A novel method for disturbance detection, localization and pattern recognition in signal images

H. G. Zadeh, Siamak Janianpour, J. Haddadnia
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Abstract

A new approach to pattern recognition and classification of non-stationary power signal is presented in this paper. In the proposed work visual localization and detection of non-stationary power signals are achieved using Image processing and its pattern is recognized and classified by MLP neural network algorithm. Also disturbance localization and classification of the signal, is done once with image processing and one more time with neural network and the results are compared with each other. In MLP neural network method we tried to reduce the disturbance localization errors of image processing method. Various non-stationary power signals are processed through image processing stage to generate property charts of the signal for extracting relevant features for pattern classification. The extracted features are clustered using MLP Neural Network algorithm to refine the cluster centers.
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一种新的信号图像干扰检测、定位和模式识别方法
提出了一种新的非平稳电力信号的模式识别与分类方法。该方法利用图像处理技术实现非平稳电力信号的视觉定位和检测,并利用MLP神经网络算法对其模式进行识别和分类。利用图像处理和神经网络分别对信号进行了扰动定位和分类,并对结果进行了比较。在MLP神经网络方法中,我们试图减小图像处理方法的干扰定位误差。通过图像处理阶段对各种非平稳电力信号进行处理,生成信号的属性图,提取相关特征进行模式分类。利用MLP神经网络算法对提取的特征进行聚类,以细化聚类中心。
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