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1. 发明公开

EP3299976A1 GENE EXPRESSION DATA CLASSIFICATION METHOD AND CLASSIFICATION SYSTEM 审中-公开
标题翻译：基因表达数据分类方法及分类系统
公开(公告)号：EP3299976A1
公开(公告)日：2018-03-28
申请号：EP16899247.7
申请日：2016-11-17
申请人： Soochow University
发明人： ZHANG, Li , HUANG, Xiaojuan , WANG, Bangjun , ZHANG, Zhao , LI, Fanzhang
IPC分类号： G06F19/24
CPC分类号： G06F19/24 , G06F17/30321 , G06F17/30598 , G06F19/18 , G06F19/22 , G06N99/005 , G16H50/20
摘要： A gene expression data classification method and a gene expression data classification system are provided. With the gene expression data classification method, a gene feature data set is acquired, and then the gene feature data set is clustered using a clustering algorithm to obtain clustering sets, the number of which is a first preset parameter, the clustering sets are processed to obtain a second sample matrix, a second training set and a feature index set, to reduce dimensionality of gene expression data. In this way, redundancy among the gene expression data is reduced, thereby greatly reducing calculation resources and calculation time consumed in a subsequent process of performing feature selection on the second training set. Also, a few calculation resources and a little calculation time are consumed in a case of clustering the gene feature data set by using the clustering algorithm, therefore, a few calculation resources and a little calculation time are consumed by classifying the gene expression data to be measured with the gene expression data classification method.
摘要翻译：提供基因表达数据分类方法和基因表达数据分类系统。利用基因表达数据分类方法，获取基因特征数据集，然后利用聚类算法对基因特征数据集进行聚类，得到聚类集，数量为第一预设参数，将聚类集处理为获得第二样本矩阵，第二训练集和特征索引集，以降低基因表达数据的维数。这样，减少了基因表达数据之间的冗余，从而大大减少了后续对第二训练集进行特征选择过程中所消耗的计算资源和计算时间。此外，在使用聚类算法对基因特征数据集进行聚类的情况下，消耗少量计算资源和少量计算时间，因此通过将基因表达数据分类为少数计算资源和少量计算时间用基因表达数据分类方法测量。

2. 发明公开

EP3346419A1 FEATURE EXTRACTION AND CLASSIFICATION METHOD BASED ON SUPPORT VECTOR DATA DESCRIPTION AND SYSTEM THEREOF 审中-公开
公开(公告)号：EP3346419A1
公开(公告)日：2018-07-11
申请号：EP16907682.5
申请日：2016-12-19
申请人： Soochow University
发明人： ZHANG, Li , LU, Xingning , WANG, Bangjun , LI, Fanzhang , ZHANG, Zhao
IPC分类号： G06K9/62
CPC分类号： G06N99/005 , G06F17/5009 , G06F2217/16 , G06K9/62 , G06N3/08
摘要： A feature extraction and classification method based on support vector data description is provided, which includes: calculating, for each sample, Euclidean distances from the sample to spherical centers of multiple hypersphere models corresponding to different data categories, where the multiple hypersphere models are acquired in advance by training using a support vector data description algorithm(s101); substituting, for each sample, the Euclidean distances and radiuses of the hypersphere models respectively corresponding to the Euclidean distances into a new feature relation equation, to acquire a new feature sample corresponding to the sample, where the new feature samples constitute a new feature sample set(s102); and performing classification on the new feature sample set using a preset classification algorithm, to acquire a classification result(s103). With the method, a calculation amount in feature extraction can be reduced, and the speed of data classification can be increased. A feature extraction and classification system based on support vector data description having the above advantages is further provided.

3. 发明公开

EP3336804A1 MOVING TARGET DETECTION METHOD AND SYSTEM 审中-公开
公开(公告)号：EP3336804A1
公开(公告)日：2018-06-20
申请号：EP16907680.9
申请日：2016-12-07
申请人： Soochow University
发明人： ZHANG, Li , PEI, Tingwei , WANG, Bangjun , ZHANG, Zhao , LI, Fanzhang
IPC分类号： G06T7/20
CPC分类号： G06T7/254 , G06T7/20 , G06T7/231 , G06T2207/10016 , G06T2207/20021
摘要： A moving object detection method and a moving object detection system are provided. The method includes: predetermining a background image corresponding to a scene monitored by a video monitoring device (S11); performing a subtraction processing on a grayscale image to be detected and the background image to acquire a difference image (S12); and binarizing the difference image and determining a moving object in the grayscale image to be detected (S13), where the determining the background image includes: dividing a first grayscale image frame and a second grayscale image frame in a grayscale image frame sequence captured by the video monitoring device into image blocks to acquire a first image block set and a second image block set respectively, and determining the background image using a difference between the first image block set and the second image block set. The effect of moving object detection is improved and the complexity of the detection process is reduced with the method and the system.

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