At present, facial expression recognition technology is widely used in artificial intelligence, transportation, medical and other aspects, so it has important research value. Traditional facial expression recognition uses manual feature extraction method with low accuracy and weak generalization ability, which is difficult to be applied in real life. With the development of deep learning, convolution neural network appears in people’s vision. Different from traditional manual feature extraction, convolution neural network can learn image features independently, and learn more features. In addition, it has the advantage of sharing the weight with the neural network. Although convolution neural network has multiple advantages, it also has some disadvantages, especially over fitting. In this paper, the model of convolution network is improved based on the classical VGGNet according to the working principle of convolution neural network. In this new model, the number of convolution kernels is reduced in parameters, and the global average pool layer is used to replace the full connection layer in the structure, so as to reduce the degree of over fitting and decrease the operation parameters. Finally, experiments show that the accuracy, generalization and consumption of resource are enhanced in the new model. It is proposed that the new method is better than the traditional convolution network VGGNet.

Pulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse disturbance signals appear as instantaneous, high-energy vertical-line pulse trains (VLPTs) on the spectrogram. This paper uses computer vision techniques and unsupervised clustering algorithms to process and analyze VLPT on very-low-frequency (VLF) waveform spectrograms collected by the China Seismo-Electromagnetic Satellite (CSES) electric field detector. First, the waveform data are transformed into time–frequency spectrograms with a duration of 8 s using the short-time Fourier transform. Then, the spectrograms are subjected to grayscale transformation, vertical line feature extraction, and binarization preprocessing. In the third step, the preprocessed data are dimensionally reduced and fed into an unsupervised K-means++ clustering model to achieve automatic recognition and labeling of VLPTs. By recognizing and studying VLPT, not only can interference be recognized, but the temporal and spatial locations of these interferences can also be determined. This lays the foundation for identifying VLPT sources and gaining deeper insights into the generation, propagation, and characteristics of electromagnetic radiation.

High-precision total electron content (TEC) prediction can improve the accuracy of the Global Navigation Satellite System (GNSS)-based applications. The existing deep learning models for TEC prediction mainly include long short-term memory (LSTM), convolutional long short-term memory (ConvLSTM), and their variants, which contain only one temporal memory. These models may result in fuzzy prediction results due to neglecting spatial memory, as spatial memory is crucial for capturing the correlations of TEC within the TEC neighborhood. In this paper, we draw inspiration from the predictive recurrent neural network (PredRNN), which has dual memory states to construct a TEC prediction model named Multichannel ED-PredRNN. The highlights of our work include the following: (1) for the first time, a dual memory mechanism was utilized in TEC prediction, which can more fully capture the temporal and spatial features; (2) we modified the n vs. n structure of original PredRNN to an encoder–decoder structure, so as to handle the problem of unequal input and output lengths in TEC prediction; and (3) we expanded the feature channels by extending the Kp, Dst, and F10.7 to the same spatiotemporal resolution as global TEC maps, overlaying them together to form multichannel features, so as to fully utilize the influence of solar and geomagnetic activities on TEC. The proposed Multichannel ED-PredRNN was compared with COPG, ConvLSTM, and convolutional gated recurrent unit (ConvGRU) from multiple perspectives on a data set of 6 years, including comparisons at different solar activities, time periods, latitude regions, single stations, and geomagnetic storm periods. The results show that in almost all cases, the proposed Multichannel ED-PredRNN outperforms the three comparative models, indicating that it can more fully utilize temporal and spatial features to improve the accuracy of TEC prediction.

The ionospheric total electron content (TEC) has complex spatiotemporal variations, making its spatiotemporal prediction challenging. Capturing long-range spatial dependencies is of great significance for improving the spatiotemporal prediction accuracy of TEC. Existing work based on Convolutional Long Short-Term Memory (ConvLSTM) primarily relies on convolutional operations for spatial feature extraction, which are effective at capturing local spatial correlations, but struggle to model long-range dependencies, limiting their predictive performance. Self-Attention Convolutional Long Short-Term Memory (SA-ConvLSTM) can selectively store and focus on long-range spatial dependencies, but it requires the input length and output length to be the same due to its “n vs. n” structure, limiting its application. To solve this problem, this paper proposes an encoder-decoder SA-ConvLSTM, abbreviated as ED-SA-ConvLSTM. It can effectively capture long-range spatial dependencies using SA-ConvLSTM and achieve unequal input-output lengths through encoder–decoder structure. To verify its performance, the proposed ED-SA-ConvLSTM was compared with C1PG, ConvLSTM, and PredRNN from multiple perspectives in the area of 12.5° S–87.5° N, 25° E–180° E, including overall quantitative comparison, comparison across different months, comparison at different latitude regions, visual comparisons, and comparison under extreme situations. The results have shown that, in the vast majority of cases, the proposed ED-SA-ConvLSTM outperforms the comparative models.

The electric field detector of the CSES satellite has captured a vast number of lightning whistler events. To recognize them effectively from the massive amount of electric field detector data, a recognition algorithm based on speech technology has attracted attention. However, this approach has failed to recognize the lightning whistler events which are contaminated by other low-frequency electromagnetic disturbances. To overcome this limitation, we apply the single-channel blind source separation method and audio recognition approach to develop a novel model, which consists of two stages. (1) The training stage: Firstly, we preprocess the electric field detector wave data into the audio fragment. Then, for each audio fragment, mel-frequency cepstral coefficients are extracted and input into the long short-term memory network for training the novel lightning whistler recognition model. (2) The inference stage: Firstly, we process each audio fragment with the single-channel blind source to generate two different sub-signals. Then, for each sub-signal, the mel-frequency cepstral coefficient features are extracted and input into the lightning whistler recognition model to recognize the lightning whistler. Finally, the two results above are processed by decision fusion to obtain the final recognition result. Experimental results based on the electric field detector data of the CSES satellite demonstrate the effectiveness of the algorithm. Compared with classical methods, the accuracy, recall, and F1-score of this algorithm can be increased by 17%, 62.2%, and 50%, respectively. However, the time cost only increases by 0.41 s.

The manufacturing process of ceramic materials and exquisite patterns is not simply artistic acts. At the same time, it condensed the excellent traditional culture of ancient China. The application of ceramic materials in modern furniture can not only enhance the taste of living room culture but also show the charm of Chinas ceramic culture. Objective. To optimize the scientific data model required by traditional ceramic materials. On the basis of image difference prediction calculation, the data of ceramic material color matching collection points were analyzed and processed, and the image difference prediction model was established. This paper introduces the application and development of ceramic materials in furniture industry. On the basis of the traditional image difference prediction algorithm, the consideration of image difference and difference prediction expansion is added, and the image difference prediction calculation model of ceramic material color matching is established. The survey results show that 83 of customers are very satisfied with the color matching of new ceramic materials calculated by image difference prediction. This experiment uses image difference prediction model to remove the redundant pixels of ceramic material color and make multiple judgments on color display. According to the initial color data of the collection points, this experiment finally obtained a new combination color matching of ceramic wardrobe, which is more suitable for the minimalist and quiet styles.