지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 중앙대학교 영상콘텐츠융합연구소 TECHART: Journal of Arts and Imaging Science TECHART: Journal of Arts and Imaging Science Vol.12 No.1
- 발행연도
- 2025.1
- 수록면
- 13 - 19 (7page)
- DOI
- 10.15323/techart.2025.1.12.1.13
이용수
초록· 키워드
오류제보하기
Metabolomics studies face several critical challenges: scarcity of available data, difficulty in obtaining reliable labeled samples because of the expensive and time-consuming labeling process, and lack of consistency between data samples due to variations in extraction times. To address these challenges, we present a novel data augmentation framework that combines temporal alignment preprocessing with a 1D variational autoencoder (VAE). The preprocessing step eliminates the temporal inconsistencies between samples as they do not represent meaningful metabolomic characteristics. The VAE generates synthetic data based on these aligned samples, which are then utilized in a semi-supervised anomaly-detection framework. When applied to coffee and Aralia variety authentication, the framework successfully classified pure samples and detected mixed samples as anomalies despite using only 20% of the available labeled data for training. The proposed approach effectively addresses real-world scenarios in which labeled and unlabeled data are scarce.
목차
Abstract
1. Introduction
2. Related works
3. Metabolic data analysis
4. Methodology
5. Experimental Results
6. Conclusion
References
참고문헌 (0)
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