A Power Transformer Event Classification Technique Based on Support Vector Machine

2020 ◽  
Author(s):  
Lucas D. Simoes ◽  
Bruna L. Souza ◽  
Hagi J. D. Costa ◽  
Rodrigo P. de Medeiros ◽  
V. S. Orivaldo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Power Transformer ◽  
Support Vector ◽  
Event Classification ◽  
Classification Technique

scholarly journals Hydrometeor classification from two-dimensional video disdrometer data

2014 ◽  
Vol 7 (9) ◽  
pp. 2869-2882 ◽  
Author(s):  
J. Grazioli ◽  
D. Tuia ◽  
S. Monhart ◽  
M. Schneebeli ◽  
T. Raupach ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Visual Inspection ◽  
Particle Image ◽  
Support Vector ◽  
Two Dimensional ◽  
Time Intervals ◽  
Classification Technique ◽  
Statistical Behavior ◽  
Over Time

Abstract. The first hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data is presented. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using the statistical behavior of a set of particle descriptors as input, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over 60 s precipitation time steps labeled by visual inspection. In this way, eight dominant hydrometeor classes can be discriminated. The algorithm achieved high classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.

SUPPORT VECTOR MACHINES FOR THAI PHONEME RECOGNITION

2001 ◽  
Vol 09 (06) ◽  
pp. 803-813 ◽  
Author(s):  
NUTTAKORN THUBTHONG ◽  
BOONSERM KIJSIRIKUL
Keyword(s):  
Support Vector Machine ◽  
Dimensional Space ◽  
High Dimensional ◽  
Support Vector ◽  
Phoneme Recognition ◽  
Classification Technique ◽  
Separating Hyperplane ◽  
Vector Machines ◽  
Tone Recognition ◽  
Vowel Recognition

The Support Vector Machine (SVM) has recently been introduced as a new pattern classification technique. It learns the boundary regions between samples belonging to two classes by mapping the input samples into a high dimensional space, and seeking a separating hyperplane in this space. This paper describes an application of SVMs to two phoneme recognition problems: 5 Thai tones, and 12 Thai vowels spoken in isolation. The best results on tone recognition are 96.09% and 90.57% for the inside test and outside test, respectively, and on vowel recognition are 95.51% and 87.08% for the inside test and outside test, respectively.

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