随着城市化交通的发展,感知计算在智慧城市起着重要的作用。针对传统密度聚类算法无法适配海量出租车GPS轨迹数据及可视化的问题,提出了BCS-DBSCAN(Big-Data Cluster Center Statistics Density-Based Spatial Clustering of Applicatio...随着城市化交通的发展,感知计算在智慧城市起着重要的作用。针对传统密度聚类算法无法适配海量出租车GPS轨迹数据及可视化的问题,提出了BCS-DBSCAN(Big-Data Cluster Center Statistics Density-Based Spatial Clustering of Applications with Noise)聚类算法。该算法可以对轨迹数据切分及并行化聚类且能够提取最大密度簇心,并将结果适配可视化模型。实验结果表明,与其它流行的方法相比,在海量数据下提取城市载客热点区域的聚类速度、精确化及可视化方面具有十分显著的优势,对进一步提升城市规划、提高交通效率提供了重要的决策信息。展开更多
以美国科学情报所(ISI)的Web of science数据库所收录的2000年以来的56篇跳马研究相关文献和中国知网(CNKI)数据库自1959年以来所收录的656篇跳马相关文献为研究对象,利用CitesaceⅢ软件对其进行可视化分析,揭示了国内外跳马研究的现状...以美国科学情报所(ISI)的Web of science数据库所收录的2000年以来的56篇跳马研究相关文献和中国知网(CNKI)数据库自1959年以来所收录的656篇跳马相关文献为研究对象,利用CitesaceⅢ软件对其进行可视化分析,揭示了国内外跳马研究的现状,探测了其研究热点、研究现状、发展趋势等,剖析了跳马研究在研究领域、思路、侧重点等方面的差异,旨在为促进中国跳马研究明确方向,为我国竞技体操的发展提供理论指导。展开更多
With the development of short video industry,video and bullet screen have become important ways to spread public opinions.Public attitudes can be timely obtained through emotional analysis on bullet screen,which can a...With the development of short video industry,video and bullet screen have become important ways to spread public opinions.Public attitudes can be timely obtained through emotional analysis on bullet screen,which can also reduce difficulties in management of online public opinions.A convolutional neural network model based on multi-head attention is proposed to solve the problem of how to effectively model relations among words and identify key words in emotion classification tasks with short text contents and lack of complete context information.Firstly,encode word positions so that order information of input sequences can be used by the model.Secondly,use a multi-head attention mechanism to obtain semantic expressions in different subspaces,effectively capture internal relevance and enhance dependent relationships among words,as well as highlight emotional weights of key emotional words.Then a dilated convolution is used to increase the receptive field and extract more features.On this basis,the above multi-attention mechanism is combined with a convolutional neural network to model and analyze the seven emotional categories of bullet screens.Testing from perspectives of model and dataset,experimental results can validate effectiveness of our approach.Finally,emotions of bullet screens are visualized to provide data supports for hot event controls and other fields.展开更多
文摘随着城市化交通的发展,感知计算在智慧城市起着重要的作用。针对传统密度聚类算法无法适配海量出租车GPS轨迹数据及可视化的问题,提出了BCS-DBSCAN(Big-Data Cluster Center Statistics Density-Based Spatial Clustering of Applications with Noise)聚类算法。该算法可以对轨迹数据切分及并行化聚类且能够提取最大密度簇心,并将结果适配可视化模型。实验结果表明,与其它流行的方法相比,在海量数据下提取城市载客热点区域的聚类速度、精确化及可视化方面具有十分显著的优势,对进一步提升城市规划、提高交通效率提供了重要的决策信息。
文摘以美国科学情报所(ISI)的Web of science数据库所收录的2000年以来的56篇跳马研究相关文献和中国知网(CNKI)数据库自1959年以来所收录的656篇跳马相关文献为研究对象,利用CitesaceⅢ软件对其进行可视化分析,揭示了国内外跳马研究的现状,探测了其研究热点、研究现状、发展趋势等,剖析了跳马研究在研究领域、思路、侧重点等方面的差异,旨在为促进中国跳马研究明确方向,为我国竞技体操的发展提供理论指导。
基金National Natural Science Foundation of China(No.61562057)Gansu Science and Technology Plan Project(No.18JR3RA104)。
文摘With the development of short video industry,video and bullet screen have become important ways to spread public opinions.Public attitudes can be timely obtained through emotional analysis on bullet screen,which can also reduce difficulties in management of online public opinions.A convolutional neural network model based on multi-head attention is proposed to solve the problem of how to effectively model relations among words and identify key words in emotion classification tasks with short text contents and lack of complete context information.Firstly,encode word positions so that order information of input sequences can be used by the model.Secondly,use a multi-head attention mechanism to obtain semantic expressions in different subspaces,effectively capture internal relevance and enhance dependent relationships among words,as well as highlight emotional weights of key emotional words.Then a dilated convolution is used to increase the receptive field and extract more features.On this basis,the above multi-attention mechanism is combined with a convolutional neural network to model and analyze the seven emotional categories of bullet screens.Testing from perspectives of model and dataset,experimental results can validate effectiveness of our approach.Finally,emotions of bullet screens are visualized to provide data supports for hot event controls and other fields.
