Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-eff...Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-effective geophysical technique can acquire high-density data;however,uncertainty and nonuniqueness inherent in ERT impede its usage for stratigraphy identification.This paper integrates ERT and onsite observations for the first time to propose a novel method for characterizing stratigraphic profiles.The method consists of two steps:(1)ERT for prior knowledge:ERT data are processed by soft clustering using the Gaussian mixture model,followed by probability smoothing to quantify its depthdependent uncertainty;and(2)Observations for calibration:a spatial sequential Bayesian updating(SSBU)algorithm is developed to update the prior knowledge based on likelihoods derived from onsite observations,namely topsoil and boreholes.The effectiveness of the proposed method is validated through its application to a real slope site in Foshan,China.Comparative analysis with advanced borehole-driven methods highlights the superiority of incorporating ERT data in stratigraphic modeling,in terms of prediction accuracy at borehole locations and sensitivity to borehole data.Informed by ERT,reduced sensitivity to boreholes provides a fundamental solution to the longstanding challenge of sparse measurements.The paper further discusses the impact of ERT uncertainty on the proposed model using time-lapse measurements,the impact of model resolution,and applicability in engineering projects.This study,as a breakthrough in stratigraphic modeling,bridges gaps in combining geophysical and geotechnical data to address measurement sparsity and paves the way for more economical geotechnical exploration.展开更多
Human beings’ intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms.It is detailed in this paper how to utilize the hierarchical reasonin...Human beings’ intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms.It is detailed in this paper how to utilize the hierarchical reasoning on the basis of greedy and directional strategy to establish a spatial heuristic,so as to improve running efficiency and suitability of shortest path algorithm for traffic network.The authors divide urban traffic network into three hierarchies and set forward a new node hierarchy division rule to avoid the unreliable solution of shortest path.It is argued that the shortest path,no matter distance shortest or time shortest,is usually not the favorite of drivers in practice.Some factors difficult to expect or quantify influence the drivers’ choice greatly.It makes the drivers prefer choosing a less shortest,but more reliable or flexible path to travel on.The presented optimum path algorithm,in addition to the improvement of the running efficiency of shortest path algorithms up to several times,reduces the emergence of those factors,conforms to the intellection characteristic of human beings,and is more easily accepted by drivers.Moreover,it does not require the completeness of networks in the lowest hierarchy and the applicability and fault tolerance of the algorithm have improved.The experiment result shows the advantages of the presented algorithm.The authors argued that the algorithm has great potential application for navigation systems of large_scale traffic networks.展开更多
Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recogni...Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases.展开更多
Considering the characteristics of spatial straightness error, this paper puts forward a kind of evaluation method of spatial straightness error using Geometric Approximation Searching Algorithm (GASA). According to t...Considering the characteristics of spatial straightness error, this paper puts forward a kind of evaluation method of spatial straightness error using Geometric Approximation Searching Algorithm (GASA). According to the minimum condition principle of form error evaluation, the mathematic model and optimization objective of the GASA are given. The algorithm avoids the optimization and linearization, and can be fulfilled in three steps. First construct two parallel quadrates based on the preset two reference points of the spatial line respectively;second construct centerlines by connecting one quadrate each vertices to another quadrate each vertices;after that, calculate the distances between measured points and the constructed centerlines. The minimum zone straightness error is obtained by repeating comparing and reconstructing quadrates. The principle and steps of the algorithm to evaluate spatial straightness error is described in detail, and the mathematical formula and program flowchart are given also. Results show that this algorithm can evaluate spatial straightness error more effectively and exactly.展开更多
As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.I...As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.In contrast with SDEEM2015,SDEEM2019,the latest version,extends the orbital range from the Low Earth Orbit(LEO)to Geosynchronous Orbit(GEO)for the years 1958-2050.In this paper,improved modeling algorithms used by SDEEM2019 in propagating simulation,spatial density distribution,and spacecraft flux evaluation are presented.The debris fluxes of SDEEM2019 are compared with those of three typical models,i.e.,SDEEM2015,Orbital Debris Engineering Model 3.1(ORDEM 3.1),and Meteoroid and Space Debris Terrestrial Environment Reference(MASTER-8),in terms of two assessment modes.Three orbital cases,including the Geostationary Transfer Orbit(GTO),Sun-Synchronous Orbit(SSO)and International Space Station(ISS)orbit,are selected for the spacecraft assessment mode,and the LEO region is selected for the spatial density assessment mode.The analysis indicates that compared with previous algorithms,the variable step-size orbital propagating algorithm based on semi-major axis control is more precise,the spatial density algorithm based on the second zonal harmonic of the non-spherical Earth gravity(J_(2))is more applicable,and the result of the position-centered spacecraft flux algorithm is more convergent.The comparison shows that SDEEM2019 and MASTER-8 have consistent trends due to similar modeling processes,while the differences between SDEEM2019 and ORDEM 3.1 are mainly caused by different modeling approaches for uncatalogued debris.展开更多
This paper describes the nearest neighbor (NN) search algorithm on the GBD(generalized BD) tree. The GBD tree is a spatial data structure suitable for two-or three-dimensional data and has good performance characteris...This paper describes the nearest neighbor (NN) search algorithm on the GBD(generalized BD) tree. The GBD tree is a spatial data structure suitable for two-or three-dimensional data and has good performance characteristics with respect to the dynamic data environment. On GIS and CAD systems, the R-tree and its successors have been used. In addition, the NN search algorithm is also proposed in an attempt to obtain good performance from the R-tree. On the other hand, the GBD tree is superior to the R-tree with respect to exact match retrieval, because the GBD tree has auxiliary data that uniquely determines the position of the object in the structure. The proposed NN search algorithm depends on the property of the GBD tree described above. The NN search algorithm on the GBD tree was studied and the performance thereof was evaluated through experiments.展开更多
基金the financial support from the National Key R&D Program of China(Grant No.2021YFC3001003)Science and Technology Development Fund,Macao SAR(File No.0056/2023/RIB2)Guangdong Provincial Department of Science and Technology(Grant No.2022A0505030019).
