BROWSE
Detail
Conference Paper
Power Quality Problem Classification Methods Based on Active Transfer Learning
Cited 0 time in 
Share 
Share
- 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
- 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)