In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling met...In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.展开更多
An accurate probability distribution model of wind speed is critical to the assessment of reliability contribution of wind energy to power systems. Most of current models are built using the parametric density estimat...An accurate probability distribution model of wind speed is critical to the assessment of reliability contribution of wind energy to power systems. Most of current models are built using the parametric density estimation(PDE) methods, which usually assume that the wind speed are subordinate to a certain known distribution(e.g. Weibull distribution and Normal distribution) and estimate the parameters of models with the historical data. This paper presents a kernel density estimation(KDE) method which is a nonparametric way to estimate the probability density function(PDF) of wind speed. The method is a kind of data-driven approach without making any assumption on the form of the underlying wind speed distribution, and capable of uncovering the statistical information hidden in the historical data. The proposed method is compared with three parametric models using wind data from six sites.The results indicate that the KDE outperforms the PDE in terms of accuracy and flexibility in describing the longterm wind speed distributions for all sites. A sensitivity analysis with respect to kernel functions is presented and Gauss kernel function is proved to be the best one. Case studies on a standard IEEE reliability test system(IEEERTS) have verified the applicability and effectiveness of the proposed model in evaluating the reliability performance of wind farms.展开更多
In this paper,we propose a multiphase fuzzy region competition model for texture image segmentation.In the functional,each region is represented by a fuzzy membership function and a probability density function that i...In this paper,we propose a multiphase fuzzy region competition model for texture image segmentation.In the functional,each region is represented by a fuzzy membership function and a probability density function that is estimated by a nonparametric kernel density estimation.The overall algorithm is very efficient as both the fuzzy membership function and the probability density function can be implemented easily.We apply the proposed method to synthetic and natural texture images,and synthetic aperture radar images.Our experimental results have shown that the proposed method is competitive with the other state-of-the-art segmentation methods.展开更多
Beijing Xianyukou Hutong(hutong refers to historical and cultural block in Chinese)occupies an important geographical location with unique urban fabric,and after years of renewal and protection,the commercial space of...Beijing Xianyukou Hutong(hutong refers to historical and cultural block in Chinese)occupies an important geographical location with unique urban fabric,and after years of renewal and protection,the commercial space of Xianyukou Street and has gained some recognition.This article Xianyukou takes commercial hutong in Beijing as an example,spatial analysis was carried out using methods like GIS kernel density method,space syntax after site investigation and research.Based on the street space problems found,this paper then puts forward strategies to improve and upgrade Xianyukou Street’s commercial space and improve businesses in Xianyukou Street and other similar hutong.展开更多
Aiming at the large cost of calculating variable bandwidth kernel particle filter and the high complexity of its algorithm,a self-adjusting kernel function particle filter is presented. Kernel density estimation is fa...Aiming at the large cost of calculating variable bandwidth kernel particle filter and the high complexity of its algorithm,a self-adjusting kernel function particle filter is presented. Kernel density estimation is facilitated to iterate and obtain new particle set. And the standard deviation of particle is introduced in the kernel bandwidth. According to the characteristics of particle distribution,the bandwidth is dynamically adjusted,and the particle distribution can thus be more close to the posterior probability density model of the system. Meanwhile,the kernel density is used to estimate the weight of updating particle and the system state. The simulation results show the feasibility and effectiveness of the proposed algorithm.展开更多
A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed.The proposed method overcomes the challenge of source-load random fluctuations...A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed.The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems(IESs)in the operation scheduling problem of integrated energy production units(IEPUs).First,to solve the problem of inaccurate prediction of renewable energy output,an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction.Subsequently,to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs,a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established.The system considers the further utilization of energy using hydrogen energy coupling equipment(such as hydrogen storage devices and fuel cells)and the comprehensive demand response of load-side schedulable resources.The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions,improve the source-load interaction of the IES,realize the efficient use of hydrogen energy,and improve system robustness.展开更多
