分类： 天文学 >> 天文仪器与技术 分类： 物理学 >> 核物理学 分类： 计算机科学 >> 计算机应用技术 提交时间： 2024-03-10
摘要： The HADAR experiment, which will be constructed in Tibet, China, combines the wide-angle advantages of traditional EAS array detectors with the high sensitivity advantages of focused Cherenkov detectors. Its physics objective is to observe transient sources such as gamma-ray bursts and counterparts of gravitational waves. The aim of this study is to utilize the latest AI technology to enhance the sensitivity of the HADAR experiment. We have built training datasets and models with distinctive creativity by incorporating relevant physical theories for various applications. They are able to determine the kind, energy, and direction of incident particles after careful design. We have obtained a background identification accuracy of 98.6 %, a relative energy reconstruction error of 10.0 %, and an angular resolution of 0.22-degrees in a test dataset at 10 TeV. These findings demonstrate the enormous potential for enhancing the precision and dependability of detector data analysis in astrophysical research. Thanks to deep learning techniques, the HADAR experiment’s observational sensitivity to the Crab Nebula has surpassed that of MAGIC and H.E.S.S. at energies below 0.5 TeV and remains competitive with conventional narrow-field Cherenkov telescopes at higher energies. Additionally, our experiment offers a fresh approach to dealing with strongly connected scattered data.
分类： 物理学 >> 核物理学 提交时间： 2025-05-02
Zhao, Dr. bokai
GAN, Prof. weimin
LIU, Prof. danmin
LIU, Prof. xiaolong
HOU, Mr. yuhan
LI, Dr. yuqing
li, Dr. tianfu
Sun, Prof. Kai
JIAO, Prof. xuesheng
Li, Prof. Meijuan
Chen, Prof. Dongfeng 陈东风
Zhang, Prof. Yingxun
摘要： In this study, we propose a convolutional neural network (CNN) model aimed at inferring texture types and their volume fractions from neutron diffractometer data. The model is trained using labeled texture data of face- centered cubic (FCC) materials, sourced from X-ray diffraction (XRD) measurements. The effectiveness of the model is evaluated using data obtained from neutron diffraction. Compared to traditional data analysis methods, the CNN model not only offers fast and accurate predictions of texture components and their volume fractions, but also demonstrates strong generalization ability. Even under a certain signal-to-noise ratio, the CNN model maintains high accuracy in inferring texture types and their volume fractions. This capability could facilitate the operation of neutron texture diffractometers at lower neutron beam intensities in the future, thus improving both the efficiency of texture analysis.
分类： 物理学 >> 核物理学 提交时间： 2025-04-26
Zhao, Dr. bokai
GAN, Prof. weimin
LIU, Prof. danmin
LIU, Prof. xiaolong
HOU, Mr. yuhan
LI, Dr. yuqing
li, Dr. tianfu
Sun, Prof. Kai
JIAO, Prof. xuesheng
Li, Prof. Meijuan
Chen, Prof. Dongfeng 陈东风
Zhang, Prof. Yingxun
摘要： In this study, we propose a convolutional neural network (CNN) model aimed at inferring texture types and their volume fractions from neutron diffractometer data. The model is trained using labeled texture data of face- centered cubic (FCC) materials, sourced from X-ray diffraction (XRD) measurements. The effectiveness of the model is evaluated using data obtained from neutron diffraction. Compared to traditional data analysis methods, the CNN model not only offers fast and accurate predictions of texture components and their volume fractions, but also demonstrates strong generalization ability. Even under a certain signal-to-noise ratio, the CNN model maintains high accuracy in inferring texture types and their volume fractions. This capability could facilitate the operation of neutron texture diffractometers at lower neutron beam intensities in the future, thus improving both the efficiency of texture analysis.
