Most methods for classifying hyperspectral data only consider the local spatial relation-ship among samples,ignoring the important non-local topological relationship.However,the non-local topological relationship is b...Most methods for classifying hyperspectral data only consider the local spatial relation-ship among samples,ignoring the important non-local topological relationship.However,the non-local topological relationship is better at representing the structure of hyperspectral data.This paper proposes a deep learning model called Topology and semantic information fusion classification network(TSFnet)that incorporates a topology structure and semantic information transmis-sion network to accurately classify traditional Chinese medicine in hyperspectral images.TSFnet uses a convolutional neural network(CNN)to extract features and a graph convolution network(GCN)to capture potential topological relationships among different types of Chinese herbal medicines.The results show that TSFnet outperforms other state-of-the-art deep learning classification algorithms in two different scenarios of herbal medicine datasets.Additionally,the proposed TSFnet model is lightweight and can be easily deployed for mobile herbal medicine classification.展开更多
With the rapid development of Web2.0 technology, more and more social annotation systems are emerging, such as Del.icio.us, Flickr, YouTube, and CiteULike. These systems help users to manage and share their digital re...With the rapid development of Web2.0 technology, more and more social annotation systems are emerging, such as Del.icio.us, Flickr, YouTube, and CiteULike. These systems help users to manage and share their digital resources, and have attracted a lot of users to annotate the resources with tags and bookmarks, which result in a large scale of tag data. Due to the exponential increase of social annotations, all the users are facing the same problem: How can we explore the desired resources efficiently in such a large tag dataset? Since the traditional methods such as tag cloud view and annotation match work well only in small annotation dataset, this paper studies the relationships of tag-tag, tag-resource and resource-resource through the co-occurrences and proposes a new efficient way for users to organize and explore the literature resources. Our research mainly focuses on two aspects:1) The hidden semantic relationships of popular tags and their relevant literature resources;2) the computing of literature resources similarity given a specific literature. A prototype system named PKUSpace is implemented and shows promising results.展开更多
分析了列车行车组织、列车控制、列车安全预警等综合调度平台运行所需的站场基础数据,提出铁路站场拓扑数据的建立方法、思路和关键算法,并参考铁路站场设备设置规范,使用地理信息系统(Geography Information System,GIS)技术,开发了相...分析了列车行车组织、列车控制、列车安全预警等综合调度平台运行所需的站场基础数据,提出铁路站场拓扑数据的建立方法、思路和关键算法,并参考铁路站场设备设置规范,使用地理信息系统(Geography Information System,GIS)技术,开发了相应的建立铁路站场拓扑数据的工具软件,利用此工具软件生成了铁路综合调度平台所需的站场拓扑数据.展开更多
基金supported by the National Natural Science Foundation of China(No.62001023)Beijing Natural Science Foundation(No.JQ20021)。
文摘Most methods for classifying hyperspectral data only consider the local spatial relation-ship among samples,ignoring the important non-local topological relationship.However,the non-local topological relationship is better at representing the structure of hyperspectral data.This paper proposes a deep learning model called Topology and semantic information fusion classification network(TSFnet)that incorporates a topology structure and semantic information transmis-sion network to accurately classify traditional Chinese medicine in hyperspectral images.TSFnet uses a convolutional neural network(CNN)to extract features and a graph convolution network(GCN)to capture potential topological relationships among different types of Chinese herbal medicines.The results show that TSFnet outperforms other state-of-the-art deep learning classification algorithms in two different scenarios of herbal medicine datasets.Additionally,the proposed TSFnet model is lightweight and can be easily deployed for mobile herbal medicine classification.
基金supported by the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20070001073)the National Natural Science Foundation of China(Grant Nos.90412010 and60773162)
文摘With the rapid development of Web2.0 technology, more and more social annotation systems are emerging, such as Del.icio.us, Flickr, YouTube, and CiteULike. These systems help users to manage and share their digital resources, and have attracted a lot of users to annotate the resources with tags and bookmarks, which result in a large scale of tag data. Due to the exponential increase of social annotations, all the users are facing the same problem: How can we explore the desired resources efficiently in such a large tag dataset? Since the traditional methods such as tag cloud view and annotation match work well only in small annotation dataset, this paper studies the relationships of tag-tag, tag-resource and resource-resource through the co-occurrences and proposes a new efficient way for users to organize and explore the literature resources. Our research mainly focuses on two aspects:1) The hidden semantic relationships of popular tags and their relevant literature resources;2) the computing of literature resources similarity given a specific literature. A prototype system named PKUSpace is implemented and shows promising results.
文摘分析了列车行车组织、列车控制、列车安全预警等综合调度平台运行所需的站场基础数据,提出铁路站场拓扑数据的建立方法、思路和关键算法,并参考铁路站场设备设置规范,使用地理信息系统(Geography Information System,GIS)技术,开发了相应的建立铁路站场拓扑数据的工具软件,利用此工具软件生成了铁路综合调度平台所需的站场拓扑数据.
