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
An improved clustering algorithm was presented based on density-isoline clustering algorithm. The new algorithm can do a better job than density-isoline clustering when dealing with noise, not having to literately cal...An improved clustering algorithm was presented based on density-isoline clustering algorithm. The new algorithm can do a better job than density-isoline clustering when dealing with noise, not having to literately calculate the cluster centers for the samples batching into clusters instead of one by one. After repeated experiments, the results demonstrate that the improved density-isoline clustering algorithm is significantly more efficiency in clustering with noises and overcomes the drawbacks that traditional algorithm DILC deals with noise and that the efficiency of running time is improved greatly.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
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
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
针对静态和动态救援场景下的多无人机协同任务调度问题,提出基于密度的噪声应用空间聚类-一致性包算法(density-based spatial clustering of applications with noise-consensus-based bundle algorithm,DBSCAN-CBBA)。首先,针对任务...针对静态和动态救援场景下的多无人机协同任务调度问题,提出基于密度的噪声应用空间聚类-一致性包算法(density-based spatial clustering of applications with noise-consensus-based bundle algorithm,DBSCAN-CBBA)。首先,针对任务执行阶段存在的场景不确定以及无人机携带物资载荷限制等问题,建立了一种更为符合救援实际的多任务分配模型。然后,优化了一致性包算法的任务包构建结构以提高算法效率和搜索最优解的能力。第1阶段通过基于密度聚类算法生成候选任务集合,并通过随机方式构建非候选任务集合;第2阶段通过无人机之间的通信,消解它们因独立构建任务包而产生的冲突。最后,将该算法分别应用于静态和实时动态任务分配场景。仿真实验结果表明,该算法可较为高效地找到合理的任务分配方案。展开更多
为有效识别和剔除风电机组实测数据中的异常数据,通过分析风电机组实测数据的高维特征,提出一种基于流形学习的异常数据识别算法。首先,采用k-近邻互信息算法实现风电机组特征变量选择;随后,使用将样本间距离度量替换为欧几里得度量和...为有效识别和剔除风电机组实测数据中的异常数据,通过分析风电机组实测数据的高维特征,提出一种基于流形学习的异常数据识别算法。首先,采用k-近邻互信息算法实现风电机组特征变量选择;随后,使用将样本间距离度量替换为欧几里得度量和局部主成分分析(local principal component analysis,LPCA)差别加权和的优化t-分布随机近邻嵌入(t-distributed stochastic neighbor embedding,t-SNE)算法挖掘出高维流形数据中具有内在规律的低维特征,使得具有不同分布特征的数据在可视化二维空间中显著分离;最后,采用基于密度的噪声空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法对二维空间中的数据进行聚类。结果表明,与主成分分析(principal component analysis,PCA)算法、局部线性嵌入(locally linear embedding,LLE)算法和原t-SNE算法相比,所提方法能够对各种复杂工况数据进行可视化分离聚类,并对异常数据进行识别和剔除。展开更多
为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical de...为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical density-based spatial clustering of applications with noise,HDBSCAN)提取用户在不同场景下的典型日负荷曲线,并利用改进的K-means算法对提取出的典型日负荷曲线进行聚类分析,构建行业的典型负荷形态;其次,提出一种多维场景负荷特征异常智能研判方法,通过构造用户的负荷特征,使用熵权法评估行业典型场景的相对重要性,并采用单分类支持向量机(one-class support vector machine,OCSVM)算法量化每个场景下的用户负荷特征的异常程度,通过加权计算得到用户的综合嫌疑得分并排序,从而实现对负荷特征异常用户的准确辨识。最后,采用某地区实际用户数据进行算例验证。仿真结果表明,所提方法在行业典型负荷场景构建及负荷特征异常辨识方面表现出良好的可行性与实用价值。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
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 materia...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.展开更多
通过分析基于密度的带噪空间聚类算法(DBSCAN,density-based spatial clustering of applications with noise)和模糊C均值(FCM,fuzzy C-means)聚类算法的聚类性能,本文提出一种快速的基于几何代数的自适应典型航迹生成算法。首先,利用K...通过分析基于密度的带噪空间聚类算法(DBSCAN,density-based spatial clustering of applications with noise)和模糊C均值(FCM,fuzzy C-means)聚类算法的聚类性能,本文提出一种快速的基于几何代数的自适应典型航迹生成算法。首先,利用K-means聚类算法进行航班运行时间的归一化;然后,利用几何代数优越的时空表达和计算能力,给出了航迹转弯判定、DBSCAN聚类和FCM聚类的几何代数描述;最后,在几何代数空间中对转弯运动状态和直线运动状态的航迹分别自适应地进行DBSCAN聚类和FCM聚类形成典型航迹.实验结果表明,本文自适应典型航迹的生成速度较欧氏空间方法可提升30%以上。展开更多
新型配电系统柔性消弧装置及定位技术均需充分挖掘相电流暂态特征来实现选相、选线和故障定位。针对此问题,对新型配电系统单相接地故障相电流暂态分布特性进行分析,提出了一种基于相电流多维时频分布特征差异的新型配电系统单相接地故...新型配电系统柔性消弧装置及定位技术均需充分挖掘相电流暂态特征来实现选相、选线和故障定位。针对此问题,对新型配电系统单相接地故障相电流暂态分布特性进行分析,提出了一种基于相电流多维时频分布特征差异的新型配电系统单相接地故障定位新方法。依据故障相电流故障暂态量与非故障相电流故障暂态量的差异性,通过灰色关联度算法完成故障选相;对各出线始端监测点以及疑似故障馈线分支监测点的相电流暂态波形进行26维多维时频特征的提取,通过经方差优化的t-分布近邻嵌入算法(variance-optimized t-distributed stochastic neighbor embedding,VTSNE)进行筛选和降维,并对处理后的特征数据进行基于密度的有噪空间聚类算法(density-based special clustering of application with noise,DBSCAN)聚类完成故障选线和故障区段定位。该方法在某绿色港口10 kV新型配电系统模型中得到验证,在不同故障初相角、不同过渡电阻等故障场景下均可准确可靠定位故障位置,对采样同步精度及采样频率要求低,易于工程实现。展开更多
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An improved clustering algorithm was presented based on density-isoline clustering algorithm. The new algorithm can do a better job than density-isoline clustering when dealing with noise, not having to literately calculate the cluster centers for the samples batching into clusters instead of one by one. After repeated experiments, the results demonstrate that the improved density-isoline clustering algorithm is significantly more efficiency in clustering with noises and overcomes the drawbacks that traditional algorithm DILC deals with noise and that the efficiency of running time is improved greatly.
