최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기KSII Transactions on internet and information systems : TIIS, v.18 no.2, 2024년, pp.348 - 369
Haoyi Zhong (Department of Computer Engineering, Chonnam National University) , Yongjiang Zhao (Department of Computer Engineering, Chonnam National University) , Chang Gyoon Lim (Department of Computer Engineering, Chonnam National University)
With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate freque...
P. K. Malik, R. Sharma, R. Singh, A. Gehlot, S. C. Satapathy, W. S. Alnumay, D. Pelusi, U. Ghosh, and J. Nayak, "Industrial Internet of Things and its Applications in Industry 4.0: State of The Art," Computer Communications, vol. 166, pp. 125-139, Jan. 2021.
G. Toh and J. Park, "Review of vibration-based structural health monitoring using deep learning," Applied Sciences, vol. 10(5), p. 1680, Mar. 2020.
M. Alarcon, F. M. Martinez-Garcia, and F. C. Gomez de Leon Hijes, "Energy and maintenance management systems in the context of industry 4.0. Implementation in a real case," Renewable and Sustainable Energy Reviews, vol. 142, p. 110841, May 2021.
H. Dhiman, D. Deb, S. M. Muyeen, and I. Kamwa, "Wind Turbine Gearbox Anomaly Detection Based on Adaptive Threshold and Twin Support Vector Machines," IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 3462-3469, Dec. 2021.
A. K. Pathak, S. Saguna, K. Mitra, and C. Ahlund, "Anomaly Detection using Machine Learning to Discover Sensor Tampering in IoT Systems," in Proc. of ICC 2021 - IEEE International Conference on Communications, Montreal, QC, Canada, 2021.
J. Gao, X. Song, Q. Wen, P. Wang, L. Sun, and H. Xu, "RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks," Cornell University - arXiv, Cornell University - arXiv, Feb. 2020.
M. Thill, W. Konen, H. Wang, and T. Back, "Temporal convolutional autoencoder for unsupervised anomaly detection in time series," Applied Soft Computing, vol. 122, p. 107751, Nov. 2021.
S. Ramaswamy, R. Rastogi, and K. Shim, "Efficient algorithms for mining outliers from large data sets," in Proc. of ACM SIGMOD Record, pp. 427-438, Jun. 2000.
F. T. Liu, K. M. Ting, and Z.-H. Zhou, "Isolation Forest," in Proc. of 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, 2008.
A. Toshniwal, K. Mahesh, and J. R., "Overview of Anomaly Detection techniques in Machine Learning," in Proc. of 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 2020.
O. I. Provotar, Y. M. Linder, and M. M. Veres, "Unsupervised Anomaly Detection in Time Series Using LSTM-Based Autoencoders," in Proc. of 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine, 2019.
G. Spigler, "Denoising Autoencoders for Overgeneralization in Neural Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 4, pp. 998-1004, Apr. 2020.
J. W. Cooley and J. W. Tukey, "An algorithm for the machine calculation of complex Fourier series," Mathematics of Computation, vol. 19, pp. 297-301, 1965.
F. Jurado and J. R. Saenz, "Comparison between discrete STFT and wavelets for the analysis of power quality events," Electric Power Systems Research, vol. 62, no. 3, pp. 183-190, Jul. 2002.
Z. Wang, J. Zhou, J. Wang, W. Du, J. Wang, X. Han, and G. He, "A Novel Fault Diagnosis Method of Gearbox Based on Maximum Kurtosis Spectral Entropy Deconvolution," IEEE Access, vol. 7, pp. 29520-29532, Jan. 2019.
A. A. Cook, G. Misirli, and Z. Fan, "Anomaly Detection for IoT Time-Series Data: A Survey," IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6481-6494, Jul. 2020.
X. Xia, X. Pan, N. Li, X. He, L. Ma, X. Zhang, and N. Ding, "GAN-based Anomaly Detection: A Review," Neurocomputing, vol. 493, pp. 497-535, Jul. 2022.
M. Hassan, M. Rehmani, and J. Chen, "Anomaly Detection in Blockchain Networks: A Comprehensive Survey," IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 289-318, 2023.
W. A. Chaovalitwongse, Y.-J. Fan, and R. C. Sachdeo, "On the Time Series K-Nearest Neighbor Classification of Abnormal Brain Activity," IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 37, no. 6, pp. 1005-1016, Nov. 2007.
M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, "LOF: identifying density-based local outliers," in Proc. of ACM SIGMOD Record, pp. 93-104, Jun. 2000.
