Five factors expressing greenbelt quality and one factor expressing quantity were adopted for evaluation of the residential greenbelt, and the AHP （Analytical Hierarchy Process） method was used to determine the valu...Five factors expressing greenbelt quality and one factor expressing quantity were adopted for evaluation of the residential greenbelt, and the AHP （Analytical Hierarchy Process） method was used to determine the value of factors. Thirty residential areas were selected as the samples. Two principal components were extracted and their expression was constructed by method of factor anlysis, therefore, quality evaluation of residential greenbelt was obtained. The accuracy of the function and implement quality classification toward the residential greenbelts in Xinxiang City were validated by clustering analysis method. The results showed that the greenbelt quality of fourteen residential areas was higher than the average level, of which eleven were newly-built residential areas. The 30 residential areas were classified into three types according to their greenbelt features and their formation by clustering analysis method. Finally rational proposal basing on aforesaid evaluating results was proposed for construction and renewal of residential greenbelt, upon which directive basis was provided for construction and renewal of residential greenbelt.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
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
Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the S...Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.展开更多
A multi-parameter signal sorting algorithm for interleaved radar pulses in dense emitter environment is presented. The algorithm includes two parts, pulse classification and pulse repetition interval （PRI） analysis....A multi-parameter signal sorting algorithm for interleaved radar pulses in dense emitter environment is presented. The algorithm includes two parts, pulse classification and pulse repetition interval （PRI） analysis. Firstly, we propose the dynamic distance clustering （DDC） for classification. In the clustering algorithm, the multi-dimension features of radar pulse are used for reliable classification. The similarity threshold estimation method in DDC is derived, which contributes to the efficiency of the algorithm. However, DDC has large computation with many signal pulses. Then, in order to sort radar signals in real time, the improved DDC （IDDC） algorithm is proposed. Finally, PRI analysis is adopted to complete the process of sorting. The simulation experiments and hardware implementations show both algorithms are effective.展开更多
With the SPSS and the help of factor method and hierarchical clustered method,journal articles on digital information resources(DIR) from CNKI in the past ten years are analyzed with a co-word analytical method in thi...With the SPSS and the help of factor method and hierarchical clustered method,journal articles on digital information resources(DIR) from CNKI in the past ten years are analyzed with a co-word analytical method in this paper. The hot issues of studies on DIR and the relationship between those subjects are analyzed in this investigation as well.展开更多
In order to implement the robust cluster analysis,solve the problem that the outliers in the data will have a serious disturbance to the probability density parameter estimation,and therefore affect the accuracy of cl...In order to implement the robust cluster analysis,solve the problem that the outliers in the data will have a serious disturbance to the probability density parameter estimation,and therefore affect the accuracy of clustering,a robust cluster analysis method is proposed which is based on the diversity self-paced t-mixture model.This model firstly adopts the t-distribution as the submodel which tail is easily controllable.On this basis,it utilizes the entropy penalty expectation conditional maximal algorithm as a pre-clustering step to estimate the initial parameters.After that,this model introduces l2,1-norm as a self-paced regularization term and developes a new ECM optimization algorithm,in order to select high confidence samples from each component in training.Finally,experimental results on several real-world datasets in different noise environments show that the diversity self-paced t-mixture model outperforms the state-of-the-art clustering methods.It provides significant guidance for the construction of the robust mixture distribution model.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
Chosen-message pair Simple Power Analysis (SPA) attacks were proposed by Boer, Yen and Homma, and are attack methods based on searches for collisions of modular multiplication. However, searching for collisions is dif...Chosen-message pair Simple Power Analysis (SPA) attacks were proposed by Boer, Yen and Homma, and are attack methods based on searches for collisions of modular multiplication. However, searching for collisions is difficult in real environments. To circumvent this problem, we propose the Simple Power Clustering Attack (SPCA), which can automatically identify the modular multiplication collision. The insignificant effects of collision attacks were validated in an Application Specific Integrated Circuit (ASIC) environment. After treatment with SPCA, the automatic secret key recognition rate increased to 99%.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
