改进PSO-SVM在说话人识别中的应用

为了加快粒子群优化算法的收敛速度,增强全局的搜索能力,通过对粒子群优化算法中惯性权重和全局最优值的分析,提出了一种根据迭代次数而自适应变化的惯性权重的粒子群优化方法。改进后的粒子群算法在防止陷入局部最优的能力方面有了明显的增强,同时,给出了应用粒子群优化算法训练支持向量机的方法,并将其应用于说话人识别。实验结果证实了在说话人识别中改进PSO-SVM方法比其他传统方法能获得更好的识别精度和识别速度。     

A survey of scheduling problems with late work criteria

The paper presents the first complete survey of scheduling problems with the late work criteria. Late work objective functions estimate the quality of a schedule based on durations of late parts of jobs, not taking into account the amount of delay for fully late jobs. The paper provides a formal definition of the late work parameter and compares the criteria based on it with other classical performance measures. It shows the relationship between the late work model and the imprecise computation model known from the hard real-time literature. Moreover, the paper presents a few real world applications of the late work objective function. The paper lists results obtained for nearly forty problems of scheduling jobs on a single machine, parallel (identical and uniform) machines and dedicated machines, investigated in the literature since 1984.     

我国英语翻译教学研究:问题与对策

对近年来我国翻译教学研究的相关文献调查分析后发现,目前我国英语翻译教学研究在教学研究内容、研究途径和方法以及教学指导思想等四个方面存在着一些不足,即翻译教学研究学科单一化,翻译教学研究手段单一化,翻译教学研究定位笼统化和翻译教学研究内容技能化。作者在分析了相关的原因背景基础之上简要概述了相应的对策。     

Bayesian Additive Machine: classification with a semiparametric discriminant function

In this paper, we propose a new Bayesian inference approach for classification based on the traditional hinge loss used for classical support vector machines, which we call the Bayesian Additive Machine (BAM). Unlike existing approaches, the new model has a semiparametric discriminant function where some feature effects are nonlinear and others are linear. This separation of features is achieved automatically during model fitting without user pre-specification. Following the literature on sparse regression of high-dimensional models, we can also identify the irrelevant features. By introducing spike-and-slab priors using two sets of indicator variables, these multiple goals are achieved simultaneously and automatically, without any parameter tuning such as cross-validation. An efficient partially collapsed Markov chain Monte Carlo algorithm is developed for posterior exploration based on a data augmentation scheme for the hinge loss. Our simulations and three real data examples demonstrate that the new approach is a strong competitor to some approaches that were proposed recently for dealing with challenging classification examples with high dimensionality.     

新型注射机控制系统设计

介绍了用S5－115U型PC对注射机实行闭环控制的原理，软件、硬件组成等内容，用本系统控制的注射机具有控制功能多、精度多、可靠性强、灵活方便等特点。     

基于支持向量机回归模型的重庆市房地产金融风险预警研究

以房地产金融风险单项指标的评价标准为依据,利用基于支持向量机（SVM）的回归分析模型对重庆市房地产的金融风险状况进行预警分析研究。通过研究评价指标变量与模型输出数值之间的关系,分析房地产金融风险状况与各评价指标变量的非线性关系,预测出各年度的金融风险状况及金融风险的走势,从而为有针对性地降低金融风险水平制定相应的宏观调控政策提供理论参考。     

基于TwinCAT的袖衩机数控系统

针对目前市场上的数控系统在袖衩机上应用的开放性、通用性以及兼容性程度低的问题,从新型自动袖衩机的 工作原理和机械结构出发,对控制系统的硬件进行了选型,并采用了基于IPC平台结合PROFIBUS现场总线,利用某公 司的TwinCAT工控软件平台的软件式控制方案,设计出了符合系统要求的人机界面,实现了控制系统软件部分的功能。 结果表明该系统简化了袖衩机数控系统操作程序,提高了袖衩机数控系统的开放性、通用性和兼容性。     

新型全电动式注塑机直拉式电动射移机构

采用射移丝杠安装于机架方式的全电动式注塑机电动射移机构,易造成推力块的力臂结构长度过长。当喷嘴顶 靠于模具时,由于过大偏载力矩的作用而产生上翘现象。针对性地设计开发了新型直拉式电动射移机构。该机构将注 射单元通过直拉式射移机构直拉于定模板上,大大减小了全电动式注塑机射移力系的封闭回路里出力机构的射移丝杠 中心线与机筒中心线的距离,使机架不再需要承受原有设计由于射移最终阶段喷嘴顶紧模具浇口产生的顶靠力,从而大 大改善了射移机构和喷嘴的受力情况,同时也简化了机架结构,进而减轻了机架的结构重量。     

机械零件图像表面瑕疵的检测算法

针对机械零件表面瑕疵检测问题,将机器视觉技术用于零件表面图像瑕疵的提取和分析,提出一种基于粒子群 优化算法加权模糊C均值聚类的零件缺陷图像智能分割算法,精确定位了机械零件表面的瑕疵区域。缺陷的形状特征 是判断其类型的重要依据,提取缺陷的形状特征,设计支持向量机分类器来检测划痕、裂纹、砂眼等表面瑕疵。研究结果 表明,该方法具有较强的实用性,在实验数据库上达到90%以上的正确识别率。     

Runtime and memory consumption analyses for machine learning R programs

R is a multi-paradigm language with a dynamic type system, different object systems and functional characteristics. These characteristics support the development of statistical algorithms at a high level of abstraction. Although R is commonly used in the statistics domain a big disadvantage are its runtime problems when handling computation-intensive algorithms. Especially in the domain of machine learning the execution of pure R programs is often unacceptably slow. Our long-term goal is to resolve these issues and in this contribution we used the traceR tool to analyse the bottlenecks arising in this domain. Here we measured the runtime and overall memory consumption on a well-defined set of classical machine learning applications and gained detailed insights into the performance issues of these programs.