高分辨力地震是提高中国南方碳酸盐岩地震勘探效能的基础——一个由失败到成功的史例

南方碳酸盐岩复杂地区的地震工作经历了长期艰难曲折的道路,其中异常判别的经验教训极为深刻。本文从地震异常分析入手,揭示其实质,寻求问题的关键,探讨解决的办法,介绍高分辨率地震方法技术,展示高分辨率地震新成果,以得出有意义的结论和建议。     

Time series clustering and classification by the autoregressive metric

The statistical properties of the autoregressive (AR) distance between ARIMA processes are investigated. In particular, the asymptotic distribution of the squared AR distance and an approximation which is computationally efficient are derived. Moreover, the problem of time series clustering and classification is discussed and the performance of the AR distance is illustrated by means of some empirical applications.     

Novel e-nose for the discrimination of volatile organic biomarkers with an array of carbon nanotubes (CNT) conductive polymer nanocomposites (CPC) sensors

We report the original design of a new type of electronic nose (e-nose) consisting of only five sensors made of hierarchically structured conductive polymer nanocomposites (CPC). Each sensor benefits from both the exceptional electrical properties of carbon nanotubes (CNT) used to build the conductive architecture and the spray layer by layer (sLbL) assembly technique, which provides the transducers with a highly specific 3D surface structure. Excellent sensitivity and selectivity were obtained by optimizing the amount of CNT with five different polymer matrices: poly(caprolactone) (PCL), poly(lactic acid) (PLA), poly(carbonate) (PC), poly(methyl methacrylate) (PMMA) and a biobased polyester (BPR). The ability of the resulting e-nose to detect nine organic solvent vapours (isopropanol, tetrahydrofuran, dichloromethane, n-heptane, cyclohexane, methanol, ethanol, water and toluene), as well as biomarkers for lung cancer detection in breath analysis, has been demonstrated. Principal component analysis (PCA) proved to be an excellent pattern recognition tool to separate vapour clusters.     

Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools

Electronic nose (E-nose) technique was attempted to discriminate green tea quality instead of human panel test in this work. Four grades of green tea, which were classified by the human panel test, were attempted in the experiment. First, the E-nose system with eight metal oxide semiconductors gas sensors array was developed for data acquisition; then, the characteristic variables were extracted from the responses of the sensors; next, the principal components (PCs), as the input of the discrimination model, were extracted by principal component analysis (PCA); finally, three different linear or nonlinear classification tools, which were K-nearest neighbors (KNN), artificial neural network (ANN) and support vector machine (SVM), were compared in developing the discrimination model. The number of PCs and other model parameters were optimized by cross-validation. Experimental results showed that the performance of SVM model was superior to other models. The optimum SVM model was achieved when 4 PCs were included. The back discrimination rate was equal to 100% in the training set, and predictive discrimination rate was equal to 95% in the prediction set, respectively. The overall results demonstrated that E-nose technique with SVM classification tool could be successfully used in discrimination of green tea's quality, and SVM algorithm shows its superiority in solution to classification of green tea's quality using E-nose data.     

Gamma mixture models for target recognition

This paper considers a mixture model approach to automatic target recognition using high-resolution radar measurements. The mixture model approach is motivated from several perspectives including requirements that the target classifier is robust to uncertainty in amplitude scaling, rotation and translation of the target. Estimation of the model parameters is achieved using the expectation-maximisation (EM) algorithm. Gamma mixtures are introduced and the re-estimation equations derived. The models are applied to the classification of high-resolution radar range profiles of ships and results compared with a previously published self-organising map approach.     

多波段多极化成像雷达图象识别森林类型效果分析

要陕西省宜川县三北防护林地区获取的航天飞机成像雷达ＳＩＲ－Ｃ／Ｘ－ＳＡＲ数据为基础,进行了森林类型的识别与分类,区分出针叶林、阔叶林和混交林３种森林类型,并通过从ＳＩＲ－Ｃ／Ｘ－ＳＡＲ数据中提取这３种类型森林的后向散射系数,分析了多波段、多极化雷达在森林类型识别中的作用。     

模糊判决支持向量机在自动语种辨识中的研究

支持向量机(SVM)是在统计学习理论的基础上发展起来的一种新的通用学习方法。自动语种辨识是语音信号处理中新出现的分支,也是一项较难的课题。该文提出的模糊判决支持向量机(FDSVM)是对支持向量机的判决结果的合理化改进,并应用于自动语种辨识系统。利用OGI-TS电话语音库对新算法的性能进行测试,然后给出实验结果。结果表明,该算法相对于传统算法是一种更有效的方法。     

Unsound kernel identification using linear colour charge‐coupled device

Wheat is one of the most consumed grains in the world. The identification of wheat based on surface characteristics is important for the market. This study is aimed at identifying unsound kernels (Triticum durum Desf), including 710 black germ kernels, 627 broken kernels and 1169 sound kernels from several seed distributors in China. The system is mainly composed of a liner charge‐coupled device for image capture and a software package for extracting various morphological, colour and texture features. The models built by partial least squares discriminate analysis, support vector machine discrimination analysis (SVMDA) and principal component analysis‐artificial neural networks for identifying the unsound kernels have been explored. After comparisons of these three methods, it has been found that SVMDA got the best accuracy: 95.1%, 96.0% and 98.3% (black germ kernels, broken kernels and sound kernels). Obviously, the experimental results have shown that SVMDA is the most feasible and effective choice for the identification.     

Factorial 2 p–q Plans Robust Against Linear and Quadratic Trends

When the substrate of a 2 ^n–q factorial or fractional factorial experiment may be expected to show trends representable by linear and quadratic terms in time, then certain orderings spaced at equal time-intervals permit better estimation of the effects than do others. Some of these ordered plans are given for p – q = 2, 3, 4, 5. Simple methods are given for computing effects, trends, and efficiencies.     

Varietal discrimination of Chilean wines by direct injection mass spectrometry analysis combined with multivariate statistics

A simple, direct injection, electrospray ionization Fourier transform mass spectrometry (ESI FT-MS) method, in combination with multivariate statistics, was used for the characterization and sorting of Chilean wines. 47 commercial red wines labelled as Cabernet Sauvignon, Carménère, Syrah, and Pinot noir, and 25 white wines of the varieties Chardonnay and Sauvignon blanc were diluted, directly infused into the mass spectrometer, and analyzed in negative ion mode. The signature ions used for statistical analyses were manually filtered out from the signals with m/z ratios over 10%. The results of principal component analysis allowed a good sorting of white wines, but not so in the case of reds. The main three principal components explained 96.82% and 85.65% of the variance for white and red wines, respectively. Instead, linear discriminant analysis, allowed the correct discrimination of 100.00% of white and 95.74% of red samples. The validation of these results using the leave-one-out cross-validation method gave lower percentages of correct classification (76.00% and 61.70% of white and red samples respectively), suggesting that some of the wine samples analyzed might have been blends of more than one variety. Consequently, ESI FT-MS direct injection analysis of wines can be used for sample discrimination, but requires a stronger mathematical model built with spectral information of pure and blended samples before improving the percentages of classification.