• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.2
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• pp.55-67
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• 2023

This study aims to develop a model that can automatically identify the rooftop shape of apartment buildings using GIS and machine learning algorithms, and apply it to analyze the relationship between rooftop shape and characteristics of apartment complexes. A database of rooftop data for each building in an apartment complex was constructed using geospatial data, and individual buildings within each complex were classified into flat type, tower type, and mixed types using the random forest algorithm. In addition, the relationship between the proportion of rooftop shapes, development density, height, and other characteristics of apartment complexes was analyzed to propose the potential application of geospatial information in the real estate field. This study is expected to serve as a basic research on AI-based building type classification and to be utilized in various spatial and real estate analyses.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.31-31
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• 2022

기후변화, 물 부족, 인구 증가와 도시화로 인한 물 수요 증가, 수질 악화, 노후화된 인프라와 같은 세계적인 물문제의 증가로 인해, 도시 물순환 시스템 관리는 더 큰 어려움을 겪고 있다. 취수, 도·송수, 정수처리, 배·급수, 용수 사용, 하수 집수, 하수 처리, 재이용 및 배출 과정을 포함하는 도시 물순환 시스템의 과정은 매우 에너지 집약적인 활동이며, 이와 같은 에너지 소비는 탄소 배출과 양의 직접적인 상관관계가 있다. 따라서 자원 관리 및 데이터 관리를 최적화하기 위해 넥서스 관점의 접근법이 도시 물순환 시스템에 점차적으로 도입되고 있는 추세이다. 도시 물순환 시스템 넥서스에서는 일반적으로 에너지 인텐시티로 표현되는 물을 위한 에너지를 이해하는 것이 중요하다. 에너지 인텐시티의 차이는 기후(연평균 강수량, 단기 기후 변동성, 기후패턴 등), 지리적 특징(표고차, 평지비, 위치 등), 시스템 특성(총급수량, 인구, 인구밀도, 관로 연장 등) 및 운영관리 효율(수압, 누수율, 에너지 효율 등)과 밀접한 관계가 있다. 그리고 도시 물순환 시스템에서 에너지 관리를 증진시킨 방안은 유지관리 효율 개선(물·에너지 관리전략, 물손실 관리, 수요 관리 및 수요 대응 등), 신기술 도입, 그리고 에너지 회수로 나누어진다. 본 연구에서는 기존 문헌의 자료를 분석하여 도시 물순환 시스템의 각 공정별 에너지 인텐시티를 분석하였으며, 시스템 다이나믹스를 적용하여 다양한 도시 여건(인구, lpcd, 누수율, 취수원, 에너지 인텐시티)에서 외부영향(기후변화, 도시화)과 운영효율 변동(운영효율 향상, 신시술 도입)에 따른 도시 물순환 시스템 내 자원 사용 및 이동을 분석하였다. 에너지 인텐시티는 전체 도시 물순환 시스템, 상수 시스템, 하수시스템에서 각각 2.334 kWh/m³, 1.029 kWh/m³, 1.024 kWh/m³를 나타내었으며, 용수사용, 담수화, 재이용 과정에서는 매우 높은 값이 나타났다. 에너지 인틴시티의 값은 외부 영향에 크게 좌우되는 것으로 분석되었으며, 운영효율의 변동에 따라서 물 및 에너지 사용량은 변화하였지만 에너지 인텐시티의 변동은 크지 않았다. 이에 따라 도시 물순환 시스템을 넥서스 관점에서 관리하기 위해서는 에너지 인텐시티 이외에 물 및 에너지 사용량, 유수수량 관점 에너지 인텐시티, 사용수량 관점 에너지 인텐시티를 종합적으로 고려하는 것이 필요하다.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.2
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• pp.54-67
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• 2023

Mount Tai, with an elevation of 1,532 meters, has a reputation as ''The Most Revered of the Five Sacred Mountains (五嶽獨尊)", despite not being the highest mountain in China. A guidebook is a book or pamphlet that contains an introduction and description of specific activities or facilities, especially detailed and accurate information about scenic spots, which provide superior vistas to than other commercially publicized locations. The study aims to investigate Mount Tai's landscape characteristics by analyzing the landscape types and elements, the Kernel Density, the Mean Center and the Standard Deviational Ellipse of the landscape elements appearing in guidebooks introducing Mount Tai. The research results of this study are summarized as follows. First, the landscape type characteristics of Mount Tai are dominated by natural landscapes, which are different from what was shown highlighted in poems and Big Data as they proposed that the landscape characteristics of Mount Tai is dominated by human activities. Second, from the perspective of subdivided landscape types, the landscape elements that appeared in Mount Tai are topography, structure, architecture, plants, semantics, human beings and image orderly, based on the proportion of landscape elements. Third, from the perspective of landscape elements by times series, "Fengshan (封禅)", "sacrifices (祭祀)" and "legends" mostly appeared in the 1950s and 1980s, and after the 1990s, "climbing" and "overlooking" mostly appeared. Fourth, the landscape elements of Mount Tai are concentrated in Daiding (岱顶) and Dai Temple (岱庙) in all periods in terms of spatial distribution. This will become an important space for Mount Tai scenic spots in the future. Moreover, as a whole, the landscape elements of Mount Tai have changed from the concentrated distribution form in Mount Tai scenic spot to the scattered distribution form including Mount Tai and Tai'an City. This will provide necessary enlightenment for the landscape preservation and the re-production of guidebooks of Mount Tai scenic spot in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3C
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• pp.179-186
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• 2008

