참고문헌
- Aizenberg, I., Aizenberg, N. N. and Vandewalle, J. P. L. (2000), Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications, Springer Science & Business Media. pp.81-137.
- Bengio, Y. (1991), Artificial Neural Networks and their Application to Speech/Sequence Recognition, McGill University Ph.D. thesis.
- Bengio, Y. (2012), Practical recommendations for gradient-based training of deep architectures, Neural Networks, Tricks of the Trade, pp.437-478.
- Dang, L. M., Kyeong, S. J., Li, Y., Wang, H., Nguyen, T. N. and Moon, H. J. (2021), Deep learning-based sewer defect classification for highly imbalanced dataset, Computers & Industrial Engineering, Vol.161, 107630. https://doi.org/10.1016/j.cie.2021.107630
- De Carvalho, A. C. L. F., Fairhurst, M. C. and Bisset, D. (1994), An integrated Boolean neural network for pattern classification, Pattern Recognition Letters, Vol.15, No.8, pp.807-813. https://doi.org/10.1016/0167-8655(94)90009-4
- Dechter, R. (1986), Learning while searching inconstraint-satisfaction problems, University of California, Computer Science Department, Cognitive Systems Laboratory. pp.178-183.
- Deng, L., Hassanein, K. and Elmasry, M. (1994), Analysis of correlation structure for a neural predictive model with applications to speech recognition, Neural Networks, Vol.7, No.2, pp.331-339. https://doi.org/10.1016/0893-6080(94)90027-2
- Fukushima, K. (1980), Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biol, Cybern, Vol.36, No.4, pp.193-202. https://doi.org/10.1007/BF00344251
- Hinton, G. E. (2007), Learning multiple layers of representation, Trends in Cognitive Sciences, Vol.11, No.10, pp.428-434. https://doi.org/10.1016/j.tics.2007.09.004
- Hi nton, G. E., Dayan, P., Frey, B. J. and Neal, R. (1995), The wake-sleep algorithm for unsupervised neural networks, Vol.268, No.5214, pp.1158-1161. https://doi.org/10.1126/science.7761831
- Hinton, G. E., Osindero, S. and Teh, Y. W. (2006), A Fast Learning Algorithm for Deep Belief Nets, Neural Computation, Vol.18, No.7, pp.1527-1554. https://doi.org/10.1162/neco.2006.18.7.1527
- Hochreiter, S. (1991), Untersuchungen zu dynamischen neuronalen Netzen Archived 2015-03-06atthe Wayback Machine, Diploma thesis, Institut f. Informatik, Technische Univ.
- Hwang, C. H., Kim, H. S. and Jung, H. K. (2018), Detection and Correction Method of Erroneous Data Using Quantile Pattern and LSTM, JICCE, Vol.16, No.4, pp.242-247.
- Ivakhnenko, A. G. (1971), Polynomial theory of complex systems, IEEE Transactions on Systems, Man and Cybernetics, Vol.4, pp.364-378. https://doi.org/10.1109/TSMC.1971.4308320
- Ivakhnenko, A. G., Lapa, V. G. and McDonough, R. N. (1967), Cybernetics and forecasting techniques, American Elsevier, New York.
- Jiang, J., Li, C., Sun, L., Guo, D., Zhang, Y. and Wang, W. (2021), A deep learning algorithm for multi-source data fusion to predict water quality of urban sewer networks, Journal of Cleaner Production, Vol.318, No.10. pp. 2411-2502.
- McKim, R. A., and Sinha, S. K. (1999), Condition assessment of underground sewer pipes using a modified digital image processing paradigm, Tunnelling and Underground Space Technology, Vol.14, pp.29-37. https://doi.org/10.1016/S0886-7798(00)00021-3
- Moselhi, O. and Shehab-Eldeen, T. (1999), Automated detection of surface defects in water and sewer pipes, Automation in Construction, Vol.8, No.5, pp.581-588. https://doi.org/10.1016/S0926-5805(99)00007-2
- Nguyen, V. Q., Ma, L. V. and Kim, J. (2018), LSTM-based anomaly detection on big data for smart factory monitoring, Journal of Digital Contents Society, Vol.19, No.4, pp.789-79. https://doi.org/10.9728/DCS.2018.19.4.789
- Samuel, A. L. (1959), Some Studies in Machine Learning Using the Game of Checkers Offsite Link, IBM Journal of Research and Development, Vol.3, No.3, pp. 206-226. https://doi.org/10.1147/rd.33.0210
- Son, B. J. and Lee, K. H. (2017), Crack Recognition of Sewer with Low Resolution using Convolutional Neural Network(CNN) Method, Journal of Korean Society for Advanced Composite Structures, Vol.8, No.4, pp.58-65. https://doi.org/10.11004/kosacs.2017.8.4.058
- Wang, M. Z., Kumar, S. S. and Cheng, J. C. P. (2021), Automated sewer pipe defect tracking in CCTV videos based on defect detection and metric learning, Automation in Construction 121.103438 https://doi.org/10.1016/j.autcon.2020.103438
- Xu, K., Lxmoore, A. R. and Davies, T. (1998), Sewer pipe deformation assessment by image analysis of video surveys, Pattern Recognition, Vol.31, No.2, pp.169-180. https://doi.org/10.1016/S0031-3203(97)00037-X
- Yang, M. D., and Su, T. C. (2008), Automated diagnosis of sewer pipe defects based on machine learning approaches, Expert Systems with Applications, Vol.35, No.3, pp.1327-1337. https://doi.org/10.1016/j.eswa.2007.08.013
- Yang, M. D., Su, T. C., Pan, N. F., and Yang, Y. F. (2011), Systematic image quality assessment for sewer inspection, Expert Systems with Applications, Vol.38, No.3, pp.1766-1776. https://doi.org/10.1016/j.eswa.2010.07.103