Partial Discharge Diagnostics: Data Cleaning and Feature Extraction

Partial Discharge Feature Extraction Based on Ensemble Empirical Mode Decomposition and Sample Entropy

Partial Discharge (PD) pattern recognition plays an important part in electrical equipment fault diagnosis and maintenance. Feature extraction could greatly affect recognition results. Traditional PD feature extraction methods suffer from high-dimension calculation and signal attenuation. In this study, a novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and ...

Feature Extraction from Visual Data Feature Extraction from Visual Data

Local feature extraction and matching partial objects

A primary shortcoming of existing techniques for 3D model matching is the reliance on global information of model’s structure. Models are matched in their entirety, depending on overall topology and geometry information. A current open challenge is how to perform partial matching. Partial matching is important for finding similarities across part models with different global shape properties an...

Feature Extraction of Meris Data

A feature extraction method is at the core of most multispectral or hyperspectral processing algorithms aiming at classification, compression, colour representation etc. In this work a comparison between three different feature extraction methods, for the case of MERIS data is carried out. This comparison reveals the different properties and drawbacks of each method.

Feature Extraction of Multispectral Data