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NTIS 바로가기品質經營學會誌 = Journal of Korean society for quality management, v.49 no.4, 2021년, pp.581 - 594
서재홍 (연세대학교 산업공학과) , 박준성 (연세대학교 산업공학과) , 유준우 (연세대학교 산업공학과) , 박희준 (연세대학교 산업공학과)
Purpose: The purpose of this study is to compare machine learning models for anomaly detection of mechanical facility equipment and suggest an anomaly detection system for mechanical facility equipment in subway stations. It helps to predict failures and plan the maintenance of facility. Ultimately ...
Akcay S, Atapour-Abarghouei A. Breckon T P. 2019. Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. 2019 International Joint Conference on Neural Networks (IJCNN). IEEE.
An J. and Cho S. 2015. Variational Autoencoder based AnomalyDetection using Reconstruction Probability. Technical Report. SNU Data MiningCenter. 1-18.
Bank D., Koenigstein N., and Giryes R. 2020. Autoencoders. arXiv eprints.
Baur C., Wiestler B., Albarqouni S., and Navab N. 2018. Deep autoencoding models for unsupervised anomaly seg- mentation in brain MR images. In Proceeding of International MICCAI Brainlesion Workshop. 11383:161-169.
Chalapathy R. and Chawla S. 2019. Deep Learning for Anomaly Detection: A Survey. CoRR, (abs/1901.03407).
Chandola V., Banerjee A., and Kumar V. 2009. Anomaly detection : A survey, ACM computing surveys (CSUR), 41(3):15.
DataRPM, P. 2017. Anomaly Detection & Prediction Decoded: 6 Industries. Copious Challenges. Extraordinary Impact. Technical Report.
Guo Y., Liao W., Wang Q., Yu L., Ji T., and Li P. 2018. Multidimensional Time Series Anomaly Detection: A GRU-based Gaussian Mixture Variational Autoencoder Approach. Proceedings of the 10th Asian Conference on Machine Learning, PMLR. 95:97-112.
Harmeling S., Dornhege G., Tax D., Meinecke F., and Muller K R. 2006. From outliers to prototypes : ordering data. Neurocomputing 69(13-15):1608-1618.
Hasan M., Islam M M, Zarif M I I, and Hashem M M A. 2019. Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things. 7:100059
Hodge V. and Austin, J. 2004. A survey of outlier detection methodologies. Artificial Intelligence Review 22(2):85-126.
K.H. Sun, H Huh, Tama, B A, S.Y Lee, J.H. Jung, and S. Lee, 2020. Vision-Based Fault Diagnostics Using Explainable Deep Learning with Class Activation Map. IEEE Access 8:129169-129179.
Li D, Chen D, Goh J, and Ng S K. 2018. Anomaly detection with generative adversarial networks for multivariate time series. arXiv preprint arXiv:1809.04758.
M.J. Kim, Y.H. Park, T.K. Kim, J.S. Jung. 2019. A Study on the Prediction Diagnosis System Improvement by Error Terms and Learning Methodologies Application. Journal of Korean Society for Quality Management. 47(4):783-793.
Maesschalck R D, Jouan-Rimbaud D, and Massart D L. 2000. The Mahalanobis Distance. Chemometrics and Intelligent Laboratory Systems 50(1):1-18.
Malhotra P, Vig L, Shroff G, and Agarwal P. 2015. Long short term memory networks for anomaly detection in time series. In Proceedings of Presses Universitaires de Louvain 89:89-94.
Munawar A, Vinayavekhin P, and Magistris G. D. 2017. Limiting the reconstruction capability of generative neural network using negative learning. 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE.
N.Y. Choi, and W.H. Kim. 2019. Detecting user behavior anomalies using Generative Adversarial Networks. Intelligence Information Research 25(3):43-62.
Park D, Hoshi Y, and Kemp C C. 2018. A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder. IEEE Robotics and Automation Letters 3(3):1544-1551.
Perera P, Nallapati R, and Xiang B. 2019. Ocgan: One-class novelty detection using gans with constrained latent representations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
Pokrajac D, Lazarevic A, and Latecki L. 2007. Incremental Local Outlier Detection for Data Streams. CIDM Conference.
Rousseeuw P. and Leroy A. 2003. Robust Regression and Outlier Detection. Wiley.
Son V H, Daisuke U, Kiyoshi H, Kazuki M, Pranata S, and Shen S. M. 2019. Anomaly detection with adversarial dual autoencoders. arXiv eprint.
Wei Q, Ren Y, Hou R, Shi B, Lo J Y, and Carin L. 2018. Anomaly detection for medical images based on a one-class classification. In Proceeding of Medical Imaging 2018: Computer-Aided Diagnosis.
Yamanaka Y, Iwata T, Takahashi H, Yamada M., and Kanai S. 2019. Autoencoding binary classifiers for supervised anomaly detection. Pacific Rim International Conference on Artificial Intelligence.
Zong B, Song Q, Min M R, Cheng W. Lumezanu C, Cho D, and Chen H. 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In Proceeding of International Conference on Learning Representations.
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