292,291 research outputs found
A new method for automatic Multiple Partial Discharge Classification
A new wavelet based feature parameter have been developed to represent the characteristics of PD activities, i.e. the wavelet decomposition energy of PD pulses measured from non-conventional ultra wide bandwidth PD sensors such as capacitive couplers (CC) or high frequency current transformers (HFCT). The generated feature vectors can contain different dimensions depending on the length of recorded pulses. These high dimensional feature vectors can then be processed using Principal Component Analysis (PCA) to map the data into a three dimensional space whilst the first three most significant components representing the feature vector are preserved. In the three dimensional mapped space, an automatic Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is then applied to classify the data cluster(s) produced by the PCA. As the procedure is undertaken in a three dimensional space, the obtained clustering results can be easily assessed. The classified PD sub-data sets are then reconstructed in the time domain as phase-resolved patterns to facilitate PD source type identification. The proposed approach has been successfully applied to PD data measured from electrical machines and power cables where measurements were undertaken in different laboratories
Identifying structural changes with unsupervised machine learning methods
Unsupervised machine learning methods are used to identify structural changes
using the melting point transition in classical molecular dynamics simulations
as an example application of the approach. Dimensionality reduction and
clustering methods are applied to instantaneous radial distributions of atomic
configurations from classical molecular dynamics simulations of metallic
systems over a large temperature range. Principal component analysis is used to
dramatically reduce the dimensionality of the feature space across the samples
using an orthogonal linear transformation that preserves the statistical
variance of the data under the condition that the new feature space is linearly
independent. From there, k-means clustering is used to partition the samples
into solid and liquid phases through a criterion motivated by the geometry of
the reduced feature space of the samples, allowing for an estimation of the
melting point transition. This pattern criterion is conceptually similar to how
humans interpret the data but with far greater throughput, as the shapes of the
radial distributions are different for each phase and easily distinguishable by
humans. The transition temperature estimates derived from this machine learning
approach produce comparable results to other methods on similarly small system
sizes. These results show that machine learning approaches can be applied to
structural changes in physical systems
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