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.
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.
文摘针对静态和动态救援场景下的多无人机协同任务调度问题,提出基于密度的噪声应用空间聚类-一致性包算法(density-based spatial clustering of applications with noise-consensus-based bundle algorithm,DBSCAN-CBBA)。首先,针对任务执行阶段存在的场景不确定以及无人机携带物资载荷限制等问题,建立了一种更为符合救援实际的多任务分配模型。然后,优化了一致性包算法的任务包构建结构以提高算法效率和搜索最优解的能力。第1阶段通过基于密度聚类算法生成候选任务集合,并通过随机方式构建非候选任务集合;第2阶段通过无人机之间的通信,消解它们因独立构建任务包而产生的冲突。最后,将该算法分别应用于静态和实时动态任务分配场景。仿真实验结果表明,该算法可较为高效地找到合理的任务分配方案。
文摘为有效识别和剔除风电机组实测数据中的异常数据,通过分析风电机组实测数据的高维特征,提出一种基于流形学习的异常数据识别算法。首先,采用k-近邻互信息算法实现风电机组特征变量选择;随后,使用将样本间距离度量替换为欧几里得度量和局部主成分分析(local principal component analysis,LPCA)差别加权和的优化t-分布随机近邻嵌入(t-distributed stochastic neighbor embedding,t-SNE)算法挖掘出高维流形数据中具有内在规律的低维特征,使得具有不同分布特征的数据在可视化二维空间中显著分离;最后,采用基于密度的噪声空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法对二维空间中的数据进行聚类。结果表明,与主成分分析(principal component analysis,PCA)算法、局部线性嵌入(locally linear embedding,LLE)算法和原t-SNE算法相比,所提方法能够对各种复杂工况数据进行可视化分离聚类,并对异常数据进行识别和剔除。
文摘为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical density-based spatial clustering of applications with noise,HDBSCAN)提取用户在不同场景下的典型日负荷曲线,并利用改进的K-means算法对提取出的典型日负荷曲线进行聚类分析,构建行业的典型负荷形态;其次,提出一种多维场景负荷特征异常智能研判方法,通过构造用户的负荷特征,使用熵权法评估行业典型场景的相对重要性,并采用单分类支持向量机(one-class support vector machine,OCSVM)算法量化每个场景下的用户负荷特征的异常程度,通过加权计算得到用户的综合嫌疑得分并排序,从而实现对负荷特征异常用户的准确辨识。最后,采用某地区实际用户数据进行算例验证。仿真结果表明,所提方法在行业典型负荷场景构建及负荷特征异常辨识方面表现出良好的可行性与实用价值。
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.
基金funded by the National Natural Science Foundation of China(42071014).
文摘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.
文摘通过分析基于密度的带噪空间聚类算法(DBSCAN,density-based spatial clustering of applications with noise)和模糊C均值(FCM,fuzzy C-means)聚类算法的聚类性能,本文提出一种快速的基于几何代数的自适应典型航迹生成算法。首先,利用K-means聚类算法进行航班运行时间的归一化;然后,利用几何代数优越的时空表达和计算能力,给出了航迹转弯判定、DBSCAN聚类和FCM聚类的几何代数描述;最后,在几何代数空间中对转弯运动状态和直线运动状态的航迹分别自适应地进行DBSCAN聚类和FCM聚类形成典型航迹.实验结果表明,本文自适应典型航迹的生成速度较欧氏空间方法可提升30%以上。
文摘新型配电系统柔性消弧装置及定位技术均需充分挖掘相电流暂态特征来实现选相、选线和故障定位。针对此问题,对新型配电系统单相接地故障相电流暂态分布特性进行分析,提出了一种基于相电流多维时频分布特征差异的新型配电系统单相接地故障定位新方法。依据故障相电流故障暂态量与非故障相电流故障暂态量的差异性,通过灰色关联度算法完成故障选相;对各出线始端监测点以及疑似故障馈线分支监测点的相电流暂态波形进行26维多维时频特征的提取,通过经方差优化的t-分布近邻嵌入算法(variance-optimized t-distributed stochastic neighbor embedding,VTSNE)进行筛选和降维,并对处理后的特征数据进行基于密度的有噪空间聚类算法(density-based special clustering of application with noise,DBSCAN)聚类完成故障选线和故障区段定位。该方法在某绿色港口10 kV新型配电系统模型中得到验证,在不同故障初相角、不同过渡电阻等故障场景下均可准确可靠定位故障位置,对采样同步精度及采样频率要求低,易于工程实现。
