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Prediction of HSV color model parameter values of cloud movement picture based on artificial neural networks

机译:基于人工神经网络的云运动图片HSV颜色模型参数值预测

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In order to predict the exact moment of Sun shading by clouds and Sun cover duration to optimize the energy flow in the microgrid with solar photo electric system, it is essential to transform cloud images from RGB color model into HSV color model to be able to precisely detect cloud edges and determine the position of centroids for prediction of cloud movements. Parameters that define the quality of the image depend on the range of values for Hue, Saturation and Value (HSV) components. The dynamics of clouds and changing their shapes, sizes and colors require constant adjustments of those parameters by a human to get the best results. This paper deals with prediction and automatic setting of the HSV parameters by using artificial neural network and supervised learning. The image processing and parameters prediction was performed by an application developed in Java programming language based on JavaCV library and Encog framework for implementation of the artificial neural network.
机译:为了预测太阳被云遮挡的确切时刻和太阳覆盖持续时间,以优化具有太阳能光电系统的微电网中的能量流,必须将云图像从RGB颜色模型转换为HSV颜色模型,以便能够精确地检测云的边缘并确定质心的位置以预测云的运动。定义图像质量的参数取决于色相,饱和度和值(HSV)分量的值范围。云的动态变化及其形状,大小和颜色的变化需要人类不断调整这些参数才能获得最佳效果。本文利用人工神经网络和监督学习对HSV参数进行预测和自动设置。图像处理和参数预测由基于JavaCV库和Encog框架的Java编程语言开发的应用程序执行,以实现人工神经网络。

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