Cycle slip detection and repair is one of the key technologies for GNSS high-precision positioning.We introduce an enhanced methodology for detecting and repairing BDS four-frequency cycle slips,utilizing fuzzy cluste...Cycle slip detection and repair is one of the key technologies for GNSS high-precision positioning.We introduce an enhanced methodology for detecting and repairing BDS four-frequency cycle slips,utilizing fuzzy clustering analysis.Firstly,based on fuzzy clustering analysis,the optimal combinations for the BDS four-frequency,including extra-wide lane(EWL),wide lane(WL),and narrow lane(NL),were selected.Secondly,the feasibility of this method was verified using actual static and dynamic observation data,and different types of cycle slips were simulated for further validation.Meanwhile,the proposed method was compared with the classical Turbo-Edit method through experiments.Finally,cycle slips were repaired using the least squares method.According to the experimental results,the optimal geometry-free phase combinations(-2,2,1,-1),(1,-1,1,-1),(3,2,-2,-3),and the pseudo-range phase combination(-1,1,1,-1),selected based on fuzzy clustering analysis,were used for cycle slip detection.The proposed method accurately detected small,large,and specific cycle slips simulated in the actual data.Compared with the Turbo-Edit method,the proposed methodwas able to detect specific cycle slips that Turbo-Edit could not.It is worth noting that during the repair process,the coefficients of the combined observation values are integers,preserving the integer cycle characteristic of the observation values,which allows cycle slips to be fixed directly,eliminating the need for complex searching procedures.Consequently,by enhancing the precision and reliability of the detection of BDS four-frequency cycle slips,our proposed method provides the support for the high-precision localization of BDS multi-frequency observations.展开更多
With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing hig...With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods,this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification.To resolve the scalability issue of coupled transmission and distribution power systems,clustering is first carried out based on the distribution system states.As the data and models of the transmission system and distribution systems are not shared.For the transmission system,equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system.Further,the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models.Then,a parameter identification method is proposed to obtain the parameters of the equivalent load model.Finally,a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model.The results of the numerical analysis show that compared with the traditional master-slave splitting method,the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy.展开更多
Inter-simple sequence repeat（ISSR） molecular markers were applied to analyze the genetic diversity and clustering of 48 introduced and bred cultivars of Olea euyopaea L. Totally 106 DNA bands were amplified by 11 sc...Inter-simple sequence repeat（ISSR） molecular markers were applied to analyze the genetic diversity and clustering of 48 introduced and bred cultivars of Olea euyopaea L. Totally 106 DNA bands were amplified by 11 screened primers, including 99 polymorphic bands; the percentage of polymorphic loci was 93.40%, indicating a rich genetic diversity in Olea euyopaea L. germplasm resources. Based on Nei＇s genetic distances between various cultivars, a dendrogram of 48 cultivars of Olea euyopaea L. was constructed using unweighted pair-group（UPMGA）method,which showed that 48 cultivars were clustered into four main categories; 84.6% of native cultivars were clustered into two categories; most of introduced cultivars were clustered based on their sources and main usages but not on their geographic origins. This study will provide references for the utilization and further genetic improvement of Olea euyopaea L. germplasm resources.展开更多
In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising...In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising data based on a semantic description in coal mines is studied.First,the semantic and numerical-based hybrid description method of security supervising data in coal mines is described.Secondly,the similarity measurement method of semantic and numerical data are separately given and a weight-based hybrid similarity measurement method for the security supervising data based on a semantic description in coal mines is presented.Thirdly,taking the hybrid similarity measurement method as the distance criteria and using a grid methodology for reference,an improved CURE clustering algorithm based on the grid is presented.Finally,the simulation results of a security supervising data set in coal mines validate the efficiency of the algorithm.展开更多
[Objective] This study aimed to develop ACGM markers for the clustering analysis of large grained Brassica napus materials. [Method] A total of 44 pairs of ACGM primers were designed according to 18 genes related to A...[Objective] This study aimed to develop ACGM markers for the clustering analysis of large grained Brassica napus materials. [Method] A total of 44 pairs of ACGM primers were designed according to 18 genes related to Arabidopsis grain development and their homologous rape EST sequences. After electrophoresis, 18 pairs of ACGM primers were selected for the clustering analysis of 16 larger grained samples and four fine grained samples of rapeseed. [Result] PCR result showed that 2-6 specific bands were respectively amplified by each pair of primes, and all the bands were polymorphic and repeatable, suggesting that the optimized ACGM markers were useful for clustering analysis of B. napus species. Clustering analysis revealed that the 20 rapeseed samples were divided into three clusters A, B, and C at similarity coefficient 0.6. Then, the clusters A and B were further divided into five sub clusters A1, A2, A3, B1 and B2 at similarity coefficient 0.67. [Conclusion] This study will provide theoretical and practical values for rape breeding.展开更多
