Target tracking is very important in computer vision and related areas. It is usually difficult to accurately track fast motion target with appearance variations. Sometimes the tracking algorithms fail for heavy appea...Target tracking is very important in computer vision and related areas. It is usually difficult to accurately track fast motion target with appearance variations. Sometimes the tracking algorithms fail for heavy appearance variations. A multiple template method to track fast motion target with appearance changes is presented under the framework of appearance model with Kalman filter. Firstly, we construct a multiple template appearance model, which includes both the original template and templates affinely transformed from original one. Generally speaking, appearance variations of fast motion target can be covered by affine transformation. Therefore, the affine tr templates match the target of appearance variations better than conventional models. Secondly, we present an improved Kalman filter for approx- imate estimating the motion trail of the target and a modified similarity evaluation function for exact matching. The estimation approach can reduce time complexity of the algorithm and keep accuracy in the meantime. Thirdly, we propose an adaptive scheme for updating template set to alleviate the drift problem. The scheme considers the following differences： the weight differences in two successive frames; different types of affine transformation applied to templates. Finally, experiments demonstrate that the proposed algorithm is robust to appearance varia- tion of fast motion target and achieves real-time performance on middle/low-range computing platform.展开更多
针对传统静态状态估计方法的缺点,提出了一种改进的电力系统状态估计方法,即将部分节点相量测量单元(phasor measurement unit,PMU)量测数据与监控数据采集(supervisory control and data acquisition,SCADA)量测数据融合进行电力系统...针对传统静态状态估计方法的缺点,提出了一种改进的电力系统状态估计方法,即将部分节点相量测量单元(phasor measurement unit,PMU)量测数据与监控数据采集(supervisory control and data acquisition,SCADA)量测数据融合进行电力系统的全网状态估计。该方法简化了系统的雅可比矩阵,缩短了计算时间。文章研究了PMU和SCADA系统融合改进后的快速分解法,针对SCADA量测数据的缺点,通过历史数据库对潮流数据进行预测,并依据PMU量测量对系统进行分析,继而进行系统全网状态的动态监测。通过算例证明,与传统的估计方法相比,该方法改善了状态估计的精确性,减少了迭代次数,细致地描绘了电网状态的变化过程,为调度中心下一步的决策提供了依据。展开更多
针对在线支持向量回归(online support vector regression,online SVR)算法进行复杂时间序列精确预测时效率较低的问题,提出一种改进减量训练策略的快速预测方法,通过对非支持向量样本的采样选择,采取加速减量训练实现对在线训练数据集...针对在线支持向量回归(online support vector regression,online SVR)算法进行复杂时间序列精确预测时效率较低的问题,提出一种改进减量训练策略的快速预测方法,通过对非支持向量样本的采样选择,采取加速减量训练实现对在线训练数据集规模的缩减,从而达到快速在线训练和预测的目的.将该算法应用于黑龙江移动通信话务量数据的预测中,实验结果表明,在保持OnlineSVR预测精度的条件下,算法执行效率得到大幅提高。展开更多
基金Supported by the National Science Foundation of China(61472289)Hubei Province Science Foundation(2015CFB254)
文摘Target tracking is very important in computer vision and related areas. It is usually difficult to accurately track fast motion target with appearance variations. Sometimes the tracking algorithms fail for heavy appearance variations. A multiple template method to track fast motion target with appearance changes is presented under the framework of appearance model with Kalman filter. Firstly, we construct a multiple template appearance model, which includes both the original template and templates affinely transformed from original one. Generally speaking, appearance variations of fast motion target can be covered by affine transformation. Therefore, the affine tr templates match the target of appearance variations better than conventional models. Secondly, we present an improved Kalman filter for approx- imate estimating the motion trail of the target and a modified similarity evaluation function for exact matching. The estimation approach can reduce time complexity of the algorithm and keep accuracy in the meantime. Thirdly, we propose an adaptive scheme for updating template set to alleviate the drift problem. The scheme considers the following differences： the weight differences in two successive frames; different types of affine transformation applied to templates. Finally, experiments demonstrate that the proposed algorithm is robust to appearance varia- tion of fast motion target and achieves real-time performance on middle/low-range computing platform.
文摘针对传统静态状态估计方法的缺点,提出了一种改进的电力系统状态估计方法,即将部分节点相量测量单元(phasor measurement unit,PMU)量测数据与监控数据采集(supervisory control and data acquisition,SCADA)量测数据融合进行电力系统的全网状态估计。该方法简化了系统的雅可比矩阵,缩短了计算时间。文章研究了PMU和SCADA系统融合改进后的快速分解法,针对SCADA量测数据的缺点,通过历史数据库对潮流数据进行预测,并依据PMU量测量对系统进行分析,继而进行系统全网状态的动态监测。通过算例证明,与传统的估计方法相比,该方法改善了状态估计的精确性,减少了迭代次数,细致地描绘了电网状态的变化过程,为调度中心下一步的决策提供了依据。
文摘针对在线支持向量回归(online support vector regression,online SVR)算法进行复杂时间序列精确预测时效率较低的问题,提出一种改进减量训练策略的快速预测方法,通过对非支持向量样本的采样选择,采取加速减量训练实现对在线训练数据集规模的缩减,从而达到快速在线训练和预测的目的.将该算法应用于黑龙江移动通信话务量数据的预测中,实验结果表明,在保持OnlineSVR预测精度的条件下,算法执行效率得到大幅提高。
