Road network is a critical component of public infrastructure,and the supporting system of social and economic development.Based on a modified kernel density estimate(KDE)algorithm,this study evaluated the road servic...Road network is a critical component of public infrastructure,and the supporting system of social and economic development.Based on a modified kernel density estimate(KDE)algorithm,this study evaluated the road service capacity provided by a road network composed of multi-level roads(i.e.national,provincial,county and rural roads),by taking account of the differences of effect extent and intensity for roads of different levels.Summarized at town scale,the population burden and the annual rural economic income of unit road service capacity were used as the surrogates of social and economic demands for road service.This method was applied to the road network of the Three Parallel River Region,the northwestern Yunnan Province,China to evaluate the development of road network in this region.In results,the total road length of this region in 2005 was 3.70×104km,and the length ratio between national,provincial,county and rural roads was 1∶2∶8∶47.From 1989 to 2005,the regional road service capacity increased by 13.1%,of which the contributions from the national,provincial,county and rural roads were 11.1%,19.4%,22.6%,and 67.8%,respectively,revealing the effect of′All Village Accessible′policy of road development in the mountainous regions in the last decade.The spatial patterns of population burden and economic requirement of unit road service suggested that the areas farther away from the national and provincial roads have higher road development priority(RDP).Based on the modified KDE model and the framework of RDP evaluation,this study provided a useful approach for developing an optimal plan of road development at regional scale.展开更多
Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific ...Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.展开更多
As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configu...As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares technique.Secondly,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty weight.Comparison experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and verified.Experimental results show that the RSCN has an excellent performance for the PS estimation.展开更多
Holter usually monitors electrocardiogram(ECG)signals for more than 24 hours to capture short-lived cardiac abnormalities.In view of the large amount of Holter data and the fact that the normal part accounts for the m...Holter usually monitors electrocardiogram(ECG)signals for more than 24 hours to capture short-lived cardiac abnormalities.In view of the large amount of Holter data and the fact that the normal part accounts for the majority,it is reasonable to design an algorithm that can automatically eliminate normal data segments as much as possible without missing any abnormal data segments,and then take the left segments to the doctors or the computer programs for further diagnosis.In this paper,we propose a preliminary abnormal segment screening method for Holter data.Based on long short-term memory(LSTM)networks,the prediction model is established and trained with the normal data of a monitored object.Then,on the basis of kernel density estimation,we learn the distribution law of prediction errors after applying the trained LSTM model to the regular data.Based on these,the preliminary abnormal ECG segment screening analysis is carried out without R wave detection.Experiments on the MIT-BIH arrhythmia database show that,under the condition of ensuring that no abnormal point is missed,53.89% of normal segments can be effectively obviated.This work can greatly reduce the workload of subsequent further processing.展开更多
It is one of the responsibilities of the navigation support department to ensure the correct layout position of the light buoy and provide as accurate position information as possible for ship navigation and positioni...It is one of the responsibilities of the navigation support department to ensure the correct layout position of the light buoy and provide as accurate position information as possible for ship navigation and positioning.If the position deviation of the light buoy is too large to be detected in time,sending wrong navigation assistance information to the ship will directly affect the navigation safety of the ship and increase the pressure on the management department.Therefore,mastering the offset characteristics of light buoy is of great significance for the maintenance of light buoy and improving the navigation aid efficiency of light buoy.Kernel density estimation can intuitively express the spatial and temporal distribution characteristics of buoy position,and indicates the intensive areas of buoy position in the channel.In this paper,in order to speed up deciding the optimal variable width of kernel density estimator,an improved adaptive variable width kernel density estimator is proposed,which reduces the risk of too smooth probability density estimation phenomenon and improves the estimation accuracy of probability density.A fractional recurrent neural network is designed to search the optimal bandwidth of kernel density estimator.It not only achieves faster training speed,but also improves the estimation accuracy of probability density.展开更多
