A Discrimination Method of Saturated Sand Liquefaction Possibility Based on Support Vector Machine

2014 ◽  
Vol 509 ◽  
pp. 38-43
Author(s):  
Zhong Jie Fan ◽  
Yan Qiu Leng ◽  
Yong Long Xu ◽  
Zheng Jiang Meng ◽  
Ji Wei Xu
Keyword(s):  
Support Vector Machine ◽  
Influence Factors ◽  
Kernel Functions ◽  
Small Sample ◽  
Support Vector ◽  
Saturated Sand ◽  
Risk Minimization ◽  
Sand Liquefaction ◽  
Discriminant Model ◽  
Svm Model

Based on the analysis of influence factors of saturated sand, this paper expounds the limitations of traditional evaluation of liquefaction, and introduces the criterion of support vector machine (SVM) based on the principle of structural risk minimization. According to the main influence factors of sand liquefaction, a SVM discriminant model of sand liquefaction with different kernel functions is established. Through studying small sample data, this model can establish nonlinear mapping relationship between influence factors and liquefaction type. On the basis of seismic data, a radial based kernel function is selected to predict sand liquefaction type. The research results show that the predicted magnitude is identical with the actual result, to prove that it is effective to apply this SVM model to evaluate the level of sand liquefaction.

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

2013 ◽  
Vol 438-439 ◽  
pp. 1167-1170
Author(s):  
Xu Chao Shi ◽  
Ying Fei Gao
Keyword(s):  
Support Vector Machine ◽  
Influence Factors ◽  
Support Vector ◽  
Compression Index ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Structural Risk Minimization Principle ◽  
Svm Model ◽  
Genetic Arithmetic ◽  
Practical Tool

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.

scholarly journals Real-time flood forecast using the coupling support vector machine and data assimilation method

2013 ◽  
Vol 16 (5) ◽  
pp. 973-988 ◽  
Author(s):  
Xiao-Li Li ◽  
Haishen Lü ◽  
Robert Horton ◽  
Tianqing An ◽  
Zhongbo Yu
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Time Scale ◽  
Support Vector ◽  
Flood Forecast ◽  
Risk Minimization ◽  
Svm Model ◽  
The Impact ◽  
Assimilation Time ◽  
Better Than

An accurate and real-time flood forecast is a crucial nonstructural step to flood mitigation. A support vector machine (SVM) is based on the principle of structural risk minimization and has a good generalization capability. The ensemble Kalman filter (EnKF) is a proven method with the capability of handling nonlinearity in a computationally efficient manner. In this paper, a type of SVM model is established to simulate the rainfall–runoff (RR) process. Then, a coupling model of SVM and EnKF (SVM + EnKF) is used for RR simulation. The impact of the assimilation time scale on the SVM + EnKF model is also studied. A total of four different combinations of the SVM and EnKF models are studied in the paper. The Xinanjiang RR model is employed to evaluate the SVM and the SVM + EnKF models. The study area is located in the Luo River Basin, Guangdong Province, China, during a nine-year period from 1994 to 2002. Compared to SVM, the SVM + EnKF model substantially improves the accuracy of flood prediction, and the Xinanjiang RR model also performs better than the SVM model. The simulated result for the assimilation time scale of 5 days is better than the results for the other cases.

Support Vector Machine-Based Aqueduct Safety Assessment

2011 ◽  
Vol 368-373 ◽  
pp. 531-536
Author(s):  
Qiang Qu ◽  
Ming Qi Chang ◽  
Lei Xu ◽  
Yue Wang ◽  
Shao Hua Lu
Keyword(s):  
Support Vector Machine ◽  
Safety Assessment ◽  
Optimal Solution ◽  
Indicator System ◽  
Small Sample ◽  
Assessment Model ◽  
Irrigation District ◽  
Support Vector ◽  
Risk Minimization ◽  
Safety Standards

According to water power, structure and foundation conditions of aqueduct, it has established aqueduct safety assessment indicator system and standards. Based on statistical learning theory, support vector machine shifts the learning problems into a convex quadratic programming problem with structural risk minimization criterion, which could get the global optimal solution, and be applicable to solving the small sample, nonlinearity classification and regression problems. In order to evaluate the safety condition of aqueduct, it has established the aqueduct safety assessment model which is based on support vector machine. It has divided safety standards into normal, basically normal, abnormal and dangerous. According to the aqueduct safety assessment standards and respective evaluation level, the sample set is generated randomly, which is used to build a pair of classifier with many support vectors. The results show that the method is feasible, and it has a good application prospect in irrigation district canal building safety assessment.

