• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2003년도 International Symposium on Clean Environment
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• pp.212-215
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• 2003

The aim of this study was to test the air quality modelling system (HIRES-AIRCHEM) of The University of New South Wales particularly with regard to forecast ozone distribution. This was achieved by assimilating the New South Wales State EPA emissions inventory consisting of road and non-road sources and running the system over the Sydney metropolitan area for the four day period 25-28 February 1998. During this period ozone readings exceeded the EPA＇s goal of 80ppb on several occasions. The model forecasts of ozone distribution verified well with the EPA＇s ozone readings. This result has important implications for possible future use of the system as a tool for routinely assessing air quality.

• 한국시스템다이내믹스연구
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• 제1권1호
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• pp.103-131
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• 2000

If a system such as a port has a large boundary and complexity, and the system's substance is considered as a black box, forecast accuracy will be very low. Futhermore various components in a port exert significant influence on each other. To copy with these problem the form of structure models were introduced by using SD method. The Competitive Ports Model had several sub-systems consisting of each Unit Port models, and each Unit Port model was made by quantitative, qualitative factors and their feedback loops. The fact that all components of one port have influence on the components of the other ports should be taken into account to construct Competitive Port Models. However, with the current approach that is impossible, and in this paper therefore, models were simplified by HFP adapted to integrate level variables of unit port models. Although many studies on modelling of port competitive situation have been conducted, both theoretical frame and methodology are still very weak. In this study, a new algorithm called ESD(Extensional System Dynamics) for the evaluation of port competition was presented, and applied to simulate port systems in northeast asia.

• 한국실내디자인학회논문집
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• 제29호
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• pp.19-26
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• 2001

The aim of this study is to forecast characteristics of dynamic expression in the architecture design by analyzing characteristics and trends in dynamic expressions of the architecture classified by period. The various architectural trends, even in the transition of new paradigm after collapse of the CIAM, consistently expresses dynamics of architecture. In this study, it regards the inherent characteristics of the architecture design as expression of space organization and modelling and it views dynamic expression is important element of the architecture design. The case study shows that the architecture of passed 50 years, the key element of the dynamic expressions are expression of new technology and expression of one's experience. Also, it shows that the dynamic expressions of architecture in 1990's are expression of new technology which consists of lightness, fragmentation, and symbolism that reflects the trend of arts in 1990's.

• Structural Engineering and Mechanics
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• 제19권3호
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• pp.237-260
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• 2005

This paper presents an experimental as well as a numerical analysis of the in-plane shear behaviour of hollow, $870{\times}840{\times}100mm$ masonry walls, externally strengthened with FRP composites. The experimental approach is devoted to the evaluation of the effectiveness of different composite strengthening configurations and the methodology consists in the diagonal compression of masonry walls. The numerical study assesses the stress and strain state distribution in the unreinforced and strengthened panels using a commercial finite element code. The effect of FRP reinforcement on the masonry behaviour and the capability of modelling to forecast a representative failure mode of the unreinforced and reinforced masonry walls is investigated.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• 전기전자학회논문지
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• 제21권1호
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• pp.13-23
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• 2017

공항의 안정적인 운영을 위하여 승객의 단기예측은 매우 중요하다. 본 논문에서는 인천공항의 출입국자 예측을 위하여 출입국자의 대부분을 차지하는 한국인과 중국인의 출국자의 예측 모델링을 수행하였다. 예측 모델링 정확도 향상을 위해 네이버와 바이두 검색 트렌드 데이터를 활용하였다. 출국자 수들과 관련 검색 트렌드 데이터 간 Granger Causality 테스트를 수행하여 상관관계가 있음을 확인하였다. "출국자 수" 단독으로 예측하는 것보다 "출국자 수"와 "검색어 트렌드" 자료를 합하여 예측하는 것이 정확도가 향상됨을 알 수 있었다. 이는 검색이 어떤 일을 수행하기 전에 하는 행위이기 때문이고, 검색 트렌드 데이터 내에 태생적으로 예측 기재가 존재함을 본 연구를 통하여 확인할 수 있었다.

