In this paper, we study a spatially periodic system with spatial noises. Study shows that the correlation between the spatial noises can produce the nonzero flux, even in the absence of the asymmetry of the system.
In this paper, we study a fluctuating potential barrier system with correlated spatial noises. Study shows that for this system, there is the resonant activation over the fluctuating potential barrier, and that the co...In this paper, we study a fluctuating potential barrier system with correlated spatial noises. Study shows that for this system, there is the resonant activation over the fluctuating potential barrier, and that the correlation between the different spatial noises can enhance (or weaken) the resonant activation.展开更多
The phenomenon of stochastic synchronization in globally coupled FitzHugh–Nagumo（FHN） neuron system subjected to spatially correlated Gaussian noise is investigated based on dynamical mean-field approximation（DMA...The phenomenon of stochastic synchronization in globally coupled FitzHugh–Nagumo（FHN） neuron system subjected to spatially correlated Gaussian noise is investigated based on dynamical mean-field approximation（DMA） and direct simulation（DS）. Results from DMA are in good quantitative or qualitative agreement with those from DS for weak noise intensity and larger system size. Whether the consisting single FHN neuron is staying at the resting state, subthreshold oscillatory regime, or the spiking state, our investigation shows that the synchronization ratio of the globally coupled system becomes higher as the noise correlation coefficient increases, and thus we conclude that spatial correlation has an active effect on stochastic synchronization, and the neurons can achieve complete synchronization in the sense of statistics when the noise correlation coefficient tends to one. Our investigation also discloses that the noise spatial correlation plays the same beneficial role as the global coupling strength in enhancing stochastic synchronization in the ensemble. The result might be useful in understanding the information coding mechanism in neural systems.展开更多
As cultural facilities,physical bookstore is an important part of urban infrastructure.Influenced by the development of social economy and the internet,physical bookstores also have become a combination of cultural sp...As cultural facilities,physical bookstore is an important part of urban infrastructure.Influenced by the development of social economy and the internet,physical bookstores also have become a combination of cultural space and tourism experience.In this case,it is necessary to explore the spatial characteristics and influencing factors of physical bookstores.This study uses Density-Based Spatial Clustering of Applications with Noise(DBSCAN),spatial analysis and geographical detectors to calculate the spatial distribution pattern and factors influencing physical bookstores in national central cities/municipality(hereafter using cities)in western China.Based on spatial data,population density,road density and other data,this study constructed a data set of the influencing factors of physical bookstores,consisting of 11 factors along 6 dimensions for 3 national central cities in western China.The results are as follows:first,the spatial distribution pattern of physical bookstores in Xi’an,Chengdu,and Chongqing is unbalanced.The spatial distribution of physical bookstores in Xi’an and Chongqing is from southwest to northeast and are relatively clustered,while those in Chengdu are relatively discrete.Second,the spatial distribution pattern of physical bookstores has been formed under the influence of different factors.The intensity and significance of influencing factors differ in the case cities.However,in general,the social factor,business factor,the density of research facilities,tourism factor and road density are the main driving factors in the three cities.There is a synergistic relationship between public libraries and physical bookstores.Third,the explanatory power becomes stronger after the interaction between various factors.In Xi’an and Chengdu,the density of communities and the density of research facilities have stronger explanatory power for the dependent variable after interacting with other factors.However,in Chongqing,the traffic factors have stronger explanatory power for the dependent variable after interacting with other factors.The results could provide a practical reference for the sustainable development of physical bookstores and encourage a love of reading among the public.展开更多
There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteri...There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration areas.This study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to 2022.The results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to 2022.However,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and volume.Urban B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level clusters.While the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban areas.Conversely,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial distribution.In terms of differences,population agglomeration had a positive impact on the distribution of urban B&Bs and a negative effect on the distribution of rural B&Bs.Rural B&Bs clustering was more influenced by tourism resources compared with urban B&Bs,but increasing tourist stay duration remains an urgent issue to be addressed.The findings of this study could provide a more precise basis for government planning and management of urban and rural B&B agglomeration areas.展开更多
