Search

Dear Editor, This letter is concerned with wood crack detection which is important to guarantee the quality of wooden products. In the wood industry, the crack detection is one of the most challenging tasks in the wood defects detection, since the detection accuracy may be reduced due to the stains on the boards, the tiny cracks, and some cracks that are similar to the sound region. To overcome these challenges, we propose a data-driven semantic segmentation network based on U-Net, which is called WCU-Net, for wood crack detection. Specifically, a position attention mechanism is firstly proposed to exaggerate the wood crack positions, then the feature enhancement mechanism is designed to selectively derive more diluted information of tiny crack. Moreover, a residual block is adopted to obtain and fuse multi-scale receptive fields for finding more crack areas that are similar to the sound region. The experimental results show that WCU-Net can improve the accuracy of wood crack detection.

Wireless sensor networks (WSNs) are energyconstrained, so energy saving is one of the most important issues in typical applications. The clustered WSN topology is considered in this paper. To achieve the balance of energy consumption and utility of network resources, we explicitly model and factor the effect of power and rate. A novel joint optimization model is proposed with the protection for cluster head. By the mean of a choice of two appropriate sub-utility functions, the distributed iterative algorithm is obtained. The convergence of the proposed iterative algorithm is proved analytically. We consider general dual decomposition method to realize variable separation and distributed computation, which is practical in large-scale sensor networks. Numerical results show that the proposed joint optimal algorithm converges to the optimal power allocation and rate transmission, and validate the performance in terms of prolonging of network lifetime and improvement of throughput.