A novel automated separator based on dual energy gamma-rays transmission

Xu, Qi; Zhang, Yongchuan

doi:10.1088/0957-0233/11/9/319

Cited by 9 publications

(2 citation statements)

References 8 publications

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“…Therefore, many coal mining companies prefer fully automated methods to the manual approach. In fully automated coal preparation plants, two main techniques are used to separate gangue from coal: mechanical wet separation [2,6] and dry separation, where the latter includes the use of gamma rays [7] or X-rays [8]. Despite the advantages of mechanical wet separation (e.g., a high separation efficiency and a large handling capacity), this technique requires a large amount of water, and the discharged materials pollute the surrounding environment [9][10][11], as illustrated in Table I.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithm for Computer Vision with a New Technique and Concept: PIDC-NN for Binary Classification Tasks in a Coal Preparation Plant (MinerNet)

Eshaq¹

2023

Preprint

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To Whom It May Concern Members of Scientific Community Dear colleagues: I hope my letter finds you well. My name is REFAT MOHAMMED ABDULLAH ESHAQ ( <a href="https://orcid.org/0000-0002-6448-4054" target="_blank">https://orcid.org/0000-0002-6448-4054</a> ). I have created a new algorithm, namely Proportional–Integral–Derivative–Cumulative (PIDC), also called MinerNet. This algorithm work based on the PID controller that was created by the inventor Elmer Sperry in 1910. Although convolutional neural networks (CNNs) have achieved great successes in computer vision and pattern recognition, they have some shortcomings. In this article, a novel deep learning algorithm for binary classification is proposed to distinguish between coal and gangue infrared images. First, a Proportional–Integral–Derivative–Cumulative (PIDC) algorithm is created, which works based on the concept of a PID controller, in order to quickly extract features from infrared images and also to control the performance of Artificial Neural Networks (ANNs). Second, an ANN is designed for binary classification tasks (coal/gangue). Third, the PIDC algorithm and the ANN algorithm are connected to create a new learning system, namely, the Proportional–Integral–Derivative–Cumulative Neural Network (PIDC-NN), also called MinerNet. The proposed PIDC-NN architecture works without any traditional layers of deep CNNs such as convolutional layers, nonlinear activation functions layers, batch normalization layers, polling layers, or dropout layers. The results of the training and test processes demonstrate that the proposed PIDC-NN architecture alleviates the oscillation and overfitting problems of existing CNNs. Moreover, it solves the problem of dead neurons and big data that are required to train CNNs. Additionally, it provides robust and resilient control by tuning the gain coefficients KP, KI, and KD; the sampling time (dt); and arbitrary value (AV). A comparison between the proposed PIDC-NN architecture and state-of-the-art CNNs proves the effectiveness of the proposed method in accelerating both the training and test processes with competitive loss and accuracy. I emphasize that this algorithm (PIDC) that I created through my own effort, can provide optimal control to any system (not only ANN) whether linear or nonlinear with multiple inputs. Furthermore, this algorithm (PIDC) can control multiple complicated random inputs and make the system linear even with inputs, their amounts, and values are huge numbers (goes to infinity). The code is licensed under GNU Affero General Public License Version 3 (GNU AGPLv3); for more information, see <a href="https://www.gnu.org/licenses/agpl-3.0.en.html" target="_blank">https://www.gnu.org/licenses/agpl-3.0.en.html</a>. The dataset (Coal and Gangue Infrared Images in BMP file format (Data.rar)) is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0); for more information, see <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. · The code has been released on GitHub, see <a href="https://github.com/REFATESHAQ" target="_blank">https://github.com/REFATESHAQ</a> · The data (Coal and Gangue Infrared Images in BMP file format (Data.rar)) has been released on IEEE Dataport. <a href="https://dx.doi.org/10.21227/v3m7-dk11" target="_blank">https://dx.doi.org/10.21227/v3m7-dk11</a> This work has been supported by my livelihood and my family's aid. The code and data is connected to article, entitled “Deep Learning Algorithm for Computer Vision with a New Technique and Concept: PIDC-NN for Binary Classification Tasks in a Coal Preparation Plant (MinerNet)” TechRxiv (10.36227/techrxiv.23266301). Note that, the article is under review. Yours faithfully ESHAQ Web of Science ResearcherID: AAJ-8724-2020 ResearchGate: <a href="https://www.researchgate.net/profile/Refat-Eshaq" target="_blank">https://www.researchgate.net/profile/Refat-Eshaq</a> Google Scholar: <a href="https://scholar.google.com/citations?user=_mmSzykAAAAJ&hl=en" target="_blank">https://scholar.google.com/citations?user=_mmSzykAAAAJ&hl=en</a> Author's Email: <a href="mailto:refateshaq1993@gmail.com" target="_blank">refateshaq1993@gmail.com</a>; <a href="mailto:refateshaq@hotmail.com" target="_blank">refateshaq@hotmail.com</a>; <a href="mailto:fs18050005@cumt.edu.cn" target="_blank">fs18050005@cumt.edu.cn</a>;

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Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithm for Computer Vision with a New Technique and Concept: PIDC-NN for Binary Classification Tasks in a Coal Preparation Plant (MinerNet)

Eshaq¹

2023

Preprint

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“…Dry techniques apply water-free devices with γ-rays or X-rays in the existing industrial applications. However, the problems of poor recognition rate and the cumbersome management of radiation sources remain unsolved [3].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

2018

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Abstract:The separation of coal and gangue is an important process of the coal preparation technology. The conventional way of manual selection and separation of gangue from the raw coal can be replaced by computer vision technology. In the literature, research on image recognition and classification of coal and gangue is mainly based on the grayscale and texture features of the coal and gangue. However, there are few studies on characteristics of coal and gangue from the perspective of their outline differences. Therefore, the multifractal detrended fluctuation analysis (MFDFA) method is introduced in this paper to extract the geometric features of coal and gangue. Firstly, the outline curves of coal and gangue in polar coordinates are detected and achieved along the centroid, thereby the multifractal characteristics of the series are analyzed and compared. Subsequently, the modified local singular spectrum widths ∆h of the outline curve series are extracted as the characteristic variables of the coal and gangue for pattern recognition. Finally, the extracted geometric features by MFDFA combined with the grayscale and texture features of the images are compared with other methods, indicating that the recognition rate of coal gangue images can be increased by introducing the geometric features.

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Particle size distribution of coal and gangue after impact-crush separation

Yang

et al. 2017

J. Cent. South Univ.

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A novel automated separator based on dual energy gamma-rays transmission

Cited by 9 publications

References 8 publications

Deep Learning Algorithm for Computer Vision with a New Technique and Concept: PIDC-NN for Binary Classification Tasks in a Coal Preparation Plant (MinerNet)

Deep Learning Algorithm for Computer Vision with a New Technique and Concept: PIDC-NN for Binary Classification Tasks in a Coal Preparation Plant (MinerNet)

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

Particle size distribution of coal and gangue after impact-crush separation

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