Managing readiness-relevant activities for the organizational dimension of ERP implementation

An organization requires performing readiness-relevant activities to ensure successful implementation of an enterprise resource planning (ERP) system. This paper develops a novel approach to managing these interrelated activities to get ready for implementing an ERP system. The approach enables an organization to evaluate its ERP implementation readiness by assessing the degree to which it can achieve the interrelated readiness relevant activities using fuzzy cognitive maps. Based on the interrelationship degrees among the activities, the approach clusters the activities into manageable groups and prioritizes them. To help work out a readiness improvement plan, scenario analysis is conducted.     

ECG heart beat classification method based on modified ABC algorithm

Electrocardiogram is the most commonly used tool for the diagnosis of cardiologic diseases. In order to help cardiologists to diagnose the arrhythmias automatically, new methods for automated, computer aided ECG analysis are being developed. In this paper, a Modified Artificial Bee Colony (MABC) algorithm for ECG heart beat classification is introduced. It is applied to ECG data set which is obtained from MITBIH database and the result of MABC is compared with seventeen other classifier's accuracy.In classification problem, some features have higher distinctiveness than others. In this study, in order to find higher distinctive features, a detailed analysis has been done on time domain features. By using the right features in MABC algorithm, high classification success rate (99.30%) is obtained. Other methods generally have high classification accuracy on examined data set, but they have relatively low or even poor sensitivities for some beat types. Different data sets, unbalanced sample numbers in different classes have effect on classification result. When a balanced data set is used, MABC provided the best result as 97.96% among all classifiers.Not only part of the records from examined MITBIH database, but also all data from selected records are used to be able to use developed algorithm on a real time system in the future by using additional software modules and making adaptation on a specific hardware.     

Modeling and analysis of passive worm propagation in the P2P file-sharing network

A number of worms, named P2P (peer-to-peer) passive worms, have recently surfaced, which propagate in P2P file-sharing networks and have posed heavy threats to these networks. In contrast to the majority of Internet worms, it is by exploiting users’ legitimate activities instead of vulnerabilities of networks in which P2P passive worms propagate. This feature evidently slows down their propagation, which results in them not attracting an adequate amount of attention in literature. Meanwhile, this feature visibly increases the difficulty of detecting them, which makes it very possible for them to become epidemic. In this paper, we propose an analytical model for P2P passive worm propagation by adopting epidemiological approaches so as to identify their behaviors and predict the tendency of their propagation accurately. Compared with a few existing models, dynamic characteristics of P2P networks are taken into account. Based on this proposed model, the sufficient condition for the global stability of the worm free equilibrium is derived by applying epidemiological theories. Large scale simulation experiments have validated both the proposed model and the condition.     

市区微蜂窝传播路径损耗模式的比较检验

讨论了几种微蜂窝路径损耗模式,并用我国某城市测量的微蜂窝传播数据对COST231WI,修正COST231WI和Xia模式进行了检验,结果表明修正COST231模式的误差最小,Xia模式次之,COST231WI模式最差。从而得到修正COST231模式比较符合我国实际的微蜂窝传播预测模式。     

基于聚类算法实现信号盲分类

本文解决了信号处理、工业控制等领域存在的非平稳信号盲分类问题。在聚类中广泛应用的K-Means算法及其它基于中心的聚类算法有两个共同的缺陷-需要预先确定类数目且随机初始化中心引起性能不稳定。本文提出的算法较好地解决了这两个问题，提高了算法稳定性，实现了非平稳信号盲分类。提取非平稳信号的小波系数作为聚类的样本空间，分析聚类结果的统计偏差以估计类的数目，采用调和均值准则进行分类。最后给出的仿真结果表明本文提出的方法较传统的K-Means算法明显降低分类错误率。     

关于海面无线传播模型的探讨

现有文献中一般都认为海面环境的无线传播模型近似于自由空间传播模型，但在实际测试中发现，自由空间模型预测值与实测值差别较大，不能很好地解释此类环境的信号传播。笔者根据多次海面覆盖测试的数据，分析并拟合出了新的海面传播模型。     

Thin hard coatings fracture propagation during the impact test

One of the most significant applications of the impact test is to investigate thin hard coatings fatigue properties. Herein the test conditions and duration up to the film damage initiation, are considered in order to determine the critical stresses associated with the coating fatigue strength. Moreover, the subsequent film damage propagation is a significant mechanism as well, since it refers to the ability of the coating to withstand loads after its fatigue damage initiation. In order to describe the film fracture propagation the failed area ratio was introduced and an algorithm to determine this magnitude developed, based on the analysis of imprint scanning electron microscopy graphs. The application of the coating failed area ratio will be demonstrated in various impact test film cases, also with superficial thin layers. The top layers do not affect, in general, the failure initiation of the basic coating. However, according to the obtained results, the superficial films influence the basic coating failure propagation rate during the impact test.     

Elastoplastic modelling of subsurface crack growth in rail/wheel contact problems

Propagation of small subsurface cracks subjected to shear under repeated rolling contact load is studied. An analytical crack model (Dugdale) with plastic strips at the two crack tips is employed. Compressive stresses promoting crack closure and friction between crack faces are considered. The triaxial stress state is used in the yield criterion. A damage criterion is suggested based on experimental LCF data. In a numerical study, critical crack lengths are found below which propagation of an existing crack should be effectively suppressed.     

A fuzzy clustering algorithm enhancing local model interpretability

In this work, simple modifications on the cost index of particular local-model fuzzy clustering algorithms are proposed in order to improve the readability of the resulting models. The final goal is simultaneously providing local linear models (reasonably close to the plant’s Jacobian) and clustering in the input space so that desirable characteristics (regarding final model accuracy, and convexity and smoothness of the cluster membership functions) are improved with respect to other proposals in literature. Some examples illustrate the proposed approach.