• Korean Journal of Heritage: History & Science
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• v.43 no.2
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• pp.40-59
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• 2010

Anssolim is the unique technique which standing columns lean in a inward direction of buildings in traditional architecture, which has not been thoroughly investigated to this day. With a dearth of previous studies, the anssolim technique can only be examined through detailed three-dimensional surveys. The main halls of Korean palaces can be seen as buildings that were built with the regulations of the day in mind, making them excellent research subjects when studying the anssolim technique. The findings can be summarized as follows. 1. In the main halls that were studied, anssolim was applied most to main space (eokan) columns, then lessened for peripheral columns. 2. The largest second-floor cheoma columns were placed inward in the eokan, then became smaller as with the peripheral columns. In the case of the eokan, the columns were arranged according to the size of the anssolim. 3. The second-floor cheoma column anssolim in the middle-floor main hall were generally a third or a quarter of the size of those on the first floor. As on the first floor, the largest anssolim were applied to the eokan columns, then became gradually smaller towards the periphery columns. 4. In the palace main halls, the largest anssolim were used for the eokan columns, and became smaller with the peripheral columns. This unique structure can be seen to be a Korean technique that deviates from the Chinese "Yingzaofashi(營造法式)" techniques. Although this study is limited in that it only studies the main hall of Korean palaces, it is significant in that it shed new light on the technological implications of the anssolim technique, and can be used as important data for research into the history of technology. Although this type of data is difficult to extrapolate, it has been made as accurate as possible by minimizing the margin of error in the data for the palaces that were actually studied.

• Science of Emotion and Sensibility
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• v.21 no.4
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• pp.55-62
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• 2018

The two main requirements of wearable optical fiber fabrics are that they must presuppose a high degree of flexibility and they must maintain the luminance effect in both flat and bent conformations. Therefore, woven optical fiber fabrics that satisfy the above conditions were developed by both weaving and by using computer embroidery. First, we measured the brightness of the wearable optical fiber fabric in the flat state at a total of 10 measurement points at intervals of 1 cm. Second, the wearable optical fiber fabric was placed horizontally on the forearm, where three-dimensional bending occurs, and the luminance values were recorded at the same 10 measurement points. For the woven fabric in the flat state, the maximum, minimum, average, and standard deviation luminance values were $5.23cd/m^2$, $2.74cd/m^2$, $3.56cd/m^2$, and $1.11cd/m^2$, respectively. The corresponding luminance values from the bent forearm were $7.92cd/m^2$ (maximum), $2.37cd/m^2$ (minimum), $4.42cd/m^2$ (average), and $2.16cd/m^2$ (standard deviation). In the case of the computer-embroidered fabric, the maximum, minimum, average, and standard deviation luminance values in the flat state were $7.56cd/m^2$, $3.84cd/m^2$, $5.13cd/m^2$, and $1.04cd/m^2$, respectively, and in the bent forearm state were $9.6cd/m^2$, $3.63cd/m^2$, $6.13cd/m^2$, and $2.26cd/m^2$, respectively. Therefore, the computer-embroidered fabric exhibited a higher luminous effect than the woven fabric because the detailed structure reduced light-loss due to the backside fabric. In both types of wearable optical fiber fabric the luminance at the forearm was 124% and 119%, respectively, and the light emitting effect of the optical fiber fabric was maintained even when bent by the human body. This is consistent with the principle of Huygens, which defines the wave theory of light, and also the Huygens-Fresnel-Kirchhoff principle, which states that the intensity of light increases according to the magnitude of the angle of propagation of the light wavefront (${\theta}$).

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• The Journal of Korean Academy of Prosthodontics
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• v.57 no.3
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• pp.203-210
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• 2019