Objective To develop an individual noise exposure dose estimation model based on spatial distribution of noise in order to provide reference for occupational health management and hearing loss risk assessment caused b...Objective To develop an individual noise exposure dose estimation model based on spatial distribution of noise in order to provide reference for occupational health management and hearing loss risk assessment caused by noise in workplace.Methods From July 2018 to October 2019,10 noise-exposed positions and 48 employees in 3 high-noise workplaces of a manufacturing enterprise in Sichuan Province were selected as the research subjects.Occupational health survey,fixed-point measurement of workplace noise and individual noise measurement were used to obtain noise intensity and employee exposure information.The mean noise intensity and the corresponding exposure time were weighed to estimate the individual noise exposure dose estimation model based on the spatial distribution of workplace noise,and the paired t-test was used to evaluate the accuracy of the individual noise exposure dose estimate(8 h equivalent sound level,Lex,8 h)based on the spatial distribution of workplace,noise and the measured value of individualnoise exposure dose.And the least square regression model was used to correct it.Results The daily noise exposure dose of 44(91.7%)of 48 workers in 10 types of work in 3 sites of the manufacturing enterprise exceeded the standard,and the maximum noise exposure intensity was up to 108.3 dB(A).The measured value of individual noise exposure dose was higher than Lex,s h,and the difference was statistically significant(P<0.001).The noise difference distribution was mostly positive and symmetrical(P=0.958).The measured noise exposure dose of 6 out of 10 work types in 3 sites was higher than Lex,8 h(all P<0.05),and there was a linear correlation between the measured dose and Lex.8 h(r=0.373,P<0.05).Considering the influence of systematic error and the construction,diagnosis and screening of adaptive assessment model,an individual noise exposure dose estimation model based on the spatial distribution of workplace noise was finally obtained:y=0.574x+45.250,where y was the measured value of individual noise exposure dose,x was Lex,s h value.Conclusion The high noise hazard in manufacturing industry is more serious,and the noise exposure assessment of single fixed-point detection is relatively rough.The individual noise exposure dose estimation model based on the spatial distribution of workplace noise has a certain role in the occupational health managementofworkplacenoise hazard in manufacturing industry.展开更多
Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflecti...Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflection point method”to analyze specific deviation trajectories,determine deviation thresholds,and identify commonly used deviation paths.By combining multiple similarity metrics,including Euclidean distance,Hausdorff distance,and sector edit distance,with the density-based spatial clustering of applications with noise(DBSCAN)algorithm,the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs.A case study of 23578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework.Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances,with higher silhouette coefficients and lower Davies⁃Bouldin indices,ensuring better intra-cluster compactness and inter-cluster separation.Based on clustering results,19 representative trajectory options are identified,covering both nominal and deviation paths,which significantly enhance route diversity and reflect actual flight practices.This provides a practical basis for optimizing flight paths and scheduling,enhancing the flexibility of route selection for flights between city pairs.展开更多
针对点云数据中噪声点的剔除问题,提出了一种基于改进DBSCAN(density-based spatial clustering of applications with noise)算法的多尺度点云去噪方法。应用统计滤波对孤立离群点进行预筛选,去除点云中的大尺度噪声;对DBSCAN算法进行...针对点云数据中噪声点的剔除问题,提出了一种基于改进DBSCAN(density-based spatial clustering of applications with noise)算法的多尺度点云去噪方法。应用统计滤波对孤立离群点进行预筛选,去除点云中的大尺度噪声;对DBSCAN算法进行优化,减少算法时间复杂度和实现参数的自适应调整,以此将点云分为正常簇、疑似簇及异常簇,并立即去除异常簇;利用距离共识评估法对疑似簇进行精细判定,通过计算疑似点与其最近的正常点拟合表面之间的距离,判定其是否为异常,有效保持了数据的关键特征和模型敏感度。利用该方法对两个船体分段点云进行去噪,并与其他去噪算法进行对比,结果表明,该方法在去噪效率和特征保持方面具有优势,精确地保留了点云数据的几何特性。展开更多
This paper addresses the problem of four-dimensional angle and Doppler frequency estimation for bistatic multiple-input multiple-output （MIMO） radar with arbitrary arrays in spatial co- lored noise. A novel method f...This paper addresses the problem of four-dimensional angle and Doppler frequency estimation for bistatic multiple-input multiple-output （MIMO） radar with arbitrary arrays in spatial co- lored noise. A novel method for joint estimation of Doppler fre- quency, two-dimensional （2D） direction of departure and 2D direc- tion of arrival based on the propagator method （PM） for arbitrary arrays is discussed. A special matrix is constructed to eliminate the influence of spatial colored noise. The four-dimensional （4D） angle and Doppler frequency are extracted from the matrix and the three- dimensional （3D） coordinates of the targets are then calculated on the basis of these angles. The proposed algorithm provides a lower computational complexity and has a parameter estimation very close to that of the ESPRIT algorithm and the DOA-matrix al- gorithm in the high signal to noise ratio and the Cramer-Rao bound （CRB） is given. Furthermore, multi-dimensional parameters can be automatically paired by this algorithm to avoid performance degra- dation resulting from wrong pairing. Simulation results demonstrate the effectiveness of the proposed method.展开更多
