• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.979-984
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• 2004

The ball bearing having faults generally shows, nonlinear vibration characteristics. For the effective method of fault diagnosis on bail bearing, non-linear diagnostic methods can be used. In this paper, the correlation dimension analysis based on nonlinear timeseries was applied to diagnose the faults of ball bearing. The correlation dimension analysis shows some Intrinsic information of underlying dynamical systems, and clear the classification of the fault of ball bearing.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.2
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• pp.138-145
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• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.5
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• pp.261-273
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• 2024

Automobiles are one of the major sources of air pollution, and analyzing data on air pollutants, where vehicles are the primary pollutants, can help elucidate the correlation between factors like electric vehicles, traffic volume, and actual air pollution. Ensuring the reliability of air pollutant data is crucial for such analyses. This paper proposes a method for detecting sections of data exhibiting 'baseline anomalies' measured at air pollutant monitoring stations across the country by combining deep learning models with algorithms such as dynamic time warping and change point detection. While previous studies have focused on detecting data with unprecedented patterns and defined them as anomalies, this approach was not suitable for detecting baseline anomalies. In this study, we modify the U-Net model, typically used for image segmentation, to be more suitable for time-series data and apply dynamic time warping and change point detection algorithms to compare with nearby monitoring stations, thereby minimizing false detections.