A novel three-dimensional MnO2 catalyst have been successfully prepared by a facile hydrothermal route. They were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). In addition, they showed excellent catalytic activity over the aqueous degradation of methylene blue (MB).
With the development of convolutional neural networks, aiming at the problem of low efficiency and low accuracy in the process of wood species recognition, a recognition method using an improved convolutional neural network is proposed in this article. First, a large-scale wood dataset was constructed based on the WOOD-AUTH dataset and the data collected. Then, a new model named W_IMCNN was constructed based on Inception and mobilenetV3 networks for wood species identification. Experimental results showed that compared with other models, the proposed model had better recognition performance, such as shorter training time and higher recognition accuracy. In the data set constructed by us, the accuracy of the test set reaches 96.4%. We used WOOD-AUTH dataset to evaluate the model, and the recognition accuracy reached 98.8%. Compared with state-of-the-art methods, the effectiveness of the W_IMCNN were confirmed.
Many restaurants have a certain amount of food waste. The monitoring of food waste will help restaurants to eliminate some dishes with outrageous waste and reduce waste from the source. In view of this, this research proposed a method to detect waste of dishes through image processing and deep learning technology. According to the remaining quantity of the dishes, the collected dish images were preliminarily divided into six levels, which were used as sample labels, and then the image of the uneaten dishes and the image of the dishes after eating were stacked as the input of the network. Trained in the InceptionV3, Xception, and ResNet18 network models, we find that compared with the single image data as the input, the effect of stacking the two images data as the input was better. The accuracy of sample label recognition increased by 6.97%, 5.81%, and 4.1% respectively. Further analysis discovered that the sample that predicted wrong on the test set data, its true label, and the predicted wrong label were adjacent. Therefore, with the help of the probability vector output by the trained network model, the definition method of the level of dish waste degree and its corresponding accuracy metric standard was further given. Finally, the recognition accuracy of the best network structure InceptionV3 on the test set data can reach 98.47%.
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