• Korean Journal of Heritage: History & Science
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• v.55 no.1
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• pp.5-34
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• 2022

The purpose of this study is to investigate the unique contrasting landscape characteristics of imperial tombs of Qing dynasty by examining the characteristics of facility layout in which location, Feng shui, and siblings are harmonized with the 12 Qing Dynasty Hwanggyeongchim. Through literature surveys, field observation and interviews, videos and drawing comparison, and inductive contrast analysis, the contrasting landscape characteristics of imperial tombs in the Qing Dynasty were analyzed by classifying them into natural environment, location, Feng shui, and formal esthetics. As a result, the characteristics of the location type and the layout plan of the Qing dynasty imperial tombs were derived from the analysis of Feng shui shape, axis extension distance along the midaxial line, the width of the ridge, the formality of the facility layout according to the difference between the height of the starting point and the end point, the leftward direction of the tomb, and the space ratio of the 'entry space' - 'ritual space' - 'burial mound space', etc. In addition, it was possible to derive the facility arrangement characteristics of tombstones through the analysis of the types and the arrangement order of tombs facilities, as well as the distribution, quantity and types of stone figures, while also revealing some contrasting characteristics different from those of the Ming Dynasty. In addition, it was confirmed that the spatial division effect through the water system and the view effect of the view from the midaxial line and the vista are the representative view effect found in the Qing Dynasty imperial tombs along with the density contrast.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1407-1422
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• 2022

This study was conducted to classify tree species and assess the classification accuracy, using SE-Inception, a classification-based deep learning model. The input images of the dataset used Worldview-3 and GeoEye-1 images, and the size of the input images was divided into 10 × 10 m, 30 × 30 m, and 50 × 50 m to compare and evaluate the accuracy of classification of tree species. The label data was divided into five tree species (Pinus densiflora, Pinus koraiensis, Larix kaempferi, Abies holophylla Maxim. and Quercus) by visually interpreting the divided image, and then labeling was performed manually. The dataset constructed a total of 2,429 images, of which about 85% was used as learning data and about 15% as verification data. As a result of classification using the deep learning model, the overall accuracy of up to 78% was achieved when using the Worldview-3 image, the accuracy of up to 84% when using the GeoEye-1 image, and the classification accuracy was high performance. In particular, Quercus showed high accuracy of more than 85% in F₁ regardless of the input image size, but trees with similar spectral characteristics such as Pinus densiflora and Pinus koraiensis had many errors. Therefore, there may be limitations in extracting feature amount only with spectral information of satellite images, and classification accuracy may be improved by using images containing various pattern information such as vegetation index and Gray-Level Co-occurrence Matrix (GLCM).

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.108-117
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• 2021

The educational approach using Capstone Design is gradually expanding with the change in social and educational paradigms. Course development utilizing the Capstone Design in models in the department of fashion can create a positive effect in that it enhances the practical capabilities of the fashion model and expands the perspective of various fields related to fashion shows. This study proved the educational efficacy by applying the Capstone Design to the model work presentation course and investigating the implications of the design from the instructor's perspective. The research methods used to guide this course utilizing the Capstone Design were theatrical research and model development research. This study showed that learners' satisfaction for this course combined with Capstone Design was very high, and students were very satisfied with the progress of the class. The instructor's point of view in progressing this course showed the need for education from an in-depth and convergence perspective related to fashion, improvement of temporal and spatial utilization of space, concerns about establishing connections with experts and various industries, and expanding the scope of education through continuous exchanges and cooperation with industry.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.93-103
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• 2020

This study aims to explore the difficulties of Korean dance instructors targeting senior people and extends the difficulties found to developing PBL problems applicable to the instructors' education. To address the goal, this author employed an open-ended questionnaire consisting of four questions and 1:1 interview and collected data. According to the study results, total nine subfactors were drawn from four difficulties associated with student management, the curriculum, performances, and class environment. First, about difficulties related to student management, 'conflicts between students' and 'demand for personalized class' were explored. Second, regarding difficulties about the curriculum, 'refusal against new teaching methods' and 'level difference according to the ability of acquisition' were explored. Third, concerning difficulties related to having performances, 'lack of time for practicing', 'needs to achieve excellent performances', and 'the administration of organizations in charge' were explored. Fourth, about difficulties associated with class environment, 'environment in general' and 'spatial environment' were explored. Also, based on the difficulties explored from dance instructors for senior people, this researcher has developed four PBL problems through community dance for harmony, joint choreography-based creative dance, playful dance allowing role division, and mirroring-based dance.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.47-63
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• 2023

