• Journal of KIISE:Computer Systems and Theory
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• v.27 no.8
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• pp.701-709
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• 2000

Real world wind can be classified into two categories: natural wind and artificial wind. Artificial wind can be generated by human beings, air conditioners, electric fans, etc. In this paper, a model for artificial wind is presented. We also present methods to efficiently calculate the forces applied to the objects under influence of the artificial wind. Our model is designed for real-time applications such as virtual environments. A general wind generating system can be established through integrating our model with previous wind models those are concentrated on the natural wind generation.

• Journal of Digital Convergence
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• v.9 no.2
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• pp.141-152
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• 2011

The data center has become an increasingly important part of most business operations. An increasing demand for computation has led to increasing industry energy consumption. Therefore, higher-than-normal rates of energy efficiency have become a core issue in the life cycle of data center. In this paper, we proposed the framework of level diagnosis for green data centre that can be used to diagnose the levels of capability maturity model. This framework contains the 5 key areas such as construction, air-conditioning, electricity, information technology, organization and indicators that can be applied as basic level diagnosis guide for green data center.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.324-324
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• 2022

정확한 오염물질 예측은 기상학, 자연재해, 기후변화 연구 등 현장에서 필수적인 과제 중 하나이다. 주변 관측소에서 얻은 데이터를 사용하는 경우 모델 학습을 위한 불필요한 데이터로 인해 예측 결과에 왜곡 문제가 있을 수 있습니다. 따라서, 우리는 종합적인 대기질 지수 행동에 영향을 미치는 요인을 제공하는 최적의 데이터 소스를 찾기 위해 네트워크 방식을 사용했습니다. 본 연구에서는 2015년부터 2020년까지 우리나라의 6개 오염물질과 종합적인 대기질 지수 예측에 대한 네트워크 기법을 적용한 LSTM 및 DNN 모델을 적용하였다. 본 연구는 미세먼지(PM10), 초미세먼지(PM2.5), 오존(O3), 이산화황(SO2), 이산화질소(NO2), 일산화탄소(CO) 등 6가지 오염물질을 기반으로 종합적인 대기질 지수를 예측하는 2단계로 구성되어 있다. LSTM을 이용하여, 개별적으로 예측된 6가지 오염물질을 이용하여 DNN 모형을 이용하여 종합적인 대기질 지수를 예측한다. 6가지 오염물질에 대한 각 모델의 예측능력과 종합적인 대기질 지수 예측은 관측된 대기질 데이터와 비교하여 평가하였다. 본 연구는 심층신경망 모델과 네트워크 방식을 결합한 것이 높은 예측력을 제공함을 보여주었으며, 종합적인 대기질 지수 예측을 위한 최적의 모델로 선정되었다. 재난관리의 필요성이 증가함에 따라 네트워크 방식의 딥러닝 모델은 자연재해 피해를 줄이고 재난관리를 개선할 수 있는 충분한 잠재력을 가질 것으로 기대된다.

• Journal of Navigation and Port Research
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• v.31 no.8
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• pp.733-739
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• 2007

Demand for Sea & Air intermodal transport has increased between north-China costal cities and Incheon since China's international airline network was not established completely. It will be big opportunity for Incheon to be logistics hub of Sea & Air intermodal transport in the north-east Asia, musing large sea-air cargoes to be transferred at the port of Incheon. Therefore, this study aims to propose competitive strategy on Sea & Air intermodal transport system of Incheon. In this analysis results, this paper shows that sea & air cargoes rather from china to U.S. than from China to Europe is very significant, considering geographically for Incheon and is also devote to not only providing high quality services but also activating RFS(Road Feeder Service) system, enlarging toward Shanghai, Weihai, and Yantai.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.1
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• pp.53-61
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• 2021

This paper is a preliminary study for the development of a drone for inspection inside a boiler in a thermal power plant, which is a high-temperature environment, and validated whether the drone can fly normally through a high-temperature environment simulation using AirSim. In a high-temperature flight environment, the aerodynamic characteristics of the air density and viscosity are different from room temperature, and the flight performance of the drone is also changed accordingly. Therefore, in order to confirm the change of the aerodynamic characteristics of the propeller according to the temperature change, the propeller analysis and thrust test through JBLADE, and the operation characteristics prediction through the electric propulsion system performance prediction model were performed. In addition, the analysis and performance prediction results were applied to AirSim for simulation, and the aircraft redesigned through the analysis of the results. As a result of the redesign, it was confirmed that about 65% of the maximum power used before the redesign was reduced to 52% to obtain the necessary thrust when hovering in an environment of 80℃.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.