For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic...For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique(SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The densitybased spatial clustering of applications with noise(DBSCAN) is not rigorous when dealing with the samples near the borderline.We optimize the DBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique(DSMOTE). First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples,different strategies are used to over-sample core samples and borderline samples. Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.展开更多
Clustering is one of the unsupervised learning problems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morphology,overlapping and the large number of clu...Clustering is one of the unsupervised learning problems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morphology,overlapping and the large number of clusters at the same time.Many scientific communities have used the clustering algorithm from the perspective of density,which is one of the best methods in clustering.This study proposes a density-based spatial clustering of applications with noise(DBSCAN)algorithm based on the selected high-density areas by automatic fuzzy-DBSCAN(AFD)which works with the initialization of two parameters.AFD,by using fuzzy and DBSCAN features,is modeled by the selection of high-density areas and generates two parameters for merging and separating automatically.The two generated parameters provide a state of sub-cluster rules in the Cartesian coordinate system for the dataset.The model overcomes the problems of clustering such as morphology,overlapping,and the number of clusters in a dataset simultaneously.In the experiments,all algorithms are performed on eight data sets with 30 times of running.Three of them are related to overlapping real datasets and the rest are morphologic and synthetic datasets.It is demonstrated that the AFD algorithm outperforms other recently developed clustering algorithms.展开更多
The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of ...The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.展开更多
Electric vehicle(EV)charging load is greatly affected by many traffic factors,such as road congestion.Accurate ultra short-term load forecasting(STLF)results for regional EV charging load are important to the scheduli...Electric vehicle(EV)charging load is greatly affected by many traffic factors,such as road congestion.Accurate ultra short-term load forecasting(STLF)results for regional EV charging load are important to the scheduling plan of regional charging load,which can be derived to realize the optimal vehicle to grid benefit.In this paper,a regional-level EV ultra STLF method is proposed and discussed.The usage degree of all charging piles is firstly defined by us based on the usage frequency of charging piles,and then constructed by our collected EV charging transactiondata in thefield.Secondly,these usagedegrees are combinedwithhistorical charging loadvalues toform the inputmatrix for the deep learning based load predictionmodel.Finally,long short-termmemory(LSTM)neural network is used to construct EV charging load forecastingmodel,which is trained by the formed inputmatrix.The comparison experiment proves that the proposed method in this paper has higher prediction accuracy compared with traditionalmethods.In addition,load characteristic index for the fluctuation of adjacent day load and adjacent week load are proposed by us,and these fluctuation factors are used to assess the prediction accuracy of the EV charging load,together with the mean absolute percentage error(MAPE).展开更多
In order to diagnose the common faults of railway switch control circuit,a fault diagnosis method based on density-based spatial clustering of applications with noise(DBSCAN)and self-organizing feature map(SOM)is prop...In order to diagnose the common faults of railway switch control circuit,a fault diagnosis method based on density-based spatial clustering of applications with noise(DBSCAN)and self-organizing feature map(SOM)is proposed.Firstly,the three-phase current curve of the switch machine recorded by the micro-computer monitoring system is dealt with segmentally and then the feature parameters of the three-phase current are calculated according to the action principle of the switch machine.Due to the high dimension of initial features,the DBSCAN algorithm is used to separate the sensitive features of fault diagnosis and construct the diagnostic sensitive feature set.Then,the particle swarm optimization(PSO)algorithm is used to adjust the weight of SOM network to modify the rules to avoid“dead neurons”.Finally,the PSO-SOM network fault classifier is designed to complete the classification and diagnosis of the samples to be tested.The experimental results show that this method can judge the fault mode of switch control circuit with less training samples,and the accuracy of fault diagnosis is higher than that of traditional SOM network.展开更多
