Intelligent perception means that with the assistance of artificial intelligence(AI)-motivated brain,flexible sensors achieve the ability of memory,learning,judgment,and reasoning about external information like the h...Intelligent perception means that with the assistance of artificial intelligence(AI)-motivated brain,flexible sensors achieve the ability of memory,learning,judgment,and reasoning about external information like the human brain.Due to the superiority of machine learning(ML)algorithms in data processing and intelligent recognition,intelligent perception systems possess the ability to match or even surpass human perception systems.However,the built-in flexible sensors in these systems need to work on dynamic and irregular surfaces,inevitably affecting the precision and fidelity of the acquired data.In recent years,the strategy of introducing the developed functional materials and innovative structures into flexible sensors has made some progress toward the above challenges,and with the blessing of ML algorithms,accurate perception and reasoning in various scenarios have been achieved.Here,the most representative functional materials and innovative structures for constructing flexible sensors are comprehensively reviewed,the research progress of intelligent perception systems based on flexible sensors and ML algorithms is further summarized,and the intersection of the two is expected to unlock new opportunities for next-stage AI development.展开更多
Safety is essential when building a strong transportation system.As a key development direction in the global railway system,the intelligent railway has safety at its core,making safety a top priority while pursuing t...Safety is essential when building a strong transportation system.As a key development direction in the global railway system,the intelligent railway has safety at its core,making safety a top priority while pursuing the goals of efficiency,convenience,economy,and environmental friendliness.This paper describes the state of the art and proposes a system architecture for intelligent railway systems.It also focuses on the development of railway safety technology at home and abroad,and proposes the active safety method and technology system based on advanced theoretical methods such as the in-depth integration of cyber–physical systems(CPS),data-driven models,and intelligent computing.Finally,several typical applications are demonstrated to verify the advancement and feasibility of active safety technology in intelligent railway systems.展开更多
An increase in car ownership brings convenience to people’s life.However,it also leads to frequent traffic accidents.Precisely forecasting surrounding agents’future trajectories could effectively decrease vehicle-ve...An increase in car ownership brings convenience to people’s life.However,it also leads to frequent traffic accidents.Precisely forecasting surrounding agents’future trajectories could effectively decrease vehicle-vehicle and vehicle-pedestrian collisions.Long-short-term memory(LSTM)network is often used for vehicle trajectory prediction,but it has some shortages such as gradient explosion and low efficiency.A trajectory prediction method based on an improved Transformer network is proposed to forecast agents’future trajectories in a complex traffic environment.It realizes the transformation from sequential step processing of LSTM to parallel processing of Transformer based on attentionmechanism.To performtrajectory predictionmore efficiently,a probabilistic sparse self-attention mechanism is introduced to reduce attention complexity by reducing the number of queried values in the attention mechanism.Activate or not(ACON)activation function is adopted to select whether to activate or not,hence improving model flexibility.The proposed method is evaluated on the publicly available benchmarks nextgeneration simulation(NGSIM)and ETH/UCY.The experimental results indicate that the proposed method can accurately and efficiently predict agents’trajectories.展开更多
Perception is the interaction interface between an intelligent system and the real world. Without sophisticated and flexible perceptual capabilities, it is impossible to create advanced artificial intelligence （AI） ...Perception is the interaction interface between an intelligent system and the real world. Without sophisticated and flexible perceptual capabilities, it is impossible to create advanced artificial intelligence （AI） systems. For the next-generation AI, called ＇AI 2.0＇, one of the most significant features will be that AI is empowered with intelligent perceptual capabilities, which can simulate human brain＇s mechanisms and are likely to surpass human brain in terms of performance. In this paper, we briefly review the state-of-the-art advances across different areas of perception, including visual perception, auditory perception, speech perception, and perceptual information processing and learning engines. On this basis, we envision several R＆D trends in intelligent perception for the forthcoming era of AI 2.0, including： （1） human-like and transhuman active vision; （2） auditory perception and computation in an actual auditory setting; （3） speech perception and computation in a natural interaction setting; （4） autonomous learning of perceptual information; （5） large-scale perceptual information processing and learning platforms; and （6） urban omnidirectional intelligent perception and reasoning engines. We believe these research directions should be highlighted in the future plans for AI 2.0.展开更多
基金Basic Science Research Program through the National Research Foundation of Korea(NRF),Grant/Award Numbers:2018R1D1A1A09083353,2018R1A6A1A03025242Korea Ministry of Environment(MOE)Graduate School specialized in Integrated Pollution Prevention and Control ProjectResearch Grant of Kwangwoon University in 2022。
文摘Intelligent perception means that with the assistance of artificial intelligence(AI)-motivated brain,flexible sensors achieve the ability of memory,learning,judgment,and reasoning about external information like the human brain.Due to the superiority of machine learning(ML)algorithms in data processing and intelligent recognition,intelligent perception systems possess the ability to match or even surpass human perception systems.However,the built-in flexible sensors in these systems need to work on dynamic and irregular surfaces,inevitably affecting the precision and fidelity of the acquired data.In recent years,the strategy of introducing the developed functional materials and innovative structures into flexible sensors has made some progress toward the above challenges,and with the blessing of ML algorithms,accurate perception and reasoning in various scenarios have been achieved.Here,the most representative functional materials and innovative structures for constructing flexible sensors are comprehensively reviewed,the research progress of intelligent perception systems based on flexible sensors and ML algorithms is further summarized,and the intersection of the two is expected to unlock new opportunities for next-stage AI development.
