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Applied Sciences
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Artificial Intelligence Applications in Mining and Mineral Processing
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Artificial Intelligence Applications in Mining and Mineral Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 October 2024 | Viewed by 2631

Special Issue Editor

Prof. Dr. Deqiang Cheng

E-Mail Website
Guest Editor
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: image processing; objective recognition; video analyzing

Special Issue Information

Dear Colleagues,

This Special Issue, entitled “Artificial Intelligence Applications in Mining and Mineral Processing”, focuses on presenting research into image-based or video-based AI techniques used in mines. Our objective is to establish effective communication between AI researchers and the intelligent mining community, push forward applications of AI-driven machine vision techniques for intelligent mining, present research articles and encourage discussions centered around the utilization of artificial intelligence techniques in mining and mineral processing. In particular, this includes methods of image super-resolution reconstruction for the degraded images of mine roadways with special lighting and dust environments, unsupervised cross-view re-recognition methods for the weak characteristic personnel in the mine roadways with complex backgrounds, identification methods and lightweight recognition models for safety hazards in the key production scenes of mines, and the three-dimensional virtual reconstruction technology of mining scenes based on video content analysis and real-time control data and image-based particle shape measurement of pulverized coal.

Kind regards,

Prof. Dr. Deqiang Cheng
Guest Editor

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

  • super-resolution reconstruction of mine roadway image(or video) in special lighting and dust environment
  • pedestrian re-identification in the mine roadway with complex background
  • AI-driven mining surveillance and abnormal situation monitoring
  • three-dimensional reconstruction of real mining scenes
  • image-based particle shape measurement of pulverized coal

Published Papers (2 papers)

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Research

12 pages, 1203 KiB  
Article
Application of Machine Learning Methods to Assess Filtration Properties of Host Rocks of Uranium Deposits in Kazakhstan
by Yan Kuchin, Ravil Mukhamediev, Nadiya Yunicheva, Adilkhan Symagulov, Kirill Abramov, Elena Mukhamedieva, Elena Zaitseva and Vitaly Levashenko
Appl. Sci. 2023, 13(19), 10958; https://doi.org/10.3390/app131910958 - 04 Oct 2023
Cited by 3 | Viewed by 944
Abstract
The uranium required for power plants is mainly extracted by two methods in roughly equal amounts: quarries (underground and open pit) and in situ leaching (ISL). Uranium mining by in situ leaching is extremely attractive because it is economical and has a minimal [...] Read more.
The uranium required for power plants is mainly extracted by two methods in roughly equal amounts: quarries (underground and open pit) and in situ leaching (ISL). Uranium mining by in situ leaching is extremely attractive because it is economical and has a minimal impact on the region’s ecology. The effective use of ISL requires, among other things, the accurate assessment of the host rocks’ filtration characteristics. An accurate assessment of the filtration properties of the host rocks allows optimizing the mining process and improving the quality of the ore reserve prediction. At the same time, in Kazakhstan, this calculation is still based on methods that were developed more than 50 years ago and, in some cases, produce inaccurate results. According to our estimates, this method provides a prediction of filtration properties with a determination coefficient R2 = 0.32. This paper describes a method of calculating the filtration coefficient of ore-bearing rocks using machine learning methods. The proposed approach was based on nonlinear regression models providing a 20–75% increase in the accuracy of the filtration coefficient assessment compared with the current methodology. The work used different types of machine learning algorithms based on the gradient boosting technique, bagging technique, feed-forward neural networks, support vector machines, etc. The results of logging, core sampling, and hydrogeological studies obtained during the exploration stage of the Inkai deposit were used as the initial data. All used machine learning models demonstrated significantly better results than the old method. This resulted in improved results compared with previous studies. The LightGBM regressor demonstrated the best result (R2 = 0.710). Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Mining and Mineral Processing)
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15 pages, 3746 KiB  
Article
Research on Improved Retinex-Based Image Enhancement Method for Mine Monitoring
by Feng Tian, Tingting Chen and Jing Zhang
Appl. Sci. 2023, 13(4), 2672; https://doi.org/10.3390/app13042672 - 19 Feb 2023
Cited by 1 | Viewed by 1130
Abstract
An improved Retinex fusion image enhancement algorithm is proposed for the traditional image denoising methods and problems of halo enlargement and image overexposure after image enhancement caused by the existing Retinex algorithm. First, a homomorphic filtering algorithm is used to enhance each RGB [...] Read more.
An improved Retinex fusion image enhancement algorithm is proposed for the traditional image denoising methods and problems of halo enlargement and image overexposure after image enhancement caused by the existing Retinex algorithm. First, a homomorphic filtering algorithm is used to enhance each RGB component of the underground coal mine surveillance image and convert the image from RGB space to HSV space. Second, bilateral filtering and multi-scale retinex with color restoration (MSRCR) fusion algorithms are used to enhance the luminance V component while keeping the hue H component unchanged. Third, adaptive nonlinear stretching transform is used for the saturation S-component. Last, the three elements are combined and converted back to RGB space. MATLAB simulation experiments verify the superiority of the improved algorithm. Based on the same dataset and experimental environment, the improved algorithm has a more uniform histogram distribution than the multi-scale Retinex (msr) algorithm and MSRCR algorithm through comparative experiments. At the same time, the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), standard deviation, average gradient, mean value, and colour picture information entropy of the images were improved by 8.28, 0.15, 4.39, 7.38, 52.92 and 2.04, respectively, compared to the MSR algorithm, and 3.97, 0.02, 34.33, 60.46, 26.21, and 1.33, respectively, compared to the MSRCR algorithm. The experimental results show that the image quality, brightness and contrast of the images enhanced by the improved Retinex algorithm are significantly enhanced, and the amount of information in the photos increases, the halo and overexposure in the images are considerably reduced, and the anti-distortion performance is also improved. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Mining and Mineral Processing)
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