Purpose: The purpose of this study was to verify the effect of the abutment superimposition process on the final virtual model in the scanning process of single and 3-units bridge model using a dental model scanner. Materials and methods: A gypsum model for single and 3-unit bridges was manufactured for evaluating. And working casts with removable dies were made using Pindex system. A dental model scanner (3Shape E1 scanner) was used to obtain CAD reference model (CRM) and CAD test model (CTM). The CRM was scanned without removing after dividing the abutments in the working cast. Then, CTM was scanned with separated from the divided abutments and superimposed on the CRM (n=20). Finally, three-dimensional analysis software (Geomagic control X) was used to analyze the root mean square (RMS) and Mann-Whitney U test was used for statistical analysis (${\alpha}=.05$). Results: The RMS mean abutment for single full crown preparation was $10.93{\mu}m$ and the RMS average abutment for 3 unit bridge preparation was $6.9{\mu}m$. The RMS mean of the two groups showed statistically significant differences (P<.001). In addition, errors of positive and negative of two groups averaged $9.83{\mu}m$, $-6.79{\mu}m$ and 3-units bridge abutment $6.22{\mu}m$, $-3.3{\mu}m$, respectively. The mean values of the errors of positive and negative of two groups were all statistically significantly lower in 3-unit bridge abutments (P<.001). Conclusion: Although the number of abutments increased during the scan process of the working cast with removable dies, the error due to the superimposition of abutments did not increase. There was also a significantly higher error in single abutments, but within the range of clinically acceptable scan accuracy.

• Journal of Life Science
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• v.29 no.2
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• pp.215-222
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• 2019

Muscle dysfunction may arise from skeletal muscle atrophy caused by aging, injury, oxidative stress, and hereditary disease. Powdered heat-killed Enterococcus faecalis (EF-2001) has anti-allergy, anti-inflammatory, and anti-tumor effects. However, its antioxidant and anti-atrophy effects are poorly characterized. In this study, we examined the effects of EF-2001 on muscle atrophy. To determine the protective effect of EF-2001 on oxidative stress, C2C12 myoblasts were treated with $H_2O_2$ to induce oxidative stress. This induced cell damage, which was reduced by treatment with EF-2001. The mechanism of EF-2001's effect was examined in response to oxidative stress. Treatment with EF-2001 reversed the expression of HSP70 and SOD1 proteins. Also, mRNA levels of Atrogin-1/MAFbx and MuRF1 increased under oxidative stress conditions but decreased following EF-2001 treatment. To evaluate muscle volume, two and three dimensional models of the muscles were analyzed using micro-CT. As expected, muscle volume decreased after sciatic denervation and recovered after oral administration of EF-2001. Therefore, EF-2001 is a candidate for the treatment of muscular atrophy, and future discovery of the additional effects of EF-2001 may yield further applications as a functional food with useful activities in various fields.

• Korean Journal of Breeding Science
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• v.50 no.4
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• pp.396-405
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• 2018

The areas of soybean (Glycine max (L.) Merrill) cultivation in Gangwon-do have increased due to the growing demand for well-being foods. The soybean barcode system is a useful tool for cultivar identification and diversity analysis, which could be used in the seed production system for soybean cultivars. We genotyped cultivars using 202 insertion and deletion (InDel) markers specific to dense variation blocks (dVBs), and examined their ability to identify cultivars and analyze diversity by comparison to the database in the soybean barcode system. The genetic homology of "Cheonga," "Gichan," "Daewang," "Haesal," and "Gangil" to the 147 accessions was lower than 81.2%, demonstrating that these barcodes have potentiality in cultivar identification. Diversity analysis of one hundred and fifty-three soybean cultivars revealed four subgroups and one admixture (major allele frequency <0.6). Among the accessions, "Heugcheong," "Hoban," and "Cheonga" were included in subgroup 1 and "Gichan," "Daewang," "Haesal," and "Gangil" in the admixture. The genetic regions of subgroups 3 and 4 in the admixture were reshuffled for early maturity and environmental tolerance, respectively, suggesting that soybean accessions with new dVB types should be developed to improve the value of soybean products to the end user. These results indicated that the two-dimensional barcodes of soybean cultivars enable not only genetic identification, but also management of genetic resources through diversity analysis.

• Journal of Environmental Impact Assessment
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• v.30 no.5
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• pp.271-296
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• 2021

