One of the most important issues in inertial confinement fusion (ICF) is to study the uniformity of the radiation field around the implosion pellet containing fuel.To this end,a numerical method linking Monte Carlo wi...One of the most important issues in inertial confinement fusion (ICF) is to study the uniformity of the radiation field around the implosion pellet containing fuel.To this end,a numerical method linking Monte Carlo with iteration method is presented for calculating the radiation transfer problems in a cavity.The detail of the calculation scheme is described and some numerical examples are also given.展开更多
基金Project supported in part by the National Natural Science Foundation of China and the National High-Tech ICF Committee in China.
文摘One of the most important issues in inertial confinement fusion (ICF) is to study the uniformity of the radiation field around the implosion pellet containing fuel.To this end,a numerical method linking Monte Carlo with iteration method is presented for calculating the radiation transfer problems in a cavity.The detail of the calculation scheme is described and some numerical examples are also given.
文摘孤立森林是一种高效的异常检测算法,但其轴平行线隔离的方式难以检测到非线性可分离数据中的异常。针对此问题,提出了一种利用神经网络随机投影融合孤立森林(isolation Forest, iForest)的半监督离群点检测方法——稀疏深度随机映射正向隔离林(Sparse deep Random Projection Positive isolation Forest, SRP-PiF)。算法通过神经网络进行非线性随机映射,将不同大小的子空间进行非线性划分,为分类提供有效的低维特征属性;设置超参数稀疏率来调整神经元中的权重,提高运算效率,最后结合半监督学习方式进行训练和预测。所提方法在真实世界10个数据集上进行验证,结果表明:相比于同类异常检测算法,SRP-PiF在异常检测性能指标AUC-ROC中有明显提升;在样本数量较多的数据集下,该算法识别精确度比孤立森林算法和经验累积分布函数的无监督离群值检测方法(ECOD)高出3%~20%。