[Objective] This study aimed to investigate the trace elements in Rehman- nia glutinosa Libosch. by using principal component analysis and clustering analysis. [Method] Principal component analysis and clustering anal...[Objective] This study aimed to investigate the trace elements in Rehman- nia glutinosa Libosch. by using principal component analysis and clustering analysis. [Method] Principal component analysis and clustering analysis of R. glutinosa medicinal materials from different sources were conducted with contents of six trace elements as indices. [Result] The principal component analysis could comprehen- sively evaluate the quality of R. glutinosa samples with objective results which was consistent with the results of clustering analysis. [Conclusion] Principal component analysis and clustering analysis methods can be used for the quality evaluation of Chinese medicinal materials with multiple indices.展开更多
The goal of this study was to optimize the constitutive parameters of foundation soils using a k-means algorithm with clustering analysis. A database was collected from unconfined compression tests, Proctor tests and ...The goal of this study was to optimize the constitutive parameters of foundation soils using a k-means algorithm with clustering analysis. A database was collected from unconfined compression tests, Proctor tests and grain distribution tests of soils taken from three different types of foundation pits： raft foundations, partial raft foundations and strip foundations. k-means algorithm with clustering analysis was applied to determine the most appropriate foundation type given the un- confined compression strengths and other parameters of the different soils.展开更多
With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings,crowd monitoring has taken a considerable attentions in many disciplines such as psychology,sociology,engine...With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings,crowd monitoring has taken a considerable attentions in many disciplines such as psychology,sociology,engineering,and computer vision.This is due to the fact that,monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents(e.g.sports).One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles(UAVs),because UAVs have the capability to acquiring fast,low costs,high-resolution and real-time images over crowd areas.In addition,geo-referenced images can also be provided through integration of on-board positioning sensors(e.g.GPS/IMU)with vision sensors(digital cameras and laser scanner).In this paper,a new testing procedure based on feature from accelerated segment test(FAST)algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions.The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order.A single pixel which takes the ranking number 9(for FAST-9)or 12(for FAST-12)was then compared with the center pixel.Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features.The results show that the proposed algorithms are able to extract crowd features from different UAV images.Overall,the values of Completeness range from 55 to 70%whereas the range of correctness values was 91 to 94%.展开更多
Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at ...Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.展开更多
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
A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Th...A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Then some representative examples are chosen from each of them to construct SVM components. At last,the outputs of the individual classifiers are fused through ma-jority voting method to obtain the final decision. Comparisons of performance between the proposed method and other popular ensemble approaches,such as Bagging,Adaboost and k.-fold cross valida-tion,are carried out on synthetic and UCI datasets. The experimental results show that our method has higher classification accuracy since the example distribution information is considered during en-semble through clustering analysis. It further indicates that our method needs a much smaller size of training subsets than Bagging and Adaboost to obtain satisfactory classification accuracy.展开更多
A novel multivariate similarity clustering analysis (MSCA) approach was used to estimate a biogeographical division scheme for the global terrestrial fauna and was compared against other widely used clustering algorit...A novel multivariate similarity clustering analysis (MSCA) approach was used to estimate a biogeographical division scheme for the global terrestrial fauna and was compared against other widely used clustering algorithms. The faunal dataset included almost all terrestrial and freshwater fauna, a total of 4631 families, 141,814 genera, and 1,334,834 species. Our findings demonstrated that suitable results were only obtained with the MSCA method, which was associated with distinct hierarchies, reasonable structuring, and furthermore, conformed to biogeographical criteria. A total of seven kingdoms and 20 sub-kingdoms were identified. We discovered that the clustering results for the higher and lower animals did not differ significantly, leading us to consider that the analysis result is convincing as the first zoogeographical division scheme for global all terrestrial animals.展开更多
