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Advanced Intelligent Mining Technology
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Advanced Intelligent Mining Technology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (30 October 2023) | Viewed by 2904

Special Issue Editors

Prof. Dr. Shibo Wang

E-Mail Website
Guest Editor
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: intelligent mining equipment; positioning and navigation; coal rock cutting mechanism
Prof. Dr. Shaofeng Wang

E-Mail Website
Guest Editor
School of Resources and Safety Enginerring, Central South University, Changsha 410083, China
Interests: rock mechanics; geomechanics; geomaterials
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiusheng Bao

E-Mail Website
Guest Editor
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: mine equipment and its intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mining workers and researchers aim to use intelligent mining technology to improve mining productivity, increase personnel safety, and secure environmental sustainability. The mining process is dependent upon the mining equipment and rock mass. Intelligent mining is not commonly used because of the absence of intelligent decision making, scene perception, and accurate control. Thus, intelligent mining is not as productive as human miners are.

This Special Issue focuses on the advances in intelligent mining technology. We encourage the contribution of original papers on intelligent decision making, scene perception, accurate control, model simulation, positioning and navigation, mining mechanisms, etc., to improve intelligent mining. We also welcome original research on innovative technologies and interdisciplinary studies, e.g., artificial intelligence, digital twin, new applications and viewpoints of intelligent mining technology.

Prof. Dr. Shibo Wang
Prof. Dr. Shaofeng Wang
Prof. Dr. Jiusheng Bao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent decision
  • scene perception
  • accurate controlling
  • model simulation
  • positioning and navigation
  • rock mechanism
  • artificial intelligence
  • digital twin

Published Papers (3 papers)

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Research

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13 pages, 40866 KiB  
Article
Coal–Rock Data Recognition Method Based on Spectral Dimension Transform and CBAM-VIT
by Jianjian Yang, Yuzeng Zhang, Kaifan Wang, Yibo Tong, Jinteng Liu and Guoyong Wang
Appl. Sci. 2024, 14(2), 593; https://doi.org/10.3390/app14020593 - 10 Jan 2024
Viewed by 706
Abstract
Coal–gangue sorting is a vital component of intelligent mine construction. As intelligent manufacturing continued to advance, data-driven coal–gangue recognition emerged as a prominent research topic. However, conventional data-driven methods for coal–gangue recognition heavily rely on expert-extracted features. The process of feature extraction is [...] Read more.
Coal–gangue sorting is a vital component of intelligent mine construction. As intelligent manufacturing continued to advance, data-driven coal–gangue recognition emerged as a prominent research topic. However, conventional data-driven methods for coal–gangue recognition heavily rely on expert-extracted features. The process of feature extraction is labor-intensive and significantly impacts the final outcome. Deep learning (DL) offers an effective approach to automatically extract features from raw data. Among the various DL techniques, convolutional neural networks (CNNs) have proven to be particularly effective. In this paper, we propose an intelligent method for recognizing coal–rock by fusing multiple preprocessing techniques applied to near-infrared spectra and employing dual attention. Initially, a signal-to-RGB image conversion method is applied to fuse three types of preprocessing data, namely first-order differential, second-order differential, and standard normal transform, into an RGB image representation. Subsequently, we propose a neural network model (CBAM-VIT) that integrates the convolutional block attention mechanism (CBAM) and Vision Transformer (VIT). When evaluated on the coal–rock dataset, this model achieves an accuracy of 98.5%, surpassing the performance of VIT (95.3%), VGG-16 (89%), and AlexNet (82%). The comparative results clearly demonstrate that the proposed coal–gangue recognition method yields significant improvements in classification outcomes. Full article
(This article belongs to the Special Issue Advanced Intelligent Mining Technology)
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16 pages, 3260 KiB  
Article
Longwall Face Automation: Coal Seam Floor Cutting Path Planning Based on Multiple Hierarchical Clustering
by Zenglun Guan, Shibo Wang, Jingqian Wang and Shirong Ge
Appl. Sci. 2023, 13(18), 10242; https://doi.org/10.3390/app131810242 - 12 Sep 2023
Viewed by 785
Abstract
Space adaptability between mining equipment and coal-rock mass, to ensure the machines cut in a coal seam, is an importance technique in longwall mining automation. In order to guide the mining equipment cutting in the coal seam, a cutting path planning method based [...] Read more.
Space adaptability between mining equipment and coal-rock mass, to ensure the machines cut in a coal seam, is an importance technique in longwall mining automation. In order to guide the mining equipment cutting in the coal seam, a cutting path planning method based on multiple hierarchical clustering was proposed. Morphology similarity and the coplanarity measurement method were defined to evaluate the similarity of clusters. The coal seam floor series in the face-advancing direction were clustered according to the morphology similarity and coplanarity, respectively. Taking the morphology-based and coplanarity-based cluster centers as generating lines and stretching angle, respectively, the coal seam floor was reconstructed. The reconstructed floor can be regarded as the cutting path. The coal seam geological model of the 18,201 longwall face was analyzed with the proposed cutting path planning method. Comparing the reconstructed floor and original floor, the amounts of coal left and cut gangue were 1999 m3 and 1856 m3, respectively, for the segmental floor. For the case of whole floor, the amounts of coal left and cut gangue were 5642 m3 and 5463 m3, respectively. The coal loss rates only were 0.57% and 0.87% for the segmental and whole coal seam, respectively. Full article
(This article belongs to the Special Issue Advanced Intelligent Mining Technology)
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Review

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17 pages, 9615 KiB  
Review
Longwall Mining Automation—The Shearer Positioning Methods between the Longwall Automation Steering Committee and China University of Mining and Technology
by Weiwei Dai, Shijia Wang and Shibo Wang
Appl. Sci. 2023, 13(22), 12168; https://doi.org/10.3390/app132212168 - 9 Nov 2023
Viewed by 1030
Abstract
The shearer positioning method is of great significance to the automation of longwall mining. The research teams in the Longwall Automation Steering Committee (LASC) of Australia and China University of Mining and Technology (CUMT) have focused on shearer positioning and identified the shearer [...] Read more.
The shearer positioning method is of great significance to the automation of longwall mining. The research teams in the Longwall Automation Steering Committee (LASC) of Australia and China University of Mining and Technology (CUMT) have focused on shearer positioning and identified the shearer inertial navigation system, the measurement of longwall retreat and creep displacement, and the backward calibration of the shearer trajectory as three key technologies to obtain accurate shearer positioning information. In underground environments without GPS, due to the characteristics of inertial navigation system (INS) autonomous full-parameter navigation, shearer positioning based on INS is adopted by the LASC and CUMT, and error reduction algorithms are proposed to inhibit the rapid error accumulation of INS. In order to obtain the periodic calibration information when the shearer reaches the end of the longwall face, it is necessary to measure the retreat and creep displacements in order to back-correct the shearer trajectory. Finding a suitable measurement method for this task is challenging, especially in the presence of dust and moisture. The LASC used a scanning laser and FMR 250 microwave radar to measure these two displacements, while CUMT adopted an ultra-wideband (UWB) radar. In terms of the backward calibration method, minimum-variance fixed-interval smoothing (MFS) proposed by LASC and the global optimization model (GOM) for the shearer trajectory from CUMT are described in detail. The experiment demonstrates that the GOM outperforms MFS in terms of accuracy but requires more computational resources. Therefore, our next research objective is to develop an efficient and accurate algorithm for performing backward calibration on the shearer trajectory. Full article
(This article belongs to the Special Issue Advanced Intelligent Mining Technology)
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