The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity,multi-failure regions,and small failure probability,which brings in unacceptable computi...The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity,multi-failure regions,and small failure probability,which brings in unacceptable computing efficiency and accuracy of the current analysismethods.In this case,by fitting the implicit limit state function(LSF)with active Kriging(AK)model and reducing candidate sample poolwith adaptive importance sampling(AIS),a novel AK-AIS method is proposed.Herein,theAKmodel andMarkov chainMonte Carlo(MCMC)are first established to identify the most probable failure region(s)(MPFRs),and the adaptive kernel density estimation(AKDE)importance sampling function is constructed to select the candidate samples.With the best samples sequentially attained in the reduced candidate samples and employed to update the Kriging-fitted LSF,the failure probability and sensitivity indices are acquired at a lower cost.The proposed method is verified by twomulti-failure numerical examples,and then applied to the reliability and sensitivity analyses of a typical stator blade regulator.Withmethods comparison,the proposed AK-AIS is proven to hold the computing advantages on accuracy and efficiency in complex reliability and sensitivity analysis problems.展开更多
The digital economy has become an important driver for stimulating economic growth.The digital economy has now widely penetrated the fields of economy and society,providing new opportunities for the develop‐ment of u...The digital economy has become an important driver for stimulating economic growth.The digital economy has now widely penetrated the fields of economy and society,providing new opportunities for the develop‐ment of urban-rural integration.Based on panel data for 30 provinces in China from 2011 to 2020,this study constructed an index system for the integration of the digital economy and the development of urban-rural areas and conducted a systematic measurement analysis.Additionally,we used a two-step system of GMM estimation to analyze the impact of the digital economy on the development of urban-rural integra‐tion.The findings demonstrate the significant imbalance paradox of China’s digital economy development,which is shown in a gradient where the eastern region is higher than the center and the central region is higher than the west.Urban-rural integration levels in China fluctuate and display geographical variance,typically displaying high levels in the east and low levels in the west.Urban-rural integration is significantly encouraged by the digital economy,yet it varies in variability between different areas and dimensions.Addi‐tionally,rural human capital moderates the favorable effects of the digital economy on urban-rural integra‐tion.As a result,in order to achieve the integrated development of urban and rural areas,it is imperative to fully exploit the active role of the digital economy,better support the development of rural revitalization,bridge the“digital divide”between urban and rural development,and build a strong foundation for the for‐mation of a digital urban-rural integrated development pattern with urban and rural areas and common con‐struction and sharing.展开更多
Cold storage is the vital infrastructure of cold chain logistics. In this study, we analyzed the spatial pattern evolution characteristics, spatial autocorrelation and influencing factors of cold storage in China by u...Cold storage is the vital infrastructure of cold chain logistics. In this study, we analyzed the spatial pattern evolution characteristics, spatial autocorrelation and influencing factors of cold storage in China by using kernel density estimation(KDE), spatial autocorrelation analysis(SAA), and spatial error model(SEM). Results showed that: 1) the spatial distribution of cold storage in China is unbalanced, and has evolved from ‘one core’ to ‘one core and many spots’, that is, ‘one core’ refers to the Bohai Rim region mainly including Beijing, Tianjin, Hebei, Shandong and Liaoning regions, and ‘many spots’ mainly include the high-density areas such as Shanghai, Fuzhou, Guangzhou, Zhengzhou, Hefei, Wuhan, ürümqi. 2) The distribution of cold storage has significant global spatial autocorrelation and local spatial autocorrelation, and the ‘High-High’ cluster area is the most stable, mainly concentrated in the Bohai Rim;the ‘Low-Low’ cluster area is grouped in the southern China. 3) Economic development level, population density, traffic accessibility, temperature and land price, all affect the location choice of cold storage in varying degrees, while the impact of market demand on it is not explicit.展开更多
Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connect...Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods.In this paper,a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation.A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance.The power forecast errors in different weather types are analyzed,and an energy storage system is used to compensate for the errors.The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system;the power and capacity of the energy storage unit are calculated at different confidence levels.The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction.The proposed method was validated using actual operating data from a PV power station.The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.展开更多
In order to obtain accurate conflict risks in terminal airspace design,the concept and calculation model of potential conflict frequency for intersected routes are proposed.Conflict frequency is represented by the pro...In order to obtain accurate conflict risks in terminal airspace design,the concept and calculation model of potential conflict frequency for intersected routes are proposed.Conflict frequency is represented by the product of horizontal conflict frequency and vertical conflict probability.The horizontal conflict frequency is derived from the probability density distribution of conflicts in a period of time.Based on the recorded radar trajectory data,the concept and model of ROUTE distance are proposed,and the probability density function of aircraft height at a specified ROUTE distance is deduced by kernel density estimation.Furthermore,vertical conflict probability and its horizontal distribution are achieved.Examples of three intersected arrival and departure route design schemes are studied.Compared with scheme 1,the conflict frequency values of the other two improved schemes decrease to53% and 24%,respectively.The results show that the model can quantify potential conflict frequency of intersected routes.展开更多