文摘Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-effective geophysical technique can acquire high-density data;however,uncertainty and nonuniqueness inherent in ERT impede its usage for stratigraphy identification.This paper integrates ERT and onsite observations for the first time to propose a novel method for characterizing stratigraphic profiles.The method consists of two steps:(1)ERT for prior knowledge:ERT data are processed by soft clustering using the Gaussian mixture model,followed by probability smoothing to quantify its depthdependent uncertainty;and(2)Observations for calibration:a spatial sequential Bayesian updating(SSBU)algorithm is developed to update the prior knowledge based on likelihoods derived from onsite observations,namely topsoil and boreholes.The effectiveness of the proposed method is validated through its application to a real slope site in Foshan,China.Comparative analysis with advanced borehole-driven methods highlights the superiority of incorporating ERT data in stratigraphic modeling,in terms of prediction accuracy at borehole locations and sensitivity to borehole data.Informed by ERT,reduced sensitivity to boreholes provides a fundamental solution to the longstanding challenge of sparse measurements.The paper further discusses the impact of ERT uncertainty on the proposed model using time-lapse measurements,the impact of model resolution,and applicability in engineering projects.This study,as a breakthrough in stratigraphic modeling,bridges gaps in combining geophysical and geotechnical data to address measurement sparsity and paves the way for more economical geotechnical exploration.
文摘Human beings’ intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms.It is detailed in this paper how to utilize the hierarchical reasoning on the basis of greedy and directional strategy to establish a spatial heuristic,so as to improve running efficiency and suitability of shortest path algorithm for traffic network.The authors divide urban traffic network into three hierarchies and set forward a new node hierarchy division rule to avoid the unreliable solution of shortest path.It is argued that the shortest path,no matter distance shortest or time shortest,is usually not the favorite of drivers in practice.Some factors difficult to expect or quantify influence the drivers’ choice greatly.It makes the drivers prefer choosing a less shortest,but more reliable or flexible path to travel on.The presented optimum path algorithm,in addition to the improvement of the running efficiency of shortest path algorithms up to several times,reduces the emergence of those factors,conforms to the intellection characteristic of human beings,and is more easily accepted by drivers.Moreover,it does not require the completeness of networks in the lowest hierarchy and the applicability and fault tolerance of the algorithm have improved.The experiment result shows the advantages of the presented algorithm.The authors argued that the algorithm has great potential application for navigation systems of large_scale traffic networks.
基金Supported by the Open Researches Fund Program of L IESMARS(WKL(0 0 ) 0 30 2 )
文摘Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases.
文摘Considering the characteristics of spatial straightness error, this paper puts forward a kind of evaluation method of spatial straightness error using Geometric Approximation Searching Algorithm (GASA). According to the minimum condition principle of form error evaluation, the mathematic model and optimization objective of the GASA are given. The algorithm avoids the optimization and linearization, and can be fulfilled in three steps. First construct two parallel quadrates based on the preset two reference points of the spatial line respectively;second construct centerlines by connecting one quadrate each vertices to another quadrate each vertices;after that, calculate the distances between measured points and the constructed centerlines. The minimum zone straightness error is obtained by repeating comparing and reconstructing quadrates. The principle and steps of the algorithm to evaluate spatial straightness error is described in detail, and the mathematical formula and program flowchart are given also. Results show that this algorithm can evaluate spatial straightness error more effectively and exactly.
文摘As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.In contrast with SDEEM2015,SDEEM2019,the latest version,extends the orbital range from the Low Earth Orbit(LEO)to Geosynchronous Orbit(GEO)for the years 1958-2050.In this paper,improved modeling algorithms used by SDEEM2019 in propagating simulation,spatial density distribution,and spacecraft flux evaluation are presented.The debris fluxes of SDEEM2019 are compared with those of three typical models,i.e.,SDEEM2015,Orbital Debris Engineering Model 3.1(ORDEM 3.1),and Meteoroid and Space Debris Terrestrial Environment Reference(MASTER-8),in terms of two assessment modes.Three orbital cases,including the Geostationary Transfer Orbit(GTO),Sun-Synchronous Orbit(SSO)and International Space Station(ISS)orbit,are selected for the spacecraft assessment mode,and the LEO region is selected for the spatial density assessment mode.The analysis indicates that compared with previous algorithms,the variable step-size orbital propagating algorithm based on semi-major axis control is more precise,the spatial density algorithm based on the second zonal harmonic of the non-spherical Earth gravity(J_(2))is more applicable,and the result of the position-centered spacecraft flux algorithm is more convergent.The comparison shows that SDEEM2019 and MASTER-8 have consistent trends due to similar modeling processes,while the differences between SDEEM2019 and ORDEM 3.1 are mainly caused by different modeling approaches for uncatalogued debris.
文摘This paper describes the nearest neighbor (NN) search algorithm on the GBD(generalized BD) tree. The GBD tree is a spatial data structure suitable for two-or three-dimensional data and has good performance characteristics with respect to the dynamic data environment. On GIS and CAD systems, the R-tree and its successors have been used. In addition, the NN search algorithm is also proposed in an attempt to obtain good performance from the R-tree. On the other hand, the GBD tree is superior to the R-tree with respect to exact match retrieval, because the GBD tree has auxiliary data that uniquely determines the position of the object in the structure. The proposed NN search algorithm depends on the property of the GBD tree described above. The NN search algorithm on the GBD tree was studied and the performance thereof was evaluated through experiments.