Affected by many involved factors, different dimensions, data with large difference, incomplete information and so on, the most optimal selection of regional outburst prevention measures for outburst mine has become a...Affected by many involved factors, different dimensions, data with large difference, incomplete information and so on, the most optimal selection of regional outburst prevention measures for outburst mine has become a complicated system project. The traditional way of outburst prevention measure selection belongs to qualitative method, which may cause high-cost of gas control, huge quantities of drilling work, long construction time and even secondary disaster. To solve the above-mentioned problems, in light of occurrence status of coal seam gas in No. 21 mining area of Jinzhushan Tuzhu Mine, through grey fixed weight clustering theory and a combination method of qualitative and quantitative analysis, the judging model with multi-objective classification for optimization of outburst prevention measures was established. The three weight coefficients of outburst prevention technology scheme are sorted, in order to determine the advantages and disadvantages of each outburst prevention technology scheme under the comprehensive evaluation of multi-target. Finally, the problem of quantitative selection for regional outburst prevention technology scheme is solved under the situation of multi-factor mode and incomplete information, which provides reasonable and effective technical measures for prevention of coal and gas outburst disaster.展开更多
The main task of provenance analysis is to determine the source of sediments and the position of parent rocks.Provenance analysis may find out the relationship between erosion districts and sediment zone,between the u...The main task of provenance analysis is to determine the source of sediments and the position of parent rocks.Provenance analysis may find out the relationship between erosion districts and sediment zone,between the uplift and the depression in the process of basin development.The authors use the method of heavy mineral clustering analysis and estimate the provenance direction of Huanghua Depression in the Paleogene Kong 2 Member.Research shows that there were five provenance areas of Kong 2 Member in Kongnan area.They are western(Shenusi),northwestern(Cangzhou),eastern(Ganhuatun),northeastern and southeastern.The main provenance areas were northwestern and western,while the southern provenance could not be ruled out.And these areas are consistent with the known provenance areas.展开更多
In this study,the world’s land(except Antarctica)is divided into 67 basic geographical units according to ecological types.Using our newly proposed MSCA(Multivariate Similarity Clustering Analysis)method,7,591 specie...In this study,the world’s land(except Antarctica)is divided into 67 basic geographical units according to ecological types.Using our newly proposed MSCA(Multivariate Similarity Clustering Analysis)method,7,591 species of modern terrestrial mammals belonging to 1,374 genera in 162 families and 2,378 species of mammals in the Wallace era before 1876 are quantitatively analyzed,and almost the same clustering results are obtained,with clear levels and reasonable clustering,which conform to the principles of geography,statistics,ecology and biology.It not only affirms and supports the reasonable kernel of Wallace’s scheme,but also puts forward suggestions that should be revised and improved.The large or small differences between the clustering results and the mammalian geographical zoning schemes of contemporary scholars are caused by different analysis methods,and they are highly consistent with the analysis results of chordates,angiosperms and insects in the world analyzed by the same method.Once again,it confirms the homogeneity of the global biological distribution pattern of major groups,and the possibility of building a unified biogeographic zoning system in the world.展开更多
Identifying user electric events is of significant importance for uncovering patterns in user electric consumption behavior and enhancing the level of energy efficiencymanagement on the user side.To promptly and effec...Identifying user electric events is of significant importance for uncovering patterns in user electric consumption behavior and enhancing the level of energy efficiencymanagement on the user side.To promptly and effectively detect electric events embedded in the electric data of the user,this paper introduces a cluster-based electric event identification model designed based on the electric current state sequence dataset.Based on extracting features from the electric current state sequence,this model treats the identification of electric events in the current sequence as a clustering partition problem utilizing the feature set derived from the electric current state sequence.To assess the model’s efficacy,twometrics were utilized:the silhouette coefficient and precision,to evaluate its performance.The experiments demonstrate that compared to the identification model for the electric event of the user based on k-means clustering,SOM clustering,and FCM clustering,the identification model for the electric event of the user based on hierarchical clustering is more effective in identifying electric events.展开更多
