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
Computational phantoms play an essential role in radiation dosimetry and health physics.Although mesh-type phantoms offer a high resolution and adjustability,their use in dose calculations is limited by their slow com...Computational phantoms play an essential role in radiation dosimetry and health physics.Although mesh-type phantoms offer a high resolution and adjustability,their use in dose calculations is limited by their slow computational speed.Progress in heterogeneous computing has allowed for substantial acceleration in the computation of mesh-type phantoms by utilizing hardware accelerators.In this study,a GPU-accelerated Monte Carlo method was developed to expedite the dose calculation for mesh-type computational phantoms.This involved designing and implementing the entire procedural flow of a GPUaccelerated Monte Carlo program.We employed acceleration structures to process the mesh-type phantom,optimized the traversal methodology,and achieved a flattened structure to overcome the limitations of GPU stack depths.Particle transport methods were realized within the mesh-type phantom,encompassing particle location and intersection techniques.In response to typical external irradiation scenarios,we utilized Geant4 along with the GPU program and its CPU serial code for dose calculations,assessing both computational accuracy and efficiency.In comparison with the benchmark simulated using Geant4 on the CPU using one thread,the relative differences in the organ dose calculated by the GPU program predominantly lay within a margin of 5%,whereas the computational time was reduced by a factor ranging from 120 to 2700.To the best of our knowledge,this study achieved a GPU-accelerated dose calculation method for mesh-type phantoms for the first time,reducing the computational time from hours to seconds per simulation of ten million particles and offering a swift and precise Monte Carlo method for dose calculation in mesh-type computational phantoms.展开更多
With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyz...With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyze the charging load characteristics of six battery electric vehicle categories in Hebei Province,leveraging multi-source probabilistic distribution data under typical operational scenarios.The findings reveal that electric vehicle charging loads are primarily concentrated during midday and nighttime periods,with significant load fluctuations exerting substantial pressure on the grid.In response,this paper proposes strategic interventions including optimized charging infrastructure planning,time-of-use electricity pricing mechanisms,and smart charging technologies to balance grid loads.The results provide a theoretical foundation for electric vehicle load forecasting,smart grid dispatching,and vehicle-grid integration,thereby enhancing grid operational efficiency and sustainability.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
针对多部位损伤(Multiple Site Damage,MSD)结构安全性评估问题,通过Monte-Carlo方法对MSD结构的失效概率进行预测和分析。首先,基于多孔铝板的多裂纹萌生试验,得出裂纹萌生寿命服从对数正态分布,为多裂纹萌生分析提供支持;通过多孔铝...针对多部位损伤(Multiple Site Damage,MSD)结构安全性评估问题,通过Monte-Carlo方法对MSD结构的失效概率进行预测和分析。首先,基于多孔铝板的多裂纹萌生试验,得出裂纹萌生寿命服从对数正态分布,为多裂纹萌生分析提供支持;通过多孔铝板的剩余强度试验得到铆钉孔直径、铆钉孔间距和裂纹萌生位置对结构剩余强度均有一定影响。其次,通过对裂纹萌生寿命分布进行随机抽样生成初始裂纹并使用组合法结合Paris公式,实现多裂纹随机扩展的模拟;在试验数据基础上,对传统的Irwin塑性区连通准则进行改进,发现改进的Irwin塑性区连通准则在孔间距大于10mm时的误差大大降低,并结合净截面屈服准则以获得更好的剩余强度预测结果;将随机性的裂纹萌生和扩展过程与确定性的剩余强度预测方法相结合,建立基于Monte-Carlo方法的MSD结构的失效概率预测模型。最后,通过算例分析,该模型能够得到MSD结构的失效概率曲线,实现结构安全性评估。结果表明MSD结构的失效概率会在短时间内迅速增加,需要在裂纹萌生寿命附近进行限制。展开更多
In response to the demand for rapid geometric modeling in Monte Carlo radiation transportation calculations for large-scale and complex geometric scenes,functional improvements,and algorithm optimizations were perform...In response to the demand for rapid geometric modeling in Monte Carlo radiation transportation calculations for large-scale and complex geometric scenes,functional improvements,and algorithm optimizations were performed using CAD-to-Monte Carlo geometry conversion(CMGC)code.Boundary representation(BRep)to constructive solid geometry(CSG)conversion and visual CSG modeling were combined to address the problem of non-convertible geometries such as spline surfaces.The splitting surface assessment method in BRep-to-CSG conversion was optimized to reduce the number of Boolean operations using an Open Cascade.This,in turn,reduced the probability of CMGC conversion failure.The auxiliary surface generation algorithm was optimized to prevent the generation of redundant auxiliary surfaces that cause an excessive decomposition of CAD geometry solids.These optimizations enhanced the usability and stability of the CMGC model conversion.CMGC was applied successfully to the JMCT transportation calculations for the conceptual designs of five China Fusion Engineering Test Reactor(CFETR)blankets.The rapid replacement of different blanket schemes was achieved based on the baseline CFETR model.The geometric solid number of blankets ranged from hundreds to tens of thousands.The correctness of the converted CFETR models using CMGC was verified through comparisons with the MCNP calculation results.The CMGC supported radiation field evaluations for a large urban scene and detailed ship scene.This enabled the rapid conversion of CAD models with thousands of geometric solids into Monte Carlo CSG models.An analysis of the JMCT transportation simulation results further demonstrated the accuracy and effectiveness of the CMGC.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supported by the National Natural Science Foundation of China(Nos.U2167209 and 12375312)Open-end Fund Projects of China Institute for Radiation Protection Scientific Research Platform(CIRP-HYYFZH-2023ZD001).
