Abstract
Vehicle-related engineers develop techniques for improving vehicle functions using vehicle data and test the performance of the devised techniques, where they need to secure vehicle data with various characteristics for more exact tests. However, it is actually hard to directly collect such vehicle data from the real world because of time and cost constraints. For this reason, we propose a method for generating vehicle data with various characteristics. Our approach can create vehicle data considering changes of vehicle attribute values such as increase or decrease in a linear form. Numerous types of vehicle data generated by the proposed method can be employed in a variety of data mining areas utilizing vehicle data such as clustering, monitoring, diagnosis, classification, and prognosis methods.
This research was supported by the MSIP (Ministry of Science, ICT & Future Planning), Korea, under ICT/SW Creative research program supervised by the NIPA (National ICT Industry Promotion Agency) (NIPA-2014-H0502-14-3008) and the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF No. 2013-005682).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Franck, G., Jean-Michel, C., Stéphane, G., Olivier, L., Abderrafiaa, K.: Virtual intelligent vehicle urban simulator: Application to vehicle platoon evaluation. Simulation Modelling Practice and Theory 24, 103–114 (2012)
Haigang, G., Nianbo, L., Lingfei, Y., Chao, S.: An Efficient Data Dissemination Protocol with Roadside Parked Vehicles’ Assistance in Vehicular Networks. IJDSN 2013, 1–12 (2013)
Ines, K., Guy, B., Fabian, R., Berthold, F.: Driving Behavior and Simulator Sickness While Driving the Vehicle in the Loop: Validation of Longitudinal Driving Behavior. IEEE Intelligent Transportation Systems Magazine 5(1), 42–57 (2013)
Kargupta, H., Puttagunta, V., Klein, M., Sarkar, K.: On-board Vehicle Data Stream Monitoring using MineFleet and Fast Resource Constrained Monitoring of Correlation Matrices. New Gwneration Computing 5(1), 5–32 (2006)
Leman, A., Christos, F.: RTG: a recursive realistic graph generator using random typing. Data Mining and Knowledge Discovery 19(2), 194–209 (2009)
Moataz, A., Irman, H.: GA-based multiple paths test data generator. COR 35(10), 3107–3124 (2008)
Stephen, O., Geraldine, G.: Development and Evaluation of a Dataset Generator Tool for Generating Synthetic Log Files Containing Computer Attack Signatures. IJACI 3(2), 64–76 (2011)
Tim, V.B., Koen, V.L., Bart, N., Piet, R., Hongwu, M., Alain, V., Bart, D.M., Kathleen, M.: SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinformatics 7(43), 1–12 (2006)
Witold, P., Hamid, R.K., Kjell, G.R.: Data-based modeling of vehicle collisions by nonlinear autoregressive model and feedforward neural network. Info.Sci. 235, 65–79 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yun, U., Ryang, H., Kim, J. (2015). Vehicle Data Generating Technique Considering Vehicles with Incrementally Changing States Based on Growth and Decline Functions. In: Park, J., Stojmenovic, I., Jeong, H., Yi, G. (eds) Computer Science and its Applications. Lecture Notes in Electrical Engineering, vol 330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45402-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-662-45402-2_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45401-5
Online ISBN: 978-3-662-45402-2
eBook Packages: EngineeringEngineering (R0)