Scientifically constructing an ecological security pattern(ESP)is an important spatial analysis approach to improve ecological functions in arid areas and achieve sustainable development.However,previous research meth...Scientifically constructing an ecological security pattern(ESP)is an important spatial analysis approach to improve ecological functions in arid areas and achieve sustainable development.However,previous research methods ignored the complex trade-offs between ecosystem services in the process of constructing ESP.Taking the mainstream of the Tarim River Basin(MTRB),China as the study area,this study set seven risk scenarios by applying Ordered Weighted Averaging(OWA)model to trade-off the importance of the four ecosystem services adopted by this study(water conservation,carbon storage,habitat quality,and biodiversity conservation),thereby identifying priority protection areas for ecosystem services.And then,this study identified ecological sources by integrating ecosystem service importance with eco-environmental sensitivity.Using circuit theory,the ecological corridors and nodes were extracted to construct the ESP.The results revealed significant spatial heterogeneity in the four ecosystem services across the study area,primarily driven by hydrological gradients and human activity intensity.The ESP of the MTRB included 34 ecological sources with a total area of 1471.38 km^(2),66 ecological corridors with a length of about 1597.45 km,11 ecological pinch points,and 13 ecological barrier points distributed on the ecological corridors.The spatial differentiation of the ESP was obvious,with the upper and middle reaches of the MTRB having a large number of ecological sources and exhibiting higher clustering of ecological corridors compared with the lower reaches.The upper and middle reaches require ecological protection to sustain the existing ecosystem,while the lower reaches need to carry out ecological restoration measures including desertification control.Overall,this study makes up for the shortcomings of constructing ESP simply by spatial superposition of ecosystem service functions and can effectively improve the robustness and stability of ESP construction.展开更多
设施POI(point of interest)在城市地理空间中往往聚集分布,呈现热点特征。对该类POI分布热点的分析大多采用基于欧氏距离的空间密度估计,忽略了城市空间通达、连接是沿着街道路径的事实,从而很难准确、客观地反映城市功能的热点布局。...设施POI(point of interest)在城市地理空间中往往聚集分布,呈现热点特征。对该类POI分布热点的分析大多采用基于欧氏距离的空间密度估计,忽略了城市空间通达、连接是沿着街道路径的事实,从而很难准确、客观地反映城市功能的热点布局。本研究针对该缺陷,利用基于网络路径距离的核密度计算方法确定热点的区域密度,并提出了一种简单、高效的网络分析算法。该算法扩展二维栅格膨胀操作,以一维形态算子的连续扩展计算POI在网络单元上的密度值,通过评价试验表明,该算法比现有算法具有更好的性能和可扩展性。通过实际POI数据分析发现,考虑街道网络约束的热点范围可凸显设施功能沿交通网络布局的空间特征,为区域规划、导航以及地理信息查询等应用提供有价值的空间知识与信息服务。展开更多
城市空间POI点的分布模式、分布密度在基础设施规划、城市空间分析中具有重要意义,表达该特征的核密度法(kernel density estimation)由于顾及了地理学第一定律的区位影响,比其他密度表达方法(如样方密度、基于Voronoi图密度)占优。然而...城市空间POI点的分布模式、分布密度在基础设施规划、城市空间分析中具有重要意义,表达该特征的核密度法(kernel density estimation)由于顾及了地理学第一定律的区位影响,比其他密度表达方法(如样方密度、基于Voronoi图密度)占优。然而,传统的核密度计算方法往往基于二维延展的欧氏空间,忽略了城市网络空间中设施点的服务功能及相互联系发生于网络路径距离而非欧氏距离的事实。本研究针对该缺陷,给出了网络空间核密度计算模型,分析了核密度方法在置入网络结构中受多种约束条件的扩展模式,讨论了衰减阈值及高度极值对核密度特征表达的影响。通过实际多种POI点分布模式(随机型、稀疏型、区域密集型、线状密集型)下的核密度分析试验,讨论了POI基础设施在城市区域中的分布特征、影响因素、服务功能。展开更多
基金Under the auspices of National Natural Science Foundation of China(No.41371190,31021001)Scientific and Tech-nical Projects of Western China Transportation Construction,Ministry of Transport of China(No.2008-318-799-17)
