• 한국해양환경ㆍ에너지학회지
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• 제13권4호
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• pp.234-240
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• 2010

연안개발로 인해 발생하는 해양환경 변화를 정확히 예측하여 해양환경을 효율적으로 관리하기 위해서는 정확한 조위 및 조류 분포에 관한 자료의 확보가 필수적이다. 그러나 현재 대부분의 환경영향평가에서는 조석 수치모의에서 제한된 조석분조 만을 사용하여 조위와 조류분포를 예측하여 많은 분조의 합성에 의해 발생되는 실제 조석현상을 정확하게 계산하지 못하고 있으며, 이로 인해 환경영향평가에 오류가 발생하고 있다. 본 연구에서는 제한된 분조의외해 개방경계에서 조화상수를 가지고도 연안에서 관측된 조위자료를 활용하여 실시간으로 정확하게 조위 및 조류 분포를 예측할 수 있는 방법을 제안하였다. 4개 분조에 의한 조위와 38개 분조에 의한 조차의 비 그리고 모의조차와 관측조차의 비를 가지고 보정한 조위 예측결과는 관측조위와 잘 일치하였다.

• 한국도로학회논문집
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• 제10권3호
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• pp.199-208
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• 2008

포장상태 예측은 의사결정과정에서 포장의 공용성능을 평가하고 사업대상구간의 우선순위를 선정하기 위한 적정한 정보를 제공해준다. 근래들어 현재의 포장상태가 장래에 어느 정도 저하되는지를 예측하려는 많은 접근이 있었으나 포장의 서비스수명을 적정히 예측하는 데에는 한계를 보여왔다. 본 논문에서는 포장상태 예측방법을 개선하기 위하여 포장상태 공용성모형과 포장상태 예측모형을 개발하였다. 공용성 모형은 실제 포장상태 분석결과를 회귀분석하여 포장의 종류별, 교통량별로 백분위 50%, 25%, 15%, 5%의 확률분포 모형을 도출한 것이다. 예측모형은 앞서 도출된 공용성모형 모형식을 기준으로 하여 대상구간 각각의 포장상태 측정값에 의해 포장상태 확률을 결정한다. 개발된 예측모형의 검증을 위하여 비교대상구간을 선정하였고, HPCI의 평균값 표준편차, 3.0이하 비율을 비교분석하였다. 이를 통하여 기존예측모형이 안고 있는 교통량, 재령, 현재 포장 상태를 고려하여 보다 현실에 부합되는 포장상태를 예측하는 방법을 제공하고자 한다.

• 대기
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• 제30권2호
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• pp.195-207
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• 2020

This study aims to introduce the structure of the impact-based heat health warning system on 165 counties in South Korea developed by the National Institute of Meteorological Sciences. This system was developed using the daily maximum perceived temperature (PTmax), which is a human physiology-based thermal comfort index, and the Local ENSemble prediction system for the probability forecasts. Also, A risk matrix proposed by the World Meteorological Organization was employed for the impact-based forecasts of this system. The threshold value of the risk matrix was separately set depending on regions. In this system, the risk level was issued as four levels (GREEN, YELLOW, ORANGE, RED) for first, second, and third forecast lead-day (LD1, LD2, and LD3). The daily risk level issued by the system was evaluated using emergency heat-related patients obtained at six cities, including Seoul, Incheon, Daejeon, Gwangju, Daegu, and Busan, for LD1 to LD3. The high-risks level occurred more consistently in the shorter lead time (LD3 → LD1) and the performance (r_s) was increased from 0.42 (LD3) to 0.45 (LD1) in all cities. Especially, it showed good performance (r_s = 0.51) in July and August, when heat stress is highest in South Korea. From an impact-based forecasting perspective, PTmax is one of the most suitable temperature indicators for issuing the health risk warnings by heat in South Korea.

• Journal of Electrical Engineering and Technology
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• 제10권3호
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• pp.877-887
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• 2015

Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm.

• The Journal of Asian Finance, Economics and Business
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• 제8권1호
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• pp.665-673
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• 2021

Default has become an extreme concern in the current world due to the financial crisis. The previous prediction of companies' bankruptcy exhibits evidence of decision assistance for financial and regulatory bodies. Notwithstanding numerous advanced approaches, this area of study is not outmoded and requires additional research. The purpose of this research is to find the best classifier to detect a company's default risk and bankruptcy. This study used secondary data from the Pakistan Stock Exchange (PSX) and it is time-series data to examine the impact on the determinants. This research examined several different classifiers as per their competence to properly categorize default and non-default Pakistani companies listed on the PSX. Additionally, PSX has remained consistent for some years in terms of growth and has provided benefits to its stockholders. This paper utilizes machine learning techniques to predict financial distress in companies listed on the PSX. Our results indicate that most multi-stage mixture of classifiers provided noteworthy developments over the individual classifiers. This means that firms will have to work on the financial variables such as liquidity and profitability to not fall into the category of liquidation. Moreover, Adaptive Boosting (Adaboost) provides a significant boost in the performance of each classifier.

