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김기현,Kim, Gi-Hyeon 한국과학기술단체총연합회 1986 과학과 기술 Vol.19 No.8
◇이 글은 지난 7월 8일부터 10일까지 건국대에서 개최된 "'86국내외한국과학기술자학술회의 하계심포지움"의 개회식에서 행한 재미과협 김기현회장(미노드캐로라이나 센추럴대교수ㆍ물리학)의 특별강연 내용을 정리한 것이다. <편집자 주>
푸리에 변환 및 이미지 증강을 통한 분류 성능 최적화에 관한 연구
김기현,김성목,김용수 한국품질경영학회 2023 품질경영학회지 Vol.51 No.1
Purpose: This study proposes a classification model for implementing condition-based maintenance (CBM) by monitoring the real-time status of a machine using acceleration sensor data collected from a vehicle. Methods: The classification model's performance was improved by applying Fourier transform to convert the acceleration sensor data from the time domain to the frequency domain. Additionally, the Generative Adversarial Network (GAN) algorithm was used to augment images and further enhance the classification model's performance. Results: Experimental results demonstrate that the GAN algorithm can effectively serve as an image augmentation technique to enhance the performance of the classification model. Consequently, the proposed approach yielded a significant improvement in the classification model's accuracy. Conclusion: While this study focused on the effectiveness of the GAN algorithm as an image augmentation method, further research is necessary to compare its performance with other image augmentation techniques. Additionally, it is essential to consider the potential for performance degradation due to class imbalance and conduct follow-up studies to address this issue.