Selection of Parameters of the Support Vector Machine Method to the Problem of Subsidence Modelling Due to Drainage

2016 ◽  
pp. 651-656
Author(s):  
Wojciech T. Witkowski ◽  
Ryszard Hejmanowski
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Selection Of

Research of Diver Sonar Image Recognition Based on Support Vector Machine

2013 ◽  
Vol 785-786 ◽  
pp. 1437-1440 ◽  
Author(s):  
Ke Li ◽  
Chong Lun Li ◽  
Wei Zhang
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Small Object ◽  
Accuracy Rate ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Feature Parameters ◽  
Sonar Images ◽  
The Right ◽  
Selection Of

To recognize small diver target from the dim special diver sonar images accurately, the Support Vector Machine method is used as classifier. According to the main characteristics of diver, five feature parameters, including Average-scale, Velocity, Shape, Direction, Included angle, are chosen as the input of characteristics vectors to train the net. And then the testing images are classified and identified. The experimental results show that accuracy rate of recognition reaches 94.5% for as many as 200 testing images. The experiment indicates that small object recognition from complex sonar images based on the right selection of feature parameters is of good performance by using the SVM method as well as good engineering foreground.

scholarly journals A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668596 ◽  
Author(s):  
Fuqiang Sun ◽  
Xiaoyang Li ◽  
Haitao Liao ◽  
Xiankun Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Remaining Useful Life ◽  
Lifetime Prediction ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Microwave Component ◽  
Useful Life ◽  
Bayesian Least Squares

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

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