目标机动规范模型研究

分析和总结了飞机各种机动动作的内在因素,提出了目标机动规范模型,研究了模型的数学描述方式、控制参量的相互约束关系及其组织形式,并通过模拟导弹对目标的攻击过程对模型进行了验证。     

稀溶液黏度法测定聚合物共混物相容性理论及应用

以经典的Huggins方程和特性黏度为理论基础，通过测定聚合物共混物稀溶液黏度来确定共混物相容性。对相容性判据△b、△B、α、μ和k23进行了分析归纳，并用实例说明了此方法的可行性。     

大浓度范围内PV型旋风分离器粒级效率的计算方法

探讨了旋风分离器入口含尘浓度对其分离性能及粒级效率的影响机理。对旋风分离器内部气固两相运动进行了相似分析,得到了包括入口含尘浓度在内的一系列影响分离性能的相似准数。通过相似试验及试验数据的多元回归分析,建立了各相似准数与粒级效率的关系,得出了能精确估计大浓度范围内PV型旋风分离器粒级效率的计算公式。     

螺带式搅拌器在假塑液中的混合及放大的研究

本文综合研究了螺带式搅拌器在假塑性液体中的混合特性及动力特性，测定了双螺带，内外螺带及螺带螺旋３种类型搅拌器的功率常数Ｋｐ和Ｍｅｔａｎｅｒ常数Ｋｓ，并得出混匀操作和传质工艺过程的放大准则。     

核岭回归的邻域保持最大间隔分析的人脸识别

邻域保持嵌入是局部线性嵌入的线性近似,强调保持数据流形的局部结构.改进的最大间隔准则重视数据流形的判别和几何结构,提高了对数据的分类性能.文中提出的核岭回归的邻域保持最大间隔分析既保持流形的局部结构,又使不同类别的数据保持最大间隔,以此构建算法的目标函数.为了解决数据流形高度非线性化的问题,算法采用核岭回归计算特征空间的变换矩阵.先求解数据样本在核子空间中降维映射的结果,再解得核子空间.在标准人脸数据库上的实验表明该算法正确有效,并且识别性能优于普通的流形学习算法.     

递归程序设计方法的分析探讨

递归是设计程序的一种重要方法,文中对递归的定义及递归的基本条件进行了分析,给出了递归程序设计的基本方法,并结合数据结构中二叉树遍历和单链表结点输出的实例进行了验证,最后对递归程序进行了深度分析,说明了递归是一种有效的程序设计方法。     

粒子群算法求解组合投资问题

本文在分析概率准则下的组合投资问题模型的基础上,提出了一种基于粒子群优化算法的求解方案,并用C++加以实的收敛性和计算效率,为组合证券投资者提供了一种高效的决策方法。现,然后结合实例,和传统方法、随机模拟结合遗传算法,遗传—禁忌算法等进行比较。证明了文中采用算法具有较高     

Formalizing interoperability for test case generation purpose

This study deals with interoperability testing of protocol implementations. The objective is to define a method for automatic test derivation based on formal definitions. First, the notion of interoperability criteria is introduced. They formally describes the conditions that two implementations must verify in order to be considered interoperable. Then, based on the equivalence of two of the defined interoperability criteria, we propose a method to derive automatically interoperability test cases that avoids the state-space explosion problem.     

Modified versions of the Bayesian Information Criterion for sparse Generalized Linear Models

The classical model selection criteria, such as the Bayesian Information Criterion (BIC) or Akaike information criterion (AIC), have a strong tendency to overestimate the number of regressors when the search is performed over a large number of potential explanatory variables. To handle the problem of the overestimation, several modifications of the BIC have been proposed. These versions rely on supplementing the original BIC with some prior distributions on the class of possible models. Three such modifications are presented and compared in the context of sparse Generalized Linear Models (GLMs). The related choices of priors are discussed and the conditions for the asymptotic equivalence of these criteria are provided. The performance of the modified versions of the BIC is illustrated with an extensive simulation study and a real data analysis. Also, simplified versions of the modified BIC, based on least squares regression, are investigated.     

Automatic transaction of signal via statistical modeling

The statistical information processing can be characterized by the likelihood function defined by giving an explicit form for an approximation to the true distribution. This mathematical representation, which is usually called a model, is built based on not only the current data but also prior knowledge on the object and the objective of the analysis. Akaike^2,3) showed that the log-likelihood can be considered as an estimate of the Kullback-Leibler (K-L) information which measures the similarity between the predictive distribution of the model and the true distribution. Akaike information criterion (AIC) is an estimate of the K-L information and makes it possible to evaluate and compare the goodness of many models objectively. In consequence, the minimum AIC procedure allows us to develop automatic modeling and signal extraction procedures. In this article, we give a simple explanation of statistical modeling based on the AIC and demonstrate four examples of applying the minimum AIC procedure to an automatic transaction of signals observed in the earth sciences. Genshiro, Kitagawa, Ph.D.: He is a Professor in the Department of Prediction and Control at the Institute of Statistical Mathematics. He is currently Deputy Director of the Institute of Statistical Mathematics and Professor of Statistical Science at the Graduate University for Advanced Study. He obtained his Ph.D. from the Kyushu University in 1983. His primary research interests are in time series analysis, non-Gaussian nonlinear filtering, and statistical modeling. He has published over 50 research papers. He was awarded the 2nd Japan Statistical Society Prize in 1997. Tomoyuki Higuchi, Ph.D.: He is an Associate Professor in the Department of Prediction and Control at the Institute of Statistical Mathematics. He is currently an Associate Professor of Statistical Science at the Graduate University for Advanced Study. He obtained his Ph.D. from the University of Tokyo in 1989. His research interests are in statistical modeling of space-time data, stochastic optimization techniques, and data mining. He has published over 30 research papers.