Urban areas and its evolution are important anthropogenic indicators and human ecological footprints, and play decisive roles in environmental change analysis, global geo-conditional monitoring, and sustainable develo...Urban areas and its evolution are important anthropogenic indicators and human ecological footprints, and play decisive roles in environmental change analysis, global geo-conditional monitoring, and sustainable development. China has the highest rate of urban expansion and has emerged as an urban expansion hotspot worldwide. In this paper, the progress of studies on Chinese urban expansion based on remote sensing technology are summarized and analyzed from the aspects of urban area definition, remotely sensed imagery applied in urban expansion, monitoring methods of urban expansion, and urban expansion applications. Existing issues and future directions of Chinese urban expansion are discussed and proposed. Results indicate that: 1) The fusion of multi-source remotely sensed imagery is imperative to meet the needs of urban expansion with various monitoring terms and frequencies on different scales and dimensions. 2) To guarantee the classification accuracy and efficiency and describe urban expansion and its influences on local land use simultaneously, the combination of visual interpretation and automatic classification is the tendency of future monitoring methods of urban areas. 3) Urban expansion data have become the prerequisite for recognizing the urban development process, excavating its driving forces, simulating and predicting the future development directions, and also is conducive to revealing and explaining urban ecological and environmental issues. 4) In the past decades, Chinese scholars have promoted the application of remote sensing technology in the urban expansion field, with data construction, methods and models developing from the quotation stage to improvement and innovation stage; however, an independent and consistent urban expansion data on the national scale with long-term and high-frequency(such as annual monitoring) monitoring is still lacking.展开更多
In order to improve the automatic retrieval ability of English vocabulary, for the distribution of semantic attributes in English vocabulary, an automatic classification method of English vocabulary is proposed based ...In order to improve the automatic retrieval ability of English vocabulary, for the distribution of semantic attributes in English vocabulary, an automatic classification method of English vocabulary is proposed based on association rules, English vocabulary data storage model is constructed, a two element linguistic feature function is constructed for describing the directionality of English lexical retrieval scheduling, English vocabulary classification decision making model is constructed based on contextual relations of English vocabulary, the features of the association rules of English vocabulary are extracted, the adaptive learning method is used to realize the automatic classification of English vocabulary. The simulation results show that the method of English vocabulary classification has good performance, the classification error rate is low, the retrieval precision is high, and the computational overhead is small.展开更多
Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing auto...Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution（TIE） algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.展开更多
基金Under the auspices of National Major Science and Technology Program for Water Pollution Contro and Treatment(No.2017ZX07101001)International Partnership Program of Chinese Academy of Sciences(No.131C11KYSB20160061)
文摘Urban areas and its evolution are important anthropogenic indicators and human ecological footprints, and play decisive roles in environmental change analysis, global geo-conditional monitoring, and sustainable development. China has the highest rate of urban expansion and has emerged as an urban expansion hotspot worldwide. In this paper, the progress of studies on Chinese urban expansion based on remote sensing technology are summarized and analyzed from the aspects of urban area definition, remotely sensed imagery applied in urban expansion, monitoring methods of urban expansion, and urban expansion applications. Existing issues and future directions of Chinese urban expansion are discussed and proposed. Results indicate that: 1) The fusion of multi-source remotely sensed imagery is imperative to meet the needs of urban expansion with various monitoring terms and frequencies on different scales and dimensions. 2) To guarantee the classification accuracy and efficiency and describe urban expansion and its influences on local land use simultaneously, the combination of visual interpretation and automatic classification is the tendency of future monitoring methods of urban areas. 3) Urban expansion data have become the prerequisite for recognizing the urban development process, excavating its driving forces, simulating and predicting the future development directions, and also is conducive to revealing and explaining urban ecological and environmental issues. 4) In the past decades, Chinese scholars have promoted the application of remote sensing technology in the urban expansion field, with data construction, methods and models developing from the quotation stage to improvement and innovation stage; however, an independent and consistent urban expansion data on the national scale with long-term and high-frequency(such as annual monitoring) monitoring is still lacking.
文摘In order to improve the automatic retrieval ability of English vocabulary, for the distribution of semantic attributes in English vocabulary, an automatic classification method of English vocabulary is proposed based on association rules, English vocabulary data storage model is constructed, a two element linguistic feature function is constructed for describing the directionality of English lexical retrieval scheduling, English vocabulary classification decision making model is constructed based on contextual relations of English vocabulary, the features of the association rules of English vocabulary are extracted, the adaptive learning method is used to realize the automatic classification of English vocabulary. The simulation results show that the method of English vocabulary classification has good performance, the classification error rate is low, the retrieval precision is high, and the computational overhead is small.
文摘Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution（TIE） algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.
