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Deep Denoising Autoencoding Method for Feature Extraction and Recognition of Vehicle Adhesion Status
Joint Authors
Zhang, Chang-fan
He, Jing
Sun, Jian
Zhao, Kaihui
Liu, Linfan
Li, Peng
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-16
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Feature extraction and classification for deep learning are studied to recognize the problem of vehicle adhesion status.
Data concentration acquired by automobile sensors contains considerable noise.
Thus, a sparse autoencoder (stacked denoising autoencoder) is introduced to achieve network weight learning, restore original pure signal data by use of overlapping convergence strategy, and construct multiclassification support vector machine (SVM) for classification.
The sensors are adopted in different road environments to acquire data signals and recognize the adhesion status online.
Results show that the proposed method can achieve higher accuracies than those of the adhesion status recognition method based on SVM and extreme learning machine.
American Psychological Association (APA)
He, Jing& Liu, Linfan& Zhang, Chang-fan& Zhao, Kaihui& Sun, Jian& Li, Peng. 2018. Deep Denoising Autoencoding Method for Feature Extraction and Recognition of Vehicle Adhesion Status. Journal of Sensors،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1201564
Modern Language Association (MLA)
He, Jing…[et al.]. Deep Denoising Autoencoding Method for Feature Extraction and Recognition of Vehicle Adhesion Status. Journal of Sensors No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1201564
American Medical Association (AMA)
He, Jing& Liu, Linfan& Zhang, Chang-fan& Zhao, Kaihui& Sun, Jian& Li, Peng. Deep Denoising Autoencoding Method for Feature Extraction and Recognition of Vehicle Adhesion Status. Journal of Sensors. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1201564
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1201564