Various types of plasma events emerge in specific parameter ranges and exhibit similar characteristics in diagnostic signals,which can be applied to identify these events.A semisupervised machine learning algorithm,th...Various types of plasma events emerge in specific parameter ranges and exhibit similar characteristics in diagnostic signals,which can be applied to identify these events.A semisupervised machine learning algorithm,the k-means clustering algorithm,is utilized to investigate and identify plasma events in the J-TEXT plasma.This method can cluster diverse plasma events with homogeneous features,and then these events can be identified if given few manually labeled examples based on physical understanding.A survey of clustered events reveals that the k-means algorithm can make plasma events(rotating tearing mode,sawtooth oscillations,and locked mode)gathering in Euclidean space composed of multi-dimensional diagnostic data,like soft x-ray emission intensity,edge toroidal rotation velocity,the Mirnov signal amplitude and so on.Based on the cluster analysis results,an approximate analytical model is proposed to rapidly identify plasma events in the J-TEXT plasma.The cluster analysis method is conducive to data markers of massive diagnostic data.展开更多
Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experien...Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.展开更多
The original temporal clustering analysis (OTCA) is an effective technique for obtaining brain activation maps when the timing and location of the activation are completely unknown, but its deficiency of sensitivity i...The original temporal clustering analysis (OTCA) is an effective technique for obtaining brain activation maps when the timing and location of the activation are completely unknown, but its deficiency of sensitivity is exposed in processing brain activation signal which is relatively weak. The time slice analysis method based on OTCA is proposed considering the weakness of the functional magnetic resonance imaging (fMRI) signal of the rat model. By dividing the stimulation period into several time slices and analyzing each slice to detect the activated pixels respectively after the background removal, the sensitivity is significantly improved. The inhibitory response in the hypothalamus after glucose loading is detected successfully with this method in the experiment on rat. Combined with the OTCA method, the time slice analysis method based on OTCA is effective on detecting when, where and which type of response will happen after stimulation, even if the fMRI signal is weak.展开更多
Supervised learning methods（eg.PLS-DA,SVM,etc.） have been widely used with laser-induced breakdown spectroscopy（LIBS） to classify materials;however,it may induce a low correct classification rate if a test sample ...Supervised learning methods（eg.PLS-DA,SVM,etc.） have been widely used with laser-induced breakdown spectroscopy（LIBS） to classify materials;however,it may induce a low correct classification rate if a test sample type is not included in the training dataset.Unsupervised cluster analysis methods（hierarchical clustering analysis,K-means clustering analysis,and iterative self-organizing data analysis technique） are investigated in plastics classification based on the line intensities of LIBS emission in this paper.The results of hierarchical clustering analysis using four different similarity measuring methods（single linkage,complete linkage,unweighted pair-group average,and weighted pair-group average） are compared.In K-means clustering analysis,four kinds of choosing initial centers methods are applied in our case and their results are compared.The classification results of hierarchical clustering analysis,K-means clustering analysis,and ISODATA are analyzed.The experiment results demonstrated cluster analysis methods can be applied to plastics discrimination with LIBS.展开更多
This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverag...This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverage time, the frequency of coverage, average time per coverage and maximum coverage gap, etc. is required simultaneously. By introducing index normalization method to convert performance indices into dimensionless variables within the range of [0, 1], a design optimization method based on the principal component analysis and cluster analysis is proposed, which consists of index normalization method, principal component analysis, multiple-level cluster analysis and weighted evaluation method. The results of orbit optimization for earth observation satellites show that the optimal orbit can be obtained by using the proposed method. The principal component analysis can reduce the total number of indices with a non-independent relationship to save computing time. Similarly, the multiple-level cluster analysis with parallel computing could save computing time.展开更多
Compared with hyperbolic velocity estimation methods,nonhyperbolic methods(such as shifted hyperbola)are better choices for large offsets or vertical transverse isotropy(VTI)media.Since local seismic event slope conta...Compared with hyperbolic velocity estimation methods,nonhyperbolic methods(such as shifted hyperbola)are better choices for large offsets or vertical transverse isotropy(VTI)media.Since local seismic event slope contains subsurface information,they can be used to estimate zero-offset two-way traveltime and normal moveout velocity.The traditional velocity estimation methods require a great deal of manual work and are also prone to human error.In order to estimate the traveltime parameters for VTI media automatically,in this paper,we propose to use predictive painting and similarity-weighted clustering to obtain traveltime parameters.The predictive painting is used to estimate zero-offset two-way traveltime,and the shifted-hyperbola traveltime equation is used to obtain velocity and anisotropy attributes.We first map local slopes to zero-offset two-way traveltime and moveout-parameters domain and then use similarity-weightedk-means clustering to find the maximum likelihood anisotropy parameters of the main subsurface structures.In order to demonstrate that,we apply the similarity-weighted clustering method to synthetic and field data examples and the results are of higher accuracy when compared to the ones obtained using multiparameter semblance-based method.From estimation error section,it can be seen that the estimation error of multiparameter semblance-based method is about 3-5 times that of the proposed method.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supported by the National Magnetic Confinement Fusion Science Program of China(Nos.2018YFE0301104 and 2018YFE0301100)National Natural Science Foundation of China(Nos.12075096 and 51821005)。
文摘Various types of plasma events emerge in specific parameter ranges and exhibit similar characteristics in diagnostic signals,which can be applied to identify these events.A semisupervised machine learning algorithm,the k-means clustering algorithm,is utilized to investigate and identify plasma events in the J-TEXT plasma.This method can cluster diverse plasma events with homogeneous features,and then these events can be identified if given few manually labeled examples based on physical understanding.A survey of clustered events reveals that the k-means algorithm can make plasma events(rotating tearing mode,sawtooth oscillations,and locked mode)gathering in Euclidean space composed of multi-dimensional diagnostic data,like soft x-ray emission intensity,edge toroidal rotation velocity,the Mirnov signal amplitude and so on.Based on the cluster analysis results,an approximate analytical model is proposed to rapidly identify plasma events in the J-TEXT plasma.The cluster analysis method is conducive to data markers of massive diagnostic data.