Purpose: To present a method for systematically mapping diversity of publication patterns at the author level in the social sciences and humanities in terms of publication type, publication language and co-authorship....Purpose: To present a method for systematically mapping diversity of publication patterns at the author level in the social sciences and humanities in terms of publication type, publication language and co-authorship.Design/methodology/approach: In a follow-up to the hard partitioning clustering by Verleysen and Weeren in 2016, we now propose the complementary use of fuzzy cluster analysis, making use of a membership coefficient to study gradual differences between publication styles among authors within a scholarly discipline. The analysis of the probability density function of the membership coefficient allows to assess the distribution of publication styles within and between disciplines.Findings: As an illustration we analyze 1,828 productive authors affiliated in Flanders, Belgium. Whereas a hard partitioning previously identified two broad publication styles, an international one vs. a domestic one, fuzzy analysis now shows gradual differences among authors. Internal diversity also varies across disciplines and can be explained by researchers’ specialization and dissemination strategies.Research limitations: The dataset used is limited to one country for the years 2000–2011; a cognitive classification of authors may yield a different result from the affiliation-based classification used here.Practical implications: Our method is applicable to other bibliometric and research evaluation contexts, especially for the social sciences and humanities in non-Anglophone countries.Originality/value: The method proposed is a novel application of cluster analysis to the field of bibliometrics. Applied to publication patterns at the author level in the social sciences and humanities, for the first time it systematically documents intra-disciplinary diversity.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 article aims to investigate the current situation of the international almond trade and its consumption patterns. Traditionally, almonds are characterized by their good taste and high quality and regarded as an i...This article aims to investigate the current situation of the international almond trade and its consumption patterns. Traditionally, almonds are characterized by their good taste and high quality and regarded as an ideal source of several natural health nutrients. At present, the United States is the leading almond producer and exporter in the world, accompanied by Germany, Spain and Japan, the biggest almond importing countries. In order to study almond consumption patterns, two indicators were used in our study, the Food Consumer Location Ratio （FCLR） and the Food Consumer Location Relative Ratio （FCLRR）. Furthermore, to identify the almond consumption groups, we carried out two cluster analyses based on FCLR and FCLRR values, Finally, an analysis of the factors which have an impact on a country＇s almond consumption was conducted. It shows that income level, endowment of resources and tradition as well as dietary habits are key factors that help to shape a country＇s almond consumption pattern.展开更多
Lepidium sativum（commonly known as garden cress） belongs to the family Brassicaceae. It is a fastgrowing erect, annual herbaceous plant. Its seeds possess significant fracture healing, anti-asthmatic, anti-diabetic,...Lepidium sativum（commonly known as garden cress） belongs to the family Brassicaceae. It is a fastgrowing erect, annual herbaceous plant. Its seeds possess significant fracture healing, anti-asthmatic, anti-diabetic,hypoglycemic, nephrocurative and nephroprotective activities. In the present study, we assessed the genetic diversity of various genotypes of L. sativum using inter-simple sequence repeat（ISSR） markers. Out of 41 ISSR primers screened, 32 primers showed significant, clear and reproducible bands. A total of 510 amplified bands were obtained using 32 ISSR primers, out of which 422 bands were polymorphic and 88 bands were monomorphic. The percentage of polymorphism was found to be 82. A total of 35 unique alleles ranging insize from 200 to 2,900 bp were observed.Cluster analysis based on unweighted pair-group method,arithmetic mean divided the 18 genotypes into two main clusters, with the first having only HCS-08 genotype of L.sativum and other having all of the other 17 genotypes. The Jaccard similarity coefficient revealed a broad range32–72 % genetic relatedness among the 18 genotypes.展开更多
Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management...Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.展开更多