• Journal of Advanced Navigation Technology
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• v.28 no.5
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• pp.760-766
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• 2024

Machine learning and deep learning are being researched in various fields and applied in real life. Designing reliable models is crucial, and understanding the results of these models is necessary. This paper analyzes the impact of variables on prediction values using SHAP. Prediction models for PM_2.5 were designed using DNN and LSTM algorithms. The training and test data were composed by selecting weather data and air pollutant data through correlation analysis. The RMSE and accuracy for AQI categories were checked for both prediction models, with the LSTM algorithm showing slightly better performance. The contribution of variables to the prediction values of both models was confirmed using SHAP. It was found that air pollutant data had a high contribution in predicting PM_2.5, and temperature among weather data had a high contribution in the prediction process of both models. Both models showed that high values of temperature, wind speed, and sea level pressure decreased prediction values, while low values increased them. For NO₂ , PM₁₀, and SO₂, the LSTM model showed a bidirectional impact on prediction values, unlike the DNN model.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.383-383
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• 2023

오늘날 수문학 분야에서는 유역에 대한 강우-유출 시뮬레이션을 머신 러닝(ML: Machine Learning)을 활용하여 다양한 연구를 실행하고 있다. 본 연구에서는 시간별 강우-유출 예측 모델인 GR4H(Génie Rural à 4 paramètres Horaires)를 사용하여 충주댐 유역을 대상으로 연구를 수행하였다. 유역의 속성에 따라서 모델의 성능이 어떻게 달라지는지 비교하여 특성에 맞는 모델을 알아내고. 또한 이 과정에서 기상 및 유출 데이터의 보정 길이를 가지고 어느 정도의 데이터 기간이 모델에서 좋은 성능을 보이는지 파악하였다. 뿐만 아니라 모델에 필요한 선행기간의 데이터가 있는 경우와 없는 경우를 비교하여 어떠한 차이를 보이는지, 그리고 선행기간은 얼마나 필요한지 연구를 통하여 알아냈다. 본 연구를 통하여 충주댐 유역에 대한 모델의 적용성 및 성능을 파악하고 수문 모형 구축에 제한이 있는 유역에 대해서도 사용이 가능한지 판단한다. 실험 유역의 관측 값을 모델에 입력한 후 각 모델에 해당하는 매개변수의 최적값을 찾아내는 과정을 거쳐 시뮬레이션을실 행했다. 본 연구에서 사용한 강우-유출 모델인 GR4H는 프랑스의 INRAE-Antony(Institut National de la recherche agronomique-Antony)에서 만들어진 airGR의 일종으로, 시간별 강우-유출 예측을 위해 개발된 공정 기반(process-based)의 집중적, 개념적 수문학 모델이다. 4개의 매개변수(parameter)가 있으며 이는 유역의 특정 속성을 나타낸다. GR4H를 시뮬레이션 하는 과정에서 매개변수의 최적화를 위해 적절한 보정 길이를 파악하여야 한다. 이러한 과정은 4년, 5년, 6년 등 1년씩 데이터의 양을 늘려가며 매개변수를 최적화한다. 이 과정에서 기상 및 유출 데이터의 적절한 보정 길이를 찾아낸다. 시뮬레이션을 통해 얻은 데이터를 관측 값과 비교하여 모델의 성능을 평가하고 다른 관측 값을 통해 시뮬레이션을 실행하여 검증을 거친다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.803-804
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• 2012

• Proceedings of the Korea Air Pollution Research Association Conference
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• 1996.04a
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• pp.140-143
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• 1996