Heavy-haul locomotives operate in complex and dynamic environments.Drivers are required to frequently monitor numerous displays within the cab and track conditions,and switching the line of sight will result in drivin...Heavy-haul locomotives operate in complex and dynamic environments.Drivers are required to frequently monitor numerous displays within the cab and track conditions,and switching the line of sight will result in driving blind for a certain distance.This can easily lead to visual fatigue and safety incidents over extended periods.In this paper,we propose a dual-focal-plane augmented reality head-up display(AR-HUD)system adapted for heavy-haul locomotives,integrating dual picture generation units(PGUs)with two freeform surfaces to achieve the display of near-field and far-field.Due to the large inclination angle of the windshield in heavy-haul locomotives,there are significant aberrations of the optical system and the virtual image quality of the far-field needs to be improved.To address this issue,we introduce two freeform surfaces into the optical path of the far-field to reduce aberration.The increased structural degrees of freedom facilitate subsequent optimization.Following optimization of the system,the maximum root mean square(RMS)radius within the eyebox regions E1 to E5 was smaller than the Airy disk radius,and the modulation transfer function(MTF)value at the cutoff frequency exceeded 0.3.Grid distortion was less than 5%,and at the cutoff frequency of 4.31 line pairs per millimeter(lp/mm),over 98%of the MTF was greater than 0.3.The image quality and overall imaging performance were excellent,with reasonable tolerance distribution,demonstrating the feasibility of this design.This configuration allows for the simultaneous display of both far-field and near-field images,enhancing its applicability in rail transport.The feasibility of this innovative AR-HUD system has been validated through user interface(UI)simulation.展开更多
Considering the rapid advancements in AI technologies such as reinforcement learning,ChatGPT,and deep learning,this paper conducts a comprehensive survey of the tech-nological landscape of AI in the energy and agricul...Considering the rapid advancements in AI technologies such as reinforcement learning,ChatGPT,and deep learning,this paper conducts a comprehensive survey of the tech-nological landscape of AI in the energy and agriculture sectors.It delineates the evolu-tionary path of AI technologies in smart grids and precision agriculture,highlighting significant advancements in energy prediction,optimisation of production and con-sumption,and intelligent management.Furthermore,the paper identifies key AI tech-nologies crucial for the Agricultural Energy Internet(AEI),offering specialised exploration into AI applications for crop cultivation and fisheries,including disease detection,yield prediction,and resource management.The research provides essential theoretical foundations for AI integration in each of these agricultural domains.In addition,the paper envisions the future integration of ChatGPT in coupled modelling of agriculture and energy systems,enhancing synergistic intelligent control,and AI-driven carbon tracking technologies within the AEI.This study facilitates a greater grasp of the transformative potential of AI in reshaping the nexus of agriculture and energy.展开更多
The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability a...The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability and sustainable development.However,the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low efficiency.The recognition method based on pattern recognition and deep learning can automatically fit image features,and use features to classify and predict images.This study introduced the improved Vision Transformer(ViT)method for crop pest image recognition.Among them,the region with the most obvious features can be effectively selected by block partition.The self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion area.In the experiment,data with 7 classes of examples are used for verification.It can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology,accurately judge the crop diseases and pests category,provide method reference for agricultural diseases and pests identification research,and further optimize the crop diseases and pests control work for agricultural workers in need.展开更多
As the new generation of artificial intelligence(AI)continues to evolve,weather big data and statistical machine learning(SML)technologies complement each other and are deeply integrated to significantly improve the p...As the new generation of artificial intelligence(AI)continues to evolve,weather big data and statistical machine learning(SML)technologies complement each other and are deeply integrated to significantly improve the processing and forecasting accuracy of fishery weather.Accurate fishery weather services play a crucial role in fishery production,serving as a great safeguard for economic benefits and personal safety,enabling fishermen to carry out fishery production better,and contributing to the sustainable development of the fishery industry.The objective of this paper is to offer an understanding of the present state of research and development in SML technology for simulating and forecasting fishery weather.Specifically,we analyze the current state of research and technical features of SML in weather and summarize the applications of SML in simulation and forecasting of fishery weather,which mainly include three aspects:fishery weather scenario generation,fishery weather forecasting,and fishery extreme weather warning.We also illustrate the main technical means and principles of SML technology.Finally,we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.展开更多
基金supported by the Key R&D Plan Projects in Zhejiang Province(No.2023C01243)China and the National Natural Science Foundation of China(Nos.52472422,52293424,and 52477065).
