Terra SAR-X图像的几何校正和辐射定标对于获取地面目标高精度地理位置信息和辐射特征具有重要意义。采用距离-多普勒间接定位模型和仿射校正模型进行SAR影像几何校正,同时完成地形辐射校正并对辐射定标精度进行验证。距离向和方位向上...Terra SAR-X图像的几何校正和辐射定标对于获取地面目标高精度地理位置信息和辐射特征具有重要意义。采用距离-多普勒间接定位模型和仿射校正模型进行SAR影像几何校正,同时完成地形辐射校正并对辐射定标精度进行验证。距离向和方位向上达到了约0.3 m和0.4 m的几何定位精度;相对辐射定标精度优于0.3 d B,绝对辐射定标精度为0.22 d B,所以该模型能达到方法较高的精度且简单快捷。展开更多
We present initial results on the temporal evolution of the phase space density (PSD) of the outer radiation belt energetic electrons driven by the superluminous R-X mode waves. We calculate diffusion rates in pitch a...We present initial results on the temporal evolution of the phase space density (PSD) of the outer radiation belt energetic electrons driven by the superluminous R-X mode waves. We calculate diffusion rates in pitch angle and momentum assuming the standard Gaussian distributions in both wave frequency and wave normal angle at the location L=6.5. We solve a 2D momentum-pitch-angle Fokker-Planck equation using those diffusion rates as inputs. Numerical results show that R-X mode can produce significant acceleration of relativistic electrons around geostationary orbit,supporting previous findings that superluminous waves potentially contribute to dramatic variation in the outer radiation belt electron dynamics.展开更多
Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can b...Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.展开更多
文摘Terra SAR-X图像的几何校正和辐射定标对于获取地面目标高精度地理位置信息和辐射特征具有重要意义。采用距离-多普勒间接定位模型和仿射校正模型进行SAR影像几何校正,同时完成地形辐射校正并对辐射定标精度进行验证。距离向和方位向上达到了约0.3 m和0.4 m的几何定位精度;相对辐射定标精度优于0.3 d B,绝对辐射定标精度为0.22 d B,所以该模型能达到方法较高的精度且简单快捷。
基金supported by the National Natural Science Foundation of China(Grant Nos.40774078,40925014,40874076and40931053)the Special Fund for Public Welfare Industry(Meteorology)GYHY200806024the Specialized Research Fund for State Key Laboratories of China.
文摘We present initial results on the temporal evolution of the phase space density (PSD) of the outer radiation belt energetic electrons driven by the superluminous R-X mode waves. We calculate diffusion rates in pitch angle and momentum assuming the standard Gaussian distributions in both wave frequency and wave normal angle at the location L=6.5. We solve a 2D momentum-pitch-angle Fokker-Planck equation using those diffusion rates as inputs. Numerical results show that R-X mode can produce significant acceleration of relativistic electrons around geostationary orbit,supporting previous findings that superluminous waves potentially contribute to dramatic variation in the outer radiation belt electron dynamics.
文摘Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.
