A wide range of research problems in physics and engineering involve the acquisition of high-resolution data. Recently, deep learning has proved to be a prospective technique for super-resolution (SR) reconstruction of fluid flows. General deep learning methods develop temporal multi-branch networks to improve SR accuracy while ignoring computational efficiency. Further, the generalization ability of the deep learning model in different fluid flow scenarios is still an unstudied issue. In this article, we propose an efficient multi-scale integration network called FlowSRNet to reconstruct the high-resolution flow fields. Specifically, we elaborately design a lightweight multi-scale aggregation block (LMAB) to capture multi-scale features of fluid data, which contains a parallel cascading architecture (PCA) and feature aggregation module (FAM). The residual backbone of FlowSRNet is built by cascading the lightweight multi-scale aggregation blocks (LMABs) (cascaded blocks number N=8) in a serial manner. Also, we present a small architecture LiteFlowSRNet (cascaded blocks number N=2) for comparison. In addition, a corresponding SR dataset is constructed to train and test the proposed model, which contains different kinds of fluid flows. Finally, extensive experiments are performed on different fluid data to evaluate the performance of the proposed model. The results demonstrate that our approach achieve state-of-the-art SR performance on various fluid flow fields. Notably, our method enjoys merit of lightweight, which facilitates the development of the complicated calculation in computational fluid dynamics.

WiFi localization based on channel state information (CSI) fingerprints has become the mainstream method for indoor positioning due to the widespread deployment of WiFi networks, in which fingerprint database building is critical. However, issues, such as insufficient samples or missing data in the collection fingerprint database, result in unbalanced training data for the localization system during the construction of the CSI fingerprint database. To address the above issue, we propose a deep learning-based oversampling method, called Self-Attention Synthetic Minority Oversampling Technique (SASMOTE), for complementing the fingerprint database to improve localization accuracy. Specifically, a novel self-attention encoder-decoder is firstly designed to compress the original data dimensionality and extract rich features. The synthetic minority oversampling technique (SMOTE) is adopted to oversample minority class data to achieve data balance. In addition, we also construct the corresponding CSI fingerprinting dataset to train the model. Finally, extensive experiments are performed on different data to verify the performance of the proposed method. The results show that our SASMOTE method can effectively solve the data imbalance problem. Meanwhile, the improved location model, 1D-MobileNet, is tested on the balanced fingerprint database to further verify the excellent performance of our proposed methods.

To address the problem that the direction of pipe cracks is difficult to detect, a crack direction recognition method based on prototype learning is proposed with a prototype network as a framework. First, through the convolutional layer of the prototype network, shallow features of crack directions are extracted to improve the generalization ability of the model on the data set of this paper. then, by improving the High-Resolution Network and introducing a location self-attention mechanism, and combined with a migration training method for the data set of this paper, a category that can accurately reflect the crack directions is constructed prototype learning mechanism. Finally, pattern recognition is performed by the metric classification methods, the effective classification of crack direction under small sample condition is achieved. The experimental results show that the recognition accuracy of the crack direction recognition method based on prototype learning can reach 99.2% with the sample parameters unchanged.