• Proceedings of the Korea Concrete Institute Conference
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• 2002.05a
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• pp.773-778
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• 2002

The variance characteristics of the calcium carbonate contents along to the concrete cover depth takes the prediction method of remaining service life of concrete. Calcium carbonate contents were measured by the Thermo Gravimetric/Differential Thermal Analysis method at three point, depth of 0.25cm, 0.75cm, 1.25cm from the surface of concrete. This prediction method contain some assumption that the chemical protection conferred on steel is through a passive protective oxide film which forms on steel in an environment at or above a pH of 10.5$^{4)}$ .

• Journal of Biosystems Engineering
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• v.16 no.3
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• pp.281-289
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• 1991

During the storage and transport of fruits in the bulk state, significant damage by dead load may occur. To reduce such damage, the prediction model of allowable pile depth, duration and optimum thickness of packaging cushion for fruits was developed in this study. From the preliminary experiment and some assumptions, the derived equations were verified to be a good prediction of the above three parameters.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.203-204
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• 2007

In this work, we propose a compression algorithm for depth images, which are obtained from multi-view sequences. The proposed algorithm represents a depth image using a 3-D regular triangular mesh and predictively encodes the mesh vertices using a linear prediction scheme. The prediction errors are encoded with a arithmetic coder. Simulation results demonstrate that the proposed algorithm provides better performances than the JPEG2000 lossless coder.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.314-321
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• 2022

Depth information used in SLAM and visual odometry is essential in robotics. Depth information often obtained from sensors or learned by networks. While learning-based methods have gained popularity, they are mostly limited to RGB images. However, the limitation of RGB images occurs in visually derailed environments. Thermal cameras are in the spotlight as a way to solve these problems. Unlike RGB images, thermal images reliably perceive the environment regardless of the illumination variance but show lacking contrast and texture. This low contrast in the thermal image prohibits an algorithm from effectively learning the underlying scene details. To tackle these challenges, we propose multi-channel remapping for contrast. Our method allows a learning-based depth prediction model to have an accurate depth prediction even in low light conditions. We validate the feasibility and show that our multi-channel remapping method outperforms the existing methods both visually and quantitatively over our dataset.

• Journal of ILASS-Korea
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• v.14 no.2
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• pp.77-83
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• 2009

In this study, the effect of important parameters such as the pixel size and number of a CCD, the object distance, the wavelength of laser, and the particle diameter on the depth of focus in digital in-line particle holography were investigated. The depth of focus in several different cases was calculated using simulation holograms and detailed description of the depth of focus in digital particle holography was presented. The depth of focus is directly proportional to the object distance and the particle size. With the increase of the wavelength of laser, the depth of focus is decreased. The depth of focus is also inversely proportional to the pixel size and number of a CCD. Using the data of depth of focus from simulation holograms and a data-fitting software, we obtained the prediction equations of depth of focus for typical CCD cameras. Finally, the prediction equations of depth of focus in digital particle holography were verified by investigating real holograms of the calibration target in different cases and satisfied agreement between measured values and predicted values was confirmed.

• Atmosphere
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• v.25 no.1
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• pp.155-167
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• 2015

A dynamical seasonal prediction system for boreal winter utilizing cryospheric information was developed. Using the Community Atmospheric Model, version3, (CAM3) as a modeling system, newly developed snow depth initialization method and sea ice concentration treatment were implemented to the seasonal prediction system. Daily snow depth analysis field was scaled in order to prevent climate drift problem before initializing model's snow fields and distributed to the model snow-depth layers. To maximize predictability gain from land surface, we applied one-month-long training procedure to the prediction system, which adjusts soil moisture and soil temperature to the imposed snow depth. The sea ice concentration over the Arctic region for prediction period was prescribed with an anomaly-persistent method that considers seasonality of sea ice. Ensemble hindcast experiments starting at 1st of November for the period 1999~2000 were performed and the predictability gain from the imposed cryospheric informations were tested. Large potential predictability gain from the snow information was obtained over large part of high-latitude and of mid-latitude land as a result of strengthened land-atmosphere interaction in the modeling system. Large-scale atmospheric circulation responses associated with the sea ice concentration anomalies were main contributor to the predictability gain.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.8
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• pp.1503-1512
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• 1992

The strength evaluation and life prediction on the corrosion part of structure is one of the most important subjects, as a viewpoint of reducing economic loss by regular inspection, maintenance, repair and replace. For this purpose, it has been difficult to obtain the available data on growth of pit depth or growth rate of each pit which depends on time. In this paper, the life prediction and strength evaluation method was suggested for the structure with irregular stress concentration part by surface corrosion. The statistical distribution pattern of corrosion depth and the degree of fatigue strength decline were confirmed according to corrosion period by artificial corrosion of SS41 steel. The life prediction and the fatigue strength evaluation of materials with consideration of the corrosion period on the extreme value statistic analysis by the data of maximum depth of corrosion and on random variable was studied.

• Journal of Korea Water Resources Association
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• v.30 no.6
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• pp.743-751
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• 1997

A thorough literatural review of researches on the experimental prediction of scour depths caused by jets was carried out to find out a measure of scour prediction. A new experimental equation for predicting scour depths due to a vortical or an inclined jet was developed from five hundreds of experimental data which were carefully reviewed and adopted from the previous researches. The developed equation shows 0.941 statistical correlation coefficient. It was found that the parameters governing the scour depth were the size of bed material, flow rate through unit width of spillway, and head drop across structure. The predicted scour depth could be expressed as the deepest scouring depth below tail water surface with correlation coefficient ${\gamma}$ = 0.941.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.906-913
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• 2022

In this paper, we propose a new deep network architecture using nearest neighbor kernel for the estimation of dense depth map from its sparse map and corresponding color information. First, we propose to decompose the depth map signal into the structure and details for easier prediction. We then propose two separate subnetworks for prediction of both structure and details using classification and regression approaches, respectively. Moreover, the nearest neighboring kernel method has been newly proposed for accurate prediction of structure signal. As a result, the proposed method showed better results than other methods quantitatively and qualitatively.

• Earthquakes and Structures
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• v.6 no.2
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• pp.141-161
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• 2014

This article presents a new generation of empirical ground motion models for the prediction of response spectral accelerations in soil conditions, specifically developed for the Vrancea intermediate-depth seismic source. The strong ground motion database from which the ground motion prediction model is derived consists of over 800 horizontal components of acceleration recorded from nine Vrancea intermediate-depth seismic events as well as from other seventeen intermediate-depth earthquakes produced in other seismically active regions in the world. Among the main features of the new ground motion model are the prediction of spectral ordinates values (besides the prediction of the peak ground acceleration), the extension of the magnitudes range applicability, the use of consistent metrics (epicentral distance) for this type of seismic source, the extension of the distance range applicability to 300 km, the partition of total standard deviation in intra- and inter-event standard deviations and the use of a national strong ground motion database more than two times larger than in the previous studies. The results suggest that this model is an improvement of the previous generation of ground motion prediction models and can be properly employed in the analysis of the seismic hazard of Romania.