The knowledge of bubble profiles in gas-liquid two-phase flows is crucial for analyzing the kinetic processes such as heat and mass transfer, and this knowledge is contained in field data obtained by surface-resolved ...The knowledge of bubble profiles in gas-liquid two-phase flows is crucial for analyzing the kinetic processes such as heat and mass transfer, and this knowledge is contained in field data obtained by surface-resolved computational fluid dynamics (CFD) simulations. To obtain this information, an efficient bubble profile reconstruction method based on an improved agglomerative hierarchical clustering (AHC) algorithm is proposed in this paper. The reconstruction method is featured by the implementations of a binary space division preprocessing, which aims to reduce the computational complexity, an adaptive linkage criterion, which guarantees the applicability of the AHC algorithm when dealing with datasets involving either non-uniform or distorted grids, and a stepwise execution strategy, which enables the separation of attached bubbles. To illustrate and verify this method, it was applied to dealing with 3 datasets, 2 of them with pre-specified spherical bubbles and the other obtained by a surface-resolved CFD simulation. Application results indicate that the proposed method is effective even when the data include some non-uniform and distortion.展开更多
To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed t...To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T;spectrums based on the Gaussian mixture model(GMM). Firstly, We conducted the principal component analysis on T;spectrums in order to reduce the dimension data and the dependence of the original variables. Secondly, the dimension-reduced data was fitted using the GMM probability density function, and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion. Finally, the T;spectrum features and pore structure types of different clustering groups were analyzed and compared with T;geometric mean and T;arithmetic mean. The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data. The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T;spectrum, pore structure, and petroleum productivity, providing a new means for quantitative identification of pore structure, reservoir grading, and oil and gas productivity evaluation.展开更多
The cluster analysis method needs continuous improvement and perfection in the research and application of the spatial differentiation and change of pollutants.In this paper,the date of monthly highest concentration o...The cluster analysis method needs continuous improvement and perfection in the research and application of the spatial differentiation and change of pollutants.In this paper,the date of monthly highest concentration of ozone(O_(3))and the concentration value of that day were selected as the similarity coefficient between classes.Single-factor cluster analysis was performed on O_(3)during 2016-2019 and the COVID-19 outbreak of 2020 in Hunan Province using the Ward method.The clustering results showed that the spatial distribution of atmospheric O_(3)in the 14 regions of Hunan Province was most suitable to be classified according to class III clustering areas.That is,the Changsha-Zhuzhou-Xiangtan urban agglomeration was the center,and the high-value area was in northern Hunan.The transition area was in central and southern Hunan,while the low-value area was centered in western Hunan.The partition results were in good agreement with the homogeneous subset of one-way ANOVA and the distribution of monitoring values during the same period.The comparison showed that the inter-class plates in the two periods corresponded well,and the intra-class area showed a continuous geographical distribution,and there were dynamic changes in the spatial differentiation of the O_(3)plates in different periods.In 2020,the center of the O_(3)high-value area plate in Hunan Province moved eastward and extended southward,focusing on the middle and lower reaches of the Xiangjiang River basin,and extending to the upstream area;the regional plate in the transition area expanded significantly;the low-value area plate shrank to the two cities in western Hunan.The abnormal emissions and abnormal climate during the COVID-19 epidemic had an impact on the spatial differentiation of O_(3)in Hunan Province.展开更多
In the field of data mining and machine learning,clustering is a typical issue which has been widely studied by many researchers,and lots of effective algorithms have been proposed,including K-means,fuzzy c-means(FCM)...In the field of data mining and machine learning,clustering is a typical issue which has been widely studied by many researchers,and lots of effective algorithms have been proposed,including K-means,fuzzy c-means(FCM)and DBSCAN.However,the traditional clustering methods are easily trapped into local optimum.Thus,many evolutionary-based clustering methods have been investigated.Considering the effectiveness of brain storm optimization(BSO)in increasing the diversity while the diversity optimization is performed,in this paper,we propose a new clustering model based on BSO to use the global ability of BSO.In our experiment,we apply the novel binary model to solve the problem.During the period of processing data,BSO was mainly utilized for iteration.Also,in the process of K-means,we set the more appropriate parameters selected to match it greatly.Four datasets were used in our experiment.In our model,BSO was first introduced in solving the clustering problem.With the algorithm running on each dataset repeatedly,our experimental results have obtained good convergence and diversity.In addition,by comparing the results with other clustering models,the BSO clustering model also guarantees high accuracy.Therefore,from many aspects,the simulation results show that the model of this paper has good performance.展开更多