Application of a new hybrid particle swarm optimization-mixed kernels function-based support vector machine model for reservoir porosity prediction: A case study in Jacksonburg-Stringtown oil field, West Virginia, USA

2019 ◽  
Vol 7 (1) ◽  
pp. T97-T112 ◽  
Author(s):  
Zhi Zhong ◽  
Timothy R. Carr
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Absolute Error ◽  
Kernel Functions ◽  
Oil Field ◽  
Support Vector ◽  
Well Log ◽  
Swarm Optimization ◽  
Svm Model

Porosity is a fundamental property that characterizes the storage capability of fluid and gas-bearing formations in a reservoir. An accurate porosity value can be measured from core samples in the laboratory; however, core analysis is expensive and time consuming. Well-log data can be used to calculate porosity, but the availability of log suites is often limited in mature fields. Therefore, robust porosity prediction requires integration of core-measured porosity with available well-log suites to control for changes in lithology and fluid content. A support vector machine (SVM) model with mixed kernel function (MKF) is used to construct the relationship between limited conventional well-log suites and sparse core data. Porosity is the desired output, and two conventional well-log responses (gamma ray [GR] and bulk density) and three well-log-derived parameters (the slope of GR, the slope of density, and [Formula: see text]) are input parameters. A global stochastic searching algorithm, particle swarm optimization (PSO), is applied to improve the efficiency of locating the appropriate values of five control parameters in MKF-SVM model. The results of SVM with different traditional kernel functions were compared, and the MKF-SVM model provided an improvement over the traditional SVM model. To confirm the advantage of the hybrid PSO-MKF-SVM model, the results from three models: (1) radial basis function (RBF)-based least-squares SVM, (2) multilayer perceptron artificial neural network (ANN), and (3) RBF ANN, are compared with the result of the hybrid PSO-MKF-SVM model. The results indicate that the hybrid PSO-MKF-SVM model improves porosity prediction with the highest correlation coefficient ([Formula: see text] of 0.9560), the highest coefficient of determination ([Formula: see text] of 0.9140), the lowest root-mean-square error (1.6505), average absolute error value (1.4050), and maximum absolute error (2.717).

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

2015 ◽  
Vol 1120-1121 ◽  
pp. 1385-1389
Author(s):  
Xin Yin ◽  
Yuan Peng Liu ◽  
Xian Zhang Feng
Keyword(s):  
Support Vector Machine ◽  
Wavelet Packet ◽  
Support Vector ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Structural Risk Minimization Principle ◽  
Signal Characteristics ◽  
Svm Model ◽  
Structural Risk ◽  
Multi Classification

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.

Leak Detection Method Based on Support Vector Machine

2008 ◽  
Author(s):  
XiaoJing Fan ◽  
LaiBin Zhang ◽  
Wei Liang ◽  
ZhaoHui Wang
Keyword(s):  
Support Vector Machine ◽  
Statistical Learning ◽  
Learning Theory ◽  
Statistical Learning Theory ◽  
Leak Detection ◽  
Small Sample ◽  
Support Vector ◽  
Svm Classifier ◽  
Risk Minimization ◽  
Sample Set