Purpose:This study analyzes the profiles of elite Brazilian researchers,recognized through the prestigious CNPq productivity scholarships.By identifying distinct researcher clusters,the study sheds light on different ...Purpose:This study analyzes the profiles of elite Brazilian researchers,recognized through the prestigious CNPq productivity scholarships.By identifying distinct researcher clusters,the study sheds light on different academic strategies,levels of productivity,and academic contributions within the Brazilian higher education system.Design/methodology/approach:The research analyzes a comprehensive dataset of 14,003 researchers,employing principal component analysis(PCA)followed by cluster analysis to group researchers based on their academic attributes.The clusters reflect diverse aspects of research productivity,graduate supervisions,and publication patterns.Findings:The analysis reveals the existence of three distinct researcher profiles(the Advanced Supervisors,the Book Publishers/Organizers,and the Generalists).The study also highlights the characteristics of highcaliber scientists,representing the upper echelon of Brazilian researchers in terms of productivity and impact.Research limitations:Although the study provides a robust analysis of the Brazilian system,the results reflect specific characteristics of the Brazilian academic context.Furthermore,the analysis was restricted to normalized annual data,which may overlook temporal variations in researcher productivity.Pratical implications:The findings provide valuable insights for policy makers,funding agencies(such as CNPq),and university administrators who aim to develop tailored support programs for different researcher profiles.Originality/value:The cluster-based profiling offers a novel perspective on how different academic trajectories coexist within a national science system,offering lessons for other emerging economies.展开更多
Traditional Chinese medicine(TCM)has played a significant role in the prevention and treatment of chronic heart failure(CHF).To study TCM diagnosis of CHF,a total of 278 Chinese clinical research articles on the study...Traditional Chinese medicine(TCM)has played a significant role in the prevention and treatment of chronic heart failure(CHF).To study TCM diagnosis of CHF,a total of 278 Chinese clinical research articles on the study of CHF syndromes in recent 40 years retrieved from Web of Science,Scopus,Pub Med,Embase,CNKI,Wanfang Data,Cq VIP,and Sino Med.According to cumulative frequency analysis,network analysis,and hierarchical cluster analysis,the study found the distribution of CHF syndromes was syndrome of qi deficiency with blood stasis,syndrome of qi and yin deficiency,syndrome of yang deficiency with water flooding,syndrome of heart blood stasis obstruction,syndrome of turbid phlegm,and syndrome of collapse due to primordial yang deficiency.The syndrome elements on location of illness were heart,kidney,lung,and spleen.The syndrome elements on nature of illness were qi deficiency,blood stasis,yang deficiency,yin deficiency,water retention,and turbid phlegm.These findings can provide reference to the research on diagnosis and treatment of CHF,and contribute to the study on syndrome standardization and objective research of TCM diagnosis.展开更多
The accuracy and reliability of non-destructive testing(NDT)approaches in detecting interior corrosion problems are critical,yet research in this field is limited.This work describes a novel way to monitor the structu...The accuracy and reliability of non-destructive testing(NDT)approaches in detecting interior corrosion problems are critical,yet research in this field is limited.This work describes a novel way to monitor the structural integrity of steel gas pipelines that uses advanced numerical modeling techniques to anticipate fracture development and corrosion effects.The objective is to increase pipeline dependability and safety through more precise,real-time health evaluations.Compared to previous approaches,our solution provides higher accuracy in fault detection and quantification,making it ideal for pipeline integritymonitoring in real-world applications.To solve this issue,statistical analysis was conducted on the size and directional distribution features of about 380,000 sets of internal corrosion faults,as well as simulations of erosion and wear patterns on bent pipes.Using real defectmorphologies,we developed a modeling framework for typical interior corrosion flaws.We evaluated and validated the applicability and effectiveness of in-service inspection processes,as well as conducted on-site comparison tests.The results show that(1)the length and width of corrosion defects follow a log-normal distribution,the clock orientation follows a normal distribution,and the peak depth follows a Freundlich EX function distribution pattern;(2)pipeline corrosion defect data can be classified into three classes using the K-means clustering algorithm,allowing rapid and convenient acquisition of typical size and orientation characteristics of internal corrosion defects;(3)the applicability range and boundary conditions of various NDT techniques were verified,establishing comprehensive selection principles for internal corrosion defect detection technology;(4)on-site inspection results showed a 31%The simulation and validation platform for typical interior corrosion issues greatly enhances the accuracy and reliability of detection data.展开更多
基金supported by the National Natural Science Foundation of China(42174003)the Gansu Provincial Department of Education:Innovation Fund Project for College Teachers(2023A-035)+1 种基金Gansu Provincial Science and Technology Program(Joint Research Fund),24JRRA856the Lanzhou Talent Innovation Project,2023-RC-31.