分类： 物理学 >> 核物理学 提交时间： 2025-03-08
Zhao, Dr. bokai
GAN, Prof. weimin
LIU, Prof. danmin
LIU, Prof. xiaolong
HOU, Mr. yuhan
LI, Dr. yuqing
li, Dr. tianfu
Sun, Prof. Kai
JIAO, Prof. xuesheng
Li, Prof. Meijuan
Chen, Prof. Dongfeng 陈东风
Zhang, Prof. Yingxun
摘要： In this study, we propose a convolutional neural network (CNN) model aimed at inferring texture types and their volume fractions from neutron diffractometer data. The model is trained using labeled texture data of face-centered cubic (FCC) materials, primarily sourced from X-ray diffraction (XRD) measurements. The ef- fectiveness of the model is evaluated using data obtained from neutron diffraction. Compared to traditional data analysis methods, the CNN model not only offers faster and more accurate predictions of texture components and their volume fractions, but also demonstrates strong generalization ability. Even under a certain signal-to- noise ratio, the CNN model maintains high accuracy in inferring texture types and their volume fractions. This capability could facilitate the operation of neutron texture diffractometers at lower neutron beam intensities in the future, thus improving both the efficiency and precision of texture analysis.
分类： 物理学 >> 核物理学 提交时间： 2024-12-12
Zhao, Dr. bokai
GAN, Prof. weimin
LIU, Prof. danmin
LIU, Prof. xiaolong
HOU, Mr. yuhan
LI, Dr. yuqing
li, Dr. tianfu
Sun, Prof. Kai
JIAO, Prof. xuesheng
Li, Prof. Meijuan
Chen, Prof. Dongfeng 陈东风
Zhang, Prof. Yingxun
摘要： In this study, we propose a convolutional neural network (CNN) model aimed at inferring texture types and their volume fractions from neutron diffractometer data. The model is trained using labeled texture data of face-centered cubic (FCC) materials, primarily sourced from X-ray diffraction (XRD) measurements. The ef- fectiveness of the model is evaluated using data obtained from neutron diffraction. Compared to traditional data analysis methods, the CNN model not only offers faster and more accurate predictions of texture components and their volume fractions, but also demonstrates strong generalization ability. Even under a certain signal-to- noise ratio, the CNN model maintains high accuracy in inferring texture types and their volume fractions. This capability could facilitate the operation of neutron texture diffractometers at lower neutron beam intensities in the future, thus improving both the efficiency and precision of texture analysis.
分类： 计算机科学 >> 计算机科学技术其他学科 提交时间： 2023-03-02
摘要： In this paper, we propose a disentangled representation transformer network (DRTN) approach for 3D dense face alignment and reconstruction.Unlike traditional 3DMM-based approaches in which the target parameters, namely the shape, expression, and pose parameters, are all individually estimated, without considering their direct influences on one another, although they are jointly optimized our DRTN aims to enhance the representation of facial attributes in a semantic sense by learning the correlation of different 3D facial attribute parameters.To achieve this we present a novel strategy to design disentangled 3D face attribute representation,which decomposes the given facial attributes into identity, expression, and poses parts. Specifically, the estimate of 3D face parameters in the regression network depends on the correlation of other face attribute parameters rather than being independent. The branching of the identity component aims to reinforce the learning of expression and pose attributes by preserving the overall face geometry structure and keeping the identity intact. Accordingly, the expression and pose parts of the branch maintain the consistency of expression and pose attributes, respectively. It helps refine the reconstruction and alignment of face details in large poses mainly by coupling other facial attribute parameters. Extensive qualitative and quantitative experimental results on widely-evaluated benchmarking datasets demonstrate that our approach achieves competitive performance compared to state-of-the-art methods.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-27 合作期刊: 《数据智能（英文）》
摘要： Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and varied in their structure and components, continued innovation of modelling strategies is required to identify architectures that can best model outcomes. In this work, we trained a Heterogeneous Graph Model (HGM) on electronic health record (EHR) data and used the resulting embedding vector as additional information added to a Convolutional Neural Network (CNN) model for predicting in-hospital mortality. We show that the additional information provided by including time as a vector in the embedding captured the relationships between medical concepts, lab tests, and diagnoses, which enhanced predictive performance. We found that adding HGM to a CNN model increased the mortality prediction accuracy up to 4%. This framework served as a foundation for future experiments involving different EHR data types on important healthcare prediction tasks.
点击量
点击量
下载量
评论
待评议
