随着电磁频谱设备在各个领域的广泛应用,用频装备互相干扰导致设备无法正常工作的事件时有发生,保障装备的安全可靠用频是频谱管理部门十分重要的工作。为快速准确检测出异常信号并进行识别,本文提出一种基于动态时间规整算法(Dynamic T...随着电磁频谱设备在各个领域的广泛应用,用频装备互相干扰导致设备无法正常工作的事件时有发生,保障装备的安全可靠用频是频谱管理部门十分重要的工作。为快速准确检测出异常信号并进行识别,本文提出一种基于动态时间规整算法(Dynamic Time Warping,DTW)的频谱异常信号检测方法,首先通过对频段扫描数据进行统计分析,对异常频点信号进行快速提取,对历史积累的异常信号进行加权统计建模形成异常信号模板库,然后采用DTW算法和异常信号模板库中的信号进行匹配计算;最后根据相似系数对异常信号进行识别。展开更多
The Schatten p-quasi-norm regularized minimization problem has attracted extensive attention in machine learning, image recognition, signal reconstruction, etc. Meanwhile, the l_(2,1)-regularized matrix optimization m...The Schatten p-quasi-norm regularized minimization problem has attracted extensive attention in machine learning, image recognition, signal reconstruction, etc. Meanwhile, the l_(2,1)-regularized matrix optimization models are also popularly used for its joint sparsity. Naturally, the pseudo matrix norm l_(2,p) is expected to carry over the advantages of both l_p and l_(2,1). This paper proposes a mixed l_(2,q)-l_(2,p) matrix minimization approach for multi-image face recognition. To uniformly solve this optimization problem for any q ∈ [1,2] and p ∈(0,2], an iterative quadratic method(IQM) is developed. IQM is proved to iescend strictly until it gets a stationary point of the mixed l_(2,q)-l_(2,p)matrix minimization. Moreover, a more practical IQM is presented for large-scale case. Experimental results on three public facial image databases show that the joint matrix minimization approach with practical IQM not only saves much computational cost but also achievez better performance in face recognition than state-of-the-art methods.展开更多
文摘随着电磁频谱设备在各个领域的广泛应用,用频装备互相干扰导致设备无法正常工作的事件时有发生,保障装备的安全可靠用频是频谱管理部门十分重要的工作。为快速准确检测出异常信号并进行识别,本文提出一种基于动态时间规整算法(Dynamic Time Warping,DTW)的频谱异常信号检测方法,首先通过对频段扫描数据进行统计分析,对异常频点信号进行快速提取,对历史积累的异常信号进行加权统计建模形成异常信号模板库,然后采用DTW算法和异常信号模板库中的信号进行匹配计算;最后根据相似系数对异常信号进行识别。
基金supported by National Natural Science Foundation of China(Grant Nos.11471159 and 61661136001)
文摘The Schatten p-quasi-norm regularized minimization problem has attracted extensive attention in machine learning, image recognition, signal reconstruction, etc. Meanwhile, the l_(2,1)-regularized matrix optimization models are also popularly used for its joint sparsity. Naturally, the pseudo matrix norm l_(2,p) is expected to carry over the advantages of both l_p and l_(2,1). This paper proposes a mixed l_(2,q)-l_(2,p) matrix minimization approach for multi-image face recognition. To uniformly solve this optimization problem for any q ∈ [1,2] and p ∈(0,2], an iterative quadratic method(IQM) is developed. IQM is proved to iescend strictly until it gets a stationary point of the mixed l_(2,q)-l_(2,p)matrix minimization. Moreover, a more practical IQM is presented for large-scale case. Experimental results on three public facial image databases show that the joint matrix minimization approach with practical IQM not only saves much computational cost but also achievez better performance in face recognition than state-of-the-art methods.
