Efficient prolog programming

This paper presents some basic programming strategies for Prolog programmers. The strategies are based on knowledge about the operational behaviour of the Warren Abstract Machine (WAM). The aim is to provide simple principles for making programs both faster and less space-consuming. The programming hints given are mostly local, i.e. only a single clause or procedure need be considered at a time.     

Integrated distributed intelligent system for process-control system design

In process-control system design, the Computer Aided Design (CAD) techniques have evolved a new generation of design techniques. This also paves the way for implementing Computer Integrated Manufacturing Systems (CIMS). However, the key issue to accomplishing the objectives in CAD is the automation of conceptual design. This paper first introduces the fundamentals of conceptual design for process-control system design. Then, a strategy to solve the problems in conceptual design is proposed. The associated system configuration and software implementation platform for developing intelligent systems for process control system design are presented. Finally, an application case is studied.     

Quadratic optimization fine tuning for the Support Vector Machines learning phase

This work presents a comparative analysis of specific, rather than general, mathematical programming implementation techniques of the quadratic optimization problem (QP) based on Support Vector Machines (SVM) learning process. Considering the Karush–Kuhn–Tucker (KKT) optimality conditions, we present a strategy of implementation of the SVM-QP following three classical approaches: (i) active set, also divided in primal and dual spaces, methods, (ii) interior point methods and (iii) linearization strategies. We also present the general extension to treat large-scale applications consisting in a general decomposition of the QP problem into smaller ones, conserving the exact solution approach. In the same manner, we propose a set of heuristics to take into account for a better than a random selection process for the initialization of the decomposition strategy. We compare the performances of the optimization strategies using some well-known benchmark databases.     

An integrated job release and scheduling approach on parallel machines: An application in electric wire-harness industry

This paper presents a new job release (JR) and scheduling methodology for one-stage parallel machines where sequence dependent setup times exist. A decision support system (DSS) based on job release is developed in order to enable the application of the methodology. First, mathematical programming models for both job release and job scheduling are devised. Then, due to the NP-hard nature of the problems, heuristics are proposed. As for the interaction between JR and scheduling, job scheduling is integrated with job release for the proposed heuristic solutions so that the capacity achievement provided by scheduling can be utilized for job release. In brief, product design characteristics oriented scheduling affects JR in the proposed approach. Moreover, value stream mapping (VSM) approach is used with the aim of stating the effect of the proposed methodology. Furthermore, the presented methodology was applied in a real life electric wire-harness production system. The application, based on 120-day production data, revealed that the proposed methodology provided 25% decrease in in-plant manufacturing lead time.     

Feature selection via sensitivity analysis of SVM probabilistic outputs

Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection method for the Support Vector Machine (SVM) learning. Like most feature-selection methods, the proposed method ranks all features in decreasing order of importance so that more relevant features can be identified. It uses a novel criterion based on the probabilistic outputs of SVM. This criterion, termed Feature-based Sensitivity of Posterior Probabilities (FSPP), evaluates the importance of a specific feature by computing the aggregate value, over the feature space, of the absolute difference of the probabilistic outputs of SVM with and without the feature. The exact form of this criterion is not easily computable and approximation is needed. Four approximations, FSPP1-FSPP4, are proposed for this purpose. The first two approximations evaluate the criterion by randomly permuting the values of the feature among samples of the training data. They differ in their choices of the mapping function from standard SVM output to its probabilistic output: FSPP1 uses a simple threshold function while FSPP2 uses a sigmoid function. The second two directly approximate the criterion but differ in the smoothness assumptions of criterion with respect to the features. The performance of these approximations, used in an overall feature-selection scheme, is then evaluated on various artificial problems and real-world problems, including datasets from the recent Neural Information Processing Systems (NIPS) feature selection competition. FSPP1-3 show good performance consistently with FSPP2 being the best overall by a slight margin. The performance of FSPP2 is competitive with some of the best performing feature-selection methods in the literature on the datasets that we have tested. Its associated computations are modest and hence it is suitable as a feature-selection method for SVM applications. Editor: Risto Miikkulainen.     

基于ECoG的运动想象脑-机接口分类方法

脑—机接口BCI(Brain-Computer Interface)技术是近年来国际上的研究热点之一,它通常利用脑电EEG(electroencephalo-gram)来实现无动作的人机交互,运动想象是其中一种重要的BCI实验范式。有关研究表明,脑皮层电位ECoG(electrocorticogram)具有更好的信噪比与频带特性。研究基于ECoG的运动想象BCI系统,针对ECoG信号的特点,改进了信号处理方法,提取数据的公共空间模式CSP(Common Spatial Pattern)特征,并利用支持向量机SVM(Support Vector Machines)进行分类器设计,提高了运动意向的识别正确率。用相应方法处理2005年脑-机接口竞赛中的一组实验数据,正确率达到92%,相比于当时参赛时所用的方法提高了6%。实验还发现,支持向量机在克服"维数灾难"和"过拟合"方面具有更好的鲁棒性。     

一种改进的在线最小二乘支持向量机回归算法

针对一般最小二乘支持向量机处理大规模数据集会出现训练速度幔、计算量大、不易在线训练的缺点,将修正后的遗忘因子矩形窗方法与支持向量机相结合,提出一种基于改进的遗忘因子矩形窗算法的在线最小二乘支持向量机回归算法,既突出了当前窗口数据的作用,又考虑了历史数据的影响.所提出的算法可减少计算量,提高在线辨识精度.仿真算例表明了该方法的有效性.     

嵌入式系统控制设计思想在材料试验机中的应用

介绍了嵌入式系统控制设计思想的优势,根据嵌入式系统控制设计的特点和技术要求,阐明材料试验机的特点,并以电子蠕变及持久强度试验机为例说明了嵌入式控制系统设计思想的多方面的优势和发展前景。     

一种基于双线性链表结构编码的遗传算法

针对基于以往经典编码方式的遗传算法解决并行机大批量成组调度问题时的弊端,提出了一种基于双线性链表结构编码的遗传算法（DLGA）,并设计了相应的遗传算子。应用于某轮胎厂的硫化工序调度问题,进行了仿真分析。     

基于支持向量机的计算资源反馈调度

在实时控制系统(RTCS)中,计算资源一般受限。由于诸多不确定性因素和工作负载的动态变化,系统总是运行在不可预期的开放环境中。为保证系统的稳定运行,提出了一种基于支持向量机(SVM)的计算资源反馈调度方法。它周期性地监测系统计算资源,通过SVM在线预测,得到各个控制回路的下一个采样周期,从而实现系统计算资源的动态分配。通过仿真实验验证了该反馈调度方法的性能,并与理想情况、传统开环调度方法进行了比较,显示了其优越性。