Spatial Distribution Pattern and Influencing Factors of Bed-and-breakfasts(B&Bs)from the Perspective of Urban-rural Differences:A Case Study of Jiaodong Peninsula,China

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.

Effect of spatially correlated noise on stochastic synchronization in globally coupled FitzHugh–Nagumo neuron systems

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.

Spatial Distribution Pattern and Influencing Factors of Physical Bookstores of Large Cities:A Case Study of Three National Central Cities in Western China

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.

Three-dimensional(3D)parametric measurements of individual gravels in the Gobi region using point cloud technique

Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.

A Novel Ultra Short-Term Load Forecasting Method for Regional Electric Vehicle Charging Load Using Charging Pile Usage Degree

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).

Fast multi-parameter estimation and localization for MIMO radar

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.

Automatic fuzzy-DBSCAN algorithm for morphological and overlapping datasets

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.

Over-sampling algorithm for imbalanced data classification

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.

Towed line array sonar platform noise suppression based on spatial matrix filtering technology

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.

Speckle noise reduction in digital holography with spatial light modulator and nonlocal means algorithm

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.

Spatial and temporal white noises under sublinear G-expectation

Under the framework of sublinear expectation,we introduce a new type of G-Gaussian random fields,which contains a type of spatial white noise as a special case.Based on this result,we also introduce a spatial-temporal G-white noise.Different from the case of linear expectation,in which the probability measure needs to be known,under the uncertainty of probability measures,spatial white noises are intrinsically different from temporal cases.

The effect of ocean wave on the vertical spatial correlation of ocean ambient noise

The effect of the correlation function of noise sources derived from the ocean wave spectrum on the vertical spatial correlation of ocean ambient noise is investigated. The spatial correlation models of ocean ambient noise usually assume that the surface noise sources are uncorrelated. This assumption can be used to explain some physical phenomena, but it is not consistent with the real situation. Considering the relation between the ocean wave motion and the ambient noise generated by wind, the spectrum of ocean wave is introduced to calculate the vertical correlation of ocean ambient noise as the correlation function of noise sources by using the Kuperman-Ingenito （K/I） noise model. The comparison of the simulations and the experimental data shows that the simulations of vertical correlation of ambient noise have some differences with the experimental data by assuming the noise sources are uncorrelated and the simulations of vertical correlation of ambient noise have a good agreement with the experimental data by using the correlation function of noise sources derived from the ocean wave spectrum under the situation of high wind speed.

A theory on spatial correlation and vertical directivity of surface-generated ambient noise in the sea

A theoretical model of ambient sea noise including surface noise sources and stratified medium ocean is discussed. The noise sources are assumed to be statistically independent directional point sources distributed over the ocean surface, and the effects of ocean environment on ambient noise are studied. The normal-mode theory of surface-generated noise is developed, and the normal-mode formula of the directional density function suitable for small grazing angles is analytically continued for being suitable for great grazing angles and consistent with the ray formula. The unified formulae for calculating the intensities, spatial correlation and vertical directivity of ambient sea noise are presented.

Radar false alarm plots elimination based on multi-feature extraction and classification

Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination method based on multi-feature extraction and classification is proposed to effectively eliminate false alarm plots.Firstly,the density based spatial clustering of applications with noise(DBSCAN)algorithm is used to cluster the radar echo data processed by constant false-alarm rate(CFAR).The multi-features including the scale features,time domain features and transform domain features are extracted.Secondly,a feature evaluation method combining pearson correlation coefficient(PCC)and entropy weight method(EWM)is proposed to evaluate interrelation among features,effective feature combination sets are selected as inputs of the classifier.Finally,False alarm plots classified as clutters are eliminated.The experimental results show that proposed method can eliminate about 90%false alarm plots with less target loss rate.

The Stochastic Wave Equations Driven by Fractional and Colored Noises

We investigate a wave equation in the plane with an additive noise which is fractional in time and has a non-degenerate spatial covariance. The equation is shown to admit a process-valued solution. Also we give a continuity modulus of the solution, and the HSlder continuity is presented.

基于改进DBSCAN和距离共识评估的分段点云去噪方法

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。

一种基于时空轨迹挖掘的即时配送末端路径指引策略

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。

HTDet:A Clustering Method Using Information Entropy for Hardware Trojan Detection

Hardware Trojans(HTs)have drawn increasing attention in both academia and industry because of their significant potential threat.In this paper,we propose HTDet,a novel HT detection method using information entropybased clustering.To maintain high concealment,HTs are usually inserted in the regions with low controllability and low observability,which will result in that Trojan logics have extremely low transitions during the simulation.This implies that the regions with the low transitions will provide much more abundant and more important information for HT detection.The HTDet applies information theory technology and a density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise(DBSCAN)to detect all suspicious Trojan logics in the circuit under detection.The DBSCAN is an unsupervised learning algorithm,that can improve the applicability of HTDet.In addition,we develop a heuristic test pattern generation method using mutual information to increase the transitions of suspicious Trojan logics.Experiments on circuit benchmarks demonstrate the effectiveness of HTDet.

Hitting Probabilities and the Hausdorff Dimension of the Inverse Images of a Class of Anisotropic Random Fields

Let X = {X（t）：t ∈ R^N} be an anisotropic random field with values in R^d.Under certain conditions on X,we establish upper and lower bounds on the hitting probabilities of X in terms of respectively Hausdorff measure and Bessel-Riesz capacity.We also obtain the Hausdorff dimension of its inverse image,and the Hausdorff and packing dimensions of its level sets.These results are applicable to non-linear solutions of stochastic heat equations driven by a white in time and spatially homogeneous Gaussian noise and anisotropic Guassian random fields.

An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference

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.