The spatial matrix filter was designed and used for solving the problem to detect a weak target who was influenced by the strong nearby platform noise interference of the towed line array sonar. The MFP technology and...The spatial matrix filter was designed and used for solving the problem to detect a weak target who was influenced by the strong nearby platform noise interference of the towed line array sonar. The MFP technology and the DOA estimation technology were combined together by using the sound propagation characteristics of both target and interference. The spatial matrix filter with platform noise zero response constraint was designed by the near-field platform noise normal modes copy vectors and the far-field plane wave bearing vectors together. The optimal solution of the optimization problem for designing the spatial matrix filter was deduced directly, and it was simplified by the generalized singular value decomposition. The total response error to the plane wave bearing vectors and the total response to the platform noise copy vectors were given. The phenomena that strong interferences existed in the bearing course and blind areas existed after filtering were analyzed by the correlation between the plat- form noise copy vectors and the plane wave bearing vectors. It could be found from simulations that it has less blind area and higher detection ability by using the spatial matrix filtering technology.展开更多
Performing cluster analysis on molecular conformation is an important way to find the representative conformation in the molecular dynamics trajectories.Usually,it is a critical step for interpreting complex conformat...Performing cluster analysis on molecular conformation is an important way to find the representative conformation in the molecular dynamics trajectories.Usually,it is a critical step for interpreting complex conformational changes or interaction mechanisms.As one of the density-based clustering algorithms,find density peaks(FDP)is an accurate and reasonable candidate for the molecular conformation clustering.However,facing the rapidly increasing simulation length due to the increase in computing power,the low computing efficiency of FDP limits its application potential.Here we propose a marginal extension to FDP named K-means find density peaks(KFDP)to solve the mass source consuming problem.In KFDP,the points are initially clustered by a high efficiency clustering algorithm,such as K-means.Cluster centers are defined as typical points with a weight which represents the cluster size.Then,the weighted typical points are clustered again by FDP,and then are refined as core,boundary,and redefined halo points.In this way,KFDP has comparable accuracy as FDP but its computational complexity is reduced from O(n^(2))to O(n).We apply and test our KFDP method to the trajectory data of multiple small proteins in terms of torsion angle,secondary structure or contact map.The comparing results with K-means and density-based spatial clustering of applications with noise show the validation of the proposed KFDP.展开更多
An integrated method based on optical and digital image processing is presented to suppress speckle in digital holography. A spatial light modulator is adopted to introduce random phases to the illuminating beam. Mult...An integrated method based on optical and digital image processing is presented to suppress speckle in digital holography. A spatial light modulator is adopted to introduce random phases to the illuminating beam. Multiple holograms are reconstructed and superimposed, and the intensity is averaged to smooth the noise. The adaptive algorithm based on the nonlocal means is designed to further suppress the speckle. The presented method is compared with other methods reduction is improved, and the proposed method is effective The experimental results show that speckle and feasible.展开更多
As the demand for bike-sharing has been increasing,the oversupply problem of bike-sharing has occurred,which leads to the waste of resources and disturbance of the urban environment.In order to regulate the supply vol...As the demand for bike-sharing has been increasing,the oversupply problem of bike-sharing has occurred,which leads to the waste of resources and disturbance of the urban environment.In order to regulate the supply volume of bike-sharing reasonably,an estimating model was proposed to quantify the urban carrying capacity(UCC)for bike-sharing through the demand data.In this way,the maximum supply volume of bike-sharing that a city can accommodate can be obtained.The UCC on bike-sharing is reflected in the road network carrying capacity(RNCC)and parking facilities’carrying capacity(PFCC).The space-time consumption method and density-based spatial clustering of application with noise(DBSCAN)algorithm were used to explore the RNCC and PFCC for bike-sharing.Combined with the users’demand,the urban load ratio on bike-sharing can be evaluated to judge whether the UCC can meet users’demand,so that the supply volume of bike-sharing and distribution of the related facilities can be adjusted accordingly.The application of the model was carried out by estimating the UCC and load ratio of each traffic analysis zone in Nanjing,China.Compared with the field survey data,the effect of the proposed algorithm was verified.展开更多
This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the ...This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.展开更多
针对即时配送“最后一公里”的问题,综合利用订单取送点、即时配送骑手历史时空轨迹、兴趣面(area of interest,AOI)空间范围与门禁位置等数据,精确预估AOI内部各兴趣点(point of interest,POI)到相应可通行门禁点的时间、距离及路径。...针对即时配送“最后一公里”的问题,综合利用订单取送点、即时配送骑手历史时空轨迹、兴趣面(area of interest,AOI)空间范围与门禁位置等数据,精确预估AOI内部各兴趣点(point of interest,POI)到相应可通行门禁点的时间、距离及路径。在此基础上设计了配套的调用选优策略,获得最优的末端指引方案,以有效提高即时配送路径质量及时间距离预估准确性。展开更多
文摘In this paper, we study a spatially periodic system with spatial noises. Study shows that the correlation between the spatial noises can produce the nonzero flux, even in the absence of the asymmetry of the system.
