Distinguishing between coal and gangue in the production lines of mining factories based on the thermal energy and infrared radiation emission of an object is feasible. In this paper, we use an infrared camera (IC) to distinguish between coal and gangue in the industrial mining field. Additionally, this system is considered to be a binary classification system that has two classes. We analyze the infrared images of coal and gangue; then extract the appropriate texture features from the infrared images in order to develop an accurate classification system by using support vector machine (SVM). The method applied in this work essentially depends on feature extraction of images. The statistical features based on gray level information (GLI), grey-level cooccurrence matrix (GLCM) and visual features are executed. Thus, we suggest preparation steps to obtain one select feature before importing the data into the SVM classifier, and this approach is adopted as the fundamental basis for our work. We exploit only one feature of the infrared image, namely, Cb, which is extracted from the YCbCr color space, and then compute the mean value of Cb after heating and capturing the photos for the coal and gangue samples. The proposed method achieves a high classification accuracy 97.83 % by using Gaussian-SVM.
Recognition and separation of Coal/Gangue are important phases in the coal industries for many aspects. This paper addressed the topic of Coal/Gangue recognition and built a new model called (CGR-CNN) based on Convolutional Neural network (CNN) and using thermal images as standard images for Coal/Gangue recognition. The CGR-CNN model has been developed, augmentation principle has been applied in order to increase the dataset and the best experimental results have been achieved (99.36%) learning accuracy and (95.09%) validation accuracy, in the prediction phase (160) new images of coal and gangue (80 for both) have been tested to measure the efficiency of the work, the prediction result comes with (100%) for coal recognition accuracy and (97.5%) gangue recognition accuracy giving an overall prediction accuracy (98.75%).
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
<p><strong>To Whom It May Concern</strong></p> <p>Members of Scientific Community</p> <p>Dear colleagues:</p> <p>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. </p> <p>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 <em>KP</em>, <em>KI</em>, and <em>KD</em>; the sampling time (<em>dt</em>); and <em>arbitrary value </em>(<em>AV</em>). 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. </p> <p><strong>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).</strong> </p> <p><u><strong>The code is licensed under GNU Affero General Public License Version 3 (GNU AGPLv3); for more information, see </strong></u><a href="https://www.gnu.org/licenses/agpl-3.0.en.html" target="_blank"><strong>https://www.gnu.org/licenses/agpl-3.0.en.html</strong></a><u><strong>. 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 </strong></u><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank"><strong>https://creativecommons.org/licenses/by-nc-sa/4.0/</strong></a><u><strong>. </strong></u></p> <p>· The code has been released on GitHub, see <a href="https://github.com/REFATESHAQ" target="_blank">https://github.com/REFATESHAQ</a> </p> <p>· 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> </p> <p>This work has been supported by my livelihood and my family's aid. The code and data is connected to article, entitled “<strong>Deep Learning Algorithm for Computer Vision with a New Technique and Concept: PIDC-NN for Binary Classification Tasks in a Coal Preparation Plant (MinerNet)</strong>” TechRxiv (10.36227/techrxiv.23266301). Note that, the article is under review. </p> <p>Yours faithfully</p> <p>ESHAQ</p> <p><br></p> <p>Web of Science ResearcherID: AAJ-8724-2020</p> <p>ResearchGate: <a href="https://www.researchgate.net/profile/Refat-Eshaq" target="_blank">https://www.researchgate.net/profile/Refat-Eshaq</a></p> <p>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></p> <p>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>; </p>
<p><strong>To Whom It May Concern</strong></p> <p>Members of Scientific Community</p> <p>Dear colleagues:</p> <p>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. </p> <p>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 <em>KP</em>, <em>KI</em>, and <em>KD</em>; the sampling time (<em>dt</em>); and <em>arbitrary value </em>(<em>AV</em>). 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. </p> <p><strong>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).</strong> </p> <p><u><strong>The code is licensed under GNU Affero General Public License Version 3 (GNU AGPLv3); for more information, see </strong></u><a href="https://www.gnu.org/licenses/agpl-3.0.en.html" target="_blank"><strong>https://www.gnu.org/licenses/agpl-3.0.en.html</strong></a><u><strong>. 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 </strong></u><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank"><strong>https://creativecommons.org/licenses/by-nc-sa/4.0/</strong></a><u><strong>. </strong></u></p> <p>· The code has been released on GitHub, see <a href="https://github.com/REFATESHAQ" target="_blank">https://github.com/REFATESHAQ</a> </p> <p>· 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> </p> <p>This work has been supported by my livelihood and my family's aid. The code and data is connected to article, entitled “<strong>Deep Learning Algorithm for Computer Vision with a New Technique and Concept: PIDC-NN for Binary Classification Tasks in a Coal Preparation Plant (MinerNet)</strong>” TechRxiv (10.36227/techrxiv.23266301). Note that, the article is under review. </p> <p>Yours faithfully</p> <p>ESHAQ</p> <p><br></p> <p>Web of Science ResearcherID: AAJ-8724-2020</p> <p>ResearchGate: <a href="https://www.researchgate.net/profile/Refat-Eshaq" target="_blank">https://www.researchgate.net/profile/Refat-Eshaq</a></p> <p>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></p> <p>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>; </p>
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