• Atmosphere
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• v.31 no.5
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• pp.563-576
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• 2021

The performance of East Asian summer monsoon is assessed for GC2 and GC3.1, which are climate change models of the current and next climate prediction system in the Korea Meteorological Administration (KMA), GloSea5 and GloSea6. The most pronounced characteristics of GC models are strong monsoon trough and the weakening of the Western North Pacific Subtropical High (WNPSH). These are related to the weakening of the southwesterly wind and resulting weak monsoon band toward the Korean Peninsula. The GC3.1 is known to have improved the model configuration version compared to GC2, such as cloud physics and ocean parameters. We can confirm that the overall improvements of GC3.1 against GC2, especially in pressure, 850 hPa wind fields, and vertical wind shear. Also, the precipitation band stagnant in the south of 30°N in late spring is improved, therefore the biases of rainy onset and withdrawal on the Korean Peninsula are reduced by 2~4 pentad. We also investigate the impact of initialization in comparison with GloSea5 hindcast. Compared with GCs, hindcast results show better simulation within 1 month lead time, especially in pressure and 850 hPa wind fields, which can be expected to the improvement of WNPSH. Therefore, it is expected that the simulation performance of WNPSH will be improved in the result of applying the initialization of GloSea6.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.4
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• pp.105-112
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• 2022

Recently, many studies using reinforcement learning-based autoscaling have been performed to make autoscaling policies that are adaptive to changes in the environment and meet specific purposes. However, training the reinforcement learning-based Horizontal Pod Autoscaler(HPA) policy in a real environment requires a lot of money and time. And it is not practical to retrain the reinforcement learning-based HPA policy from scratch every time in a real environment. In this paper, we implement a reinforcement learning-based HPA in Kubernetes, and propose a transfer leanring technique using a queuing model-based simulation to accelerate the training of a reinforcement learning-based HPA policy. Pre-training using simulation enabled training the policy through simulation experience without consuming time and resources in the real environment, and by using the transfer learning technique, the cost was reduced by about 42.6% compared to the case without transfer learning technique.

• Journal of Environmental Impact Assessment
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• v.31 no.6
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• pp.369-377
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• 2022

The purpose of this study is to explore whether open spaces with land use characteristics of forest green areas can have different influence on the urban heat reduction depending on the location distribution, through the case of Gangneung-si downtown area. As a research method, the InVest Urban Cooling Model, which is a thermal phenomenon analysis model, is employed based on the most recent data available in 2018. In order to focus on the effect of location distribution of open space in the city, the downtown area is set as the observation area, not the entire city. The analysis of the land use location distribution scenarios shows that large-scale forests or clustered forests are more effective in reducing atmospheric heat in the region than several small-scale forests.

• Journal of the Korean Institute of Gas
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• v.26 no.2
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• pp.39-48
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• 2022

The domestic gas detector installation standards applied to gas detectors, which are one of the facilities that can prevent accidents such as fire, explosion, and leakage that can cause serious industrial accidents, do not take into account the behavioral characteristics of hazardous chemicals in the atmosphere. It can be seen that the technical basis is insufficient because the standard is applied. Therefore, in this study, the size of the leak hole for each facility mainly used in chemical plants and the diffusion distance according to the concentration of interest of hazardous chemicals were analyzed, and based on this, the optimal installation distance for gas detectors for each material was suggested. Using the method presented in this study, more economical and effective gas detector installation can be expected, and furthermore, it can be expected to help prevent serious industrial accidents.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.1
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• pp.33-40
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• 2023

With the development of supercomputing technology and hardware technology, numerical computation methods are also being advanced. Accordingly, improved weather prediction becomes possible. In this paper, we propose to apply the LAPACK(Linear Algebra PACKage) BLAS(Basic Linear Algebra Subprograms) library to the linear algebraic numerical computation part within the source code to improve the performance of the cumulative parametric code, Unicon(A Unified Convection Scheme), which is included in SCAM(Single-Columns Atmospheric Model, simplified version of CESM(Community Earth System Model)) and performs standby operations. In order to analyze this, an overall execution structure diagram of SCAM was presented and a test was conducted in the relevant execution environment. Compared to the existing source code, the SCOPY function achieved 0.4053% performance improvement, the DSCAL function 0.7812%, and the DDOT function 0.0469%, and all of them showed a 0.8537% performance improvement. This means that the LAPACK BLAS application method, a library for high-density linear algebra operations proposed in this paper, can improve performance without additional hardware intervention in the same CPU environment.

