农残水平测试中数据多态性分析及指定值估计研究

对全国农残水平测试中毒死蜱、氯氰菊酯数据进行统计分析,在数据统计分布特征研究基础上,使用内核密度估计进行数据多态性分析,使用bootstrap模拟取样法对数据样本值重复取样,以获得稳健的水平测试样品待测物含量代表值估计、标准误差及置信区间描述,证明以bootstrap模拟取样法获取的均值与标准偏差作为有限单次样本代表值是合理、有效的,解决了四分位稳健统计方法对非正态多态分布代表值估计不稳定问题及取样理论中取样样本数限制的瓶颈,为能力验证计划指定值的获取提供了一种新方法。     

一种新型软件开发工具NST的研制

本文介绍了一种直接面向机器的、采用特殊方法进行存贮空间分配的新型软件开发工具NST,其基础是IBM PC/XT的核心软件CC-BIOS。NST的研制对改变我国目前微机闲置率高等状况有很大的价值,对“系统软件国产化”也有很深远的意义。     

两种特征提取技术在入侵检测中的应用

在介绍主成分分析方法和核主成分分析方法原理基础上,分别采用这两种方法对KDDCUP99中的入侵检测数据进行特征提取,然后把特征提取后的数据送入神经网络进行训练。仿真实验结果表明,两种方法中核主成分分析方法具有更优秀的特征提取性能。     

支持向量机及其在羽绒识别中的应用研究

在分析支持向量机原理的基础上,对支持向量机近年来在羽绒识别中的应用进行了综述和探讨.实践和实验证明基于支持向量机的羽绒自动识别是羽绒检测领域的一个创新和尝试,最后,指出了支持向量机的优点和不足,并且对其在羽绒识别方面的应用前景进行了展望.     

Estimation of heavy-tailed probability density function with application to Web data

Summary  Common non-parametric estimators of a probability density function (PDF) show bad performance for heavy-tailed PDFs. Using a parametric approximation of the true cumulative distribution function (CDF), the transformation-retransformation of the data is explored here as a useful tool for the reliable PDF prediction. The PDF estimators are compared by their capacity to solve a classification problem. Simulation results and an application to Web data analysis are presented, too.     

Profitability in the car industry: New measures for estimating targets and target directions

In this paper we study the profitability of car manufacturers in relation to industry-wide profitability targets such as industry averages. Specifically we are interested in whether firms adjust their profitability in the direction of these targets, whether it is possible to detect any such change, and, if so, what the precise nature is of these changes.     

Algorithmic inference: From information granules to subtending functions

To get a true hybrid framework for taking operational decisions from data, we extend the Algorithmic Inference approach to the Granular Computing paradigm. The key idea is that whether or not we need to make decisions instead of mere computations depends on the fact that collected data are not sufficiently definite; rather, they are representative of whole sets of data that could be virtually observed, and we need to manage this indeterminacy. The distinguishing feature is that we face indeterminacy exactly where it affects the quality of the decision. This gives rise to a family of inference algorithms which can be tailored to many specific decisional problems that are generally solved only in approximate ways. In the paper we discuss the bases of the paradigm and provide some examples of its implementation.     

Singular Value Decomposition Solution of the Schrödinger Equation in the Presence of Exchange Terms

A new numerical method of solving integro-differential equations appearing in the theory of atomic and nuclear scattering systems has been devised. It is termed Singular Value Decomposition Method (SVD). It consists in expanding the exchange kernel into a number of separable terms by means of the Singular Value Decomposition and then iterating over the remainder. In this paper, we extend our SVD method to the scattering of low energy electron-helium which has been the subject of interest, both theoretically and experimentally. We compare our results with the Moments Method which is widely used. The Moments Method consists of making an expansion of the solution into an especially favorable basis that takes care of the non-exchange part of the Hamiltonian.     

Kernel density in the study of the strong stability of the queueing system

We use kernel density with correction of boundary effects to study the strong stability of the M/M/1 system after perturbation of arrival flow (respectively service times), to evaluate the proximity of G/M/1 (respectively M/G/1) and M/M/1 systems when the distribution G is unknown. Simulation studies are performed to support the results.