South America’s climatic diversity is a product of its vast geographical expanse, encompassing tropical to subtropical latitudes. The variations in precipitation and temperature across the region stem from the influe...South America’s climatic diversity is a product of its vast geographical expanse, encompassing tropical to subtropical latitudes. The variations in precipitation and temperature across the region stem from the influence of distinct atmospheric systems. While some studies have characterized the prevailing systems over South America, they often lacked the utilization of statistical techniques for homogenization. On the other hand, other research has employed multivariate statistical methods to identify homogeneous regions regarding temperature and precipitation, but their focus has been limited to specific areas, such as the south, southeast, and northeast. Surprisingly, there is a lack of work that compares various multivariate statistical techniques to determine homogeneous regions across the entirety of South America concerning temperature and precipitation. This paper aims to address this gap by comparing three such techniques: Cluster Analysis (K-means and Ward) and Self Organizing Maps, using data from different sources for temperature (ERA5, ERA5-Land, and CRU) and precipitation (ERA5, ERA5-Land, and CPC). Spatial patterns and time series were generated for each region over the period 1981-2010. The results from this analysis of spatially homogeneous regions concerning temperature and precipitation have the potential to significantly benefit climate analysis and forecasts. Moreover, they can offer valuable insights for various climatological studies, guiding decision-making processes in diverse fields that rely on climate information, such as agriculture, disaster management, and water resources planning.展开更多
A total of 10 indices of regional economic development in Guangxi are selected.According to the relevant economic data,regional economic development in Guangxi is analyzed by using System Clustering Method and Princip...A total of 10 indices of regional economic development in Guangxi are selected.According to the relevant economic data,regional economic development in Guangxi is analyzed by using System Clustering Method and Principal Component Analysis Method.Result shows that System Clustering Method and Principal Component Analysis Method have revealed similar results analysis of economic development level.Overall economic strength of Guangxi is weak and Nanning has relatively high scores of factors due to its advantage of the political,economic and cultural center.Comprehensive scores of other regions are all lower than 1,which has big gap with the development of Nanning.Overall development strategy points out that Guangxi should accelerate the construction of the Ring Northern Bay Economic Zone,create a strong logistics system having strategic significance to national development,use the unique location advantage and rely on the modern transportation system to establish a logistics center and business center connecting the hinterland and the Asean Market.Based on the problems of unbalanced regional economic development in Guangxi,we should speed up the development of service industry in Nanning,construct the circular economy system of industrial city,and accelerate the industrialization process of tourism city in order to realize balanced development of regional economy in Guangxi,China.展开更多
This study focuses on the critical importance of compliant landing procedures for commercial aircraft to mitigate the risk of accidents,incidents,and financial losses.The research aims to raise awareness of these proc...This study focuses on the critical importance of compliant landing procedures for commercial aircraft to mitigate the risk of accidents,incidents,and financial losses.The research aims to raise awareness of these procedures and reduce their associated risks.This study uses K-means,the Gaussian Mixture Model(GMM),and Balanced Iterative Reducing and Clustering using Hierarchies(BIRCH)to find the best algorithm for finding unusual landing procedures based on vertical speed and elevation angle.It also uses clustering techniques to look at the data that was collected.The evaluation using the silhouette score,Davies-Bouldin(DB)index,and the Calinski-Harabasz(CH)index reveals that the GMM is the most stable method for forming clusters.Analysis of the anomalies revealed that the vertical speed rule identified 100%of the data as anomalies,whereas elevation-based anomalies accounted for only 0.8%of the total data.Limitations of the study include a limited number of features and a brief two-month data collection period.Future research should incorporate additional features,extend the data collection period,and explore more algorithms to enhance the accuracy and robustness of the analysis.展开更多
Detection of Autism Spectrum Disorder(ASD)is a crucial area of research,representing a foundational aspect of psychological studies.The advancement of technology and the widespread adoption of machine learning methodo...Detection of Autism Spectrum Disorder(ASD)is a crucial area of research,representing a foundational aspect of psychological studies.The advancement of technology and the widespread adoption of machine learning methodologies have brought significant attention to this field in recent years.Interdisciplinary efforts have further propelled research into detection methods.Consequently,this study aims to contribute to both the fields of psychology and computer science.Specifically,the goal is to apply machine learning techniques to limited data for the detection of Autism Spectrum Disorder.This study is structured into two distinct phases:data preprocessing and classification.In the data preprocessing phase,four datasets—Toddler,Children,Adolescent,and Adult—were converted into numerical form,adjusted as necessary,and subsequently clustered.Clustering was performed using six different methods:Kmeans,agglomerative,DBSCAN(Density-Based Spatial Clustering of Applications with Noise),mean shift,spectral,and Birch.In the second phase,the clustered ASD data were classified.The model’s accuracy was assessed using 5-fold cross-validation to ensure robust evaluation.In total,ten distinct machine learning algorithms were employed.The findings indicate that all clustering methods demonstrated success with various classifiers.Notably,the K-means algorithm emerged as particularly effective,achieving consistent and significant results across all datasets.This study is expected to serve as a guide for improving ASD detection performance,even with minimal data availability.展开更多