• Journal of Industrial Convergence
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• v.20 no.11
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• pp.127-134
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• 2022

With the recent advent of the Fourth Industrial Revolution, the importance of the platform business is increasing. Most global companies with high market value are known as platform companies. This change is changing the business model of companies in various industries. However, existing studies have mainly focused on information-intensive industries and large companies. Therefore, this study attempted to analyze the case of Everybot, which is successfully growing in the service robot industry. Everybot is known as a company that produces robot cleaners. However, according to the result, the company has focused on developing autonomous driving technologies and pursuing platform-based business strategies rather than product-based ones. The results of this study have theoretical and practical implications by showing how domestic small and medium-sized robot companies apply platform-based business strategies to achieve long-term growth with gaining leadership in the personal service robotics market.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.289-289
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• 2023

4차산업 혁명이 도래한 이후로 전세계적으로 AI 기술이 유래 없는 속도로 발달 및 활용되고 있으며, 다양한 분야에서 AI 기법을 도입한 연구가 활발히 진행 중에 있다. 최근 수자원 분야에서는 단기 강우 예측, 댐 유입량 예측 및 하천 수위 예측 등의 분야에서 AI 기술이 접목되어 꾸준한 기술 개발이 이루어지고 있다. 그러나 단변량으로 축척된 자료를 활용하여 중·장기 모형 개발 연구가 다수 진행되고 있지만, 급격한 기후변화 현상과 복잡한 매커니즘을 보이고 있는 기상현상의 경우 단변량 분석으로서는 정확도가 저하 될 수 있는 우려가 있는 것이 현실이다. 이에 본 연구에서는 상기에 제시된 단점을 극복하고자 다양한 기상자료를 검증·예측인자로 활용함과 동시에 Deeplearning 모형과 결합하여 신뢰성 있는 단기 강수 예측이 가능한 모형을 개발하였다. 본 연구에서는 유럽중기예보센터(ECMWF, European Center for Medium-Range Weather Forecasts)에서 제공하고 있는 ERA5 재해석 자료를 활용하였으며, Deeplearning 모형과 결합하여 단기 강우 예측이 가능한 모형을 개발하였다. 1차적으로 격자자료(25km×25km)로 제공되고 있는 ERA5 자료를 상세화(downscaling) 모형에 적용하여 기상청 관측소와 비교·검증하였으며, Deeplearning 모형을 통해 단기 예측이 가능한 모형으로 확장하였다. 이때 Deeplearning의 다양한 모형 중 시계열 분석에 있어 예측 성능이 높은 LSTM 모형을 활용하였으며, 제공되고 있는 대기 변수의 상호관계를 노드간 연결을 통해 결과의 정확도와 신뢰성을 확보하였다. 본 연구 결과는 기관별로 제공하고 있는 예측 수준을 상회하는 결과를 도출하였으며, 홍수기에 집중되는 강우량을 예측하여 대비·대책을 선제적으로 마련할 수 있는 자료로써의 활용성이 높을 것으로 사료된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.268-268
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• 2023

