Facial paralysis refers to a facial nerve disordering, with which people may lose the abilities to accurately control their facial muscles for certain facial performances. The diagnosis of such disordering is mainly based on the observation of patient's face in terms of the facial spatial information, such as facial asymmetry. Up to now, this area is still dominated by therapists' subjective examinations clinically. Therefore, automations for this task receive wide attentions in both academic and industrial fields. Recently, the deep learning based methods, the convolutional neural networks (CNNs) more specifically, demonstrate their competitive performance compared with traditional approaches in many areas. However, due to the lack of the structured/labelled facial paralysis data as training data, those deep learning based solutions are still not able to fully attach their superiorities to the facial paralysis evaluation tasks. Another essential aspect for automation in facial paralysis analysis is the facial spatial information extraction. Semantic segmentation is a better choice than traditional template-based facial landmark detection for analysing facial paralysis images, which contain faces in uncommon patterns. However, most existing semantic segmentation approaches are made for indoor or outdoor scene parsing. To this end, this paper presents a deep learning-based approach for automatic facial paralysis grading prediction. The proposed model utilizes a cascaded encoder structure, which explores the advantages of the facial semantic feature for facial spatial information extraction, and then benefits the facial paralysis assessment. A dual-stage cascaded training process is adopted to utilize a mixture of normal and paralysed faces as training data, which exports a well-trained deep neural network model for facial paralysis evaluation. Experiments are conducted in two aspects to demonstrate the performance of each components of the proposed model. Encouraging results are illustrated compared with several existing approaches in the related areas.

Significant wave height (SWH) prediction plays an important role in marine engineering areas such as fishery, exploration, power generation, and ocean transportation. For long-term forecasting of a specific location, classical numerical model wave height forecasting methods often require detailed climatic data and incur considerable calculation costs, which are often impractical in emergencies. In addition, how to capture and use the dynamic correlation between multiple variables is also a major research challenge for multivariate SWH prediction. To explore a new method for predicting SWH, this paper proposes a deep neural network model for multivariate time series SWH prediction—namely, CLTS-Net. In this study, the sea surface wind and wave height in the ERA5 dataset of the relevant points P1, P2, and P3 from 2011 to 2018 were used as input information to train the model and evaluate the model’s SWH prediction performance. The results show that the correlation coefficients (R) of CLTS-Net are 0.99 and 0.99, respectively, in the 24 h and 48 h SWH forecasts at point P1 along the coast. Compared with the current mainstream artificial intelligence-based SWH solutions, it is much higher than ANN (0.79, 0.70), RNN (0.82, 0.83), LSTM (0.93, 0.91), and Bi-LSTM (0.95, 0.94). Point P3 is located in the deep sea. In the 24 h and 48 h SWH forecasts, the R of CLTS-Net is 0.97 and 0.98, respectively, which are much higher than ANN (0.71, 0.72), RNN (0.85, 0.78), LSTM (0.85, 0.78), and Bi-LSTM (0.93, 0.93). Especially in the 72 h SWH forecast, when other methods have too large errors and have lost their practical application value, the R of CLTS-Net at P1, P2, and P3 can still reach 0.81, 0.71, and 0.98. The results also show that CLTS-Net can capture the short-term and long-term dependencies of data, so as to accurately predict long-term SWH, and has wide applicability in different sea areas.

Understanding the distribution and development patterns of mining-induced fissures is crucial for environmental protection and geological hazard prevention. To address labor-intensive manual inspection, an automated approach leveraging Convolutional Neural Networks (CNNs) and Unmanned Aerial System Photogrammetry (UASP) is proposed for fissure identification and mapping. Initially, the ResNet-50 network was employed for the binary classification of the cropped UASP orthophoto images. A comparative analysis was conducted to determine the optimal model between DeepLabv3+ and U-Net. Subsequently, the identified fissures were mosaicked and spatially projected onto the original orthophoto image, incorporating precise projection data, thereby furnishing a spatial reference for environmental governance. The results indicate a classification accuracy of 93% for the ResNet-50 model, with the U-Net model demonstrating a superior identification performance. Fissure orientation and distribution patterns are influenced by the mining direction, ground position of the mining workface, and topographic undulations. Enhancing the CNN performance can be achieved by incorporating variables such as slope indices, vegetation density, and mining workface locations. Lastly, a remote unmanned approach is proposed for the automated mapping of mining-induced fissures, integrated with UAS automated charging station technology. This study contributes to the advancement of intelligent, labor-saving, and unmanned management approaches advocated by the mining industry, with potential for broad applications in mining environmental protection efforts.

Based on the linear wave superposition model, we realize the numerical simulation of three-dimensional (3-D) surface waves combined with JONSWAP spectrum and stereo wave observation project (SWOP) directional function. According to the formation characteristics of freak waves to concentrate the wave energy at a specific location, the component waves are modulated. A complete numerical simulation model of time-invariant 3-D freak waves evolution is first proposed in this study. Then, the accuracy of the model is verified from the aspects of wave height distribution, frequency spectrum estimation, and freak wave parameters. The effectiveness of wave steepness as the discrimination condition of freak waves is discussed through experiments. In terms of the electromagnetic scattering characteristics of freak waves, we construct an electromagnetic scattering model, fitting the time-invariant 3-D freak wave, based on the two-scale method (TSM). By comparing and analysing the scattering characteristics D-value of synthetic aperture radar (SAR) image of the freak wave and the background wave, the rationality of the electromagnetic scattering characteristics of the freak wave as its feature identification is verified. Comparing the normalized radar cross section (NRCS) of freak waves and background sea waves, the experiment shows that the NRCS value of freak waves is the lowest, and the calculation results of the two have obvious differences. The research conclusions above can provide effective data support for the identification and detection of freak waves in practical offshore engineering.