The use of metal oxides has been extensively documented in the literature and applied in a variety of contexts,including but not limited to energy storage,chemical sensors,and biomedical applications.One of the most s...The use of metal oxides has been extensively documented in the literature and applied in a variety of contexts,including but not limited to energy storage,chemical sensors,and biomedical applications.One of the most significant applications of metal oxides is heterogeneous catalysis,which represents a pivotal technology in industrial production on a global scale.Catalysts serve as the primary enabling agents for chemical reactions,and among the plethora of catalysts,metal oxides including magnesium oxide(MgO),ceria(CeO_(2))and titania(TiO_(2)),have been identified to be particularly effective in catalyzing a variety of reactions[1].Theoretical calculations based on density functional theory(DFT)and a multitude of other quantum chemistry methods have proven invaluable in elucidating the mechanisms of metal-oxide-catalyzed reactions,thereby facilitating the design of high-performance catalysts[2].展开更多
Web data extraction has become a key technology for extracting valuable data from websites.At present,most extraction methods based on rule learning,visual pattern or tree matching have limited performance on complex ...Web data extraction has become a key technology for extracting valuable data from websites.At present,most extraction methods based on rule learning,visual pattern or tree matching have limited performance on complex web pages.Through ana-lyzing various statistical characteristics of HTML el-ements in web documents,this paper proposes,based on statistical features,an unsupervised web data ex-traction method—traversing the HTML DOM parse tree at first,calculating and generating the statistical matrix of the elements,and then locating data records by clustering method and heuristic rules that reveal in-herent links between the visual characteristics of the data recording areas and the statistical characteristics of the HTML nodes—which is both suitable for data records extraction of single-page and multi-pages,and it has strong generality and needs no training.The ex-periments show that the accuracy and efficiency of this method are equally better than the current data extrac-tion method.展开更多
Having estimates of wave climate parameters and extreme values play important roles for a variety of different societal activities,such as coastal management,design of inshore and offshore structures,marine transport,...Having estimates of wave climate parameters and extreme values play important roles for a variety of different societal activities,such as coastal management,design of inshore and offshore structures,marine transport,coastal recreational activities,fisheries,etc.This study investigates the efficiency of a state-of-the-art spatial neutral gas clustering method in the classification of wind/wave data and the evaluation of extreme values of significant wave heights(Hs),mean wave direction(MWD)and mean wave periods(T0)for two 39-year time periods;from 1979 to 2017 for the present climate,and from 2060 to 2098,for a future climate change scenario in the Northwest Atlantic.These data were constructed by application of a numerical model,WAVEWATCHIII TM(hereafter,WW3),to simulate the wave climate for the study area for both present and future climates.Data from the model was extracted for the wave climate,in terms of the wave parameters,specifically Hs,MWD and T0,which were analyzed and compared for winter and summer seasons,for present and future climates.In order to estimate extreme values in the study area,a Natural Gas(hereafter,NG)clustering method was applied,separate clusters were identified,and corresponding centroid points were determined.To analyze data at each centroid point,time series of wave parameters were extracted,and using standard stochastic models,such as Gumbel,exponential and Weibull distribution functions,the extreme values for 50 and 100-year return periods were estimated.Thus,the impacts of climate change on wave regimes and extreme values can be specified.展开更多
In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields loc...In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.展开更多
Fuzzy mathematics is an important means to quantitatively evaluate the properties of fault sealing in petroleum reservoirs.To accurately study fault sealing,the comprehensive quantitative evaluation method of fuzzy ma...Fuzzy mathematics is an important means to quantitatively evaluate the properties of fault sealing in petroleum reservoirs.To accurately study fault sealing,the comprehensive quantitative evaluation method of fuzzy mathematics is improved based on a previous study.First,the single-factor membership degree is determined using the dynamic clustering method,then a single-factor evaluation matrix is constructed using a continuous grading function,and finally,the probability distribution of the evaluation grade in a fuzzy evaluation matrix is analyzed.In this study,taking the F1 fault located in the northeastern Chepaizi Bulge as an example,the sealing properties of faults in different strata are quantitatively evaluated using both an improved and an un-improved comprehensive fuzzy mathematics quantitative evaluation method.Based on current oil and gas distribution,it is found that our evaluation results before and after improvement are significantly different.For faults in＂best＂and＂poorest＂intervals,our evaluation results are consistent with oil and gas distribution.However,for the faults in＂good＂or＂poor＂intervals,our evaluation is not completelyconsistent with oil and gas distribution.The improved evaluation results reflect the overall and local sealing properties of target zones and embody the nonuniformity of fault sealing,indicating the improved method is more suitable for evaluating fault sealing under complicated conditions.展开更多
