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Author:

Wang, Zixuan (Wang, Zixuan.) | Tang, Jian (Tang, Jian.) | Cui, Chengyu (Cui, Chengyu.) | Li, Weitao (Li, Weitao.) | Xu, Zhe (Xu, Zhe.) | Han, Honggui (Han, Honggui.) (Scholars:韩红桂)

Indexed by:

EI Scopus

Abstract:

With the increase of the replacement frequency of mobile phones, the recycling of used mobile phones (UMPs) has gradually become a hot topic. UMP recycling equipment based on the internet of things technology has become one type of the main devices to recycle UMPs, but there are still some problems such that low recognition success rates and tedious recycling steps. The key to solving these problems is to improve the accuracy of UMP recognition (UMPR). In view of the above problems, this paper reviews the methods of UMPR for UMP recycling equipment, which provides support for improving recycling efficiency. First, we briefly introduce the structure and recycling process of UMP recycling equipment. Next, the four typical methods of UMPR are addressed in detail and their respective shortcomings are analyzed. Then, these methods are summarized and analyzed, and the shortcomings and deficiencies of the current UMPR methods based on artificial intelligence, especially the image recognition method are addressed. Finally, the directions for future research on UMPR are given out. © 2020 Technical Committee on Control Theory, Chinese Association of Automation.

Keyword:

Image recognition Cellular telephones Recycling Artificial intelligence Electronic Waste

Author Community:

  • [ 1 ] [Wang, Zixuan]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 2 ] [Wang, Zixuan]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 3 ] [Tang, Jian]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 4 ] [Tang, Jian]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 5 ] [Cui, Chengyu]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 6 ] [Cui, Chengyu]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 7 ] [Li, Weitao]School of Electrical Engineering and Automation, Hefei University of Technology, Hefei; 230009, China
  • [ 8 ] [Xu, Zhe]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 9 ] [Xu, Zhe]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 10 ] [Han, Honggui]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 11 ] [Han, Honggui]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China

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Source :

ISSN: 1934-1768

Year: 2020

Volume: 2020-July

Page: 1105-1110

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 10

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