Evaluating urban land use efficiency(ULUE) provides insights into the interactions between land use systems and their external environment. Specifically, changes in ULUE are important for monitoring urban transformati...Evaluating urban land use efficiency(ULUE) provides insights into the interactions between land use systems and their external environment. Specifically, changes in ULUE are important for monitoring urban transformation in developing countries. In this study, using a traditional input-output index model, we incorporated slack-based measurements and undesirable outputs into a SBM-UN(slack-based measure-undesirable) model to investigate ULUE within the context of increasing environmental restrictions in China. The model was used to estimate the ULUE of 26 cities in the highly developed urban agglomeration of the Yangtze River Delta from 2000 to2018. The average ULUE in the Yangtze River Delta was relatively low compared to that of developed city regions in the European Union(EU) and North America and exhibited a U-shaped curve over the study period. Incorporating undesirable outputs, such as environmental pollution, into the model reduced ULUE by 19.06%. ULUE varied spatially, with the kernel density estimation exhibiting a bimodal distribution. Efficiency decomposition analysis showed that scale efficiency made a greater contribution to ULUE than pure technical efficiency. Based on our findings, recommended approaches to improve ULUE include optimizing factor allocation, reducing undesirable outputs, and increasing the effective output per land unit. The study suggests that ULUE and the SBM-UN model are useful planning tools for sustainable urban development.展开更多
In this paper,we mainly study how to estimate the error density in the ultrahigh dimensional sparse additive model,where the number of variables is larger than the sample size.First,a smoothing method based on B-splin...In this paper,we mainly study how to estimate the error density in the ultrahigh dimensional sparse additive model,where the number of variables is larger than the sample size.First,a smoothing method based on B-splines is applied to the estimation of regression functions.Second,an improved two-stage refitted crossvalidation(RCV)procedure by random splitting technique is used to obtain the residuals of the model,and then the residual-based kernel method is applied to estimate the error density function.Under suitable sparse conditions,the large sample properties of the estimator,including the weak and strong consistency,as well as normality and the law of the iterated logarithm,are obtained.Especially,the relationship between the sparsity and the convergence rate of the kernel density estimator is given.The methodology is illustrated by simulations and a real data example,which suggests that the proposed method performs well.展开更多
Traditional methods for early warning of dam displacements usually assume that residual displacements follow a normal distribution.This assumption deviates from the reality,thereby affecting the reliability of early w...Traditional methods for early warning of dam displacements usually assume that residual displacements follow a normal distribution.This assumption deviates from the reality,thereby affecting the reliability of early warning results and leading to misjudgments of dam displacement behavior.To solve this problem,this study proposed an early warning method using a non-normal distribution function.A new early warning index was developed using cumulative distribution function(CDF)values.The method of kernel density estimation was used to calculate the CDF values of residual displacements at a single point.The copula function was used to compute the CDF values of residual displacements at multiple points.Numerical results showed that,with residual displacements in a non-normal distribution,the early warning method proposed in this study accurately reflected the dam displacement behavior and effectively reduced the frequency of false alarms.This method is expected to aid in the safe operation of dams.展开更多
Controlled experiments are widely used in many applications to investigate the causal relationship between input factors and experimental outcomes.A completely randomised design is usually used to randomly assign trea...Controlled experiments are widely used in many applications to investigate the causal relationship between input factors and experimental outcomes.A completely randomised design is usually used to randomly assign treatment levels to experimental units.When covariates of the experimental units are available,the experimental design should achieve covariate balancing among the treatment groups,such that the statistical inference of the treatment effects is not confounded with any possible effects of covariates.However,covariate imbalance often exists,because the experiment is carried out based on a single realisation of the complete randomisation.It is more likely to occur and worsen when the size of the experimental units is small or moderate.In this paper,we introduce a new covariate balancing criterion,which measures the differences between kernel density estimates of the covariates of treatment groups.To achieve covariate balance before the treatments are randomly assigned,we partition the experimental units by minimising the criterion,then randomly assign the treatment levels to the partitioned groups.Through numerical examples,weshow that the proposed partition approach can improve the accuracy of the difference-in-mean estimator and outperforms the complete randomisation and rerandomisation approaches.展开更多
