The Experts below are selected from a list of 13977 Experts worldwide ranked by ideXlab platform

Qiao Tiezhu - One of the best experts on this subject based on the ideXlab platform.

  • Research On ADCN Method For Damage Detection Of Mining Conveyor Belt
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Qu Dingran, Qiao Tiezhu, Pang Y., Yi Yang, Zhang Haitao
    Abstract:

    Belt Conveyor is considered as a momentous component of modern coal mining transportation system, and thus it is an essential task to diagnose and monitor the damage of Belt in real time and accurately. Based on the deep learning algorithm, this present study proposes a method of Conveyor Belt damage detection based on ADCN (Adaptive Deep Convolutional Network). A deep convolution network with unique adaptability is built to extract the different scale features of visible light image of Conveyor Belt damage, and the target is classified and located in the form of anchor boxes. A data set with data diversity is collected according to the actual working conditions of the Conveyor Belt. After training and regression, the ADCN model can perfectly capture and classify the damaged target in the video of the Conveyor running. Compared with the SVM based method, the method based on ADCN can better meet the real-time and reliability requirements of Belt damage detection, and it has the positioning function which SVM does not have.Accepted Author ManuscriptTransport Engineering and Logistic

  • Multispectral visual detection method for Conveyor Belt longitudinal tear
    'Elsevier BV', 2019
    Co-Authors: Hou Chengcheng, Qiao Tiezhu, Zhang Haitao, Pang Y., Xiong Xiaoyan
    Abstract:

    As an important part of modern coal mine production, Conveyor Belts are widely used in the coal collection and transportation. In order to ensure the safe operation of the coal mine Conveyor Belt and solve the drawbacks of the existing Conveyor Belt longitudinal tear detection technology, a multispectral visual detection method for Conveyor Belt longitudinal tear is proposed in this paper. The experimental results show that the multispectral visual detection method not only can identify the Conveyor Belt longitudinal tear, but also accurately classifies and identify other states of the Conveyor Belt. The accuracy of multispectral visual detection method is over 90.06%, and the precision of longitudinal tearing recognition is over 92.04%. The proposed method is verified to meet the requirements of reliability and real-time in the industrial field.Accepted Author ManuscriptTransport Engineering and Logistic

  • Research on conditional characteristics vision real-time detection system for Conveyor Belt longitudinal tear
    'Institution of Engineering and Technology (IET)', 2017
    Co-Authors: Qiao Tiezhu, Pang Y., Li Xinyu, Lü Yuxiang, Wang Feng, Jin Baoquan
    Abstract:

    Conveyor Belt longitudinal tear is one of the most serious problems in coal mining. Existing systems cannot realise lossless and real-time detection for longitudinal tear of Conveyor Belt. Currently, visual detecting systems are proposed by many researchers and are becoming the future trend. A visual recognition system based on using laser and area light sources is designed in this study, which can recognise and count abrasions, incomplete-tears, and complete-tears. The advantage of the system is to prevent longitudinal tear based on multi-feature information. In the process of detecting conditional characteristics, laser and area light sources are responsible for enhancing contrast between conditional features and Conveyor Belt surface, meanwhile false corner filtration and single-point feature identification method are designed for improving recognition accuracy of the system. Compared with several current systems, the designed system has a better performance on recognising complex tear characteristics of Conveyor Belt, thus the problem of starting warning only based on single feature can be effectively avoided.

  • Research on conditional characteristics vision real-time detection system for Conveyor Belt longitudinal tear
    'Institution of Engineering and Technology (IET)', 2017
    Co-Authors: Qiao Tiezhu, Pang Y., Li Xinyu, Lü Yuxiang, Wang Feng, Jin Baoquan
    Abstract:

    Conveyor Belt longitudinal tear is one of the most serious problems in coal mining. Existing systems cannot realise lossless and real-time detection for longitudinal tear of Conveyor Belt. Currently, visual detecting systems are proposed by many researchers and are becoming the future trend. A visual recognition system based on using laser and area light sources is designed in this study, which can recognise and count abrasions, incomplete-tears, and complete-tears. The advantage of the system is to prevent longitudinal tear based on multi-feature information. In the process of detecting conditional characteristics, laser and area light sources are responsible for enhancing contrast between conditional features and Conveyor Belt surface, meanwhile false corner filtration and single-point feature identification method are designed for improving recognition accuracy of the system. Compared with several current systems, the designed system has a better performance on recognising complex tear characteristics of Conveyor Belt, thus the problem of starting warning only based on single feature can be effectively avoided.Accepted Author ManuscriptTransport Engineering and Logistic

Kun Chen - One of the best experts on this subject based on the ideXlab platform.

