Shift Quality Classifier Using Deep Neural Networks on Small Data with Dropout and Semi-Supervised Learning-Reference-Cited by-同舟云学术

Shift Quality Classifier Using Deep Neural Networks on Small Data with Dropout and Semi-Supervised Learning

Published:2023-12-01 Issue:12 Volume:E106.D Page:2078-2084
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

KAWAKAMI Takefumi¹,IDE Takanori¹,HOKI Kunihito²,MURAMATSU Masakazu²

Affiliation:

1. Advanced Development Department, AISIN CORPORATION

2. Graduate School of Informatics and Engineering, The University of Electro-Communications

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E106.D/12/E106.D_2023EDP7033/_pdf

Reference22 articles.

1. [1] T. Kawakami, T. Ide, E. Moriyama, K. Hoki, and M. Muramatsu, “Development of Artificial Intelligence to Classify Quality of Transmission Shift Control Using Deep Convolutional Neural Networks,” IEEE Trans. Veh. Technol., vol.69, no.12, pp.16168-16172, 2020. 10.1109/tvt.2020.3032191

2. [2] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res., vol.15, no.1, pp.1929-1958, 2014.

3. [3] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.

4. [4] D. Kim, M.G. Jeong, B.G. Bae, and C. Ahn, “Design of a Human Evaluator Model for the Ride Comfort of Vehicle on a Speed Bump Using a Neural Artistic Style Extraction,” Sensors, vol.19, no.24, p.5407, 2019. 10.3390/s19245407

5. [5] E. Chabrier and M. Grima, “Subjective and Objective Vehicle Tests, Two Parallel Vehicle Handling Evaluations,” Proc. FISITA 2012 World Automotive Congress, Berlin, Heidelberg, pp.1767-1775, Springer Berlin Heidelberg, 2013. 10.1007/978-3-642-33738-3_70