Density-based nonparametric clustering techniques,such as the mean shift algorithm,are well known for their flexibility and effectiveness in real-world vision-based problems.The underlying kernel density estimation pr...Density-based nonparametric clustering techniques,such as the mean shift algorithm,are well known for their flexibility and effectiveness in real-world vision-based problems.The underlying kernel density estimation process can be very expensive on large datasets.In this paper,the divide-and-conquer method is proposed to reduce these computational requirements.The dataset is first partitioned into a number of small,compact clusters.Components of the kernel estimator in each local cluster are then fit to a single,representative density function.The key novelty presented here is the efficient derivation of the representative density function using concepts from function approximation,such that the expensive kernel density estimator can be easily summarized by a highly compact model with very few basis functions.The proposed method has a time complexity that is only linear in the sample size and data dimensionality.Moreover,the bandwidth of the resultant density model is adaptive to local data distribution.Experiments on color image filtering/segmentation show that,the proposed method is dramatically faster than both the standard mean shift and fast mean shift implementations based on kd-trees while展开更多
In this study,the entropy weight method was used to measure the agricultural versatility of 30 provinces in China(excluding Tibet,Hong Kong,Macao,and Taiwan)from 2008 to 2019.In addition,the Theil index method and ker...In this study,the entropy weight method was used to measure the agricultural versatility of 30 provinces in China(excluding Tibet,Hong Kong,Macao,and Taiwan)from 2008 to 2019.In addition,the Theil index method and kernel density estimation were used to analyze the spatiotemporal characteristics of the agricultural versatility in each province.The results show that the agricultural product supply and social security functions rapidly developed,but the economic development was weak.From 2008 to 2019,the total functional index of agriculture increased by 6.74%;the functional index of the agricultural product supply,social security,and ecological services increased by 12.72%,5.53%,and 2.05%,respectively;and the functional index of economic development decreased by 1.32%.The development of agricultural multifunctions in China is regionally heterogeneous.Based on the Theil index method,the differences in the agricultural functions of the three regions are mainly due to intragroup differences.The contribution of intragroup differences of the eight economic regions is significantly lower than that of the three regions.However,intragroup differences dominate the agricultural product supply,economic development,and social security functions and intergroup differences control the ecological service function.The kernel density estimation curve shows that the overall agricultural functional evaluation index increased,among which the agricultural product supply function increased the most.展开更多
This paper characterizes the rarely noticeable hot-cutting defect and statistically models the fracture governed by this new type defect. The morphology of the defect on fired ceramic is examined and quantitatively fe...This paper characterizes the rarely noticeable hot-cutting defect and statistically models the fracture governed by this new type defect. The morphology of the defect on fired ceramic is examined and quantitatively featured through comparing the fracture strength governed by intrinsic defect and hot-cutting defect. Weibull distribution is utilized to fit the observed strength data and chi-square goodness-of-fit test is conducted to analyze the deviation. Kernel density estimation is introduced to explore the underlying strength distribution dominated by hot-cutting defect. Based on the shape information from kernel density estimating,gamma and lognormal distribution are compared and the hot-cutting defect governing fracture statistics is finally confirmed by chisquare test. Results show that the newly-defined hot-cutting defect is more dangerous than the intrinsic defect and the priori Weibull distribution fails to describe the fracture statistic governed by the edge defect while the lognormal with a slightly right skewed shape fits it very well.展开更多
Ba<span style="font-family:Verdana;">sed on the urban resident population statistics from 2005 to 2018, thi</span><span style="font-family:Verdana;">s paper analyzes the distribut...Ba<span style="font-family:Verdana;">sed on the urban resident population statistics from 2005 to 2018, thi</span><span style="font-family:Verdana;">s paper analyzes the distribution and evolution of city scale in China by screening city samples according to the threshold criteria and using empirical research methods such as the City Primacy Index, the Rank-Scale Rule, the Gini coefficient of city scale, Kernel Density Estimation and Markov transfer matrix. The results show that: the most populous city in China has obvious advantages. The population distribution is concentrated in high order cities and in accordance with the law of order</span><span style="font-family:Verdana;">-</span><span style="font-family:""><span style="font-family:Verdana;">scale;the economic scale of cities is in a concentrated state, the gap between the economic development levels of different types of cities is large, and the megacities are more attractive, which to a certain extent limit the development of the scale of the rest of the cities;th</span><span style="font-family:Verdana;">e number of China’s city population is increasing, however, the ga</span><span style="font-family:Verdana;">p between the</span></span><span style="font-family:""> </span><span style="font-family:Verdana;">population scale of other cities and the most populous city continues to be large, and the structure of city population scale is not reasonable enough;megacities and megalopolises keep their original scale levels unchanged to a large extent, and the scale transition between the two types of cities is rather difficult. Finally, based on the explanatory framework of the dynamics of city scale evolution, policy recommendations are proposed to promote a more balanced distribution of city scale.</span>展开更多
基金supported by Science and Technology project of the State Grid Corporation of China“Research on Active Development Planning Technology and Comprehensive Benefit Analysis Method for Regional Smart Grid Comprehensive Demonstration Zone”National Natural Science Foundation of China(51607104)
文摘In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.