  • plasmonic graded nano disks as nano optical Conveyor Belt
    Optics Express, 2014
    Co-Authors: Zhiwen Kang, Jiajie Chen, Kun Chen
    Abstract:

    We propose a plasmonic system consisting of nano-disks (NDs) with graded diameters for the realization of nano-optical Conveyor Belt. The system contains a couple of NDs with individual elements coded with different resonant wavelengths. By sequentially switching the wavelength and polarization of the excitation source, optically trapped target nano-particle can be transferred from one ND to another. The feasibility of such function is verified based on the three-dimensional finite-difference time-domain technique and the Maxwell stress tensor method. Our design may provide an alternative way to construct nano-optical Conveyor Belt with which target molecules can be delivered between trapping sites, thus enabling many on-chip optofluidic applications.

Pang Y. - One of the best experts on this subject based on the ideXlab platform.

  • Research On ADCN Method For Damage Detection Of Mining Conveyor Belt
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Qu Dingran, Qiao Tiezhu, Pang Y., Yi Yang, Zhang Haitao
    Abstract:

    Belt Conveyor is considered as a momentous component of modern coal mining transportation system, and thus it is an essential task to diagnose and monitor the damage of Belt in real time and accurately. Based on the deep learning algorithm, this present study proposes a method of Conveyor Belt damage detection based on ADCN (Adaptive Deep Convolutional Network). A deep convolution network with unique adaptability is built to extract the different scale features of visible light image of Conveyor Belt damage, and the target is classified and located in the form of anchor boxes. A data set with data diversity is collected according to the actual working conditions of the Conveyor Belt. After training and regression, the ADCN model can perfectly capture and classify the damaged target in the video of the Conveyor running. Compared with the SVM based method, the method based on ADCN can better meet the real-time and reliability requirements of Belt damage detection, and it has the positioning function which SVM does not have.Accepted Author ManuscriptTransport Engineering and Logistic

  • Multispectral visual detection method for Conveyor Belt longitudinal tear
    'Elsevier BV', 2019
    Co-Authors: Hou Chengcheng, Qiao Tiezhu, Zhang Haitao, Pang Y., Xiong Xiaoyan
    Abstract:

    As an important part of modern coal mine production, Conveyor Belts are widely used in the coal collection and transportation. In order to ensure the safe operation of the coal mine Conveyor Belt and solve the drawbacks of the existing Conveyor Belt longitudinal tear detection technology, a multispectral visual detection method for Conveyor Belt longitudinal tear is proposed in this paper. The experimental results show that the multispectral visual detection method not only can identify the Conveyor Belt longitudinal tear, but also accurately classifies and identify other states of the Conveyor Belt. The accuracy of multispectral visual detection method is over 90.06%, and the precision of longitudinal tearing recognition is over 92.04%. The proposed method is verified to meet the requirements of reliability and real-time in the industrial field.Accepted Author ManuscriptTransport Engineering and Logistic

  • Research on conditional characteristics vision real-time detection system for Conveyor Belt longitudinal tear
    'Institution of Engineering and Technology (IET)', 2017
    Co-Authors: Qiao Tiezhu, Pang Y., Li Xinyu, Lü Yuxiang, Wang Feng, Jin Baoquan
    Abstract:

    Conveyor Belt longitudinal tear is one of the most serious problems in coal mining. Existing systems cannot realise lossless and real-time detection for longitudinal tear of Conveyor Belt. Currently, visual detecting systems are proposed by many researchers and are becoming the future trend. A visual recognition system based on using laser and area light sources is designed in this study, which can recognise and count abrasions, incomplete-tears, and complete-tears. The advantage of the system is to prevent longitudinal tear based on multi-feature information. In the process of detecting conditional characteristics, laser and area light sources are responsible for enhancing contrast between conditional features and Conveyor Belt surface, meanwhile false corner filtration and single-point feature identification method are designed for improving recognition accuracy of the system. Compared with several current systems, the designed system has a better performance on recognising complex tear characteristics of Conveyor Belt, thus the problem of starting warning only based on single feature can be effectively avoided.