Regarding high drilling costs,an effort should be made to substantially reduce the drilling operation.To achieve this goal,exploration and development stages should be carried out precisely with maximum information ac...Regarding high drilling costs,an effort should be made to substantially reduce the drilling operation.To achieve this goal,exploration and development stages should be carried out precisely with maximum information acquired from the reservoir.The use of multi-attribute matching technology to predict sedimentary system has always been a very important but challenging task.To resolve the challenges,we utilized a quantitative analysis method of seismic attributes based on geological models involving high resolution 3D seismic data for sedimentary facies.We developed a workflow that includes core data,seismic attribute analysis,and well logging to highlight the benefit of understanding the facies distribution in the 3 rd Member of the Lower Jurassic Badaowan Formation,Hongshanzui area,Junggar Basin,China.1)Data preprocessing.2)Cluster analysis.3)RMS attribute based on a normal distribution constrains facies boundary.4)Mapping the sedimentary facies by using MRA(multiple regression analysis)prediction model combined with the lithofacies assemblages and logging facies assemblages.The confident level presented in this research is 0.745,which suggests that the methods and data-mining techniques are practical and efficient,and also be used to map facies in other similar geological settings.展开更多
In this study, whole-oil gas chromatographic fingerprint analyses were performed on oils from the Es3^3 reservoir in the Liubei area of the Nanpu Sag. The gas chromatographic peaks of cyclic and branched alkanes with ...In this study, whole-oil gas chromatographic fingerprint analyses were performed on oils from the Es3^3 reservoir in the Liubei area of the Nanpu Sag. The gas chromatographic peaks of cyclic and branched alkanes with relatively high resolution from nCl0 to nC25 were selected to establish a database of whole-oil gas chromatographic peak height ratio fingerprints. Reservoir fluid connectivity was identified by using clustering analysis. This method can reflect the gas chromatography fingerprint information accurately and entirely, and avoid the one-sidedness of the star diagram method which only selects several fixed gas chromatographic peaks.展开更多
This paper presents a method of modeling a fuzzy system with fuzzy and nonlinear border,obtaining systematic structure by clustering analysis,applying BP network (BPN) to generate rule bases antecedent function and us...This paper presents a method of modeling a fuzzy system with fuzzy and nonlinear border,obtaining systematic structure by clustering analysis,applying BP network (BPN) to generate rule bases antecedent function and using RBF network (RBFN) to approximate each rules conclusion function not only because of efficient capability of approximation nonlinear function of BPN and RBFN but also because of quickness of training speed of RBFN. In addition,structure design and training of relevant networks are discussed in detail. Finally,the structure optimization and overstudy of RBFN are discussed.展开更多
The normalized central moments are widely used in pattern recognition because of scale and translation invariance. The moduli of normalized central moments of the 1-dimensional complex range profiles are used here as ...The normalized central moments are widely used in pattern recognition because of scale and translation invariance. The moduli of normalized central moments of the 1-dimensional complex range profiles are used here as feature vector for radar target recognition. The common feature extraction method for high resolution range profile obtained by using Fourier-modified direct Mellin transform is inefficient and unsatisfactory in recognition rate And. generally speaking, the automatic target recognition method based on inverse synthetic aperture radar 2-dimensional imaging is not competent for real time object identification task because it needs complicated motion compensation which is sometimes too difficult to carry out. While the method applied here is competent for real-time recognition because of its computational efficiency. The result of processing experimental data indicates that this method is good at recognition.展开更多
We investigated the composition of plant communities to quantify their relationships with environmental parameters in the Chitral Hindukush range of Pakistan. We sampled tree vegetation using the Point Centered Quart...We investigated the composition of plant communities to quantify their relationships with environmental parameters in the Chitral Hindukush range of Pakistan. We sampled tree vegetation using the Point Centered Quarter （PCQ） method while understory vegetation was sampled in 1.5-m circular quadrats. Cedrus deodara is the national symbol of Pakistan and was dominant in the sampled communities. Because environmental variables determine vegetation types, we analyzed and evaluated edaphic and topographic factors. DCA-Ordination showed the major gradient as an amalgam of elevation （p〈0.05） and slope （p〈0.01） as the topographic factors correlated with species distribution. Soil variables were the factors of environmental significance along DCA axes. However, among these factors, Mg2＋ , K ＋ and N2＋ contributed not more than 0.054% 0.20% and 0.073%, respectively, to variation along the first ordination axis. We conclude that the principal reason for weak or no correlation with many edaphic variables was the anthropogenic disturbance of vegetation. The understory vegetation was composed of perennial herbs in most communities and was most dense under the tree canopy. The understory vegetation strongly regulates tree seedling growth and regeneration patterns. We recommend further study of the understory vegetation using permanent plots to aid development of forest regeneration strategies.展开更多
