• Proceedings of the KIPE Conference
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• 2011.07a
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• pp.113-114
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• 2011

PV(Photovoltaic)는 일사량 및 온도에 의해 얻을 수 있는 최대 전력이 변화하고, PV의 전압에 따라 출력 전력이 변화한다. 따라서 PV를 이용한 인버터는 최대 전력점에서 동작하게 하는 최대 전력점 추정(Maximum Power Point Tracking) 제어시스템이 필요하다. 본 논문에서는 인버터 제어에 필요한 여러가지 MPPT 기능 실험이 가능하게 하는 저가형 태양전지 시뮬레이터 시스템의 제어알고리즘을 제안한다. 이 시스템은 PV의 출력특성에 따른 출력값을 설정하고, 다양한 출력특성 변화의 실시간 변화가 가능하게 하는 새로운 알고리즘을 적용하였다. 이를 통해 PV가 설치되지 않은 곳에서 인버터의 MPPT 성능 시험이 가능하며, 실제 인버터에 구현된 P&O-MPPT와 연동하여 제안된 제어기법의 타당성을 검증하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.1A
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• pp.38-47
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• 2006

In this paper, we propose a velocity-adaptive channel estimation scheme using the simple zero-forcing technique in the frequency domain. Channel estimation is performed by removing frequency components that are higher than the maximum Doppler frequency in the received signal. The proposed scheme can be extended to the combined estimation scheme for channel coefficients and mobile velocity using one FFT/IFFT module. Simulation results show that the proposed scheme outperforms conventional schemes for a wide range of mobile velocities($3{\sim}300\;Km/h$). Finally, the MSE for the proposed channel estimation scheme is analyzed.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.383-384
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• 2020

본 논문에서는 고승압이 가능한 2-Stage Boost 컨버터의 최대효율 운전을 위한 손실 분석을 수행한다. 2-Stage Boost 컨버터의 최대효율 지점 추정을 위해 앞단 Boost 컨버터의 출력 전압인 V_mid의 변동에 따른 2-Stage Boost 컨버터의 상세한 손실을 분석하며, 손실이 가장 적은 V_mid지점을 도출하였으며 실험을 통해 검증한다.

• Proceedings of the Korean Reliability Society Conference
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• 2000.11a
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• pp.315-320
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• 2000

본 연구에서는 수리 가능한 시스템에서 고장 강도와 수리 효과에 대한 모수 추정 문제를 다룬다. 시스템이 노후화로 인한 고장이 발생할 경우 최소수리가 행해지고 계획된 예방정비에서는 비례적 수명 감소가 이루어지는 수명 데이터에 대해서 고장 강도 함수의 모수와 정비의 수리효과를 추정하기 위해서 최대 우도 함수 방법을 이용한다. 또한 유전자 알고리즘을 이용해서 우도 함수를 최대화시키는 절차를 개발하고 수치 예제를 나타낸다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.56-56
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• 2022

지속시간 동안 물리적으로 발생 가능한 최대의 강수량으로 정의되는 PMP(Probable Maximum Precipitation)를 나타내는 한 가지 방법으로 포락곡선이 있다. 포락곡선은 최대강수량을 지속시간에 대해 도시한 것으로 역사적으로 발생한 극한 강수현상의 특징을 그대로 나타낸다. 오랜 기간 동안 강수 관측이 이루어졌다면 관측자료로부터 직접 포락곡선을 그릴 수 있겠으나, 지금까지 우리나라에서는 통계적 추정 방법을 통해 간접적으로 접근해왔다. 하지만, 강우관측자료가 상당히 누적된 지금에 이르러서는 직접 포락곡선을 도출하는 것이 가능해졌다. 이 연구에서는 50년 이상의 강우관측년도를 보유하고 있는 24개 종관기상관측 지점의 최대강우량을 종합하여 우리나라 기후조건을 대표할 수 있는 포락곡선을 산정하였다. 이번에 산정된 포락곡선을 우리나라 선행연구들에서 제시된 PMP와 비교하였으며, 전세계의 지속시간에 따른 최대강수량을 대표하는 Jennings law와도 비교하였다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.1
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• pp.138-149
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• 2014

