Lei He

Anhui University of Science and Technology

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i-10 index

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Co-Authors

Yongcun Guo

Anhui University of Science and Technology

Shuang Wang

Anhui University of Science and Technology

Gang Cheng

Anhui University of Science and Technology

Yanqiu Zhao

Anhui University of Science and Technology

Deyong Li

Anhui University of Science and Technology

Qisheng Luo

Anhui University of Science and Technology

Kun Hu

Anhui University of Science and Technology

Xinquan Wang

China Energy Engineering Corporation (China)

Shuang Wang

Shandong University of Technology

Xin Li

China University of Mining and Technology

Cooperative Institutions

Anhui University of Science and Technology

Huainan Mining Industry Group (China)

Chinese Academy of Sciences

Harbin Institute of Technology

China University of Mining and Technology

Huazhong University of Science and Technology

Central South University

Shenyang Ligong University

Materials Science & Engineering

Liaoning Technical University

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Small targets detection in dual-energy X-ray images of coal and gangue based on APSO-C_Otsu

Research Square (Research Square) (2022)

Yanqiu Zhao Shuang Wang Yongcun Guo Lei He Wenshan Wang

Abstract The positioning accuracy of coal and gangue is related to the discharge accuracy of gangue, which will affect the utilization rate of coal. But the detectability of the small coal and gangue is poor due to the fewer number of pixels and texture information in coal and gangue dual-energy X-ray images. So, the Otsu with crotch structure based on Adaptive Partical Swarm Optimization (APSO) for small targets detection is proposed, called after APSO-C_Otsu. Firstly, the Otsu with crotch structure is used to perform multi-threshold segmentation of coal and gangue dual-energy X-ray images to increase the contrast between small target and background. Meanwhile, the APSO algorithm was used to optimize the Otsu algorithm with crotch structure in order to improve its convergence speed and reduce its calculation amount. Finally, the processed image is binarized, and the location of the target was labeled based on the bwlabel algorithm. The experimental results revealed that the APSO-C_Otsu algorithm could effectively detect the small pixel size (less than 8 × 8 pixels) of coal and gangue with a particle size of 6 ~ 30 mm, and was also applicable to the coal and gangue with the particle size larger than 30 mm, which was of great significance for accurate separation of coal and gangue and the improvement of coal utilization.

Otsu's method

Gangue

10.21203/rs.3.rs-2092600/v1

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Citations (1)

Detection of Pits by Conjugate Lines: An Algorithm for Segmentation of Overlapping and Adhesion Targets in DE-XRT Sorting Images of Coal and Gangue

Applied Sciences (2022)

Lei He Shuang Wang Yongcun Guo

In lump coal and gangue separation based on photoelectric technology, the prerequisite of using a dual-energy X-ray to locate and identify coal and gangue is to obtain the independent target area. However, with the increase in the input of the sorting system, the actual collected images had adhesion and overlapping targets. This paper proposes a pit point detection and segmentation algorithm to solve the problem of overlapping and adhesion targets. The adhesion forms are divided into open and closed-loop adhesion (OLA and CLA). Then, an open- and closed-loop crossing algorithm (OLCA and CLCA) is proposed. We used the conjugate lines to detect the pit and judge the position and distance of the pixel point relative to the conjugate lines. Then, we set the constraint of the distance of the pixel point and the relatively straight line position to complete the pit detection. Finally, the minimum distance search method was used to obtain the dividing line corresponding to the pit to complete the image segmentation. The experiment results demonstrate that the segmentation accuracy of the overlapping target was 90.73%, and the acceptable segmentation accuracy was 94.15%.