[Objective] The aim was to assess regional eco-environmental quality by means of grey clustering method based on normalized index value. [Method] Eco-environmental quality in Chaohu basin was assessed by using grey cl...[Objective] The aim was to assess regional eco-environmental quality by means of grey clustering method based on normalized index value. [Method] Eco-environmental quality in Chaohu basin was assessed by using grey clustering method based on normalized index value, and the evaluation results were compared with those of unascertained measure method to verify the feasibility of grey clustering method used to evaluate regional eco-environmental quality. [Result] In the grey clustering assessment method based on normalized index value, indices whose standard normalized values in the same grade were close to each other were classified into one class and had the same whitening function, which reduced the number of whitening functions. Grey clustering method based on normalized index value was used to assess eco-environmental quality in Chaohu basin, and the evaluation results were basically in accordance with those of unascertained measure method, namely eco-environmental quality in Hefei, Chaohu and Lu’an belonged to the third (pass), fourth (worse) and fifth grade (bad), except for one grade difference in overall basin, and the results showed that the method had practicality and could be applied to assess regional eco-environmental quality. [Conclusion] The study could provide theoretical foundation for the establishment of comprehensive management countermeasures of regional ecological environment.展开更多
A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared...A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.展开更多
Performing analytics on the load curve(LC)of customers is the foundation for demand response which requires a better understanding of customers'consumption pattern(CP)by analyzing the load curve.However,the perfor...Performing analytics on the load curve(LC)of customers is the foundation for demand response which requires a better understanding of customers'consumption pattern(CP)by analyzing the load curve.However,the performances of previous widely-used LC clustering methods are poor in two folds:larger number of clusters,huge variances within a cluster(a CP is extracted from a cluster),bringing huge difficulty to understand the electricity consumption pattern of customers.In this paper,to improve the performance of LC clustering,a clustering framework incorporated with community detection is proposed.The framework includes three parts:network construction,community detection,and CP extraction.According to the cluster validity index(CVI),the integrated approach outperforms the previous state-of-the-art method with the same amount of clusters.And the approach needs fewer clusters to achieve the same performance measured by CVI.展开更多
Neutron shielding materials face imbalanced behaviors among shielding,strength,and ductility properties.Based on the requirement of the high property shielding particles,a superior semi-coherentτ(Al4 MgGd)phase was d...Neutron shielding materials face imbalanced behaviors among shielding,strength,and ductility properties.Based on the requirement of the high property shielding particles,a superior semi-coherentτ(Al4 MgGd)phase was designed and predicted by cluster expansion(CE)method using density functional theory calculations.To realize its shielding property,the Powder Metallurgy-based routines(i.e.,powder fabrication,spark plasma sintering,and hot extrusion techniques)are used to fabricate 6TiB_(2)/Al-6Mg-5Gd(wt.%)composite with dispersed refinedτphases and homogenized TiB_(2) distribution.The atomic structure of ternary phase τ is examined by aberration-corrected high-angle annual dark-field(HAADF)scanning transmission electron microscope(STEM)and energy dispersive X-ray spectroscopy(EDXS)STEM experiments,which is well complied with the calculated compound(Al_(4)MgGd).In detail,theτ(Al_(4)MgGd)phase has a semi-coherent interface both with α-Al and TiB_(2),which is consistent with the prediction of interface relationships.With the optimized interfaces,the TiB_(2) and τ phases can effectively promote recrystallization and suppress grain growth,leading to the formation of ultra-fine grain structure.Then,the composite exhibits advanced shielding properties(Macroscopic transmission cross section ~24.1/cm,higher than 30%B_(4) C/Al)and optimized synergic mechanical properties(Ultimate tensile strength ~506 MPa,elongation ~12.9%),which are far higher than available Al-based neutron shielding materials.Finally,the underlying strength-ductility mechanisms are discussed.Critically,the design and optimization of shielding particle interfaces are reliable strategies for developing novel structural-functional integrated materials.展开更多
We study the thermodynamic properties of the classical one-dimensional generalized nonlinear Klein-Gordon lattice model(n≥2)by using the cluster variation method with linear response theory.The results of this method...We study the thermodynamic properties of the classical one-dimensional generalized nonlinear Klein-Gordon lattice model(n≥2)by using the cluster variation method with linear response theory.The results of this method are exact in the thermodynamic limit.We present the single-site reduced densityρ^((1))(z),averages such as(z^(2)),<|z^(n)|>,and<(z_(1)-z_(2))^(2)>,the specific heat C_(v),and the static correlation functions.We analyze the scaling behavior of these quantities and obtain the exact scaling powers at the low and high temperatures.Using these results,we gauge the accuracy of the projective truncation approximation for theφ^(4)lattice model.展开更多
In this paper, we conduct research on the novel natural image reconstruction and representation algorithm based on clustenng and modified neural network. Image resolution enhancement is one of the earliest researches ...In this paper, we conduct research on the novel natural image reconstruction and representation algorithm based on clustenng and modified neural network. Image resolution enhancement is one of the earliest researches of single image interpolation. Although the traditional interpolation and method for single image amplification is effect, but did not provide more useful information. Our method combines the neural network and the clustering approach. The experiment shows that our method performs well and satisfactory.展开更多
The combined finiteediscrete element method （FDEM） belongs to a family of methods of computationalmechanics of discontinua. The method is suitable for problems of discontinua, where particles aredeformable and can f...The combined finiteediscrete element method （FDEM） belongs to a family of methods of computationalmechanics of discontinua. The method is suitable for problems of discontinua, where particles aredeformable and can fracture or fragment. The applications of FDEM have spread over a number of disciplinesincluding rock mechanics, where problems like mining, mineral processing or rock blasting canbe solved by employing FDEM. In this work, a novel approach for the parallelization of two-dimensional（2D） FDEM aiming at clusters and desktop computers is developed. Dynamic domain decompositionbased parallelization solvers covering all aspects of FDEM have been developed. These have beenimplemented into the open source Y2D software package and have been tested on a PC cluster. Theoverall performance and scalability of the parallel code have been studied using numerical examples. Theresults obtained confirm the suitability of the parallel implementation for solving large scale problems. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.展开更多
