• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.158-158
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• 2015

대부분의 대규모 배수지는 고지대에 위치함으로써 자연유하를 통해 배, 급수지역으로 용수를 공급한다. 이를 위해 배수지 전단에는 가압장이 위치하여 정수장에서 처리된 용수를 고지대에 위치한 배수지로 송수한다. 이때 가압장에서 발생하는 전력소비량이 매우 높은 것으로 알려져 있으며, 효율적인 펌프운영을 통해 상당한 전력비용 절감이 가능할 것으로 판단된다. 일반적인 가압장의 운영은 시스템 운영자의 경험을 토대로 해당 가압장에 연결된 배수지의 수위에 따라 펌프의 작동여부를 결정하는 방법이 주로 사용되고 있다. 이러한 운영방법은 용수공급의 안전성을 우선시함으로써 배수지의 수위를 일정하게 유지하고자 빈번하게 펌프를 작동하게 되고 따라서 가압장에서 소모되는 전력량이 커서 운영효율 측면에서는 바람직하지 않다고 할 수 있다. 또한 빈번한 펌프의 작동으로 인해 펌프의 수명이 단축될 뿐만 아니라, 배수지내 용수의 수질저하 문제도 발생할 수 있다. 본 연구에서는 효율적인 펌프장 운영을 위해 급수지역의 24시간 용수사용량을 예측하고, 그에 따른 펌프장의 가압 유량 및 양정을 파악하여 적정용량의 펌프를 선정하고 운영함으로써 펌프의 운영비용의 최소화 및 안정적인 용수공급을 동시에 달성하고자 한다. 이를 위해, 실시간 최적화 모형을 개발하였다. 개발된 최적화 모형은 상수관망해석 프로그램(EPAENT)을 연계하여 수요절점의 수압조건 및 운영상황을 모의하였다. 최적화 기법으로는 유전자알고리즘을 사용하였으며, 실제 시스템의 운영상황를 반영하기 위한 다양한 제약조건(operational constraints)을 적용하였다. 개발된 모형은 정속펌프(혹은 On/Off 펌프) 뿐만 아니라, 최근 실무에서 널리 사용되고 있는 변속펌프(variable speed pump)를 추가적으로 고려하였다. 개발된 모형은 국내에서 실제 운영되고 있는 송, 배수 시스템에 적용하여 모형의 실무 적용가능성을 검증하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.459-459
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• 2017

본 연구에서는 순물소모량 개념의 농업용수 수요량 추정방법을 적용하여 기후변화에 따른 제주도의 미래 수요량 변화를 추정 분석하였다. 지하수를 주 수원으로 하고 관정에 의한 밭작물 위주의 작물재배와, 일정 규모 이상의 강우시에만 유출이 발생하며, 유출량의 대부분이 지하수로 침투되는 물순환 특성 등을 고려할 수 있는 제주도 지역에 적합한 순물소모량 산정방법을 적용하였다. 순물소모량 산정에 필요한 실제증발산량 및 잠재증발산량 등은 유역모형인 SWAT을 이용하여 산정하였다. SWAT 모형의 구동에 필요한 미래 기후자료는 10개의 대표적인 대순환모델(General Circulation Model, GCM) 결과로부터 상세화(Downscaling) 기법을 통해 적용하였으며, RCP 4.5와 RCP 8.5 시나리오를 중심으로 미래 기후변화에 따른 영향을 분석하였다. 미래(2010-2099)의 수문성분별 변화를 살펴본 결과, 연도별 증감과 GCM 모델별 차이는 있으나, 평균적으로 강수량, 잠재증발산량, 실제증발산량, 함양량 등이 점차 증가하는 것으로 나타났으며, RCP 4.5보다는 RCP 8.5 시나리오에서 증가현상이 좀 더 크게 나타났다. 순물소모량 또한 2010년에 비해 2099년을 기준으로 약 100~200mm 정도 증가하는 것으로 나타났으며, RCP 8.5 시나리오에서 증가폭이 크게 나타났다. 그러나 이는 자연적인 기후변화에 따른 단위면적당 순물소모량으로서, 인위적인 요인인 농업형태의 변화(관개면적의 증감, 작물품종의 변화, 인위적 용수절감 등)에 따라 실제 지역별 농업용수 수요량은 다른 경향을 나타낼 수도 있다. 특히 농업용수는 계절별, 지역별 편차가 크게 나타나므로, 자연적 조건에 의한 가용수자원량과 지역별 공급시설에 의한 용수공급량 및 수요예측량의 상호분석을 통해 안정적 물수급을 위한 대응책 마련이 필요할 것으로 판단된다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.17-31
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• 2023

An activity-based model requires detailed population information to model individual travel behavior in a disaggregated manner. The recent innovative approach developed deep generative models with novel regularization terms that improves fidelity and diversity for population synthesis. Since the method relies on measuring the distance between distribution boundaries of the sample data and the generated sample, it is crucial to obtain well-defined continuous representation from the discretized dataset. Therefore, we propose an improved entity embedding models to enhance the performance of the regularization terms, which indirectly supports the synthesis in terms of feasible and diverse populations. Our results show a 28.87% improvement in the F1 score compared to the baseline method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.477-483
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• 2010

The existing mass transport system, with its limited capacity and the saturated road networks, has given cause for a new means of transport to be developed, and strong demands for such new means of transport are observed more than ever. However, the traffic authority is seeking a new transport system that focuses more on LRT(Light Rail Transit), a downsized version of the existing urban railroad, rather than one that is appropriate to solve the traffic problems. Moreover, local governments are experiencing difficulties in planning their own mass transportation(bus or urban railroad) as they have no specified criteria for selecting a mass transport system. Accordingly, there has been an increasingly loud voice that calls for criteria to determine which mass transport system befits each transportation corridor. This paper develops a mass-transport demand forecasting model based on the GIS Buffering analysis of each transportation corridor in the city, sets up the capacity for each mass transport system and presents the criteria for selecting an optimal mass transport system for each transportation corridor. It also presents a methodology that identifies necessary and sufficient conditions for selection and evaluation, since it is most important to select the optimal mass-transport system that can meet the demand by each mass-transportation corridor.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.322-340
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• 2021

