An Improved Support Vector Machine Method for Transient Stability Assessment in Bulk Power Grid

2020 ◽  
Author(s):  
Changjiang Wang ◽  
Fusuo Liu
Keyword(s):  
Support Vector Machine ◽  
Transient Stability ◽  
Power Grid ◽  
Support Vector ◽  
Stability Assessment ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Bulk Power ◽  
Improved Support Vector Machine

scholarly journals Temperature drift modeling and compensation of micro-electro-mechanical system gyroscope based on improved support vector machine algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 155014772090819
Author(s):  
Xinwang Wang ◽  
Huiliang Cao
Keyword(s):  
Support Vector Machine ◽  
Mechanical System ◽  
Micro Electro Mechanical System ◽  
Support Vector ◽  
Temperature Drift ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Noise Characteristics ◽  
Improved Support Vector Machine

This article suggested two methods to compensate for the temperature drift of the micro-electro-mechanical system gyroscopes, which are support vector machine method and C-means support vector machine. The output of X axis which was ranged from −40°C to 60°C based on the micro-electro-mechanical system gyroscope is reduced and analyzed in this article. The results showed the correctness of the two methods. The final results indicate that when the temperature is ranged from −40°C to 60°C, the factor of B is reduced from 0.424 [Formula: see text] to 0.02194 [Formula: see text], and when the temperature is ranged from 60°C to −40°C, the factor of B is reduced from 0.1056 [Formula: see text] to 0.0329 [Formula: see text], and the temperature drift trend and noise characteristics are improved clearly.

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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