• Fire Science and Engineering
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• v.28 no.5
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• pp.23-29
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• 2014

The present study has been conducted to investigate the validity of the computational fire model and the results predicted by BRANZFIRE zone model and FDS field model are compared with a real scale fire test with spray fire in a multi-compartment. The liquid spray fires fueled with toluene and methanol are used as the fire source and the quantitative measurement of heat release rate is performed in an isolated ISO-9705 compartment with a standard door opening. The temperature field predicted by FDS model showed good agreement with the measurement in the fire room and the corridor, and BRANZFIRE model also gave acceptable result in spite of its simplicity and roughness. The mean temperature predicted by FDS model corresponds with measurement within maximum discrepancy range of 25% and the overall mean value of FDS model matched well with experimental data less than 10%. This study can contribute to establish the limitation and application scope of computational fire model and provide reference data for applying to reliable fire risk assessment.

• Journal of The Korean Association For Science Education
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• v.36 no.3
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• pp.389-397
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• 2016

This study aims to test the efficacy of English-based automated computer scoring models and machine translation to score Korean college students' written responses on natural selection concept items. To this end, I collected 128 pre-service biology teachers' written responses on four-item instrument (total 512 written responses). The machine translation software (i.e., Google Translate) translated both original responses and spell-corrected responses. The presence/absence of five scientific ideas and three $na{\ddot{i}}ve$ ideas in both translated responses were judged by the automated computer scoring models (i.e., EvoGrader). The computer-scored results (4096 predictions) were compared with expert-scored results. The results illustrated that no significant differences in both average scores and statistical results using average scores was found between the computer-scored result and experts-scored result. The Pearson correlation coefficients of composite scores for each student between computer scoring and experts scoring were 0.848 for scientific ideas and 0.776 for $na{\ddot{i}}ve$ ideas. The inter-rater reliability indices (Cohen kappa) between computer scoring and experts scoring for linguistically simple concepts (e.g., variation, competition, and limited resources) were over 0.8. These findings reveal that the English-based automated computer scoring models and machine translation can be a promising method in scoring Korean college students' written responses on natural selection concept items.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.3
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• pp.72-81
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• 2022

Positions of five drifting buoys deployed on August 2020 near southwestern area of Jeju Island and numerically predicted velocities were used to develop five Artificial Intelligence-based models (AI models) for the prediction of particle tracks. Five AI models consisted of three machine learning models (Extra Trees, LightGBM, and Support Vector Machine) and two deep learning models (DNN and RBFN). To evaluate the prediction accuracy for six models, the predicted positions from five AI models and one numerical model were compared with the observed positions from five drifting buoys. Three skills (MAE, RMSE, and NCLS) for the five buoys and their averaged values were calculated. DNN model showed the best prediction accuracy in MAE, RMSE, and NCLS.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.1117-1119
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• 2015

In this letter, we propose a mathematical model of variable-length payloads transmitted in EDCA and transmitted using MU-MIMO. Assuming fixed-length or the use of mean value of payload length leads to discordant results while calculating the total payload length of variable-length frames transmitted within a fixed duration. Using the proposed model results in accurate results (less than 3% relative errors) for total payload length under variable-length traffic.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.18 no.4
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• pp.359-367
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• 2000

This paper suggests that coefficients models of the Earth's gravitational potential can be used to calculate height anomalies which are then reduced to the geoid undulation to determine more precise geoid undulation. The potential coefficients and modified coefficients of EGM96 and KODEM33 digital elevation model in and around the Korean peninsula were used for this study. The magnitude of height anomaly computed in this study reached 0.025 m and the mean vaule showed -0.015 m. In this study, geometrical geoid undulation was derived from GPS/Leveling data for evaluating the precisely computed geoid undulation. In comparison with geometric and gravimetric geoid undulations, mean value and standard deviation of the differences showed 0.0114 m and 0.2817 m respectively and it showed the improvement of results.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.10
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• pp.49-55
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• 2008

This paper presents a novel evaluation method of life expectancy of power system equipment. The life expectancy means expected remaining lifetime; it can be usefully utilized to maintenance planning, equipment replacement planning, and reliability assessment. The proposed method is composed of three steps. Firstly, a cumulative probability for future years is evaluated for targeted age year. Secondly, the cumulative probability is modeled by well-blown cumulative distribution function(CDF) such as Weibull distribution. Lastly, life expectancy is evaluated as the mean value of the model. Since the model CDF is established in the proposed method, it can also evaluate the probability of equipment retirement within specific years. The developed method is applied to examples of generators of combined cycle power plants to show its effectiveness.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1186-1189
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• 2016

