This study focused on the durability of basalt fibers, glass fibers, basalt‐fiber‐reinforced polymer (BFRP) bars, and glass‐fiber‐reinforced polymer (GFRP) bars in an artificial seawater. The specimens were immersed in the artificial seawater at 20, 40, and 60°C; the specimen immersed in distilled water at a temperature of 60°C was also used as a reference. The tensile strength of the monofilament basalt fibers and glass fibers immersed in the seawater for 90 days at a temperature of 60°C decreases by 61% and 59% respectively, showing the most significant degradation; the brittleness of the fibers also increases due to the decomposition of their sizing agent. The chloride ions in the seawater are beneficial to enhancing the resistance of the composites to moisture absorption. In addition, the tensile strength retention of the fiber‐reinforced polymer (FRP) bars is higher than that of the fibers due to the protection by the epoxy resin. The mechanical properties of the FRP bars immersed in the seawater improve after removing their moisture. The mechanical performance of the BFRP bars is inferior to that of the GFRP bars due to their higher water absorption and weak bonding between the basalt fibers and the polymer matrix.
In the real world, the degradation of images taken under haze can be quite complex, where the spatial distribution of haze is varied from image to image. Recent methods adopt deep neural networks to recover clean scenes from hazy images directly. However, due to the paradox caused by the variation of real captured haze and the fixed degradation parameters of the current networks, the generalization ability of recent dehazing methods on real-world hazy images is not ideal. To address the problem of modeling real-world haze degradation, we propose to solve this problem by perceiving and modeling density for uneven haze distribution.We propose a novel Separable Hybrid Attention (SHA) module to encode haze density by capturing features in the orthogonal directions to achieve this goal. Moreover, a density map is proposed to model the uneven distribution of the haze explicitly. The density map generates positional encoding in a semi-supervised way-such a haze density perceiving and modeling capture the unevenly distributed degeneration at the feature level effectively. Through a suitable combination of SHA and density map, we design a novel dehazing network architecture, which achieves a good complexity-performance trade-off.The extensive experiments on two large-scale datasets demonstrate that our method surpasses all state-of-the-art approaches by a large margin both quantitatively and qualitatively, boosting the best published PSNR metric from 28.53 dB to 33.49 dB on the Haze4k test dataset and from 37.17 dB to 38.41 dB on the SOTS indoor test dataset.
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