基于互关联后继树的多时间序列关联模式挖掘

时间序列是现实生活中常见的数据形式之一，在时间序列中发现频繁模式是分析时间序列变化规律的一项重要任务．提出基于互关联后继树的多时间序列关联模式挖掘算法．该算法首先用Allen逻辑位置关系来描述序列状态关系，根据这些关系在时间窗口内顺序或并行出现情况，获得一个由这些关系组成的特殊序列．在此基础上提出了一个基于互关联后继树的新型挖掘模型，实现了序列间关联模式的挖掘．与其他方法相比，该算法简单、直观，而且整个挖掘过程不需要生成候选模式，大大提高挖掘效率．     

Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine

Since abnormal control chart patterns (CCPs) are indicators of production processes being out-of-control, it is a critical task to recognize these patterns effectively based on process measurements. Most methods on CCP recognition assume that the process data only suffers from single type of unnatural pattern. In reality, the observed process data could be the combination of several basic patterns, which leads to severe performance degradations in these methods. To address this problem, some independent component analysis (ICA) based schemes have been proposed. However, some limitations are observed in these algorithms, such as lacking of the capability of monitoring univariate processes with only one key measurement, misclassifications caused by the inherent permutation and scaling ambiguities, and inconsistent solution. This paper proposes a novel hybrid approach based on singular spectrum analysis (SSA) and support vector machine (SVM) to identify concurrent CCPs. In the proposed method, the observed data is first separated by SSA into multiple basic components, and then these separated components are classified by SVM for pattern recognition. The scheme is suitable for univariate concurrent CCPs identification, and the results are stable since it does not have shortcomings found in the ICA-based schemes. Furthermore, it has good generalization performance of dealing with the small samples. Superior performance of the proposed algorithm is achieved in simulations.     

基于控制流数据保护的缓冲区溢出防御方法

根据栈缓冲区溢出的基本原理,介绍了三种缓冲区溢出攻击的基本模式,分析了现有的动态防御方法所存在的优缺点。以此为基础,提出了一种基于控制流相关数据保护的栈缓冲区溢出动态防御方法,引入了加密机制,有效地防御攻击者对保护数据的篡改。设计并实现了针对目标文件为对象的二进制文件重构工具,通过理论分析和实验表明该方法能够极大概率防御各种缓冲区溢出攻击。     

A new model for fingerprint classification by ridge distribution sequences

In this paper, a new method is introduced which is a combination of structural and syntactic approaches for fingerprint classification. The goal of the proposed ridge distribution (R-D) model is to present the idea of the possibility for classifying a fingerprint into the complete seven classes in the Henry's classification. From our observation, there exist only 10 basic ridge patterns which construct fingerprints. Fingerprint classes can be interpreted as a combination of these 10 ridge patterns with different ridge distribution sequences. In this paper, the classification task is performed depending on the global distribution of the 10 basic ridge patterns by analyzing the ridge shapes and the sequence of ridges distribution. The regular expression for each class is formulated and a NFA model is constructed accordingly. An explicit rejection criterion is also defined in this paper. For the seven-class fingerprint classification problem, our method can achieve the classification accuracy of 93.4% with 5.1% rejection rate. For the five-class problem, the accuracy rate of 94.8% is achieved. Experimental results reveal the feasibility and validity of the proposed approach in fingerprint classification.     

基于图模型理论的决策者组合行为冲突分析

依据现有4种基本稳定性能够获取冲突均衡解,但该过程通常假设决策者具有相同的行为模式.为了研究各个决策者行为模式的差异性对冲突演化分析与求解的影响,提出一种决策者组合行为冲突分析方法.首先,基于冲突分析图模型4种稳定性概念,通过预见力和风险态度两项指标识别不同决策者的行为模式;其次,定义规范化的组合均衡解概念,以此反映决策者不同行为模式对冲突决策的影响;接着,给出基于矩阵行为模式分析函数的组合均衡求解方法,以此提高均衡解的计算效率;最后,运用新方法解决企业员工体面劳动保障制度实施冲突问题.研究表明,所提出方法能够较好地提高冲突分析图模型理论的战略解析能力和决策水平.     

Mobility prediction in mobile wireless networks

In realistic mobile ad-hoc network scenarios, the hosts usually travel to the pre-specified destinations, and often exhibit non-random motion behaviors. In such mobility patterns, the future motion behavior of the mobile is correlated with its past and current mobility characteristics. Therefore, the memoryless mobility models are not capable of realistically emulating such a mobility behavior. In this paper, an adaptive learning automata-based mobility prediction method is proposed in which the prediction is made based on the Gauss–Markov random process, and exploiting the correlation of the mobility parameters over time. In this prediction method, using a continuous-valued reinforcement scheme, the proposed algorithm learns how to predict the future mobility behaviors relying only on the mobility history. Therefore, it requires no a prior knowledge of the distribution parameters of the mobility characteristics. Furthermore, since in realistic mobile ad hoc networks the mobiles move with a wide variety of the mobility models, the proposed algorithm can be tuned for duplicating a wide spectrum of the mobility patterns with various randomness degrees. Since the proposed method predicts the basic mobility characteristics of the host (i.e., speed, direction and randomness degree), it can be also used to estimate the various ad-hoc network parameters like link availability time, path reliability, route duration and so on. In this paper, the convergence properties of the proposed algorithm are also studied and a strong convergence theorem is presented to show the convergence of the algorithm to the actual characteristics of the mobility model. The simulation results conform to the theoretically expected convergence results and show that the proposed algorithm precisely estimates the motion behaviors.     

