内部电网地理信息系统(Geographic Information Systern,GIS)数据体量增加,对电网数据存储性能造成了极大的困难,为此,提出一种基于随机森林的电网GIS数据分布式存储方法。以跨域资源共享(Cross-Origin Resource Sharing,CORS)技术在电...内部电网地理信息系统(Geographic Information Systern,GIS)数据体量增加,对电网数据存储性能造成了极大的困难,为此,提出一种基于随机森林的电网GIS数据分布式存储方法。以跨域资源共享(Cross-Origin Resource Sharing,CORS)技术在电网GIS空间信息服务平台中获取的电网GIS数据为基础,根据类区分度数值选择电网GIS数据特征,引入随机森林算法分类处理电网GIS数据,将其合理分发给不同的服务器,采用并行处理手段存储分类数据,从而实现了电网GIS数据的分布式存储。实验数据显示:应用所提方法后,电网GIS数据分类精度达到了96.8%,电网GIS数据分布式存储时间最小值为5.2 s,充分证实了所提方法数据存储性能更佳。展开更多
为提高说话人确认系统在噪声环境下的鲁棒性,在利用听觉外周模型改进Mel频率倒谱系数(Mel Frequency Cepstral Coefficient,MFCC)的基础上,结合感知线性预测系数(Perceptual Linear Predictive Coefficient,PLPC),以类间区分度为依据,...为提高说话人确认系统在噪声环境下的鲁棒性,在利用听觉外周模型改进Mel频率倒谱系数(Mel Frequency Cepstral Coefficient,MFCC)的基础上,结合感知线性预测系数(Perceptual Linear Predictive Coefficient,PLPC),以类间区分度为依据,在特征域对两种声纹特征进行融合,提出一种新的声纹特征提取方法,并对基于该特征的说话人确认系统的噪声鲁棒性进行研究。针对不同信噪比的语音信号进行了融合特征与原始特征的对比实验,结果表明,融合特征在模拟餐厅噪声环境中的错误率更低,较MFCC与PLPC分别降低了2.2%和3.1%,说话人确认系统在噪声中的鲁棒性得到提升。展开更多
The complexity of natural conditions leads to the complexity of vegetation types of Taiwan of China, which has both tropical and cold-temperate vegetation types, and could be depicted as the vegetation miniature of Ch...The complexity of natural conditions leads to the complexity of vegetation types of Taiwan of China, which has both tropical and cold-temperate vegetation types, and could be depicted as the vegetation miniature of China or even for the world. The physiognomic-floristic principle was adopted for the vegetation classification of Taiwan. The units of rank from top to bottom are: class of vegetation-type, order of vegetation-type, vegetation-type, alliance group, alliance and association. The high-rank units (class, order and vegetation-type) are classified by ecological physiognomy, while the median and lower units by the species composition of community. At the same time the role of dominant species and character species will also be considered. The dominant species are the major factor concerned with the median ranks (alliance group, and alliance) because they are the chief components of community, additionally their remarkable appearance is easy to identify; the character species (or diagnostic species) are for relatively low ranks (association) because they will clearly show the interspecies relation-ship and the characteristics of community. According to this principle, vegetation of Taiwan is classi-fied into five classes of vegetation-types (forests, thickets, herbaceous vegetation, rock fields vegetation, swamps and aquatic vegetation), 29 orders of vegetation-types (cold-temperate needle-leaved forests, cool-temperate needle-leaved forests, warm-temperate needle-leaved forests, warm needle-leaved forests, deciduous broad-leaved forests, mixed evergreen and deciduous broad-leaved forests, evergreen mossy forests, evergreen sclerophyllous forests, evergreen broad-leaved forests, tropical rain forests, tropical monsoon forests, coastal forests, warm bamboo forests, evergreen needle-leaved thickets, sclerophyllous thickets, deciduous broad-leaved thickets, evergreen broad-leaved thickets, xerothermic thorn-succulent thickets, bamboo thickets, meadows, sparse shrub grasslands, savannahic grasslands, sparse scree communities, chasmophytic vegetation, woody swamps, herbaceous swamps, moss bogs, fresh water aquatic vegetation, salt water aquatic vegetation) and 53 vegetation-types. The main alliances of each vegetation-type are described.展开更多
Clustering data with varying densities and complicated structures is important,while many existing clustering algorithms face difficulties for this problem. The reason is that varying densities and complicated structu...Clustering data with varying densities and complicated structures is important,while many existing clustering algorithms face difficulties for this problem. The reason is that varying densities and complicated structure make single algorithms perform badly for different parts of data. More intensive parts are assumed to have more information probably,an algorithm clustering from high density part is proposed,which begins from a tiny distance to find the highest density-connected partition and form corresponding super cores,then distance is iteratively increased by a global heuristic method to cluster parts with different densities. Mean of silhouette coefficient indicates the cluster performance. Denoising function is implemented to eliminate influence of noise and outliers. Many challenging experiments indicate that the algorithm has good performance on data with widely varying densities and extremely complex structures. It decides the optimal number of clusters automatically.Background knowledge is not needed and parameters tuning is easy. It is robust against noise and outliers.展开更多
