In optical performance monitoring system,the analog to digital converter is needed to detect the peak of nanosecond pulse and get the signal envelope.A scheme based on a designed anti-aliasing filter and analog to dig...In optical performance monitoring system,the analog to digital converter is needed to detect the peak of nanosecond pulse and get the signal envelope.A scheme based on a designed anti-aliasing filter and analog to digital converter is proposed to broaden the nanosecond pulse and make it easier for the analog to digital converter to catch the peak of the nanosecond pulse.The experimental results demonstrate that,with the proposed scheme,the optical performance system needs less time to get the recovered eye-diagram of high speed optical data signal,and is robust to phase mismatch in the analog to digital converter circuit.展开更多
In this paper a real-time peak detection method based on modified Automatic Multiscale Field Detection (AMPD) algorithm and Field Programmable Gate Arrays (FPGA) technologies of a time series data is studied, and opti...In this paper a real-time peak detection method based on modified Automatic Multiscale Field Detection (AMPD) algorithm and Field Programmable Gate Arrays (FPGA) technologies of a time series data is studied, and optimum scaling is highlighted after testing several scales. To validate the results obtained from modified algorithm, they are compared with the results of original AMPD method. As data of this study, three-phase voltage values of a power station are used. A detail detective sensitivity analysis of phase-to-phase voltage values is tried at different scales. Moreover, the original algorithm is tested regarding the off-line mode to obtain optimum scaling for real-time peak point detection. It is concluded that the peak detection of minimum and maximum points of data series achieved by modified algorithm is very close to the results of original AMPD algorithm.展开更多
离群点检测任务是指检测与正常数据在特征属性上存在显著差异的异常数据。大多数基于聚类的离群点检测方法主要从全局角度对数据集中的离群点进行检测,而对局部离群点的检测性能较弱。基于此,本文通过引入快速搜索和发现密度峰值方法改...离群点检测任务是指检测与正常数据在特征属性上存在显著差异的异常数据。大多数基于聚类的离群点检测方法主要从全局角度对数据集中的离群点进行检测,而对局部离群点的检测性能较弱。基于此,本文通过引入快速搜索和发现密度峰值方法改进K-means聚类算法,提出了一种名为KLOD(local outlier detection based on improved K-means and least-squares methods)的局部离群点检测方法,以实现对局部离群点的精确检测。首先,利用快速搜索和发现密度峰值方法计算数据点的局部密度和相对距离,并将二者相乘得到γ值。其次,将γ值降序排序,利用肘部法则选择γ值最大的k个数据点作为K-means聚类算法的初始聚类中心。然后,通过K-means聚类算法将数据集聚类成k个簇,计算数据点在每个维度上的目标函数值并进行升序排列。接着,确定数据点的每个维度的离散程度并选择适当的拟合函数和拟合点,通过最小二乘法对升序排列的每个簇的每1维目标函数值进行函数拟合并求导,以获取变化率。最后,结合信息熵,将每个数据点的每个维度目标函数值乘以相应的变化率进行加权,得到最终的异常得分,并将异常值得分较高的top-n个数据点视为离群点。通过人工数据集和UCI数据集,对KLOD、LOF和KNN方法在准确度上进行仿真实验对比。结果表明KLOD方法相较于KNN和LOF方法具有更高的准确度。本文提出的KLOD方法能够有效改善K-means聚类算法的聚类效果,并且在局部离群点检测方面具有较好的精度和性能。展开更多
在被动声纳对宽带接收信号的处理过程中,常规波束形成器存在波束较宽,方向分辨率低,输出信噪比低等问题,进而导致信号源的方向估计不准确。论文针对常规波束形成器的上述问题,提出了反卷积峰值能量检测算法(Deconvolved Peak Energy Det...在被动声纳对宽带接收信号的处理过程中,常规波束形成器存在波束较宽,方向分辨率低,输出信噪比低等问题,进而导致信号源的方向估计不准确。论文针对常规波束形成器的上述问题,提出了反卷积峰值能量检测算法(Deconvolved Peak Energy Detection Method,DPED)。该算法通过对常规波束形成器的输出进行反卷积计算,提高了输出信噪比,优化了常规波束形成器的空间谱显示。文章讨论了反卷积波束形成的基本方法,给出了反卷积峰值能量检测算法的基本实现流程。通过仿真验证了该算法的有效性。展开更多
基金supported by National 863 Program of China(2013AA013401),P.R.ChinaNational Natural Science Foundation of China under Grant No.61177067,No.61027007,and No.61331010
文摘In optical performance monitoring system,the analog to digital converter is needed to detect the peak of nanosecond pulse and get the signal envelope.A scheme based on a designed anti-aliasing filter and analog to digital converter is proposed to broaden the nanosecond pulse and make it easier for the analog to digital converter to catch the peak of the nanosecond pulse.The experimental results demonstrate that,with the proposed scheme,the optical performance system needs less time to get the recovered eye-diagram of high speed optical data signal,and is robust to phase mismatch in the analog to digital converter circuit.
