Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.     

Natural ventilation in buildings: measurement in a wind tunnel and numerical simulation with large-eddy simulation

Natural ventilation in buildings can create a comfortable and healthy indoor environment, and can save energy compared to mechanical ventilation systems. In building design the prediction of ventilation can be difficult; cases of wind-driven single-sided ventilation, where the effects of turbulence dominate, are particularly problematic to simulate. In order to investigate the mechanism of natural ventilation driven by wind force, large-eddy simulation (LES) is used. In the meanwhile, detailed airflow fields, such as mean and fluctuating velocity and pressure distribution inside and around building-like models were measured by wind tunnel tests and compared to LES results for model validation. Three ventilation cases, single-sided ventilation with an opening in windward wall, single-sided ventilation with an opening in leeward wall, and cross ventilation, are studied. In the wind tunnel, a laser Doppler anemometry was used to provide accurate and detailed velocity data. In LES calculations, two subgrid-scale (SS) models, a Smagorinsky SS model and a filtered dynamic SS model, were used. The numerical results from LES are in good agreement with the experimental data, in particular with the predicted airflow patterns and velocities around and within, and the surface pressures over, the models. This is considered to establish confidence in the application of the LES methods to the calculation of ventilation in buildings, in particular for single-sided ventilation cases.     

基于情感词典与LDA模型的股市文本情感分析

建立了一种基于股票情感词典与LDA分析股票文本情感倾向的模型。针对股票文本情感分析中情感词典不全面与句子分析片面的问题,构建较为全面的股票情感词典,同时以句子的倾向性、程度性与相关性三方面分析股票文本情感。引入针对股票的词语、程度性词语与转折性词语构建较为全面的情感词典;抽取预处理之后的股票文本句子的情感词;利用句子算法计算句子倾向、程度向量,并对句子向量利用支持向量机(SVM)和K均值算法分类;利用LDA(latent dirichlet allocation)对情感词计算文档 主题、文档 词语概率分布,以此概率分布获取句子的相关性;综合句子的倾向性、程度性、相关性计算句子情感;最后,通过句子情感获取股票文本的情感倾向比例。通过对百度新闻经济板块收集的股票文本进行实验并与其他算法比较,该模型对句子与文本分类准确率提高到82.78%与84.14%。     

基于线性判别分析和分步机器学习的变压器故障诊断

为了改善当前变压器故障诊断在特征量选取和使用单一诊断模型进行故障诊断上的不足,提高变压器故障诊断的准确率和效率,提出基于线性判别分析（LDA）的特征选取方法,建立基于分步机器学习的诊断模型. 该模型选取16组油中溶解气体体积分数比值的多特征参数,运用线性判别分析对参数进行降维作为输入特征向量;运用概率神经网络对变压器故障做出初步诊断,区分出易混淆故障;使用基于灰狼群算法优化的支持向量机对易混淆故障做进一步的区分. 最终实验诊断准确率为97.27%,诊断时间为4.87 s. 与单一机器学习模型相比,所提出的模型不仅具有更高的准确率,还具有更高的效率. 实例分析表明,本研究方法能有效弥补单一机器学习的缺陷,为故障样本有限情况下的电力变压器故障诊断提供参考.     

In vitro and in silico (IVIS) flow characterization in an idealized human airway geometry using laser Doppler anemometry and computational fluid dynamics techniques

The air flow in idealized human airway geometry was studied using computational and experimental methods. A computational fluid dynamics model developed to determine the air flow characteristics in airways was validated by comparison of the experimental velocity profiles obtained with laser Doppler anemometric measurements with numerical data. A good correlation was found between the values obtained with the two methods. Both the measurements and the calculations showed the flow to be laminar in the trachea region of the airway model, but it is affected by the airway geometry in subsequent airways.     

