Graphene nanowalls (GNWs) with different sizes (i.e., length and height) were grown directly on the surface of individual carbon fibers (CFs) using a radio frequency plasma-enhanced chemical vapor deposition (RF-PECVD) technique. The size was controlled by varying the deposition time. The GNW-modified CFs were embedded into epoxy resin matrix to prepare a series of carbon-fiber-reinforced composites (CFRCs). The results indicated that GNWs were remarkably effective in improving the interfacial shear strength (IFSS) and interlaminar shear strength (ILSS) of the carbon-fiber-reinforced composites. The enhancement effect on the strength strongly depended on the size of GNWs. It increased with the increase in the GNWs’ size and reached the maximum upon the incorporation of GNWs that were grown for 45 min. Noticeable increases of 222.8% and 41.1% were observed in IFSS and ILSS, respectively. The enhancement mechanism was revealed by means of scanning electron microscope (SEM) fractography analysis. However, further increase of GNW size led to no more improvement in the shear strength. It could result from the increased defect concentration and wrinkle size in the GNWs, which deteriorated the strength.

Automatic License plate recognition (ALPR) remains a challenging task in face of some difficulties such as multi-line character distribution and license plate (LP) deformation due to camera angles. Most existing ALPR methods either focus on single-line LP or perform horizontal multi-line LP detection and recognition with character-level annotations. In this paper, we propose a novel end-to-end irregular license plate recognition (EILPR) to detect and recognize the LP of multi-line text or arbitrary shooting angles, using only plate-level annotations for training. In EILPR, a coarse-to-fine strategy is adopted to extract the LP features accurately for sequence recognition. Firstly, a coarse rectangular box of the LP is located, along with the corresponding predicted LP class which is single-line or double-line. Then, considering the fact that a LP mainly generates perspective distortion in the image due to its rigid feature, we propose a new automatic perspective alignment network (APAN) to extract the fine LP features connecting the detection and recognition. For recognition, a location-aware 2D attention based recognition network is performed to recognize the multi-line and multinational LP based on the extracted features. Experiments on several datasets show that EILPR achieves the state-of-the-art performance, demonstrating the effectiveness of the proposed method.

Deep learning models are essential tools for mid- to long-term runoff prediction. However, the influence of the input time lag and output lead time on the prediction results in deep learning models has been less studied. Based on 290 schemas, this study specified different time lags by sliding windows and predicted the runoff process by RNN (Recurrent Neural Network), LSTM (Long–short-term Memory), and GRU (Gated Recurrent Unit) models at five hydrological stations in the upper Yangtze River during 1980–2018 at daily, ten-day, and monthly scales. Different models have different optimal time lags; therefore, multiple time lags were analyzed in this paper to find out the relationship between the time intervals and the accuracy of different river runoff predictions. The results show that the optimal time-lag settings for the RNN, LSTM, and GRU models in the daily, ten-day, and monthly scales were 7 days, 24 ten days, 27 ten days, 24 ten days, 24 months, 27 months, and 21 months, respectively. Furthermore, with the increase of time lags, the simulation accuracy would stabilize after a specific time lag at multiple time scales of runoff prediction. Increased lead time was linearly related to decreased NSE at daily and ten-day runoff prediction. However, there was no significant linear relationship between NSE and lead time at monthly runoff prediction. Choosing the smallest lead time could have the best prediction results at different time scales. Further, the RMSE of the three models revealed that RNN was inferior to LSTM and GRU in runoff prediction. In addition, RNN, LSTM, and GRU models could not accurately predict extreme runoff events at different time scales. This study highlights the influence of time-lag setting and lead-time selection in the mid- to long-term runoff prediction results for the upper Yangtze River basin. It is recommended that researchers should evaluate the effect of time lag before using deep learning models for runoff prediction, and to obtain the best prediction, the shortest lead-time length can be chosen as the best output for different time scales.

Object detection is one of the core technologies in aerial image processing and analysis. Although existing aerial image object detection methods based on deep learning have made some progress, there are still some problems remained: (1) Most existing methods fail to simultaneously consider multi-scale and multi-shape object characteristics in aerial images, which may lead to some missing or false detections; (2) high precision detection generally requires a large and complex network structure, which usually makes it difficult to achieve the high detection efficiency and deploy the network on resource-constrained devices for practical applications. To solve these problems, we propose a slimmer network for more efficient object detection in aerial images. Firstly, we design a polymorphic module (PM) for simultaneously learning the multi-scale and multi-shape object features, so as to better detect the hugely different objects in aerial images. Then, we design a group attention module (GAM) for better utilizing the diversiform concatenation features in the network. By designing multiple detection headers with adaptive anchors and the above-mentioned two modules, we propose a one-stage network called PG-YOLO for realizing the higher detection accuracy. Based on the proposed network, we further propose a more efficient channel pruning method, which can slim the network parameters from 63.7 million (M) to 3.3M that decreases the parameter size by 94.8%, so it can significantly improve the detection efficiency for real-time detection. Finally, we execute the comparative experiments on three public aerial datasets, and the experimental results show that the proposed method outperforms the state-of-the-art methods.