Mining closed patterns in multi-sequence time-series databases

In this paper, we propose an efficient algorithm, called CMP-Miner, to mine closed patterns in a time-series database where each record in the database, also called a transaction, contains multiple time-series sequences. Our proposed algorithm consists of three phases. First, we transform each time-series sequence in a transaction into a symbolic sequence. Second, we scan the transformed database to find frequent patterns of length one. Third, for each frequent pattern found in the second phase, we recursively enumerate frequent patterns by a frequent pattern tree in a depth-first search manner. During the process of enumeration, we apply several efficient pruning strategies to remove frequent but non-closed patterns. Thus, the CMP-Miner algorithm can efficiently mine the closed patterns from a time-series database. The experimental results show that our proposed algorithm outperforms the modified Apriori and BIDE algorithms.     

An approach for temporal analysis of email data based on segmentation

Many kinds of information are hidden in email data, such as the information being exchanged, the time of exchange, and the user IDs participating in the exchange. Analyzing the email data can reveal valuable information about the social networks of a single user or multiple users, the topics being discussed, and so on. In this paper, we describe a novel approach for temporally analyzing the communication patterns embedded in email data based on time series segmentation. The approach computes egocentric communication patterns of a single user, as well as sociocentric communication patterns involving multiple users. Time series segmentation is used to uncover patterns that may span multiple time points and to study how these patterns change over time. To find egocentric patterns, the email communication of a user is represented as an item-set time series. An optimal segmentation of the item-set time series is constructed, from which patterns are extracted. To find sociocentric patterns, the email data is represented as an item-setgroup time series. Patterns involving multiple users are then extracted from an optimal segmentation of the item-setgroup time series. The proposed approach is applied to the Enron email data set, which produced very promising results.     

An optimized sequential pattern matching methodology for sequence classification

In this paper we present a novel methodology for sequence classification, based on sequential pattern mining and optimization algorithms. The proposed methodology automatically generates a sequence classification model, based on a two stage process. In the first stage, a sequential pattern mining algorithm is applied to a set of sequences and the sequential patterns are extracted. Then, the score of every pattern with respect to each sequence is calculated using a scoring function and the score of each class under consideration is estimated by summing the specific pattern scores. Each score is updated, multiplied by a weight and the output of the first stage is the classification confusion matrix of the sequences. In the second stage an optimization technique, aims to finding a set of weights which minimize an objective function, defined using the classification confusion matrix. The set of the extracted sequential patterns and the optimal weights of the classes comprise the sequence classification model. Extensive evaluation of the methodology was carried out in the protein classification domain, by varying the number of training and test sequences, the number of patterns and the number of classes. The methodology is compared with other similar sequence classification approaches. The proposed methodology exhibits several advantages, such as automated weight assignment to classes using optimization techniques and knowledge discovery in the domain of application.

基于稀疏性非负矩阵分解和支持向量机的时频图像识别

针对机械故障诊断领域对反映设备运行状态的图像识别困难以及选择和提取敏感特征困难的问题, 将基于图像的机械设备运行状态判别问题当作图像的识别问题来处理, 提出使用稀疏性非负矩阵分解(Sparse non-negative matrix factorization, SNMF)和支持向量机(Support vector machine, SVM)对时频图像进行识别进而判断机器运行状态, 从而避免特征的选择和提取. 稀疏性非负矩阵分解在对时频图像进行大规模压缩的同时, 能够很好地保留图像的隐含特征, 从而大大减少自动识别时频图像的计算复杂度, 并有效地提高支持向量机的识别精度. 此外, 本文还对影响识别率的稀疏性非负矩阵分解的各参数进行了讨论. 实验结果表明, 该方法对时频处理方法依赖性低, 在大多数情况下都能获得较传统方法高的识别率.     

Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases

Abstract: The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.     

A novel hybrid immune algorithm for global optimization in design and manufacturing

This paper presents a new hybrid optimization approach based on immune algorithm and hill climbing local search algorithm. The purpose of the present research is to develop a new optimization approach for solving design and manufacturing optimization problems. This research is the first application of immune algorithm to the optimization of machining parameters in the literature. In order to evaluate the proposed optimization approach, single objective test problem, multi-objective I-beam and machine-tool optimization problems taken from the literature are solved. Finally, the hybrid approach is applied to a case study for milling operations to show its effectiveness in machining operations. The results of the hybrid approach for the case study are compared with those of genetic algorithm, the feasible direction method and handbook recommendation.     

Multi-agent collaborative 3D design with geometric model at different levels of detail

This paper presents a scheme for collaborative 3D design using product model at various levels of detail (LODs). Design features are selectively hidden at each level from certain participants, depending on their actual needs and individual accessibility in the collaboration. A tree data structure represents the feature hierarchy of CAD construction, the link between feature and LOD, and 2D mesh data for display control of each feature. An XML/XSLT-based approach is proposed to enable real-time visualization of different LOD models in distributed environment. A collaborative design system is implemented using multi-agent technologies, which focuses on function design of each agent, interactions among agents, the client–server structure, and generation of the LOD data using the XML/XSLT approach. A scenario of synchronous 3D mold assembly demonstrates that geometric categorization of product model provides an operational mechanism for assuring security of information sharing in engineering collaborations over the Internet. It also validates the effectiveness of the agent technologies for automating complex engineering activities.     

Grounded design: Design patterns as the link between theory and practice

The process of designing a networked learning environment can be characterized by a high degree of uncertainty. This is especially true when the design includes innovative technologies. It is difficult to design a networked learning environment that closely fits the requirements of the educational practice. Often the technology seems leading in the design process because it is the most tangible aspect of the e-learning environment. This brings along the risk that the technology becomes detached from its context of use.Design patterns are a means to overcome this problem of detachment between design and practice. Design patterns make the tight relationship between the two more explicit. In this paper we further elaborate on the relationship between design and practice. We will stress the importance of theory in the development of design patterns. Theory development is an essential aspect of our approach to the development of design patterns. The approach is oriented towards a systematic analysis of problems and the development of conceptual models that guide the definition and evaluation of design patterns. In this paper we present a case study of how the approach was used to develop design patterns for networked learning in the classroom.     

流密码算法RC4的教学设计

针对流密码算法RC4教学过程中存在难以理解的问题,在分析RC4算法的基础上,本文提出了一种基于摸球模型的RC4算法教学模型和教学过程,在密码学课程教学方面取得了良好的效果。     

军工软件测试管理与文档生成工具的研究与实现

本文从军工软件测试流程出发,探讨了军工测试管理与文档生成工具的研究与实现。文章首先对军工测试管理工具软件系统架构、软件设计模型进行分析,并以此为基础,对军工软件测试管理工具的服务器端、客户端、文档生成工具进行设计与实现。实现的军工软件测试管理与文档生成工具很大程度上解决了现有工具存在的中文本地化支持不好、测试用例管理不稳定、与现有军工测试流程不符、操作复杂、不能生成军工测试各阶段文档等问题,并已在实际的军工测试项目中得到广泛应用。