基金supported in part by the National Natural Science Foundation of China(No.51307185)Natural Science Foundation Project of CQ CSTC(No.cstc2012jjA90004)the Fundamental Research Funds for the Central Universities(No.CDJPY12150002)
文摘An accurate probability distribution model of wind speed is critical to the assessment of reliability contribution of wind energy to power systems. Most of current models are built using the parametric density estimation(PDE) methods, which usually assume that the wind speed are subordinate to a certain known distribution(e.g. Weibull distribution and Normal distribution) and estimate the parameters of models with the historical data. This paper presents a kernel density estimation(KDE) method which is a nonparametric way to estimate the probability density function(PDF) of wind speed. The method is a kind of data-driven approach without making any assumption on the form of the underlying wind speed distribution, and capable of uncovering the statistical information hidden in the historical data. The proposed method is compared with three parametric models using wind data from six sites.The results indicate that the KDE outperforms the PDE in terms of accuracy and flexibility in describing the longterm wind speed distributions for all sites. A sensitivity analysis with respect to kernel functions is presented and Gauss kernel function is proved to be the best one. Case studies on a standard IEEE reliability test system(IEEERTS) have verified the applicability and effectiveness of the proposed model in evaluating the reliability performance of wind farms.
基金supported partially by RGC 201508,HKBU FRGsThe Research Fund for the Doctoral Program of Higher Education(200802691037)the Natural Science Foundation of Shanghai(10ZR1410200).
文摘In this paper,we propose a multiphase fuzzy region competition model for texture image segmentation.In the functional,each region is represented by a fuzzy membership function and a probability density function that is estimated by a nonparametric kernel density estimation.The overall algorithm is very efficient as both the fuzzy membership function and the probability density function can be implemented easily.We apply the proposed method to synthetic and natural texture images,and synthetic aperture radar images.Our experimental results have shown that the proposed method is competitive with the other state-of-the-art segmentation methods.
基金Beijing Zheshe Base Construction Project:Research on Urban Renewal and Comprehensive Environmental Management of the Old Community in Beijing(110051360022XN121-05)。
文摘Beijing Xianyukou Hutong(hutong refers to historical and cultural block in Chinese)occupies an important geographical location with unique urban fabric,and after years of renewal and protection,the commercial space of Xianyukou Street and has gained some recognition.This article Xianyukou takes commercial hutong in Beijing as an example,spatial analysis was carried out using methods like GIS kernel density method,space syntax after site investigation and research.Based on the street space problems found,this paper then puts forward strategies to improve and upgrade Xianyukou Street’s commercial space and improve businesses in Xianyukou Street and other similar hutong.
基金Supported by the National Natural Science Foundation of China(60972059)the General Project of Science and Technology of Xuzhou City(XM12B002)
文摘Aiming at the large cost of calculating variable bandwidth kernel particle filter and the high complexity of its algorithm,a self-adjusting kernel function particle filter is presented. Kernel density estimation is facilitated to iterate and obtain new particle set. And the standard deviation of particle is introduced in the kernel bandwidth. According to the characteristics of particle distribution,the bandwidth is dynamically adjusted,and the particle distribution can thus be more close to the posterior probability density model of the system. Meanwhile,the kernel density is used to estimate the weight of updating particle and the system state. The simulation results show the feasibility and effectiveness of the proposed algorithm.
基金supported by the National Key Research and Development Project of China(2018YFE0122200).
文摘A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed.The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems(IESs)in the operation scheduling problem of integrated energy production units(IEPUs).First,to solve the problem of inaccurate prediction of renewable energy output,an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction.Subsequently,to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs,a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established.The system considers the further utilization of energy using hydrogen energy coupling equipment(such as hydrogen storage devices and fuel cells)and the comprehensive demand response of load-side schedulable resources.The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions,improve the source-load interaction of the IES,realize the efficient use of hydrogen energy,and improve system robustness.
基金supported by the National Natural Science Foundation of China under Grant Nos.52105136,51975028China Postdoctoral Science Foundation under Grant[No.2021M690290]the National Science and TechnologyMajor Project under Grant No.J2019-IV-0002-0069.
文摘The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity,multi-failure regions,and small failure probability,which brings in unacceptable computing efficiency and accuracy of the current analysismethods.In this case,by fitting the implicit limit state function(LSF)with active Kriging(AK)model and reducing candidate sample poolwith adaptive importance sampling(AIS),a novel AK-AIS method is proposed.Herein,theAKmodel andMarkov chainMonte Carlo(MCMC)are first established to identify the most probable failure region(s)(MPFRs),and the adaptive kernel density estimation(AKDE)importance sampling function is constructed to select the candidate samples.With the best samples sequentially attained in the reduced candidate samples and employed to update the Kriging-fitted LSF,the failure probability and sensitivity indices are acquired at a lower cost.The proposed method is verified by twomulti-failure numerical examples,and then applied to the reliability and sensitivity analyses of a typical stator blade regulator.Withmethods comparison,the proposed AK-AIS is proven to hold the computing advantages on accuracy and efficiency in complex reliability and sensitivity analysis problems.
