scholarly journals A rapid pattern-recognition method for driving styles using clustering-based support vector machines

2016 ◽  
Author(s):  
Wenshuo Wang ◽  
Junqiang Xi
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machines ◽  
Support Vector ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Vector Machines

scholarly journals Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jingzong Yang ◽  
Xiaodong Wang ◽  
Zao Feng ◽  
Guoyong Huang
Keyword(s):  
Pattern Recognition ◽  
Impulse Response ◽  
Information Entropy ◽  
Operating Conditions ◽  
Support Vector ◽  
Entropy Theory ◽  
Pattern Recognition Method ◽  
Response Signal ◽  
Recognition Method ◽  
Learning Machine

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

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

2009 ◽  
Vol 119 (1-2) ◽  
pp. 32-38 ◽  
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

scholarly journals Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7674
Author(s):  
Ruixu Zhou ◽  
Wensheng Gao ◽  
Weidong Liu ◽  
Dengwei Ding ◽  
Bowen Zhang
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Pulse Current ◽  
State Of The Art ◽  
Selection Criterion ◽  
Component Analysis ◽  
Support Vector ◽  
Stable Operation ◽  
Pattern Recognition Method ◽  
Recognition Method

Accurately identifying the types of insulation defects inside a gas-insulated switchgear (GIS) is of great significance for guiding maintenance work as well as ensuring the safe and stable operation of GIS. By building a set of 220 kV high-voltage direct current (HVDC) GIS experiment platforms and manufacturing four different types of insulation defects (including multiple sizes and positions), 180,828 pulse current signals under multiple voltage levels are successfully measured. Then, the apparent discharge quantity and the discharge time, two inherent physical quantities unaffected by the experimental platform and measurement system, are obtained after the pulse current signal is denoised, according to which 70 statistical features are extracted. In this paper, a pattern recognition method based on generalized discriminant component analysis driven support vector machine (SVM) is detailed and the corresponding selection criterion of involved parameters is established. The results show that the newly proposed pattern recognition method greatly improves the recognition accuracy of fault diagnosis in comparison with 36 kinds of state-of-the-art dimensionality reduction algorithms and 44 kinds of state-of-the-art classifiers. This newly proposed method not only solves the difficulty that phase-resolved partial discharge (PRPD) cannot be applied under DC condition but also immensely facilitates the fault diagnosis of HVDC GIS.

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