基金Projects(51634010,51676211) supported by the National Natural Science Foundation of ChinaProject(2017SK2253) supported by the Key Research and Development Program of Hunan Province,China
文摘The knowledge of bubble profiles in gas-liquid two-phase flows is crucial for analyzing the kinetic processes such as heat and mass transfer, and this knowledge is contained in field data obtained by surface-resolved computational fluid dynamics (CFD) simulations. To obtain this information, an efficient bubble profile reconstruction method based on an improved agglomerative hierarchical clustering (AHC) algorithm is proposed in this paper. The reconstruction method is featured by the implementations of a binary space division preprocessing, which aims to reduce the computational complexity, an adaptive linkage criterion, which guarantees the applicability of the AHC algorithm when dealing with datasets involving either non-uniform or distorted grids, and a stepwise execution strategy, which enables the separation of attached bubbles. To illustrate and verify this method, it was applied to dealing with 3 datasets, 2 of them with pre-specified spherical bubbles and the other obtained by a surface-resolved CFD simulation. Application results indicate that the proposed method is effective even when the data include some non-uniform and distortion.
基金Supported by the National Natural Science Foundation of China (42174142)National Science and Technology Major Project (2017ZX05039-002)+2 种基金Operation Fund of China National Petroleum Corporation Logging Key Laboratory (2021DQ20210107-11)Fundamental Research Funds for Central Universities (19CX02006A)Major Science and Technology Project of China National Petroleum Corporation (ZD2019-183-006)。
文摘To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T;spectrums based on the Gaussian mixture model(GMM). Firstly, We conducted the principal component analysis on T;spectrums in order to reduce the dimension data and the dependence of the original variables. Secondly, the dimension-reduced data was fitted using the GMM probability density function, and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion. Finally, the T;spectrum features and pore structure types of different clustering groups were analyzed and compared with T;geometric mean and T;arithmetic mean. The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data. The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T;spectrum, pore structure, and petroleum productivity, providing a new means for quantitative identification of pore structure, reservoir grading, and oil and gas productivity evaluation.
文摘The cluster analysis method needs continuous improvement and perfection in the research and application of the spatial differentiation and change of pollutants.In this paper,the date of monthly highest concentration of ozone(O_(3))and the concentration value of that day were selected as the similarity coefficient between classes.Single-factor cluster analysis was performed on O_(3)during 2016-2019 and the COVID-19 outbreak of 2020 in Hunan Province using the Ward method.The clustering results showed that the spatial distribution of atmospheric O_(3)in the 14 regions of Hunan Province was most suitable to be classified according to class III clustering areas.That is,the Changsha-Zhuzhou-Xiangtan urban agglomeration was the center,and the high-value area was in northern Hunan.The transition area was in central and southern Hunan,while the low-value area was centered in western Hunan.The partition results were in good agreement with the homogeneous subset of one-way ANOVA and the distribution of monitoring values during the same period.The comparison showed that the inter-class plates in the two periods corresponded well,and the intra-class area showed a continuous geographical distribution,and there were dynamic changes in the spatial differentiation of the O_(3)plates in different periods.In 2020,the center of the O_(3)high-value area plate in Hunan Province moved eastward and extended southward,focusing on the middle and lower reaches of the Xiangjiang River basin,and extending to the upstream area;the regional plate in the transition area expanded significantly;the low-value area plate shrank to the two cities in western Hunan.The abnormal emissions and abnormal climate during the COVID-19 epidemic had an impact on the spatial differentiation of O_(3)in Hunan Province.
基金supported by Natural Science Foundation of Jiangsu Province(Grant No.BK20141005)by Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.14KJB520025).
文摘In the field of data mining and machine learning,clustering is a typical issue which has been widely studied by many researchers,and lots of effective algorithms have been proposed,including K-means,fuzzy c-means(FCM)and DBSCAN.However,the traditional clustering methods are easily trapped into local optimum.Thus,many evolutionary-based clustering methods have been investigated.Considering the effectiveness of brain storm optimization(BSO)in increasing the diversity while the diversity optimization is performed,in this paper,we propose a new clustering model based on BSO to use the global ability of BSO.In our experiment,we apply the novel binary model to solve the problem.During the period of processing data,BSO was mainly utilized for iteration.Also,in the process of K-means,we set the more appropriate parameters selected to match it greatly.Four datasets were used in our experiment.In our model,BSO was first introduced in solving the clustering problem.With the algorithm running on each dataset repeatedly,our experimental results have obtained good convergence and diversity.In addition,by comparing the results with other clustering models,the BSO clustering model also guarantees high accuracy.Therefore,from many aspects,the simulation results show that the model of this paper has good performance.