基金Ministry of Education Humanities and Social Science Foundation Youth Project“Micro-quantification,Action Mechanism and Impact Research on Financialization of Entity Enterprises”[Grant number.19YJC790106]National Social Science Fund“Mechanism Analysis and Optimization Path Research of Digital Finance Supporting the Im‐provement of Development Efficiency of SMEs”[Grant number.21BJY047]+2 种基金Science and Technology Research Program of Chongqing Education Commission of China:“Optimization Path Research of Or‐ganizational Effectiveness of SOEs in Chongqing Based on Multi-Level Organizational Citizenship Behavior”[Grant number.17SKG036]Chongqing Social Science Planning Major Project“Research on the Technological Progress Path and Countermeasure System of Innovation-driven Manufacturing Upgrade in Chongqing”[Grant num‐ber.2020ZDJJ01]Chongqing Municipal Education Commission Hu‐manities and Social Sciences Research Project“Western Region Finan‐cial Development and Manufacturing Traditional Comparative Advan‐tage Transformation:Efficiency Measurement,Action Mechanism and Research on Spatial Effects”[Grant number.20SKGH040].
文摘The digital economy has become an important driver for stimulating economic growth.The digital economy has now widely penetrated the fields of economy and society,providing new opportunities for the develop‐ment of urban-rural integration.Based on panel data for 30 provinces in China from 2011 to 2020,this study constructed an index system for the integration of the digital economy and the development of urban-rural areas and conducted a systematic measurement analysis.Additionally,we used a two-step system of GMM estimation to analyze the impact of the digital economy on the development of urban-rural integra‐tion.The findings demonstrate the significant imbalance paradox of China’s digital economy development,which is shown in a gradient where the eastern region is higher than the center and the central region is higher than the west.Urban-rural integration levels in China fluctuate and display geographical variance,typically displaying high levels in the east and low levels in the west.Urban-rural integration is significantly encouraged by the digital economy,yet it varies in variability between different areas and dimensions.Addi‐tionally,rural human capital moderates the favorable effects of the digital economy on urban-rural integra‐tion.As a result,in order to achieve the integrated development of urban and rural areas,it is imperative to fully exploit the active role of the digital economy,better support the development of rural revitalization,bridge the“digital divide”between urban and rural development,and build a strong foundation for the for‐mation of a digital urban-rural integrated development pattern with urban and rural areas and common con‐struction and sharing.
基金Under the auspices of the National Social Science Fund of China(No.15BGL185,19XJL004)General Project of Humanities and Social Sciences Research and Planning Fund of Ministry of Education(No.19YJA790097)+1 种基金Social Science Fund of Fujian Province(No.FJ2017C080)A Key Discipline of Henan University of Animal Husbandry and Economy‘Business Enterprise Management’(No.MXK2016201)。
文摘Cold storage is the vital infrastructure of cold chain logistics. In this study, we analyzed the spatial pattern evolution characteristics, spatial autocorrelation and influencing factors of cold storage in China by using kernel density estimation(KDE), spatial autocorrelation analysis(SAA), and spatial error model(SEM). Results showed that: 1) the spatial distribution of cold storage in China is unbalanced, and has evolved from ‘one core’ to ‘one core and many spots’, that is, ‘one core’ refers to the Bohai Rim region mainly including Beijing, Tianjin, Hebei, Shandong and Liaoning regions, and ‘many spots’ mainly include the high-density areas such as Shanghai, Fuzhou, Guangzhou, Zhengzhou, Hefei, Wuhan, ürümqi. 2) The distribution of cold storage has significant global spatial autocorrelation and local spatial autocorrelation, and the ‘High-High’ cluster area is the most stable, mainly concentrated in the Bohai Rim;the ‘Low-Low’ cluster area is grouped in the southern China. 3) Economic development level, population density, traffic accessibility, temperature and land price, all affect the location choice of cold storage in varying degrees, while the impact of market demand on it is not explicit.
基金supported by Nation Key R&D Program of China(2021YFE0102400).
文摘Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods.In this paper,a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation.A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance.The power forecast errors in different weather types are analyzed,and an energy storage system is used to compensate for the errors.The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system;the power and capacity of the energy storage unit are calculated at different confidence levels.The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction.The proposed method was validated using actual operating data from a PV power station.The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.
