人的视觉心理艺术浅析

本文就人的视觉在对事物的色彩、形式、形状、光和影等诸方面,所产生的心理艺术影响作了剖析。并论述了视觉心理在电影、绘画等艺术创作中的重要性。从而激励人们更好地关注和创作美的作品。     

数值解法中的单元形状函数技巧

本文介绍从一组互不相关的函数中推导数值法中单元形状函数的简便而有效的算法,以特定的一维内插方法导出各种二维、三维的内插方法,它们对奇异性的模拟是有益的;为了适用于延伸到无限远的单元,假设形状函数类似于Lagrange多项式,但包含了一个按指数衰减的项。     

西部民族地区社会现代化与现代人格的塑造

为适应西部民族地区社会现代化的发展,必须要塑造与之相应的现代人格。文章对现代人格的特征以及西部民族地区塑造现代人格的现实可行性进行了阐述,并提出了塑造现代人格的基本对策。     

涡轮叶片尾缘针肋的换热研究

以涡轮叶片尾缘中针肋通道的换热为研究对象，讨论并计算出了一定条件下获得最大针肋通道换热量时针肋的形状曲线和换热优势，系统分析了这种拥有最佳形状曲线针肋的通道散热量随针肋物性参数及几何特征变化的规律。     

浅谈我国传统图形艺术与现代标志设计

现代标志设计具有很强的现代感、信息感,传达了一种特定的信息,传统的造型艺术为现代标志设计提供了更多的思维路径,带来更多的启示。那么如何将中国传统图形与现代设计相结合,设计出具有文化韵味的现代标志是值得我们深入探讨的。     

Longitudinal shape analysis by using the spherical coordinates

One of the important topics in morphometry that received high attention recently is the longitudinal analysis of shape variation. According to Kendall's definition of shape, the shape of object appertains on non-Euclidean space, making the longitudinal study of configuration somehow difficult. However, to simplify this task, triangulation of the objects and then constructing a non-parametric regression-type model on the unit sphere is pursued in this paper. The prediction of the configurations in some time instances is done using both properties of triangulation and the size of great baselines. Moreover, minimizing a Euclidean risk function is proposed to select feasible weights in constructing smoother functions in a non-parametric smoothing manner. These will provide some proper shape growth models to analysis objects varying in time. The proposed models are applied to analysis of two real-life data sets.     

小型变流量电磁泵的结构优化设计

本文在介绍电磁泵的结构组成的基础上,详细介绍了采用复合形法对电磁泵进行优化设计的具体步骤。     

光谱法测量等离子体离子温度和再循环粒子流速

利用多道光学分析仪（OSMA）测量HT-6M托卡马克限制器前Hα线形分布,通过高斯拟合由多普勒展宽和多普勒频移分别得出等离子体离子温度和再循环粒子流速。     

Regression for non-Euclidean data using distance matrices

Regression methods for common data types such as measured, count and categorical variables are well understood but increasingly statisticians need ways to model relationships between variable types such as shapes, curves, trees, correlation matrices and images that do not fit into the standard framework. Data types that lie in metric spaces but not in vector spaces are difficult to use within the usual regression setting, either as the response and/or a predictor. We represent the information in these variables using distance matrices which requires only the specification of a distance function. A low-dimensional representation of such distance matrices can be obtained using methods such as multidimensional scaling. Once these variables have been represented as scores, an internal model linking the predictors and the responses can be developed using standard methods. We call scoring as the transformation from a new observation to a score, whereas backscoring is a method to represent a score as an observation in the data space. Both methods are essential for prediction and explanation. We illustrate the methodology for shape data, unregistered curve data and correlation matrices using motion capture data from an experiment to study the motion of children with cleft lip.