High-Resolution Image Classification Using the Dynamic Differential Evolutionary Algorithm Optimized Multi-scale Kernel Support Vector Machine Method

2018 ◽  
pp. 334-341
Author(s):  
Xueqian Rong ◽  
Aizhu Zhang ◽  
Genyun Sun ◽  
Hui Huang ◽  
Ping Ma
Keyword(s):  
Support Vector Machine ◽  
High Resolution ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Resolution Image ◽  
Multi Scale ◽  
High Resolution Image ◽  
Kernel Support Vector Machine ◽  
Differential Evolutionary Algorithm

Anomaly Detection of Electric Gate Valve Based on Multi-Kernel Support Vector Machine

2021 ◽  
Author(s):  
Jing Luo ◽  
Hang Wang ◽  
Minjun Peng
Keyword(s):  
Support Vector Machine ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Anomaly Detection ◽  
Nuclear Power ◽  
Gate Valve ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Kernel Support Vector Machine

Abstract Valve is an indispensable fluid control component in nuclear power system. Nuclear power station has a large number of gate valve equipment, which works under high temperature, high pressure, high radioactivity and other harsh conditions. In nuclear power plant accidents and economic losses, a considerable part of them are caused by valve failure. Aiming at the fault of electric gate valve, this paper proposes an anomaly detection method based on multi-kernel support vector machine. Firstly, the acoustic emission instrument is used to measure the fault state data and extract the fault features. Secondly, on the basis of classical support vector machine, multiple kernel function combinations are selected to decompose the model into convex optimization problems to realize the abnormal state detection of internal leakage fault of electric gate valve in nuclear power plant. The results show that, compared with the classical support vector machine method, the constructed support vector machine method based on multikernel learning has better effect and higher accuracy in anomaly detection of electric gate valve.

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.

scholarly journals Using a support vector machine method to predict the development indices of very high water cut oilfields

2010 ◽  
Vol 7 (3) ◽  
pp. 379-384 ◽  
Author(s):  
Yihua Zhong ◽  
Lei Zhao ◽  
Zhibin Liu ◽  
Yao Xu ◽  
Rong Li
Keyword(s):  
Support Vector Machine ◽  
High Water ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
High Water Cut ◽  
Water Cut ◽  
Very High

Urban water resources allocation and shortage risk mapping with support vector machine method

2015 ◽  
Vol 81 (2) ◽  
pp. 1209-1228 ◽  
Author(s):  
Qian Zhang ◽  
Xiujuan Liang ◽  
Zhang Fang ◽  
Tao Jiang ◽  
Yubo Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Water Resources ◽  
Risk Mapping ◽  
Support Vector ◽  
Resources Allocation ◽  
Urban Water ◽  
Water Resources Allocation ◽  
Machine Method ◽  
Support Vector Machine Method
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