文摘Cycle slip detection and repair is one of the key technologies for GNSS high-precision positioning.We introduce an enhanced methodology for detecting and repairing BDS four-frequency cycle slips,utilizing fuzzy clustering analysis.Firstly,based on fuzzy clustering analysis,the optimal combinations for the BDS four-frequency,including extra-wide lane(EWL),wide lane(WL),and narrow lane(NL),were selected.Secondly,the feasibility of this method was verified using actual static and dynamic observation data,and different types of cycle slips were simulated for further validation.Meanwhile,the proposed method was compared with the classical Turbo-Edit method through experiments.Finally,cycle slips were repaired using the least squares method.According to the experimental results,the optimal geometry-free phase combinations(-2,2,1,-1),(1,-1,1,-1),(3,2,-2,-3),and the pseudo-range phase combination(-1,1,1,-1),selected based on fuzzy clustering analysis,were used for cycle slip detection.The proposed method accurately detected small,large,and specific cycle slips simulated in the actual data.Compared with the Turbo-Edit method,the proposed methodwas able to detect specific cycle slips that Turbo-Edit could not.It is worth noting that during the repair process,the coefficients of the combined observation values are integers,preserving the integer cycle characteristic of the observation values,which allows cycle slips to be fixed directly,eliminating the need for complex searching procedures.Consequently,by enhancing the precision and reliability of the detection of BDS four-frequency cycle slips,our proposed method provides the support for the high-precision localization of BDS multi-frequency observations.
基金State Grid Jiangsu Electric Power Co.,Ltd.Technology Project(J2023121).
文摘With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods,this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification.To resolve the scalability issue of coupled transmission and distribution power systems,clustering is first carried out based on the distribution system states.As the data and models of the transmission system and distribution systems are not shared.For the transmission system,equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system.Further,the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models.Then,a parameter identification method is proposed to obtain the parameters of the equivalent load model.Finally,a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model.The results of the numerical analysis show that compared with the traditional master-slave splitting method,the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy.
基金Supported by Key Project of New Product Development in Yunnan Province(2009BB006)~~
文摘Inter-simple sequence repeat（ISSR） molecular markers were applied to analyze the genetic diversity and clustering of 48 introduced and bred cultivars of Olea euyopaea L. Totally 106 DNA bands were amplified by 11 screened primers, including 99 polymorphic bands; the percentage of polymorphic loci was 93.40%, indicating a rich genetic diversity in Olea euyopaea L. germplasm resources. Based on Nei＇s genetic distances between various cultivars, a dendrogram of 48 cultivars of Olea euyopaea L. was constructed using unweighted pair-group（UPMGA）method,which showed that 48 cultivars were clustered into four main categories; 84.6% of native cultivars were clustered into two categories; most of introduced cultivars were clustered based on their sources and main usages but not on their geographic origins. This study will provide references for the utilization and further genetic improvement of Olea euyopaea L. germplasm resources.
基金The National Natural Science Foundation of China(No.50674086)Specialized Research Fund for the Doctoral Program of Higher Education(No.20060290508)the Postdoctoral Scientific Program of Jiangsu Province(No.0701045B)
文摘In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising data based on a semantic description in coal mines is studied.First,the semantic and numerical-based hybrid description method of security supervising data in coal mines is described.Secondly,the similarity measurement method of semantic and numerical data are separately given and a weight-based hybrid similarity measurement method for the security supervising data based on a semantic description in coal mines is presented.Thirdly,taking the hybrid similarity measurement method as the distance criteria and using a grid methodology for reference,an improved CURE clustering algorithm based on the grid is presented.Finally,the simulation results of a security supervising data set in coal mines validate the efficiency of the algorithm.