文摘In this paper, we study a fluctuating potential barrier system with correlated spatial noises. Study shows that for this system, there is the resonant activation over the fluctuating potential barrier, and that the correlation between the different spatial noises can enhance (or weaken) the resonant activation.
基金supported by the National Natural Science Foundation of China(11072182 and 11272241)
文摘The phenomenon of stochastic synchronization in globally coupled FitzHugh–Nagumo（FHN） neuron system subjected to spatially correlated Gaussian noise is investigated based on dynamical mean-field approximation（DMA） and direct simulation（DS）. Results from DMA are in good quantitative or qualitative agreement with those from DS for weak noise intensity and larger system size. Whether the consisting single FHN neuron is staying at the resting state, subthreshold oscillatory regime, or the spiking state, our investigation shows that the synchronization ratio of the globally coupled system becomes higher as the noise correlation coefficient increases, and thus we conclude that spatial correlation has an active effect on stochastic synchronization, and the neurons can achieve complete synchronization in the sense of statistics when the noise correlation coefficient tends to one. Our investigation also discloses that the noise spatial correlation plays the same beneficial role as the global coupling strength in enhancing stochastic synchronization in the ensemble. The result might be useful in understanding the information coding mechanism in neural systems.
基金Under the auspices of National Natural Science Foundation of China(No.41271179)。
文摘As cultural facilities,physical bookstore is an important part of urban infrastructure.Influenced by the development of social economy and the internet,physical bookstores also have become a combination of cultural space and tourism experience.In this case,it is necessary to explore the spatial characteristics and influencing factors of physical bookstores.This study uses Density-Based Spatial Clustering of Applications with Noise(DBSCAN),spatial analysis and geographical detectors to calculate the spatial distribution pattern and factors influencing physical bookstores in national central cities/municipality(hereafter using cities)in western China.Based on spatial data,population density,road density and other data,this study constructed a data set of the influencing factors of physical bookstores,consisting of 11 factors along 6 dimensions for 3 national central cities in western China.The results are as follows:first,the spatial distribution pattern of physical bookstores in Xi’an,Chengdu,and Chongqing is unbalanced.The spatial distribution of physical bookstores in Xi’an and Chongqing is from southwest to northeast and are relatively clustered,while those in Chengdu are relatively discrete.Second,the spatial distribution pattern of physical bookstores has been formed under the influence of different factors.The intensity and significance of influencing factors differ in the case cities.However,in general,the social factor,business factor,the density of research facilities,tourism factor and road density are the main driving factors in the three cities.There is a synergistic relationship between public libraries and physical bookstores.Third,the explanatory power becomes stronger after the interaction between various factors.In Xi’an and Chengdu,the density of communities and the density of research facilities have stronger explanatory power for the dependent variable after interacting with other factors.However,in Chongqing,the traffic factors have stronger explanatory power for the dependent variable after interacting with other factors.The results could provide a practical reference for the sustainable development of physical bookstores and encourage a love of reading among the public.
基金Under the auspices of National Social Science Foundation of China (No.21BJY202)。
文摘There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration areas.This study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to 2022.The results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to 2022.However,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and volume.Urban B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level clusters.While the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban areas.Conversely,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial distribution.In terms of differences,population agglomeration had a positive impact on the distribution of urban B&Bs and a negative effect on the distribution of rural B&Bs.Rural B&Bs clustering was more influenced by tourism resources compared with urban B&Bs,but increasing tourist stay duration remains an urgent issue to be addressed.The findings of this study could provide a more precise basis for government planning and management of urban and rural B&B agglomeration areas.
