基于降噪自编码器特征学习的作者识别及其在《西游记》诗词上的应用

由于作者归属问题较为复杂,采用传统自然语言处理模型难以完成作者识别.为了深入挖掘作者归属问题,首先采用降噪自编码器深度模型提取文本结构特征,再采用支持向量机分类器完成作者识别.模型的优势在于能够考虑未知文本特征的噪声多样性和复杂性,且能够重构添加噪声的原始文本输入.将该方法应用于吴承恩、王廷陈、薛蕙等人的诗词作者识别,识别准确率最高为78.2%,验证了该方法的有效性,进一步将该方法应用于《西游记》诗词作者识别.国家自然科学基金(61673322,61573294

基于涡合成方法的 可压缩入口湍流条件的生成

为了给大涡模拟LES计算提供合适的湍流入口信息，本文采用了一种改进的涡合成方法(synthetic eddy methods)来生成指定速度和雷诺应力分布下的湍流入口条件。该方法根据可压缩平板边界层内的湍流结构特征将入口截面沿壁面法向方向分为若干区域，并为它们指定不同的形状函数、特征迁移速度与流动尺度来描述相应的湍流结构，并最终生成含有特定湍流脉动信息的入口条件。但由于涡合成方法生成的湍流脉动量无法满足连续性条件，流场下游会出现较强的虚假压力脉动，海绵边界条件(sponge layer)的引入很好地抑制了此种现象。计算结果表明：在满足必要的网格分辨率下，采用涡合成方法的来流马赫数为2的可压缩平板边界层可以在8&delta;的过渡距离内形成完全发展的湍流流场；数值模拟得到的时均统计量结果（壁面摩擦系数、速度型、雷诺应力、湍动能能量谱密度）与可压缩流动的理论结果以及DNS模拟结果符合良好，进一步验证了涡合成方法在可压缩湍流边界层入口条件生成上的应用前景。</p

Research on the Situation of Elderly Disability and its Impact Factors in Xiamen

目的了解厦门市老年人失能现状,并分析失能影响因素。方法采用多阶段抽样的方法,对厦门市60岁及以上户籍老年人进行面对面问卷调查;利用kATz指数量表评价老年人日常生活自理能力(Adl),进而判断其失能状况,采用克朗巴赫α系数评价量表的信度;应用lOgISTIC回归模型分别对失能影响因素进行单因素和多因素分析。结果本次共调查厦门市6个区,38个街道/乡镇,173个社区,回收有效问卷14292份,其中失能老年人610名,失能率为4.27%;量表的克朗巴赫α系数为0.973,洗澡失能率最高,吃饭失能率最低;女性、非在婚、非独居、小学文化程度、个人经济状况差和患有3种及以上慢性病者的标准化失能率较高;多因素分析显示年龄、婚姻状况、家庭居住方式、个人经济状况、慢性病患病情况为失能影响因素。结论厦门市老年人的总体失能率低于全国水平,但是年龄≥80岁、非在婚、个人经济状况差和患有慢性病等老年人的失能问题仍尤为突出,应针对以上高危人群开展防控措施。Objective To reveal the features of elderly disability and identify its impact factors.Methods A cross-sectional study was designed.The subjects in our study were aged ≥60 years and recruited by multi-stage sampling.Data were collected through face-to-face questionnaires.The activity of daily living( ADL) was assessed by the Katz Index Scale and the disability of elderly was evaluated based on it.The reliability of the scale was assessed by Cronbach's alpha.Logistic regression was applied to analyze the influencing factors of disability.Results A total of 14,292 valid samples were collected from 6 districts,38 subdistricts and 173 communities.610 were disabilities and the disability prevalence was 4.27%.The greatest prevalence of disability occurred with regard to bathing,and the smallest one occurred in feeding.The Cronbach's alpha of the ADL scale was 0.973.The elderly who were female,unmarried,live with family,lower educational level,poor personal economic status and suffer three kinds of chronic disease had a greater prevalence of disability.Logistic regression analysis showed that the major impact factors of disability were age,marital status,living arrangement,personal economic status and the number of chronic diseases.Conclusion The prevalence of elderly disability in Xiamen was lower than the national level,how ever,we also need to pay attention to high risk group of the elderly who were aged ≥80 years,unmarried,poor economic status and various chronic diseases.厦门市社会科学规划项目(XDHT2013357A

Reconstruction model for heat release rate based on artificial neural network

Optimizing the distribution of heat release rate (HRR) is the key to improve the performance of various combustors. However, limited by current diagnostic techniques, the spatial measurement of HRR in many realistic combustion devices is often difficult or even impossible. HRR prediction is theoretically possible through establishing correlations between HRR and other quantities (e.g., chemiluminescence intensity) that can be experimentally determined; however, up to now, few universal correlations have been established. A novel artificial neural network (ANN) approach was adopted to build the mapping relationship between the combustion heat release rate and the measurable chemiluminescent species. Proper orthogonal -12omposition (POD) technology is used to extract the combustion physics and reduce the data of the spatial-temporally high-resolution combustion field. The correlation between the reduced-order HRR and chemiluminescent species is built using an ANN model. A unique segmentation approach was proposed to improve the training efficiency and accuracy. Validation in a supersonic hydrogen-oxygen nonpremixed flame proves the accuracy and efficiency of the proposed HRR reconstruction model based on the reduced-order POD method and data-driven ANN model. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved

Low-Dissipative Hybrid Compressible Solver Designed for Large-Eddy Simulation of Supersonic Turbulent Flows

To reduce the numerical dissipation in turbulence modeling while maintaining the numerical stability around flow discontinuities in supersonic flowfield, a low-dissipative compressible solver is developed for large-eddy simulation within the OpenFOAM framework. To achieve the aforementioned goals, the low-dissipative solver adopts the hybrid scheme approach through combining the dissipative Kurganov-Tadmor scheme with the nondissipative central scheme via a shock sensor. In the construction of the central scheme, a robust skew-symmetric form of the convective term is adopted to preserve the local kinetic energy without adding an explicit dissipative term. Another feature of the low-dissipative solver is the implementation of an optimal explicit strong stability-preserving linear third-order total variation diminishing Runge-Kutta method for the temporal discretization. Numerical tests for a series of canonical flow problems are carried out to validate the solver&#39;s good performance in the flowfield either with strong discontinuities or with continuous spectrum characteristics. Large-eddy simulation of a scramjet combustor with supersonic airstream passing over the flame holder is conducted to validate the low-dissipative solver&#39;s reliability in a realistic flow with the complex interaction of shock discontinuities and turbulence.</p

Development and application of ANN model for property prediction of supercritical kerosene

Three artificial neural network (ANN) models were developed to predict the fluid properties of China RP3 kerosene under supercritical pressure in replacement of the time-consuming property calculations by the principle of Extended Corresponding State (ECS). The analysis shows that the properties predicted by the trained ANN models agree well with the calculations by the ECS method. The correlation coefficients (R) between the ANN predictions and the ECS calculations are higher than 0.99, and most of the relative errors are lower than 0.1%. The prediction by the ANN models is of several orders (104) faster than that by the ECS method, especially near the critical points. The trained ANN model was further coupled with the CFD modeling of a realistic kerosene jet, where high efficiency and satisfactory accuracy were shown compared with the direct ECS calculations.</p