基金Supported by the National Natural Science Foundation of China(61039001)the State Technology Supporting Plan(2011BAH24B08)
文摘In order to obtain accurate conflict risks in terminal airspace design,the concept and calculation model of potential conflict frequency for intersected routes are proposed.Conflict frequency is represented by the product of horizontal conflict frequency and vertical conflict probability.The horizontal conflict frequency is derived from the probability density distribution of conflicts in a period of time.Based on the recorded radar trajectory data,the concept and model of ROUTE distance are proposed,and the probability density function of aircraft height at a specified ROUTE distance is deduced by kernel density estimation.Furthermore,vertical conflict probability and its horizontal distribution are achieved.Examples of three intersected arrival and departure route design schemes are studied.Compared with scheme 1,the conflict frequency values of the other two improved schemes decrease to53% and 24%,respectively.The results show that the model can quantify potential conflict frequency of intersected routes.
基金Under the auspices of the Project Supported by Natural Science Foundation of Jiangsu Province (No. BK20200109)the Open Fund of Key Laboratory of Coastal Zone Exploitation and Protection,Ministry of Natural Resource (No. 2021CZEPK05)+1 种基金National Natural Science Foundation of China (No. 42001225)the Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province,China (No. 2022SJYB0287)。
文摘Evaluating urban land use efficiency(ULUE) provides insights into the interactions between land use systems and their external environment. Specifically, changes in ULUE are important for monitoring urban transformation in developing countries. In this study, using a traditional input-output index model, we incorporated slack-based measurements and undesirable outputs into a SBM-UN(slack-based measure-undesirable) model to investigate ULUE within the context of increasing environmental restrictions in China. The model was used to estimate the ULUE of 26 cities in the highly developed urban agglomeration of the Yangtze River Delta from 2000 to2018. The average ULUE in the Yangtze River Delta was relatively low compared to that of developed city regions in the European Union(EU) and North America and exhibited a U-shaped curve over the study period. Incorporating undesirable outputs, such as environmental pollution, into the model reduced ULUE by 19.06%. ULUE varied spatially, with the kernel density estimation exhibiting a bimodal distribution. Efficiency decomposition analysis showed that scale efficiency made a greater contribution to ULUE than pure technical efficiency. Based on our findings, recommended approaches to improve ULUE include optimizing factor allocation, reducing undesirable outputs, and increasing the effective output per land unit. The study suggests that ULUE and the SBM-UN model are useful planning tools for sustainable urban development.
基金supported by National Natural Science Foundation of China (Grant Nos. 11971324 and 11471223)Interdisciplinary Construction of Bioinformatics and StatisticsAcademy for Multidisciplinary Studies, Capital Normal University
文摘In this paper,we mainly study how to estimate the error density in the ultrahigh dimensional sparse additive model,where the number of variables is larger than the sample size.First,a smoothing method based on B-splines is applied to the estimation of regression functions.Second,an improved two-stage refitted crossvalidation(RCV)procedure by random splitting technique is used to obtain the residuals of the model,and then the residual-based kernel method is applied to estimate the error density function.Under suitable sparse conditions,the large sample properties of the estimator,including the weak and strong consistency,as well as normality and the law of the iterated logarithm,are obtained.Especially,the relationship between the sparsity and the convergence rate of the kernel density estimator is given.The methodology is illustrated by simulations and a real data example,which suggests that the proposed method performs well.
基金supported by the National Natural Science Foundation of China(Grant No.52109156)the Science and Technology Project of the Jiangxi Provincial Education Department(Grant No.GJJ190970).
文摘Traditional methods for early warning of dam displacements usually assume that residual displacements follow a normal distribution.This assumption deviates from the reality,thereby affecting the reliability of early warning results and leading to misjudgments of dam displacement behavior.To solve this problem,this study proposed an early warning method using a non-normal distribution function.A new early warning index was developed using cumulative distribution function(CDF)values.The method of kernel density estimation was used to calculate the CDF values of residual displacements at a single point.The copula function was used to compute the CDF values of residual displacements at multiple points.Numerical results showed that,with residual displacements in a non-normal distribution,the early warning method proposed in this study accurately reflected the dam displacement behavior and effectively reduced the frequency of false alarms.This method is expected to aid in the safe operation of dams.
基金supported by Division of Mathematical Sciences[grant number 1916467].
文摘Controlled experiments are widely used in many applications to investigate the causal relationship between input factors and experimental outcomes.A completely randomised design is usually used to randomly assign treatment levels to experimental units.When covariates of the experimental units are available,the experimental design should achieve covariate balancing among the treatment groups,such that the statistical inference of the treatment effects is not confounded with any possible effects of covariates.However,covariate imbalance often exists,because the experiment is carried out based on a single realisation of the complete randomisation.It is more likely to occur and worsen when the size of the experimental units is small or moderate.In this paper,we introduce a new covariate balancing criterion,which measures the differences between kernel density estimates of the covariates of treatment groups.To achieve covariate balance before the treatments are randomly assigned,we partition the experimental units by minimising the criterion,then randomly assign the treatment levels to the partitioned groups.Through numerical examples,weshow that the proposed partition approach can improve the accuracy of the difference-in-mean estimator and outperforms the complete randomisation and rerandomisation approaches.