文摘Computational phantoms play an essential role in radiation dosimetry and health physics.Although mesh-type phantoms offer a high resolution and adjustability,their use in dose calculations is limited by their slow computational speed.Progress in heterogeneous computing has allowed for substantial acceleration in the computation of mesh-type phantoms by utilizing hardware accelerators.In this study,a GPU-accelerated Monte Carlo method was developed to expedite the dose calculation for mesh-type computational phantoms.This involved designing and implementing the entire procedural flow of a GPUaccelerated Monte Carlo program.We employed acceleration structures to process the mesh-type phantom,optimized the traversal methodology,and achieved a flattened structure to overcome the limitations of GPU stack depths.Particle transport methods were realized within the mesh-type phantom,encompassing particle location and intersection techniques.In response to typical external irradiation scenarios,we utilized Geant4 along with the GPU program and its CPU serial code for dose calculations,assessing both computational accuracy and efficiency.In comparison with the benchmark simulated using Geant4 on the CPU using one thread,the relative differences in the organ dose calculated by the GPU program predominantly lay within a margin of 5%,whereas the computational time was reduced by a factor ranging from 120 to 2700.To the best of our knowledge,this study achieved a GPU-accelerated dose calculation method for mesh-type phantoms for the first time,reducing the computational time from hours to seconds per simulation of ten million particles and offering a swift and precise Monte Carlo method for dose calculation in mesh-type computational phantoms.
基金funded by Humanities and Social Sciences of Ministry of Education Planning Fund of China,grant number 21YJA790009National Natural Science Foundation of China,grant number 72140001.
文摘With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyze the charging load characteristics of six battery electric vehicle categories in Hebei Province,leveraging multi-source probabilistic distribution data under typical operational scenarios.The findings reveal that electric vehicle charging loads are primarily concentrated during midday and nighttime periods,with significant load fluctuations exerting substantial pressure on the grid.In response,this paper proposes strategic interventions including optimized charging infrastructure planning,time-of-use electricity pricing mechanisms,and smart charging technologies to balance grid loads.The results provide a theoretical foundation for electric vehicle load forecasting,smart grid dispatching,and vehicle-grid integration,thereby enhancing grid operational efficiency and sustainability.
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.
文摘针对多部位损伤(Multiple Site Damage,MSD)结构安全性评估问题,通过Monte-Carlo方法对MSD结构的失效概率进行预测和分析。首先,基于多孔铝板的多裂纹萌生试验,得出裂纹萌生寿命服从对数正态分布,为多裂纹萌生分析提供支持;通过多孔铝板的剩余强度试验得到铆钉孔直径、铆钉孔间距和裂纹萌生位置对结构剩余强度均有一定影响。其次,通过对裂纹萌生寿命分布进行随机抽样生成初始裂纹并使用组合法结合Paris公式,实现多裂纹随机扩展的模拟;在试验数据基础上,对传统的Irwin塑性区连通准则进行改进,发现改进的Irwin塑性区连通准则在孔间距大于10mm时的误差大大降低,并结合净截面屈服准则以获得更好的剩余强度预测结果;将随机性的裂纹萌生和扩展过程与确定性的剩余强度预测方法相结合,建立基于Monte-Carlo方法的MSD结构的失效概率预测模型。最后,通过算例分析,该模型能够得到MSD结构的失效概率曲线,实现结构安全性评估。结果表明MSD结构的失效概率会在短时间内迅速增加,需要在裂纹萌生寿命附近进行限制。
基金supported by the National Natural Science Foundation of China(No.U23B2067)Innovation Program of CAEP(No.CX20210045)。
文摘In response to the demand for rapid geometric modeling in Monte Carlo radiation transportation calculations for large-scale and complex geometric scenes,functional improvements,and algorithm optimizations were performed using CAD-to-Monte Carlo geometry conversion(CMGC)code.Boundary representation(BRep)to constructive solid geometry(CSG)conversion and visual CSG modeling were combined to address the problem of non-convertible geometries such as spline surfaces.The splitting surface assessment method in BRep-to-CSG conversion was optimized to reduce the number of Boolean operations using an Open Cascade.This,in turn,reduced the probability of CMGC conversion failure.The auxiliary surface generation algorithm was optimized to prevent the generation of redundant auxiliary surfaces that cause an excessive decomposition of CAD geometry solids.These optimizations enhanced the usability and stability of the CMGC model conversion.CMGC was applied successfully to the JMCT transportation calculations for the conceptual designs of five China Fusion Engineering Test Reactor(CFETR)blankets.The rapid replacement of different blanket schemes was achieved based on the baseline CFETR model.The geometric solid number of blankets ranged from hundreds to tens of thousands.The correctness of the converted CFETR models using CMGC was verified through comparisons with the MCNP calculation results.The CMGC supported radiation field evaluations for a large urban scene and detailed ship scene.This enabled the rapid conversion of CAD models with thousands of geometric solids into Monte Carlo CSG models.An analysis of the JMCT transportation simulation results further demonstrated the accuracy and effectiveness of the CMGC.