文摘Road network is a critical component of public infrastructure,and the supporting system of social and economic development.Based on a modified kernel density estimate(KDE)algorithm,this study evaluated the road service capacity provided by a road network composed of multi-level roads(i.e.national,provincial,county and rural roads),by taking account of the differences of effect extent and intensity for roads of different levels.Summarized at town scale,the population burden and the annual rural economic income of unit road service capacity were used as the surrogates of social and economic demands for road service.This method was applied to the road network of the Three Parallel River Region,the northwestern Yunnan Province,China to evaluate the development of road network in this region.In results,the total road length of this region in 2005 was 3.70×104km,and the length ratio between national,provincial,county and rural roads was 1∶2∶8∶47.From 1989 to 2005,the regional road service capacity increased by 13.1%,of which the contributions from the national,provincial,county and rural roads were 11.1%,19.4%,22.6%,and 67.8%,respectively,revealing the effect of′All Village Accessible′policy of road development in the mountainous regions in the last decade.The spatial patterns of population burden and economic requirement of unit road service suggested that the areas farther away from the national and provincial roads have higher road development priority(RDP).Based on the modified KDE model and the framework of RDP evaluation,this study provided a useful approach for developing an optimal plan of road development at regional scale.
基金Project(2020YFC2008605)supported by the National Key Research and Development Project of ChinaProject(52072412)supported by the National Natural Science Foundation of ChinaProject(2021JJ30359)supported by the Natural Science Foundation of Hunan Province,China。
文摘Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.
基金Projects(61603393,61741318)supported in part by the National Natural Science Foundation of ChinaProject(BK20160275)supported by the Natural Science Foundation of Jiangsu Province,China+1 种基金Project(2015M581885)supported by the Postdoctoral Science Foundation of ChinaProject(PAL-N201706)supported by the Open Project Foundation of State Key Laboratory of Synthetical Automation for Process Industries of Northeastern University,China
文摘As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares technique.Secondly,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty weight.Comparison experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and verified.Experimental results show that the RSCN has an excellent performance for the PS estimation.
文摘Holter usually monitors electrocardiogram(ECG)signals for more than 24 hours to capture short-lived cardiac abnormalities.In view of the large amount of Holter data and the fact that the normal part accounts for the majority,it is reasonable to design an algorithm that can automatically eliminate normal data segments as much as possible without missing any abnormal data segments,and then take the left segments to the doctors or the computer programs for further diagnosis.In this paper,we propose a preliminary abnormal segment screening method for Holter data.Based on long short-term memory(LSTM)networks,the prediction model is established and trained with the normal data of a monitored object.Then,on the basis of kernel density estimation,we learn the distribution law of prediction errors after applying the trained LSTM model to the regular data.Based on these,the preliminary abnormal ECG segment screening analysis is carried out without R wave detection.Experiments on the MIT-BIH arrhythmia database show that,under the condition of ensuring that no abnormal point is missed,53.89% of normal segments can be effectively obviated.This work can greatly reduce the workload of subsequent further processing.
基金the Natural Science Foundation of Fujian Province(No.2021J01819)。
文摘It is one of the responsibilities of the navigation support department to ensure the correct layout position of the light buoy and provide as accurate position information as possible for ship navigation and positioning.If the position deviation of the light buoy is too large to be detected in time,sending wrong navigation assistance information to the ship will directly affect the navigation safety of the ship and increase the pressure on the management department.Therefore,mastering the offset characteristics of light buoy is of great significance for the maintenance of light buoy and improving the navigation aid efficiency of light buoy.Kernel density estimation can intuitively express the spatial and temporal distribution characteristics of buoy position,and indicates the intensive areas of buoy position in the channel.In this paper,in order to speed up deciding the optimal variable width of kernel density estimator,an improved adaptive variable width kernel density estimator is proposed,which reduces the risk of too smooth probability density estimation phenomenon and improves the estimation accuracy of probability density.A fractional recurrent neural network is designed to search the optimal bandwidth of kernel density estimator.It not only achieves faster training speed,but also improves the estimation accuracy of probability density.
