• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.536-542
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• 2023

Satellites represent cutting-edge technology, of ering significant advantages in spatial and temporal observations. National agencies worldwide harness satellite data to respond to marine accidents and analyze ocean fluctuations effectively. However, challenges arise with high-resolution satellite-based sea surface temperature data (Operational Sea Surface Temperature and Sea Ice Analysis, OSTIA), where gaps or empty areas may occur due to satellite instrumentation, geographical errors, and cloud cover. These issues can take several hours to rectify. This study addressed the issue of missing OSTIA data by employing LaMa, the latest deep learning-based algorithm. We evaluated its performance by comparing it to three existing image processing techniques. The results of this evaluation, using the coefficient of determination (R²) and mean absolute error (MAE) values, demonstrated the superior performance of the LaMa algorithm. It consistently achieved R² values of 0.9 or higher and kept MAE values under 0.5 ℃ or less. This outperformed the traditional methods, including bilinear interpolation, bicubic interpolation, and DeepFill v1 techniques. We plan to evaluate the feasibility of integrating the LaMa technique into an operational satellite data provision system.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.70-78
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• 2023

The ordinary concentrically braced frame has an advantage of having simple design procedure. For this reason, it has been widely used for the small-sized frame structures subject to moderate or lower magnitude earthquake, even though its seismic performance against the earthquake load is not much effective compared to that of other frame systems. To enhance seismic performance of the ordinary concentrically braced frame where the bracing has a weakness for compressive behavior under lateral earthquake, seismic retrofitting by viscous damper has been commonly introduced. However, the viscous damper, itself, generally does not have stiffness for restoring the structure to the original position. This may cause residual displacement to the structure. In this paper, a self-centering viscous damper system in which upper and lower beams having flexural rigidity play a role as a nonlinear-elastic spring, restoring the spring-damper system subject to external displacement history to its original location, is developed. The numerical analysis for a simplified frame structure shows how including the developed self-centering viscous damper system leads to an enhanced seismic performance of the frame structure through energy dissipation during earthquake excitation.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.164-170
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• 2021

Research was conducted to analyze and improve the kinetic performance of offshore wind power generators. The shape used in this study was taken with reference to the previous paper, and the size of the repair area was designed at 80%, 60%, 40%, and 20%, respectively, and the exercise performance was confirmed accordingly. The sea state was calculated in Sea State 4, 5, and 6. In the calculation process, the calculation was performed using commercial computational hydrodynamics (ANSYS) and AQUA. In the case of overall exercise performance, it was confirmed that the smaller the size of the repair area, the smaller the exercise such as heave, roll, and pitch. However, it was confirmed that in the case of a shape in which the size of the repair area was rapidly reduced, there may be cases in which the restoration performance was not satisfied when the restoration calculation was performed. In addition, it was confirmed that there may be an appropriate repair surface depending on the sea condition.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.377-387
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• 2014

This study mainly treats a new type of the bracing friction damper system, which is able to minimize structural damage under earthquake loads. The slotted bolt holes are placed on the shear faying surfaces with an intention to dissipate considerable amount of friction energy. The superelastic shape memory alloy (SMA) wire strands are installed crossly between two plates for the purpose of enhancing recentering force that are able to reduce permanent deformation occurring at the friction damper system. The smart recentering friction damper system proposed in this study can be expected to reduce repair cost as compared to the conventional damper system because the proposed system mitigates the inter-story drift of the entire frame structure. The response mechanism of the proposed damper system is firstly investigated in this study, and then numerical analyses are performed on the component spring models calibrated to the experimental results. Based on the numerical analysis results, the seismic performance of the recentering friction damper system with respect to recentering capability and energy dissipation are investigated before suggesting optimal design methodology. Finally, nonlinear dynamic analyses are conducted by using the frame models designed with the proposed damper systems so as to verify superior performance to the existing damper systems.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.6 s.360
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• pp.1-7
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• 2007

This paper proposes CMOS semi-digital data recovery with 2X converse oversampling to reduce power consumption and chid area of high definition multimedia interface (HDMI) receivers. Proposed recovery can reduce its power and the effective area by using nt converse oversampling algorithm and semi-digital architecture. Proposed circuit is fabricated using 0.18um CMOS process and measured results demonstrated the power consumption of 14.4mW, the effective area of $0.152mm^2$ and the jitter tolerance of 0.7UIpp with 1.8V supply voltage.)