文摘Heavy-haul locomotives operate in complex and dynamic environments.Drivers are required to frequently monitor numerous displays within the cab and track conditions,and switching the line of sight will result in driving blind for a certain distance.This can easily lead to visual fatigue and safety incidents over extended periods.In this paper,we propose a dual-focal-plane augmented reality head-up display(AR-HUD)system adapted for heavy-haul locomotives,integrating dual picture generation units(PGUs)with two freeform surfaces to achieve the display of near-field and far-field.Due to the large inclination angle of the windshield in heavy-haul locomotives,there are significant aberrations of the optical system and the virtual image quality of the far-field needs to be improved.To address this issue,we introduce two freeform surfaces into the optical path of the far-field to reduce aberration.The increased structural degrees of freedom facilitate subsequent optimization.Following optimization of the system,the maximum root mean square(RMS)radius within the eyebox regions E1 to E5 was smaller than the Airy disk radius,and the modulation transfer function(MTF)value at the cutoff frequency exceeded 0.3.Grid distortion was less than 5%,and at the cutoff frequency of 4.31 line pairs per millimeter(lp/mm),over 98%of the MTF was greater than 0.3.The image quality and overall imaging performance were excellent,with reasonable tolerance distribution,demonstrating the feasibility of this design.This configuration allows for the simultaneous display of both far-field and near-field images,enhancing its applicability in rail transport.The feasibility of this innovative AR-HUD system has been validated through user interface(UI)simulation.
文摘Considering the rapid advancements in AI technologies such as reinforcement learning,ChatGPT,and deep learning,this paper conducts a comprehensive survey of the tech-nological landscape of AI in the energy and agriculture sectors.It delineates the evolu-tionary path of AI technologies in smart grids and precision agriculture,highlighting significant advancements in energy prediction,optimisation of production and con-sumption,and intelligent management.Furthermore,the paper identifies key AI tech-nologies crucial for the Agricultural Energy Internet(AEI),offering specialised exploration into AI applications for crop cultivation and fisheries,including disease detection,yield prediction,and resource management.The research provides essential theoretical foundations for AI integration in each of these agricultural domains.In addition,the paper envisions the future integration of ChatGPT in coupled modelling of agriculture and energy systems,enhancing synergistic intelligent control,and AI-driven carbon tracking technologies within the AEI.This study facilitates a greater grasp of the transformative potential of AI in reshaping the nexus of agriculture and energy.
基金the National Natural Science Foundation of China under Grant 52007193 and The 2115 Talent Development Program of China Agricultural University.
文摘The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability and sustainable development.However,the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low efficiency.The recognition method based on pattern recognition and deep learning can automatically fit image features,and use features to classify and predict images.This study introduced the improved Vision Transformer(ViT)method for crop pest image recognition.Among them,the region with the most obvious features can be effectively selected by block partition.The self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion area.In the experiment,data with 7 classes of examples are used for verification.It can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology,accurately judge the crop diseases and pests category,provide method reference for agricultural diseases and pests identification research,and further optimize the crop diseases and pests control work for agricultural workers in need.
基金the National Natural Science Foundation of China under Grant 52007193 and The 2115 Talent Development Program of China Agricultural University.
文摘As the new generation of artificial intelligence(AI)continues to evolve,weather big data and statistical machine learning(SML)technologies complement each other and are deeply integrated to significantly improve the processing and forecasting accuracy of fishery weather.Accurate fishery weather services play a crucial role in fishery production,serving as a great safeguard for economic benefits and personal safety,enabling fishermen to carry out fishery production better,and contributing to the sustainable development of the fishery industry.The objective of this paper is to offer an understanding of the present state of research and development in SML technology for simulating and forecasting fishery weather.Specifically,we analyze the current state of research and technical features of SML in weather and summarize the applications of SML in simulation and forecasting of fishery weather,which mainly include three aspects:fishery weather scenario generation,fishery weather forecasting,and fishery extreme weather warning.We also illustrate the main technical means and principles of SML technology.Finally,we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.
