卷积神经网络模型在儿科疾病预测中的应用

目的:针对儿童看病需求量大导致的儿科诊疗服务效率和准确率偏低等问题,利用自然语言处理和深度学习技术,从儿科历史病历数据中自动\"学习\"专家医生诊断模式,形成智能辅助诊断模型,从而对新的儿科病历数据输出疾病诊断决策。结果:基于深度卷积神经网络的七分类疾病智能诊断模型的正确率为84.26%,F1-score为84.33%,基本达到可投入实际应用的级别。结论:智能诊断决策作为预诊信息提供给医生进行确诊参考,对提升医生诊断速度效果明显。国家自然科学基金面上项目(编号:71571056);;福建省自然科学基金面上项目(编号:2012J01274)~

MLICP-CNN:基于CNN与ICP的多标记胸片置信诊断模型

针对胸片的多标记预测集缺少可校准性的缺陷,提出一种基于卷积神经网络(Convolutional Neural Networks,CNN)与归纳一致性预测器(Inductive Conformal Prediction,ICP)的多标记胸片置信诊断模型MLICP-CNN。该模型将学习数据划分为训练集和校准集,通过使用CNN从训练集中学习出规则D。基于规则D和校准集使用算法随机性对被测数据进行置信预测,即为每个被测数据提供附带置信度的多标记预测集。在对Chest X-ray14胸片数据集的实验结果表明,该模型在临床常用的95%置信度下,模型准确率为95%,体现了置信度评估的恰好可校准性。在CNN架构为Resenet50并采用LS-MLICP为奇异值映射函数下,模型性能最好,其确定预测率为96.43%,理想预测率为92.31%。另外,CNN架构对预测效率的影响程度远远小于奇异值映射函数。国家自然科学基金面上项目(61673186);;\n福建省自然科学基金面上项目(2012J01274

Stochastic dynamical model for space-time energy spectra in turbulent shear flows

Space-time energy spectra describe the distribution of energy density over space and timescales, which are fundamental to studying dynamic coupling at spatial and temporal scales and turbulence-generated noise. The present paper develops a dynamic autoregressive (DAR) random forcing model for space-time energy spectra in turbulent shear flows. This model includes the two essential mechanisms of statistical decorrelation: the convection proposed by Taylor's model and the random sweeping proposed by the Kraichnan-Tennekes model. The new development is that DAR random forcing is introduced to represent the random sweeping effect. The resulting model can correctly reproduce the convection velocity and spectral bandwidths, while a white-in-time random forcing model makes erroneous predictions on spectral bandwidths. The DAR model is further combined with linear stochastic estimation (LSE) to reconstruct the near-wall velocity fluctuations of the desired space-time energy spectra. Direct numerical simulation of turbulent channel flows is used to validate the DAR model and evaluate the Werner-Wengle wall model and the LSE approach. Both the wall model and LSE incorrectly estimate the spectral bandwidths

Local modulated wave model for the reconstruction of space-time energy spectra in turbulent flows

A statistical model is developed to reconstruct space-time energy spectra in turbulent flows from a non-extensive dataset comprising a time series of velocity fluctuations at a finite number of measurement points. This model is based on a higher approximation of energetic flow structures and developed by using local modulated waves. As a result, it can correctly predict the mean wavenumbers and spectral bandwidths. In contrast, Taylor's frozen-flow hypothesis incorrectly predicts the spectral bandwidths to be zero, and the local wavenumber model significantly under-predicts the spectral bandwidths. An analytical example is formulated to illustrate the present model, and datasets from direct numerical simulations of turbulent channel flows are used to validate this model. The present statistical model is also discussed in terms of the dominating processes of temporal decorrelation in turbulent flows