文摘Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.
基金the National Natural Science Foundation of China (30370432)
文摘The original temporal clustering analysis (OTCA) is an effective technique for obtaining brain activation maps when the timing and location of the activation are completely unknown, but its deficiency of sensitivity is exposed in processing brain activation signal which is relatively weak. The time slice analysis method based on OTCA is proposed considering the weakness of the functional magnetic resonance imaging (fMRI) signal of the rat model. By dividing the stimulation period into several time slices and analyzing each slice to detect the activated pixels respectively after the background removal, the sensitivity is significantly improved. The inhibitory response in the hypothalamus after glucose loading is detected successfully with this method in the experiment on rat. Combined with the OTCA method, the time slice analysis method based on OTCA is effective on detecting when, where and which type of response will happen after stimulation, even if the fMRI signal is weak.
基金supported by Beijing Natural Science Foundation of China(No.4132063)
文摘Supervised learning methods（eg.PLS-DA,SVM,etc.） have been widely used with laser-induced breakdown spectroscopy（LIBS） to classify materials;however,it may induce a low correct classification rate if a test sample type is not included in the training dataset.Unsupervised cluster analysis methods（hierarchical clustering analysis,K-means clustering analysis,and iterative self-organizing data analysis technique） are investigated in plastics classification based on the line intensities of LIBS emission in this paper.The results of hierarchical clustering analysis using four different similarity measuring methods（single linkage,complete linkage,unweighted pair-group average,and weighted pair-group average） are compared.In K-means clustering analysis,four kinds of choosing initial centers methods are applied in our case and their results are compared.The classification results of hierarchical clustering analysis,K-means clustering analysis,and ISODATA are analyzed.The experiment results demonstrated cluster analysis methods can be applied to plastics discrimination with LIBS.
基金Funded by 973 Program of Ministry of National Defense of China(Grant No.613237)
文摘This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverage time, the frequency of coverage, average time per coverage and maximum coverage gap, etc. is required simultaneously. By introducing index normalization method to convert performance indices into dimensionless variables within the range of [0, 1], a design optimization method based on the principal component analysis and cluster analysis is proposed, which consists of index normalization method, principal component analysis, multiple-level cluster analysis and weighted evaluation method. The results of orbit optimization for earth observation satellites show that the optimal orbit can be obtained by using the proposed method. The principal component analysis can reduce the total number of indices with a non-independent relationship to save computing time. Similarly, the multiple-level cluster analysis with parallel computing could save computing time.
基金supported by the National Natural Science Foundation of China(41574113)the Important National Science and Technology Specific Projects of China(Grant 2016ZX05026-002-006).
文摘Compared with hyperbolic velocity estimation methods,nonhyperbolic methods(such as shifted hyperbola)are better choices for large offsets or vertical transverse isotropy(VTI)media.Since local seismic event slope contains subsurface information,they can be used to estimate zero-offset two-way traveltime and normal moveout velocity.The traditional velocity estimation methods require a great deal of manual work and are also prone to human error.In order to estimate the traveltime parameters for VTI media automatically,in this paper,we propose to use predictive painting and similarity-weighted clustering to obtain traveltime parameters.The predictive painting is used to estimate zero-offset two-way traveltime,and the shifted-hyperbola traveltime equation is used to obtain velocity and anisotropy attributes.We first map local slopes to zero-offset two-way traveltime and moveout-parameters domain and then use similarity-weightedk-means clustering to find the maximum likelihood anisotropy parameters of the main subsurface structures.In order to demonstrate that,we apply the similarity-weighted clustering method to synthetic and field data examples and the results are of higher accuracy when compared to the ones obtained using multiparameter semblance-based method.From estimation error section,it can be seen that the estimation error of multiparameter semblance-based method is about 3-5 times that of the proposed method.
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。
