Generating Test Sets from Non-Deterministic Stream X-Machines

X-machines were proposed by Holcombe as a possible specification language and since then a number of further investigations have demonstrated that the model is intuitive and easy to use as well as general enough to cater for a wide range of applications. In particular (generalised) stream X-machines have been found to be extremely useful as a specification method and most of the theory developed so far has concentrated on this particular class of X-machines. Furthermore, a method for testing systems specified by stream X-machines exists and is proved to detect all faults of the implementation provided that the system meets certain initial requirements. However, this method can only be used to generate test sequences from deterministic X-machine specifications. In this paper we present the theoretical basis for a method for generating test sets from non-deterministic generalised stream X-machines. Received November 1999 / Accepted in revised form September 2000     

Tabu search for scheduling on identical parallel machines to minimize mean tardiness

This paper presents a tabu search approach for scheduling jobs on identical parallel machines with the objective of minimizing the mean tardiness. Initially, we consider a basic tabu search that uses short term memory only. Local search is performed on a neighborhood defined by two types of moves. Insert moves consist of transferring each job from one machine to another and swap moves are those obtained by exchanging each pair of jobs between two machines. Next, we analyze the incorporation of two diversification strategies with the aim of exploring unvisited regions of the solution space. The first strategy uses long term memory to store the frequency of the moves executed throughout the search and the second makes use of influential moves. Computational tests are performed on problems with up to 10 machines and 150 jobs. The heuristic performance is evaluated through a lower bound given by Lagrangean relaxation. A comparison is also made with respect to the best constructive heuristic reported in the literature.     

核方法的探讨及其应用

核方法是机器学习中一种新的强有力的学习方法。针对核方法进行了探讨,给出了核方法的基本思想和优点。同时,描述了核方法的算法实现并举例进行了说明。     

Parameter determination of support vector machine and feature selection using simulated annealing approach

Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that does not degrade the SVM classification accuracy. This study develops a simulated annealing (SA) approach for parameter determination and feature selection in the SVM, termed SA-SVM.To measure the proposed SA-SVM approach, several datasets in UCI machine learning repository are adopted to calculate the classification accuracy rate. The proposed approach was compared with grid search which is a conventional method of performing parameter setting, and various other methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of grid search and other approaches. The SA-SVM is thus useful for parameter determination and feature selection in the SVM.     

Vector control for loss minimization of induction motor using GA–PSO

This paper deals with the GA–PSO (genetic algorithm–particle swarm optimization) based vector control for loss minimization operation of induction motor. It is estimated that more than around 50% of the world electric energy generated is consumed by electric machines such as induction motor, dc motor. So, improving efficiency in electric drives is important and control strategy for minimum energy loss is needed as one of optimal operation strategies. The vector control of induction motor has been widely used to operate in a wide speed range by using flux weakening at rated speed. However, it is still necessary to advance because of coupling is behavior between fluxes in motor. In this paper, vector control approach is suggested for an optimal operation of induction motor using variable acceleration and GA–PSO tuning method through simulation. We can obtain satisfactory results for energy saving control.     

A distributed PSO–SVM hybrid system with feature selection and parameter optimization

This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to improve the classification accuracy with a small and appropriate feature subset. This optimization mechanism combined the discrete PSO with the continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVM kernel parameter setting. The hybrid PSO–SVM data mining system was implemented via a distributed architecture using the web service technology to reduce the computational time. In a heterogeneous computing environment, the PSO optimization was performed on the application server and the SVM model was trained on the client (agent) computer. The experimental results showed the proposed approach can correctly select the discriminating input features and also achieve high classification accuracy.     

基于二叉级联结构的并行极速学习机算法

为解决因庞大的矩阵存储和计算,ELM（Extreme Learning Machines）难以应用到大规模、高维数据集的问题,提出一种基于＂分而治之＂策略的并行极速学习机算法。该算法利用二叉级联结构,将大规模数据集分派到多个计算节点上,并行地更新单隐层前馈网络的输出权值,且能有限步地单调收敛到最小二乘解。实验结果表明,该算法不仅泛化性能优异,并且具有非常高的加速比和并行效率。     

基于云隶属度支持向量机的舰船购置费时间序列预测

模糊支持向量机降低了传统支持向量机对异常点的敏感度,但其模糊隶属度函数对样本点的分类缺乏模糊性,影响舰船购置费预测的精度。因此,利用云理论能够科学表达模糊性的特点,设计了一种面向异常点模糊分类的云隶属度发生器;在支持向量机中引入这种云隶属度发生器,提出了一种基于云隶属度的支持向量机算法;构建了基于云隶属度支持向量机的舰船购置费时间序列预测模型。实验证明：该算法模糊地降低了模型对异常点的敏感度,并自适应地对支持向量约束水平进行寻优,提高了舰船购置费预测的精度。     

A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach

Predicting corporate credit-rating using statistical and artificial intelligence (AI) techniques has received considerable research attention in the literature. In recent years, multi-class support vector machines (MSVMs) have become a very appealing machine-learning approach due to their good performance. Until now, researchers have proposed a variety of techniques for adapting support vector machines (SVMs) to multi-class classification, since SVMs were originally devised for binary classification. However, most of them have only focused on classifying samples into nominal categories; thus, the unique characteristic of credit-rating - ordinality - seldom has been considered in the proposed approaches. This study proposes a new type of MSVM classifier (named OMSVM) that is designed to extend the binary SVMs by applying an ordinal pairwise partitioning (OPP) strategy. Our model can efficiently and effectively handle multiple ordinal classes. To validate OMSVM, we applied it to a real-world case of bond rating. We compared the results of our model with those of conventional MSVM approaches and other AI techniques including MDA, MLOGIT, CBR, and ANNs. The results showed that our proposed model improves the performance of classification in comparison to other typical multi-class classification techniques and uses fewer computational resources.     

Virtio network paravirtualization driver: Implementation and performance of a de-facto standard

One of the techniques used to improve I/O performance of virtual machines is paravirtualization. Paravirtualized devices are intended to reduce the performance overhead on full virtualization where all hardware devices are emulated. The interface of a paravirtualized device is not identical to that of the underlying hardware. The OS of the virtual guest machine must be ported in order to use a paravirtualized device. In this paper, the network virtualization done by the Kernel-based Virtual Machine (KVM) is described. The KVM model is different from other Virtual Machines Monitors (VMMs) because the KVM is a Linux kernel model and it depends on hardware support. In this work, the overhead of using such virtual networks is been measured. A paravirtualized model by using the virtio [38] network driver is described, and some performance results of web benchmark on the two models are presented.