基金funded by the Xinjiang Uygur Autonomous Region Tianshan Talent Training Program(2023TSYCLJ0047)the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2023D01D18)+1 种基金the Key Research and Development Project of Xinjiang(2022B03024-1)the Science and Technology Planning Project of Xinjiang Production and Construction Corps(2022DB023)。
文摘Scientifically constructing an ecological security pattern(ESP)is an important spatial analysis approach to improve ecological functions in arid areas and achieve sustainable development.However,previous research methods ignored the complex trade-offs between ecosystem services in the process of constructing ESP.Taking the mainstream of the Tarim River Basin(MTRB),China as the study area,this study set seven risk scenarios by applying Ordered Weighted Averaging(OWA)model to trade-off the importance of the four ecosystem services adopted by this study(water conservation,carbon storage,habitat quality,and biodiversity conservation),thereby identifying priority protection areas for ecosystem services.And then,this study identified ecological sources by integrating ecosystem service importance with eco-environmental sensitivity.Using circuit theory,the ecological corridors and nodes were extracted to construct the ESP.The results revealed significant spatial heterogeneity in the four ecosystem services across the study area,primarily driven by hydrological gradients and human activity intensity.The ESP of the MTRB included 34 ecological sources with a total area of 1471.38 km^(2),66 ecological corridors with a length of about 1597.45 km,11 ecological pinch points,and 13 ecological barrier points distributed on the ecological corridors.The spatial differentiation of the ESP was obvious,with the upper and middle reaches of the MTRB having a large number of ecological sources and exhibiting higher clustering of ecological corridors compared with the lower reaches.The upper and middle reaches require ecological protection to sustain the existing ecosystem,while the lower reaches need to carry out ecological restoration measures including desertification control.Overall,this study makes up for the shortcomings of constructing ESP simply by spatial superposition of ecosystem service functions and can effectively improve the robustness and stability of ESP construction.
文摘设施POI(point of interest)在城市地理空间中往往聚集分布,呈现热点特征。对该类POI分布热点的分析大多采用基于欧氏距离的空间密度估计,忽略了城市空间通达、连接是沿着街道路径的事实,从而很难准确、客观地反映城市功能的热点布局。本研究针对该缺陷,利用基于网络路径距离的核密度计算方法确定热点的区域密度,并提出了一种简单、高效的网络分析算法。该算法扩展二维栅格膨胀操作,以一维形态算子的连续扩展计算POI在网络单元上的密度值,通过评价试验表明,该算法比现有算法具有更好的性能和可扩展性。通过实际POI数据分析发现,考虑街道网络约束的热点范围可凸显设施功能沿交通网络布局的空间特征,为区域规划、导航以及地理信息查询等应用提供有价值的空间知识与信息服务。
文摘城市空间POI点的分布模式、分布密度在基础设施规划、城市空间分析中具有重要意义,表达该特征的核密度法(kernel density estimation)由于顾及了地理学第一定律的区位影响,比其他密度表达方法(如样方密度、基于Voronoi图密度)占优。然而,传统的核密度计算方法往往基于二维延展的欧氏空间,忽略了城市网络空间中设施点的服务功能及相互联系发生于网络路径距离而非欧氏距离的事实。本研究针对该缺陷,给出了网络空间核密度计算模型,分析了核密度方法在置入网络结构中受多种约束条件的扩展模式,讨论了衰减阈值及高度极值对核密度特征表达的影响。通过实际多种POI点分布模式(随机型、稀疏型、区域密集型、线状密集型)下的核密度分析试验,讨论了POI基础设施在城市区域中的分布特征、影响因素、服务功能。
