目的目标检测在智能交通、自动驾驶以及安防监控中均有重要的地位,Vi Be算法是常用的运动目标检测算法,它主要由背景模型初始化、前景检测、背景模型更新3部分组成,其思想简单,易于实现,运算效率高,但当初始帧有运动目标时,检测结果会出...目的目标检测在智能交通、自动驾驶以及安防监控中均有重要的地位,Vi Be算法是常用的运动目标检测算法,它主要由背景模型初始化、前景检测、背景模型更新3部分组成,其思想简单,易于实现,运算效率高,但当初始帧有运动目标时,检测结果会出现"鬼影"现象,且易受噪声和光照变化影响,不能适应动态场景。同时,其逐帧逐像素进行前景检测,在计算复杂度方面有较大提升空间。为解决这些问题,提出一种改进的Vi Be算法,称为Vi Be Imp算法。方法在背景模型初始化时,用多帧平均法给出初始背景,采用该初始背景构建初始背景样本模型。在前景检测过程中,采用背景差分法、帧差法与OTSU算法相结合给出半径阈值的自适应计算方法。同时,根据背景差分法找出运动区域,只对运动区域进行前景判断和模型更新,降低算法的计算复杂度。结果对25个不同场景视频分别给出Vi Be Imp算法在初始化背景,自适应半径阈值和计算复杂度方面改进的结果及有效性指标,实验结果表明,与Vi Be、Vi Be Diff2、Vi Be IniR,以及Surendra等算法和高斯混合模型相比,Vi Be Imp算法对噪声、光照和背景动态变化有较好的鲁棒性,检测结果更完整,且实时性较好。同时,Vi Be Imp算法将Vi Be算法的查准率、查全率以及F1值分别提高了17. 98%、11. 40%和15. 96%。结论 Vi Be Imp算法采用多帧平均法构建初始背景可有效地消除"鬼影",并给出半径阈值的自适应计算方法,使Vi Be算法更快适应视频环境变化,准确且完整地检测出运动目标,具有较低的误检率和漏检率。该方法克服了Vi Be算法对初始背景以及视频环境的依赖,很大程度上提高了运算速度,具有很好的鲁棒性和适用性。展开更多
The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants,...The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method(IIDM) using an intrinsic mode function(IMF)feature-based grey wolf optimized artificial neural network(GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.展开更多
文摘目的目标检测在智能交通、自动驾驶以及安防监控中均有重要的地位,Vi Be算法是常用的运动目标检测算法,它主要由背景模型初始化、前景检测、背景模型更新3部分组成,其思想简单,易于实现,运算效率高,但当初始帧有运动目标时,检测结果会出现"鬼影"现象,且易受噪声和光照变化影响,不能适应动态场景。同时,其逐帧逐像素进行前景检测,在计算复杂度方面有较大提升空间。为解决这些问题,提出一种改进的Vi Be算法,称为Vi Be Imp算法。方法在背景模型初始化时,用多帧平均法给出初始背景,采用该初始背景构建初始背景样本模型。在前景检测过程中,采用背景差分法、帧差法与OTSU算法相结合给出半径阈值的自适应计算方法。同时,根据背景差分法找出运动区域,只对运动区域进行前景判断和模型更新,降低算法的计算复杂度。结果对25个不同场景视频分别给出Vi Be Imp算法在初始化背景,自适应半径阈值和计算复杂度方面改进的结果及有效性指标,实验结果表明,与Vi Be、Vi Be Diff2、Vi Be IniR,以及Surendra等算法和高斯混合模型相比,Vi Be Imp算法对噪声、光照和背景动态变化有较好的鲁棒性,检测结果更完整,且实时性较好。同时,Vi Be Imp算法将Vi Be算法的查准率、查全率以及F1值分别提高了17. 98%、11. 40%和15. 96%。结论 Vi Be Imp算法采用多帧平均法构建初始背景可有效地消除"鬼影",并给出半径阈值的自适应计算方法,使Vi Be算法更快适应视频环境变化,准确且完整地检测出运动目标,具有较低的误检率和漏检率。该方法克服了Vi Be算法对初始背景以及视频环境的依赖,很大程度上提高了运算速度,具有很好的鲁棒性和适用性。
基金supported by the National Research Foundation (NRF) of South Korea funded by the Ministry of Science, ICT & Future Planning (MSIP) of the Korean government (No.2018R1A2A1A05078680)。
文摘The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method(IIDM) using an intrinsic mode function(IMF)feature-based grey wolf optimized artificial neural network(GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.
