涡流空气分级机分级功能区研究

从理论上分析了涡流空气分级机的分级过程,在此基础上把涡流空气分级机内部分为主要分散区、分散分离区、第二分离区、辅助分离区、阻力区五个功能区,讨论了各功能区在分级过程中的作用,用物料试验证明了作者的分区正确.在此基础上改进了主要分散区、分散分离区、第二分离区三个功能区,得到更好的分级效果;这将对理解和改进涡流空气分级机分级性能进而开发出性能更好的涡流空气分级机具有指导作用.     

蛋白质规则二级结构中亲疏水氨基酸紧邻关联特性

将氨基酸椐亲 -疏水性按 5度简并进行分类 ,然后映射于蛋白质规则二级结构中 ,对规则二级结构中氨基酸的亲 -疏水性紧邻关联作了统计分析 ,并给出规则二级结构中亲 -疏水氨基酸的位置择优规律性     

基于邻域的混合象元解译

根据地物的自然连续性,认为混合象元中最终光谱单元的组成与其邻域相关,据此提出了一种十分简单的混合象元解译方法———基于邻域的混合象元解译．给出了解译的基本步骤与线性算法,讨论了最终光谱单元的标准及其搜集方法,并探讨了混合象元中与邻域无关的孤立最终光谱单元的探测问题．     

A detection strategy of multi-pose face in compressed domain

In this paper, we present a strategy to implement multi pose face detection in compressed domain. The strategy extracts firstly feature vectors from DCT domain, and then uses a boosting algorithm to build classifiers to distinguish faces and non-faces. Moreover. to get more accurate results of the face detection, we present a kernel function and a linear combination to build incrementally the strong classifiers based on the weak classifiers. Through comparing and analyzing results of some experiments on the synthetic data and the natural data, we can get more satisfied results by the strong classifiers than by the weak classifies.     

基于最邻近聚类和向量模糊c-均值的混沌预测

针对混沌时间序列难预测的问题,提出一种新的基于最邻近聚类和向量模糊c-均值(FCMV)聚类算法的模糊建模方法。其前提参数辨识分两步,首先用最近邻聚类法初始划分输入空间,得到规则数及初始聚类中心,再用FCMV把具有相同收敛向量的聚类中心归到同一个区域来优化前一步得到的聚类中心,得到前提参数;采用递推最小二乘算法辨识模型的结论参数。最后通过对Mackey-Glass混沌时间序列的建模和预测验证了该方法的有效性与实用性。     

A multiple intelligent agent system for credit risk prediction via an optimization of localized generalization error with diversity

Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank＇s loan approval decision process. Traditional financial models for credit risk prediction are no longer adequate for describing today＇s complex relationship between the financial health and potential bankruptcy of a company. In this work, a multiple classifier system （embedded in a multiple intelligent agent system） is proposed to predict the financial health of a company. In our model, each individual agent （classifier） makes a prediction on the likelihood of credit risk based on only partial information of the company. Each of the agents is an expert, but has limited knowledge （represented by features） about the company. The decisions of all agents are combined together to form a final credit risk prediction. Experiments show that our model out-performs other existing methods using the benchmarking Compustat American Corporations dataset.     

基于Adaboost的行道线检测

行道线检测是主动安全和视觉导航技术中的一个重要研究课题.在总结前人检测算法的基础上,设计了基于Adaboost算法的行道线检测方法.Adaboost算法作为一种新型的机器学习算法,可以在比随机预测略好的弱分类器基础上构建高精度的强分类器.该算法简单可靠、学习效率高,较好地解决了实时检测系统中速度和精度的矛盾.实验结果表明该方法有较好的检测效果.     

非线性半参数模型最小二乘近邻估计

目前对非线性半参数模型的研究尚处于初级阶段,关于非线性半参数模型的计算理论还未见实质性报道.基于非线性半参数模型最小二乘近邻估计,给出了其参数分量和非参数分量估计的构造式,导出了参数分量和非参数分量顾及二次项直接解法的非线性直接解法.基于工程实际中的非线性模型用模拟数据进行计算,证明对于非线性半参数模型最小二乘近邻估计,可采用顾及二次项的直接解法.     

量子K最近邻算法

为减少经典K最近邻算法的时间复杂度,提出了量子K最近邻算法(QKNN)。介绍了QKNN算法的构造步骤,然后为减少量子计数子程序的运行时间,进一步将固定的K值修改为可变的k,形成改进的k可变的量子最近邻算法(QkvNN)。为弥补由于最近邻个数K变化带来的分类错误率上升的影响,在Boosting算法框架下,用三个由QkvNN算法训练的弱分类器,去构造了一个强分类器,从而提高单独运行QkvNN的分类精度。在此算法中,由于利用了量子计算的强大能力,使得经典K最近邻算法的时间复杂度从O(N)减小为O(N)。     

Metabonomic analysis of hepatitis B virus-induced liver failure： identification of potential diagnostic biomarkers by fuzzy support vector machine

Hepatitis B virus （HBV）-induced liver failure is an emergent liver disease leading to high mortality. The severity of liver failure may be reflected by the profile of some metabolites. This study assessed the potential of using metabolites as biomarkers for liver failure by identifying metabolites with good discriminative performance for its phenotype. The serum samples from 24 HBV-indueed liver failure patients and 23 healthy volunteers were collected and analyzed by gas chromatography-mass spectrometry （GC-MS） to generate metabolite profiles. The 24 patients were further grouped into two classes according to the severity of liver failure. Twenty-five eommensal peaks in all metabolite profiles were extracted, and the relative area values of these peaks were used as features for each sample. Three algorithms, F-test, k-nearest neighbor （KNN） and fuzzy support vector machine （FSVM） combined with exhaustive search （ES）, were employed to identify a subset of metabolites （biomarkers） that best predict liver failure. Based on the achieved experimental dataset, 93.62% predictive accuracy by 6 features was selected with FSVM-ES and three key metabolites, glyeerie acid, cis-aeonitie acid and citric acid, are identified as potential diagnostic biomarkers.