Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Security as a safety issue in rail communications
SCS '03 Proceedings of the 8th Australian workshop on Safety critical systems and software - Volume 33
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Classifying imbalanced data using a bagging ensemble variation (BEV)
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
Improving railroad wheel inspection planning using classification methods
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Survey of Wireless Communications Applications in the Railway Industry
AUSWIRELESS '07 Proceedings of the The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications
On learning algorithm selection for classification
Applied Soft Computing
Optimal Track Access Rights Allocation for Agent Negotiation in an Open Railway Market
IEEE Transactions on Intelligent Transportation Systems
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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The performance of rail vehicles running on railway tracks is governed by the dynamic behaviors of railway bogies, particularly in cases of lateral instability and track irregularities. To ensure reliable, safe, and secure operation of railway systems, it is desirable to adopt intelligent monitoring systems for railway wagons. In this paper, a forecasting model is developed to investigate the vertical-acceleration behavior of railway wagons that are attached to a moving locomotive using modern machine-learning techniques. Both front- and rear-body vertical-acceleration conditions are predicted using popular regression algorithms. Different types of models can be built using a uniform platform to evaluate their performance. The estimation techniques' performance has been measured using a set of attributes' correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE), and computational complexity for each of the algorithms. Statistical hypothesis analysis is applied to determine the most suitable regression algorithm for this application. Finally, spectral analysis of the front- and rear-body vertical condition is produced from the predicted data using the fast Fourier transform (FFT) and is used to generate precautionary signals and system status that can be used by a locomotive driver for necessary actions.