전 지구적 급격한 기후변화로 인해 수문기상인자들의 비선형적 변동성이 발생함과 동시에 가뭄, 홍수와 같은 수재해의 발생빈도 및 강도가 증가하고 있는 추세이다. 이에 따라, 세계의 유수기관 (NASA, ESA 등)에서는 대기모형과 해양 모형의 결합 및 수치해석적 접근법을 활용하여 계절내-계절 (Subseasonal to seasonal; S2S) 예측치를 생산하여 제공하고 있다. 이에 따라, 본 연구에서는 European Centre for Medium-Range Weather Forecast (ECMWF)에서 산정되는 수문기상인자 (강수량, 증발산량 및 유출량)에 대한 정확도를 평가하고자 한다. 연구지역으로는 다양한 기후대 및 토지 피복으로 구성되어 있으며, El-Nino-Southern Oscillation (ENSO), Indian Ocean Diapole (IOD)와 같은 기후 현상이 빈번히 발생하는 호주지역을 대상으로 연구를 수행하였다. ECMWF S2S 자료에 대한 통계적 검증은 1) 지점 기반 관측치와 더불어 2) 물수지 모델 기반 수문 추정치 (The Australian Water Resources Assessment Landscape Model; AWRA-L)와 비교하였다. 연구 결과 S2S 강우 및 증발산량 산정치의 경우 비교적 짧은 예측기간(약 2주)에서 상대적으로 높은 상관관계 (R=0.5~0.6)와 낮은 편차 (강수량 = 0.10 mm/day, 증발산량 = 0.21 mm/day)를 나타내었다. 유출량의 경우, 강우 및 증발산량에 비해 상대적으로 낮은 정확도를 나타내었으며, 예측 기간이 길어짐에 따라 불확실성이 상당히 높아지는 것으로 확인되었다. 이는, S2S 계산과정에서 강우 및 증발산량 뿐만아니라 지표 유출로 도달하기 전까지의 수문기상인자들의 불확실성이 모두 모여 유출량의 불확실성이 높아진 것으로 확인할 수 있었다. 계절적 검증에서는, 강우 및 증발산량 모두 여름철에 높은 상관관계를 나타내었지만 불확실성은 상대적으로 큰 값을 나타내었다. 자세한 분석을 위해, 공간적인 불확실성을 분석해본 결과 ECMWF S2S가 매우 습윤하거나 건조한 지역에서 수문기상인자를 예측하는데 있어 한계성이 나타난 것을 확인하였다. 본 연구를 토대로, 추후 S2S 예측치에 대한 보정과 더불어 미래의 수재해 발생 위험도에 대한 정보를 획득하는데 적용될 수 있을 것으로 판단된다.

• Korean Journal of Construction Engineering and Management
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• v.24 no.3
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• pp.42-51
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• 2023

Freeze and burst water meter in winter causes many social costs, such as meter replacement cost, inability of water use, and secondary damage by freezing water. The government is making efforts to modernize local waterworks, and in particular, is promoting SWM(Smart Water Management) project nationwide. In this study suggests a new freeze risk notification information service based on the temperature by IoT sensor inside the water meter box rather than outside temperature. In addition, in order to overcome the quantitative and regional limitation of IoT temperature sensors installed nationwide, and AI based temperature prediction model was developed that predicts the temperature inside water meter boxes based on data acquired from IoT temperature sensors and other information. Through the prediction model optimization process, a nationwide water meter freezing risk information service was convinced.

• Journal of the Korean GEO-environmental Society
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• v.24 no.6
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• pp.5-11
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• 2023

The engineering industry heavily relies on fossil fuels such as coal and petroleum to generate energy through combustion. However, this process emits carbon dioxide into the atmosphere, leading to global warming. To mitigate this issue, researchers have explored various methods to reduce carbon dioxide emissions, one of which is carbon dioxide underground storage technology. This innovative technology involves capturing carbon dioxide from industrial plants and injecting it into the saturated ground layer beneath the earth's surface, storing it securely underground. Despite its potential benefits, carbon dioxide underground storage efficiency needs improvement to optimize storage in a limited space. To address this challenge, our research team has focused on improving storage efficiency by utilizing surfactants. Furthermore, we evaluated how different carbon dioxide states, including gaseous, liquid, and supercritical, impact storage efficiency based on their respective pressures and temperatures within the underground reservoir. Our findings indicate that using surfactants and optimizing the injection rate can effectively enhance storage efficiency across all carbon dioxide states. This research will pave the way for more efficient carbon dioxide underground storage, contributing to mitigating the environmental impact of fossil fuels on the planet.