Signal Detection of Welding Joint Flaw Based on Wavelet-Support Vector Machine

The friction welded joints made by GH4169 heat metal alloys are detected by U1traPAC system of the ultrasonic wave explore instrument. Aimed at the blemish signal characteristics, this article introduce Support Vector Machine (SVM) theory, which is based on statistical theory and structural risk minimization principle, to carry out multi-classification study of the detection signal. We decompose de-noising signals with wavelet packet transform, and extract energy eigenvalues according to "energy- defects". In accordance with designed "1-to-v" SVMs scheme, we respectively input normalized eigenvector to the SVM model to obtain the Forecast data. It is verificated that the limited existing data and information is well used by SVM and the signal is accurately been classificated. All of these verify that SVM has a strong generalization ability.

Application of Genetic Arithmetic and Support Vector Machine in Prediction of Compression Index of Clay

The compression index is an important soil property that is essential to many geotechnical designs. As the determination of the compression index from consolidation tests is relatively time-consuming. Support Vector Machine (SVM) is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. Considering the fact that parameters in SVM model are difficult to be decided, a genetic SVM was presented in which the parameters in SVM method are optimized by Genetic Algorithm (GA). Taking plasticity index, water content, void ration and density of soil as primary influence factors, the prediction model of compression index based on GA-SVM approach was obtained. The results of this study showed that the GA-SVM approach has the potential to be a practical tool for predicting compression index of soil.

The Bound of Local Risk Minimization Estimation on Quasi-Probability Space

the ideas of local risk minimization estimation problem on quasi-probability space is presented; In order to make structural risk minimization principle apply to the problem of local risk minimization estimation, the paper gives and proves the bounds of the bound of local risk minimization estimation on quasi-probability.

Modeling river stage–discharge–sediment rating relation using support vector regression

A variety of data-driven approaches have been developed in the recent past to capture the properties of hydrological data for improved modeling. These include artificial neural networks (ANNs), fuzzy logic and evolutionary algorithms, amongst others. Of late, kernel-based machine learning approaches have become popular due to their inherent advantages over traditional modeling techniques. In this work, support vector machines (SVMs), a kernel-based learning approach, has been investigated for its suitability to model the relationship between the river stage, discharge, and sediment concentration. SVMs are an approximate implementation of the structural risk minimization principle that aims at minimizing a bound on the generalization error of a model. These have been found to be promising in many areas including hydrology. Application of SVMs to regression problems is known as support vector regression (SVR). This paper presents an application of SVR to model river discharge and sediment concentration rating relation. The results obtained using SVR were compared with those from ANNs and it was found that the SVR approach is better when compared with ANNs.