Position (finance)

Line segment

Line (geometry)

Gangue

10.3390/app12199850

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Citations (2)

Research on identification of coal and gangue under complex working conditions based on relief feature

International Journal of Coal Preparation and Utilization (2023)

Qisheng Luo Shuang Wang Yongcun Guo Deyong Li Lei He

To improve the identification accuracy of coal gangue under the complex working conditions of pulverized coal and moisture adhesion on the surface, this paper studies the distribution difference of characteristic curves under five working conditions of different proportions of moisture and pulverized coal, and finds that moisture has a greater influence on gradient features, while pulverized coal has a greater influence on gray texture features. Then this paper studies the distribution of coarse texture and fine texture in coal and gangue, and finds that there are many fine textures and few coarse textures on the surface of gangue, while coal shows the opposite law. In addition, the ratio of pixels with large gray value to pixels with small gray value can reduce the occurrence of noncompliance with the above laws. Finally, through the combination of coarse texture, fine texture, and gray value difference, the relief feature is put forward. Compared with different features, the average feature overlap ratio proposed in this paper is lower, at 23.83%. Compared with different methods, this method has a higher identification rate under different working conditions, and the average identification accuracy rate is 89.59%, which is at least 13.93% higher than other methods.

Pulverized coal-fired boiler

Gangue

Texture (cosmology)

Feature (linguistics)

Gray (unit)

10.1080/19392699.2023.2282633

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Citations (1)

Research on the fine identification of coal types and gangue based on X-ray diffraction principle

International Journal of Coal Preparation and Utilization (2022)

Yanqiu Zhao Shuang Wang Yongcun Guo Gang Cheng Lei He

Currently, new sorting technology of coal and gangue not merely focuses on the identification of coal and gangue, but also emphasizes the fine identification of coal. Therefore, this paper takes the main coal types, including one-third coking coal, gas coal, and gangue in Huainan coalfield as the research object, and proposes a method to identify coal types and gangue. The ADXRD patterns and EDXRD patterns of the samples were obtained through X-ray diffractometer and EDXRD test platform, respectively. The differences between the phase composition and the internal connection between the ADXRD pattern and EDXRD pattern of the samples were discussed, and the conversion of ADXRD pattern to EDXRD pattern of coal and gangue was simulated, the PSO-SVM identification model of coal types and gangue was established. The experimental results show that this method provides a new and effective technical way for coal types and gangue identification, which has important reference significance for fine identification of coal and gangue.

Gangue

Identification

Diffractometer

10.1080/19392699.2022.2140335

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Citations (3)

Dual-energy X-ray transmission identification method of multi-thickness coal and gangue based on SVM distance transformation

Fuel (2023)

Shuang Wang Lei He Yongcun Guo Kun Hu Deyong Li

Gangue

10.1016/j.fuel.2023.129593

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Citations (13)

Study on coal and gangue recognition method based on the combination of X-ray transmission and diffraction principle

Energy Sources Part A Recovery Utilization and Environmental Effects (2022)

Yanqiu Zhao Shuang Wang Gang Cheng Lei He

Coal and gangue separation technology based on X-ray has broad development prospects due to its low energy consumption and pollution-free environment, which is also the key part in the green and intelligent mines. Aiming at the low recognition accuracy for coal and gangue with the particle size of 5–15 mm for the dual-energy X-ray coal and gangue separation technology, the coal and gangue recognition method based on the combination of X-ray transmission and diffraction principle was proposed. The dual-energy X-ray images based on the X-ray transmission principle were collected for coal and gangue with particle size larger than 15 mm, and Rc, Glc, Gl, Ra were extracted as the recognition features. EDXRD patterns based on the X-ray diffraction principle were collected for coal and gangue with particle size less than 15 mm, and the characteristic diffraction peaks were extracted as the recognition features. Then, the PSO-SVM model was established for coal and gangue recognition. The test results show that the proposed method can broaden the particle size range for dry coal preparation based on X-ray, and the recognition accuracy of coal and gangue with particle size less than 15 mm is 98%, which is 16.7% higher than that of the method based on the X-ray transmission principle alone. The comprehensive recognition accuracy of coal and gangue with particle size of 5–100 mm reached 97.5%. Consequently, this paper provides a new technical approach for coal and gangue identification.

