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• The Journal of the Korean dental association
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• The Mathematical Education
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• The Journal of the Korean dental association
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• 1992

• The Journal of the Korean dental association
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• The Journal of the Korean dental association
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• 1984

• Food Industry And Nutrition
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• pp.61-65
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• 1999

• Proceedings of the Korean Information Science Society Conference
• /
• 2011.06c
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• pp.176-179
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• 2011

본 연구에서는 T-Test와 Genetic Algorithm을 사용해 Luminex 사용 환경에서 난소암을 진단할 수 있는 바이오마커의 조합을 찾고 Cancer와 Normal간의 분류 성능을 평가해 보았다. 바이오마커는 혈액, 체액 내의 특정 질환 여부나 상태를 나타내는 단백질, DNA들의 지표 물질이다. 정상인과는 다른 분포를 가진 성분이 환자의 혈액이나 체액에서 발견되면 이를 토대로 질병유무와 상태를 판단할 수 있다. 난소암을 진단할 수 있는 바이오마커 조합을 찾기 위해 T-Test와 Genetic Algorithm를 사용하여 분류성능이 좋은 바이오마커 조합을 각각 선별해 보았고, 선별된 각각의 마커조합을 선형분류기(LDA)를 사용해 평균 민감도, 특이도, 정확도를 비교해 보았다. 실험데이터는 두 곳의 병원에서 제공받은 총 58명(Cancer 27명, Normal 31명)의 혈청에서 21 종류의 바이오마커 데이터를 Luminex-PRA를 통해 얻었다. 본 연구에서는 T-Test로 만들어진 마커조합이 Genetic algorithm으로 만들어진 마커조합 보다 더 좋은 민감도, 특이도, 분류정확도를 보여주었다.

• Smart Media Journal
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• v.9 no.1
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• pp.16-22
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• 2020

In this study, a neural network method performing both Detection and Classification of diseases and insects in paprika is proposed with Multi-Tasking U-net. Paprika on farms does not have a wide variety of diseases in this study, only two classes such as powdery mildew and mite, which occur relatively frequently are made as the targets. Aiming to this, a U-net is used as a backbone network, and the last layers of the encoder and the decoder of the U-net are utilized for classification and segmentation, respectively. As the result, the encoder of the U-net is shared for both of detection and classification. The training data are composed of 680 normal leaves, 450 mite-damaged leaves, and 370 powdery mildews. The test data are 130 normal leaves, 100 mite-damaged leaves, and 90 powdery mildews. Its test results shows 89% of recognition accuracy.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.9-17
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• 2023

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.