基金supported by the Science and Technology Project of Henan Provincial Science and Technology Department (No.0424490012 )Major Program of Henan Institute of Science and Technology (No.040132)
文摘Five factors expressing greenbelt quality and one factor expressing quantity were adopted for evaluation of the residential greenbelt, and the AHP （Analytical Hierarchy Process） method was used to determine the value of factors. Thirty residential areas were selected as the samples. Two principal components were extracted and their expression was constructed by method of factor anlysis, therefore, quality evaluation of residential greenbelt was obtained. The accuracy of the function and implement quality classification toward the residential greenbelts in Xinxiang City were validated by clustering analysis method. The results showed that the greenbelt quality of fourteen residential areas was higher than the average level, of which eleven were newly-built residential areas. The 30 residential areas were classified into three types according to their greenbelt features and their formation by clustering analysis method. Finally rational proposal basing on aforesaid evaluating results was proposed for construction and renewal of residential greenbelt, upon which directive basis was provided for construction and renewal of residential greenbelt.
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 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Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.
基金supported by the National Defense Pre-research Fund of China under Grant No 41101030401
文摘A multi-parameter signal sorting algorithm for interleaved radar pulses in dense emitter environment is presented. The algorithm includes two parts, pulse classification and pulse repetition interval （PRI） analysis. Firstly, we propose the dynamic distance clustering （DDC） for classification. In the clustering algorithm, the multi-dimension features of radar pulse are used for reliable classification. The similarity threshold estimation method in DDC is derived, which contributes to the efficiency of the algorithm. However, DDC has large computation with many signal pulses. Then, in order to sort radar signals in real time, the improved DDC （IDDC） algorithm is proposed. Finally, PRI analysis is adopted to complete the process of sorting. The simulation experiments and hardware implementations show both algorithms are effective.
基金supported by the Fund for Philosophy and Social Sciences,Ministry of Education of China(Grant No.05JZD00024)
文摘With the SPSS and the help of factor method and hierarchical clustered method,journal articles on digital information resources(DIR) from CNKI in the past ten years are analyzed with a co-word analytical method in this paper. The hot issues of studies on DIR and the relationship between those subjects are analyzed in this investigation as well.
基金Supported by the 13th 5-Year National Science and Technology Supporting Project(2018YFC2000302)。
文摘In order to implement the robust cluster analysis,solve the problem that the outliers in the data will have a serious disturbance to the probability density parameter estimation,and therefore affect the accuracy of clustering,a robust cluster analysis method is proposed which is based on the diversity self-paced t-mixture model.This model firstly adopts the t-distribution as the submodel which tail is easily controllable.On this basis,it utilizes the entropy penalty expectation conditional maximal algorithm as a pre-clustering step to estimate the initial parameters.After that,this model introduces l2,1-norm as a self-paced regularization term and developes a new ECM optimization algorithm,in order to select high confidence samples from each component in training.Finally,experimental results on several real-world datasets in different noise environments show that the diversity self-paced t-mixture model outperforms the state-of-the-art clustering methods.It provides significant guidance for the construction of the robust mixture distribution model.
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supported in part by the National Natural Science Foundation of China under Grant No. 60873216Scientific and Technological Research Priority Projects of Sichuan Province under Grant No. 2012GZ0017Basic Research of Application Fund Project of Sichuan Province under Grant No. 2011JY0100
文摘Chosen-message pair Simple Power Analysis (SPA) attacks were proposed by Boer, Yen and Homma, and are attack methods based on searches for collisions of modular multiplication. However, searching for collisions is difficult in real environments. To circumvent this problem, we propose the Simple Power Clustering Attack (SPCA), which can automatically identify the modular multiplication collision. The insignificant effects of collision attacks were validated in an Application Specific Integrated Circuit (ASIC) environment. After treatment with SPCA, the automatic secret key recognition rate increased to 99%.