文摘In this paper a real-time peak detection method based on modified Automatic Multiscale Field Detection (AMPD) algorithm and Field Programmable Gate Arrays (FPGA) technologies of a time series data is studied, and optimum scaling is highlighted after testing several scales. To validate the results obtained from modified algorithm, they are compared with the results of original AMPD method. As data of this study, three-phase voltage values of a power station are used. A detail detective sensitivity analysis of phase-to-phase voltage values is tried at different scales. Moreover, the original algorithm is tested regarding the off-line mode to obtain optimum scaling for real-time peak point detection. It is concluded that the peak detection of minimum and maximum points of data series achieved by modified algorithm is very close to the results of original AMPD algorithm.
文摘离群点检测任务是指检测与正常数据在特征属性上存在显著差异的异常数据。大多数基于聚类的离群点检测方法主要从全局角度对数据集中的离群点进行检测,而对局部离群点的检测性能较弱。基于此,本文通过引入快速搜索和发现密度峰值方法改进K-means聚类算法,提出了一种名为KLOD(local outlier detection based on improved K-means and least-squares methods)的局部离群点检测方法,以实现对局部离群点的精确检测。首先,利用快速搜索和发现密度峰值方法计算数据点的局部密度和相对距离,并将二者相乘得到γ值。其次,将γ值降序排序,利用肘部法则选择γ值最大的k个数据点作为K-means聚类算法的初始聚类中心。然后,通过K-means聚类算法将数据集聚类成k个簇,计算数据点在每个维度上的目标函数值并进行升序排列。接着,确定数据点的每个维度的离散程度并选择适当的拟合函数和拟合点,通过最小二乘法对升序排列的每个簇的每1维目标函数值进行函数拟合并求导,以获取变化率。最后,结合信息熵,将每个数据点的每个维度目标函数值乘以相应的变化率进行加权,得到最终的异常得分,并将异常值得分较高的top-n个数据点视为离群点。通过人工数据集和UCI数据集,对KLOD、LOF和KNN方法在准确度上进行仿真实验对比。结果表明KLOD方法相较于KNN和LOF方法具有更高的准确度。本文提出的KLOD方法能够有效改善K-means聚类算法的聚类效果,并且在局部离群点检测方面具有较好的精度和性能。
文摘在被动声纳对宽带接收信号的处理过程中,常规波束形成器存在波束较宽,方向分辨率低,输出信噪比低等问题,进而导致信号源的方向估计不准确。论文针对常规波束形成器的上述问题,提出了反卷积峰值能量检测算法(Deconvolved Peak Energy Detection Method,DPED)。该算法通过对常规波束形成器的输出进行反卷积计算,提高了输出信噪比,优化了常规波束形成器的空间谱显示。文章讨论了反卷积波束形成的基本方法,给出了反卷积峰值能量检测算法的基本实现流程。通过仿真验证了该算法的有效性。