基金Supported by the National Natural Science Foundation of China(30860147)Open Funds of National Key Laboratory of Crop Genetic Improvement(ZK200902)Natural Science Foundation of Yunnan Province(2011FB117)~~
文摘[Objective] This study aimed to develop ACGM markers for the clustering analysis of large grained Brassica napus materials. [Method] A total of 44 pairs of ACGM primers were designed according to 18 genes related to Arabidopsis grain development and their homologous rape EST sequences. After electrophoresis, 18 pairs of ACGM primers were selected for the clustering analysis of 16 larger grained samples and four fine grained samples of rapeseed. [Result] PCR result showed that 2-6 specific bands were respectively amplified by each pair of primes, and all the bands were polymorphic and repeatable, suggesting that the optimized ACGM markers were useful for clustering analysis of B. napus species. Clustering analysis revealed that the 20 rapeseed samples were divided into three clusters A, B, and C at similarity coefficient 0.6. Then, the clusters A and B were further divided into five sub clusters A1, A2, A3, B1 and B2 at similarity coefficient 0.67. [Conclusion] This study will provide theoretical and practical values for rape breeding.
基金Supported by Fund of Sichuan Provincial Administration of traditional Chinese Medicine(2008-12)~~
文摘[Objective] This study aimed to investigate the trace elements in Rehman- nia glutinosa Libosch. by using principal component analysis and clustering analysis. [Method] Principal component analysis and clustering analysis of R. glutinosa medicinal materials from different sources were conducted with contents of six trace elements as indices. [Result] The principal component analysis could comprehen- sively evaluate the quality of R. glutinosa samples with objective results which was consistent with the results of clustering analysis. [Conclusion] Principal component analysis and clustering analysis methods can be used for the quality evaluation of Chinese medicinal materials with multiple indices.
文摘The goal of this study was to optimize the constitutive parameters of foundation soils using a k-means algorithm with clustering analysis. A database was collected from unconfined compression tests, Proctor tests and grain distribution tests of soils taken from three different types of foundation pits： raft foundations, partial raft foundations and strip foundations. k-means algorithm with clustering analysis was applied to determine the most appropriate foundation type given the un- confined compression strengths and other parameters of the different soils.
文摘With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings,crowd monitoring has taken a considerable attentions in many disciplines such as psychology,sociology,engineering,and computer vision.This is due to the fact that,monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents(e.g.sports).One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles(UAVs),because UAVs have the capability to acquiring fast,low costs,high-resolution and real-time images over crowd areas.In addition,geo-referenced images can also be provided through integration of on-board positioning sensors(e.g.GPS/IMU)with vision sensors(digital cameras and laser scanner).In this paper,a new testing procedure based on feature from accelerated segment test(FAST)algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions.The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order.A single pixel which takes the ranking number 9(for FAST-9)or 12(for FAST-12)was then compared with the center pixel.Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features.The results show that the proposed algorithms are able to extract crowd features from different UAV images.Overall,the values of Completeness range from 55 to 70%whereas the range of correctness values was 91 to 94%.
基金This work has been supported by.Central University Research Fund(No.2016MS116,No.2016MS117,No.2018MS074)the National Natural Science Foundation(51677072).
文摘Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.
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 (No.60472072)the Specialized Research Foundation for the Doctoral Program of Higher Educa-tion of China (No.20040699034).
文摘A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Then some representative examples are chosen from each of them to construct SVM components. At last,the outputs of the individual classifiers are fused through ma-jority voting method to obtain the final decision. Comparisons of performance between the proposed method and other popular ensemble approaches,such as Bagging,Adaboost and k.-fold cross valida-tion,are carried out on synthetic and UCI datasets. The experimental results show that our method has higher classification accuracy since the example distribution information is considered during en-semble through clustering analysis. It further indicates that our method needs a much smaller size of training subsets than Bagging and Adaboost to obtain satisfactory classification accuracy.
