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

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.

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

Corrosion Science ◽

10.1016/j.corsci.2008.10.038 ◽

2009 ◽

Vol 51 (2) ◽

pp. 349-355 ◽

Cited By ~ 75

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

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The Progressive Optimization Model on the Corrosion Rate of the Grounding Grid

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.712-715.1090 ◽

2013 ◽

Vol 712-715 ◽

pp. 1090-1095

Author(s):

Fen Lei Dai ◽

Wen Qiang Xie

Keyword(s):

Neural Network ◽

Corrosion Rate ◽

Performance Optimization ◽

Cross Validation ◽

Principal Component ◽

Training Model ◽

Main Element ◽

Network Training ◽

Grounding Grid ◽

Neural Network Input

Established a cross-validation of the model optimization algorithm in order to predict the corrosion rate of the grounding grid. Forward neural network input variables by principal component analysis to extract the main element, eliminating the correlation between variables; cross-validation method and change the termination of the neural network training conditions, the selection of network training model. The final simulation results show that, get a good grounding grid corrosion rate stability and generalization performance optimization prediction model can predict the corrosion rate of the grounding grid.

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Integrating principal component analysis and vector quantization with support vector regression for sulfur content prediction in HDS process

Chemical Industry and Chemical Engineering Quarterly ◽

10.2298/ciceq140418039s ◽

2015 ◽

Vol 21 (3) ◽

pp. 379-390 ◽

Cited By ~ 3

Author(s):

Saeid Shokri ◽

Mohammad Sadeghi ◽

Mahdi Marvast ◽

Shankar Narasimhan

Keyword(s):

Support Vector Regression ◽

Sulfur Content ◽

Vector Quantization ◽

Prediction Accuracy ◽

Principal Component ◽

Component Analysis ◽

Test Time ◽

Support Vector ◽

Predicting Performance ◽

Input Variables

An accurate prediction of sulfur content is very important for the proper operation and product quality control in hydrodesulfurization (HDS) process. For this purpose, a reliable data- driven soft sensors utilizing Support Vector Regression (SVR) was developed and the effects of integrating Vector Quantization (VQ) with Principle Component Analysis (PCA) were studied on the assessment of this soft sensor. First, in pre-processing step the PCA and VQ techniques were used to reduce dimensions of the original input datasets. Then, the compressed datasets were used as input variables for the SVR model. Experimental data from the HDS setup were employed to validate the proposed integrated model. The integration of VQ/PCA techniques with SVR model was able to increase the prediction accuracy of SVR. The obtained results show that integrated technique (VQ-SVR) was better than (PCA-SVR) in prediction accuracy. Also, VQ decreased the sum of the training and test time of SVR model in comparison with PCA. For further evaluation, the performance of VQ-SVR model was also compared to that of SVR. The obtained results indicated that VQ-SVR model delivered the best satisfactory predicting performance (AARE= 0.0668 and R2= 0.995) in comparison with investigated models.

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Modeling color fading ozonation of reactive-dyed cotton using the Extreme Learning Machine, Support Vector Regression and Random Forest

Textile Research Journal ◽

10.1177/0040517519883059 ◽

2019 ◽

Vol 90 (7-8) ◽

pp. 896-908 ◽

Cited By ~ 2

Author(s):

Zhenglei He ◽

Kim-Phuc Tran ◽

Sébastien Thomassey ◽

Xianyi Zeng ◽

Jie Xu ◽

...

Keyword(s):

Random Forest ◽

Support Vector Regression ◽

Extreme Learning Machine ◽

Support Vector ◽

Training Time ◽

Single Output ◽

Predicting Performance ◽

Color Fading ◽

Learning Machine ◽

Temperature Water

Textile products with a faded effect achieved via ozonation are increasingly popular nowadays. In order to better understand and apply this process, the complex factors and effects of color fading ozonation are investigated via process modeling in terms of pH, temperature, water pick-up, time (of process) and original color (of textile) affecting the color performance ( K/ S, L*, a*, b* values) of reactive-dyed cotton using the Extreme Learning Machine (ELM), Support Vector Regression (SVR) and Random Forest (RF), respectively. It is found that the RF and SVR perform better than the ELM as the latter were very unstable in the case of predicting a certain single output. Both the RF and SVR are potentially applicable, but SVR would be more recommended to be used in the real application due to its balancer predicting performance and lower training time cost.

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