J. Baihong, C. Yuxin, L. Dan, P. Kameshwar, and S.-V. Alberto, "A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection," in Proc. of IEEE Conference Proceedings, IEEE Conference Proceedings, Jan. 2019.
S. Zhong, S. Fu, L. Lin, X. Fu, Z. Cui, and R. Wang, "A novel unsupervised anomaly detection for gas turbine using Isolation Forest," in Proc. of 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), San Francisco, CA, USA, 2019.
V. Aggarwal, V. Gupta, P. Singh, K. Sharma, and N. Sharma, "Detection of Spatial Outlier by Using Improved Z-Score Test," in Proc. of 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2019.
F. E. Grubbs, "Sample Criteria for Testing Outlying Observations," The Annals of Mathematical Statistics, vol. 21, no. 1, pp. 27-58, 1950.
K. Choi, J. Yi, C. Park, and S. Yoon, "Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines," IEEE Access, vol. 9, pp. 120043-120065, Jan. 2021.
P. Matias, D. Folgado, H. Gamboa, and A. Carreiro, "Robust Anomaly Detection in Time Series through Variational AutoEncoders and a Local Similarity Score," in Proc. of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, Online Streaming, --- Select a Country ---, 2021.
H. Liu, J. Zhou, Y. Zheng, W. Jiang, and Y. Zhang, "Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders," ISA Transactions, vol. 77, pp. 167-178, Jun. 2018.
B. Du, X. Sun, J. Ye, K. Cheng, J. Wang, and L. Sun, "GAN-Based Anomaly Detection for Multivariate Time Series Using Polluted Training Set," IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12208-12219, 2023.
D. J. Bartholomew, G. E. P. Box, and G. M. Jenkins, "Time Series Analysis Forecasting and Control," Operational Research Quarterly (1970-1977), vol. 22, no. 2, p. 199, Jun. 1971.
C. Chakrabarti, M. Vishwanath, and R. M. Owens, "Architectures for wavelet transforms: A survey," Journal of VLSI signal processing systems for signal, image and video technology, vol. 14, no. 2, pp. 171-192, Nov. 1996.
N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," in Proc. of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, pp. 903-995, Mar. 1998.
A. Gupta, P. Mishra, C. Pandey, U. Singh, C. Sahu, and A. Keshri, "Descriptive statistics and normality tests for statistical data," Annals of Cardiac Anaesthesia, vol. 22, no. 1, pp. 67-72, Jan. 2019.
R. G. Baraniuk, P. Flandrin, A. J. E. M. Janssen, and O. J. J. Michel, "Measuring time-frequency information content using the Renyi entropies," IEEE Transactions on Information Theory, vol. 47, no. 4, pp. 1391-1409, May 2001.
J. Weston, S. Chopra, and A. Bordes, "Memory networks," in Proc. of inInternational Conference on Learning Representations (ICLR), 2015.
S. Sukhbaatar, a. szlam, J. Weston, and R. Fergus, "End-to-end memory networks," in Proc. of Neural Information Processing Systems, 2015.
A. Kumar, O. Irsoy, P. Ondruska, M. Iyyer, J. Bradbury, I. Gulrajani, V. Zhong, R. Paulus, and R. Socher, "Ask Me Anything: Dynamic Memory Networks for Natural Language Processing," arXiv: Computation and Language, arXiv: Computation and Language, Jun. 2015.
D. Gong, L. Liu, V. Le, B. Saha, M. R. Mansour, S. Venkatesh, and A. Van Den Hengel, "Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection," in Proc. of 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019.
N. Helwig, E. Pignanelli, A. Schtze, "Condition monitoring of hydraulic systems," UCI Machine Learning Repository, 2018. [Online] . Available: https://archive.ics.uci.edu/dataset/447/condition+monitoring+of+hydraulic+systems
Case Western Reserve University Bearing Data Center, Dec. 2019. [Online] . Available: https://engineering.case.edu/bearingdatacenter
H. Ringberg, A. Soule, J. Rexford, and C. Diot, "Sensitivity of PCA for traffic anomaly detection," in Proc. of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems, San Diego California USA, pp. 109-120, 2007.
B. Scholkopf, RobertC. Williamson, AlexanderJ. Smola, J. Shawe-Taylor, and J. Platt, "Support Vector Method for Novelty Detection," Neural Information Processing Systems, Neural Information Processing Systems, Nov. 1999.
S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, Nov. 1997.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.