• Journal of IKEEE
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• v.22 no.2
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• pp.491-494
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• 2018

This paper presents a similarity search method based on mixed norm for enhancing self-example-based single image super-resolution. In order to incorporate the local statistical characteristics of the patches into the super-resolution image reconstruction, we propose a method to determine the order of the norm according to the patch inclination and use it as a similarity search between patches. Experimental results demonstrate that the proposed similarity search method has the capability to improve the performance of existing search method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.104-107
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• 2022

autoencoder deep learning model has excellent ability to restore abnormal data to normal data, so it is not appropriate for anomaly detection. In addition, the Inpainting method, which is a method of restoring hidden data after masking (masking) a part of the data, has a problem in that the restoring ability is poor for noisy images. In this paper, we use a method of modifying and improving the MLP-Mixer model to mask the image at a certain ratio and to reconstruct the image by delivering compressed information of the masked image to the model. After constructing a model learned with normal data from the MVTec AD dataset, a reconstruction error was obtained by inputting normal and abnormal images, respectively, and anomaly detection was performed through this. As a result of the performance evaluation, it was found that the proposed method has superior anomaly detection performance compared to the existing method.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.205-210
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• 2008

A new approach is proposed for restoration of corrupted regions and segmentation in natural text images. The challenge is to fill in the corrupted regions on the basis of color feature analysis by second order symmetric stick tensor. It is show how feature analysis can benefit from analyzing features using tensor voting with chromatic and achromatic components. The proposed method is applied to text images corrupted by manifold types of various noises. Firstly, we decompose an image into chromatic and achromatic components to analyze images. Secondly, selected feature vectors are analyzed by second-order symmetric stick tensor. And tensors are redefined by voting information with neighbor voters, while restore the corrupted regions. Lastly, mode estimation and segmentation are performed by adaptive mean shift and separated clustering method respectively. This approach is automatically done, thereby allowing to easily fill-in corrupted regions containing completely different structures and surrounding backgrounds. Applications of proposed method include the restoration of damaged text images; removal of superimposed noises or streaks. We so can see that proposed approach is efficient and robust in terms of restoring and segmenting text images corrupted.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1980-1987
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• 1999

Restored image is always lower quality than original one due to distortion and noise. The purpose of image restoration is to improve the image quality by fixing the noise or distortion information. One category of spatial filters for image restoration is linear filter. This filter algorithm is easily implemented and can be suppressed the Gaussian noise effectively, but not so good performance for spot or impulse noise. In this paper, we propose the nonlinear spatial filter algorithm for image restoration called the optimal adaptive multistage filter(OAMF). The OAMF is used to reduce the filtering time, increases the noise suppression ratio and preserves the edge information. The OAMF optimizes the adaptive multistage filter(AMF) by using weight learning algorithm of back-propagation learning algorithm. Simulation results of this filter algorithm are presented and discussed.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.1-9
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• 2019

The image obtained from systems such as autonomous driving cars or fire-fighting robots often suffer from several degradation such as noise, motion blur, and compression artifact due to multiple factor. It is difficult to apply image recognition to these degraded images, then the image restoration is essential. However, these systems cannot recognize what kind of degradation and thus there are difficulty restoring the images. In this paper, we propose the deep neural network, which restore natural images from images degraded in several ways such as noise, blur and JPEG compression in situations where the distortion applied to images is not recognized. We adopt the channel attention modules and skip connections in the proposed method, which makes the network focus on valuable information to image restoration. The proposed method is simpler to train than other methods, and experimental results show that the proposed method outperforms existing state-of-the-art methods.