The identification of ore grades is a critical step in mineral resource exploration and mining.Prompt gamma neutron activation analysis(PGNAA)technology employs gamma rays generated by the nuclear reactions between ne...The identification of ore grades is a critical step in mineral resource exploration and mining.Prompt gamma neutron activation analysis(PGNAA)technology employs gamma rays generated by the nuclear reactions between neutrons and samples to achieve the qualitative and quantitative detection of sample components.In this study,we present a novel method for identifying copper grade by combining the vision transformer(ViT)model with the PGNAA technique.First,a Monte Carlo simulation is employed to determine the optimal sizes of the neutron moderator,thermal neutron absorption material,and dimensions of the device.Subsequently,based on the parameters obtained through optimization,a PGNAA copper ore measurement model is established.The gamma spectrum of the copper ore is analyzed using the ViT model.The ViT model is optimized for hyperparameters using a grid search.To ensure the reliability of the identification results,the test results are obtained through five repeated tenfold cross-validations.Long short-term memory and convolutional neural network models are compared with the ViT method.These results indicate that the ViT method is efficient in identifying copper ore grades with average accuracy,precision,recall,F_(1)score,and F_(1)(-)score values of 0.9795,0.9637,0.9614,0.9625,and 0.9942,respectively.When identifying associated minerals,the ViT model can identify Pb,Zn,Fe,and Co minerals with identification accuracies of 0.9215,0.9396,0.9966,and 0.8311,respectively.展开更多
Accurately predicting geomagnetic field is of great significance for space environment monitoring and space weather forecasting worldwide.This paper proposes a vision Transformer(ViT)hybrid model that leverages aurora...Accurately predicting geomagnetic field is of great significance for space environment monitoring and space weather forecasting worldwide.This paper proposes a vision Transformer(ViT)hybrid model that leverages aurora images to predict local geomagnetic station component,breaking the spatial limitations of geomagnetic stations.Our method utilizes the ViT backbone model in combination with convolutional networks to capture both the large-scale spatial correlation and distinct local feature correlation between aurora images and geomagnetic station data.Essentially,the model comprises a visual geometry group(VGG)image feature extraction network,a ViT-based encoder network,and a regression prediction network.Our experimental findings indicate that global features of aurora images play a more substantial role in predicting geomagnetic data than local features.Specifically,the hybrid model achieves a 39.1%reduction in root mean square error compared to the VGG model,a 29.5%reduction compared to the ViT model and a 35.3%reduction relative to the residual network(ResNet)model.Moreover,the fitting accuracy of the model surpasses that of the VGG,ViT,and ResNet models by 2.14%1.58%,and 4.1%,respectively.展开更多
Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates dise...Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates disease severity,supports effective treatment strategies,and reduces reliance on excessive pesticide use.Traditional machine learning approaches have been applied for automated rice disease diagnosis;however,these methods depend heavily on manual image preprocessing and handcrafted feature extraction,which are labor-intensive and time-consuming and often require domain expertise.Recently,end-to-end deep learning(DL) models have been introduced for this task,but they often lack robustness and generalizability across diverse datasets.To address these limitations,we propose a novel end-toend training framework for convolutional neural network(CNN) and attention-based model ensembles(E2ETCA).This framework integrates features from two state-of-the-art(SOTA) CNN models,Inception V3 and DenseNet-201,and an attention-based vision transformer(ViT) model.The fused features are passed through an additional fully connected layer with softmax activation for final classification.The entire process is trained end-to-end,enhancing its suitability for realworld deployment.Furthermore,we extract and analyze the learned features using a support vector machine(SVM),a traditional machine learning classifier,to provide comparative insights.We evaluate the proposed E2ETCA framework on three publicly available datasets,the Mendeley Rice Leaf Disease Image Samples dataset,the Kaggle Rice Diseases Image dataset,the Bangladesh Rice Research Institute dataset,and a combined version of all three.Using standard evaluation metrics(accuracy,precision,recall,and F1-score),our framework demonstrates superior performance compared to existing SOTA methods in rice disease diagnosis,with potential applicability to other agricultural disease detection tasks.展开更多
基金supported by the National Natural Science Foundation of China(Nos.U2BB2077 and 42374226)the Natural Science Foundation of Jiangxi Province(20232BAB201043 and 20232BCJ23006)the Nuclear energy development project of the National Defense Science and Industry Bureau(Nos.20201192-01,20201192-03).
