grant No.2022YFC3003600National Natural Science Foundation of China(NSFC)under Grant No.51978023。
文摘Due to uncertainties in seismic pipeline damage and post-earthquake recovery processes,probabilistic characteristics such as mean value,standard deviation,probability density function,and cumulative distribution function provide valuable information.In this study,a simulation-based framework to evaluate these probabilistic characteristics in water distribution systems(WDSs)during post-earthquake recovery is developed.The framework first calculates pipeline failure probabilities using seismic fragility models and then generates damage samples through quasi-Monte Carlo simulations with Sobol’s sequence for faster convergence.System performance is assessed using a hydraulic model,and recovery simulations produce time-varying performance curves,where the dynamic importance of unrepaired damage determines repair sequences.Finally,the probabilistic characteristics of seismic performance indicators,resilience index,resilience loss,and recovery time are evaluated.The framework is applied in two benchmark WDSs with different layouts to investigate the probabilistic characteristics of their seismic performance and resilience.Application results show that the cumulative distribution function reveals the variations in resilience indicators for different exceedance probabilities,and there are dramatic differences among the recovery times corresponding to the system performance recovery targets of 80%,90%,and 100%.
基金The National Natural Science Foundation of China(No.20406016)the Project of Fujian Petrochemical Company of SIN-OPEC (No.MS/FJ-08-JS-15-2005-01).
文摘The elementary reactions of propylene polymerization catalyzed by conventional Ziegler-Natta catalysts was proposed according to the comprehensive view and without considering the effect of any impurity in the material on propylene polymerization. The Monte Carlo simulation technique was employed to investigate the kinetics of propylene polymerization in order to determine the validity of the stationary state assumption and the effects of the polymerization temperature on the polymerization. The simulated total amount of active species, which only increases quickly at the beginning of the polymerization, indicates that the stationary state assumption in the studied system is valid. Moreover, significant effects of polymerization temperature on the polymerization conversion, and the molecular weight and its distribution were also analyzed. The simulated results show that the consumption rate of propylene increases with the increase of polymerization temperature; the maximum values of the number-average degree of polymerization are constant at different polymerization temperatures, however, the peak appears earlier with the higher temperature; as the polymerization temperature increases, the average molecular weight decreases and the molecular weight distribution changes greatly.
基金Supported by the National Natural Science Foundation of China (60672104,10675013)the Na-tional Basic Research Program of China ("973"Program)(2006CB705705)+1 种基金the 10th Five-Year Plan of the Ministry of Science and Technology of China(2001BA706B-05)the Joint Research Foundation of Beijing Municipal Commissionof Education~~
文摘A method for designing an X-ray flatness filter for medical electron linac is developed. It is used in the optimization process in the electron beam radiation system. Monte Carlo simulation method is used and two examples of real radiation system optimization processes for China-made medical electron linac are provided： 15 MV X- ray system of BJ-20 linac, and 12 MeV electron system of BJ-14. Results are verified by using the traditional method.
文摘The Monte Carlo simulators with the three valley model and the full band Monte Carlo model are used to explore electron transport in bulk wurtzite gallium nitride (GaN).Comparison of the results based on the two models is made.The results based on both models are basically the same at the lower field region,but exhibit some differences at the higher field region.The electron average energy exhibits obvious difference at the high field region between the two models.This difference further causes several other differences of GaN properties,such as the drift velocity versus field characteristics,the repopulation.Because of the complicated energy band structures at the high energy region for wurtzite GaN,the analytical band structures in the three valley model can not cover all properties of the band structures of wurtzite GaN,so the results based on the full band Monte Carlo model should be more exact.
文摘To get the probability of long span bridges under the influence of external random factors, the Monte Carlo method using Latin hypercube sampling is applied. Combined with the condition assessment system on Runyang Suspension Bridge, which is the longest suspension bridge in China, the structural probabilities in normal and damaged situations are calculated with the external random factors considered including environmental temperature, wind load, load of vehicles, etc. The main assessment items contain the maximal vertical displacement of girder, the maximal stress of cables, the maximal horizontal displacement of towers etc. Finally, the probabilities and their cumulative distribution functions are provided. The analysis results can be plotted on line in a clear and vivid way, so the efficiency of assessment is increased and the decision-making of maintenance is more objective and accurate.
基金financially supported by the Strategic Scholarships Fellowship Research Network,Commission on Higher Education,Ministry of Education Thailand
文摘The conformational and dynamic properties of polypropylene （PP） for both pure melts and blends with different chain tacticity were investigated by Monte Carlo simulation of isotactic （iPP）, atactic （aPP） and syndiotactic （sPP） polypropylenes. The simulation of coarse-grained PP models was performed on a high coordination lattice incorporating short- and long-range intramolecular interactions from the rotational isomeric state （RIS） model and Lennard-Jones （LJ） potential function of propane pairs, respectively. The dynamics of chains in binary PP/PP mixture were investigated with the composition of C150H302 with different chain taciticity. The diffusion rates of PP with different stereochemistry are generally in the order as： iPP 〉 aPP 〉〉 sPP. For PP/PP blends with 50：50 wt% binary mixtures, immiscibility was observed when sPP was introduced into the mixtures. The diffusion rate of iPP and aPP became slower after mixing, while sPP diffuses significantly faster in the binary mixtures. The mobility of PP chains depends on both intramolecular （molecular size and chain stiffness） and intermolecular （chain packing） interactions. The effect of intramolecular contribution is greater than that of intermolecular contribution for iPP and aPP chains in binary mixtures. For sPP chain, intermolecular interaction has greater influence on the dynamics than intramolecular contribution.