Gangue

Particle (ecology)

10.1080/15567036.2022.2137600

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Citations (9)

Study of raw coal identification method by dual-energy X-ray and dual-view visible light imaging

International Journal of Coal Preparation and Utilization (2022)

Lei He Shuang Wang Yongcun Guo Kun Hu Gang Cheng

In the process of pre-discharging gangue via the dual-energy X-ray coal and gangue sorting device, the pseudo-medium coal in the raw coal will be identified as gangue and then discarded. Clean coal can be recovered and resource waste can be reduced via the crushing and re-separation of pseudo-medium coal, which is the critical step in the treatment procedure of a coal preparation plant. By virtue of dual-view visible-light imaging, an image identification method was proposed to recover the pseudo-medium coal identified as gangue by dual-energy X-ray. The pseudo-medium coal was determined based on the differences in coal and gangue surface organization and reflective properties. Linear dispersion was used to evaluate the distinguishability of features, and the elements were sorted. The recognition effect of the classifier model on pseudo-medium coal and gangue under different feature combinations was experimentally observed. The results unraveled that the identification rate of mixed samples of pseudo-medium coal and gangue that were selected from Dayugou Coal Mine in Zhengzhou City, Henan Province, could reach 95% using the method herein. The invention can effectively recover pseudo-medium coal from the waste rock pre-discharged by the dual-energy X-ray sorting device, realizing the multi-classification of raw coal.

Gangue

10.1080/19392699.2022.2051013

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Citations (24)

Corrosion behavior of Inconel 718 superalloy utilized in an entrained-flow pulverized coal gasifier burner

Engineering Failure Analysis (2024)

Lei He Longfei Zeng Xiaoyan Wu Shuai Liu

Inconel

Pulverized coal-fired boiler

Wood gas generator

10.1016/j.engfailanal.2024.109154

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Shape selection recognition and scattering distribution prediction of adhesion targets in multi-scale dual-energy X-ray images of coal and gangue

International Journal of Coal Preparation and Utilization (2022)

Lei He Shuang Wang Yongcun Guo Kun Hu Gang Cheng

Identifying and predicting the distribution of scattered coal and gangue is the premise of locating them. By analyzing the shape of multi-scale coal and gangue, an approach for shape selection and recognition is provided. The issues of independent and adhesion target recognition, adhesion type recognition, and distribution prediction have been resolved. The binary target, circumscribed convex hull, and concave defect images are used to extract a total of 27 shape features. The ReliefF algorithm is employed to select features. The shape recognition model 1 shows the highest adhesion and independent recognition rates of 98.54%. For adhesion kinds, shape recognition model 2 gets the highest recognition accuracy of 92%. According to the experimental findings, the difference between predicted and actual results for the parameters describing the dispersed distribution of targets, such as target number, distribution overlap rate, and distribution density, is less than 4.1%, which is acceptable for use in real-world scenarios.

10.1080/19392699.2022.2122453

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Citations (6)

Rapid Detection of Incomplete Coal and Gangue Based on Improved Pspnet

SSRN Electronic Journal (2022)

Xi Wang Yongcun Guo Shuang Wang Gang Cheng Lei He

Aiming at the rapid identification difficulty of coal and gangue under the conditions of multi-scale, adhesion and half-occlusion, we propose a semantic segmentation network of coal and gangue image (SSNet_CG). The attention mechanism is embedded in the inverted residual block (IRB) to strengthen the detailed feature information of coal and gangue image; depthwise separable convolution (DSC) and atrous convolution (AC) are used to replace typical convolution to reduce the number of parameters; The pyramid pooling module(PPM)'s structure is optimized to minimize parameter redundancy; Two feature fusion channels are added to refine the segmentation boundary of coal and gangue in an adhesive state by connecting the preceding position information. The results show that our method has the best effects, the MPA, mIoU and F1_scores are respectively 97.3, 95.4 and 0.98, and the single image test time is 0.027s. This method can accurately identify multi-scale and partially blocked coal and gangue.

10.2139/ssrn.4062783

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