大功率激光器阵列光场分布测试方法的研究

针对大功率半导体激光器阵列难以进行光场分布测试评价的问题,设计了大功率激光器光场分布测试系统。从测试系统探测器的抗损伤阈值方面,分析了对激光衰减的比例要求,提出了几种衰减的方法,设计了低透射系数高抗激光损伤的衰减方案,并进行逐一的测试比较,得到了理想的衰减效果。结合测试系统要求,完成了大功率半导体激光器光场分布的测试与评价。     

基于改进用户兴趣模型的个性化图书推荐算法

为了有效提高推荐算法的精确度,提出了一种适用于个性化图书推荐的改进隐含狄利克雷分配(Latent Dirichlet Allocation,LDA)用户兴趣模型。首先在借阅者-借阅者评分矩阵的基础上,通过增加借阅者特征信息相似度计算和借阅者-图书属性相似度计算,对图书内容相似度计算方法进行了改进。然后采用LDA主题挖掘模型来实现个性化图书推荐,并给出了相应的参数估计过程。实验结果显示:相比传统算法,提出的算法具有较高的准确度,能有效对图书进行挖掘,为借阅者推荐个性化和潜在感兴的书籍。     

Color space normalization: Enhancing the discriminating power of color spaces for face recognition

This paper presents the concept of color space normalization (CSN) and two CSN techniques, i.e., the within-color-component normalization technique (CSN-I) and the across-color-component normalization technique (CSN-II), for enhancing the discriminating power of color spaces for face recognition. Different color spaces usually display different discriminating power, and our experiments on a large scale face recognition grand challenge (FRGC) problem reveal that the RGB and XYZ color spaces are weaker than the I₁I₂I₃, YUV, YIQ, and LSLM color spaces for face recognition. We therefore apply our CSN techniques to normalize the weak color spaces, such as the RGB and the XYZ color spaces, the three hybrid color spaces XGB, YRB and ZRG, and 10 randomly generated color spaces. Experiments using the most challenging FRGC version 2 Experiment 4 with 12,776 training images, 16,028 controlled target images, and 8,014 uncontrolled query images, show that the proposed CSN techniques can significantly and consistently improve the discriminating power of the weak color spaces. Specifically, the normalized RGB, XYZ, XGB, and ZRG color spaces are more effective than or as effective as the I₁I₂I₃, YUV, YIQ and LSLM color spaces for face recognition. The additional experiments using the AR database validate the generalization of the proposed CSN techniques. We finally explain why the CSN techniques can improve the recognition performance of color spaces from the color component correlation point of view.     

OPTIMIZATION AND NEURAL MODELLING OF PULSE COMBUSTORS FOR DRYING APPLICATIONS

ABSTRACT

Results of investigations of a valved pulse combustor to choose optimal geometry, which covered measurements of the flow rates of air and fuel, pressure oscillations, including pressure amplitude and frequency and flue gas composition are presented in the paper. Experimental studies compsiring the operation of the pulse combustor coupled with a drying chamber and working separately are described. It was found that coupling of the pulse combustor with a drying chamber had no significant effect on the pulse combustion process. Smoother runs of pressure oscillations in the combustion chamber, lower noise level and slightly higher NO_x emission were observed. The velocity flow field inside the drying chamber was measured by LDA technique. Results confirmed a complex character of pulsating flow in the chamber. A large experimental data set obtained from measurements enabled developing a neural model of pulse combustion process. Artificial neural networks were trained to predict amplitudes and frequencies of pressure oscillations, temperatures in the combustion chamber and emission of toxic substances. An excellent mapping performance of the developed neural models was obtained. Due to complex character of the pulse combustion process, the application of artificial neural networks seems to be the best way to predict inlet parameters of a drying agent produced by the pulse combustor     

Robust facial feature points extraction in color images

A method of facial feature points extraction based on improved active appearance model (AAM) with Gabor wavelet features was presented in the paper. After the pre-processing of a standard face detector and lighting compensation, the paper proposed a hybrid AAM by combining the local skin similarity with the original local grey-level appearance model. Moreover, the feature points by the hybrid AAM and their neighbors were considered by a classification problem to further refine the results. Namely, the Gabor feature around the feature points was extracted, trained by linear discriminant analysis (LDA) and classified by K Nearest Neighbor (KNN) to give the precise location of the feature points. Experimental results indicated that facial feature points can be located robustly and precisely by the proposed method.