高速铁路大半径曲线参数检测方法研究

对于高速铁路,维护曲线的良好状态对于提高旅客舒适度、保障行车安全极为重要。利用轨道检测车实现高速铁路的曲线线型判别和曲线要素检测是线路养护维修工作的前提。本文提出了高速铁路大半径曲线参数检测算法模型,分析影响曲线检测的关键参数,给出其合适阈值,并用现场试验予以验证,最终得到一套可应用于高速铁路大半径曲线检测的方法。

Hazard and population vulnerability analysis: a step towards landslide risk assessment

In this paper, an attempt to analyse landslide hazard and vulnerability in the municipality of Pahuatlfin, Puebla, Mexico, is presented. In order to estimate landslide hazard, the susceptibility, magnitude （area-velocity ratio） and landslide frequency of the area of interest were produced based on information derived from a geomorphological landslide inventory; the latter was generated by using very high resolution satellite stereo pairs along with information derived from other sources （Google Earth, aerial photographs and historical information）. Estimations of landslide susceptibility were determined by combining four statistical techniques： （i） logistic regression, （ii） quadratic discriminant analysis, （iii） linear discriminant analysis, and （iv） neuronal networks. A Digital Elevation Model （DEM） of lo m spatial resolution was used to extract the slope angle, aspect, curvature, elevation and relief. These factors, in addition to land cover, lithology anddistance to faults, were used as explanatory variables for the susceptibility models. Additionally, a Poisson model was used to estimate landslide temporal frequency, at the same time as landslide magnitude was obtained by using the relationship between landslide area and the velocity of movements. Then, due to the complexity of evaluating it, vulnerability of population was analysed by applying the Spatial Approach to Vulnerability Assessment （SAVE） model which considered levels of exposure, sensitivity and lack of resilience. Results were expressed on maps on which different spatial patterns of levels of landslide hazard and vulnerability were found for the inhabited areas. It is noteworthy that the lack of optimal methodologies to estimate and quantify vulnerability is more notorious than that of hazard assessments. Consequently, levels of uncertainty linked to landslide risk assessment remain a challenge to be addressed.

Kernel Model Applied in Kernel Direct Discriminant Analysis for the Recognition of Face with Nonlinear Variations

A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower （7%） error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority （1% in terms of recognition rate） to other polynomial kernel models.

Comparison of wrist motion classification methods using surface electromyogram

The Gaussian mixture model (GMM), k-nearest neighbor (k-NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Effect of feature selection in EMG signal processing was also verified by comparing classification accuracy of each feature, and the enhancement of classification accuracy by normalization was confirmed. EMG signals were acquired from two electrodes placed on the forearm of twenty eight healthy subjects and used for recognition of wrist motion. Features were extracted from the obtained EMG signals in the time domain and were applied to classification methods. The difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), mean absolute value (MAV), root mean square (RMS) were used for composing 16 double features which were combined of two channels. In the classification methods, the highest accuracy of classification showed in the GMM. The most effective combination of classification method and double feature was (MAV, DAMV) of GMM and its classification accuracy was 96.85%. The results of normalization were better than those of non-normalization in GMM, k-NN, and LDA.

Classification of breast lesions based on a dual S-shaped logistic model in dynamic contrast enhanced magnetic resonance imaging

This study proposes a novel dual S-shaped logistic model for automatically quantifying the characteristic kinetic curves of breast lesions and for distinguishing malignant from benign breast tumors on dynamic contrast enhanced (DCE) magnetic resonance (MR) images.D(,) is the diagnostic parameter derived from the logistic model.Significant differences were found in D(,) between the malignant benign groups.Fisher's Linear Discriminant analysis correctly classified more than 90% of the benign and malignant kinetic breast data using the derived diagnostic parameter (D(,)).Receiver operating characteristic curve analysis of the derived diagnostic parameter (D(,)) indicated high sensitivity and specificity to differentiate malignancy from benignancy.The dual S-shaped logistic model was effectively used to fit the kinetic curves of breast lesions in DCE-MR.Separation between benign and malignant breast lesions was achieved with sufficient accuracy by using the derived diagnostic parameter D(,) as the lesion's feature.The proposed method therefore has the potential for computer-aided diagnosis in breast tumors.

Multifractal methods for rapeseed nitrogen nutrition qualitative diagnosis modeling

Nutrition diagnosis plays a key role in the crop＇s growth, which has mainly been car- ried out in the field by agricultural workers. Currently, automatic nutrition recognition technologies have been widely used in this field. A procedure is proposed in this paper to diagnose nitrogen nutrition non-destructively for rapeseed qualitatively based on the multifractal theory. Twelve texture parameters are given by the method of multifractal detrended fluctuation （MF-DFA）, which contains six generalized Hurst exponents and six relative multifractal parameters that are used as features of the rapeseed leaf images for identifying the two nitrogen levels, namely, the N-mezzo and the N-wane. For the base leaves, central leaves and top leaves of the rapeseed plant and the three-section mixed samples, three parameters combinations are selected to conduct the work. Five classifiers of Fisher＇s linear discriminant algorithm （LDA）, extreme learning machine （ELM）, support vector machine and kernel method （SVMKM）, random decision forests （RF） and K-nearest neighbor algorithm （KNN） are employed to calculate the diagno- sis accuracy. An interesting finding is that the best diagnose accuracy is from the base leaves of the rapeseed plant. It is explained that the base leaf is the most sensitive to the nitrogen deficiency. The diagnose effect by the base leaves samples is outshining the existing result significantly for the same leaves samples. For the mixed samples, the aver- aged discriminant accuracy reaches 97.12% and 97.56% by SVMKM and RF methods with the 10-fold cross-validation respectively. The resulting high accuracy on N-levels identification shows the feasibility and efficiency of our method.