Due to climate change and its consequential spring temperature rise, flowering time of Robinia pseudoacacia has advanced and a simultaneous blooming phenomenon occurred in different regions in South Korea. These changes in flowering time became a major crisis in the domestic beekeeping industry and the demand for accurate prediction of flowering time for R. pseudoacacia is increasing. In this study, we developed and compared performance of four different models predicting flowering time of R. pseudoacacia for the entire country: a Single Model for the country (SM), Modified Single Model (MSM) using correction factors derived from SM, Group Model (GM) estimating parameters for each region, and Local Model (LM) estimating parameters for each site. To achieve this goal, the bloom date data observed at 26 points across the country for the past 12 years (2006-2017) and daily temperature data were used. As a result, bloom dates for the north central region, where spring temperature increase was more than two-fold higher than southern regions, have advanced and the differences compared with the southwest region decreased by 0.7098 days per year (p-value=0.0417). Model comparisons showed MSM and LM performed better than the other models, as shown by 24% and 15% lower RMSE than SM, respectively. Furthermore, validation with 16 additional sites for 4 years revealed co-krigging of LM showed better performance than expansion of MSM for the entire nation (RMSE: p-value=0.0118, Bias: p-value=0.0471). This study improved predictions of bloom dates for R. pseudoacacia and proposed methods for reliable expansion to the entire nation.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.62-67
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• 2023

Due to the prolonged impact of COVID-19, the demand for Information Technology (IT) products is increasing, and their production facilities are expanded. Consequently, the use of harmful and dangerous chemicals are increased, the risk of fire(s) and explosion(s) is also elevated. In order to mitigate these risks, the government sets standards, such as KS C IEC 60079-10-1, and manages explosion-prone hazardous facilities where flammable substances are manufactured, used, and handled. However, using the standards of KS, it is difficult to predict the actual possibility of an explosion in a facility, because ventilation (an important factor) is not considered when setting up a hazardous work environment. In this study, the SEMI S6, Tracer Gas Test was applied to the chemical vapor deposition (CVD) facility, a major part of the display industry, to evaluate ventilation performance and to confirm the possibility of creating a less explosive environment. Based on the results, it was confirmed that the ventilation performance in the assumed scenarios met the standards stipulated in SEMI S6, along with supporting the possibility of creating a less explosive working condition. Therefore, it is recommended to use the prediction tool using engineering techniques, as well as KS standards, in such hazardous environments to prevent accidents and/or reduce economic burden following accidents.

• Journal of Korean Society of Forest Science
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• v.81 no.4
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• pp.372-382
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• 1992

Recreation planning is essential activity to meet changing demands and to protect the resources. The recreation opportunity specturm(ROS) system is a principal part of a recreational management planning. In this study, the basic concepts and tenets of the ROS system described and reviewed the feasibility of applying to forest recreation planning to the Korean national forest. In Korea, the forest land as a major recreation place has used without the rational planning process. The control for the laissez-faire use on the forest area, the classification of recreational opportunity settings is the most important process and then it make a useful tool for providing proper recreational opportunity and site development guidance. Opportunity settings classification can help maintain diversity and enhance protection of forest resources. It can also improve the quality of recreational experiences and the management action guidances. GIS technology using the ARC/INFO could be useful in current attempts to identify analysis areas for predictive modeling of forest recreation site planning.

• Journal of Sensor Science and Technology
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• v.24 no.5
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• pp.291-298
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• 2015

There are many landslides occurred by typhoons and intense rainfall during the summer seasons in Korea. To predict a landslide triggering it is important to understand mechanisms and potential areas of landslides by the geological approaches. However, recent climate changes make difficult to predict landslide based on only conventional prediction methods. Therefore, the importance of a real-time monitoring of landslide using various sensors is emphasized in recent. Many researchers have studied monitoring techniques of landslides and suggested several monitoring systems which can be applicable to the natural terrain. Most sensors of landslide monitoring measure slope displacement, hydrogeologic properties of soils and rocks, changes of stress in soil and rock fractures, and rainfall amount and intensity. The measured values of each sensor are transmitted to a monitoring server in real-time. The ultimate goal of landslide monitoring is to warn landslide occurrence in advance and to reduce damages induced by landslides. This study introduces the current situation of landslide monitoring techniques in each country.

• Journal of the Korean Geotechnical Society
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• v.37 no.5
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• pp.47-63
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• 2021

Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.337-352
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• 2023

In general, Rainfall-Runoff model parameter set is optimized using the entire data to calculate unique parameter set. However, Korea has a large precipitation deviation according to the season, and it is expected to even worsen due to climate change. Therefore, the need for hydrological data considering seasonal characteristics. In this study, we conducted regional sensitivity analysis(RSA) using the conceptual Rainfall-Runoff model, GR4J aimed at the Soyanggang dam basin, and clustered combining the RSA results with hydrometeorological data using Self-Organizing map(SOM). In order to consider the climate characteristics in parameter estimation, the data was divided based on clustering, and a calibration approach of the Rainfall-Runoff model was developed by comparing the objective functions of the Global Optimization method. The performance of calibration was evaluated by statistical techniques. As a result, it was confirmed that the model performance during the Cold period(November~April) with a relatively low flow rate was improved. This is expected to improve the performance and predictability of the hydrological model for areas that have a large precipitation deviation such as Monsoon climate.