• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.565-571
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• 2023

With the introduction of the post-verification system, the measurement of floor impact noise performance on-site has become mandatory, and the evaluation method has changed. To track the performance changes since the policy implementation, research is needed on how the characteristics of heavyweight impact sound change according to the varied evaluation method. In this study, we analyzed the contribution rate of the frequency band-specific sound pressure level on the single-number quantity for a multi-family housing unit with the same floor plan and floor structure, comprising 59 households, based on the changed impact sources and evaluation indicators. It is difficult to compare simply because the method of calculating contributions by frequency band according to the single-day evaluation is different, but the average contribution rate of 63 Hz was 80.8 % in the evaluation method before the introduction of the post-confirmation system (Tire measurement and evaluated as L'_i,Fmax,AW), and the average contribution rate of 125 Hz was 19.2 %. The current evaluation method (rubber ball measurement and evaluation as L'_iA,Fmax) shows that the contribution rate has decreased to 33.1 % on average at 50 Hz ~ 80 Hz, 58.7 % on average at 100 Hz ~ 160 Hz, 6.9 % on average at 200 Hz ~ 315 Hz, and 1.3 % on average at 400 Hz ~ 630 Hz. This result is a case analysis for the target apartment house, and it is necessary to analyze measurement data for more diverse apartment houses.