基于自适应阈值设置的运动目标检测算法

提出一种基于自适应阈值设置的运动目标检测算法,该算法不同干传统的全局阈值设置方法,而是利用核密度对背景像素点进行密度估计,给出一种新的全局和局部阈值相结合的自适应阈值设置方法。该方法考虑不同位置的像素颜色分布复杂度不同,针对每个像素点自适应设置局部阈值,能克服全局阈值的不足,提高检测的精度。对多个标准视频进行实验,实验结果证明了该算法的有效性。     

基于KISOMAP-LDA-KNN算法TE过程故障诊断研究

针对化工连续生产过程的时序性及非线性等特征,文章提出了一种基于KISOMAP-LDA-KNN的非线性故障辨识方法。首先采用核等距映射（KISOMAP）算法在保持训练数据内在几何结构下进行非线性降维,然后使用线性判别（LDA）算法保持数据的最佳分类效果下进行降维,完成过程的特征提取,最后用K近邻（KNN）算法进行模式分类。将上述方法应用到TE过程,仿真结果验证了该故障诊断方法有较高的辨识能力。     

基于多核支持向量机的飞机重着陆诊断

将支持向量机应用于飞机重着陆的诊断研究。通过分析飞机着陆阶段的运动方程,确定造成飞机重着陆的主要影响因素,将传统的单一指标诊断扩展到多指标诊断。提出一种多核支持向量机,并在此基础上建立飞机重着陆诊断模型。与传统支持向量机和神经网络模型比较表明,该模型精度高,具有更强的泛化能力。     

基于CK-CPU的Linux2.6实时性能优化

针对Linux2.6在实时中断应用上的局限性,提出一种基于CK-CPU的实时性能优化方案。设计用于实时中断的快速中断请求(FIQ)机制,在FIQ处理程序退出时增加实时进程调度策略,将FIQ服务程序的公共代码和堆栈保留在便笺式存储器中,减少FIQ服务程序的内存访问次数并加快其执行速度。实验结果证明,该方案可显著提高Linux2.6的实时性能。     

基于树核函数的英文句子情感分类研究

最近几年,由于在线客户评论信息飞快地增长。如何把这些信息分类为正向和负向情感是一个迫切需要解决的问题。提出了一种细粒度级别(句子级别)的情感分类方法,该方法在SVM分类器中使用了树核和复合核函数来进行句子级别情感的分类。实验结果表明在句子级别的情感分类中树核和复合核的方法比线性核具有更佳的性能。     

A novel approach for analog fault diagnosis based on neural networks and improved kernel PCA

We have developed a neural-network-based fault diagnosis approach of analog circuits using maximal class separability based kernel principal components analysis (MCSKPCA) as preprocessor. The proposed approach can detect and diagnose faulty components efficiently in the analog circuits by analyzing their time responses. First, using wavelet transform to preprocess the time responses obtains the essential and reduced candidate features of the corresponding response signals. Then, the second preprocessing by MCSKPCA further reduces the dimensionality of candidate features so as to obtain the optimal features with maximal class separability as inputs to the neural networks. This simplifies the architectures reasonably and reduces the computational burden of neural networks drastically. The simulation results show that our resulting diagnostic system can classify the faulty components of analog circuits under test effectively and achieves a competitive classification performance.     

Gaussian kernel optimization: Complex problem and a simple solution

The Gaussian kernel function implicitly defines the feature space of an algorithm and plays an essential role in the application of kernel methods. The parameter of Gaussian kernel function is a scalar that has significant influences on final results. However, until now, it is still unclear how to choose an optimal kernel parameter. In this paper, we propose a novel data-driven method to optimize the Gaussian kernel parameter, which only depends on the original dataset distribution and yields a simple solution to this complex problem. The proposed method is task irrelevant and can be used in any Gaussian kernel-based approach, including supervised and unsupervised machine learning. Simulation experiments demonstrate the efficacy of the obtained results. A user-friendly online calculator is implemented at: www.csbio.sjtu.edu.cn/bioinf/kernel/ for public use.     

支持向量机的进化多核设计

为提高支持向量机分类精度,提出一种基于遗传程序设计的进化多核算法.算法中每个个体表示一个多核函数,并采用树形结构进行编码,增强了多核函数的非线性;初始种群由生长法产生,经过遗传操作后得到适合具体问题的进化多核函数.遗传程序设计的全局搜索性能使得算法设计不需要先验知识.与单核函数及其他多核函数的对比实验结果表明,进化多核...     

Gaussian kernel optimization for pattern classification

This paper presents a novel algorithm to optimize the Gaussian kernel for pattern classification tasks, where it is desirable to have well-separated samples in the kernel feature space. We propose to optimize the Gaussian kernel parameters by maximizing a classical class separability criterion, and the problem is solved through a quasi-Newton algorithm by making use of a recently proposed decomposition of the objective criterion. The proposed method is evaluated on five data sets with two kernel-based learning algorithms. The experimental results indicate that it achieves the best overall classification performance, compared with three competing solutions. In particular, the proposed method provides a valuable kernel optimization solution in the severe small sample size scenario.