This study proposes an estimation method of strain distribution for multi-span steel beam structure under unspecific loading conditions. The estimation method in this paper employs the curve fitting using the least square method from measured strain data, not analytical method. To verify the proposed estimation method, a static loading test for multi-span steel beam on which distributed and concentrated loads act was conducted. The strain data for verification was measured by FBG sensors that have multiplexing technology. The analysis of the accuracy of strain estimation for distributed and concentrated loads and the errors by considering the number of measured points used in the estimation were conducted. In the maximum strain points, the strains could be estimated with the errors of 5.89% (loading step 1) and 6.26% (loading step 2). In case of decreasing the number of sensors, it was also confirmed that the errors increased (0.26~0.37%). Through the curve fitting method, it is possible to estimate the strain distribution (maximum strains and their locations) of multi-span beam for unspecific loads and go over the limit of the analytical estimation method which is suitable for specific distributed loads.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.171-180
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• 2016

Basel committee suggests using Value-at-Risk (VaR) and expected shortfall (ES) as a measurement for market risk. Various estimation methods of VaR and ES have been studied in the literature. This paper compares semi-parametric methods, such as conditional autoregressive value at risk (CAViaR) and conditional autoregressive expectile (CARE) methods, and a Gaussian quasi-maximum likelihood estimator (QMLE)-based method through back-testing methods. We use unconditional coverage (UC) and conditional coverage (CC) tests for VaR, and a bootstrap test for ES to check the adequacy. A real data analysis is conducted for S&P 500 index and Hyundai Motor Co. stock price index data sets.

• Journal of KIISE:Databases
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• v.30 no.2
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• pp.168-175
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• 2003

Until now, substring selectivities have been estimated by two steps. First step is to build up a count-suffix tree, which has statistical information about substrings, and second step is to estimate substring selectivity using it. However, it's actually impossible to build up a count-suffix tree from biological sequences because their lengths are too long. So, this paper proposes a novel data structure, count q-gram tree, consisting of fixed length substrings. The Count q-gram tree retains the exact counts of all substrings whose lengths are equal to or less than q and this tree is generated in 0(N) time and in site not subject to total length of all sequences, N. This paper also presents an estimation technique, k-MO. k-MO can choose overlapping length of splitted substrings from a query string, and this choice will affect accuracy of selectivity and query processing time. Experiments show k-MO can estimate very accurately.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.175-183
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• 2022

The collision between a ship and bridge across a waterway may result in extremely serious consequences that may endanger the safety of life and property. Therefore, factors affecting ship bridge collision must be investigated, and the impact force should be discussed based on various collision conditions. In this study, a finite element model of ship bridge collision is established, and the peak impact force of a ship bridge collision based on 50 operating conditions combined with three input parameters, i.e., ship loading condition, ship speed, and ship bridge collision angle, is calculated via numerical simulation. Using neural network models trained with the numerical simulation results, the prediction model of the peak impact force of ship bridge collision involving an extremely short calculation time on the order of milliseconds is established. The neural network models used in this study are the basic backpropagation neural network model and Elman neural network model, which can manage temporal information. The accuracy of the neural network models is verified using 10 test samples based on the operating conditions. Results of a verification test show that the Elman neural network model performs better than the backpropagation neural network model, with a mean relative error of 4.566% and relative errors of less than 5% in 8 among 10 test cases. The trained neural network can yield a reliable ship bridge collision force instantaneously only when the required parameters are specified and a nonlinear finite element solution process is not required. The proposed model can be used to predict whether a catastrophic collision will occur during ship navigation, and thus hence the safety of crew operating the ship.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.551-559
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• 2007

The maximum likelihood estimation is not robust against outliers in the logistic regression. Thus we propose an algorithm for the robust estimation, which identifies the bad leverage points and vertical outliers by the V-mask type criterion, and then strives to dampen the effect of outliers. Our main finding is that, by an appropriate selection of weights and factors, we could obtain the logistic estimates with high breakdown point. The proposed algorithm is evaluated by means of the correct classification rate on the basis of real-life and artificial data sets. The results indicate that the proposed algorithm is superior to the maximum likelihood estimation in terms of the classification.