Pressuremeter test estimates the deformational properties of soil from the relationship between applied pressure and the displacement of cavity wall. It is general to utilize the reloading curve for the estimation of deformational properties of soil because the initial loading curve can be affected by the disturbance caused by boring. On the other hand, the instrumental resolution or the variation of measured data makes it hard to estimate the maximum shear modulus from pressuremeter test results. This study suggested the methodology estimating the maximum shear modulus from pressuremeter test directly, based on the curve fitting of reloading curve. In addition, the difference was taken into account between the stress state around the probe in reloading and that of the in-situ state. Pressuremeter tests were conducted for 15 cases using a large calibration chamber, together with a number of reference tests. The maximum shear moduli taken from suggested method were compared with those from empirical correlation and bender element test.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.425-433
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• 2006

The presented work provided a predictive equation for dynamic modulus of hot mix asphalt, which showed higher reliability and more simplicity. Lots of test result by UTM at laboratory has been used to develop the precise predictive equation. Evaluation of dynamic modulus for 13mm and 19mm surface course and 25mm of base course of hot mix asphalt with granite aggregate and two asphalt binders (AP-3 and AP-5) were carried out. Superpave Level 1 Mix Design with gyrator compactor was adopted to determine the optimum asphalt binder content (OAC) and the measured ranges of OAC were between 5.1% and 5.4% for surface HMA, and around 4.2% for base HMA. The dynamic modulus and phase angle were determined by testing on UTM, with 5 different testing temperature (-10, 5, 20, 40, & $55^{\circ}C$) and 5 different loading frequencies (0.05, 0.1, 1, 10, 25 Hz). Using the measured dynamic modulus and phase angle, the input parameters of Sigmoidal function equation to represent the master curve were determined and these will be adopted in FEM analysis for asphalt pavements. The effect of each parameter for equation has been compared. Due to the limitation of laboratory tests, the reliability of predictive equation for dynamic modulus is around 80%.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.6
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• pp.75-87
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• 2023

This study presents an experimental design for demonstrating the role of nature-based solutions to climate change in the landscape and garden sector. The study suggests spatial strategies for a carbon-neutral society and its role as a cultural industry. This paper describes the use of a low-maintenance garden as part of a strategy for carbon reduction with the goal of protecting the environment and forming a carbon-neutral society. To this end, this study involved the design and construction of a realistic model garden to provide scientific data on the functions, spatial elements, and carbon neutrality of carbon-reducing gardens. The target site is located in the Sejong National Arboretum. The test area in which the carbon-reducing function is measured is located in the centre of the site, and other spaces include dry gardens, community gardens, and flower gardens intended for exhibition and relaxation. The experimental area is divided into several smaller areas within which the carbon-reducing effect is analysed according to the amount of biochar installed, the planting density, and the plant species present. The application of facilities and construction methods to promote carbon reduction were based on the method known as '10 types of carbon gardening for the earth'. In the model garden, we employed rainwater utilization facilities and used low-carbon certified wood and local materials. The carbon reduction effect of each facility and construction method is compared and presented here. The results are expected to serve as an important basis for realizing a carbon-neutral society and can be used as a reference in various fields that require sustainable development, such as the garden industry.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.849-858
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• 2023

Investigating disaster sites such as earthquakes and landslides involves significant risks due to potential secondary disasters like facility collapse. In situations where direct access is challenging, there is a need to develop methods for safely acquiring high-precision 3D disaster information using light detection and ranging (LiDAR) equipped drone survey systems. In this study, the feasibility of using drone LiDAR in disaster scenarios was examined, focusing on the collapse accident at Jeongja Bridge in Bundang-gu, Seongnam City, in April 2023. High-density point clouds for the accident bridge were collected, and the bridge's 3D terrain information was reconstructed and compared to the measurement performance of 10 ground control points. The results showed horizontal and vertical root mean square error values of 0.032 m and 0.055 m, respectively. Additionally, when compared to a point cloud generated using ground LiDAR for the same target area, a vertical difference of approximately 0.08 m was observed, but overall shapes showed minimal discrepancies. Moreover, in terms of overall data acquisition and processing time, drone LiDAR was found to be more efficient than ground LiDAR. Therefore, the use of drone LiDAR in disaster sites with significant risks allows for safe and rapid onsite investigations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.49-62
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• 2024

Reckless development of the underground by rapid urbanization causes inspection delay on replacement of existing structure and installation new facilities. However, frequent accidents occur due to deviation in construction design planned by inaccurate location information of underground structure. Meanwhile, the electrical resistivity survey, knowns as non-destructive method, is based on the difference in the electric potential of electrodes to measure the electrical resistance of ground. This method is significantly advanced with multi-electrode and deep learning for analyzing strata. However, there is no study to quantitatively assess change in electrical resistance according to geometric conditions of structures. This study evaluates changes in electrical resistance through geometric parameters of electrodes and structure. Firstly, electrical resistance numerical module is developed using generalized mesh occurring minimal errors between theoretical and numerical resistance values. Then, changes in resistances are quantitatively compared on geometric parameters including burial depth, diameter of structure, and distance electrode and structure under steady current condition. The results show that higher electrical resistance is measured for shallow depth, larger size, and proximity to the electrode. Additionally, electric potential and current density distributions are analyzed to discuss the measured electrical resistance around the terminal electrode and structure.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.305-316
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• 2011

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.