.GN algorithm has high classification accuracy on community detection, but its time complexity is too high. In large scale network, the algorithm is lack of practical values. This paper puts forward an improved GN alg....GN algorithm has high classification accuracy on community detection, but its time complexity is too high. In large scale network, the algorithm is lack of practical values. This paper puts forward an improved GN algorithm. The algorithm firstly get the network center nodes set, then use the shortest paths between center nodes and other nodes to calculate the edge betweenness, and then use incremental module degree as the algorithm terminates standard. Experiments show that, the new algorithm not only ensures accuracy of network community division, but also greatly reduced the time complexity, and improves the efficiency of community division.展开更多
文摘为提高说话人确认系统在噪声环境下的鲁棒性,在利用听觉外周模型改进Mel频率倒谱系数(Mel Frequency Cepstral Coefficient,MFCC)的基础上,结合感知线性预测系数(Perceptual Linear Predictive Coefficient,PLPC),以类间区分度为依据,在特征域对两种声纹特征进行融合,提出一种新的声纹特征提取方法,并对基于该特征的说话人确认系统的噪声鲁棒性进行研究。针对不同信噪比的语音信号进行了融合特征与原始特征的对比实验,结果表明,融合特征在模拟餐厅噪声环境中的错误率更低,较MFCC与PLPC分别降低了2.2%和3.1%,说话人确认系统在噪声中的鲁棒性得到提升。
文摘The complexity of natural conditions leads to the complexity of vegetation types of Taiwan of China, which has both tropical and cold-temperate vegetation types, and could be depicted as the vegetation miniature of China or even for the world. The physiognomic-floristic principle was adopted for the vegetation classification of Taiwan. The units of rank from top to bottom are: class of vegetation-type, order of vegetation-type, vegetation-type, alliance group, alliance and association. The high-rank units (class, order and vegetation-type) are classified by ecological physiognomy, while the median and lower units by the species composition of community. At the same time the role of dominant species and character species will also be considered. The dominant species are the major factor concerned with the median ranks (alliance group, and alliance) because they are the chief components of community, additionally their remarkable appearance is easy to identify; the character species (or diagnostic species) are for relatively low ranks (association) because they will clearly show the interspecies relation-ship and the characteristics of community. According to this principle, vegetation of Taiwan is classi-fied into five classes of vegetation-types (forests, thickets, herbaceous vegetation, rock fields vegetation, swamps and aquatic vegetation), 29 orders of vegetation-types (cold-temperate needle-leaved forests, cool-temperate needle-leaved forests, warm-temperate needle-leaved forests, warm needle-leaved forests, deciduous broad-leaved forests, mixed evergreen and deciduous broad-leaved forests, evergreen mossy forests, evergreen sclerophyllous forests, evergreen broad-leaved forests, tropical rain forests, tropical monsoon forests, coastal forests, warm bamboo forests, evergreen needle-leaved thickets, sclerophyllous thickets, deciduous broad-leaved thickets, evergreen broad-leaved thickets, xerothermic thorn-succulent thickets, bamboo thickets, meadows, sparse shrub grasslands, savannahic grasslands, sparse scree communities, chasmophytic vegetation, woody swamps, herbaceous swamps, moss bogs, fresh water aquatic vegetation, salt water aquatic vegetation) and 53 vegetation-types. The main alliances of each vegetation-type are described.
基金Supported by the National Key Research and Development Program of China(No.2016YFB0201305)National Science and Technology Major Project(No.2013ZX0102-8001-001-001)National Natural Science Foundation of China(No.91430218,31327901,61472395,61272134,61432018)
文摘Clustering data with varying densities and complicated structures is important,while many existing clustering algorithms face difficulties for this problem. The reason is that varying densities and complicated structure make single algorithms perform badly for different parts of data. More intensive parts are assumed to have more information probably,an algorithm clustering from high density part is proposed,which begins from a tiny distance to find the highest density-connected partition and form corresponding super cores,then distance is iteratively increased by a global heuristic method to cluster parts with different densities. Mean of silhouette coefficient indicates the cluster performance. Denoising function is implemented to eliminate influence of noise and outliers. Many challenging experiments indicate that the algorithm has good performance on data with widely varying densities and extremely complex structures. It decides the optimal number of clusters automatically.Background knowledge is not needed and parameters tuning is easy. It is robust against noise and outliers.
文摘.GN algorithm has high classification accuracy on community detection, but its time complexity is too high. In large scale network, the algorithm is lack of practical values. This paper puts forward an improved GN algorithm. The algorithm firstly get the network center nodes set, then use the shortest paths between center nodes and other nodes to calculate the edge betweenness, and then use incremental module degree as the algorithm terminates standard. Experiments show that, the new algorithm not only ensures accuracy of network community division, but also greatly reduced the time complexity, and improves the efficiency of community division.