基金supported by the 2021 Chinese Academy of Engineering(CAE)International Top-level Forum on Engineering Science and Technology,“Safety and Governance of the High-Speed Railway”。
文摘Safety is essential when building a strong transportation system.As a key development direction in the global railway system,the intelligent railway has safety at its core,making safety a top priority while pursuing the goals of efficiency,convenience,economy,and environmental friendliness.This paper describes the state of the art and proposes a system architecture for intelligent railway systems.It also focuses on the development of railway safety technology at home and abroad,and proposes the active safety method and technology system based on advanced theoretical methods such as the in-depth integration of cyber–physical systems(CPS),data-driven models,and intelligent computing.Finally,several typical applications are demonstrated to verify the advancement and feasibility of active safety technology in intelligent railway systems.
基金the SuzhouKey industrial technology innovation project SYG202031the Future Network Scientific Research Fund Project,FNSRFP-2021-YB-29.
文摘An increase in car ownership brings convenience to people’s life.However,it also leads to frequent traffic accidents.Precisely forecasting surrounding agents’future trajectories could effectively decrease vehicle-vehicle and vehicle-pedestrian collisions.Long-short-term memory(LSTM)network is often used for vehicle trajectory prediction,but it has some shortages such as gradient explosion and low efficiency.A trajectory prediction method based on an improved Transformer network is proposed to forecast agents’future trajectories in a complex traffic environment.It realizes the transformation from sequential step processing of LSTM to parallel processing of Transformer based on attentionmechanism.To performtrajectory predictionmore efficiently,a probabilistic sparse self-attention mechanism is introduced to reduce attention complexity by reducing the number of queried values in the attention mechanism.Activate or not(ACON)activation function is adopted to select whether to activate or not,hence improving model flexibility.The proposed method is evaluated on the publicly available benchmarks nextgeneration simulation(NGSIM)and ETH/UCY.The experimental results indicate that the proposed method can accurately and efficiently predict agents’trajectories.
基金supported by the Strategic Consulting Research Project of Chinese Academy of Engineering(No.2016-ZD-04-03)
文摘Perception is the interaction interface between an intelligent system and the real world. Without sophisticated and flexible perceptual capabilities, it is impossible to create advanced artificial intelligence （AI） systems. For the next-generation AI, called ＇AI 2.0＇, one of the most significant features will be that AI is empowered with intelligent perceptual capabilities, which can simulate human brain＇s mechanisms and are likely to surpass human brain in terms of performance. In this paper, we briefly review the state-of-the-art advances across different areas of perception, including visual perception, auditory perception, speech perception, and perceptual information processing and learning engines. On this basis, we envision several R＆D trends in intelligent perception for the forthcoming era of AI 2.0, including： （1） human-like and transhuman active vision; （2） auditory perception and computation in an actual auditory setting; （3） speech perception and computation in a natural interaction setting; （4） autonomous learning of perceptual information; （5） large-scale perceptual information processing and learning platforms; and （6） urban omnidirectional intelligent perception and reasoning engines. We believe these research directions should be highlighted in the future plans for AI 2.0.
基金supported by the National Key Research and Development Program of China(2021YFA1401100)the National Natural Science Foundation of China(61974014)the Innovation Group Project of Sichuan Province(20CXTD0090).
