Velocity measurements and flow field characteristic analyses in a turbo air classifier

Turbo air classifier is one of the most widely used powder classification equipment. The rotor cage as a rotary component can create a forced centrifugal field, so it is a key part for turbo air classifier. In order to investigate the effect of structural variations of the rotor cage on flow field characteristics, three dimensional velocity measurements of the annular region in a turbo air classifier equipped with two different rotor cage bottom plates (A and B type) are performed by laser Doppler velocimeter (LDV). It is found that the different bottom plates have different axial and tangential velocity distributions in the annular region. However, the structural variations of the rotor cage have hardly any effect on the radial velocity. Based on the classification principle, the relation between the classification performance and the flow field characteristics is investigated in great detail.The results of the flow field measurements were tested by the classification experiments carried out with cement raw meal and ground calcium carbonate. The results demonstrate that B type bottom plate can realize the production of narrow particle size distributions, so it is more favorable for classification than A type bottom plate. Classification experiment results are in good agreement with the results of the flow field measurements.     

FO-CA:一种基于距离差异度组合权重的多属性数据分类方法

为了解决多属性数据分类问题,提出了一种基于模糊优选模型与聚类分析的分类方法(FO-CA)。首先由模糊优选模型得到有序综合指标数据集,其中在权重阶段提出了距离差异度并以此为依据构建了一种组合主客观权重的赋权方法;然后采用聚类分析将有序综合指标数据集聚类为几个簇进而分类;最后选取UCI中的Iris、Wine和Ruspini 3个数据集进行仿真实验。实验结果表明,该分类方法相比模糊优选方法及K-Means算法能获得更好的分类结果,对决策者有一定的参考价值。     

基于改进遗传算法和SVM的森林火灾视频目标鉴别

计算机视觉是目前计算机技术研究的热门课题之一,目标对象特征的选择和提取是计算机视觉的核心问题。在分析和研究火焰图像中火焰预判方法的基础上,提出了从纹理、动态、几何3个方面筛取火焰图像的组合特征,并且重点介绍了动态特征的提取方法。在火焰识别的特征选择方面做了较深入的研究,综合利用了火焰的内容特征,基于遗传算法提出了新的适应度函数,使得特征选择更加科学,而基于结构风险最小化理论的SVM在识别过程中可充分发挥优势,因此在实验中取得了理想的效果。     

基于面向分类准则的维数约简及其在人脸识别中的应用

针对高维数据导致的维数灾难问题,提出了一种基于面向分类准则的维数约简方法。所提准则使每个训练样本在特征空间中与同类样本尽可能接近,而与异类样本尽可能疏远。首先对每个训练样本定义同类样本加权平均距离和异类样本加权平均距离。然后基于上述两个概念分别定义总体同类距离和总体异类距离。以最小化总体同类距离和最大化总体异类距离为目的提出了面向分类的准则(Classification Oriented Criterion,COC)。最后,基于面向分类的准则推导出了一种新的维数约简方法。在公共人脸数据库ORL和Yale上的实验表明所提方法性能优于有代表性的维数约简方法。     

一种基于上下文的医学图像ROI分类方法

感兴趣区域(ROI)的分类是医学图像的计算机辅助诊断过程的最后一步,传统方法只针对每个ROI区域单独提取特征,再利用统计学习的方法训练分类器进行分类.然而图像中每个区域所包含的视觉特征有限,很难进行准确的分类.文中提出一种基于LDA主题模型的改进模型(LDAC),考虑ROI周围区域,即图像的上下文关系,通过利用LDA对ROI周围区域所包含的上下文信息进行建模,同时结合ROI区域的视觉信息和类别标签,从而辅助ROI区域的分类,以达到提高分类准确率的目的.乳腺图像肿块分类实验表明,文中方法可提高分类的准确性.     

水泥微细分级的试验研究

用新开发的新型旋风式微细分级机进行水泥微细分级试验.结果表明.当原动中10μm以下的颗粒含量52.6%,经分级后的细粉中小于10μm的颗粒含量达88.9%,平均粒径为4.06μm.分级切割粒径为11.8μm分级精度d_(75)/d_(25)<1.5.     

改进的支持向量机中文文本分类

支持向量机是数据挖掘的新方法,由于其优秀的学习能力而得到了广泛的应用,但是,传统的支持向量机算法在处理大规模问题时存在训练时间过长和内存空间需求过大的问题,而应用多个支持向量机构成多分类器系统进行并行学习,是目前解决文本分类中大规模数据处理问题的一种有效方法。在分析传统并行算法的基础上,提出了一种改进的基于多支持向量机的并行学习算法,实验结果表明,采用该算法可使得分类效率得到较大的程度的提高,虽然,分类精度相对传统的方法略差,但是,在可接受的范围之内。     

DEAR-MULSEMEDIA: Dataset for emotion analysis and recognition in response to multiple sensorial media

Traditionally, emotion recognition is performed in response to stimuli that engage either one (vision: image or hearing: audio) or two (vision and hearing: video) human senses. An immersive environment can be generated by engaging more than two human senses while interacting with multimedia content and is known as MULtiple SEnsorial media (mulsemedia). This study aims to create a new dataset of multimodal physiological signals to recognize emotions in response to such content. To this end, four multimedia clips are selected and synchronized with fan, heater, olfaction dispenser, and haptic vest to augment cold air, hot air, olfaction, and haptic effects respectively. Furthermore, physiological responses including electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG) are observed to analyze human emotional responses while experiencing mulsemedia content. A t-test applied using arousal and valence scores show that engaging more than two human senses evokes significantly different emotions. Statistical tests on EEG, GSR, and PPG responses also show a significant difference between multimedia and mulsemedia content. Classification accuracy of 85.18% and 76.54% is achieved for valence and arousal, respectively, using K-nearest neighbor classifier and feature-level fusion strategy.     

Asymmetric Gaussian Process multi-view learning for visual classification

Methods of multi-view learning attain outstanding performance in different fields compared with the single-view based strategies. In this paper, the Gaussian Process Latent Variable Model (GPVLM), which is a generative and non-parametric model, is exploited to represent multiple views in a common subspace. Specifically, there exists a shared latent variable across various views that is assumed to be transformed to observations by using distinctive Gaussian Process projections. However, this assumption is only a generative strategy, being intractable to simply estimate the fused variable at the testing step. In order to tackle this problem, another projection from observed data to the shared variable is simultaneously learned by enjoying the view-shared and view-specific kernel parameters under the Gaussian Process structure. Furthermore, to achieve the classification task, label information is also introduced to be the generation from the latent variable through a Gaussian Process transformation. Extensive experimental results on multi-view datasets demonstrate the superiority and effectiveness of our model in comparison to state-of-the-art algorithms.