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Conference Paper Power Quality Problem Classification Methods Based on Active Transfer Learning
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Authors
Seungmin Oh, Seokgap Ko, Eden Kim, ByungTak Lee
Issue Date
2020-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1222-1224
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289605
Abstract
In this paper, we propose approach for classification of power quality problem using active transfer learning. Active learning is suitable for solving problems with real industrial data because even a small amount of labeled data can derive good performance. Active learning can derive performance by querying human data labelers for data the model does not learn or trained. Active transfer learning utilizes transfer learning for data sampling of the trained model. However, querying too much data can put pressure on human data labelers. Thus, we suggest the solution of this problem by limiting query in two ways. One is to limit the number of queries to 100 and the other is dynamically controlled the number of queries according to the accuracy. As a result, we derive better predictive performance with suggest method and compare the results with the traditional method.
KSP Keywords
Active transfer learning, Classification method, Data Sampling, Human data, Power Quality(PQ), Predictive performance, Problem classification, Quality problem, Real industrial data, Traditional methods, active learning(AL)