基金Project (No. 9140C1204060809) supported by the National Key Laboratory Foundation of China
文摘Density-based nonparametric clustering techniques,such as the mean shift algorithm,are well known for their flexibility and effectiveness in real-world vision-based problems.The underlying kernel density estimation process can be very expensive on large datasets.In this paper,the divide-and-conquer method is proposed to reduce these computational requirements.The dataset is first partitioned into a number of small,compact clusters.Components of the kernel estimator in each local cluster are then fit to a single,representative density function.The key novelty presented here is the efficient derivation of the representative density function using concepts from function approximation,such that the expensive kernel density estimator can be easily summarized by a highly compact model with very few basis functions.The proposed method has a time complexity that is only linear in the sample size and data dimensionality.Moreover,the bandwidth of the resultant density model is adaptive to local data distribution.Experiments on color image filtering/segmentation show that,the proposed method is dramatically faster than both the standard mean shift and fast mean shift implementations based on kd-trees while
文摘In this study,the entropy weight method was used to measure the agricultural versatility of 30 provinces in China(excluding Tibet,Hong Kong,Macao,and Taiwan)from 2008 to 2019.In addition,the Theil index method and kernel density estimation were used to analyze the spatiotemporal characteristics of the agricultural versatility in each province.The results show that the agricultural product supply and social security functions rapidly developed,but the economic development was weak.From 2008 to 2019,the total functional index of agriculture increased by 6.74%;the functional index of the agricultural product supply,social security,and ecological services increased by 12.72%,5.53%,and 2.05%,respectively;and the functional index of economic development decreased by 1.32%.The development of agricultural multifunctions in China is regionally heterogeneous.Based on the Theil index method,the differences in the agricultural functions of the three regions are mainly due to intragroup differences.The contribution of intragroup differences of the eight economic regions is significantly lower than that of the three regions.However,intragroup differences dominate the agricultural product supply,economic development,and social security functions and intergroup differences control the ecological service function.The kernel density estimation curve shows that the overall agricultural functional evaluation index increased,among which the agricultural product supply function increased the most.
文摘This paper characterizes the rarely noticeable hot-cutting defect and statistically models the fracture governed by this new type defect. The morphology of the defect on fired ceramic is examined and quantitatively featured through comparing the fracture strength governed by intrinsic defect and hot-cutting defect. Weibull distribution is utilized to fit the observed strength data and chi-square goodness-of-fit test is conducted to analyze the deviation. Kernel density estimation is introduced to explore the underlying strength distribution dominated by hot-cutting defect. Based on the shape information from kernel density estimating,gamma and lognormal distribution are compared and the hot-cutting defect governing fracture statistics is finally confirmed by chisquare test. Results show that the newly-defined hot-cutting defect is more dangerous than the intrinsic defect and the priori Weibull distribution fails to describe the fracture statistic governed by the edge defect while the lognormal with a slightly right skewed shape fits it very well.
文摘Ba<span style="font-family:Verdana;">sed on the urban resident population statistics from 2005 to 2018, thi</span><span style="font-family:Verdana;">s paper analyzes the distribution and evolution of city scale in China by screening city samples according to the threshold criteria and using empirical research methods such as the City Primacy Index, the Rank-Scale Rule, the Gini coefficient of city scale, Kernel Density Estimation and Markov transfer matrix. The results show that: the most populous city in China has obvious advantages. The population distribution is concentrated in high order cities and in accordance with the law of order</span><span style="font-family:Verdana;">-</span><span style="font-family:""><span style="font-family:Verdana;">scale;the economic scale of cities is in a concentrated state, the gap between the economic development levels of different types of cities is large, and the megacities are more attractive, which to a certain extent limit the development of the scale of the rest of the cities;th</span><span style="font-family:Verdana;">e number of China’s city population is increasing, however, the ga</span><span style="font-family:Verdana;">p between the</span></span><span style="font-family:""> </span><span style="font-family:Verdana;">population scale of other cities and the most populous city continues to be large, and the structure of city population scale is not reasonable enough;megacities and megalopolises keep their original scale levels unchanged to a large extent, and the scale transition between the two types of cities is rather difficult. Finally, based on the explanatory framework of the dynamics of city scale evolution, policy recommendations are proposed to promote a more balanced distribution of city scale.</span>