互斥关系模式挖掘算法研究

序列模式挖掘是数据挖掘的一个重要领域,结构关系模式挖掘是在序列模式挖掘基础上提出的一种新的挖掘任务.重点对结构关系模式的一个重要分支--互斥关系模式进行了研究,在给出与互斥关系模式相关概念的基础上讨论了互斥关系模式挖掘的两种算法,即基本检测法和分类检测法.实验结果表明,两种算法都是有效的,在序列模式数量很大时,分类检测法的挖掘效率高于基本检测法.结构关系模式挖掘和序列模式挖掘一样在实际应用中有着重要的价值,一些在序列模式挖掘过程中不能发现的隐藏模式将在结构关系模式中被发现,互斥关系模式的研究将进一步为结构关系模式挖掘理论的完善提供支持.     

在变长模式识别中利用测地距离的非线性插值进行特征选取

讨论了变长模式识别中的特征选择问题。采用基于测地距离（Geodesic Distance）的非线性插值来进行特征选择．使得变长的模式映射为等长的模式,从而可以使用传统的等长模式的方法来解决变长模式识别问题。用非特定说话人的汉语孤立词识别来验证提出方法的性能,并采用支持向量机（Support Vector Machine,SVM）作为基本的分类方法。实验结果表明,提出的方法可以获得比传统方法诸如线性插值更好的性能,而计算量仅有很少增加。     

A case-based evolutionary model for defect classification of printed circuit board images

In this research, a case-based evolutionary identification model is developed for PCB defect classification problems. Image understanding is the first and foremost step in the inspection of printed circuit boards. This paper presents a two-phase method for the segmentation of printed circuit board (PCB) images. In the first phase, a set of defect images of several existing basic patterns are stored to form a concept space. In the second phase, a new pattern is evolutionally grabbed using some primitive operators generated by calculating the relative position of several similar cases in the concept space. The case-based reasoning system relies on the software agents derived from past experience within the domain database to determine what feature is required to deliver new patterns in satisfying user’s requirements. Experimental results show that the proposed approach is very effective in identifying the defect patterns.     

挖掘闭合多维序列模式的可行方法

为了对闭合多维序列模式进行挖掘,研究了多维序列模式的基本性质,进而提出了挖掘闭合多雏序列模式的新方法.该方法集成了闭合序列模式挖掘方法和闭合项目集模式挖掘方法,通过证明该方法的正确性,指出闭合多维序列模式集合不大于多维序列模式集合,并且能够覆盖所有多维序列模式的结果集.最后分析了该方法所具备的两个明显优点,表明了在闭合多维序列模式挖掘中的可行性.     

Mixture control chart patterns recognition using independent component analysis and support vector machine

Effective recognition of control chart patterns (CCPs) is an important issue since abnormal patterns exhibited in control charts can be associated with certain assignable causes which affect the process. Most of the existing studies assume that the observed process data which needs to be recognized are basic types of abnormal CCPs. However, in practical situations, the observed process data could be mixture patterns, which consist of two basic CCPs combined together. In this study, a hybrid scheme using independent component analysis (ICA) and support vector machine (SVM) is proposed for CCPs recognition. The proposed hybrid ICA-SVM scheme initially applies an ICA to the mixture patterns in order to generate independent components (ICs). The hidden basic patterns of the mixture patterns can be discovered in these ICs. The ICs can then serve as the input variables of the SVM for building a CCP recognition model. Experimental results revealed that the proposed scheme is able to effectively recognize mixture control chart patterns and outperform the single SVM models, which did not use an ICA as a preprocessor.     

k-近邻关系下的空间高效用核模式挖掘

空间数据挖掘旨在从空间数据库中发现和提取有价值的潜在知识.空间co-location(共存)模式挖掘一直以来都是空间数据挖掘领域的重要研究方向之一,其目的 是发现一组频繁邻近出现的空间特征子集,而空间高效用co-location模式挖掘则考虑了特征的效用属性.二者在度量空间实例的邻近关系时一般都需要预先给定一个距离阈值...     

An efficient continuous k-nearest neighbor query processing scheme for multimedia data sharing and transmission in location based services

The continuous k-nearest neighbor query is one of the most important query types to share multimedia data or to continuously identify transportable users in LBS. Various methods have been proposed to efficiently process the continuous k-NN query. However, most of the existing methods suffer from high computation time and larger memory requirement because they unnecessarily access cells to find the nearest cells on a grid index. Furthermore, most methods do not consider the movement of a query. In this paper, we propose a new processing scheme to process the continuous k nearest neighbor query for efficiently support multimedia data sharing and transmission in LBS. The proposed method uses the patterns of the distance relationships among the cells in a grid index. The basic idea is to normalize the distance relationships as certain patterns. Using this approach, the proposed scheme significantly improves the overall performance of the query processing. It is shown through various experiments that our proposed method outperforms the existing methods in terms of query processing time and storage overhead.

