• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.06b
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• pp.87-90
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• 2006

The Earth's spherical harmonic model of the main field and of the secular variation, of the geomagnetic field gives the intensity and geomagnetic structure at any location around the earth, assuming an undistorted, steady state field that no external sources or localized earth anamalies. To consider the practical use of a ship's digital compass in earth's magnetic field, Earth's spherical harmonic model is searched for the related practical methods and procedures as a basic study in this work.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.1
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• pp.59-68
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• 2002

The cylindrical Upper Guide Structure assembly of the reactor intervals wish the Core Support Barrel and the Inner Barrel Assembly is subjected to flow induced loads horizontally which include random pressure fluctuation due to turbulent flow and pump pulsation pressures. The purpose of this papers is to perform random vibration and harmonic response analyses fort flow induced loads. The dynamic response characteristics due to random turbulence and pump pulsation loads were evaluated using the lumped mass beam model. Especially the model considered the annulus effects due to water gaps existing between cylindrical structures such as the Upper Guide Structure Barrel, the Core Support Barrel, and the Inner Barrel Assembly. The effect of the Inner Barrel Assembly inside the Upper Guide Structure assembly was studied. The peak dynamic responses lot each loading condition due to the addition of IBA were affected by the natural frequencies of the structures. Therefore the peak dynamic responses of the structures should be conservatively obtained from evaluation of dynamic analysis for various loading conditions.

• 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.