A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to de...A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel(GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms.展开更多
Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is...Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. A general regularized risk functional （RRF） criterion for kernel matrix learning is proposed. Compared with the RRF criterion, general RRF criterion takes into account the geometric distributions of the embedding data points. It is proven that the distance between different geometric distdbutions can be estimated by their centroid distance in the reproducing kernel Hilbert space. Using this criterion for kernel matrix learning leads to a convex quadratically constrained quadratic programming （QCQP） problem. For several commonly used loss functions, their mathematical formulations are given. Experiment results on a collection of benchmark data sets demonstrate the effectiveness of the proposed method.展开更多
锂电池健康状态(state of health, SOH)的退化过程在一定程度上是一个非平稳随机过程,使得当前多数点估计机器学习方法在实际应用中受到限制。基于贝叶斯理论的高斯过程回归(Gaussian process regression,GPR),因可输出估计结果的不确定...锂电池健康状态(state of health, SOH)的退化过程在一定程度上是一个非平稳随机过程,使得当前多数点估计机器学习方法在实际应用中受到限制。基于贝叶斯理论的高斯过程回归(Gaussian process regression,GPR),因可输出估计结果的不确定性,近年来在锂电池SOH区间估计中得到广泛应用。然而,GPR的性能很大程度上取决于其核函数的选择,当前研究多凭借经验选用固定单一核函数,无法适应不同的数据集。为此,本文提出一种基于自适应最优组合核函数GPR的锂电池SOH区间估计方法。该方法首先从电池充放电数据中提取出多个健康因子(health factor, HF),并采用皮尔森相关系数法优选出6个与SOH高度相关的健康因子作为模型的输入。然后,在当前常用的7个核函数集合上,通过两两随机组合构造新的组合核函数,并利用交叉验证自适应优选出最优组合核函数。采用3个不同数据集对所提方法进行了验证,结果表明:本文方法具有出色的SOH区间估计性能。在3个公开数据集上,平均区间宽度指标在0.0509以内,平均区间分数大于-0.0004,均方根误差小于0.0181。展开更多
基金Project(61101185)supported by the National Natural Science Foundation of China
文摘A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel(GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms.
基金supported by the National Natural Science Fundation of China (60736021)the Joint Funds of NSFC-Guangdong Province(U0735003)
文摘Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. A general regularized risk functional （RRF） criterion for kernel matrix learning is proposed. Compared with the RRF criterion, general RRF criterion takes into account the geometric distributions of the embedding data points. It is proven that the distance between different geometric distdbutions can be estimated by their centroid distance in the reproducing kernel Hilbert space. Using this criterion for kernel matrix learning leads to a convex quadratically constrained quadratic programming （QCQP） problem. For several commonly used loss functions, their mathematical formulations are given. Experiment results on a collection of benchmark data sets demonstrate the effectiveness of the proposed method.
文摘锂电池健康状态(state of health, SOH)的退化过程在一定程度上是一个非平稳随机过程,使得当前多数点估计机器学习方法在实际应用中受到限制。基于贝叶斯理论的高斯过程回归(Gaussian process regression,GPR),因可输出估计结果的不确定性,近年来在锂电池SOH区间估计中得到广泛应用。然而,GPR的性能很大程度上取决于其核函数的选择,当前研究多凭借经验选用固定单一核函数,无法适应不同的数据集。为此,本文提出一种基于自适应最优组合核函数GPR的锂电池SOH区间估计方法。该方法首先从电池充放电数据中提取出多个健康因子(health factor, HF),并采用皮尔森相关系数法优选出6个与SOH高度相关的健康因子作为模型的输入。然后,在当前常用的7个核函数集合上,通过两两随机组合构造新的组合核函数,并利用交叉验证自适应优选出最优组合核函数。采用3个不同数据集对所提方法进行了验证,结果表明:本文方法具有出色的SOH区间估计性能。在3个公开数据集上,平均区间宽度指标在0.0509以内,平均区间分数大于-0.0004,均方根误差小于0.0181。
