Machine Learning-based Quality Control and Error Correction Using Homogeneous Temporal Data Collected by IoT Sensors |
Kim, Hye-Jin
(Dept. Computer Science, Kwangwoon University)
Lee, Hyeon Soo (R&D Center, JUBIX Co., Ltd.) Choi, Byung Jin (R&D Center, JUBIX Co., Ltd.) Kim, Yong-Hyuk (Dept. Computer Science, Kwangwoon University) |
1 | R. Kohavi. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence Organization, 14(2), 1137-1145. San Francisco : Morgan Kaufmann. |
2 | E. Frank, M. A. Hall, and I. H. Witten. (2016). Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition. Morgan Kaufmann. |
3 | M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. (2009). The WEKA data mining software: an update. Newsletter of SIGKDD Explorations, 11(1), 10-18. DOI |
4 | N.-Y. Kim, Y.-H. Kim, Y. Yoon, H.-H. Im, R. K. Y. Choi, and Y. H. Lee. (2015). Correcting air-pressure data collected by MEMS sensors in smartphones. Journal of Sensors, Article ID 245498. |
5 | U. W. Pooch. (1974). Translation of decision tables. ACM Computing Surveys, 6(2), 125-151. DOI |
6 | J.-H. Ha, Y.-H. Kim, H.-H. Im, N.-Y. Kim, S. Sim, and Y. Yoon. (2018). Error correction of meteorological data obtained with Mini-AWSs based on machine learning. Advances in Meteorology, Article ID 7210137. |
7 | Y.-H. Kim, J.-H. Ha, Y. Yoon, N.-Y. Kim, H.-H. Im, S. Sim, and R. K. Y. Choi. (2016). Improved correction of atmospheric pressure data obtained by smartphones through machine learning. Computational Intelligence and Neuroscience, Article ID 9467878. |
8 | M.-K. Lee, S.-H. Moon, Y.-H. Kim, and B.-R. Moon. (2014. October). Correcting abnormalities in meteorological data by machine learning. IEEE International Conference on Systems, Man, and Cybernetics. (pp.888-893). San Diego : IEEE |
9 | G.-D. Kim & Y.-H. Kim. (2018). Correction of drifter data using recurrent neural networks. Journal of the Korea Convergence Society, 9(3), 15-21. DOI |
10 | A. J. Smola & B. Scholkopf. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199-222. DOI |
11 | F. Rosenblatt (1961). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Washington DC : Spartan Books. |
12 | J. A. Suykens & J. Vandewalle. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293-300. DOI |
13 | M.-K. Lee, S.-H. Moon, Y. Yoon , Y.-H. Kim, and B.-R. Moon. (2018). Detecting anomalies in meteorological data using support vector regression. Advances in Meteorology, Article ID 5439256. |
14 | N. R. Draper & H. Smith. (1998). Applied Regression Analysis, Thirds Edition.Wiley. |
15 | M. Riedmiller & H. Braun. (1993). A direct adaptive method for faster backpropagation learning: the RPROP algorithm. IEEE International Conference on Neural Networks.. (pp.586-591). |