To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen s...To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen set of models accurately represents the‘true’distribution of considered observables.Furthermore,the models are chosen globally,indicating their applicability across the entire energy range of interest.However,this approach overlooks uncertainties inherent in the models themselves.In this work,we propose that instead of selecting globally a winning model set and proceeding with it as if it was the‘true’model set,we,instead,take a weighted average over multiple models within a Bayesian model averaging(BMA)framework,each weighted by its posterior probability.The method involves executing a set of TALYS calculations by randomly varying multiple nuclear physics models and their parameters to yield a vector of calculated observables.Next,computed likelihood function values at each incident energy point were then combined with the prior distributions to obtain updated posterior distributions for selected cross sections and the elastic angular distributions.As the cross sections and elastic angular distributions were updated locally on a per-energy-point basis,the approach typically results in discontinuities or“kinks”in the cross section curves,and these were addressed using spline interpolation.The proposed BMA method was applied to the evaluation of proton-induced reactions on ^(58)Ni between 1 and 100 MeV.The results demonstrated a favorable comparison with experimental data as well as with the TENDL-2023 evaluation.展开更多
目前基于多尺度分解的图像融合算法存在以下问题:1)多尺度分解时,图像边缘被平滑;2)融合结果中红外显著区域的对比度降低;3)小尺度细节受到抑制,在融合图像中显示不清晰。为解决上述问题,本文提出了一种基于BMA(Bayesian model averagi...目前基于多尺度分解的图像融合算法存在以下问题:1)多尺度分解时,图像边缘被平滑;2)融合结果中红外显著区域的对比度降低;3)小尺度细节受到抑制,在融合图像中显示不清晰。为解决上述问题,本文提出了一种基于BMA(Bayesian model averaging)滤波器和边缘的图像融合算法。首先,利用BMA滤波器分别对红外与可见光图像进行多尺度分解;其次,分别利用显著性提取和边缘权值映射算法,计算各基层和细节层的融合权值矩阵;最后通过图像重构获得融合图像。实验证明,该融合算法优于传统的图像融合算法。展开更多
基金funding from the Paul ScherrerInstitute,Switzerland through the NES/GFA-ABE Cross Project。
文摘To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen set of models accurately represents the‘true’distribution of considered observables.Furthermore,the models are chosen globally,indicating their applicability across the entire energy range of interest.However,this approach overlooks uncertainties inherent in the models themselves.In this work,we propose that instead of selecting globally a winning model set and proceeding with it as if it was the‘true’model set,we,instead,take a weighted average over multiple models within a Bayesian model averaging(BMA)framework,each weighted by its posterior probability.The method involves executing a set of TALYS calculations by randomly varying multiple nuclear physics models and their parameters to yield a vector of calculated observables.Next,computed likelihood function values at each incident energy point were then combined with the prior distributions to obtain updated posterior distributions for selected cross sections and the elastic angular distributions.As the cross sections and elastic angular distributions were updated locally on a per-energy-point basis,the approach typically results in discontinuities or“kinks”in the cross section curves,and these were addressed using spline interpolation.The proposed BMA method was applied to the evaluation of proton-induced reactions on ^(58)Ni between 1 and 100 MeV.The results demonstrated a favorable comparison with experimental data as well as with the TENDL-2023 evaluation.
文摘目前基于多尺度分解的图像融合算法存在以下问题:1)多尺度分解时,图像边缘被平滑;2)融合结果中红外显著区域的对比度降低;3)小尺度细节受到抑制,在融合图像中显示不清晰。为解决上述问题,本文提出了一种基于BMA(Bayesian model averaging)滤波器和边缘的图像融合算法。首先,利用BMA滤波器分别对红外与可见光图像进行多尺度分解;其次,分别利用显著性提取和边缘权值映射算法,计算各基层和细节层的融合权值矩阵;最后通过图像重构获得融合图像。实验证明,该融合算法优于传统的图像融合算法。