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.
文摘Purpose: To present a method for systematically mapping diversity of publication patterns at the author level in the social sciences and humanities in terms of publication type, publication language and co-authorship.Design/methodology/approach: In a follow-up to the hard partitioning clustering by Verleysen and Weeren in 2016, we now propose the complementary use of fuzzy cluster analysis, making use of a membership coefficient to study gradual differences between publication styles among authors within a scholarly discipline. The analysis of the probability density function of the membership coefficient allows to assess the distribution of publication styles within and between disciplines.Findings: As an illustration we analyze 1,828 productive authors affiliated in Flanders, Belgium. Whereas a hard partitioning previously identified two broad publication styles, an international one vs. a domestic one, fuzzy analysis now shows gradual differences among authors. Internal diversity also varies across disciplines and can be explained by researchers’ specialization and dissemination strategies.Research limitations: The dataset used is limited to one country for the years 2000–2011; a cognitive classification of authors may yield a different result from the affiliation-based classification used here.Practical implications: Our method is applicable to other bibliometric and research evaluation contexts, especially for the social sciences and humanities in non-Anglophone countries.Originality/value: The method proposed is a novel application of cluster analysis to the field of bibliometrics. Applied to publication patterns at the author level in the social sciences and humanities, for the first time it systematically documents intra-disciplinary diversity.
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.
文摘This article aims to investigate the current situation of the international almond trade and its consumption patterns. Traditionally, almonds are characterized by their good taste and high quality and regarded as an ideal source of several natural health nutrients. At present, the United States is the leading almond producer and exporter in the world, accompanied by Germany, Spain and Japan, the biggest almond importing countries. In order to study almond consumption patterns, two indicators were used in our study, the Food Consumer Location Ratio （FCLR） and the Food Consumer Location Relative Ratio （FCLRR）. Furthermore, to identify the almond consumption groups, we carried out two cluster analyses based on FCLR and FCLRR values, Finally, an analysis of the factors which have an impact on a country＇s almond consumption was conducted. It shows that income level, endowment of resources and tradition as well as dietary habits are key factors that help to shape a country＇s almond consumption pattern.
文摘Lepidium sativum（commonly known as garden cress） belongs to the family Brassicaceae. It is a fastgrowing erect, annual herbaceous plant. Its seeds possess significant fracture healing, anti-asthmatic, anti-diabetic,hypoglycemic, nephrocurative and nephroprotective activities. In the present study, we assessed the genetic diversity of various genotypes of L. sativum using inter-simple sequence repeat（ISSR） markers. Out of 41 ISSR primers screened, 32 primers showed significant, clear and reproducible bands. A total of 510 amplified bands were obtained using 32 ISSR primers, out of which 422 bands were polymorphic and 88 bands were monomorphic. The percentage of polymorphism was found to be 82. A total of 35 unique alleles ranging insize from 200 to 2,900 bp were observed.Cluster analysis based on unweighted pair-group method,arithmetic mean divided the 18 genotypes into two main clusters, with the first having only HCS-08 genotype of L.sativum and other having all of the other 17 genotypes. The Jaccard similarity coefficient revealed a broad range32–72 % genetic relatedness among the 18 genotypes.
基金The National Forestry Commission of Mexico and The Mexican National Council for Science and Technology(CONAFOR-CONACYT-115900)。
文摘Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.