A distributed uplink power control algorithm based on flocking model is proposed to improve the energy efficiency of mobiles station (MS) in cellular networks. As each bird in a flock matches its velocity with the average velocity of the adjacent birds, each MS in a cell matches its uplink rate with the average uplink rate of the co-channel MSs in adjacent cells by controlling its transmission power. Results show that the proposed algorithm effectively reduces the power consumption in the MS, while maintaining a low outage probability, which eventually improves the energy efficiency of the MS.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.11
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• pp.1272-1279
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• 2012

In this paper, models for the average-year rain rate distribution and the correlation between the worst-month and the average-year rain rate distribution in domestic environment were proposed, using the rainfall measurement data with 1-minute integration time of Korea Meteorological Administration. Comparison of the proposed model with the existing ITU-R model showed that the average rain rate of the proposed model for the exceed time rate of 0.01 % is about 28 % higher than that of the ITU-R recommendation. In addition, the correlation model between the worst-month and the average-year rain rate distribution was quite different from the ITU-R model. It is recommended that the domestic rain rate distribution model should be used for calculation of the statistical characteristics of rain attenuation(exceeded-time-rate distribution of rain attenuation) which is essential for the design of wireless communication systems in domestic environment.

• Proceedings of the Korea Database Society Conference
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• 1999.10a
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• pp.183-189
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• 1999

오늘의 기업에게 정보기술이란 필수요소이다. 정보기술을 효과적으로 활용하기 위해서는 IT 인프라가 체계적으로 구축되어 있어야 한다. 해당 조직에 적합한 정보기술 기반구조를 설계하고 이의 도입을 위한 구체적인 계획을 수립하기 위해서는 체계적이고 효과적인 정보계획 수립(Information System Planning: ISP)이 필요하다. 그러나 정보계획수립 프로젝트의 상당수가 실패로 그치고 있다. 특히 정보기술의 지속적인 변화 때문에 수립한 정보기술 기반구조 계획안이 실제 구현되지 못하고 계획으로만 남는 경향이 있다. 이러한 ISP의 어려움을 해결하기 위해서는 정보기술 참조모델(reference model)을 적극적으로 활용할 필요가 있다. 즉, 조직의 정보시스템에 공통적으로 적용할 수 있는 IT 인프라나 표준 아키텍쳐를 바탕으로 정보계획수립을 수행해 나가는 방식이 필요하다. 이와 같은 참조모델 기반의 정보계획 수립은 새로운 아키텍쳐를 추출하고 표준화를 이룸으로써 프로젝트의 생산성을 높일 수 있다는 장점을 가지고 있다. 기존의 ISP 연구는 ISP의 필요성, 과정, 성공요인 등에 국한되어 왔으며, 방법론에 대한 연구는 미비한 편이다. 최근들어 ISP의 체계적인 분류나 참조모델 기반 계획수립의 필요성이 제기되었다. 그러나 아직까지 이와같은 접근에서 참조 모델을 어떻게 구축하고 활용해 나갈 것인가에 대한 연구는 부족한 실정이다. 따라서 본 논문에서는 참조모델을 구축하기 위한 다양한 접근방법과 각각의 특징을 제시한다. 나아가서 해당 조직의 상황이나 요구수준에 따라 적합한 접근방법을 선택할 수 있게 해주는 방안을 제시한다.타냈으며, 평가결과에 대해 여러 가지 방법으로 분석하였다. 첫째, 동종제품간 평가분석을 통하여 각각의 제품을 비교하였으며, 둘째 소프트웨어 종류별 평가로 제품을 응용소프트웨어, 응용개발도구, 시스템 소프트웨어로 분류하여 평균값으로 비교하였다. 셋째, 국내외 제품별 평가분석으로 전체 제품을 국내제품과 국외제품으로 분류하여 비교하였으며, 마지막으로 총괄분석을 통해 가중치를 적용하여 전 제품의 점수를 비교하였다. 여기에서는 각 제품의 평균점수에 대한 차이를 95%의 유의수준으로 T-Test를 실시하였다.uted to the society, and what the socioeconomic impacts are resulted from the program. It would be useful for the means of (ⅰ) fulfillment of public accountability to legitimate the program and to reveal the expenditure of pubic fund, and (ⅱ) managemental and strategical learning to give information necessary to improve the making. program and policy decision making, The objectives of the study are to develop the methodology of modeling the socioeconomic evaluation, and build up the practical socioeconomic evaluation mod

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.