With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the pr...With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.展开更多
由于SSOR预条件共轭梯度算法中预条件方程求解需要前推和回代,导致算法迁移到GPU平台上并行效率不高.为此,基于诺依曼多项式分解技术,提出了一种GPU加速的SSOR稀疏近似逆预条件子(GSSORSAI).它不仅保持了原线性系统系数矩阵的稀疏和对...由于SSOR预条件共轭梯度算法中预条件方程求解需要前推和回代,导致算法迁移到GPU平台上并行效率不高.为此,基于诺依曼多项式分解技术,提出了一种GPU加速的SSOR稀疏近似逆预条件子(GSSORSAI).它不仅保持了原线性系统系数矩阵的稀疏和对称正定特性,而且预条件方程求解仅需一次稀疏矩阵矢量乘运算,避免了前推和回代过程.实验结果表明:在NVIDIA Tesla C2050GPU上,对比使用Python在单个CPU上SSOR稀疏近似逆预条件子实现方法,GSSORSAI平均快将近100倍;应用到并行的PCG算法中,相比无预条件的CG算法,平均提高了算法的3倍的收敛速度.展开更多
基金supported by the Aeronautical Science Foundation of China(20111052010)the Jiangsu Graduates Innovation Project (CXZZ120163)+1 种基金the "333" Project of Jiangsu Provincethe Qing Lan Project of Jiangsu Province
文摘With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.
文摘由于SSOR预条件共轭梯度算法中预条件方程求解需要前推和回代,导致算法迁移到GPU平台上并行效率不高.为此,基于诺依曼多项式分解技术,提出了一种GPU加速的SSOR稀疏近似逆预条件子(GSSORSAI).它不仅保持了原线性系统系数矩阵的稀疏和对称正定特性,而且预条件方程求解仅需一次稀疏矩阵矢量乘运算,避免了前推和回代过程.实验结果表明:在NVIDIA Tesla C2050GPU上,对比使用Python在单个CPU上SSOR稀疏近似逆预条件子实现方法,GSSORSAI平均快将近100倍;应用到并行的PCG算法中,相比无预条件的CG算法,平均提高了算法的3倍的收敛速度.
