Next Article in Journal
Kinematics and Dynamics Analysis of a 3-DOF Upper-Limb Exoskeleton with an Internally Rotated Elbow Joint
Next Article in Special Issue
TRSDL: Tag-Aware Recommender System Based on Deep Learning–Intelligent Computing Systems
Previous Article in Journal
Plasmonic Filter and Demultiplexer Based on Square Ring Resonator
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(3), 463; https://doi.org/10.3390/app8030463

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

1
School of Mechanical Electronic & Information Engineering, China University of Mining and Technology, Beijing 100083, China
2
Mechatronics, Embedded Systems and Automation Lab, University of California, Merced, CA 95343, USA
*
Author to whom correspondence should be addressed.
Received: 18 February 2018 / Revised: 14 March 2018 / Accepted: 15 March 2018 / Published: 17 March 2018
(This article belongs to the Special Issue Fractal Based Information Processing and Recognition)
  |  
PDF [3482 KB, uploaded 17 March 2018]
  |  

Abstract

The separation of coal and gangue is an important process of the coal preparation technology. The conventional way of manual selection and separation of gangue from the raw coal can be replaced by computer vision technology. In the literature, research on image recognition and classification of coal and gangue is mainly based on the grayscale and texture features of the coal and gangue. However, there are few studies on characteristics of coal and gangue from the perspective of their outline differences. Therefore, the multifractal detrended fluctuation analysis (MFDFA) method is introduced in this paper to extract the geometric features of coal and gangue. Firstly, the outline curves of coal and gangue in polar coordinates are detected and achieved along the centroid, thereby the multifractal characteristics of the series are analyzed and compared. Subsequently, the modified local singular spectrum widths Δ h of the outline curve series are extracted as the characteristic variables of the coal and gangue for pattern recognition. Finally, the extracted geometric features by MFDFA combined with the grayscale and texture features of the images are compared with other methods, indicating that the recognition rate of coal gangue images can be increased by introducing the geometric features. View Full-Text
Keywords: coal and gangue; features extraction; outline curve; fractional calculus; multifractal detrending fluctuation analysis coal and gangue; features extraction; outline curve; fractional calculus; multifractal detrending fluctuation analysis
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Liu, K.; Zhang, X.; Chen, Y. Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis. Appl. Sci. 2018, 8, 463.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top