• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.60.1-60.1
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• 2011

본 연구에서는 풍력 터빈 블레이드의 다분야 통합 최적 설계를 위하여, 진동하는 비정상 공력하중에 의한 작동 수명을 고려한 최적화 과정을 수행하였다. 최적화 대상으로는 NREL의 1.5MW 급 풍력터빈을 baseline 으로 하였고, NREL의 FAST 프로그램을 이용하여 발전기의 정격 출력 및 블레이드에 작용하는 비정상 공력 하중 특성을 분석하였다. 최적화 수행 시 블레이드 형상의 효율적인 구현을 위해 형상모델링 함수를 이용하여 코드 길이와 트위스트 분포를 모델링하였다. 그리고 상용 MDO Framework 인 Piano를 이용하여 블레이드 루트부의 비정상 공력하중 조건을 완화시키는 최적화 설계를 수행하였다. 정격출력을 유지하면서도 Out of Plain 방향의 하중 조건을 개선하여 보다 긴 작동 수명을 기대할 수 있는 블레이드 형상을 설계하였다.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.19-24
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• 2000

The objective of this study is to develope a computer simulation model and estimate theoretically the transient performance characteristics of heat exchangers in an automotive air-conditioning system. To do that, the mathematical modelling of heat exchangers, such as evaporator and condenser, is presented first of all. For detail calculation, evaporator and condenser are divided into many sub-sections. Each sub-section is an elemental volume for transient modelling. The elemental volume is assumed to consist of three components, refrigerant, tube with fin, and air, and various properties including temperatures of three components are determined step along sub-sections. The properties of refrigerant R134a and air are calculated directly in the program. The heat transfer coefficients and pressure drop in single or two phase are also calculated by suitable empirical correlations. The overall tendencies of the simulation results were agreed well with those of actual situation.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.11
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• pp.1089-1096
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• 2001

The cool-down performance after soaking is very important in an automotive air-conditioning system and is considered as the key design variable. Therefore, understanding of the overall transient characteristics of the system is essential to the preliminary design as well as steady-state characteristics. The objective of this study is to develop a computer simulation model and estimate theoretical1y the transient performance of an automotive air-conditioning system. To accomplish this, a mathematical modelling of each component, such as compressor, condenser, expansion valve, and evaporator, is presented first of all. For a detailed calculation, condenser and evaporator are divided into many subsections. Each sub-section is an elemental volume for modelling. In models of expansion valve and compressor, dynamic behaviors are not considered in an attempt to simplify the ana1ysis, but the quasi-static ones are just considered, such as the relation between mass flow rate and pressure drop in expansion device, polytropic process in compressor, etc. The developed simulation model is validated with a comparison to laboratory test data of an automotive air-conditioning system. The overall time-tracing properties of each component agreed fairly well wish those of test data in this case.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.387-393
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• 2002

The enhancement technique of noise signal using mixture HFM (Midden Filter Model) are proposed. Given the parameters of the clean signal and noise, noisy signal is modeled by a linear state-space model with Markov switching parameters. Estimation of state vector is required for estimating original signal. The estimation procedure is based on mixture interacting multiple model (MIMM) and the estimator of speech is given by the weighted sum of parallel Kalman filters operating interactively. Simulation results showed that the proposed method offers performance gains relative to the previous results with slightly increased complexity.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.226-230
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• 2009

Intelligent Tutoring System (ITS) is a procedure of analyzing collected data for teaming, making a strategy and performing adequate service for learners. To perform suitable service for learners, modeling is the first step to collect data from the process of their learning. The model, however, cannot be authentic if collected data can contain learners' inconsistent behaviors or unpredictable learning inclination. This study focused on how to sort normal and abnormal data by analyzing collected data from learners through visualization. A model has been set up to assort unusual data from collected learner's data by using DOLLS-HI which makes possible to diagnose learner's learning propensity based on housing interior learning contents in the experiment. The created model has been confirmed its improved reliability comparing to previous one.

• Journal of KIISE
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• v.41 no.12
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• pp.1066-1074
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• 2014

In this paper, we present a visual analytics system that uses serial-correlation to detect an abnormal event in spatio-temporal data. Our approach extracts the topic-model from spatio-temporal tweets and then filters the abnormal event candidates using a seasonal-trend decomposition procedure based on Loess smoothing (STL). We re-extract the topic from the candidates, and then, we apply STL to the second candidate. Finally, we analyze the serial-correlation between the first candidates and the second candidate in order to detect abnormal events. We have used a visual analytic approach to detect the abnormal events, and therefore, the users can intuitively analyze abnormal event trends and cyclical patterns. For the case study, we have verified our visual analytics system by analyzing information related to two different events: the 'Gyeongju Mauna Resort collapse' and the 'Jindo-ferry sinking'.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 1999.10a
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• pp.28-28
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• 1999

