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%).

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.

Owing to the enormous demand for and growing large-scale use of coal in the China, India, and USA, speculation has arisen about possible hazards to environmental quality and human health. The contents of fly ash and volatile matter in low-quality coal are extremely harmful to the environment. As a result, there is still much to be explored regarding known hazards and harms to the natural environment of the Earth. For the detection of high-quality coal, we propose a new method of distinguishing coal quality in terms of different “coal types” (i.e., anthracite, bituminous coal, subbituminous coal and lignite) and efficiently separating gangue and rock from the production lines of coal preparation plants (CPPs) by exploiting infrared machine vision and convolutional neural networks (CNNs) for deep learning, which can make coal use less harmful to humans and nature and/or more useful for general welfare. In this article, we conducted two experiments. First experiment is to study the reactions of coal types, gangue and rock with infrared radiation at temperatures of 50°C, 70°C, 90°C, 110°C, and 150°C. Second experiment, several common CNN models (i.e., AlexNet, DarkNet-53, GoogLeNet, NasNet_Mobileb, ResNet-18, MobileNet-v2, Inception-v3 and DenseNet-201) are trained and tested to classify coal types and distinguish gangue and rock. We achieve a remarkable classification accuracy of 100% in these training and testing processes when employing the ResNet-18 and DenseNet-201 models.

To Whom It May Concern Members of The Scientific Community Dear colleagues: I hope my letter finds you well. My name is REFAT MOHAMMED ABDULLAH ESHAQ (https://orcid.org/0000-0002-6448-4054). I have created a new algorithm, namely Proportional–Integral–Derivative–Cumulative Neural Networks (PIDC-NN), also called MinerNet. This algorithm work based on the PID controller that was created by the inventor Elmer Sperry in 1910. The code has been released on GitHub, see https://github.com/REFATESHAQ/PIDC-NN_MinerNet, and https://github.com/REFATESHAQ/PIDC-NN_MinerNet-Pro. The data (Coal and Gangue Infrared Images in BMP file format) has been released on IEEE Dataport. https://dx.doi.org/10.21227/v3m7-dk11 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 https://www.gnu.org/licenses/agpl-3.0.en.html . The dataset (Coal and Gangue Infrared Images in BMP file format (Data.rar)) is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. For more information, see https://creativecommons.org/licenses/by/4.0/ . 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. The question is, why do I make contact with you?  As the average review time in top journals for the first round of submission may exceed one year, they encourage the authors to submit their papers to TechRxiv, IEEE’s preprint server, and publish the code in GitHub platform in order to quickly disseminate their work to a wide audience and gain community feedback. Therefore: · If you are a researcher in medicine, you can use and develop this code to detect the cancers Benign and Malignant Tumors by using the dataset of breast mammography images as inputs to the PIDC-NN algorithm. · If you are a researcher in Ecological and Environmental Engineering, you can use and develop this code to study geological and climate changes by using Remote Sensing Images as inputs to the PIDC-NN algorithm. · If you are a researcher in Mining Engineering, you can use and develop this code to detect and explore the resources by using images you collected as inputs to the PIDC-NN algorithm. · If you are a researcher in Mechanical or Industrial Engineering, you can use and develop this code to remove unwanted material from production lines of factories or to detect defects in equipment. Also who work in Fluid Mechanics and Gas Dynamics, you can use this code in order to study the behavior of fluid by using infrared images of the movement of the fluid through the pipes and heat exchangers. · If you are a researcher in Mechatronics Engineering, you can use and develop this code and insert it in Robots as visual sense algorithm such as Robotic Arms (Manipulators), Mobile Robots, and Drones and so on. Finally, there are a lot of benefits from this algorithm (PIDC) to control thermal, electrical, and mechanical processes as long as you understand how the PIDC algorithm deal with multiple random complicated inputs to produce one or multiple stable outputs. To clarify, the PIDC algorithm can not only apply to control ANN but also to any system needs to be stable. Yours faithfully ESHAQ ResearchGate: https://www.researchgate.net/profile/Refat-Eshaq Google Scholar: https://scholar.google.com/citations?user=_mmSzykAAAAJ&hl=en Author’s Email: refateshaq1993@gmail.com; refateshaq@hotmail.com; fs18050005@cumt.edu.cn;

Breast cancer, per WHO, ranks top in diagnoses and cancer fatalities. Early detection via mammography reduces mortality significantly, yet mammogram images often have indistinct features. Hence, precise tumor edge identification requires both image enhancement and segmentation. In response, we introduce the Max Planck Algorithm, a novel segmentation method rooted in Planck’s quantum theory, specifically his thermal radiation principles. We innovatively converted this theory to create a unique segmentation tool applicable to digital image processing and medical imaging. The algorithm works by relating mammogram pixel values to X-ray wavelengths, adapting Planck’s Law to use ’temperature’ as an arbitrary variable (originally tied to actual temperature in Planck’s work). Gradually adjusting ’temperature’ optimizes the mammogram image’s meaningfulness. The Max Planck Algorithm boasts advantageous properties, delivering higher efficiency and superior segmentation results. This innovative model introduces new methods for enhancing and segmenting mammograms, establishing itself as a unique technique without comparison to existing methods. The code is available on Github , and data CMMD | The Chinese Mammography Database

Purpose. Feasibility of completely dispensing with solar collectors (SCs) that are used in heat pump (HP) systems for hot water in cold areas. Methodology. Since the temperature of gearbox oil is relatively high, lots of heat can be exploited. Therefore, the recovery of this unutilized heat from gearbox oil temperature of a wind turbine shows a promising solution in improving heat pump (HP) efficiency for hot water especially in cold environment or the district that is located in the north of our planet where the solar energy is very low. This investigation focuses on the feasibility of direct conversion of mechanical energy harvested from the wind speed into thermal energy by exploiting only the friction phenomenon inside the gearbox of the wind turbine by completely dispensing with solar collectors (SCs) that are used in combined solar assisted heat pump (SAHP) because the SCs have various problems, such as large heat loss, low efficiency, freezing and tube-burst, which may limit their applications. Findings. Results show that the wind turbine can provide the power required for HP during the winter season due to high wind speed in Xuzhou city, Jiangsu, China particularly in January. At best, coefficient of performance (COP) may reach 4.08 without SCs, thus the suggested system ensures high COP in addition to decrease in the fuel consumption by 23.25%. Originality. Wind power driven HPs have been suggested in many pervious papers as a sustainable measure to provide heat to a house; however, to improve COP of HP system, we suggest using a wind turbine to directly drive the HP and exploit gearbox oil temperature in an assistant heat exchanger installed after the HP evaporator for providing additional thermal energy to refrigerant R12 and cooling the gearbox oil. Practical value. The coefficient of performance (COP) of HP has reached 4.08 without SCs, so the suggested system demonstrates high COP in addition to the reduced fuel consumption. Approximately 23.25% of energy could be saved using this novel system compared to a fuel water heater for DHW.