文摘Objective To develop an individual noise exposure dose estimation model based on spatial distribution of noise in order to provide reference for occupational health management and hearing loss risk assessment caused by noise in workplace.Methods From July 2018 to October 2019,10 noise-exposed positions and 48 employees in 3 high-noise workplaces of a manufacturing enterprise in Sichuan Province were selected as the research subjects.Occupational health survey,fixed-point measurement of workplace noise and individual noise measurement were used to obtain noise intensity and employee exposure information.The mean noise intensity and the corresponding exposure time were weighed to estimate the individual noise exposure dose estimation model based on the spatial distribution of workplace noise,and the paired t-test was used to evaluate the accuracy of the individual noise exposure dose estimate(8 h equivalent sound level,Lex,8 h)based on the spatial distribution of workplace,noise and the measured value of individualnoise exposure dose.And the least square regression model was used to correct it.Results The daily noise exposure dose of 44(91.7%)of 48 workers in 10 types of work in 3 sites of the manufacturing enterprise exceeded the standard,and the maximum noise exposure intensity was up to 108.3 dB(A).The measured value of individual noise exposure dose was higher than Lex,s h,and the difference was statistically significant(P<0.001).The noise difference distribution was mostly positive and symmetrical(P=0.958).The measured noise exposure dose of 6 out of 10 work types in 3 sites was higher than Lex,8 h(all P<0.05),and there was a linear correlation between the measured dose and Lex.8 h(r=0.373,P<0.05).Considering the influence of systematic error and the construction,diagnosis and screening of adaptive assessment model,an individual noise exposure dose estimation model based on the spatial distribution of workplace noise was finally obtained:y=0.574x+45.250,where y was the measured value of individual noise exposure dose,x was Lex,s h value.Conclusion The high noise hazard in manufacturing industry is more serious,and the noise exposure assessment of single fixed-point detection is relatively rough.The individual noise exposure dose estimation model based on the spatial distribution of workplace noise has a certain role in the occupational health managementofworkplacenoise hazard in manufacturing industry.
基金supported in part by Boeing Company and Nanjing University of Aeronautics and Astronautics(NUAA)through the Research on Decision Support Technology of Air Traffic Operation Management in Convective Weather under Project 2022-GT-129in part by the Postgraduate Research and Practice Innovation Program of NUAA(No.xcxjh20240709)。
文摘Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflection point method”to analyze specific deviation trajectories,determine deviation thresholds,and identify commonly used deviation paths.By combining multiple similarity metrics,including Euclidean distance,Hausdorff distance,and sector edit distance,with the density-based spatial clustering of applications with noise(DBSCAN)algorithm,the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs.A case study of 23578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework.Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances,with higher silhouette coefficients and lower Davies⁃Bouldin indices,ensuring better intra-cluster compactness and inter-cluster separation.Based on clustering results,19 representative trajectory options are identified,covering both nominal and deviation paths,which significantly enhance route diversity and reflect actual flight practices.This provides a practical basis for optimizing flight paths and scheduling,enhancing the flexibility of route selection for flights between city pairs.
文摘针对点云数据中噪声点的剔除问题,提出了一种基于改进DBSCAN(density-based spatial clustering of applications with noise)算法的多尺度点云去噪方法。应用统计滤波对孤立离群点进行预筛选,去除点云中的大尺度噪声;对DBSCAN算法进行优化,减少算法时间复杂度和实现参数的自适应调整,以此将点云分为正常簇、疑似簇及异常簇,并立即去除异常簇;利用距离共识评估法对疑似簇进行精细判定,通过计算疑似点与其最近的正常点拟合表面之间的距离,判定其是否为异常,有效保持了数据的关键特征和模型敏感度。利用该方法对两个船体分段点云进行去噪,并与其他去噪算法进行对比,结果表明,该方法在去噪效率和特征保持方面具有优势,精确地保留了点云数据的几何特性。
基金supported by the National Natural Science Foundation of China(6137116961179006)+1 种基金the Jiangsu Postdoctoral Research Funding Plan(1301013B)the Nanjing University of Aeronautics and Astronautics Funding(NZ2013208)
文摘This paper addresses the problem of four-dimensional angle and Doppler frequency estimation for bistatic multiple-input multiple-output （MIMO） radar with arbitrary arrays in spatial co- lored noise. A novel method for joint estimation of Doppler fre- quency, two-dimensional （2D） direction of departure and 2D direc- tion of arrival based on the propagator method （PM） for arbitrary arrays is discussed. A special matrix is constructed to eliminate the influence of spatial colored noise. The four-dimensional （4D） angle and Doppler frequency are extracted from the matrix and the three- dimensional （3D） coordinates of the targets are then calculated on the basis of these angles. The proposed algorithm provides a lower computational complexity and has a parameter estimation very close to that of the ESPRIT algorithm and the DOA-matrix al- gorithm in the high signal to noise ratio and the Cramer-Rao bound （CRB） is given. Furthermore, multi-dimensional parameters can be automatically paired by this algorithm to avoid performance degra- dation resulting from wrong pairing. Simulation results demonstrate the effectiveness of the proposed method.
