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Simulation Study of Network Reconfiguration and Load-balancing Method for the Xinjiang Astronomical observatory Data Center

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Research in Astronomy and Astrophysics 2022年第9期22卷 278-287页

作者： Jie Wang Hailong Zhang Na Wang Xinchen Ye Wanqiong Wang Jia Li Meng Zhang Yazhou Zhang Xu Du Xinjiang Astronomical observatory Chinese Academy of SciencesUrumgi 83001lChina University of Chinese Academy of Sciences Beijing 100049China National Astronomical Data Center Beijing 100101China Key Laboratory of Radio Astronomy Chinese Academy of SciencesNanjing 210008China

The Xinjiang Astronomical observatory Data Center faces issues related to delay-affected services. As a result, these services cannot be implemented in a timely manner due to the overloading of transmission links. In this paper, the software-defined network technology is applied to the Xinjiang Astronomical observatory Data Center Network(XAODCN). Specifically, a novel reconfiguration method is proposed to realise the software-defined Xinjiang Astronomical observatory Data Center Network(SDXAO-DCN), and a network model is constructed. To overcome the congestion problem, a traffic load-balancing algorithm is designed for fast transmission of the service traffic by combining three factors: network structure, congestion level and transmission service. The proposed algorithm is compared with current commonly load-balancing algorithms which are used in data center to verify its efficiency. Simulation experiments show that the algorithm improved transmission performance and transmission quality for the SDXAO-DCN.

关键词： virtual observatory tools astronomical databases:miscellaneous methods:miscellaneous

Astronomical Knowledge Entity Extraction in Astrophysics Journal Articles via Large Language Models

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Research in Astronomy and Astrophysics 2024年第6期24卷 140-155页

作者： Wujun Shao Rui Zhang Pengli Ji Dongwei Fan Yaohua Hu Xiaoran Yan Chenzhou Cui Yihan Tao Linying Mi Lang Chen National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China National Astronomical Data Center Beijing 100101China Research Institute of Artificial Intelligence Zhejiang LabHangzhou 311100China Guilin University Guangxi 541006China Xidian University Xi’an 710126China

Astronomical knowledge entities,such as celestial object identifiers,are crucial for literature retrieval and knowledge graph construction,and other research and applications in the field of *** methods of extracting knowledge entities from texts face numerous challenging obstacles that are difficult to ***,there is a pressing need for improved methods to efficiently extract *** study explores the potential of pre-trained Large Language Models(LLMs)to perform astronomical knowledge entity extraction(KEE)task from astrophysical journal articles using *** propose a prompting strategy called PromptKEE,which includes five prompt elements,and design eight combination prompts based on *** select four representative LLMs(Llama-2-70B,GPT-3.5,GPT-4,and Claude 2)and attempt to extract the most typical astronomical knowledge entities,celestial object identifiers and telescope names,from astronomical journal articles using these eight combination *** accommodate their token limitations,we construct two data sets:the full texts and paragraph collections of 30 *** the eight prompts,we test on full texts with GPT-4and Claude 2,on paragraph collections with all *** experimental results demonstrate that pre-trained LLMs show significant potential in performing KEE tasks,but their performance varies on the two data ***,we analyze some important factors that influence the performance of LLMs in entity extraction and provide insights for future KEE tasks in astrophysical articles using ***,compared to other methods of KEE,LLMs exhibit strong competitiveness in multiple aspects.

关键词： astronomical databases:miscellaneous virtual observatory tools methods:data analysis

Resolution Enhancement of SOHO/MDI Magnetograms

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Research in Astronomy and Astrophysics 2024年第6期24卷 358-367页

作者： Ying Qin Kai-Fan Ji Hui Liu Xiao-Guang Yu Yunnan Observatories Chinese Academy of SciencesKunming 650011China University of Chinese Academy of Sciences Beijing 100049China Yunnan Key Laboratory of Solar Physics and Space Science Kunming 650216China Key Laboratory for the Structure and Evolution of Celestial Objects Chinese Academy of SciencesKunming 650216China

Research on the solar magnetic field and its effects on solar dynamo mechanisms and space weather events has benefited from the continual improvements in instrument resolution and measurement *** augmentation and assimilation of historical observational data timelines also play a significant role in understanding the patterns of solar magnetic field *** the realm of astronomical data processing,super-resolution(SR)reconstruction refers to the process of using a substantial corpus of training data to learn the nonlinear mapping between low-resolution(LR)and high-resolution(HR)images,thereby achieving higherresolution astronomical *** paper is an application study in high-dimensional nonlinear *** learning models were employed to perform SR modeling on SOHO/MDI magnetograms and SDO/HMI magnetograms,thus reliably achieving resolution enhancement of full-disk SOHO/MDI magnetograms and enhancing the image resolution to obtain more detailed *** this study,a data set comprising 9717pairs of data from 2010 April to 2011 February was used as the training set,1332 pairs from 2011 March were used as the validation set and 1034 pairs from 2011 April were used as the test *** data preprocessing,we randomly cropped 128×128 sub-images as the LR cases from the full-disk MDI magnetograms,and the corresponding 512×512 sub-images as HR ones from the HMI full-disk magnetograms for model *** tests conducted have shown that the study successfully produced reliable 4×SR reconstruction of full-disk MDI *** MESR model's results(0.911)were highly correlated with the target HMI magnetographs as indicated by the correlation coefficient ***,the method achieved the best PSNR,SSIM,MAE and RMSE values,indicating that the MESR model can effectively reconstruct magnetograms.

关键词： magnetic fields techniques:image processing virtual observatory tools Sun:magnetic fields