Research on Coal and Gangue Recognition Model Based on CAM-Hardswish with EfficientNetV2

Li, Na; Xue, Jiameng; Wu, Sibo; Qin, Keyun; Liu, Na

doi:10.3390/app13158887

Cited by 3 publications

(1 citation statement)

References 17 publications

(18 reference statements)

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“…In contrast, a convolutional neural network can automatically extract high-level features of images and respond quickly. This method is least affected by external unfavorable factors and is more conducive to achieving accurate and rapid coal gangue recognition. − …”

Section: Introductionmentioning

confidence: 99%

Research on the Strong Generalization of Coal Gangue Recognition Technology Based on the Image and Convolutional Neural Network under Complex Conditions

Xun,

Yang,

Liu

2023

ACS Omega

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A coal gangue image recognition method based on complex conditions is proposed to address the current issue of image-based coal gangue recognition being greatly affected by complex conditions. First, complex conditions such as different shooting backgrounds, shooting distances, and lighting intensities are set to simulate the underground coal mining environment. Then, based on three convolutional neural network algorithms, the coal gangue recognition model is established, and the influence of different complex conditions on coal gangue image recognition is analyzed. At the same time, a network model with a strong generalization ability is determined. The results show that the accuracy of coal gangue image recognition has no obvious regularity under different shooting background conditions, and complex environments should be the primary factor affecting the accuracy of coal gangue image recognition. The accuracy of coal gangue image recognition is negatively correlated with the increase in shooting distance, and strong light conditions are conducive to improving the accuracy of coal gangue image recognition. The LeNet network model has the strongest generalization ability, which can meet the requirements of recognition accuracy and respond quickly. The accuracy of coal gangue image recognition under different complex conditions can reach more than 0.99, and the average single image recognition time is only 177 ms. This article studies the influence law of different complex conditions on the recognition of coal and gangue images and confirms that the LeNet network has strong generalization ability, achieving accurate and fast recognition of coal gangue images under complex conditions and providing a reference basis for the deployment of underground coal gangue sorting.

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Section: Introductionmentioning

confidence: 99%

Research on the Strong Generalization of Coal Gangue Recognition Technology Based on the Image and Convolutional Neural Network under Complex Conditions

Xun,

Yang,

Liu

2023

ACS Omega

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Transfer-Learning Approach for Enhanced Brain Tumor Classification in MRI Imaging

Amarnath,

Al Bataineh,

Hansen

2024

BioMedInformatics

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Background: Intracranial neoplasm, often referred to as a brain tumor, is an abnormal growth or mass of tissues in the brain. The complexity of the brain and the associated diagnostic delays cause significant stress for patients. This study aims to enhance the efficiency of MRI analysis for brain tumors using deep transfer learning. Methods: We developed and evaluated the performance of five pre-trained deep learning models—ResNet50, Xception, EfficientNetV2-S, ResNet152V2, and VGG16—using a publicly available MRI scan dataset to classify images as glioma, meningioma, pituitary, or no tumor. Various classification metrics were used for evaluation. Results: Our findings indicate that these models can improve the accuracy of MRI analysis for brain tumor classification, with the Xception model achieving the highest performance with a test F1 score of 0.9817, followed by EfficientNetV2-S with a test F1 score of 0.9629. Conclusions: Implementing pre-trained deep learning models can enhance MRI accuracy for detecting brain tumors.

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Prediction and Deformation Mechanism Analysis of High Porosity in U–10Mo Monolithic Fuels at High Burnup

Jian,

Zhao,

Ding

2023

Metals

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High porosity phenomena of U-10Mo fuel foil appear in the U–Mo/Al monolithic fuel plate under deep burnup. In this study, the fuel skeleton creep-based bubble growth model is further improved with the intrusion effect of solid fission products, multiplying the reduction factor in the bubble volume to obtain the bubble pressure. With the locally enhanced irradiation creep of the Mo-depleted region considered, a simulation of the thermo-mechanical coupling behaviors of the monolithic fuel plate L1P7A0 is carried out, based on the commercial finite element (FE) analysis code ABAQUS. A fission-induced creep rate coefficient of 250 × 10−22 mm3/(fission·MPa) is identified for the Mo-depleted region, with the predictions of porosity and the thickness deformation of U–Mo fuel foil agreeing well with the experimental data. The research results indicate that: (1) the locally enhanced fuel skeleton creep ability is responsible for the higher porosities near the U–Mo/Zr interface; (2) the entrance of solid fission products into the fission bubbles at high burnup is the dominant factor in inducing high porosity in the regions of the most heavily irradiated fuel foil, especially near the fuel foil edge bearing the elevated external hydrostatic pressures; (3) with the intrusion effect of solid fission products considered, the prediction of the porosity increases from ~15% to ~35% near the fuel foil edge; (4) the intrusion of solid fission products leads to extra differences between the bubble pressure and the external pressure, and simultaneously results in the strengthened fuel skeleton creep deformation contributions to the bubble growth.

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Research on Coal and Gangue Recognition Model Based on CAM-Hardswish with EfficientNetV2

Cited by 3 publications

References 17 publications

Research on the Strong Generalization of Coal Gangue Recognition Technology Based on the Image and Convolutional Neural Network under Complex Conditions

Research on the Strong Generalization of Coal Gangue Recognition Technology Based on the Image and Convolutional Neural Network under Complex Conditions

Transfer-Learning Approach for Enhanced Brain Tumor Classification in MRI Imaging

Prediction and Deformation Mechanism Analysis of High Porosity in U–10Mo Monolithic Fuels at High Burnup

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