Multiple submerged breakwaters (MSBWs) are commonly used coastal protection structures due to their specific advantages over the emerged ones. Rip currents, as the inevitable natural hazard in the gaps of these constructions, are investigated numerically in the present study. A fully nonlinear mild-slope equation (NMSE) model possessing both fully nonlinear and fully dispersive properties is validated and adopted in the simulations. With four monochromatic wave conditions of different wave heights, periods and incidences representing low-energy, typical, storm and oblique waves tested, the flow patterns and the low-frequency oscillations of the rip currents are studied. For the convenience of risk assessment, the rip risk level is divided into three degrees according to the maximum rip flow speed. The effects of the configurations of the MSBWs on the rip current system as well as the rip risk level are examined, considering different breakwater widths, heights, forms, gap widths and gap numbers. Simulation results suggest that the cross-shore configurations of MSBWs influence the rip risk level by inducing different wave energy dissipations but the longshore configurations of MSBWs by changing flow field patterns.

With the rapid development of virtual reality (VR) technology, this paper proposes an access point (AP) correlation method based on VR user behavior awareness to address the problem of how current AP correlation methods only focus on the performance improvements of ordinary users and ignore the impact of VR user behavior on service quality. This paper analyzes the AP association method under the coverage scenario of a multi-access point (multi-AP) scenario environment and controls the performance improvement of VR user APs or APs under the access controller (AC) by association. Firstly, the VR network application scenario and system model were constructed, and secondly, the user behavior was sensed by analyzing the viewing habits of users. Then, the VR user association problem based on VR user behavior perception was transformed into a “many-to-many” matching problem between VR user devices and APs, and the generalized multidimensional multiple choice knapsack (GMMKP) model was established to solve the problem using the backpack problem theory; the suboptimal solution algorithm was selected to obtain the best VR user AP association strategy. The experimental results show by simulation that the proposed algorithm in this paper performed better in terms of the AP load balancing and average network download latency compared to the comparison algorithms.

In industrial vision, the lack of defect samples is one of the key constraints of depth vision quality inspection. This paper mainly studies defect detection under a small training set, trying to reduce the dependence of the model on defect samples by using normal samples. Therefore, we propose a Knowledge-Embedding Relational Network. We propose a Knowledge-Embedding Relational Network (KRN): firstly, unsupervised clustering and convolution features are used to model the knowledge of normal samples; at the same time, based on CNN feature extraction assisted by image segmentation, the conv feature is obtained from the backbone network; then, we build the relationship between knowledge and prediction samples through covariance, embed the knowledge, further mine the correlation using gram operation, normalize the power of the high-order features obtained by covariance, and finally send them to the prediction network. Our KRN has three attractive characteristics: (I) Knowledge Modeling uses the unsupervised clustering algorithm to statistically model the standard samples so as to reduce the dependence of the model on defect data. (II) Covariance-based Knowledge Embedding and the Gram Operation capture the second-order statistics of knowledge features and predicted image features to deeply mine the robust correlation. (III) Power Normalizing suppresses the burstiness of covariance module learning and the complexity of the feature space. KRN outperformed several advanced baselines in small training sets on the DAGM 2007, KSDD, and Steel datasets.