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Steel surface defect detection is an important issue when producing high-quality steel materials. Traditional defect detection methods are time-consuming and uneconomical and require manually designed prior information or extra supervisors. Surface defects have different representations and features at different scales, which make it challenging to automatically detect the locations and defect types. This paper proposes a real-time steel surface defect detection technology based on the YOLO-v5 detection network. In order to effectively explore the multi-scale information of the surface defect, a multi-scale explore block is especially developed in the detection network to improve the detection performance. Furthermore, the spatial attention mechanism is also developed to focus more on the defect information. Experimental results show that the proposed network can accurately detect steel surface defects with approximately 72% mAP and satisfies the real-time speed requirement.

Image super-resolution (SR), as one of the classic image processing issues, has attracted increasing attention from researchers. As a highly ill-conditioned, non-convex optimization issue, it is difficult for image SR to restore a high-resolution (HR) image from a given low-resolution (LR) instance. Recent researchers have tended to regard image SR as a regression task and to design an end-to-end convolutional neural network (CNN) to predict the pixels directly, which lacks inherent theoretical analysis and limits the effectiveness of the restoration. In this paper, we analyze image SR from an optimization perspective and develop a deep successive convex approximation network (SCANet) for generating HR images. Specifically, we divide non-convex optimization into several convex LASSO sub-problems and use CNN to adaptively learn the parameters. To boost network representation, we use residual feature aggregation (RFA) blocks and devise a spatial and channel attention (SACA) mechanism to improve the restoration capacity. The experimental results show that the proposed SCANet can restore HR images more effectively than other works. Specifically, SCANet achieves higher PSNR/SSIM results and generates more satisfying textures.

With greater power density, a hybrid power source that combines supercapacitors and batteries has a wide range of applications in pulse-operated power systems. In this paper, a supercapacitor/battery semi-active hybrid energy storage system (HESS) with a full current-type control strategy is presented. The studied HESS is composed of batteries, supercapacitors, and a bidirectional buck–boost converter. The converter is controlled such that supercapacitors supply load power pulses, and batteries provide the power in steady state. To realize the fast compensation of the supercapacitors to the load power pulses, a power distribution module based on hysteresis control theory is designed in the control system. Moreover, the control strategy does not require the model parameters of the converter and supercapacitors, so the control system is simplified. A complete configuration scheme and cost analysis of the proposed HESS are also presented. Obtained results show that the proposed supercapacitor/battery semi-active HESS has good performance in terms of dynamic response, weight, and energy utilization coefficient (EUC).

To improve the low acceptance ratio and revenue to cost ratio caused by the poor match between the virtual nodes and the physical nodes in the existing virtual network embedding (VNE) algorithms, we established a multi-objective optimization integer linear programming model for the VNE problem, and proposed a novel two-stage virtual network embedding algorithm based on topology potential (VNE-TP). In the node embedding stage, the field theory once used for data clustering was introduced and a node embedding function designed to find the optimal physical node. In the link embedding stage, both the available bandwidth and hops of the candidate paths were considered, and a path embedding function designed to find the optimal path. Extensive simulation results show that the proposed algorithm outperforms other existing algorithms in terms of acceptance ratio and revenue to cost ratio.