• Nuclear Engineering and Technology
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• 제30권3호
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• pp.212-221
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• 1998

In the present study, a method for the dynamic analysis of a reactor core is developed. Peak responses for the motions induced from earthquake are obtained for a core model. The dynamic responses such as fuel assembly shear force, bending moment, axial force and displacement, and spacer grid impact loads are investigated. Prediction of fuel assembly stress during an earthquake requires development of a fuel assembly stress analysis model capable of interfacing with the models and results discussed in the dynamic analysis of a reactor core. This analysis uses beam characteristics which describe the overall fuel assembly response. The stress analysis method and its application for the case of an increased seismic level are also presented.

• Steel and Composite Structures
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• 제18권5호
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• pp.1291-1303
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• 2015

Damage caused by low velocity impact is so dangerous in composites because although in most cases it is not visible to the eye, it can greatly reduce the strength of the composite material. In this paper, damage development in U-section glass/polyester pultruded beams subjected to low velocity impact was considered. Different failure criteria such as Maximum stress, Maximum strain, Hou, Hashin and the combination of Maximum strain criteria for fiber failure and Hou criteria for matrix failure were programmed and implemented in ABAQUS software via a user subroutine VUMAT. A suitable degradation model was also considered for reducing material constants due to damage. Experimental tests, which performed to validate numerical results, showed that Hashin and Hou failure criteria have better accuracy in predicting force-time history than the other three criteria. However, maximum stress and Hashin failure criteria had the best prediction for damage area, in comparison with the other three criteria. Finally in order to compare numerical model with the experimental results in terms of extent of damage, bending test was performed after impact and the behavior of the beam was considered.

• 한국전산유체공학회지
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• 제18권3호
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• pp.94-101
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• 2013

In the present study, a practical method to predict cavitation erosion, which caused a critical damage on hydraulic machineries, was developed. Impact and critical velocities were defined to develop a practical method for the prediction of cavitation erosion. To develope the practical method, the computational fluid dynamics (CFD) was introduced. Cavitating flows with erosion in a converging-diverging nozzle and around a hydrofoil were simulated by developed and validated code. Based on the CFD results, the cavitation erosion coefficient was derived by a curve fitting method. The cavitation erosion coefficient was formulated as the function of the cavitation and Reynolds numbers. A cavitating flow in an axisymmetric nozzle followed by radial divergence was simulated to validate the developed practical method. For the application to a propeller, a cavitating flow around a propeller was simulated. Predicted damage extent showed similar with damaged full-scale propeller blade.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 추계학술대회논문집
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• pp.512-517
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• 2000

The road traffic noise becomes aggravated due to the rapid increase of vehicles. It has a great effect on the dwelling environment. Therefore we investigate the characteristics and sources of the road traffic noise through grasping the status of the road traffic noise. This report is concerned with the description of the various factors affecting the generation and propagation of outdoor traffic noise. It is particularly concerned with the mathematical interpretation of these processes and the resulting development of prediction techniques which are now broadly used for both the environment impact assessment of road traffic noise and the planning and design of roads and adjoining land use.

• KDI Journal of Economic Policy
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• 제44권4호
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• pp.79-108
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• 2022

This study examines the impact of sentiment shock, which is defined as a stochastic innovation to the Housing Market Confidence Index (HMCI) that is orthogonal to past housing price changes, on aggregate housing price changes and housing price volatility. This paper documents empirical evidence that sentiment shock has a statistically significant relationship with Korea's aggregate housing price changes. Specifically, the key findings show that an increase in sentiment shock predicts a rise in the aggregate housing price and a drop in its volatility at the national level. For the Seoul Metropolitan Region (SMR), this study also suggests that sentiment shock is positively associated with one-month-ahead aggregate housing price changes, whereas an increase in sentiment volatility tends to increase housing price volatility as well. In addition, the out-of-sample forecasting exercises conducted here reveal that the prediction model endowed with sentiment shock and sentiment volatility outperforms other competing prediction models.