基金supported by the National Key Research and Development Program of China(2018YFB1003700)the Scientific and Technological Support Project(Society)of Jiangsu Province(BE2016776)+2 种基金the“333” project of Jiangsu Province(BRA2017228 BRA2017401)the Talent Project in Six Fields of Jiangsu Province(2015-JNHB-012)
文摘For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique(SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The densitybased spatial clustering of applications with noise(DBSCAN) is not rigorous when dealing with the samples near the borderline.We optimize the DBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique(DSMOTE). First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples,different strategies are used to over-sample core samples and borderline samples. Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.
文摘Clustering is one of the unsupervised learning problems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morphology,overlapping and the large number of clusters at the same time.Many scientific communities have used the clustering algorithm from the perspective of density,which is one of the best methods in clustering.This study proposes a density-based spatial clustering of applications with noise(DBSCAN)algorithm based on the selected high-density areas by automatic fuzzy-DBSCAN(AFD)which works with the initialization of two parameters.AFD,by using fuzzy and DBSCAN features,is modeled by the selection of high-density areas and generates two parameters for merging and separating automatically.The two generated parameters provide a state of sub-cluster rules in the Cartesian coordinate system for the dataset.The model overcomes the problems of clustering such as morphology,overlapping,and the number of clusters in a dataset simultaneously.In the experiments,all algorithms are performed on eight data sets with 30 times of running.Three of them are related to overlapping real datasets and the rest are morphologic and synthetic datasets.It is demonstrated that the AFD algorithm outperforms other recently developed clustering algorithms.
文摘The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.
基金supported by National Key R&D Program of China(No.2021YFB2601602).
文摘Electric vehicle(EV)charging load is greatly affected by many traffic factors,such as road congestion.Accurate ultra short-term load forecasting(STLF)results for regional EV charging load are important to the scheduling plan of regional charging load,which can be derived to realize the optimal vehicle to grid benefit.In this paper,a regional-level EV ultra STLF method is proposed and discussed.The usage degree of all charging piles is firstly defined by us based on the usage frequency of charging piles,and then constructed by our collected EV charging transactiondata in thefield.Secondly,these usagedegrees are combinedwithhistorical charging loadvalues toform the inputmatrix for the deep learning based load predictionmodel.Finally,long short-termmemory(LSTM)neural network is used to construct EV charging load forecastingmodel,which is trained by the formed inputmatrix.The comparison experiment proves that the proposed method in this paper has higher prediction accuracy compared with traditionalmethods.In addition,load characteristic index for the fluctuation of adjacent day load and adjacent week load are proposed by us,and these fluctuation factors are used to assess the prediction accuracy of the EV charging load,together with the mean absolute percentage error(MAPE).
基金High Education Research Project Funding(No.2018C-11)Natural Science Fund of Gansu Province(Nos.18JR3RA107,1610RJYA034)Key Research and Development Program of Gansu Province(No.17YF1WA 158)。
文摘In order to diagnose the common faults of railway switch control circuit,a fault diagnosis method based on density-based spatial clustering of applications with noise(DBSCAN)and self-organizing feature map(SOM)is proposed.Firstly,the three-phase current curve of the switch machine recorded by the micro-computer monitoring system is dealt with segmentally and then the feature parameters of the three-phase current are calculated according to the action principle of the switch machine.Due to the high dimension of initial features,the DBSCAN algorithm is used to separate the sensitive features of fault diagnosis and construct the diagnostic sensitive feature set.Then,the particle swarm optimization(PSO)algorithm is used to adjust the weight of SOM network to modify the rules to avoid“dead neurons”.Finally,the PSO-SOM network fault classifier is designed to complete the classification and diagnosis of the samples to be tested.The experimental results show that this method can judge the fault mode of switch control circuit with less training samples,and the accuracy of fault diagnosis is higher than that of traditional SOM network.
