Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outl...Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.展开更多
In the field of automatic target recognition and tracking,traditional image complexity metrics,such as statistical variance and signal-to-noise ratio,all focus on single-frame images.However,there are few researches a...In the field of automatic target recognition and tracking,traditional image complexity metrics,such as statistical variance and signal-to-noise ratio,all focus on single-frame images.However,there are few researches about the complexity of image sequence.To solve this problem,a criterion of evaluating image sequence complexity is proposed.Firstly,to characterize this criterion quantitatively,two metrics for measuring the complexity of image sequence,namely feature space similarity degree of global background(FSSDGB)and feature space occultation degree of local background(FSODLB)are developed.Here,FSSDGB reflects the ability of global background to introduce false alarms based on feature space,and FSODLB represents the difference between target and local background based on feature space.Secondly,the feature space is optimized by the grey relational method and relevant features are removed so that FSSDGB and FSODLB are more reasonable to establish complexity of single-frame images.Finally,the image sequence complexity is not a linear sum of the single-frame image complexity.Target tracking errors often occur in high-complexity images and the tracking effect of low-complexity images is very well.The nonlinear transformation based on median(NTM)is proposed to construct complexity of image sequence.The experimental results show that the proposed metric is more valid than other metrics,such as sequence correlation(SC)and interframe change degree(IFCD),and it is highly relevant to the actual performance of automatic target tracking algorithms.展开更多
为建立一种适宜的板栗资源果实品质评价方法,本研究以25个板栗品种为研究对象,选取21项品质指标进行测定,通过主成分分析结合相关性分析、描述性统计分析的方法筛选影响板栗品质的核心评价指标,基于熵权法对核心指标赋予权重,并建立灰...为建立一种适宜的板栗资源果实品质评价方法,本研究以25个板栗品种为研究对象,选取21项品质指标进行测定,通过主成分分析结合相关性分析、描述性统计分析的方法筛选影响板栗品质的核心评价指标,基于熵权法对核心指标赋予权重,并建立灰色关联度评价模型。结果表明,不同品种板栗多项指标存在显著差异(P<0.05),且多个指标间存在显著相关性,主成分分析确立了水分、直链淀粉与支链淀粉含量的比值(Ratio of amylose to amylopectin,AA)、总黄酮、好果率、果形指数、硬度、可溶性糖和还原糖为核心指标,熵权法计算核心指标的权重分别为14.08%、14.64%、15.64%、7.74%、9.41%、9.11%、18.90%、10.48%。灰色关联度分析结果表明,丹栗1号、丹东9113和qX-005综合品质列前三位。经聚类分析将25个品种板栗分为4类,第一类板栗适宜开发功能性饮品;第二类板栗适合取仁加工,制作罐头、果脯等产品,或加工成板栗粉用于面包、饼干等产品的制作;第三类板栗可作为优质的食品原料;第四类板栗适宜炒食,也适宜作为直售坚果。本研究结果为板栗优质资源筛选及品种的选育提供参考,也为各品种的综合利用提供了理论依据。展开更多
基金Project(61362021)supported by the National Natural Science Foundation of ChinaProject(2016GXNSFAA380149)supported by Natural Science Foundation of Guangxi Province,China+1 种基金Projects(2016YJCXB02,2017YJCX34)supported by Innovation Project of GUET Graduate Education,ChinaProject(2011KF11)supported by the Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education,China
文摘Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.
基金supported by the National Natural Science Foundation of China(61703337)Shanghai Aerospace Science and Technology Innovation Fund(SAST2017-082)
文摘In the field of automatic target recognition and tracking,traditional image complexity metrics,such as statistical variance and signal-to-noise ratio,all focus on single-frame images.However,there are few researches about the complexity of image sequence.To solve this problem,a criterion of evaluating image sequence complexity is proposed.Firstly,to characterize this criterion quantitatively,two metrics for measuring the complexity of image sequence,namely feature space similarity degree of global background(FSSDGB)and feature space occultation degree of local background(FSODLB)are developed.Here,FSSDGB reflects the ability of global background to introduce false alarms based on feature space,and FSODLB represents the difference between target and local background based on feature space.Secondly,the feature space is optimized by the grey relational method and relevant features are removed so that FSSDGB and FSODLB are more reasonable to establish complexity of single-frame images.Finally,the image sequence complexity is not a linear sum of the single-frame image complexity.Target tracking errors often occur in high-complexity images and the tracking effect of low-complexity images is very well.The nonlinear transformation based on median(NTM)is proposed to construct complexity of image sequence.The experimental results show that the proposed metric is more valid than other metrics,such as sequence correlation(SC)and interframe change degree(IFCD),and it is highly relevant to the actual performance of automatic target tracking algorithms.
文摘为建立一种适宜的板栗资源果实品质评价方法,本研究以25个板栗品种为研究对象,选取21项品质指标进行测定,通过主成分分析结合相关性分析、描述性统计分析的方法筛选影响板栗品质的核心评价指标,基于熵权法对核心指标赋予权重,并建立灰色关联度评价模型。结果表明,不同品种板栗多项指标存在显著差异(P<0.05),且多个指标间存在显著相关性,主成分分析确立了水分、直链淀粉与支链淀粉含量的比值(Ratio of amylose to amylopectin,AA)、总黄酮、好果率、果形指数、硬度、可溶性糖和还原糖为核心指标,熵权法计算核心指标的权重分别为14.08%、14.64%、15.64%、7.74%、9.41%、9.11%、18.90%、10.48%。灰色关联度分析结果表明,丹栗1号、丹东9113和qX-005综合品质列前三位。经聚类分析将25个品种板栗分为4类,第一类板栗适宜开发功能性饮品;第二类板栗适合取仁加工,制作罐头、果脯等产品,或加工成板栗粉用于面包、饼干等产品的制作;第三类板栗可作为优质的食品原料;第四类板栗适宜炒食,也适宜作为直售坚果。本研究结果为板栗优质资源筛选及品种的选育提供参考,也为各品种的综合利用提供了理论依据。