Assumptive and uncertain factors, few leak samples, complex non-linear pipeline systems are the problems often involved in the process of pipeline leak detection. Furthermore, the pressure wave changes of leakage are similar to these of valve regulation and pump closure. Thus it is difficult to establish a reliable model and to distinguish the leak signal pattern from others in pipeline leak detection. The veracity of leak detection system is limited. This paper presents a novel technique based on the statistical learning theory, support vector machine (SVM) for pipeline leak detection. Support Vector Machine (SVM) is learning system that uses a hypothesis space of linear functions in a high dimensional feature space, trained with a learning algorithm from optimization theory that implements a learning bias derived from statistical learning theory. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional techniques. Thus, SVM has good performance for classification over small sample set. In this paper, an overview of the limitations of traditional statistics and the advantage of statistical learning theory will be introduced. In this paper, an SVM classifier is used to classify the signal pattern with few samples. Firstly, the algorithm of the SVM classifier and steps of using the model to identify leakage signals are studied. Secondly, the classification results of the experiment show that SVM classifier has high recognition accuracy. In addition, SVM is compared with neural network method. Then the paper concludes that in terms of classification ability and generalization performance, SVM has clearly advantages than neural network method over small sample set, so SVM is more applicable to pipeline leak detection.

Research Status of Fault Diagnosis Based on Support Vector Machine

2013 ◽  
Vol 475-476 ◽  
pp. 787-791
Author(s):  
Li Mei Liu ◽  
Jian Wen Wang ◽  
Ying Guo ◽  
Hong Sheng Lin
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Small Sample ◽  
Learning Ability ◽  
Practical Significance ◽  
Support Vector ◽  
Limited Information ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Good Learning

Support vector machine has good learning ability and it is good to perform the structural risk minimization principle of statistical learning theory and its application in fault diagnosis of the biggest advantages is that it is suitable for small sample decision. Its nature of learning method is under the condition of limited information to maximize the implicit knowledge of classification in data mining and it is of great practical significance for fault diagnosis. This paper analyzed and summarized the present situation of application of support vector machine in fault diagnosis and made a meaningful exploration on development direction of the future.

PSO-SVM Model Based Prediction and Analysis for the Formation of Navigation Channel Silt

2014 ◽  
Vol 543-547 ◽  
pp. 4133-4136
Author(s):  
Ping Wang ◽  
Zhi Hong Qie ◽  
Fu Sheng Yang
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Deterministic Model ◽  
Deformation Monitoring ◽  
Small Sample ◽  
Support Vector ◽  
Swarm Optimization ◽  
Navigation Channel ◽  
Svm Model ◽  
Selection Of

Monitor and predict the change of navigation channel silt is important to ensure the safety of the channel while one of many difficulties is the deformation monitoring data is complicated and nonlinear, so its difficult to establish a deterministic model. Supporting vector machine could be widely used in the prediction of the formation of navigation channel silt because it has a good generalized ability, which could solve the problems like small sample, nonlinear, high-dimension. Because whether the algorithm could work or not based on the selection of the parameters, so a PSO-SVM based prediction model of the formation of the silt was established by using particle swarm optimization, which is a the fast overall optimization, and then was used to optimize the model parameter of the support vector machine. Study shows utilize this model in the silt formation in Huanghua harbor is plausible.

scholarly journals Applications of Support Vector Machine in Genomic Prediction in Pig and Maize Populations

2020 ◽  
Vol 11 ◽  
Author(s):  
Wei Zhao ◽  
Xueshuang Lai ◽  
Dengying Liu ◽  
Zhenyang Zhang ◽  
Peipei Ma ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Plant Breeding ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Prediction Performance ◽  
Kernel Functions ◽  
Support Vector ◽  
Data Sets ◽  
Svm Model ◽  
Better Than

Genomic prediction (GP) has revolutionized animal and plant breeding. However, better statistical models that can improve the accuracy of GP are required. For this reason, in this study, we explored the genomic-based prediction performance of a popular machine learning method, the Support Vector Machine (SVM) model. We selected the most suitable kernel function and hyperparameters for the SVM model in eight published genomic data sets on pigs and maize. Next, we compared the SVM model with RBF and the linear kernel functions to the two most commonly used genome-enabled prediction models (GBLUP and BayesR) in terms of prediction accuracy, time, and the memory used. The results showed that the SVM model had the best prediction performance in two of the eight data sets, but in general, the predictions of both models were similar. In terms of time, the SVM model was better than BayesR but worse than GBLUP. In terms of memory, the SVM model was better than GBLUP and worse than BayesR in pig data but the same with BayesR in maize data. According to the results, SVM is a competitive method in animal and plant breeding, and there is no universal prediction model.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

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