基金supported by the National Natural Science Foundation of China(60532040,11374001)
文摘The spatial matrix filter was designed and used for solving the problem to detect a weak target who was influenced by the strong nearby platform noise interference of the towed line array sonar. The MFP technology and the DOA estimation technology were combined together by using the sound propagation characteristics of both target and interference. The spatial matrix filter with platform noise zero response constraint was designed by the near-field platform noise normal modes copy vectors and the far-field plane wave bearing vectors together. The optimal solution of the optimization problem for designing the spatial matrix filter was deduced directly, and it was simplified by the generalized singular value decomposition. The total response error to the plane wave bearing vectors and the total response to the platform noise copy vectors were given. The phenomena that strong interferences existed in the bearing course and blind areas existed after filtering were analyzed by the correlation between the plat- form noise copy vectors and the plane wave bearing vectors. It could be found from simulations that it has less blind area and higher detection ability by using the spatial matrix filtering technology.
基金Professor Hong Yu at Intelligent Fishery Innovative Team(No.C202109)in School of Information Engineering of Dalian Ocean University for her support of this workfunded by the National Natural Science Foundation of China(No.31800615 and No.21933010)。
文摘Performing cluster analysis on molecular conformation is an important way to find the representative conformation in the molecular dynamics trajectories.Usually,it is a critical step for interpreting complex conformational changes or interaction mechanisms.As one of the density-based clustering algorithms,find density peaks(FDP)is an accurate and reasonable candidate for the molecular conformation clustering.However,facing the rapidly increasing simulation length due to the increase in computing power,the low computing efficiency of FDP limits its application potential.Here we propose a marginal extension to FDP named K-means find density peaks(KFDP)to solve the mass source consuming problem.In KFDP,the points are initially clustered by a high efficiency clustering algorithm,such as K-means.Cluster centers are defined as typical points with a weight which represents the cluster size.Then,the weighted typical points are clustered again by FDP,and then are refined as core,boundary,and redefined halo points.In this way,KFDP has comparable accuracy as FDP but its computational complexity is reduced from O(n^(2))to O(n).We apply and test our KFDP method to the trajectory data of multiple small proteins in terms of torsion angle,secondary structure or contact map.The comparing results with K-means and density-based spatial clustering of applications with noise show the validation of the proposed KFDP.
基金supported by the National Natural Science Foundation of China(No.61177018)the Program for New Century Excellent Talents in University(No.NECT-11-0596)+1 种基金the Key Program of Beijing Sci-ence and Technology Plan(No.D121100004812001)Beijing Nova Program(No.2011066)
文摘An integrated method based on optical and digital image processing is presented to suppress speckle in digital holography. A spatial light modulator is adopted to introduce random phases to the illuminating beam. Multiple holograms are reconstructed and superimposed, and the intensity is averaged to smooth the noise. The adaptive algorithm based on the nonlocal means is designed to further suppress the speckle. The presented method is compared with other methods reduction is improved, and the proposed method is effective The experimental results show that speckle and feasible.
基金Project(2018YFE0120100)supported by the National Key R&D Program of ChinaProject(YBPY2040)supported by the Scientific Research Foundation of Graduate School of Southeast University,China。
文摘As the demand for bike-sharing has been increasing,the oversupply problem of bike-sharing has occurred,which leads to the waste of resources and disturbance of the urban environment.In order to regulate the supply volume of bike-sharing reasonably,an estimating model was proposed to quantify the urban carrying capacity(UCC)for bike-sharing through the demand data.In this way,the maximum supply volume of bike-sharing that a city can accommodate can be obtained.The UCC on bike-sharing is reflected in the road network carrying capacity(RNCC)and parking facilities’carrying capacity(PFCC).The space-time consumption method and density-based spatial clustering of application with noise(DBSCAN)algorithm were used to explore the RNCC and PFCC for bike-sharing.Combined with the users’demand,the urban load ratio on bike-sharing can be evaluated to judge whether the UCC can meet users’demand,so that the supply volume of bike-sharing and distribution of the related facilities can be adjusted accordingly.The application of the model was carried out by estimating the UCC and load ratio of each traffic analysis zone in Nanjing,China.Compared with the field survey data,the effect of the proposed algorithm was verified.
文摘This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.
文摘针对即时配送“最后一公里”的问题,综合利用订单取送点、即时配送骑手历史时空轨迹、兴趣面(area of interest,AOI)空间范围与门禁位置等数据,精确预估AOI内部各兴趣点(point of interest,POI)到相应可通行门禁点的时间、距离及路径。在此基础上设计了配套的调用选优策略,获得最优的末端指引方案,以有效提高即时配送路径质量及时间距离预估准确性。