  • Research on conditional characteristics vision real-time detection system for Conveyor Belt longitudinal tear
    'Institution of Engineering and Technology (IET)', 2017
    Co-Authors: Qiao Tiezhu, Pang Y., Li Xinyu, Lü Yuxiang, Wang Feng, Jin Baoquan
    Abstract:

    Conveyor Belt longitudinal tear is one of the most serious problems in coal mining. Existing systems cannot realise lossless and real-time detection for longitudinal tear of Conveyor Belt. Currently, visual detecting systems are proposed by many researchers and are becoming the future trend. A visual recognition system based on using laser and area light sources is designed in this study, which can recognise and count abrasions, incomplete-tears, and complete-tears. The advantage of the system is to prevent longitudinal tear based on multi-feature information. In the process of detecting conditional characteristics, laser and area light sources are responsible for enhancing contrast between conditional features and Conveyor Belt surface, meanwhile false corner filtration and single-point feature identification method are designed for improving recognition accuracy of the system. Compared with several current systems, the designed system has a better performance on recognising complex tear characteristics of Conveyor Belt, thus the problem of starting warning only based on single feature can be effectively avoided.Accepted Author ManuscriptTransport Engineering and Logistic

Xuping Zhang - One of the best experts on this subject based on the ideXlab platform.

  • nano optical Conveyor Belt with waveguide coupled excitation
    Optics Letters, 2016
    Co-Authors: Guanghui Wang, Zhoufeng Ying, H P Ho, Ying Huang, Ningmu Zou, Xuping Zhang
    Abstract:

    We propose a plasmonic nano-optical Conveyor Belt for peristaltic transport of nano-particles. Instead of illumination from the top, waveguide-coupled excitation is used for trapping particles with a higher degree of precision and flexibility. Graded nano-rods with individual dimensions coded to have resonance at specific wavelengths are incorporated along the waveguide in order to produce spatially addressable hot spots. Consequently, by switching the excitation wavelength sequentially, particles can be transported to adjacent optical traps along the waveguide. The feasibility of this design is analyzed using three-dimensional finite-difference time-domain and Maxwell stress tensor methods. Simulation results show that this system is capable of exciting addressable traps and moving particles in a peristaltic fashion with tens of nanometers resolution. It is the first, to the best of our knowledge, report about a nano-optical Conveyor Belt with waveguide-coupled excitation, which is very important for scalability and on-chip integration. The proposed approach offers a new design direction for integrated waveguide-based optical manipulation devices and its application in large scale lab-on-a-chip integration.

Jin Baoquan - One of the best experts on this subject based on the ideXlab platform.

  • Research on conditional characteristics vision real-time detection system for Conveyor Belt longitudinal tear
    'Institution of Engineering and Technology (IET)', 2017
    Co-Authors: Qiao Tiezhu, Pang Y., Li Xinyu, Lü Yuxiang, Wang Feng, Jin Baoquan
    Abstract:

    Conveyor Belt longitudinal tear is one of the most serious problems in coal mining. Existing systems cannot realise lossless and real-time detection for longitudinal tear of Conveyor Belt. Currently, visual detecting systems are proposed by many researchers and are becoming the future trend. A visual recognition system based on using laser and area light sources is designed in this study, which can recognise and count abrasions, incomplete-tears, and complete-tears. The advantage of the system is to prevent longitudinal tear based on multi-feature information. In the process of detecting conditional characteristics, laser and area light sources are responsible for enhancing contrast between conditional features and Conveyor Belt surface, meanwhile false corner filtration and single-point feature identification method are designed for improving recognition accuracy of the system. Compared with several current systems, the designed system has a better performance on recognising complex tear characteristics of Conveyor Belt, thus the problem of starting warning only based on single feature can be effectively avoided.

  • Research on conditional characteristics vision real-time detection system for Conveyor Belt longitudinal tear
    'Institution of Engineering and Technology (IET)', 2017
    Co-Authors: Qiao Tiezhu, Pang Y., Li Xinyu, Lü Yuxiang, Wang Feng, Jin Baoquan
    Abstract:

    Conveyor Belt longitudinal tear is one of the most serious problems in coal mining. Existing systems cannot realise lossless and real-time detection for longitudinal tear of Conveyor Belt. Currently, visual detecting systems are proposed by many researchers and are becoming the future trend. A visual recognition system based on using laser and area light sources is designed in this study, which can recognise and count abrasions, incomplete-tears, and complete-tears. The advantage of the system is to prevent longitudinal tear based on multi-feature information. In the process of detecting conditional characteristics, laser and area light sources are responsible for enhancing contrast between conditional features and Conveyor Belt surface, meanwhile false corner filtration and single-point feature identification method are designed for improving recognition accuracy of the system. Compared with several current systems, the designed system has a better performance on recognising complex tear characteristics of Conveyor Belt, thus the problem of starting warning only based on single feature can be effectively avoided.Accepted Author ManuscriptTransport Engineering and Logistic