Corrosion rate prediction of 3C steel under different seawater environment by using support vector regression

2009 ◽  
Vol 51 (2) ◽  
pp. 349-355 ◽  
Author(s):  
Y.F. Wen ◽  
C.Z. Cai ◽  
X.H. Liu ◽  
J.F. Pei ◽  
X.J. Zhu ◽  
...  
Keyword(s):  
Corrosion Rate ◽  
Support Vector Regression ◽  
Support Vector ◽  
Rate Prediction ◽  
Seawater Environment

Support vector regression for rate prediction in distributed video coding

2014 ◽  
Vol 18 (3) ◽  
pp. 465-477
Author(s):  
Isaac Nickaein ◽  
Mohammad Rahmati ◽  
Nazanin Hamzei
Keyword(s):  
Video Coding ◽  
Support Vector Regression ◽  
Distributed Video Coding ◽  
Support Vector ◽  
Rate Prediction

A Model of Predicting Corrosion Rate for Substation Grounding Grid Based on the Similarity and Support Vector Regression

2014 ◽  
Vol 596 ◽  
pp. 271-275
Author(s):  
Jing Yi Du ◽  
Juan Han ◽  
Yue Jiao Zhao ◽  
Wen Hui Liu
Keyword(s):  
Corrosion Rate ◽  
Support Vector Regression ◽  
Feature Vector ◽  
Training Model ◽  
Support Vector ◽  
Nonlinear Contribution ◽  
Grounding Grid ◽  
Predicting Performance ◽  
Index Vector ◽  
Grid Based

In this paper, we proposed a training model to predict the corrosion rate for substation grounding grid based on the Similarity and Support Vector Regression (SSVR). In the proposed model, the effect of grounding grid corrosion rate was acted as a feature vector and processed by a dimensionless treatment. Then, the similarity between the feature vector of training terminal and index vector of actual site would be calculated. In the prediction of corrosion rate, the traditional Linear Average Method (LAM) to describe the nonlinear contribution has some fault defects. Therefore, we proposed the training model named SSVR. From the experimental results, the proposed SSVR can obtain better predicting performance than the traditional LAM.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection
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