The monitoring and warning of urban rail transit is the core of operation management, and the breadth and depth of the monitoring range directly affect the quality of urban rail transit operation. For the current dome...The monitoring and warning of urban rail transit is the core of operation management, and the breadth and depth of the monitoring range directly affect the quality of urban rail transit operation. For the current domestic monitoring system, most of the critical equipments and technologies are introduced from abroad;it is diseconomy, and also causes hidden danger. Realizing the localization of monitoring and early warning system is imperative. Based on the analysis of the present situation of urban rail transit operation safety at home and abroad, the paper proposes to use integrated technology to design basic framework of monitoring and warning system of urban rail train, and puts forward the critical technologies to realize the system. Compared with the existing monitoring system, the integrated monitoring system has the characteristics of wide monitoring range, clear division of labor, centralized management, coordination and integration operation and intelligent management, and embodies the concept of people-oriented. It has scientific significance for future construction of domestic Integrated Monitoring and Early Warning System (IMEWS) of urban rail transit.展开更多
Forested environments feature a highly complex radiation regime,and solar radiation is hindered from penetrating into the forest by the 3D canopy structure;hence,canopy shortwave radiation varies spatiotemporally,seas...Forested environments feature a highly complex radiation regime,and solar radiation is hindered from penetrating into the forest by the 3D canopy structure;hence,canopy shortwave radiation varies spatiotemporally,seasonally,and meteorologically,making the radiant flux challenging to both measure and model.展开更多
Plant phenomics,the comprehensive study of plant phenotypes,has gained prominence as a vital tool for un-derstanding the intricate relationships between genotypes and the environment.Image-based plant phenomics has pr...Plant phenomics,the comprehensive study of plant phenotypes,has gained prominence as a vital tool for un-derstanding the intricate relationships between genotypes and the environment.Image-based plant phenomics has progressed rapidly,and three-dimensional(3D)phenotyping is a valuable extension of traditional 2D phe-nomics.However,the increased data dimensionality poses challenges to feature extraction and phenotyping.In recent decades,deep learning has led to remarkable progress in revolutionizing 3D phenotyping.Therefore,this review highlights the importance of using deep learning in 3D plant phenomics.It systematically overviews the capabilities of deep learning for 3D computer vision,covering 3D representation,classification,detection and tracking,semantic segmentation,instance segmentation,and generation.Additionally,deep learning techniques for 3D point preprocessing(e.g.,annotation,downsampling,and dataset organization)and various plant phe-notyping tasks are discussed.Finally,the challenges and perspectives associated with deep learning in 3D plant phenomics are summarized,including(1)benchmark dataset construction by using synthetic datasets and methods such as generative artificial intelligence and unsupervised or weakly supervised learning;(2)accurate and efficient 3D point cloud analysis by leveraging multitask learning,lightweight models,and self-supervised learning;and(3)deep learning for 3D plant phenomics by exploring interpretability,extensibility,and multi-modal data utilization.The exploration of deep learning in 3D plant phenomics is poised to spur breakthroughs in a new dimension of plant science.展开更多
文摘The monitoring and warning of urban rail transit is the core of operation management, and the breadth and depth of the monitoring range directly affect the quality of urban rail transit operation. For the current domestic monitoring system, most of the critical equipments and technologies are introduced from abroad;it is diseconomy, and also causes hidden danger. Realizing the localization of monitoring and early warning system is imperative. Based on the analysis of the present situation of urban rail transit operation safety at home and abroad, the paper proposes to use integrated technology to design basic framework of monitoring and warning system of urban rail train, and puts forward the critical technologies to realize the system. Compared with the existing monitoring system, the integrated monitoring system has the characteristics of wide monitoring range, clear division of labor, centralized management, coordination and integration operation and intelligent management, and embodies the concept of people-oriented. It has scientific significance for future construction of domestic Integrated Monitoring and Early Warning System (IMEWS) of urban rail transit.
基金This research was financially supported by the National Key Research and Development Program of China(2020YFD1000600)This research was also funded by the National Natural Science Foundation of China(grant numbers 31770591,32071681,and 42071418)Open-ing Project Fund of Key Laboratory of Biology and Genetic Resources of Rubber Tree,Ministry of Agriculture and Rural Affairs of the People Republic of China/State Key Laboratory Breeding Base of Cultivation&Physiology for Tropical Crops/Danzhou Investigation&Experiment Station of Tropical Crops,Ministry of Agriculture and Rural Affairs of the People’s Republic of China(No.RRI-KLOF202202).
文摘Forested environments feature a highly complex radiation regime,and solar radiation is hindered from penetrating into the forest by the 3D canopy structure;hence,canopy shortwave radiation varies spatiotemporally,seasonally,and meteorologically,making the radiant flux challenging to both measure and model.
基金supported in part by the STI 2030-Major Projects(2023ZD0405605)the Fundamental Research Funds for the Central Universities(KYT2024005,QTPY2025006)+6 种基金the National Natural Science Foundation of China(32201656)the Jiangsu Province Key Research and Development Program(BE2023369)the Natural Science Foundation of Jiangsu Province(BK20231469)the JBGS Project of Seed Industry Revitalization in Jiangsu Province(JBGS[2021]007)2025 AI for Graduate Education Program(2025AIYJSZX-YB031)the Jiangs Innovation Support Program for International Science and Technology Cooperation Project(BZ2023049)the Young Elite Scientists Sponsorship Program by CAST(YESS).
文摘Plant phenomics,the comprehensive study of plant phenotypes,has gained prominence as a vital tool for un-derstanding the intricate relationships between genotypes and the environment.Image-based plant phenomics has progressed rapidly,and three-dimensional(3D)phenotyping is a valuable extension of traditional 2D phe-nomics.However,the increased data dimensionality poses challenges to feature extraction and phenotyping.In recent decades,deep learning has led to remarkable progress in revolutionizing 3D phenotyping.Therefore,this review highlights the importance of using deep learning in 3D plant phenomics.It systematically overviews the capabilities of deep learning for 3D computer vision,covering 3D representation,classification,detection and tracking,semantic segmentation,instance segmentation,and generation.Additionally,deep learning techniques for 3D point preprocessing(e.g.,annotation,downsampling,and dataset organization)and various plant phe-notyping tasks are discussed.Finally,the challenges and perspectives associated with deep learning in 3D plant phenomics are summarized,including(1)benchmark dataset construction by using synthetic datasets and methods such as generative artificial intelligence and unsupervised or weakly supervised learning;(2)accurate and efficient 3D point cloud analysis by leveraging multitask learning,lightweight models,and self-supervised learning;and(3)deep learning for 3D plant phenomics by exploring interpretability,extensibility,and multi-modal data utilization.The exploration of deep learning in 3D plant phenomics is poised to spur breakthroughs in a new dimension of plant science.
