基于MLP&ST模型的空气质量预测

针对目前大多数模型均为对已监测区域的空气质量进行预测，而少有对未监测区域进行预测的问题，综合考虑气象因素、空间相关性和时间依赖性对空气质量的影响，提出了一种联合训练模型MLP&ST （MLP&spatial-temporal），模拟预测北京市未监测区域未来时刻的空气质量指数（air quality index，AQI）。通过实验结果对比确定最佳历史时间步长P值为29；然后将模型与其他空气质量预测模型进行对比。实验结果表明，MLP&ST模型在3种评价指标下（RMSE，MAE，MAPE）皆优于其他对比模型，验证了所提出模型具有良好的预测效果。

Data Augmentation Method Based on Image Gradient

As used in classification of image recognition, convolutional neural network requires large-scale image data set for training. Due to the limitation of the number of target images to be collected and the conditions of image acquisition equipment, it is difficult to obtain enough image samples by conventional methods because of time-consuming, laborconsuming and money-consuming. In order to solve the insufficiency of image samples, a variety of sample enlargement methods have been proposed. This paper introduces the research background and significance of data augmentation. For the purpose of improving the accuracy of image recognition of convolutional neural network, a data augmentation method based on image gradient is proposed. The image gradient is selected to increase image sample and enlarge image data set by precise clipping method, and the convolutional neural network is trained with the expanded data set. By using Tensorflow deep learning framework and VGG16 network model, and selecting some data sets of PlantVillage, the training set data can be expanded to 6 times of the original. The training set before and after the expansion is trained and compared. Experimental results show that the accuracy rate of the model trained by the training set after data augmentation is increased by 4.18%.