최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기韓國컴퓨터情報學會論文誌 = Journal of the Korea Society of Computer and Information, v.26 no.8, 2021년, pp.23 - 30
Kang, Gun-Ha (Epozen's research institute) , Sohn, Jung-Mo (Epozen's research institute) , Sim, Gun-Wu (Epozen's research institute)
In this study, we present a comparative analysis of major autoencoder(AE)-based anomaly detection methods for quality determination in the manufacturing process and a new anomaly discrimination criterion. Due to the characteristics of manufacturing site, anomalous instances are few and their types g...
Yeong-Tae Baek, Jae-Gyu Sim, Chan-Young Pak, Se-Hoon Lee, PCB Defect Inspection using Deep Learning. KSCI, pp. 325-326, 2018.
Chul-jin, Park. A study on the quality information management for preventing human errors at quality of ship.. Ulsan University Graduate School of Automotive Vessel Technology, 2016, UCI:I084:48009-000002213977.
Ho Young, Lee. Changes in labour demand and acceptability due to the Fourth Industrial Revoluation. Korea Information Society Development, 2019.
Raghavendra Chalapathy and Sanjay Chawla, Deep Learning for Anomaly Detection: A Survey. CoRR, (abs/1901.03407), 2019.
Alberto Tellaeche Iglesias, Miguel Angel Campos Anaya, Gonzalo Pajares Martinsanz and Iker Pastor-Lopez, On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures, Sensors, 21(10), 3339, 2021, DOI:10.3390/s21103339.
Jungsuk Kim,Jungbeom Ko,Hojong Choi and Hyunchul Kim, Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder, Sensors, 21(15), 4968, 2021, DOI:10.3390/s21154968.
Jinwon An and Sungzoon Cho, Variational Autoencoder based AnomalyDetection using Reconstruction Probability. Technical Report, SNU Data MiningCenter, pp. 1-18, 2015.
Jonathan Masci, Ueli Meier, Dan Ciresan, and Jurgen Schmidhuber, Stacked convolutional auto-encoders for hierarchical feature extraction. In International Conference on Artificial Neural Networks, ICANN, pp. 52-59, 2011, DOI:10.1007/978-3-642-21735-7_7.
Omid E. David, Nathan S.Netanyahu, DeepPainter: Painter Classification Using Deep Convolutional Autoencoders. ICANN, Vol.9887, pp. 20-28, 2016, DOI:10/1007/978-3-319-44781-0_3.
Diederik P Kingma and Max Welling, Auto-encoding variational bayes. ICLR. arXiv preprintarXiv:1312.6114, 2013.
Taeu Kim, https://taeu.github.io/paper/deeplearning-paper-vae/.
Shengyuan Piao and Jiaming Liu, Accuracy improvement of unet based on dilated convolution. Journal of Physics: Conference Series. Vol. 1345. No. 5. IOP Publishing, 2019, DOI:10.1088/1742-6596/1345/5/052066.
VESAL.Sulaiman, RAVIKUMAR.Nishant, MAIER.Andreas. A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT. arXiv preprint arXiv:1905.07710, 2019.
Ravirajsinh Dabhi, Casting product image data for quality inspection, Kaggle, https://www.kaggle.com/ravirajsinh45/real-life-industrial-dataset-of-casting-product.
The Nature of Statistics Bigpicture, Statistics Basics Lecture 1. Mean, Deviation, Variance, Standard Deviations, https://hsm-edu.tistory.com/1182
Garlic, https://m.blog.naver.com/yk60park/222100758577
CityWizard, https://m.blog.naver.com/PostView.naver?blogIdy4769&logNo220505513170&proxyRefererhttps:%2F%2Fwww.google.com%2F
Wikipedia, Entropy(Information Theory), https://en.wikipedia.org/wiki/Entropy_(information_theory)
Kim, Chungyun, Evaluation Method of Classification Model, https://velog.io/@skyepodium/%EB%B6%84%EB%A5%98-%EB%AA%A8%EB%8D%B8-%ED%8F%89%EA%B0%80-%EB%B0%A9%EB%B2%95.
Jakub Czakon, https://neptune.ai/blog/f1-score-accuracy-roc-aucpr-auc
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.