非线性网络课件设计研究与实践

针对教材式的网络课件模式,提出非线性课件的设计思想。本文着重借鉴混沌的非线性思维,进行网络课件的非线性设计研究,在此基础上,又进一步以特定实例对非线性网络课件的设计方法、设计要点进行实践探讨。     

微分几何在非线性系统中的应用

微分几何方法作为一种新的工具,被引入控制系统特别是非线性控制系统的研究中,并得到很大发展．文中在两个不同方面就此问题进行了讨论．首先,针对非线性振动系统的模态研究,探讨模态的几何性质,以期赋予非线性模态一个更加直观的几何意义;其次,介绍基于微分几何理论,通过非线性状态反馈和非线性坐标变换实现非线性系统的完全线性化的方法．     

从科学技术、物理学、耗散结构看中国近现代社会转型时期文学的转变

文学是上层建筑,受着经济基础的决定和制约;科学技术是第一生产力,是经济基础的最终决定因素;物理学是现代科学技术的基石。因此,文学的发生发展受着科学技术和物理学的深刻制约,同时,文学作为一个耗散结构,在物理学和技术科学决定的基础上,又呈现出其特有的发展规律。     

关联规则挖掘模型在大学生评价中的应用

本文在对数据挖掘相关技术、关联规则挖掘算法进行深入研究的基础上,归纳总结了基于粗糙集理论的关联规则挖掘模型和属性约简算法,并将其成功应用于大学生评价中,为社会选拔人才提供有价值的参考。     

基于分形的快速最大熵红外图像特征检测

文章针对红外图像目标检测问题,提出一种基于分形的快速最大熵的红外图像特征检测算法.该算法利用DBC方法计算分维数,根据人造物和自然背景分形维差异,确定目标区域;最后,通过二维最大熵原则确定最佳阈值,实现对单目标或者多目标图像分割.该算法能够较好实现红外图像特征检测,有效抑制背景和噪声.     

“堡”贝太阳能式降噪除尘婴儿车的设计

基于低碳环保理念,通过分析婴儿车在使用过程中容易忽略的噪声及粉尘问题,采用环保隔音材料,在车体底部安装除尘空调,以太阳能为动力除尘,设计“堡”贝太阳能式降噪除尘婴儿车,以期最大限度降低噪声及粉尘对婴儿的危害。     

Confidence Intervals for a Semiparametric Approach to Modeling Nonlinear Relations among Latent Variables

Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) Bauer, D. J. 2005. A semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Modeling, 12: 513–535. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar] proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the service of more flexibly recovering characteristics of the latent aggregate regression function. This article develops and evaluates delta method and parametric bootstrap approaches for obtaining approximate confidence intervals for Bauer's semiparametric approach to modeling latent nonlinear functions. Coverage rates of these approximate point-wise confidence intervals or nonsimultaneous confidence bands are evaluated by Monte Carlo and recommendations for their use are suggested.     

Modeling Ability Differentiation in the Second-Order Factor Model

In this article we present factor models to test for ability differentiation. Ability differentiation predicts that the size of IQ subtest correlations decreases as a function of the general intelligence factor. In the Schmid–Leiman decomposition of the second-order factor model, we model differentiation by introducing heteroscedastic residuals, nonlinear factor loadings, and a skew-normal second-order factor distribution. Using marginal maximum likelihood, we fit this model to Spanish standardization data of the Wechsler Adult Intelligence Scale (3rd ed.) to test the differentiation hypothesis.     

Fitting Nonlinear Latent Growth Curve Models With Individually Varying Time Points

Individual growth trajectories of psychological phenomena are often theorized to be nonlinear. Additionally, individuals’ measurement schedules might be unique. In a structural equation framework, latent growth curve model (LGM) applications typically have either (a) modeled nonlinearity assuming some degree of balance in measurement schedules, or (b) accommodated truly individually varying time points, assuming linear growth. This article describes how to fit 4 popular nonlinear LGMs (polynomial, shape-factor, piecewise, and structured latent curve) with truly individually varying time points, via a definition variable approach. The extension is straightforward for certain nonlinear LGMs (e.g., polynomial and structured latent curve) but in the case of shape-factor LGMs requires a reexpression of the model, and in the case of piecewise LGMs requires introduction of a general framework for imparting piecewise structure, along with tools for its automation. All 4 nonlinear LGMs with individually varying time scores are demonstrated using an empirical example on infant weight, and software syntax is provided. The discussion highlights some advantages of modeling nonlinear growth within structural equation versus multilevel frameworks, when time scores individually vary.     

Modeling Nonlinear Structural Equation Models: A Comparison of the Two-Stage Generalized Additive Models and the Finite Mixture Structural Equation Model

Researchers have devoted some time and effort to developing methods for fitting nonlinear relationships among latent variables. In particular, most of these have focused on correctly modeling interactions between 2 exogenous latent variables, and quadratic relationships between exogenous and endogenous variables. All of these approaches require prespecification of the nonlinearity by the researcher, and are limited to fairly simple nonlinear relationships. Other work has been done using mixture structural equation models (SEMM) in an attempt to fit more complex nonlinear relationships. This study expands on this earlier work by introducing the 2-stage generalized additive model (2SGAM) approach for fitting regression splines in the context of structural equation models. The model is first described and then investigated through the use of simulated data, in which it was compared with the SEMM approach. Results demonstrate that the 2SGAM is an effective tool for fitting a variety of nonlinear relationships between latent variables, and can be easily and accurately extended to models including multiple latent variables. Implications of these results are discussed.