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
Although traditional gamma-gamma density(GGD)logging technology is widely utilized,its potential environmental risks have prompted the development of more environmentally friendly neutron-gamma density(NGD)logging tec...Although traditional gamma-gamma density(GGD)logging technology is widely utilized,its potential environmental risks have prompted the development of more environmentally friendly neutron-gamma density(NGD)logging technology.However,NGD measurements are influenced by both neutron and gamma radiations.In the logging environment,variations in the formation composition indicate different elemental compositions,which affect the neutron-gamma reaction cross-sections and gamma generation.Compared to traditional gamma sources such as Cs-137,these changes significantly affect the generation and transport of neutron-induced inelastic gamma rays and hinder accurate measurements.To address this,a novel method is proposed that incorporates the mass attenuation coefficient function to account for the effects of various lithologies and pore contents on gamma-ray attenuation,thereby achieving more accurate density measurements by clarifying the transport processes of inelastic gamma rays with varying energies and spatial distributions in varied logging environments.The proposed method avoids the complex correction of neutron transport and is verified through Monte Carlo simulations for its applicability across various lithologies and pore contents,demonstrating absolute density errors that are less than 0.02 g/cm^(3)in clean formations and indicating good accuracy.This study clarifies the NGD mechanism and provides theoretical guidance for the application of NGD logging methods.Further studies will be conducted on extreme environmental conditions and tool calibration.展开更多
Statistical distribution of residual fatigue life(RFL)of railway axles under given loading was computed using the Monte Carlo method by considering random variation of the selected input parameters.Experimental data f...Statistical distribution of residual fatigue life(RFL)of railway axles under given loading was computed using the Monte Carlo method by considering random variation of the selected input parameters.Experimental data for the EA4T railway axle steel,the loading spectrum,the press fit loading and the residual stress induced by surface hardening were considered in the crack propagation simulations.Usually,the material properties measured by tensile tests are considered to be the most informative source of material data.Under fatigue loading,however,the crack growth rates near the threshold are the most critical data.Two important influencing factors on these crack growth rates are presented:first,the air humidity and,second,the near-surface residual stress.The typical variation of these parameters in operation may change the RFL by one or two orders of magnitude.Experimentally obtained crack growth thresholds and residual stress profiles are highly affected by the used methodology.Therefore,the obtained input data may be located anywhere within a large scatter,while the experimenters are completely unaware of it.This can lead to dangerously non-conservative situations,e.g.when the thresholds are measured in a laboratory under humid air conditions and then applied to predictions of RFLs of axles operated in winter in low air humidity.This is significant for the topic of inspection interval optimisation.The results of experiments done on real 1:1 railway axles were close to the most frequent value found in the histogram of the numerically computed RFLs.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
陶瓷在长时高温和高应力下工作时会发生蠕变,蠕变损伤累积将最终导致失效发生。蠕变损伤的演化与陶瓷的微结构有十分密切的关系,建立陶瓷材料的微观有限元模型有助于更深入地了解这一关系。以氮化硅陶瓷为研究对象,提出一种基于动力学...陶瓷在长时高温和高应力下工作时会发生蠕变,蠕变损伤累积将最终导致失效发生。蠕变损伤的演化与陶瓷的微结构有十分密切的关系,建立陶瓷材料的微观有限元模型有助于更深入地了解这一关系。以氮化硅陶瓷为研究对象,提出一种基于动力学的三维晶体沉积数值模型,结合Monte Carlo Potts结晶生长模型对氮化硅陶瓷的烧结过程进行模拟,力求还原氮化硅陶瓷的动态生长过程以及结晶后晶体大小、形状、取向分布以及空洞的大小、形状、分布等微观结构特征。基于该模拟生成的几何边界描述自动生成Python脚本,在有限元软件中完成建模。利用该有限元模型对氮化硅陶瓷的统计弹性常数进行验证,计算结果与试验数据对比,相对误差约为4.5%,吻合良好。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supported by the National Natural Science Foundation of China(U23B20151 and 52171253).
文摘Although traditional gamma-gamma density(GGD)logging technology is widely utilized,its potential environmental risks have prompted the development of more environmentally friendly neutron-gamma density(NGD)logging technology.However,NGD measurements are influenced by both neutron and gamma radiations.In the logging environment,variations in the formation composition indicate different elemental compositions,which affect the neutron-gamma reaction cross-sections and gamma generation.Compared to traditional gamma sources such as Cs-137,these changes significantly affect the generation and transport of neutron-induced inelastic gamma rays and hinder accurate measurements.To address this,a novel method is proposed that incorporates the mass attenuation coefficient function to account for the effects of various lithologies and pore contents on gamma-ray attenuation,thereby achieving more accurate density measurements by clarifying the transport processes of inelastic gamma rays with varying energies and spatial distributions in varied logging environments.The proposed method avoids the complex correction of neutron transport and is verified through Monte Carlo simulations for its applicability across various lithologies and pore contents,demonstrating absolute density errors that are less than 0.02 g/cm^(3)in clean formations and indicating good accuracy.This study clarifies the NGD mechanism and provides theoretical guidance for the application of NGD logging methods.Further studies will be conducted on extreme environmental conditions and tool calibration.
基金financially supported by the Czech Science Foundation in the frame of the project No.22-28283Sby the Technology Agency of the Czech Republic through the project No.CK03000060.
文摘Statistical distribution of residual fatigue life(RFL)of railway axles under given loading was computed using the Monte Carlo method by considering random variation of the selected input parameters.Experimental data for the EA4T railway axle steel,the loading spectrum,the press fit loading and the residual stress induced by surface hardening were considered in the crack propagation simulations.Usually,the material properties measured by tensile tests are considered to be the most informative source of material data.Under fatigue loading,however,the crack growth rates near the threshold are the most critical data.Two important influencing factors on these crack growth rates are presented:first,the air humidity and,second,the near-surface residual stress.The typical variation of these parameters in operation may change the RFL by one or two orders of magnitude.Experimentally obtained crack growth thresholds and residual stress profiles are highly affected by the used methodology.Therefore,the obtained input data may be located anywhere within a large scatter,while the experimenters are completely unaware of it.This can lead to dangerously non-conservative situations,e.g.when the thresholds are measured in a laboratory under humid air conditions and then applied to predictions of RFLs of axles operated in winter in low air humidity.This is significant for the topic of inspection interval optimisation.The results of experiments done on real 1:1 railway axles were close to the most frequent value found in the histogram of the numerically computed RFLs.
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.
文摘陶瓷在长时高温和高应力下工作时会发生蠕变,蠕变损伤累积将最终导致失效发生。蠕变损伤的演化与陶瓷的微结构有十分密切的关系,建立陶瓷材料的微观有限元模型有助于更深入地了解这一关系。以氮化硅陶瓷为研究对象,提出一种基于动力学的三维晶体沉积数值模型,结合Monte Carlo Potts结晶生长模型对氮化硅陶瓷的烧结过程进行模拟,力求还原氮化硅陶瓷的动态生长过程以及结晶后晶体大小、形状、取向分布以及空洞的大小、形状、分布等微观结构特征。基于该模拟生成的几何边界描述自动生成Python脚本,在有限元软件中完成建模。利用该有限元模型对氮化硅陶瓷的统计弹性常数进行验证,计算结果与试验数据对比,相对误差约为4.5%,吻合良好。