文摘South America’s climatic diversity is a product of its vast geographical expanse, encompassing tropical to subtropical latitudes. The variations in precipitation and temperature across the region stem from the influence of distinct atmospheric systems. While some studies have characterized the prevailing systems over South America, they often lacked the utilization of statistical techniques for homogenization. On the other hand, other research has employed multivariate statistical methods to identify homogeneous regions regarding temperature and precipitation, but their focus has been limited to specific areas, such as the south, southeast, and northeast. Surprisingly, there is a lack of work that compares various multivariate statistical techniques to determine homogeneous regions across the entirety of South America concerning temperature and precipitation. This paper aims to address this gap by comparing three such techniques: Cluster Analysis (K-means and Ward) and Self Organizing Maps, using data from different sources for temperature (ERA5, ERA5-Land, and CRU) and precipitation (ERA5, ERA5-Land, and CPC). Spatial patterns and time series were generated for each region over the period 1981-2010. The results from this analysis of spatially homogeneous regions concerning temperature and precipitation have the potential to significantly benefit climate analysis and forecasts. Moreover, they can offer valuable insights for various climatological studies, guiding decision-making processes in diverse fields that rely on climate information, such as agriculture, disaster management, and water resources planning.
文摘A total of 10 indices of regional economic development in Guangxi are selected.According to the relevant economic data,regional economic development in Guangxi is analyzed by using System Clustering Method and Principal Component Analysis Method.Result shows that System Clustering Method and Principal Component Analysis Method have revealed similar results analysis of economic development level.Overall economic strength of Guangxi is weak and Nanning has relatively high scores of factors due to its advantage of the political,economic and cultural center.Comprehensive scores of other regions are all lower than 1,which has big gap with the development of Nanning.Overall development strategy points out that Guangxi should accelerate the construction of the Ring Northern Bay Economic Zone,create a strong logistics system having strategic significance to national development,use the unique location advantage and rely on the modern transportation system to establish a logistics center and business center connecting the hinterland and the Asean Market.Based on the problems of unbalanced regional economic development in Guangxi,we should speed up the development of service industry in Nanning,construct the circular economy system of industrial city,and accelerate the industrialization process of tourism city in order to realize balanced development of regional economy in Guangxi,China.
文摘This study focuses on the critical importance of compliant landing procedures for commercial aircraft to mitigate the risk of accidents,incidents,and financial losses.The research aims to raise awareness of these procedures and reduce their associated risks.This study uses K-means,the Gaussian Mixture Model(GMM),and Balanced Iterative Reducing and Clustering using Hierarchies(BIRCH)to find the best algorithm for finding unusual landing procedures based on vertical speed and elevation angle.It also uses clustering techniques to look at the data that was collected.The evaluation using the silhouette score,Davies-Bouldin(DB)index,and the Calinski-Harabasz(CH)index reveals that the GMM is the most stable method for forming clusters.Analysis of the anomalies revealed that the vertical speed rule identified 100%of the data as anomalies,whereas elevation-based anomalies accounted for only 0.8%of the total data.Limitations of the study include a limited number of features and a brief two-month data collection period.Future research should incorporate additional features,extend the data collection period,and explore more algorithms to enhance the accuracy and robustness of the analysis.
文摘Detection of Autism Spectrum Disorder(ASD)is a crucial area of research,representing a foundational aspect of psychological studies.The advancement of technology and the widespread adoption of machine learning methodologies have brought significant attention to this field in recent years.Interdisciplinary efforts have further propelled research into detection methods.Consequently,this study aims to contribute to both the fields of psychology and computer science.Specifically,the goal is to apply machine learning techniques to limited data for the detection of Autism Spectrum Disorder.This study is structured into two distinct phases:data preprocessing and classification.In the data preprocessing phase,four datasets—Toddler,Children,Adolescent,and Adult—were converted into numerical form,adjusted as necessary,and subsequently clustered.Clustering was performed using six different methods:Kmeans,agglomerative,DBSCAN(Density-Based Spatial Clustering of Applications with Noise),mean shift,spectral,and Birch.In the second phase,the clustered ASD data were classified.The model’s accuracy was assessed using 5-fold cross-validation to ensure robust evaluation.In total,ten distinct machine learning algorithms were employed.The findings indicate that all clustering methods demonstrated success with various classifiers.Notably,the K-means algorithm emerged as particularly effective,achieving consistent and significant results across all datasets.This study is expected to serve as a guide for improving ASD detection performance,even with minimal data availability.
基金financial support from the National Key R&D Program of China(2021YFB3500700)the National Natural Science Foundation of China(22473042,22003016,and 92145302).
