S-Transform-Based Classification of Converter Faults in HVDC System by Support Vector Machines

Based on Support Vector Machines (SVM) and S-transform, a novel approach to detect and classify various types of high voltage direct current (HVDC) converter faults is presented. An electro-magnetic transient state simulation software PSCAD/EMTDC was used to set up a simulation model of HVDC system to investigate the typical converter faults. For the good time-frequency characteristic of S-transform, S-transform is applied to obtain useful features of the non-stationary fault signals. Then fault types are identified through the pattern recognition classifier based on SVM classification tree. Numerical results show that the proposed classification method is an effective technique for building up a pattern recognition system for converter fault signals.

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Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines

Applied Animal Behaviour Science ◽

10.1016/j.applanim.2009.03.005 ◽

2009 ◽

Vol 119 (1-2) ◽

pp. 32-38 ◽

Cited By ~ 169

Author(s):

Paula Martiskainen ◽

Mikko Järvinen ◽

Jukka-Pekka Skön ◽

Jarkko Tiirikainen ◽

Mikko Kolehmainen ◽

...

Keyword(s):

Pattern Recognition ◽

Support Vector Machines ◽

Three Dimensional ◽

Behaviour Pattern ◽

Support Vector ◽

Vector Machines

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Bankruptcy Prediction of Engineering Companies in the EU Using Classification Methods

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis ◽

10.11118/actaun201866051347 ◽

2018 ◽

Vol 66 (5) ◽

pp. 1347-1356 ◽

Cited By ~ 1

Author(s):

Michaela Staňková ◽

David Hampel

Keyword(s):

Logistic Regression ◽

Support Vector Machines ◽

Binary Classification ◽

Classification Tree ◽

Classification Trees ◽

Bankruptcy Prediction ◽

Support Vector ◽

Type I ◽

Vector Machines ◽

The Eu

This article focuses on the problem of binary classification of 902 small- and medium‑sized engineering companies active in the EU, together with additional 51 companies which went bankrupt in 2014. For classification purposes, the basic statistical method of logistic regression has been selected, together with a representative of machine learning (support vector machines and classification trees method) to construct models for bankruptcy prediction. Different settings have been tested for each method. Furthermore, the models were estimated based on complete data and also using identified artificial factors. To evaluate the quality of prediction we observe not only the total accuracy with the type I and II errors but also the area under ROC curve criterion. The results clearly show that increasing distance to bankruptcy decreases the predictive ability of all models. The classification tree method leads us to rather simple models. The best classification results were achieved through logistic regression based on artificial factors. Moreover, this procedure provides good and stable results regardless of other settings. Artificial factors also seem to be a suitable variable for support vector machines models, but classification trees achieved better results using original data.

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