Prediction of spectral reflectance factor distribution of automotive paint finishes

It is necessary to determine the accurate reflectance of painted surfaces for the review of paint finishes by computer graphics (CG) before actual painting of the exterior color of automobiles, and for quality control during production and inspection processes. We have optimized a method for measuring reflectance by using a statistical technique. We have found that the reflectance of a painted surface is best measured at an incident angle of 60° and at five aspecular angles of 10°, 18°, 28°, 40°, and 90°. Our method makes it possible to accurately reproduce reflection characteristics of paint finishes containing special flake pigments, such as pearl mica. Also it was proved that our method can apply not only to solid and metallic coatings but to all painted surfaces. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 275–282, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20125     

The analysis of solvation in ionic liquids and organic solvents using the Abraham linear free energy relationship

The contentions made in an earlier paper [J Chem Technol Biotechnol 80 : 133–137 (2005)] that the coefficients of the Abraham solvation equation do not provide meaningful information on the molecular properties of ionic liquid solvents is refuted. The objections noted in the earlier paper disappear when the solvation equation model is correctly applied to the experimental data. It is further shown that the coefficients of the Abraham solvation equations can be used to characterize ionic liquids and can be used to select solvents for the solubility of gaseous solutes. Copyright © 2006 Society of Chemical Industry     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Reconstruction of reflectance spectra using weighted principal component analysis

The weighted principal component analysis technique is employed for reconstruction of reflectance spectra of surface colors from the related tristimulus values. A dynamic eigenvector subspace based on applying certain weights to reflectance data of Munsell color chips has been formed for each particular sample and the color difference value between the target, and Munsell dataset is chosen as a criterion for determination of weighting factors. Implementation of this method enables one to increase the influence of samples which are closer to target on extracted principal eigenvectors and subsequently diminish the effect of those samples which benefit from higher amount of color difference. The performance of the suggested method is evaluated in spectral reflectance reconstruction of three different collections of colored samples by the use of the first three Munsell bases. The resulting spectra show considerable improvements in terms of root mean square error between the actual and reconstructed reflectance curves as well as CIELAB color difference under illuminant A in comparison to those obtained from the standard PCA method. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 360–371, 2008     

BP网络模型对人脸朝向的识别

研究BP神经网络模型,通过计算机模拟人脑建立神经元网络,使用一部分人脸朝向信息作为训练的实例集,训练稳健后,可以推广应用判断其他人脸的朝向信息,以实现计算机自动识别人脸朝向。先对图片进行归一化等预处理,再应用主成分分析提取特征信息,每幅提取出的特征信息都是8个数据的列向量,构建一个8个输入、17个隐含、3个输出的三层BP网络模型。将训练实例集的特征向量代入训练,调整参数后保证其性能和收敛速度。最后通过大量实验验证,计算机识别的误判率仅为6.7%,模型可靠。     

Weighted principal component extraction with genetic algorithms

Pattern recognition techniques have been widely used in a variety of scientific disciplines including computer vision, artificial intelligence, biology, and so forth. Although many methods present satisfactory performances, they still have several weak points, thus leaving a lot of space for further improvements. In this paper, we propose two performance-driven subspace learning methods by extending the principal component analysis (PCA) and the kernel PCA (KPCA). Both methods adopt a common structure where genetic algorithms are employed to pursue optimal subspaces. Because the proposed feature extractors aim at achieving high classification accuracy, enhanced generalization ability can be expected. Extensive experiments are designed to evaluate the effectiveness of the proposed algorithms in real-world problems including object recognition and a number of machine learning tasks. Comparative studies with other state-of-the-art techniques show that the methods in this paper are capable of enhancing generalization ability for pattern recognition systems.     

基于主成分分析中的几类问题的计算公式及结论

讨论了主成分分析中的几类问题的计算方法,重点揭示在协方差矩阵中对角元素相同时的第一主成分及贡献率和对角元素不同时的主成分贡献率．经讨论后给出有关计算公式,并说明这些计算公式在多元统计分析中的实际作用     

考虑中主应力效应Gudehus-Bauer亚塑性模型的改进

针对Gudehus-Bauer亚塑性模型不能考虑中主应力影响,在Gudehus提出的响应包络线理论基础上,提出了在模型的非线性项乘以一个考虑中主应力的因子进行改进.这种改进增加了土体剪切时的强度,改变了土体受压时的变形.新模型对中主应力的变化在中主应力参数影响程度能够有一定反映.同时将改进后的模型与中主应力参数为常量的真三轴实验进行了比较.结果表明改进后的模型可以模拟中主应力对砂土强度和变形特性的影响,能够定量预测由于中主应力影响产生的体积应变.     

Robust sparse principal component analysis

The model for improving the robustness of sparse principal component analysis（PCA） is proposed in this paper. Instead of the l2-norm variance utilized in the conventional sparse PCA model,the proposed model maximizes the l1-norm variance,which is less sensitive to noise and outlier. To ensure sparsity,lp-norm（0 p 1） constraint,which is more general and effective than l1-norm,is considered. A simple yet efficient algorithm is developed against the proposed model. The complexity of the algorithm approximately linearly increases with both of the size and the dimensionality of the given data,which is comparable to or better than the current sparse PCA methods. The proposed algorithm is also proved to converge to a reasonable local optimum of the model. The efficiency and robustness of the algorithm is verified by a series of experiments on both synthetic and digit number image data.     

基于大数据的产品族本体造型意象挖掘方法研究

为提升产品造型意象成族的准确性,增强感性工学研究中意象词汇提取的规范性和知识重用性。 首先从同族、泛族、异族的概念切入,对目标产品族的造型意象本体进行了定义;再借助 word2vec 工具和主 成分分析法(PCA)分别完成了对产品族意象词汇的关联性联想和降维提取,从而构建了意象词汇挖掘机制,通 过此机制可以辅助设计师利用网络大数据资源更高效、准确地进行目标产品意象的挖掘,一定程度上解决了传 统感性工学在意象挖掘方法中模糊性。最后结合意象词汇与造型特征的映射关系,利用 Protege 工具构建了产 品族造型意象的本体模型,对目标产品的造型意象知识进行了逻辑化表征,为下一代产品继承并发展原产品族 造型意象提供参考。以马自达 MX-5 车系的造型意象为例构建了本体模型,并进行了子代产品前脸的概念设计。