• 한국해양공학회지
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• 제35권4호
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• pp.273-286
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• 2021

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen's formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA's) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts' newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA's) meso-scale forecasting data. We analyzed the accuracy of each model's results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

• 디지털융복합연구
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• 제12권3호
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• pp.243-248
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• 2014

최근 태양광에너지는 실시간 태양의 위치를 추적하여 모듈경사각과 이루는 갓을 산정하여 일사량을 보정하는 부분에서 컴퓨팅과의 결합이 확대되고 있다. 태양광 발전은 태양의 위치에 따라 출력변동이 심하고 출력 예측이 어려워 효율적인 전력 생산을 위해서 신재생에너지를 전력계통에 안정적으로 연계할 수 있는 기술이 필요하다. 본 논문에서는 실증단지 내 발전단지의 실시간 기상자료 예측값을 이용하여 최종적으로 태양광 발전량 예측값을 산정하는 태양광 발전을 위한 발전량 예측 모델을 분석한다. 태양광 발전량은 태양광 발전기별 모듈특성, 온도 등을 감안하여 보정계수를 입력하고 예측 지역의 위치 경사각을 분석하여 발전량 예측 계산 알고리즘을 통해 최종 발전량을 예측한다. 또한, 제안 모델에서는 실시간 기상청 관측자료와 실시간 중기 예측 자료를 입력 자료로 사용하여 단기 예측 모델을 수행한다.

• 대한안전경영과학회지
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• 제13권4호
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• pp.9-16
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• 2011

Modern systems become more complex and the demand for systems safety goes up sharply. Thus, the proper handling of the safety requirements in the systems design is getting greatly increased attention these days. Hazard analysis has been one of the active areas of research in connection with systems safety. In this paper, we study a subject on how the hazard analysis results can be incorporated in the systems design. To this end we set up a goal on how to systematically generate safety requirements that should reflect hazard analysis results and be implemented in the systems design and development. To do so, we first review the process for systems design and suggest the associated Model. Then the process and results of hazard analysis are analyzed and Modeled particularly with emphasis on the safety data. The resulting data Model incorporating both the hazard analysis and system life cycle is used in the generation of safety requirements. Based on the developed data Model, the generation of the requirements, the construction of requirements DB, and the change management later on is demonstrated through the use of a computer-aided software tool.

• 한국콘텐츠학회논문지
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• 제9권12호
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• pp.572-581
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• 2009

XMI 메타모델과 XML 메타데이터를 이용한 클래스에 대한 연구는 일반적으로 이용되고 있는 XML 메타데이터의 생성과 상당한 차이점이 있다. 대부분의 XML 시스템은 에디터기능과 데이터베이스 연동, 등 마크업언어의 생성부분에 많은 비중을 두고 개발하고 있다. 그러나 본 연구는 이와 달리 XMI 메타모델에서 추출되는 클래스 메타데이터의 마크업언어를 생성하는데 중점을 두었다. 또한 클래스내의 단위 엘리먼트의 속성부여와 모델내의 클래스 관계를 표현할 수 있도록 하였다. 마크업언어의 생성에서는 XML 스키마를 이용하여 세부적인 데이터타입의 선언이 가능하도록 하고 있다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.134-141
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• 2023

We present a novel method aimed at refining ground truth data through regularization and modification, particularly applicable when working with the original ground truth set. Enhancing the performance of deep neural networks is achieved by applying regularization techniques to the existing ground truth data. In many machine learning tasks requiring pixel-level segmentation sets, accurately delineating objects is vital. However, it proves challenging for thin and elongated objects such as blood vessels in X-ray coronary angiography, often resulting in inconsistent generation of ground truth data. This method involves an analysis of the quality of training set pairs - comprising images and ground truth data - to automatically regulate and modify the boundaries of ground truth segmentation. Employing the active contour model and a recursive ground truth generation approach results in stable and precisely defined boundary contours. Following the regularization and adjustment of the ground truth set, there is a substantial improvement in the performance of deep neural networks.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.591-594
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• 2002

Generating NC-code from 3D part model needs a lot of effort to make many decisions, including machining area, tool change data, tool data, cutting condition, etc., by using either manual or computer aided method. This effort can be reduced by integration of automated process planning and NC-code generation. In case of generating NC code with a help of the process planning system, many data mentioned from the process planning can be used. It means that we can create NC-code about a full part. In this study, integration of FAPPS(Feature based Automatic Process Planning) with a NC-code generating module is described and additional data to adapt NC-code for machine shop is discussed.