     

Boosting performance in attack intention recognition by integrating multiple techniques

Recognizing attack intention is crucial for security analysis. In recent years, a number of methods for attack intention recognition have been proposed. However, most of these techniques mainly focus on the alerts of an intrusion detection system and use algorithms of low efficiency that mine frequent attack patterns without reconstructing attack paths. In this paper, a novel and effective method is proposed, which integrates several techniques to identify attack intentions. Using this method, a Bayesian-based attack scenario is constructed, where frequent attack patterns are identified using an efficient data-mining algorithm based on frequent patterns. Subsequently, attack paths are rebuilt by recorrelating frequent attack patterns mined in the scenario. The experimental results demonstrate the capability of our method in rebuilding attack paths, recognizing attack intentions as well as in saving system resources. Specifically, to the best of our knowledge, the proposed method is the first to correlate complementary intrusion evidence with frequent pattern mining techniques based on the FP-Growth algorithm to rebuild attack paths and to recognize attack intentions.     

基于数据流频繁闭合模式的挖掘

用挖掘频繁闭合模式集代替挖掘频繁模式集是近年来提出的一个重要策略。根据数据流的特点,提出了一种基于滑动窗口的频繁闭合模式的新方法DSFC_Mine。该算法以滑动窗口中的基本窗口为更新单位,利用改进的CHARM算法计算每个基本窗口的潜在频繁闭合项集,将它们存储到一种新的数据结构中,利用该数据结构可以快速地挖掘滑动窗口中的所有频繁闭合项集。实验验证了该算法在时间上和空间上的可行性和有效性。     

基于CV／CAD的三维物体几何建模

在虚拟现实等技术领域中,都涉及到由现实世界中的实际景物建立对应的计算机描述的虚拟景物的问题,为此提出了利用计算机视觉与CAD几何建模技术相结合的三维珠体建模途径,首先通过编码光栅方法获取三维物体的深度图象,并采用数学形态学的方法加以分割,然后利用代数曲面拟合手段对分割后的三维曲面片进行重建,并使用CAD几何建模工具由重建的曲面片构成物体的几何模型,该文给出了初步的实验结果,证明所提出的技术途径基本可行。     

LBP与鉴别模式结合的热红外人脸识别

为了提取具有鉴别能力的红外人脸图像局部结构特征,提出一种基于LBP(local binary pattern)鉴别模式的红外人脸识别方法。传统的LBP均匀模式,提取自然图像中占主导地位的信息用于识别,但占主导地位的信息不一定是最适合识别的。为了提取有效的鉴别模式特征,基于监督学习的思想,在LBP模式下引入可分性标准,对不同LBP模式进行有效的模式选择,从而抽取适合识别的鉴别模式。最后,为了利用人脸的空间位置信息,结合分块和直方图技术得到最后的识别特征。实验结果表明,本文鉴别模式可以提取更适合识别的特征,识别性能优于传统的基于均匀模式的LBP方法。     

Parametric design of garment pattern based on body dimensions

Garment pattern-making is one of the most important parts in fashion design and production. However, the traditional pattern-making is an experience based work and very time-consuming. In this paper, we proposed a parametric design method of garment pattern based on body dimensions. Based on this method, we constructed a jeans' pattern recommendation system. The input items of the proposed system are three geometric constraint parameters (jean silhouette type, length and waist height) and three-dimensional constraint parameters (human body stature, waist girth and hip girth); the output of the proposed system are jeans' patterns. Also, four adjustable parameters (jeans' length, waist height, knee and leg opening) are designed to adjust patterns generated by the proposed system. If the jeans' pattern is not satisfying after virtual or real try-on, the adjustable input parameters of the proposed system can be applied for adjustment until the patterns are acceptable. Our proposed system can combine traditional pattern-making methods to generate jeans’ patterns automatically and rapidly, hence improving pattern-making efficiency significantly.     

A Method of Face Recognition Based on Fuzzy c-Means Clustering and Associated Sub-NNs

The face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. In this paper, we present a method for face recognition based on parallel neural networks. Neural networks (NNs) have been widely used in various fields. However, the computing efficiency decreases rapidly if the scale of the NN increases. In this paper, a new method of face recognition based on fuzzy clustering and parallel NNs is proposed. The face patterns are divided into several small-scale neural networks based on fuzzy clustering and they are combined to obtain the recognition result. In particular, the proposed method achieved a 98.75% recognition accuracy for 240 patterns of 20 registrants and a 99.58% rejection rate for 240 patterns of 20 nonregistrants. Experimental results show that the performance of our new face-recognition method is better than those of the backpropagation NN (BPNN) system, the hard c-means (HCM) and parallel NNs system, and the pattern-matching system