In recent years,the rapid development of big data technology has also been favored by more and more scholars.Massive data storage and calculation problems have also been solved.At the same time,outlier detection probl...In recent years,the rapid development of big data technology has also been favored by more and more scholars.Massive data storage and calculation problems have also been solved.At the same time,outlier detection problems in mass data have also come along with it.Therefore,more research work has been devoted to the problem of outlier detection in big data.However,the existing available methods have high computation time,the improved algorithm of outlier detection is presented,which has higher performance to detect outlier.In this paper,an improved algorithm is proposed.The SMK-means is a fusion algorithm which is achieved by Mini Batch K-means based on simulated annealing algorithm for anomalous detection of massive household electricity data,which can give the number of clusters and reduce the number of iterations and improve the accuracy of clustering.In this paper,several experiments are performed to compare and analyze multiple performances of the algorithm.Through analysis,we know that the proposed algorithm is superior to the existing algorithms.展开更多
Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized charact...Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.展开更多
文摘In recent years,the rapid development of big data technology has also been favored by more and more scholars.Massive data storage and calculation problems have also been solved.At the same time,outlier detection problems in mass data have also come along with it.Therefore,more research work has been devoted to the problem of outlier detection in big data.However,the existing available methods have high computation time,the improved algorithm of outlier detection is presented,which has higher performance to detect outlier.In this paper,an improved algorithm is proposed.The SMK-means is a fusion algorithm which is achieved by Mini Batch K-means based on simulated annealing algorithm for anomalous detection of massive household electricity data,which can give the number of clusters and reduce the number of iterations and improve the accuracy of clustering.In this paper,several experiments are performed to compare and analyze multiple performances of the algorithm.Through analysis,we know that the proposed algorithm is superior to the existing algorithms.
基金funded by National Natural Science Foundation of China(Grant Nos.42272333,42277147).
文摘Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.
