• Korean Journal of Heritage: History & Science
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• v.43 no.1
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• pp.212-235
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• 2010

The purpose of this study is to examine the characteristics of the location and the spatial composition of Uireung that is located in Seokgwan-dong, Seongbuk-gu, Seoul, in order to understand the landscape architectural characteristics. The results are as follows. First, Uireung is 6.4km from Changdeokgung Palace and 5.5km from Heunginjimun Gate. It did not violate the distance standard (40km) for the royal tombs according to Joseon Dynasty Neung-won Myo-je. Second, Uireung is in harmony with the nature and shows the authoritative characteristics of the royal authority through the spatial composition and rank(Entrance Area, Ceremonial Area, Burial Area). Third, there are burial mound, stone sheep, stone tiger, stone table, stone watch pillars in the upper platform, and stone civil official, stone horse, stone lantern in the middle platform, and stone military official, stone horse in the lower platform, and T-shape shrine, worship road in the ceremonial area. There is no pond and a tomb keeper residence, but the position, size, and form can be approximated through historical research materials. There are a colony of pine trees around the burial mound and 64 species of trees such as pine tree, zelcova tree, and fir tree below the burial mound.

• Korean Journal of Heritage: History & Science
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• v.45 no.1
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• pp.70-85
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• 2012

Uiseong Tamni-ri Five-storied Stone Pagoda is composed of andesitic tuff and partially combined with tuff breccia and fine-grained granite. The andesitic tuff is identical to basement rock of Geumseongsan Mountain based on lithological, mineralogical and geochemical characteristics. The pagoda has suffered physical weathering such as crack and scaling, discoloration and biological colonization with complex reaction. Expecially, dark gray and brown discoloration appeared whole over the surface of the pagoda, and three to five-layered exfoliation and granular disintegration dominantly occurred in the fourth and fifth roof stones. It is assuming that the stone elements of the pagoda are evaluated as third to forth grades (average third grade) of weathering compared to fresh rock in Geumseongsan Mountain. The physical strength of the stone elements shows low values in the south and west sides of the pagoda that corresponds high weathering degree of the west side due to exfoliation, crack and granular disintegration. It is necessary to investigate the pagoda for precise deterioration assessment, monitoring and conservation treatment.

• Composites Research
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• v.31 no.6
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• pp.332-339
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• 2018

Fiber metal hybrid laminate (FML) can be used as an economic material with superior mechanical properties and light weight than conventional metal by bonding of metal and FRP. However, there are disadvantages that it is difficult to predict fracture behavior because of the large difference in properties depending on the type of fiber and lamination conditions. In this paper, we study the failure behavior of hybrid materials with laminated glass fiber reinforced plastics (GFRP, GEP118, woven type) in Al6061-T6 alloy. The Al alloys were coated with GFRP 1, 3, and 5 layers, and fracture behavior was analyzed by using a static test and a low cycle fatigue test. In the low cycle fatigue test, strain - life analysis and the total strain energy density method were used to analyze and predict the fatigue life. The Al alloy did not have tensile properties strengthening effect due to the GFRP coating. The fatigue hysteresis geometry followed the behavior of the Al alloy, the base material, regardless of the GFRP coating and number of coatings. As a result of the low cycle fatigue test, the fatigue strength was increased by the coating of GFRP, but it did not increase proportionally with the number of GFRP layers.

• Korean Journal of Heritage: History & Science
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• v.54 no.3
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• pp.90-105
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• 2021

The shape of the roof is very important, as it determines the beauty of the exterior of Korean wooden architecture. In particular, the curve of the eaves is the most representative of the characteristics of Korean wooden architecture. Rafters and buyeon (浮椽), flying rafters, create curves for the eaves, and buyeon in particular makes the roof lighter and more dynamic. Although the function and role of buyeon are already known, nothing is yet clear about the beginning of its use in Korean ancient architecture. Accordingly, the purpose of this study is to determine when buyeon was first used in Korean architecture. To this end, I examined various records, buildings, remains, and artifacts that have architectural shapes in Korea, China, and Japan. The results are summarized as follows. First, the use of buyeon in China appears during the Northern Qi Dynasty (北齊) in the mid-6th century, but became common in the 7th century during the Tang (唐) Dynasty. Second, the use buyeon in Japan appears in the mid-8th century, when the capital was relocated from Asuka (飛鳥) to Nara (奈良). It corresponds with the time that Japan began importing Chinese culture directly. Third, the use of buyeon in Korea may have been introduced to Baekje from China in the mid-6th century, but it was not common. It is believed that it became common after active exchanges with the Tang Dynasty during the Unified Silla Period in the mid-7th century.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.121-129
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• 2021

