As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and...As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.展开更多
Astronomical spectroscopy is crucial for exploring the physical properties,chemical composition,and kinematic behavior of celestial objects.With continuous advancements in observational technology,astronomical spectro...Astronomical spectroscopy is crucial for exploring the physical properties,chemical composition,and kinematic behavior of celestial objects.With continuous advancements in observational technology,astronomical spectroscopy faces the dual challenges of rapidly expanding data volumes and relatively lagging data processing capabilities.In this context,the rise of artificial intelligence technologies offers an innovative solution to address these challenges.This paper analyzes the latest developments in the application of machine learning for astronomical spectral data mining and discusses future research directions in AI-based spectral studies.However,the application of machine learning technologies presents several challenges.The high complexity of models often comes with insufficient interpretability,complicating scientific understanding.Moreover,the large-scale computational demands place higher requirements on hardware resources,leading to a significant increase in computational costs.AI-based astronomical spectroscopy research should advance in the following key directions.First,develop efficient data augmentation techniques to enhance model generalization capabilities.Second,explore more interpretable model designs to ensure the reliability and transparency of scientific conclusions.Third,optimize computational efficiency and reduce the threshold for deep-learning applications through collaborative innovations in algorithms and hardware.Furthermore,promoting the integration of cross-band data processing is essential to achieve seamless integration and comprehensive analysis of multi-source data,providing richer,multidimensional information to uncover the mysteries of the universe.展开更多
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 ...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.展开更多
Due to the speed difference and the complex interaction between merging and throughlane vehicles at freeway merging sections,crashes involving both human drivers and automated vehicles(AVs)persist.To assist AVs to pre...Due to the speed difference and the complex interaction between merging and throughlane vehicles at freeway merging sections,crashes involving both human drivers and automated vehicles(AVs)persist.To assist AVs to predict the intentions of surrounding vehicles for further dynamic motion planning,researchers have focused on developing trajectory prediction algorithms.Few studies,however,have developed merging trajectory prediction models using naturalistic driving data in China,making it urgent to put it on the agenda for AVs’safety and efficiency at freeway merging sections.Based on the merging periods extracted from the Shanghai Naturalistic Driving Study(SH-NDS),this study compares merging behavior on freeways with through-lane speed limits of 80 km/h,100 km/h,and 120 km/h using analysis of variance(ANOVA).Merging trajectory prediction algorithms for these three speed limit cases are trained and tested using backpropagation neural network(BPNN)and long short-term memory neural network(LSTMNN)approaches.Results show that:1)there are significant differences among the three cases in all merging behavior variables except for longitudinal gap,and 2)the BPNN algorithm for merging trajectory prediction demonstrates superior performance compared to the LSTMNN.Two major contributions to the safe operation of AVs are provided:1)the developed algorithms can be integrated into AV systems to accurately predict real-time desired trajectories of nearby merging vehicles in uncongested traffic conditions,and assist ongoing motion planning strategies for AVs;2)the algorithms can be incorporated in simulation tests for AV safety evaluation involving freeway merging sections.展开更多
基金supported by the National Key R&D Program of China(2021YFC2203502 and 2022YFF0711502)the National Natural Science Foundation of China(NSFC)(12173077)+4 种基金the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095 and 2023TSYCCX0112)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(PTYQ2022YZZD01)China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360)。
文摘As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.
基金supported by the National Key R&D Program of China(2021YFC2203502 and 2022YFF0711502)the National Natural Science Foundation of China(NSFC)(12173077)+4 种基金the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095 and 2023TSYCCX0112)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(PTYQ2022YZZD01)China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of SciencesNatural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360).
文摘Astronomical spectroscopy is crucial for exploring the physical properties,chemical composition,and kinematic behavior of celestial objects.With continuous advancements in observational technology,astronomical spectroscopy faces the dual challenges of rapidly expanding data volumes and relatively lagging data processing capabilities.In this context,the rise of artificial intelligence technologies offers an innovative solution to address these challenges.This paper analyzes the latest developments in the application of machine learning for astronomical spectral data mining and discusses future research directions in AI-based spectral studies.However,the application of machine learning technologies presents several challenges.The high complexity of models often comes with insufficient interpretability,complicating scientific understanding.Moreover,the large-scale computational demands place higher requirements on hardware resources,leading to a significant increase in computational costs.AI-based astronomical spectroscopy research should advance in the following key directions.First,develop efficient data augmentation techniques to enhance model generalization capabilities.Second,explore more interpretable model designs to ensure the reliability and transparency of scientific conclusions.Third,optimize computational efficiency and reduce the threshold for deep-learning applications through collaborative innovations in algorithms and hardware.Furthermore,promoting the integration of cross-band data processing is essential to achieve seamless integration and comprehensive analysis of multi-source data,providing richer,multidimensional information to uncover the mysteries of the universe.
基金supported by National Key R&D Program of China No.2021YFC2203502the National Natural Science Foundation of China (NSFC)(11803080,12173077,11873082,12003062)+2 种基金the Tianshan Innovation Team Plan of Xinjiang Uygur Autonomous Region (2022D14020)the Youth Innovation Promotion Association CASNational Key R&D Program of China No.2018 YFA0404704。
文摘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.
基金sponsored by the National Natural Science Foundation of China(No.51878498)the Belt and Road Cooperation Program under the 2023 Shanghai Action Plan for Science,Technology and Innovation(No.23210750500).
文摘Due to the speed difference and the complex interaction between merging and throughlane vehicles at freeway merging sections,crashes involving both human drivers and automated vehicles(AVs)persist.To assist AVs to predict the intentions of surrounding vehicles for further dynamic motion planning,researchers have focused on developing trajectory prediction algorithms.Few studies,however,have developed merging trajectory prediction models using naturalistic driving data in China,making it urgent to put it on the agenda for AVs’safety and efficiency at freeway merging sections.Based on the merging periods extracted from the Shanghai Naturalistic Driving Study(SH-NDS),this study compares merging behavior on freeways with through-lane speed limits of 80 km/h,100 km/h,and 120 km/h using analysis of variance(ANOVA).Merging trajectory prediction algorithms for these three speed limit cases are trained and tested using backpropagation neural network(BPNN)and long short-term memory neural network(LSTMNN)approaches.Results show that:1)there are significant differences among the three cases in all merging behavior variables except for longitudinal gap,and 2)the BPNN algorithm for merging trajectory prediction demonstrates superior performance compared to the LSTMNN.Two major contributions to the safe operation of AVs are provided:1)the developed algorithms can be integrated into AV systems to accurately predict real-time desired trajectories of nearby merging vehicles in uncongested traffic conditions,and assist ongoing motion planning strategies for AVs;2)the algorithms can be incorporated in simulation tests for AV safety evaluation involving freeway merging sections.