Since the thermal stratification in a reservoir inhibits the vertical mixing of the upper and lower layers and causes the formation of a hypoxia layer and the enhancement of nutrients release from the sediment, changes in the stratification structure of the reservoir according to future climate change are very important in terms of water quality and aquatic ecology management. This study was aimed to develop a data-driven inflow water temperature prediction model for Daecheong Reservoir (DR), and to predict future inflow water temperature and the stratification structure of DR considering future climate scenarios of Representative Concentration Pathways (RCP). The random forest (RF)regression model (NSE 0.97, RMSE 1.86℃, MAPE 9.45%) developed to predict the inflow temperature of DR adequately reproduced the statistics and variability of the observed water temperature. Future meteorological data for each RCP scenario predicted by the regional climate model (HadGEM3-RA) was input into RF model to predict the inflow water temperature, and a three-dimensional hydrodynamic model (AEM3D) was used to predict the change in the future (2018~2037, 2038~2057, 2058~2077, 2078~2097) stratification structure of DR due to climate change. As a result, the rates of increase in air temperature and inflow water temperature was 0.14~0.48℃/10year and 0.21~0.41℃/10year,respectively. As a result of seasonal analysis, in all scenarios except spring and winter in the RCP 2.6, the increase in inflow water temperature was statistically significant, and the increase rate was higher as the carbon reduction effort was weaker. The increase rate of the surface water temperature of the reservoir was in the range of 0.04~0.38℃/10year, and the stratification period was gradually increased in all scenarios. In particular, when the RCP 8.5 scenario is applied, the number of stratification days is expected to increase by about 24 days. These results were consistent with the results of previous studies that climate change strengthens the stratification intensity of lakes and reservoirs and prolonged the stratification period, and suggested that prolonged water temperature stratification could cause changes in the aquatic ecosystem, such as spatial expansion of the low-oxygen layer, an increase in sediment nutrient release, and changed in the dominant species of algae in the water body.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.743-752
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• 2019

The flow in the meandering channel is characterized by the spiral motion of secondary currents that typically cause the erosion along the outer bank. Hydraulic structures, such as spur dike and groyne, are commonly installed on the channel bottom near the outer bank to mitigate the strength of secondary currents. This study is to investigate the effects of submerged vanes installed in a $90^{\circ}$ meandering channel on the development of secondary currents through three-dimensional numerical modeling using the hybrid RANS/LES method for turbulence and the volume of fluid method, based on OpenFOAM open source toolbox, for capturing the free surface at the Froude number of 0.43. We employ the second-order-accurate finite volume methods in the space and time for the numerical modeling and compare numerical results with experimental measurements for evaluating the numerical predictions. Numerical results show that the present simulations well reproduce the experimental measurements, in terms of the time-averaged streamwise velocity and secondary velocity vector fields in the bend with submerged vanes. The computed flow fields reveal that the streamwise velocity near the bed along the outer bank at the end section of bend dramatically decrease by one third of mean velocity after the installation of vanes, which support that submerged vanes mitigate the strength of primary secondary flow and are helpful for the channel stability along the outer bank. The flow between the top of vanes and the free surface accelerates and the maximum velocity of free surface flow near the flow impingement along the outer bank increases about 20% due to the installation of submerged vanes. Numerical solutions show the formations of the horseshoe vortices at the front of vanes and the lee wakes behind the vanes, which are responsible for strong local scour around vanes. Additional study on the shapes and arrangement of vanes is required for mitigate the local scour.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.3
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• pp.133-149
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• 2021

This study aims to report the behavioral mechanism of steel pipe reinforcement grouting, which is being actively used to ensure the stability of the excavation surface during tunnel excavation, based on measurements taken at the actual site. After using a 12 m steel pipe attached with a shape displacement meter and a strain gauge to reinforce the actual tunnel surface, behavioral characteristics were identified by analyzing the measured deformation and stress of the steel pipe. Taking into account that the steel pipes were overlapped every 6 m, the measured data up to 7 m of excavation were used. In addition, the behavioral characteristics of the steel pipe reinforcement according to the difference in strength were also examined by applying steel pipes with different allowable stresses (SGT275 and SGT550). As a result of analyzing the behavior of steel pipes for 7 hours after the first excavation for 1 m and before proceeding with the next excavation, the stress redistribution due to the arching effect caused by the excavation relaxation load was observed. As excavation proceeded by 1 m, the excavated section exhibited the greatest deformation during excavation of 4 to 6 m due to the stress distribution of the three-dimensional relaxation load, and deformation and stress were generated in the steel pipe installed in the ground ahead of the tunnel face. As a result of comparing the behavior of SGT275 steel pipe (yield strength 275 MPa) and SGT550 steel pipe (yield strength 550 MPa), the difference in the amount of deformation was up to 18 times and the stress was up to 12 times; the stronger the steel pipe, the better it was at responding to the relaxation load. In this study, the behavior mechanism of steel pipe reinforcement grouting in response to the arching effect due to the relaxation load was identified based on the measured data during the actual tunnel excavation, and the results were reported.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.