본 연구에서는 가스 발생기에서 발생되는 고온, 고압의 가스를 이용하여 유도탄을 수직 발사하는 사출 시스템에 대해 해석적 연구를 수행하였다. 사출 시스템에 의한 수직발사 방식은 발사관내에 설치된 가스 발생기에 의해 생성된 가스가 사출 실린더의 피스톤을 구동시켜, 피스톤에 연결된 유도탄을 요구되는 높이로 사출 시킨 후 유도탄이 점화되는 발사방식이다. 이러한 발사방식은 유도탄 자체의 부스터 발사방식에 비해 유도탄의 화염에 의한 영향이 적다. 현재 사출 시스템은 가스발생기, 가스 튜브, 사출 실린더와 피스톤으로 구성되어있다. 본 논문은 가스 발생기에서 사출 실린더까지의 내부 유동장을 일차원적으로 모델링하였고, 가스발생기, 가스튜브, 실런더 내의 유동과 열전달 과정 및 유도탄의 동적거동에 대한 미분방정식을 연립하여 4th-order Runge-Kutta 방법으로 계산하였다. 또한 가스튜브와 사출 실린더의 열전달 손실에 대하여 1차원 비정상 열전도 방정식의 수치적 계산을 통해 에너지 손실을 계산하였다. 특히 해석에 사용된 작동유체인 추진제 가스의 열학적 상태량은 온도 함수의 5차 다항식으로 표현하여 사용하였다. 이론적인 해석을 통해 사출 장치 시스템의 성능 요구조건과 신뢰성을 만족시키기 위한 가스발생기의 추진제 그레인 및 사출 시스템 설 계 조건을 도출하였다.

• Proceedings of the KIEE Conference
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• 2002.11d
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• pp.43-45
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• 2002

가스차단기의 성능은 노즐재질, 접점분리 속도 차단기의 치수 그리고 마크시간 등에 좌우되며 이러한 일련의 현상을 고찰하기 위한 유동현상을 모의하기 위해서는 기본적으로 두 접점의 상대운동 및 접점 사이에서 발생하는 아크 플라즈마(arc plasma)에 의한 전도, 대류, 복사현상 뿐만 아니라 아크전류에 의한 로랜츠힘(Lorentz's force), 용삭(ablation)에 의한 화학작용 등과 같은 매우 복잡한 물리적 현상을 고려해야 한다. 본 연구에서는 차단과정 중 대전류 영역에서의 아크특성과 마크에서 방출되는 강한 복사에너지에 의해 발생하는 PTFE 증기에 의한 영향을 고려하기 위해서 상용 CFD 프로그램인 PHOENICS에 아크 모델링과 고온에서의 $SF_6$-PTFE 혼합가스의 물성치 대입을 위한 보조 프로그램을 작성하여 해석을 수행하였다.

• Convergence Security Journal
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• v.23 no.2
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• pp.103-113
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• 2023

In recent years, Unmanned Aerial Vehicles (UAV) have been widely used in various industries. However, as the depend ence on UAV increases rapidly, concerns about the security and safety of UAV are growing. Currently, various vulnerabili ties such as stealing the control right of the UAV or the right to communicate with the UAV in the web application are being disclosed. However, there is a lack of research related to the security of UAV. Therefore, in this paper, a study was conducted to determine whether the packet data was normal or abnormal by collecting packet data of an unmanned aerial vehicle in a HITL(Hardware In The Loop) simulation environment similar to the real environment. In addition, this paper proposes a method for reducing computational cost in the modeling process and increasing the ease of data interpretation, a machine learning-based anomaly detection model that detects abnormal data by learning only normal data, and optimized hyperparameter values.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.435-442
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• 2007

To reduce damage from high concentration ozone in the air, we have researched how to predict high concentration ozone before it occurs. High concentration ozone is a rare event and its reaction mechanism has nonlinearities and complexities. In this paper, we have tried to apply and consider as many methods as we could. We clustered the data using the fuzzy c-mean method and took a rejection sampling to fill in the missing and abnormal data. Next, correlations of the input component and output ozone concentration were calculated to transform more correlated components by modified log transformation. Then, we made the prediction models using Dynamic Polynomial Neural Networks. To select the optimal model, we adopted a minimum bias criterion. Finally, to evaluate suggested models, we compared the two models. One model was trained and tested by the transformed data and the other was not. We concluded that the modified transformation effected good to ideal performance In some evaluations. In particular, the data were related to seasonal characteristics or its variation trends.