A stability classification method and its application to pipelined solution of linear recurrences

A new method of classification for numerical stability of parallel algorithms is proposed based on the theoretical foundation of forward error analysis. It partitions the algorithms according to their asymptotic stability—a measure introduced to relate the limiting behavior of the stability to the size of the problem. Using this method, the stability aspect of the pipelined solution technique for first-order and second-order linear recurrences—the core of a tridiagonal linear equation solver—is studied. In particular, it shows that the pipelined solution method of the first-order linear recurrences has the same degree of stability as the commonly used sequential evaluation algorithms. The stability problems of sequential and pipelined solution methods of the second-order linear recurrences are also studied.     

航天产品多源可靠性信息的收集与整理--建立可靠性信息数据库

航天产品可靠性信息的来源多样，可靠性信息的时间性、随机性、有价性、时效性和可追溯性等特征，决定了可靠性数据收集应满足真实性、连续性、完整性等基本要求。同时，对多源信息的收集应遵循先进行需求分析，再确定数据收集点并制定数据收集表格的程度进行。给出了信息的适用性准则和信息分类的三维模型。最后结合具体型号航天产品的可靠性工作的要求，给出了可靠性信息数据库系统的整体方案设计。     

A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering

Fuzzy clustering has played an important role in solving many problems. In this paper, we design an unsupervised neural network model based on a fuzzy objective function, called OFUNN. The learning rule for the OFUNN model is a result of the formal derivation by the gradient descent method of a fuzzy objective function. The performance of the cluster analysis algorithm is often evaluated by counting the number of crisp clustering errors. However, the number of clustering errors alone is not a reliable and consistent measure for the performance of clustering, especially in the case of input data with fuzzy boundaries. We introduce two measures to evaluate the performance of the fuzzy clustering algorithm. The clustering results on three data sets, Iris data and two artificial data sets, are analyzed using the proposed measures. They show that OFUNN is very competitive in terms of speed and accuracy compared to the fuzzy c-means algorithm.     

小波变换在手写体汉字识别中的应用

汉字识别中的结构类特征具有抗变形的优点，合适做为手写体识别过程使用，但其中笔划特征的提取非常困难。本文用小波变换这一图象分析中有力工具对汉字图象分析处理之后，再进行笔划的提取，其过程简洁、明晰。     

基于YCbCr肤色模型改进算法及区域特征的人脸检测研究

针对当前人脸检测的研究现状与难题,采用改进的YCbCr椭圆聚类肤色模型进行肤色区域提取,根据肤色在YCb’Cr’空间的分布,对于亮度小于80的非肤色像素点会误判为肤色点,则缩小椭圆聚类;对于亮度大于230的肤色像素点会误判为非肤色点,则扩大椭圆聚类,有效避免了在高亮度区域和亮度较低的区域中的肤色点误判问题。接着利用人脸的几何特征,对二值化图中的目标区域进行比例、大小结构的分析,排除不可能的人脸区域,并基于肤色和位置进行区域优化,将处理后的结果作为候选人脸区域输出。     

Parametric 3D modeling of young women's lower bodies based on shape classification

Three-dimensional (3D) human body modeling is an important research direction in the field of clothing virtual design. On the basis of 3D human body scanning, this paper studied a method to build a 3D parametric lower body model according to body classification. The research includes three main parts. (1) Anthropometry and body shape classification. We randomly selected 333 young women ages 18–25 years old in Northeast China as the experimental sample. Then we divided the lower body shape into three categories using principal component analysis and K-means clustering. (2) Determination of feature cross sections and points, and reconstruction of feature curves. According to the average values of each body type, we obtained the mean reference body by Euclidean distance method. We determined feature cross sections and points, and extracted the 3D coordinates of the feature points of the mean reference body to reconstruct the feature curves. (3) The surface lofting and establishment of parametric 3D lower body model. According to the shape characteristics of the lower body, we constructed the guiding lines for the crotch and lower limbs, and established parametric lower body models for three body types.Relevance to industry3D human modeling is an important part of garment industry digitization. This research provides an effective way to construct a parametric 3D lower body model. The method offers a reference for the parametric virtual human modeling and virtual fitting of trousers.     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

浅析管理在信息系统安全中的必要性

在信息系统安全规划和设计中,管理作为信息系统的安全基础和信息系统各安全层面的基本要素,已经成为构建安全、可用信息系统不可或缺的必要条件。文章针对管理在信息系统安全中的作用、特点进行了论述,并结合等级保护政策提出了相应的管理建议。     

面向信息处理的突发事件语料库分类体系研究

本文分析了突发事件新闻的特点、建设突发事件新闻语料库的目的和意义,研究了突发事件新闻语料的分类体系和编码。研究结果对突发事件新闻语料库的建设、突发事件新闻信息检索、国家相关部门对突发事件应急处理方案制定以及对流行病学的医学研究等领域具有一定的理论意义和应用价值。     

A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into local minima and lack of prior knowledge for optimum paramaters of the kernel functions. In this paper, to overcome these drawbacks, a new clustering method based on kernelized fuzzy c-means algorithm and a recently proposed ant based optimization algorithm, hybrid ant colony optimization for continuous domains, is proposed. The proposed method is applied to a dataset which is obtained from MIT–BIH arrhythmia database. The dataset consists of six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). Four time domain features are extracted for each beat type and training and test sets are formed. After several experiments it is observed that the proposed method outperforms the traditional fuzzy c-means and kernelized fuzzy c-means algorithms.