Semantic Internet of Things is an open-world service ecosystem formed on the basis of the Semantic Web, Internet of Things, and social networks. It forms and integrates physical space, cyberspace,and social space. The...Semantic Internet of Things is an open-world service ecosystem formed on the basis of the Semantic Web, Internet of Things, and social networks. It forms and integrates physical space, cyberspace,and social space. The data friction problem is the key issue of the semantic Internet from theory to practice. In order to solve the problem of information interoperation and data friction,it is necessary to deal with the heterogeneity, dynamicity, and uncertainty of data in the semantic Internet of Things. For this reason, this paper constructs a semantic object networking context aware system based on dynamic Bayesian network. The overall architecture of context awareness system based on semantic Internet of Things is proposed. The hierarchical structure of the framework and the functions implemented by each layer are introduced in detail. The contextual awareness system based on semantic Internet of Things is introduced into the overall design and implementation. The design and implementation process of each layer structure of the system is described in detail.展开更多
针对复杂环境下含标签货物实时记录困难的问题,提出一种面向视觉物联网(visual Internet of Things,VIoT)的文本检测方法。在视觉物联网中设计并引入基于全局上下文注意力和坐标注意力的文本检测网络(text detection network based on g...针对复杂环境下含标签货物实时记录困难的问题,提出一种面向视觉物联网(visual Internet of Things,VIoT)的文本检测方法。在视觉物联网中设计并引入基于全局上下文注意力和坐标注意力的文本检测网络(text detection network based on global context attention and coordinate attention,GCANet),首先提出一种改进型坐标注意力模块,通过水平和垂直2个并行的一维池化操作,避免了因二维全局池化造成的位置信息丢失;然后引入全局上下文注意力模块,避免在复杂的背景对文本检测的影响,并防止密集或较远间隔的文本被错误地检测。该系统中提出的GCANet在公共数据集ICDAR2015、MSRA-TD500和Total-Text上的综合指标F值分别达到87.4%、86.9%和86.3%。在工业标签数据集Label-Text上平均准确率、平均召回率和平均F值分别达到93.4%、90.9%和92.1%。此外,GCANet在矿井下的标签数据集Mine-Text上准确率、召回率和F值分别达到94.4%、84.9%和89.9%。实验结果表明,本文提出的面向视觉物联网的文本检测方法效果优异。展开更多
与互联网不同,物联网(Internet of Things,IoT)通过各类通信技术将具有标识、感知或者执行能力的物理实体互联,形成了"物物互连"的虚拟网络。随着计算机及通信技术的迅速发展,计算资源将遍布人们周围的环境,情景感知技术应运...与互联网不同,物联网(Internet of Things,IoT)通过各类通信技术将具有标识、感知或者执行能力的物理实体互联,形成了"物物互连"的虚拟网络。随着计算机及通信技术的迅速发展,计算资源将遍布人们周围的环境,情景感知技术应运而生。情景感知获得传感器采集的情景信息以后,对信息进行智能处理,自主地为用户提供服务。物联网具有海量信息的特性,传统的情景信息处理方法已不再适用。对物联网情景感知技术进行了详细的介绍,首先给出情景和情景感知的概念及其研究发展和应用。然后,结合物联网特性,以情景感知流程为主线,探讨了信息获取、建模和智能处理等内容。最后,系统结构是情景感知的关键,因此对现有的系统结构进行了分析和对比,结合物联网环境论述了当前情景感知系统的不足之处,并给出了情景感知系统的参考结构。展开更多
文摘Semantic Internet of Things is an open-world service ecosystem formed on the basis of the Semantic Web, Internet of Things, and social networks. It forms and integrates physical space, cyberspace,and social space. The data friction problem is the key issue of the semantic Internet from theory to practice. In order to solve the problem of information interoperation and data friction,it is necessary to deal with the heterogeneity, dynamicity, and uncertainty of data in the semantic Internet of Things. For this reason, this paper constructs a semantic object networking context aware system based on dynamic Bayesian network. The overall architecture of context awareness system based on semantic Internet of Things is proposed. The hierarchical structure of the framework and the functions implemented by each layer are introduced in detail. The contextual awareness system based on semantic Internet of Things is introduced into the overall design and implementation. The design and implementation process of each layer structure of the system is described in detail.
文摘针对复杂环境下含标签货物实时记录困难的问题,提出一种面向视觉物联网(visual Internet of Things,VIoT)的文本检测方法。在视觉物联网中设计并引入基于全局上下文注意力和坐标注意力的文本检测网络(text detection network based on global context attention and coordinate attention,GCANet),首先提出一种改进型坐标注意力模块,通过水平和垂直2个并行的一维池化操作,避免了因二维全局池化造成的位置信息丢失;然后引入全局上下文注意力模块,避免在复杂的背景对文本检测的影响,并防止密集或较远间隔的文本被错误地检测。该系统中提出的GCANet在公共数据集ICDAR2015、MSRA-TD500和Total-Text上的综合指标F值分别达到87.4%、86.9%和86.3%。在工业标签数据集Label-Text上平均准确率、平均召回率和平均F值分别达到93.4%、90.9%和92.1%。此外,GCANet在矿井下的标签数据集Mine-Text上准确率、召回率和F值分别达到94.4%、84.9%和89.9%。实验结果表明,本文提出的面向视觉物联网的文本检测方法效果优异。
文摘与互联网不同,物联网(Internet of Things,IoT)通过各类通信技术将具有标识、感知或者执行能力的物理实体互联,形成了"物物互连"的虚拟网络。随着计算机及通信技术的迅速发展,计算资源将遍布人们周围的环境,情景感知技术应运而生。情景感知获得传感器采集的情景信息以后,对信息进行智能处理,自主地为用户提供服务。物联网具有海量信息的特性,传统的情景信息处理方法已不再适用。对物联网情景感知技术进行了详细的介绍,首先给出情景和情景感知的概念及其研究发展和应用。然后,结合物联网特性,以情景感知流程为主线,探讨了信息获取、建模和智能处理等内容。最后,系统结构是情景感知的关键,因此对现有的系统结构进行了分析和对比,结合物联网环境论述了当前情景感知系统的不足之处,并给出了情景感知系统的参考结构。