Unmanned aerial vehicle (UAV) and sensor technologies are rapidly developing and being usefully utilized for spatial information-based agricultural management and smart agriculture. Until now, there have been many difficulties in obtaining production information in a timely manner for large-scale agriculture on reclaimed land. However, smart agriculture that utilizes sensors, information technology, and UAV technology and can efficiently manage a large amount of farmland with a small number of people is expected to become more common in the near future. In this study, we evaluated the productivity of forage maize grown on reclaimed land using UAV and sensor-based technologies. This study compared the plant height, vegetation cover ratio, fresh biomass, and dry biomass of maize grown on general farmland and reclaimed land in South Korea. A biomass model was constructed based on plant height, cover ratio, and volume-based biomass using UAV-based images and Farm-Map, and related estimates were obtained. The fresh biomass was estimated with a very precise model (R² =0.97, root mean square error [RMSE]=3.18 t/ha, normalized RMSE [nRMSE]=8.08%). The estimated dry biomass had a coefficient of determination of 0.86, an RMSE of 1.51 t/ha, and an nRMSE of 12.61%. The average plant height distribution for each field lot was about 0.91 m for reclaimed land and about 1.89 m for general farmland, which was analyzed to be a difference of about 48%. The average proportion of the maize fraction in each field lot was approximately 65% in reclaimed land and 94% in general farmland, showing a difference of about 29%. The average fresh biomass of each reclaimed land field lot was 10 t/ha, which was about 36% lower than that of general farmland (28.1 t/ha). The average dry biomass in each field lot was about 4.22 t/ha in reclaimed land and about 8 t/ha in general farmland, with the reclaimed land having approximately 53% of the dry biomass of the general farmland. Based on these results, UAV and sensor-based images confirmed that it is possible to accurately analyze agricultural information and crop growth conditions in a large area. It is expected that the technology and methods used in this study will be useful for implementing field-smart agriculture in large reclaimed areas.

• Journal of Korean Society of Forest Science
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• v.111 no.3
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• pp.345-356
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• 2022

Yokjido is a 15-km² inhabited island located at the tip of the southeastern coast of the Korean Peninsula. Its forest is mostly composed of substitutional vegetation. Our aim was to provide basic information necessary for the conservation and management of the forest vegetation in Yokjido. We classified the types of existing vegetation using methods of the Zurich-Montpellier school of phytosociology. The resulting vegetation map shows the dominant tree species in the top canopy-layer. A total of 8 vegetation types were identified, which were arranged into a vegetation unit hierarchy of 2 communities, 4 sub-communities, 6 variants, and 2 subvariants. Evaluations of each type showed large and small differences in floristic composition, which reflect anthropogenic influences, site conditions, succession stages, and the establishment period. Moreover, vegetation types differed significantly in terms of species diversity indices; in particular, overall species richness, species diversity, and species evenness tended to increase significantly as the elevation increased. The herbaceous plant species showed the highest positive (+) correlation to x. These results were consistent with those of McCain, who reported that species diversity increases in mountainous areas with relatively low elevations due to the mid-domain effect. The forest succession in Yokjido will potentially enter a mixed-forest stage and then proceed to become an all-evergreen broad-leaved forest.

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.

• Journal of the Korean Geotechnical Society
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• v.23 no.4
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• pp.15-24
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• 2007

Two kinds of temperature monitoring technology have been introduced in this study, which can measure coincidently temperatures at many points along a single length of cable. One is to use a thermal sensor cable comprizing of addressable thermal sensors. The other is to use an optic fiber sensor with Distributed Temperature Sensing (DTS) system. The differences between two technologies can be summarized as follows: A thermal sensor cable has a concept of "point sensing" that can measure temperature only at a predefined position. The accuracy and resolution of temperature measurement are up to the capability of the individual thermal sensor. On the other hand, an optic fiber sensor has a concept of "distributed sensing" because temperature is measured practically at all points along the fiber optic cable by analysing the intensity of Raman back-scattering when a laser pulse travels along the fiber. Thus, the temperature resolution depends on the measuring distance, measuring time and spatial resolution. The purpose of this study is to investigate the applicability of two different temperature monitoring techniques in technical and economical sense. To this end, diverse experiments with two techniques were performed and two techniques are applied under the same condition. Considering the results, the thermal sensor cable will be well applicable to the assessment of groundwater flow, geothermal distribution and grouting efficiency within about loom distance, and the optic fiber sensor will be suitable for long distance such as pipe line inspection, tunnel fire detection and power line monitoring etc.

• Journal of the Korean Geotechnical Society
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• v.23 no.7
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• pp.37-46
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• 2007

In the practice of preloading method for soft ground improvement, field engineers need information of ground properties, construction works and field monitoring on ground behaviors of the site. So, integrating all these informations into one database can provide more efficient way for managing and utilizing the data for construction management. In this study, integrated system for construction management of ground improvement sites under preloading is developed. The developed system consists of database (DB) and application program. The database contains all collected data in a construction site and processed data in the system with their geographic information. All informations in the database are standardized from the result of data characterization. Application program performs various functions on managing and utilizing information in the database; pre- and post- data processing with graphic visualization of output, spatial data interpolation, and prediction of ground behavior using field measuring data. And by providing integrating informations and predictions over entire project area with comprehensible visual displays, the applicability and effectiveness of the developed system for construction management were confirmed.