Ultrasound Environment Classification Based on Fuzzy Model

Zhili Long; Ronghua He; Xiaobing Zhang; Yuxiang He

doi:10.1088/1757-899X/428/1/012046

Ultrasound Environment Classification Based on Fuzzy Model

Zhili Long
, Ronghua He
, Xiaobing Zhang
, Yuxiang He

Harbin Institute of Technology Shenzhen

Research output: Contribution to journal › Conference article › peer-review

Abstract

The fuzzy model based on the centreline ultrasound signal is pro-posed to solve the poor orientation of ultrasonic sensors in the environment modelling in this paper. The locations of ultrasonic arc feature points were extracted and distinguished. For the problem of poor resolution of split type ultrasonic sensors, fuzzy algorithm is applied to identify the obstacle such as line, corner, or arc structure. Ultrasound environment map based on fuzzy is constructed and the maximum distance and angle deviation is 1.2cm and 3°, showing that the accuracy of obstacle classification based on ultrasound and Fuzzy is improved.

Original language	English
Article number	012046
Journal	IOP Conference Series: Materials Science and Engineering
Volume	428
Issue number	1
DOIs	https://doi.org/10.1088/1757-899X/428/1/012046
State	Published - 1 Oct 2018
Externally published	Yes
Event	2018 3rd International Conference on Automation, Control and Robotics Engineering, CACRE 2018 - Chengdu, China Duration: 19 Jul 2018 → 22 Jul 2018

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title = "Ultrasound Environment Classification Based on Fuzzy Model",

abstract = "The fuzzy model based on the centreline ultrasound signal is pro-posed to solve the poor orientation of ultrasonic sensors in the environment modelling in this paper. The locations of ultrasonic arc feature points were extracted and distinguished. For the problem of poor resolution of split type ultrasonic sensors, fuzzy algorithm is applied to identify the obstacle such as line, corner, or arc structure. Ultrasound environment map based on fuzzy is constructed and the maximum distance and angle deviation is 1.2cm and 3°, showing that the accuracy of obstacle classification based on ultrasound and Fuzzy is improved.",

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AU - Long, Zhili

AU - He, Ronghua

AU - Zhang, Xiaobing

AU - He, Yuxiang

N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2018/10/1

Y1 - 2018/10/1

N2 - The fuzzy model based on the centreline ultrasound signal is pro-posed to solve the poor orientation of ultrasonic sensors in the environment modelling in this paper. The locations of ultrasonic arc feature points were extracted and distinguished. For the problem of poor resolution of split type ultrasonic sensors, fuzzy algorithm is applied to identify the obstacle such as line, corner, or arc structure. Ultrasound environment map based on fuzzy is constructed and the maximum distance and angle deviation is 1.2cm and 3°, showing that the accuracy of obstacle classification based on ultrasound and Fuzzy is improved.

AB - The fuzzy model based on the centreline ultrasound signal is pro-posed to solve the poor orientation of ultrasonic sensors in the environment modelling in this paper. The locations of ultrasonic arc feature points were extracted and distinguished. For the problem of poor resolution of split type ultrasonic sensors, fuzzy algorithm is applied to identify the obstacle such as line, corner, or arc structure. Ultrasound environment map based on fuzzy is constructed and the maximum distance and angle deviation is 1.2cm and 3°, showing that the accuracy of obstacle classification based on ultrasound and Fuzzy is improved.

UR - https://www.scopus.com/pages/publications/85054873273

U2 - 10.1088/1757-899X/428/1/012046

DO - 10.1088/1757-899X/428/1/012046

M3 - 会议文章

AN - SCOPUS:85054873273

SN - 1757-8981

VL - 428

JO - IOP Conference Series: Materials Science and Engineering

JF - IOP Conference Series: Materials Science and Engineering

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M1 - 012046

T2 - 2018 3rd International Conference on Automation, Control and Robotics Engineering, CACRE 2018

Y2 - 19 July 2018 through 22 July 2018

ER -

Ultrasound Environment Classification Based on Fuzzy Model

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