文摘The use of metal oxides has been extensively documented in the literature and applied in a variety of contexts,including but not limited to energy storage,chemical sensors,and biomedical applications.One of the most significant applications of metal oxides is heterogeneous catalysis,which represents a pivotal technology in industrial production on a global scale.Catalysts serve as the primary enabling agents for chemical reactions,and among the plethora of catalysts,metal oxides including magnesium oxide(MgO),ceria(CeO_(2))and titania(TiO_(2)),have been identified to be particularly effective in catalyzing a variety of reactions[1].Theoretical calculations based on density functional theory(DFT)and a multitude of other quantum chemistry methods have proven invaluable in elucidating the mechanisms of metal-oxide-catalyzed reactions,thereby facilitating the design of high-performance catalysts[2].
文摘Web data extraction has become a key technology for extracting valuable data from websites.At present,most extraction methods based on rule learning,visual pattern or tree matching have limited performance on complex web pages.Through ana-lyzing various statistical characteristics of HTML el-ements in web documents,this paper proposes,based on statistical features,an unsupervised web data ex-traction method—traversing the HTML DOM parse tree at first,calculating and generating the statistical matrix of the elements,and then locating data records by clustering method and heuristic rules that reveal in-herent links between the visual characteristics of the data recording areas and the statistical characteristics of the HTML nodes—which is both suitable for data records extraction of single-page and multi-pages,and it has strong generality and needs no training.The ex-periments show that the accuracy and efficiency of this method are equally better than the current data extrac-tion method.
文摘Having estimates of wave climate parameters and extreme values play important roles for a variety of different societal activities,such as coastal management,design of inshore and offshore structures,marine transport,coastal recreational activities,fisheries,etc.This study investigates the efficiency of a state-of-the-art spatial neutral gas clustering method in the classification of wind/wave data and the evaluation of extreme values of significant wave heights(Hs),mean wave direction(MWD)and mean wave periods(T0)for two 39-year time periods;from 1979 to 2017 for the present climate,and from 2060 to 2098,for a future climate change scenario in the Northwest Atlantic.These data were constructed by application of a numerical model,WAVEWATCHIII TM(hereafter,WW3),to simulate the wave climate for the study area for both present and future climates.Data from the model was extracted for the wave climate,in terms of the wave parameters,specifically Hs,MWD and T0,which were analyzed and compared for winter and summer seasons,for present and future climates.In order to estimate extreme values in the study area,a Natural Gas(hereafter,NG)clustering method was applied,separate clusters were identified,and corresponding centroid points were determined.To analyze data at each centroid point,time series of wave parameters were extracted,and using standard stochastic models,such as Gumbel,exponential and Weibull distribution functions,the extreme values for 50 and 100-year return periods were estimated.Thus,the impacts of climate change on wave regimes and extreme values can be specified.
基金supported by the National Science and Technology Major Project of China(No.2011ZX05029-003)CNPC Science Research and Technology Development Project,China(No.2013D-0904)
文摘In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.
基金supported by the Science and Technology Project of Universities and Colleges in Shandong Province ‘‘Investigation on diagenetic environment and transformation pattern of red-bed reservoirs in the rift basins’’ (No. J16LH52)
文摘Fuzzy mathematics is an important means to quantitatively evaluate the properties of fault sealing in petroleum reservoirs.To accurately study fault sealing,the comprehensive quantitative evaluation method of fuzzy mathematics is improved based on a previous study.First,the single-factor membership degree is determined using the dynamic clustering method,then a single-factor evaluation matrix is constructed using a continuous grading function,and finally,the probability distribution of the evaluation grade in a fuzzy evaluation matrix is analyzed.In this study,taking the F1 fault located in the northeastern Chepaizi Bulge as an example,the sealing properties of faults in different strata are quantitatively evaluated using both an improved and an un-improved comprehensive fuzzy mathematics quantitative evaluation method.Based on current oil and gas distribution,it is found that our evaluation results before and after improvement are significantly different.For faults in＂best＂and＂poorest＂intervals,our evaluation results are consistent with oil and gas distribution.However,for the faults in＂good＂or＂poor＂intervals,our evaluation is not completelyconsistent with oil and gas distribution.The improved evaluation results reflect the overall and local sealing properties of target zones and embody the nonuniformity of fault sealing,indicating the improved method is more suitable for evaluating fault sealing under complicated conditions.
基金Supported National Natural Science Foundation of China(50739002)
文摘[Objective] The aim was to assess regional eco-environmental quality by means of grey clustering method based on normalized index value. [Method] Eco-environmental quality in Chaohu basin was assessed by using grey clustering method based on normalized index value, and the evaluation results were compared with those of unascertained measure method to verify the feasibility of grey clustering method used to evaluate regional eco-environmental quality. [Result] In the grey clustering assessment method based on normalized index value, indices whose standard normalized values in the same grade were close to each other were classified into one class and had the same whitening function, which reduced the number of whitening functions. Grey clustering method based on normalized index value was used to assess eco-environmental quality in Chaohu basin, and the evaluation results were basically in accordance with those of unascertained measure method, namely eco-environmental quality in Hefei, Chaohu and Lu’an belonged to the third (pass), fourth (worse) and fifth grade (bad), except for one grade difference in overall basin, and the results showed that the method had practicality and could be applied to assess regional eco-environmental quality. [Conclusion] The study could provide theoretical foundation for the establishment of comprehensive management countermeasures of regional ecological environment.
