选粉机气流压损分析与结构优化

根据选粉机构造与流形将选粉机的流体腔划分为6个子腔体,并采用数值模拟的方法得到各腔体的压损比例,发现送料筒腔体、出口腔体与内腔体这三个腔体的压损之和达到了整机压损的86.66%。针对送料筒腔体的气流对冲损耗,设计了侧环面风口结构。针对内腔体内的旋流损耗和出口腔体内转向损耗,设计了涡流打散锥结构。实验结果表明,所设计的优化结构不影响选粉机的分级性能,且能有效的降低选粉机气流压损,节能比例在14.30%~19.80%之间,节能效果良好。     

Single-stage recovery and concentration of mineral sands using a REFLUX™ Classifier

The REFLUX™ Classifier is a gravity separation device that consists of a system of inclined channels located above a fluidized bed. Here we report for the first time the results obtained processing a minerals sands feed using narrow 6 mm channels that promote a laminar-shear separation mechanism that enhances the separation based on density. The feed had a head grade of approximately 5 wt% heavy minerals of density greater than 2800 kg/m³, with the majority in the size range from 50 to 150 μm. The overall recovery of the heavy minerals component was approximately constant at 85% for solids throughputs of 10–18.5 t/(m² h). The unrecovered heavy mineral had an average density close to that of the gangue, hence was deemed unrecoverable by gravity separation. The recovery of the denser and more liberated zirconium mineral exceeded 95%. So this work demonstrated the potential for the heavy mineral to be upgraded by a factor of 16 or more in a single stage operation.     

Design and development of automatic visual inspection system for PCB manufacturing

Inspection of solder joints has been a critical process in the electronic manufacturing industry to reduce manufacturing cost, improve yield, and ensure product quality and reliability. This paper proposes two inspection modules for an automatic solder joint classification system. The “front-end” inspection system includes illumination normalisation, localisation and segmentation. The “back-end” inspection involves the classification of solder joints using the Log-Gabor filter and classifier fusion. Five different levels of solder quality with respect to the amount of solder paste have been defined. The Log-Gabor filter has been demonstrated to achieve high recognition rates and is resistant to misalignment. This proposed system does not need any special illumination system, and the images are acquired by an ordinary digital camera. This system could contribute to the development of automated non-contact, non-destructive and low cost solder joint quality inspection systems.     

The build of n-Bits Binary Coding ICBP Ensemble System

To optimize the construct of an ensemble of many relatively small and simple classifiers may be more realistic than to optimize the design of an individual large and complex classifier. Problems of local minima and slow convergence may be mitigated within an ensemble system where the decisions obtained from locally optimal components classifiers are integrated together. However, it is very difficult to design the structure of individual neural networks (NNs) in an ensemble and the architecture of a whole ensemble. In n-Bits Binary Coding ICBP Ensemble System (nBBC-ICBP-ES), the crucial parameter that is required to be set a priori is an appropriate number of hidden nodes of the corresponding improved circular back-propagation (ICBP) root model. Thereby, both the number of individual ICBPs and the architecture of each ICBP component in an nBBC-ICBP-ES can be decided straightly. nBBC-ICBP-ES is computationally more efficient, with relatively fewer user specified parameters, and does not need any manual division to the training data set for the purpose of its construction. It is rather easy to be understood and implemented, while inheriting the benefits of ICBP root model in a natural manner. Simulation and t-test results on four large-scale benchmark classification data sets demonstrated that, in most cases, nBBC-ICBP-ES significantly improved the classification and generalization performances of the two typically large single ICBPs, Same-2^N_h-ICBP-ES (i.e., an ensemble system with the same 2^N_h ICBP components), conventional Bagging and AdaBoost ensemble. We conclude that, for NN applications in pattern recognition, assembling many small NNs might be better than just utilizing an individual large one, and further, assembling many heterogeneous small NNs might be better than assembling many homogeneous ones. The proposed nBBC-ICBP-ES is simple but efficient and effective, and also potentially significant for the applications of NN ensemble.     

分布式多媒体服务组合中的异常预测算法

为提高分布式多媒体服务组合系统中路径的健壮性和可靠性,提出一种基于Markov链和加权朴素贝叶斯分类器(WNBC)的异常预测算法。该算法利用Markov模型预测系统节点的资源状态信息,使用WNBC对预测的节点状态进行分类,以判断节点是否可能发生异常。实验结果表明,该算法能根据节点的状态信息预测系统节点的不同异常状态,性能较同类算法有较大的改善。     

基于密度敏感距离的协同训练算法

半监督算法作为一种能有效利用大量的未标签数据来改善少量的标签数据训练出来的分类器性能的算法,无论在理论上还是实践上都很有意义。提出一种基于密度敏感距离的协同训练算法,通过引入一种能有效描述数据的内在聚类分布的距离度量,来改善用组合分类器在少量数据集上得到的弱分类器,实验表明该方法是有效的。     

基于空间支持向量域分类器的人脸识别

提出了一种基于小波变换、奇异值分解与空间支持向量域分类器相结合的人脸识别方法。在使用空间支持向量分类器对不同人脸图像的奇异特征向量进行分类时,计算所测样本到各个超球球心的距离,并根据其与超球半径的关系来判断其所归属。并在ORL人脸数据库中进行实验。实验表明提出的人脸识别方法识别精度可达97.5%。     

机器视觉和AdaBoost的柑桔溃疡病自动检测

将计算机视觉技术应用于柑桔病害识别问题,实现了快速准确的识别柑桔溃疡病.从特征构造,特征选择和识别系统设计三方面进行了研究.在特征构造上采用了Gabor变换,边缘识别等方法得到了包括颜色、纹理及形状的综合特征;在特征选择上采用了AdaBoost算法实现;最后通过AdaBoost学习方法构造分类器并利用滑动窗口技术进行病害区域检测.实验结果证明该方法对柑桔溃疡病其识别准确率高于95%,在训练轮数较多的情况下能够接近99%的识别率,且该识别率较稳定.实验结果显示计算机自动识别效果与专家目测相当,在生产中具有一定的实用价值.     

Type-2 fuzzy logic-based classifier fusion for support vector machines

As a machine-learning tool, support vector machines (SVMs) have been gaining popularity due to their promising performance. However, the generalization abilities of SVMs often rely on whether the selected kernel functions are suitable for real classification data. To lessen the sensitivity of different kernels in SVMs classification and improve SVMs generalization ability, this paper proposes a fuzzy fusion model to combine multiple SVMs classifiers. To better handle uncertainties existing in real classification data and in the membership functions (MFs) in the traditional type-1 fuzzy logic system (FLS), we apply interval type-2 fuzzy sets to construct a type-2 SVMs fusion FLS. This type-2 fusion architecture takes considerations of the classification results from individual SVMs classifiers and generates the combined classification decision as the output. Besides the distances of data examples to SVMs hyperplanes, the type-2 fuzzy SVMs fusion system also considers the accuracy information of individual SVMs. Our experiments show that the type-2 based SVM fusion classifiers outperform individual SVM classifiers in most cases. The experiments also show that the type-2 fuzzy logic-based SVMs fusion model is better than the type-1 based SVM fusion model in general.     

并行决策树算法的研究

数据挖掘在科研和商业应用中正发挥着越来越重要的作用。随着数据量的增加,数据挖掘工具处理海量数据的能力问题显得日益突出。研究并行算法,是解决这个问题的有效途径。分类器是数据挖掘的一种基本方法,决策树是一种最重要的分类器。文章首先介绍了分类器中的决策树算法,然后设计了一种并行决策树算法,最后探讨了该并行算法在PVM系统下的实现。