1 |
S.G. Kim, T.I. Oh, "Real-time PM10 Concentration Prediction LSTM Model Based on IoT Streaming Sensor Data" Korea Journal of the Korean Society of Science and Technology, Vol. 19, No. 11, pp. 310-318, 2018. DOI: https://doi.org/10.5762/KAIS.2018.19.11.310
DOI
|
2 |
B. Zong, Q. Song, M.R. Min, W. Cheng, C, Lumezanu, D. Cho, H. Chen, "Deep autoencoding gaussian mixture model for unsupervised anomaly detection", In Proceeding of International Conference on Learning Representations, 2018.
|
3 |
D. Li, D. Chen, J. Goh, S.K. Ng, "Anomaly detection with generative adversarial networks for multivariate time series", arXiv preprint arXiv:1809.04758, 2018.
|
4 |
C. Baur, B. Wiestler, S. Albarqouni, N. Navab, "Deep autoencoding models for unsupervised anomaly segmentation in brain MR images", In Proceeding of International MICCAI Brainlesion Workshop, pp. 161-169, 2018. DOI: https://doi.org/10.1007/978-3-030-11723-8_16
DOI
|
5 |
,M.R. Moore, J.M. Vann. (2019, January). Anomaly detection of cyber physical network data using 2D images. In Proceeding of 2019 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-5), 2019. DOI: https://doi.org/10.1109/ICCE.2019.8662084
DOI
|
6 |
Q. Wei, Y, Ren, R. Hou, B. Shi, J.Y. Lo, L. Carin, "Anomaly detection for medical images based on a one-class classification", In Proceeding of Medical Imaging 2018: Computer-Aided Diagnosis, 2018. DOI: https://doi.org/10.1117/12.2293408
DOI
|
7 |
M. Hasan, M.M. Islam, M.I.I. Zarif, M.M.A. Hashem, "Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches", Internet of Things, 7, 100059, 2019. DOI: https://doi.org/10.1016/j.iot.2019.100059
DOI
|
8 |
P. Malhotra, L. Vig, G. Shroff, P. Agarwal, "Long short term memory networks for anomaly detection in time series", In Proceedings of Presses universitaires de Louvain, Vol. 89, pp. 89-94, 2015.
|
9 |
M., Heinrich, A., Golz, T., Arul, S. Katzenbeisser, "Rule-based Anomaly Detection for Railway Signalling Networks", arXiv preprint arXiv:2008.05241, 2020.
|
10 |
H. Sarmadi, A. Karamodin, "A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-c lass kNN rule for structural health monitoring under environmental effects.", Mechanical Systems and Signal Processing, Vol. 140, 106495, 2020. DOI: https://doi.org/10.1016/j.ymssp.2019.106495
DOI
|
11 |
S. Lawrence, C.L. Giles, A.C. Tsoi, A.D. Back, "Face recognition: A convolutional neural-network approach." IEEE transactions on neural networks, Vol. 8, No. 1, pp. 98-113, 1997. DOI: https://doi.org/110.1109/72.554195
DOI
|
12 |
M. Xia, T. Li, L. Xu, L. Liu, C.W. de Silva, "Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks", IEEE/ASME Transactions on Mechatronics, Vol. 23, No. 1, pp. 101-110, 2018. DOI: https://doi.org/10.1109/TMECH.2017.2728371
DOI
|
13 |
S.H. Lee, J.S. Kim, B.B. Shin, "CNN-Based Noise System for Motorized Driving Unit Fault Causes Classification System", Proceedings of the Korean Computer Information Conference, Vol. 26, No. 1, pp. 7-8, 2018.
|
14 |
P. Malhotra, L. Vig, G. Shroff, P. Agarwal "Long short term memory networks for anomaly detection in time series", In Proceedings, Presses universitaires de Louvain, p.89, 2015.
|
15 |
N.Y. Choi, W.H. Kim, "Detecting user behavior anomalies using Generative Adversarial Networks", Intelligence Information Research, 25(3), 43-62, 2019.
|
16 |
F.A. Gers, J. Schmidhuber, "Recurrent nets that time and count", In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Neural Computing: New Challenges and Perspectives for the New Millennium, Vol. 3, pp. 189-194, 2000. DOI: https://doi.org/10.1109/IJCNN.2000.861302
DOI
|