Performance Analysis of Natural <i>γ</i>-Ray Coal Seam Thickness Sensor and Its Application in Automatic Adjustment of Shearer’s Arms

Yang, Zengfu; Wang, Zengcai; Yan, Ming

doi:10.1155/2020/5986013

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…The occurrence conditions of coal seams and rock layers are extremely complex. To address the problem of coal–rock identification, researchers from both domestic and international communities have conducted studies from different technical fields and proposed various solutions, including process signal monitoring, infrared thermography, image features, reflectance spectroscopy, ultrasonic detection and electromagnetic waves detection (Serhir & Lesselier, 2017; Si et al., 2019; Wang et al., 2020; Yang et al., 2020; Yu et al., 2021). Compared to other identification methods, air‐coupled ground penetrating radar (GPR) enables non‐contact and efficient detection of target bodies and is well‐suited for the working environment of mine faces (Thomas & Roy, 2020).…”

Section: Introductionmentioning

confidence: 99%

Research on intelligent identification algorithm of coal and rock strata based on Hilbert transform and amplitude stacking

Zhu,

Xu,

Peng

et al. 2024

Geophysical Prospecting

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The high precision identification of coal–rock layers is a significant challenge in intelligent mining. There is a large amount of electromagnetic noise and metal reflector signals in the full space detection environment of mining roadway, which makes it hard to distinguish the reflected waves at interface from a set of echo signals generated by the interface due to the similar amplitudes among them. So the method of identifying layers solely based on amplitude characteristics has poor stability and accuracy in coal mining environments. This paper proposes a method for identifying coal–rock layers based on Hilbert transform and tracking–scanning–stacking technology. There are two steps to achieve the recognition of air–coal–rock interfaces. First, by analysing the instantaneous amplitude spectrum obtained from the Hilbert transform, the first extreme point that is always the maximum value within a wavelength range is determined as the rough position of the air–coal interface. To solve the problem of recognition errors caused by noise and energy dispersion, the density difference method is used to remove discrete points. Second, the precise position of the air–coal interface is determined by tracking the extreme points within the 1.5 wavelength range around the rough position, and using the amplitude stacking method to quantitatively analyse and compare the degree of energy concentration. The data between zero time and the reflected waves at the air–coal interface is removed to avoid the impact of them on the recognition of the coal–rock interface. Results of physical model experiments and actual coal mine experiments show that this method yields better results and has high stability compared to conventional recognition method. Moreover, the average relative thickness errors are 4.5% for air layer and 4.2% for coal layer.

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Section: Introductionmentioning

confidence: 99%

Research on intelligent identification algorithm of coal and rock strata based on Hilbert transform and amplitude stacking

Zhu,

Xu,

Peng

et al. 2024

Geophysical Prospecting

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“…This method utilizes two γ-ray sources with different energies as an excitation and accurately identifies gangue through the intensity of the radiation flux produced by its irradiation to the gangue. Yang and his team also proposed a method for gangue identification and sorting using dual-energy γ-rays 7 , which is based on the projection method. In their study, He et al 8 used X-ray projection to identify gangue.…”

Section: Introductionmentioning

confidence: 99%

Research on lightweight algorithm for gangue detection based on improved Yolov5

Yuan,

Fu,

Zhang

et al. 2024

Sci Rep

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In order to solve the problems of slow detection speed, large number of parameters and large computational volume of deep learning based gangue target detection method, we propose an improved algorithm for gangue target detection based on Yolov5s. First, the lightweight network EfficientVIT is used as the backbone network to increase the target detection speed. Second, C3_Faster replaces the C3 part in the HEAD module, which reduces the model complexity. once again, the 20 × 20 feature map branch in the Neck region is deleted, which reduces the model complexity; thirdly, the CIOU loss function is replaced by the Mpdiou loss function. The introduction of the SE attention mechanism makes the model pay more attention to critical features to improve detection performance. Experimental results show that the improved model size of the coal gang detection algorithm reduces the compression by 77.8%, the number of parameters by 78.3% the computational cost is reduced by 77.8% and the number of frames is reduced by 30.6%, which can be used as a reference for intelligent coal gangue classification.

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“…Zhang [5] and his team proposed a gangue sorting system based on the γ-ray dual-energy projection method [6], which uses γ-ray sources of different energies as an excitation, and realises the accurate identification of gangue by irradiating the intensity of radiation flux produced by the gangue. Yang [7] and his team also proposed a method for gangue identification and sorting using dual-energy γ-rays by projection. He [8], on the other hand, used X-ray projection to identify gangue by setting a threshold based on the energy attenuation of X-rays by coal and gangue and identifying gangue by comparing the energy attenuation of X-rays by the target at the time of detection with the threshold.…”

Section: Introductionmentioning

confidence: 99%

Research on improved gangue target detection algorithm based on Yolov8s

Fu,

Yuan,

Xie

et al. 2024

PLoS ONE

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An improved algorithm based on Yolov8s is proposed to address the slower speed, higher number of parameters, and larger computational cost of deep learning in coal gangue target detection. A lightweight network, Fasternet, is used as the backbone to increase the speed of object detection and reduce the model complexity. By replacing Slimneck with the C2F part in the HEAD module, the aim is to reduce model complexity and improve detection accuracy. The detection accuracy is effectively improved by replacing the Detect layer with Detect-DyHead. The introduction of DIoU loss function instead of CIoU loss function and the combination of BAM block attention mechanism makes the model pay more attention to critical features, which further improves the detection performance. The results show that the improved model compresses the storage size of the model by 28%, reduces the number of parameters by 28.8%, reduces the computational effort by 34.8%, and improves the detection accuracy by 2.5% compared to the original model. The Yolov8s-change model provides a fast, real-time and efficient detection solution for gangue sorting. This provides a strong support for the intelligent sorting of coal gangue.

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Performance Analysis of Natural γ-Ray Coal Seam Thickness Sensor and Its Application in Automatic Adjustment of Shearer’s Arms

Cited by 7 publications

References 8 publications

Research on intelligent identification algorithm of coal and rock strata based on Hilbert transform and amplitude stacking

Research on intelligent identification algorithm of coal and rock strata based on Hilbert transform and amplitude stacking

Research on lightweight algorithm for gangue detection based on improved Yolov5

Research on improved gangue target detection algorithm based on Yolov8s

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