韦伯局部描述子(Weber Local Descriptor,WLD)被广泛用在多个领域,但由于其所具有的局限性使它对图像中的位移和旋转不具有较好的鲁棒性。因此,本文对WLD进行改进,首先,提出一种多尺度可变曲率Gabor滤波器对指静脉图像进行滤波,得到多...韦伯局部描述子(Weber Local Descriptor,WLD)被广泛用在多个领域,但由于其所具有的局限性使它对图像中的位移和旋转不具有较好的鲁棒性。因此,本文对WLD进行改进,首先,提出一种多尺度可变曲率Gabor滤波器对指静脉图像进行滤波,得到多尺度能量图和几何特征图;然后,用能量图计算差分激励,在计算时采用多尺度局部窗口,并加入方向信息;最后将几何特征图和差分激励结合形成特征。该方法的有效性将在FV-TJ和FV-USM数据库上进行验证,结果表明本文方法的识别性能要优于其他方法。展开更多
为了准确和快速地估算电动汽车运行过程中汽车电池的荷电状态(State of Charge,SOC)和健康状态(State of Health,SOH),提出一种基于遗忘因子最小二乘和可变时间尺度扩展卡尔曼滤波器的自适应联合估算算法。为了提高算法的效率和准确度,...为了准确和快速地估算电动汽车运行过程中汽车电池的荷电状态(State of Charge,SOC)和健康状态(State of Health,SOH),提出一种基于遗忘因子最小二乘和可变时间尺度扩展卡尔曼滤波器的自适应联合估算算法。为了提高算法的效率和准确度,引入自适应遗忘因子递归最小二乘(Adaptive Forgetting Factor Recursive Least Square,AFFRLS)方法来识别电池模型中的参数,并采用可变时间尺度扩展卡尔曼滤波器(Variable Time Scale Extended Kalman Filter,VEKF)来指示SOC和SOH,以满足对电池动态状况进行在线快速估算的需求。应用动态应力测试(Dynamic Stress Test,DST)数据库验证了该方法的有效性,实验结果表明,该联合估算方法可以获取准确的电池模型,并实现在线状态估算。展开更多
A learning-based deformable registration method was presented for MR brain images. First, best geometric features are selected for each location and each resolution, in order to reduce ambiguity in matching during ima...A learning-based deformable registration method was presented for MR brain images. First, best geometric features are selected for each location and each resolution, in order to reduce ambiguity in matching during image registration. The best features are obtained by solving an energy minimization problem, which requires the features to be distinctive around the neighboring points and consistency across training samples. Secondly, the set of active points is hierarchically selected based on their saliency and consistency measurements during registration, which helps to produce accurate registration results. Finally, by incorporating those learned results into the framework of HAMMER, great improvement in both real data and simulated data is achieved.展开更多
文摘韦伯局部描述子(Weber Local Descriptor,WLD)被广泛用在多个领域,但由于其所具有的局限性使它对图像中的位移和旋转不具有较好的鲁棒性。因此,本文对WLD进行改进,首先,提出一种多尺度可变曲率Gabor滤波器对指静脉图像进行滤波,得到多尺度能量图和几何特征图;然后,用能量图计算差分激励,在计算时采用多尺度局部窗口,并加入方向信息;最后将几何特征图和差分激励结合形成特征。该方法的有效性将在FV-TJ和FV-USM数据库上进行验证,结果表明本文方法的识别性能要优于其他方法。
文摘为了准确和快速地估算电动汽车运行过程中汽车电池的荷电状态(State of Charge,SOC)和健康状态(State of Health,SOH),提出一种基于遗忘因子最小二乘和可变时间尺度扩展卡尔曼滤波器的自适应联合估算算法。为了提高算法的效率和准确度,引入自适应遗忘因子递归最小二乘(Adaptive Forgetting Factor Recursive Least Square,AFFRLS)方法来识别电池模型中的参数,并采用可变时间尺度扩展卡尔曼滤波器(Variable Time Scale Extended Kalman Filter,VEKF)来指示SOC和SOH,以满足对电池动态状况进行在线快速估算的需求。应用动态应力测试(Dynamic Stress Test,DST)数据库验证了该方法的有效性,实验结果表明,该联合估算方法可以获取准确的电池模型,并实现在线状态估算。
基金National Natural Science Foundation of China(No.60271033)
文摘A learning-based deformable registration method was presented for MR brain images. First, best geometric features are selected for each location and each resolution, in order to reduce ambiguity in matching during image registration. The best features are obtained by solving an energy minimization problem, which requires the features to be distinctive around the neighboring points and consistency across training samples. Secondly, the set of active points is hierarchically selected based on their saliency and consistency measurements during registration, which helps to produce accurate registration results. Finally, by incorporating those learned results into the framework of HAMMER, great improvement in both real data and simulated data is achieved.
