This study focused on the impact of intelligent learning companions(ILC)on Chinese EFL learners’oral English ability and technology perception.Using a quasi-experimental design,this study selected 51 EFL learners fro...This study focused on the impact of intelligent learning companions(ILC)on Chinese EFL learners’oral English ability and technology perception.Using a quasi-experimental design,this study selected 51 EFL learners from a university in southeast China and randomly divided them into an experimental group(EG,n=24)and a control group(CG,n=27).During the 16-week intervention period,the experimental group adopted the intelligent learning companion teaching strategy supported by artificial intelligence technology(Relying on the IFlytek Spark Platform),while the control group adopted the learning companion strategy guided by teachers.The experimental group not only had better oral English scores(p=0.001<0.05)but also showed a significant increase in technology perception(p=0.01<0.05).The research provides a new perspective for effectively integrating artificial intelligence technology in oral English teaching.Also,it offers strong empirical support for theoretical study and practical application in enhancing the oral expression ability of the second language,English.展开更多
The increase in precision agriculture has promoted the development of picking robot technology,and the visual recognition system at its core is crucial for improving the level of agricultural automation.This paper rev...The increase in precision agriculture has promoted the development of picking robot technology,and the visual recognition system at its core is crucial for improving the level of agricultural automation.This paper reviews the progress of visual recognition tech-nology for picking robots,including image capture technology,target detection algorithms,spatial positioning strategies and scene un-derstanding.This article begins with a description of the basic structure and function of the vision system of the picking robot and em-phasizes the importance of achieving high-efficiency and high-accuracy recognition in the natural agricultural environment.Sub-sequently,various image processing techniques and vision algorithms,including color image analysis,three-dimensional depth percep-tion,and automatic object recognition technology that integrates machine learning and deep learning algorithms,were analysed.At the same time,the paper also highlights the challenges of existing technologies in dynamic lighting,occlusion problems,fruit maturity di-versity,and real-time processing capabilities.This paper further discusses multisensor information fusion technology and discusses methods for combining visual recognition with a robot control system to improve the accuracy and working rate of picking.At the same time,this paper also introduces innovative research,such as the application of convolutional neural networks(CNNs)for accurate fruit detection and the development of event-based vision systems to improve the response speed of the system.At the end of this paper,the future development of visual recognition technology for picking robots is predicted,and new research trends are proposed,including the refinement of algorithms,hardware innovation,and the adaptability of technology to different agricultural conditions.The purpose of this paper is to provide a comprehensive analysis of visual recognition technology for researchers and practitioners in the field of agricul-tural robotics,including current achievements,existing challenges and future development prospects.展开更多
Targeting malignant driving incidents involving collisions with pedestrians,such as dangerous driving on New Year's Day in the United States and intentional pedestrian ramming on Christmas in Germany,which expose ...Targeting malignant driving incidents involving collisions with pedestrians,such as dangerous driving on New Year's Day in the United States and intentional pedestrian ramming on Christmas in Germany,which expose issues of sudden aggressiveness,strong destructiveness,and criminal premeditation,this paper proposes a prevention and control system based on multimodal perception technology.It elaborates on the research process,including design concepts,technical routes,and implementation strategies.The innovation lies in integrating the Keras deep learning framework and the ResNet-50 model-based micro-expression analysis algorithm to construct a highly intelligent prevention and control system.This system realizes realtime monitoring and analysis of drivers'emotions and behaviors,and can control vehicles safely in emergency situations.Simulation tests show that under Simulink,the system exhibits excellent performance and high efficiency.For intentional acceleration and pedestrian ramming scenarios,it triggers Automatic Emergency Braking(AEB)within 1.2 seconds,reducing the collision speed to 20 km/h and mitigating casualties.Compared with traditional technologies,its safety is significantly improved,providing a new solution for the field of road traffic safety.展开更多
文摘This study focused on the impact of intelligent learning companions(ILC)on Chinese EFL learners’oral English ability and technology perception.Using a quasi-experimental design,this study selected 51 EFL learners from a university in southeast China and randomly divided them into an experimental group(EG,n=24)and a control group(CG,n=27).During the 16-week intervention period,the experimental group adopted the intelligent learning companion teaching strategy supported by artificial intelligence technology(Relying on the IFlytek Spark Platform),while the control group adopted the learning companion strategy guided by teachers.The experimental group not only had better oral English scores(p=0.001<0.05)but also showed a significant increase in technology perception(p=0.01<0.05).The research provides a new perspective for effectively integrating artificial intelligence technology in oral English teaching.Also,it offers strong empirical support for theoretical study and practical application in enhancing the oral expression ability of the second language,English.
基金supported by the National Natural Science Foundation of China(Nos.62027810 and 61733004)the National Key Research and Development Program of China(No.2020YFB1712600)+1 种基金the Hunan Science and Technology Program of Hunan Province,China(Nos.2017XK2102 and 2018GK2022)supported by the Changsha Science and Technology Innovation Fund,China(No.kq2402079).
文摘The increase in precision agriculture has promoted the development of picking robot technology,and the visual recognition system at its core is crucial for improving the level of agricultural automation.This paper reviews the progress of visual recognition tech-nology for picking robots,including image capture technology,target detection algorithms,spatial positioning strategies and scene un-derstanding.This article begins with a description of the basic structure and function of the vision system of the picking robot and em-phasizes the importance of achieving high-efficiency and high-accuracy recognition in the natural agricultural environment.Sub-sequently,various image processing techniques and vision algorithms,including color image analysis,three-dimensional depth percep-tion,and automatic object recognition technology that integrates machine learning and deep learning algorithms,were analysed.At the same time,the paper also highlights the challenges of existing technologies in dynamic lighting,occlusion problems,fruit maturity di-versity,and real-time processing capabilities.This paper further discusses multisensor information fusion technology and discusses methods for combining visual recognition with a robot control system to improve the accuracy and working rate of picking.At the same time,this paper also introduces innovative research,such as the application of convolutional neural networks(CNNs)for accurate fruit detection and the development of event-based vision systems to improve the response speed of the system.At the end of this paper,the future development of visual recognition technology for picking robots is predicted,and new research trends are proposed,including the refinement of algorithms,hardware innovation,and the adaptability of technology to different agricultural conditions.The purpose of this paper is to provide a comprehensive analysis of visual recognition technology for researchers and practitioners in the field of agricul-tural robotics,including current achievements,existing challenges and future development prospects.
文摘Targeting malignant driving incidents involving collisions with pedestrians,such as dangerous driving on New Year's Day in the United States and intentional pedestrian ramming on Christmas in Germany,which expose issues of sudden aggressiveness,strong destructiveness,and criminal premeditation,this paper proposes a prevention and control system based on multimodal perception technology.It elaborates on the research process,including design concepts,technical routes,and implementation strategies.The innovation lies in integrating the Keras deep learning framework and the ResNet-50 model-based micro-expression analysis algorithm to construct a highly intelligent prevention and control system.This system realizes realtime monitoring and analysis of drivers'emotions and behaviors,and can control vehicles safely in emergency situations.Simulation tests show that under Simulink,the system exhibits excellent performance and high efficiency.For intentional acceleration and pedestrian ramming scenarios,it triggers Automatic Emergency Braking(AEB)within 1.2 seconds,reducing the collision speed to 20 km/h and mitigating casualties.Compared with traditional technologies,its safety is significantly improved,providing a new solution for the field of road traffic safety.