文摘A novel multivariate similarity clustering analysis (MSCA) approach was used to estimate a biogeographical division scheme for the global terrestrial fauna and was compared against other widely used clustering algorithms. The faunal dataset included almost all terrestrial and freshwater fauna, a total of 4631 families, 141,814 genera, and 1,334,834 species. Our findings demonstrated that suitable results were only obtained with the MSCA method, which was associated with distinct hierarchies, reasonable structuring, and furthermore, conformed to biogeographical criteria. A total of seven kingdoms and 20 sub-kingdoms were identified. We discovered that the clustering results for the higher and lower animals did not differ significantly, leading us to consider that the analysis result is convincing as the first zoogeographical division scheme for global all terrestrial animals.
文摘Affected by many involved factors, different dimensions, data with large difference, incomplete information and so on, the most optimal selection of regional outburst prevention measures for outburst mine has become a complicated system project. The traditional way of outburst prevention measure selection belongs to qualitative method, which may cause high-cost of gas control, huge quantities of drilling work, long construction time and even secondary disaster. To solve the above-mentioned problems, in light of occurrence status of coal seam gas in No. 21 mining area of Jinzhushan Tuzhu Mine, through grey fixed weight clustering theory and a combination method of qualitative and quantitative analysis, the judging model with multi-objective classification for optimization of outburst prevention measures was established. The three weight coefficients of outburst prevention technology scheme are sorted, in order to determine the advantages and disadvantages of each outburst prevention technology scheme under the comprehensive evaluation of multi-target. Finally, the problem of quantitative selection for regional outburst prevention technology scheme is solved under the situation of multi-factor mode and incomplete information, which provides reasonable and effective technical measures for prevention of coal and gas outburst disaster.
基金Supported by Project of Dagang Branch of Petroleum Group Company Ltd,CNPC No TJDG-JZHT-2005-JSFW-0000-00339
文摘The main task of provenance analysis is to determine the source of sediments and the position of parent rocks.Provenance analysis may find out the relationship between erosion districts and sediment zone,between the uplift and the depression in the process of basin development.The authors use the method of heavy mineral clustering analysis and estimate the provenance direction of Huanghua Depression in the Paleogene Kong 2 Member.Research shows that there were five provenance areas of Kong 2 Member in Kongnan area.They are western(Shenusi),northwestern(Cangzhou),eastern(Ganhuatun),northeastern and southeastern.The main provenance areas were northwestern and western,while the southern provenance could not be ruled out.And these areas are consistent with the known provenance areas.
基金supported by the key laboratory foundation of Henna(112300413221).
文摘In this study,the world’s land(except Antarctica)is divided into 67 basic geographical units according to ecological types.Using our newly proposed MSCA(Multivariate Similarity Clustering Analysis)method,7,591 species of modern terrestrial mammals belonging to 1,374 genera in 162 families and 2,378 species of mammals in the Wallace era before 1876 are quantitatively analyzed,and almost the same clustering results are obtained,with clear levels and reasonable clustering,which conform to the principles of geography,statistics,ecology and biology.It not only affirms and supports the reasonable kernel of Wallace’s scheme,but also puts forward suggestions that should be revised and improved.The large or small differences between the clustering results and the mammalian geographical zoning schemes of contemporary scholars are caused by different analysis methods,and they are highly consistent with the analysis results of chordates,angiosperms and insects in the world analyzed by the same method.Once again,it confirms the homogeneity of the global biological distribution pattern of major groups,and the possibility of building a unified biogeographic zoning system in the world.
文摘Identifying user electric events is of significant importance for uncovering patterns in user electric consumption behavior and enhancing the level of energy efficiencymanagement on the user side.To promptly and effectively detect electric events embedded in the electric data of the user,this paper introduces a cluster-based electric event identification model designed based on the electric current state sequence dataset.Based on extracting features from the electric current state sequence,this model treats the identification of electric events in the current sequence as a clustering partition problem utilizing the feature set derived from the electric current state sequence.To assess the model’s efficacy,twometrics were utilized:the silhouette coefficient and precision,to evaluate its performance.The experiments demonstrate that compared to the identification model for the electric event of the user based on k-means clustering,SOM clustering,and FCM clustering,the identification model for the electric event of the user based on hierarchical clustering is more effective in identifying electric events.