文摘The identification of ore grades is a critical step in mineral resource exploration and mining.Prompt gamma neutron activation analysis(PGNAA)technology employs gamma rays generated by the nuclear reactions between neutrons and samples to achieve the qualitative and quantitative detection of sample components.In this study,we present a novel method for identifying copper grade by combining the vision transformer(ViT)model with the PGNAA technique.First,a Monte Carlo simulation is employed to determine the optimal sizes of the neutron moderator,thermal neutron absorption material,and dimensions of the device.Subsequently,based on the parameters obtained through optimization,a PGNAA copper ore measurement model is established.The gamma spectrum of the copper ore is analyzed using the ViT model.The ViT model is optimized for hyperparameters using a grid search.To ensure the reliability of the identification results,the test results are obtained through five repeated tenfold cross-validations.Long short-term memory and convolutional neural network models are compared with the ViT method.These results indicate that the ViT method is efficient in identifying copper ore grades with average accuracy,precision,recall,F_(1)score,and F_(1)(-)score values of 0.9795,0.9637,0.9614,0.9625,and 0.9942,respectively.When identifying associated minerals,the ViT model can identify Pb,Zn,Fe,and Co minerals with identification accuracies of 0.9215,0.9396,0.9966,and 0.8311,respectively.
基金supported by the National Natural Science Foundation of China(No.41471381)the General Project of Jiangsu Natural Science Foundation(No.BK20171410)the Major Scientific and Technological Achievements Cultivation Fund of Nanjing University of Aeronautics and Astronautics(No.1011-XBD23002)。
文摘Accurately predicting geomagnetic field is of great significance for space environment monitoring and space weather forecasting worldwide.This paper proposes a vision Transformer(ViT)hybrid model that leverages aurora images to predict local geomagnetic station component,breaking the spatial limitations of geomagnetic stations.Our method utilizes the ViT backbone model in combination with convolutional networks to capture both the large-scale spatial correlation and distinct local feature correlation between aurora images and geomagnetic station data.Essentially,the model comprises a visual geometry group(VGG)image feature extraction network,a ViT-based encoder network,and a regression prediction network.Our experimental findings indicate that global features of aurora images play a more substantial role in predicting geomagnetic data than local features.Specifically,the hybrid model achieves a 39.1%reduction in root mean square error compared to the VGG model,a 29.5%reduction compared to the ViT model and a 35.3%reduction relative to the residual network(ResNet)model.Moreover,the fitting accuracy of the model surpasses that of the VGG,ViT,and ResNet models by 2.14%1.58%,and 4.1%,respectively.
基金the Begum Rokeya University,Rangpur,and the United Arab Emirates University,UAE for partially supporting this work。
文摘Rice is one of the most important staple crops globally.Rice plant diseases can severely reduce crop yields and,in extreme cases,lead to total production loss.Early diagnosis enables timely intervention,mitigates disease severity,supports effective treatment strategies,and reduces reliance on excessive pesticide use.Traditional machine learning approaches have been applied for automated rice disease diagnosis;however,these methods depend heavily on manual image preprocessing and handcrafted feature extraction,which are labor-intensive and time-consuming and often require domain expertise.Recently,end-to-end deep learning(DL) models have been introduced for this task,but they often lack robustness and generalizability across diverse datasets.To address these limitations,we propose a novel end-toend training framework for convolutional neural network(CNN) and attention-based model ensembles(E2ETCA).This framework integrates features from two state-of-the-art(SOTA) CNN models,Inception V3 and DenseNet-201,and an attention-based vision transformer(ViT) model.The fused features are passed through an additional fully connected layer with softmax activation for final classification.The entire process is trained end-to-end,enhancing its suitability for realworld deployment.Furthermore,we extract and analyze the learned features using a support vector machine(SVM),a traditional machine learning classifier,to provide comparative insights.We evaluate the proposed E2ETCA framework on three publicly available datasets,the Mendeley Rice Leaf Disease Image Samples dataset,the Kaggle Rice Diseases Image dataset,the Bangladesh Rice Research Institute dataset,and a combined version of all three.Using standard evaluation metrics(accuracy,precision,recall,and F1-score),our framework demonstrates superior performance compared to existing SOTA methods in rice disease diagnosis,with potential applicability to other agricultural disease detection tasks.