In this study, we try to minimize the tariff risk by constructing a hazardous cargo screening model by applying Association Rule Mining, one of the data mining techniques. For this, the risk level between supply chains is calculated using the Apriori Algorithm, which is an association analysis algorithm, using the big data of the import declaration form of the Korea Customs Service(KCS). We perform data preprocessing and association rule mining to generate a model to be used in screening the supply chain. In the preprocessing process, we extract the attributes required for rule generation from the import declaration data after the error removing process. Then, we generate the rules by using the extracted attributes as inputs to the Apriori algorithm. The generated association rule model is loaded in the KCS screening system. When the import declaration which should be checked is received, the screening system refers to the model and returns the confidence value based on the supply chain information on the import declaration data. The result will be used to determine whether to check the import case. The 5-fold cross-validation of 16.6% precision and 33.8% recall showed that import declaration data for 2 years and 6 months were divided into learning data and test data. This is a result that is about 3.4 times higher in precision and 1.5 times higher in recall than frequency-based methods. This confirms that the proposed method is an effective way to reduce tariff risks.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.315-324
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• 2022

In this paper, we propose KoEPT, a Transformer-based generative model for automatic math word problems solving. A math word problem written in human language which describes everyday situations in a mathematical form. Math word problem solving requires an artificial intelligence model to understand the implied logic within the problem. Therefore, it is being studied variously across the world to improve the language understanding ability of artificial intelligence. In the case of the Korean language, studies so far have mainly attempted to solve problems by classifying them into templates, but there is a limitation in that these techniques are difficult to apply to datasets with high classification difficulty. To solve this problem, this paper used the KoEPT model which uses 'expression' tokens and pointer networks. To measure the performance of this model, the classification difficulty scores of IL, CC, and ALG514, which are existing Korean mathematical sentence problem datasets, were measured, and then the performance of KoEPT was evaluated using 5-fold cross-validation. For the Korean datasets used for evaluation, KoEPT obtained the state-of-the-art(SOTA) performance with 99.1% in CC, which is comparable to the existing SOTA performance, and 89.3% and 80.5% in IL and ALG514, respectively. In addition, as a result of evaluation, KoEPT showed a relatively improved performance for datasets with high classification difficulty. Through an ablation study, we uncovered that the use of the 'expression' tokens and pointer networks contributed to KoEPT's state of being less affected by classification difficulty while obtaining good performance.

• Clean Technology
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• v.29 no.4
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• pp.237-243
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• 2023

Aerogel is the most excellent insulation material known to date, but it is inflexible and has very low strength. A blanket containing aerogel in a nonwoven fabric or fiber is currently the most practical form. However, aerogel blankets are not yet widely used because they cannot avoid dust generation when handled, lack flexibility, and can possibly deform. In this study, vacuum treatment, surface treatment, and composite materialization technology were applied to solve this problem, and some prototypes were also made. If an aerogel blanket is wrapped in an aluminum sheet, sealed at the four ends, and vacuumed, it can become a material with better insulation than the blanket itself. An aerogel molded body can be made by coating the aerogel blanket with resin and treating the surface. If the aerogel blanket is multi-packed and laminated with resin or fiber in multiple layers to make it a composite material, it can be used as a flexible insulation material. In particular, this composite material, which utilizes a Teflon membrane with controlled pores, is breathable and waterproof, so it can be used for clothing. Prototypes of insoles for winter boots and outdoor roll mats were also produced using aerogel blanket resin and fiber composites. These prototypes showed low thermal conductivity of less than 20 mW m^-1K^-1, with good flexibility and durability.

• Conservation Science in Museum
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• v.30
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• pp.1-22
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• 2023

This study aims to document the conservation treatment of the fine-hemp official headgear housed by the National Museum of Korea, and to reconsider its existing name following the restoration of the original form of the damaged cultural heritage asset. The headgear consists of a single inner frame with a vertical line at the front, a single outer frame surrounding the inner frame, and a double-layered headband that spans the circumference of the wearer's head and joins the inner and the outer frames. This study applied a conservation treatment to the men's undyed hemp headgear of the Joseon Dynasty in order to remove contaminants and foreign substances on the surface and repair the partially deteriorated and damaged fabric, thereby restoring and stabilizing the original shape and preparing it for exhibitions. The hemp headgear was sewed both by hand and with a sewing machine. Although its overall composition and style are similar to the same type of official headgear from the Joseon Dynasty, the use of a sewing machine supports the assumption that it was produced in the early 1900s. This study identified similarities between the overall composition and shape of the fully-preserved hemp official headgear and those of the jangbogwan, a type of men's official headgear worn by Confucian scholars as part of their everyday attire, and compared it with the shape of jangbogwan seen in documentary records, illustrations, prior research, and portraits from the Joseon Dynasty, as well as with the characteristics of extant jangbowan artifacts, eventually concluding that it is appropriate to classify and name the headgear as a jangbogwan.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.