基金supporting by grant fund under the Strategic Scholarships for Frontier Research Network for the PhD Program Thai Doctoral degree
文摘A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.
基金Supported by the Major Program of National Natural Science Foundation of China(No.61432006)。
文摘Performing analytics on the load curve(LC)of customers is the foundation for demand response which requires a better understanding of customers'consumption pattern(CP)by analyzing the load curve.However,the performances of previous widely-used LC clustering methods are poor in two folds:larger number of clusters,huge variances within a cluster(a CP is extracted from a cluster),bringing huge difficulty to understand the electricity consumption pattern of customers.In this paper,to improve the performance of LC clustering,a clustering framework incorporated with community detection is proposed.The framework includes three parts:network construction,community detection,and CP extraction.According to the cluster validity index(CVI),the integrated approach outperforms the previous state-of-the-art method with the same amount of clusters.And the approach needs fewer clusters to achieve the same performance measured by CVI.
基金financially supported by the National Key Research and Development Program of China(Grant No.2021YFA1600900)the National Natural Science Foundation of China(Grant Nos.U22A20174,52071207,51971137,11875192,and U1930101)the Natural Science Foundation of Shanghai(China,Grant No.22ZR1432800).
文摘Neutron shielding materials face imbalanced behaviors among shielding,strength,and ductility properties.Based on the requirement of the high property shielding particles,a superior semi-coherentτ(Al4 MgGd)phase was designed and predicted by cluster expansion(CE)method using density functional theory calculations.To realize its shielding property,the Powder Metallurgy-based routines(i.e.,powder fabrication,spark plasma sintering,and hot extrusion techniques)are used to fabricate 6TiB_(2)/Al-6Mg-5Gd(wt.%)composite with dispersed refinedτphases and homogenized TiB_(2) distribution.The atomic structure of ternary phase τ is examined by aberration-corrected high-angle annual dark-field(HAADF)scanning transmission electron microscope(STEM)and energy dispersive X-ray spectroscopy(EDXS)STEM experiments,which is well complied with the calculated compound(Al_(4)MgGd).In detail,theτ(Al_(4)MgGd)phase has a semi-coherent interface both with α-Al and TiB_(2),which is consistent with the prediction of interface relationships.With the optimized interfaces,the TiB_(2) and τ phases can effectively promote recrystallization and suppress grain growth,leading to the formation of ultra-fine grain structure.Then,the composite exhibits advanced shielding properties(Macroscopic transmission cross section ~24.1/cm,higher than 30%B_(4) C/Al)and optimized synergic mechanical properties(Ultimate tensile strength ~506 MPa,elongation ~12.9%),which are far higher than available Al-based neutron shielding materials.Finally,the underlying strength-ductility mechanisms are discussed.Critically,the design and optimization of shielding particle interfaces are reliable strategies for developing novel structural-functional integrated materials.
基金supported by the National Natural Science Foundation of China(Grant No.11974420).
文摘We study the thermodynamic properties of the classical one-dimensional generalized nonlinear Klein-Gordon lattice model(n≥2)by using the cluster variation method with linear response theory.The results of this method are exact in the thermodynamic limit.We present the single-site reduced densityρ^((1))(z),averages such as(z^(2)),<|z^(n)|>,and<(z_(1)-z_(2))^(2)>,the specific heat C_(v),and the static correlation functions.We analyze the scaling behavior of these quantities and obtain the exact scaling powers at the low and high temperatures.Using these results,we gauge the accuracy of the projective truncation approximation for theφ^(4)lattice model.
文摘In this paper, we conduct research on the novel natural image reconstruction and representation algorithm based on clustenng and modified neural network. Image resolution enhancement is one of the earliest researches of single image interpolation. Although the traditional interpolation and method for single image amplification is effect, but did not provide more useful information. Our method combines the neural network and the clustering approach. The experiment shows that our method performs well and satisfactory.
文摘The combined finiteediscrete element method （FDEM） belongs to a family of methods of computationalmechanics of discontinua. The method is suitable for problems of discontinua, where particles aredeformable and can fracture or fragment. The applications of FDEM have spread over a number of disciplinesincluding rock mechanics, where problems like mining, mineral processing or rock blasting canbe solved by employing FDEM. In this work, a novel approach for the parallelization of two-dimensional（2D） FDEM aiming at clusters and desktop computers is developed. Dynamic domain decompositionbased parallelization solvers covering all aspects of FDEM have been developed. These have beenimplemented into the open source Y2D software package and have been tested on a PC cluster. Theoverall performance and scalability of the parallel code have been studied using numerical examples. Theresults obtained confirm the suitability of the parallel implementation for solving large scale problems. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.