• 물과 미래
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• 제18권4호
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• pp.335-345
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• 1985

추계학적 이론을 근거로 하는 하천유량의 모의발생 모형에는 여러 가지가 있으며 이는 한정된 짧은 기간동안의 유량 실측치의 통계학적 특성을 재현시키는 일련의 장기적 유량자료를 인위적으로 발생시켜 수자원 시스템의 거동예측이나 조작기준을 보다 완벽하게 설정하기 위한 풍부한 인력 자료를 제공하자는 데 목적이 있다. 본 연구에서는 연유량의 모의발생에 주로 사용되는 Monte Carlo 모형을 연유량 자료를 구성하는 월별 하천유량의 발생에 적용 가능한가를 연구 검토하였다. 비교검토의 목적으로 실측된 월별 유량의 적정분포형을 설정한 후 Monte Carlo 방법에 의해 발생된 월별량과Autoregressive 모형중의 하나인 Thomas-Fiering의 다계절 모형에 의해 발생된 월류량의 통계학적 특성치의 실측치의 특성치와 비교하였다. 한편, 월유량 발생자료의 합성에 의한 연류량 자료의 특성치가 실측 월류량의 합성에 의한 월류량 특성치를 얼마나 잘 재현시키는가를 검사하기 위해 Monte Carlo 및 Thomas-Fiering 모형에 의해 발생시킨 연류량의 통계학적 특성치를 실측류량의 통계특성치와 비교평가하였다.

• 한국태양광발전학회지
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• 제4권2호
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• pp.45-53
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• 2018

Natural gas demand for power generation continued to increase until 2013 due to the expansion of large-scale LNG power plants after the black-out of 2011. However, natural gas demand for power generation has decreased sharply due to the increase of nuclear power and coal power generation. But demand for power generation has increased again as energy policies have changed, such as reducing nuclear power and coal power plants, and abnormal high temperatures and cold waves have occurred. If the gas pipeline pressure can be properly maintained by predicting these fluctuations, it can contribute to enhancement of operation efficiency by minimizing the operation time of facilities required for production and supply. In this study, we have developed a regression model with daily power demand and base power generation capacity as explanatory variables considering characteristics by day of week. The model was constructed using data from January 2013 to December 2016, and it was confirmed that the error rate was 4.12% and the error rate in the 90th percentile was below 8.85%.

• 정보보호학회논문지
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• 제29권6호
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• pp.1383-1392
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• 2019

적대적 공격은 기계학습 분류 모델의 오분류를 유도하는 적대적 데이터를 생성하는 공격으로, 실생활에 적용된 분류 모델에 혼란을 야기하여 심각한 피해를 발생시킬 수 있다. 이러한 적대적 공격 중 블랙박스 방식의 공격은, 대상 모델과 유사한 대체 모델을 학습시켜 대체 모델을 이용해 적대적 데이터를 생성하는 공격 방식이다. 이 때 사용되는 야코비 행렬 기반의 데이터 어그멘테이션 기법은 합성되는 데이터의 왜곡이 심해진다는 단점이 있다. 본 논문은 기존의 데이터 어그멘테이션 방식에 존재하는 단점을 보완하기 위해 감쇠 요소를 추가한 데이터 어그멘테이션을 사용하여 대체 모델을 학습시키고, 이를 이용해 적대적 데이터를 생성하는 방안을 제안한다. 실험을 통해, 기존의 연구 결과보다 공격 성공률이 최대 8.5% 가량 높음을 입증하였다.