• 한국지구과학회지
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• 제26권3호
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• pp.268-275
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• 2005

대표적인 엘니뇨 지수인 태평양 Nino 해역의 표층 수온을 예측하기 위해 비선형 통계모델 중의 하나인 신경망 기법을 적용하였다. 신경망 모델 학습 과정의 입력 자료로 1951년부터 1993년까지의 태평양 해역$(120^{\circ}\;E,\;20^{\circ}\;S-20^{\circ}\;N)$ NCEP/NCAR의 재분석 표층 수온 편차의 경험적 직교함수 7개 주모드를 사용하였고, 그 중 1994년부터 2003년까지의 10년 결과를 분석하였다. 모든 해역에서의 9개월까지의 신경망 모델의 예측력은 비교적 우수하였으며, 특히 1997년과 1998년의 강한 엘니뇨의 발달 및 소멸도 잘 예측함을 확인할 수 있었다. 해역별로는 Nino3 지역의 예측성능이 가장 높았으며, 9개월 이후부터는 그 예측력이 급격히 감소하였다. 한편 지역적인 영향이 커 예측력이 낮은 동태평양 연안의 Nino1+2 지역은 9개월 이후에도 예측력의 감소가 관찰되지 않았다.

• 한국수자원학회논문집
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• 제52권7호
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• pp.451-461
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• 2019

장기 기상전망 기반 댐 유입량 전망은 가뭄 대비, 용수 공급 관리 등에 활용성이 높다. 본 연구에서는 국내 7개 다목적댐 유역에 대해 유입량 전망을 수행하고 장기 기상전망 정확도가 댐 유입량 전망 정확도에 미치는 영향을 분석하였다. 강우-유출 모델의 입력자료로 활용된 장기 기상전망 자료는 기상청 GloSea5의 과거재현자료(hindcast) 및 미래전망자료(forecast)를 활용하였다. 강우-유출 모델은 다양한 특성을 가지고 있는 TANK, ABCD, K-DRUM, PRMS를 활용하였다. 댐 유입량 전망 정확도는 과거재현기간(1996~2009)과 미래전망기간(2015~2016)에 대하여 평가하였다. 댐 유입량 전망 평가결과 전망값은 관측값에 비해 과소추정하는 경향을 보였으며, 매개변수 검보정이 적절히 수행된 강우-유출 모델은 댐 유입량 전망 정확도에 미치는 영향이 거의 없는 것으로 나타났다. 반면 장기 기상전망 자료, 특히 강수량은 댐 유입량 전망 정확도에 매우 큰 영향을 미치는 것으로 나타났다. 현업에서 댐 유입량 전망 자료 활용시 과소추정하는 경향을 고려하여 활용할 필요가 있다. 향후 댐 유입량 전망 정확도 개선은 강우-유출 모델 보다 장기 기상전망의 강수량 정확도 향상을 위주로 수행할 필요가 있다.

• 한국건설관리학회논문집
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• 제2권2호
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• pp.90-97
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• 2001

공공기관에서 발주하는 건설공사의 계약에 있어서 가장 중요한 것은 합리적인 방법으로 결정된 적정한 예정가격을 기준으로 계약을 체결하는 것이다. 적정예정가격을 산정하기 위해서는 먼저 기수행한 실적공사비를 근간으로 하여 건설공사의 다양성과 불확실성을 반영할 수 있는 비용자료의 축적이 선결과제라 할 수 있다. 따라서 본 연구는 실적공사비적산방식에 따라 과거의 가장 유사한 실적자료에 기초한 확률적 비용개념을 도입하여 실적공사비 데이터 베이스 구축 모형과 이를 통한 예정공사비 산정방법 및 전산화 방안을 제시하고자 한다.