Space-time energy spectra in turbulent shear flows

This article presents a review and perspectives on the models for space-time energy spectra in turbulent shear flows. The Taylor, Kraichnan-Tennekes, and elliptic approximation (EA) models are re-examined in terms of the picture of turbulent passage, which is proposed by Taylor's frozen-flow hypothesis and the Kraichnan-Tennekes random sweeping hypothesis; the stochastic dynamic models for reproduction of space-time energy spectra, such as dynamic autoregression model, are discussed; and the statistical models for reconstruction of space-time energy spectra from incomplete data sets in experimental measurements are revisited. We present three distinct approaches of successive approximation for developing the models of space-time energy spectra and use the conditional moments of energy distribution to characterize space-time energy spectra, such as propagation velocities and spectral bandwidths

Composition of resolvents enhanced by random sweeping for large scale structures in turbulent channel flows

Composite sweeping enhanced resolvents, referred to as the R s(2) model, are proposed to predict the space time statistics of large scale structures in turbulent channel flows. This model incorporates two key mechanisms: (i) eddy damping is introduced to represent random sweeping decorrelation caused by nonlinear forcing, leading to a sweeping enhanced resolvent R s; and (ii) the sweeping enhanced resolvent R s is composited into its iterations R s(2) to yield non zero Taylor time microscales. The resulting R s(2) model can correctly predict the frequency spectra and two point cross spectra of large scale structures. This model is compared numerically with eddy viscosity enhanced resolvent models. The latter are designed to represent energy transfer instead for time decorrelation, and thus underpredict the characteristic decay time scales. The R s(2) model correctly yields the characteristic decay time scales in turbulent channel flows

Independent component analysis of streamwise velocity fluctuations in turbulent channel flows

Independent component analysis (ICA) is used to study the multiscale localised modes of streamwise velocity fluctuations in turbulent channel flows. ICA aims to decompose signals into independent modes, which may induce spatially localised objects. The height and size are defined to quantify the spatial position and extension of these ICA modes, respectively. In contrast to spatially extended proper orthogonal decomposition (POD) modes, ICA modes are typically localised in space, and the energy of some modes is distributed across the near-wall region. The sizes of ICA modes are multiscale and are approximately proportional to their heights. ICA modes can also help to reconstruct the statistics of turbulence, particularly the third-order moment of velocity fluctuations, which is related to the strongest Reynolds shear-stressproducing events. The results reported in this paper indicate that the ICA method may connect statistical descriptions and structural descriptions of turbulence. (c) 2022 The Authors. Published by Elsevier Ltd on behalf of The Chinese Society of Theoretical and Applied Mechanics. This is an open access article under the CC BY-NC-ND licens

近壁湍流的时空特征与模型

湍流的时空能谱具有重要的理论和工程意义。本文的目标是发展湍流速度场的动力学模型,使其产生适当的时空能谱和时空关联。通过引入对流、下扫、耗散和畸变等因素,本文提出了随机强迫的Taylor模型来构造近壁湍流小尺度流动的随机动力学方程。此外,本文采用线性随机估计方法来确定与外层流动相关的大尺度流动。与白噪声激励的Taylor模型不同,本文模型可以正确表征时空能谱的谱宽特征。基于槽道湍流的直接数值模拟数据,本文计算了该模型得到的时空能谱的对流速度、谱宽和时空关联等结果,这些结果与直接数值模拟结果相符。因此本文模型正确表征了湍流脉动的传播与去关联机制,并且表明了在使用随机强迫的模型来表征湍流时空能谱时,考虑随机下扫效应的重要性

中国科学院力学研究所非线性国家重点实验室;

使用非结构网格有限体积方法,通过大涡模拟与壁模型计算了周期山状流。基于流动整体速度和山顶高度的雷诺数为10595。大涡模拟采用的网格可以解析下壁面的壁面层,而无法解析上壁面的壁面层。首先,比较了上壁面不采用壁模型时Vreman亚格子模式与动态Smagorinsky模式这两种亚格子模式的计算结果,发现在周期山状流中动态Smagorinsky模式的计算结果更接近参考值。其次,在采用动态Smagorinsky模式的情况下,比较了上壁面加上Werner-Wengle壁模型后的结果与不加壁模型的结果,发现壁模型对计算结果有一定影响。最后,还分析了周期山状流中的瞬态流场结构