文摘Purpose:This study analyzes the profiles of elite Brazilian researchers,recognized through the prestigious CNPq productivity scholarships.By identifying distinct researcher clusters,the study sheds light on different academic strategies,levels of productivity,and academic contributions within the Brazilian higher education system.Design/methodology/approach:The research analyzes a comprehensive dataset of 14,003 researchers,employing principal component analysis(PCA)followed by cluster analysis to group researchers based on their academic attributes.The clusters reflect diverse aspects of research productivity,graduate supervisions,and publication patterns.Findings:The analysis reveals the existence of three distinct researcher profiles(the Advanced Supervisors,the Book Publishers/Organizers,and the Generalists).The study also highlights the characteristics of highcaliber scientists,representing the upper echelon of Brazilian researchers in terms of productivity and impact.Research limitations:Although the study provides a robust analysis of the Brazilian system,the results reflect specific characteristics of the Brazilian academic context.Furthermore,the analysis was restricted to normalized annual data,which may overlook temporal variations in researcher productivity.Pratical implications:The findings provide valuable insights for policy makers,funding agencies(such as CNPq),and university administrators who aim to develop tailored support programs for different researcher profiles.Originality/value:The cluster-based profiling offers a novel perspective on how different academic trajectories coexist within a national science system,offering lessons for other emerging economies.
基金financed by the grants from the National Natural Science Foundation of China(No.81803996)Shanghai Key Laboratory of Health Identification and Assessment(No.21DZ2271000)。
文摘Traditional Chinese medicine(TCM)has played a significant role in the prevention and treatment of chronic heart failure(CHF).To study TCM diagnosis of CHF,a total of 278 Chinese clinical research articles on the study of CHF syndromes in recent 40 years retrieved from Web of Science,Scopus,Pub Med,Embase,CNKI,Wanfang Data,Cq VIP,and Sino Med.According to cumulative frequency analysis,network analysis,and hierarchical cluster analysis,the study found the distribution of CHF syndromes was syndrome of qi deficiency with blood stasis,syndrome of qi and yin deficiency,syndrome of yang deficiency with water flooding,syndrome of heart blood stasis obstruction,syndrome of turbid phlegm,and syndrome of collapse due to primordial yang deficiency.The syndrome elements on location of illness were heart,kidney,lung,and spleen.The syndrome elements on nature of illness were qi deficiency,blood stasis,yang deficiency,yin deficiency,water retention,and turbid phlegm.These findings can provide reference to the research on diagnosis and treatment of CHF,and contribute to the study on syndrome standardization and objective research of TCM diagnosis.
基金The“13th Five-Year Plan”National Science and Technology Major Project,2016ZX05052,Changchao QiThe China National Petroleum Corporation Science and Technology Project,2021DJ6505,Changchao Qi.
文摘The accuracy and reliability of non-destructive testing(NDT)approaches in detecting interior corrosion problems are critical,yet research in this field is limited.This work describes a novel way to monitor the structural integrity of steel gas pipelines that uses advanced numerical modeling techniques to anticipate fracture development and corrosion effects.The objective is to increase pipeline dependability and safety through more precise,real-time health evaluations.Compared to previous approaches,our solution provides higher accuracy in fault detection and quantification,making it ideal for pipeline integritymonitoring in real-world applications.To solve this issue,statistical analysis was conducted on the size and directional distribution features of about 380,000 sets of internal corrosion faults,as well as simulations of erosion and wear patterns on bent pipes.Using real defectmorphologies,we developed a modeling framework for typical interior corrosion flaws.We evaluated and validated the applicability and effectiveness of in-service inspection processes,as well as conducted on-site comparison tests.The results show that(1)the length and width of corrosion defects follow a log-normal distribution,the clock orientation follows a normal distribution,and the peak depth follows a Freundlich EX function distribution pattern;(2)pipeline corrosion defect data can be classified into three classes using the K-means clustering algorithm,allowing rapid and convenient acquisition of typical size and orientation characteristics of internal corrosion defects;(3)the applicability range and boundary conditions of various NDT techniques were verified,establishing comprehensive selection principles for internal corrosion defect detection technology;(4)on-site inspection results showed a 31%The simulation and validation platform for typical interior corrosion issues greatly enhances the accuracy and reliability of detection data.
