The combination of features from the convolutional layer and the fully connected layer of a convolutional neural network (CNN) provides an effective way to improve the performance of crime scene investigation (CSI) image classification. However, in existing work, as the weights in feature fusion do not change after the training phase, it may produce inaccurate image features which affect classification results. To solve this problem, this paper proposes an adaptive feature fusion method based on an auto-encoder to improve classification accuracy. The method includes the following steps: Firstly, the CNN model is trained by transfer learning. Next, the features of the convolution layer and the fully connected layer are extracted respectively. These extracted features are then passed into the autoencoder for further learning with Softmax normalisation to obtain the adaptive weights for performing final classification. Experiments demonstrated that the proposed method achieves higher CSI image classification performance compared with fix weights feature fusion.
In planetary science, it is an important basic work to recognize and classify the features of topography and geomorphology from the massive data of planetary remote sensing.Therefore, this paper proposes a lightweight model based on VGG-16, which can selectively extract some features of remote sensing images, remove redundant information, and recognize and classify remote sensing images. This model not only ensures the accuracy, but also reduces the parameters of the model.According to our experimental results, our model has a great improvement in remote sensing image classification, from the original accuracy of 85% to 98% now. At the same time, the model has a great improvement in convergence speed and classification performance.By inputting the remote sensing image data of ultralow pixels (64 * 64) into our model, we prove that our model still has a high accuracy rate of 95% for the remote sensing image with ultra-low pixels and less feature points.Therefore, the model has a good application prospect in remote sensing image fine classification, very low pixel, less image classification.
Pentafiuoroethane (HFC-125) and trifluoroiodomethane (CF3I) are considered as promising refrigerant alternatives, especially as components in mixtures, to replace CFCs or HCFCs. Effective uses of HFC-125 and CF3I require that the therrnophysical properties be accurately measured. In the present work, vapor pressure data of HFC-125 and CF3I have been measured in the temperature range from 292 to 337 K and 288 to 336 K, respectively. Maximum total pressure uncertainty of HFC-125 data is estimated to be within ±1.2 kPa and ±780 Pa for CF3I. Based on the data set and literature values, the vapor pressure equations for HFC-125 and CF3I have been developed. The relative deviation of the equations correlate the measurements within 0.022% for HFC-125 and 0.068% for CF3I, respectively.
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