[目的/意义]随着技术的发展和环境的变化,用户对于移动社交媒体的依赖程度和使用频率激增,错失焦虑症(fear of missing out,FoMO)已经从原先的个体现象演化为一种广泛存在的社会症候群。对这一新兴群体现象进行系统梳理及分析,以更好地...[目的/意义]随着技术的发展和环境的变化,用户对于移动社交媒体的依赖程度和使用频率激增,错失焦虑症(fear of missing out,FoMO)已经从原先的个体现象演化为一种广泛存在的社会症候群。对这一新兴群体现象进行系统梳理及分析,以更好地理解移动社交媒体环境下用户信息行为及其背后的认知模式和心理因素。[方法/过程]以国外现有相关研究文献为基础,结合沉浸体验、网络/手机上瘾等相关研究成果,从移动社交媒体环境下FoMO的概念阐述、测量量表以及影响因素3方面进行研究回顾,最后从理论方法和实践操作的角度对移动社交媒体环境下FoMO的未来研究方向进行展望。[结果/结论]以往的FoMO研究对于概念辨析、情境界定以及多方法的融合方面具有较大的局限性,且针对我国移动互联网和移动社交媒体开展的FoMO理论和实证研究几乎为空白。因此,这一命题对于我国信息管理研究者,尤其是用户信息行为研究方向有很大的探索空间。展开更多
Gangue is inevitably mixed with coal during mining and transportation.Currently,the manual sorting and conventional mechanical separation technologies widely adopted in the coal mining industry are plagued by low effi...Gangue is inevitably mixed with coal during mining and transportation.Currently,the manual sorting and conventional mechanical separation technologies widely adopted in the coal mining industry are plagued by low efficiency,poor identification accuracy,severe environmental pollution,and other drawbacks.This paper proposes a machine vision-based intelligent coal gangue sorting robot system.Firstly,the OpenMV captures images of coal gangue and utilizes the MobileNetV20.35 lightweight convolutional neural network to train the FOMO(Faster Objects,More Objects)target detection model in the cloud.This enables prediction and recognition of gangue,along with the acquisition of its center point pixel coordinates.Secondly,the position information of the gangue is sent to the STM32 microcontroller using the serial communication protocol for coordinate system conversion,pose algorithm,and path planning.Finally,the STM32 microcontroller controls the start and stop of the conveyor belt through the working status of the relay.When the relay is absorbed,the conveyor belt stops,and at the same time,the robotic arm grasps the gangue for transfer action,thus realizing the sorting of coal and gangue.The experimental results demonstrate that the cloud-trained FOMO neural network model achieves an F1 score of 95.5%and a recall of 91.3%,with a test accuracy of 97.56%.The quantified model deployed on OpenMV can accurately identify multiple gangues and output their position information.The success rate of the robotic arm in tracking and sorting gangue reaches 90.13%,and the positioning error of the robotic arm is[9,12.5]mm.This system realizes high-precision identification,positioning,and intelligent sorting of coal and gangue,meeting the basic requirements for gangue sorting in coal mines.展开更多
文摘[目的/意义]随着技术的发展和环境的变化,用户对于移动社交媒体的依赖程度和使用频率激增,错失焦虑症(fear of missing out,FoMO)已经从原先的个体现象演化为一种广泛存在的社会症候群。对这一新兴群体现象进行系统梳理及分析,以更好地理解移动社交媒体环境下用户信息行为及其背后的认知模式和心理因素。[方法/过程]以国外现有相关研究文献为基础,结合沉浸体验、网络/手机上瘾等相关研究成果,从移动社交媒体环境下FoMO的概念阐述、测量量表以及影响因素3方面进行研究回顾,最后从理论方法和实践操作的角度对移动社交媒体环境下FoMO的未来研究方向进行展望。[结果/结论]以往的FoMO研究对于概念辨析、情境界定以及多方法的融合方面具有较大的局限性,且针对我国移动互联网和移动社交媒体开展的FoMO理论和实证研究几乎为空白。因此,这一命题对于我国信息管理研究者,尤其是用户信息行为研究方向有很大的探索空间。
基金Supported by the National Natural Science Foundation of China(52074273)Natural Science Research Project of Universities in Anhui Province(2023AH050343)+4 种基金Anhui Innovative Team for Pollutant Sensitive Monitoring and Application(2023AH010043)Anhui Province Graduate Education Quality Project(2024jyjxggyjY204)Innovation and Entrepreneurship Training Programme for College Students in Anhui Province(S202410373037)Huaibei Normal University’s Postgraduate Education Quality Project(2024jgxm003)Open Project Funded by Anhui Province Key Laboratoryof Intelligent Computing and Applications(AFZNJS2025KF08)。
文摘Gangue is inevitably mixed with coal during mining and transportation.Currently,the manual sorting and conventional mechanical separation technologies widely adopted in the coal mining industry are plagued by low efficiency,poor identification accuracy,severe environmental pollution,and other drawbacks.This paper proposes a machine vision-based intelligent coal gangue sorting robot system.Firstly,the OpenMV captures images of coal gangue and utilizes the MobileNetV20.35 lightweight convolutional neural network to train the FOMO(Faster Objects,More Objects)target detection model in the cloud.This enables prediction and recognition of gangue,along with the acquisition of its center point pixel coordinates.Secondly,the position information of the gangue is sent to the STM32 microcontroller using the serial communication protocol for coordinate system conversion,pose algorithm,and path planning.Finally,the STM32 microcontroller controls the start and stop of the conveyor belt through the working status of the relay.When the relay is absorbed,the conveyor belt stops,and at the same time,the robotic arm grasps the gangue for transfer action,thus realizing the sorting of coal and gangue.The experimental results demonstrate that the cloud-trained FOMO neural network model achieves an F1 score of 95.5%and a recall of 91.3%,with a test accuracy of 97.56%.The quantified model deployed on OpenMV can accurately identify multiple gangues and output their position information.The success rate of the robotic arm in tracking and sorting gangue reaches 90.13%,and the positioning error of the robotic arm is[9,12.5]mm.This system realizes high-precision identification,positioning,and intelligent sorting of coal and gangue,meeting the basic requirements for gangue sorting in coal mines.
