Linear Feature Extraction: Infomax Principle

1996 ◽  
pp. 41-63
Author(s):  
Gustavo Deco ◽  
Dragan Obradovic
Keyword(s):  
Feature Extraction ◽  
Linear Feature ◽  
Linear Feature Extraction

scholarly journals Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2748
Author(s):  
Jersson X. Leon-Medina ◽  
Maribel Anaya ◽  
Núria Parés ◽  
Diego A. Tibaduiza ◽  
Francesc Pozo
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Wind Turbine ◽  
Structural Damage ◽  
Principal Component ◽  
Gradient Boosting ◽  
Damage Classification ◽  
Linear Feature ◽  
Linear Feature Extraction ◽  
Extreme Gradient Boosting

Damage classification is an important topic in the development of structural health monitoring systems. When applied to wind-turbine foundations, it provides information about the state of the structure, helps in maintenance, and prevents catastrophic failures. A data-driven pattern-recognition methodology for structural damage classification was developed in this study. The proposed methodology involves several stages: (1) data acquisition, (2) data arrangement, (3) data normalization through the mean-centered unitary group-scaling method, (4) linear feature extraction, (5) classification using the extreme gradient boosting machine learning classifier, and (6) validation applying a 5-fold cross-validation technique. The linear feature extraction capabilities of principal component analysis are employed; the original data of 58,008 features is reduced to only 21 features. The methodology is validated with an experimental test performed in a small-scale wind-turbine foundation structure that simulates the perturbation effects caused by wind and marine waves by applying an unknown white noise signal excitation to the structure. A vibration-response methodology is selected for collecting accelerometer data from both the healthy structure and the structure subjected to four different damage scenarios. The datasets are satisfactorily classified, with performance measures over 99.9% after using the proposed damage classification methodology.

scholarly journals DNN Transfer Learning Based Non-Linear Feature Extraction for Acoustic Event Classification

2017 ◽  
Vol E100.D (9) ◽  
pp. 2249-2252 ◽  
Author(s):  
Seongkyu MUN ◽  
Minkyu SHIN ◽  
Suwon SHON ◽  
Wooil KIM ◽  
David K. HAN ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Transfer Learning ◽  
Event Classification ◽  
Acoustic Event ◽  
Linear Feature ◽  
Linear Feature Extraction ◽  
Non Linear
Sign in / Sign up

Export Citation Format

Share Document