基金supported by the National Natural Science Foundation of China(41902109)Tianshan Youth Program(2020Q064)+1 种基金National Major Projects(2017ZX05001004)Tianshan Innovation Team Program(2020D14023)。
文摘Regarding high drilling costs,an effort should be made to substantially reduce the drilling operation.To achieve this goal,exploration and development stages should be carried out precisely with maximum information acquired from the reservoir.The use of multi-attribute matching technology to predict sedimentary system has always been a very important but challenging task.To resolve the challenges,we utilized a quantitative analysis method of seismic attributes based on geological models involving high resolution 3D seismic data for sedimentary facies.We developed a workflow that includes core data,seismic attribute analysis,and well logging to highlight the benefit of understanding the facies distribution in the 3 rd Member of the Lower Jurassic Badaowan Formation,Hongshanzui area,Junggar Basin,China.1)Data preprocessing.2)Cluster analysis.3)RMS attribute based on a normal distribution constrains facies boundary.4)Mapping the sedimentary facies by using MRA(multiple regression analysis)prediction model combined with the lithofacies assemblages and logging facies assemblages.The confident level presented in this research is 0.745,which suggests that the methods and data-mining techniques are practical and efficient,and also be used to map facies in other similar geological settings.
基金funded by Shandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Minerals (Project DMSM201009)Key Laboratory of Tectonics and Petroleum Resources (China University of Geosciences), Ministry of Education, China (Project TPR-2010-29)
文摘In this study, whole-oil gas chromatographic fingerprint analyses were performed on oils from the Es3^3 reservoir in the Liubei area of the Nanpu Sag. The gas chromatographic peaks of cyclic and branched alkanes with relatively high resolution from nCl0 to nC25 were selected to establish a database of whole-oil gas chromatographic peak height ratio fingerprints. Reservoir fluid connectivity was identified by using clustering analysis. This method can reflect the gas chromatography fingerprint information accurately and entirely, and avoid the one-sidedness of the star diagram method which only selects several fixed gas chromatographic peaks.
文摘This paper presents a method of modeling a fuzzy system with fuzzy and nonlinear border,obtaining systematic structure by clustering analysis,applying BP network (BPN) to generate rule bases antecedent function and using RBF network (RBFN) to approximate each rules conclusion function not only because of efficient capability of approximation nonlinear function of BPN and RBFN but also because of quickness of training speed of RBFN. In addition,structure design and training of relevant networks are discussed in detail. Finally,the structure optimization and overstudy of RBFN are discussed.
文摘The normalized central moments are widely used in pattern recognition because of scale and translation invariance. The moduli of normalized central moments of the 1-dimensional complex range profiles are used here as feature vector for radar target recognition. The common feature extraction method for high resolution range profile obtained by using Fourier-modified direct Mellin transform is inefficient and unsatisfactory in recognition rate And. generally speaking, the automatic target recognition method based on inverse synthetic aperture radar 2-dimensional imaging is not competent for real time object identification task because it needs complicated motion compensation which is sometimes too difficult to carry out. While the method applied here is competent for real-time recognition because of its computational efficiency. The result of processing experimental data indicates that this method is good at recognition.
基金supported by the Higher Education Commission of Pakistan
文摘We investigated the composition of plant communities to quantify their relationships with environmental parameters in the Chitral Hindukush range of Pakistan. We sampled tree vegetation using the Point Centered Quarter （PCQ） method while understory vegetation was sampled in 1.5-m circular quadrats. Cedrus deodara is the national symbol of Pakistan and was dominant in the sampled communities. Because environmental variables determine vegetation types, we analyzed and evaluated edaphic and topographic factors. DCA-Ordination showed the major gradient as an amalgam of elevation （p〈0.05） and slope （p〈0.01） as the topographic factors correlated with species distribution. Soil variables were the factors of environmental significance along DCA axes. However, among these factors, Mg2＋ , K ＋ and N2＋ contributed not more than 0.054% 0.20% and 0.073%, respectively, to variation along the first ordination axis. We conclude that the principal reason for weak or no correlation with many edaphic variables was the anthropogenic disturbance of vegetation. The understory vegetation was composed of perennial herbs in most communities and was most dense under the tree canopy. The understory vegetation strongly regulates tree seedling growth and regeneration patterns. We recommend further study of the understory vegetation using permanent plots to aid development of forest regeneration strategies.
