• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.237-252
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• 2013

An estimation model for premiums and components is essential to determine reasonable insurance premiums. In this study, we introduce diverse models for the estimation of property damage premiums(premium, depth and frequency) that include a regression model using a dummy variable, additive independent variable model, autoregressive error model, seasonal ARIMA model and intervention model. In addition, the actual property damage premium data was used to estimate the premium, depth and frequency for each model. The estimation results of the models are comparatively examined by comparing the RMSE(Root Mean Squared Errors) of estimates and actual data. Based on real data analysis, we found that the autoregressive error model showed the best performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.299-299
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• 2016

미농무성에서 개발된 유역수문모형 SWAT을 국내유역에 적용할 경우 일반적으로 일단위 유출 수문곡선의 첨두부가 관측치에 비해 과소하게 모의되는 경향이 있다. 본 연구에서는 이러한 원인에 대해서 고찰하고, 첨두부가 작게 모의되는 문제를 해결하기 위해서 지표유출의 지체와 관련된 서브루틴을 개선하였다. SWAT 모형에서는 지표유출의 지체는 운동파 집중시간(kinematic wave time of concentration)을 변수로 하는 지수형 감쇠함수를 사용하고 있다. 그러나 집중시간 계산식에서 지표유출에 기여하는 초과우량을 6.35mm/hr로 작은 고정값으로 가정하고 있어 큰 호우가 발생한 경우에도 집중시간이 길게 계산되는 구조를 가지고 있다. 이로 인해 지표유출의 지체 효과가 커서 첨두유량이 과소하게 산정되는 문제가 발생한다. 따라서 본 연구에서는 집중시간 계산시 고정값 6.35mm/hr 대신에 일 단위로 모의된 지표유출 발생량이 입력되도록 알고리즘을 수정하였다. 이 방법은 지표유출량의 크기에 따라 집중시간을 가변적으로 산정되게 함으로써 수문곡선의 첨두부를 보다 유연하게 구현할 수 있는 장점이 있다. 모형의 개선 효과를 평가하기 위해서 충주댐 상류유역을 대상으로 개선 전, 후의 일 단위 유출수문곡선의 첨두부를 비교하였으며, 그 결과 큰 홍수가 발생한 기간의 첨두유량이 10 % 이상 증가하는 것으로 분석되었다.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.11-19
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• 2021

Linear variable differential transformer (LVDT) is a displacement sensor and is commonly used owing to its wide measurement range, excellent linearity, high sensitivity, and precision. To improve the output characteristics of LVDT, a few studies have been conducted to analyze the output using a theoretical method or a finite element method. However, the material properties of the core and the electromagnetic force acting on the core were not considered in the previous studies. In this study, a finite element analysis model was proposed considering the characteristics of the LVDT composed of coils, core, magnetic shell and electric circuit, and the core displacement. Using the proposed model, changes in sensitivity and linear region of LVDT according to changes in process and material parameters were analyzed. The outputs of the LVDT model were compared with those of the theoretical analysis, and then, the proposed analysis model was validated. When the electrical conductivity of the core was high and the relative magnetic permeability was low, the decrease in sensitivity was large. Additionally, an increase in the frequency of the power led to further decrease in sensitivity. The electromagnetic force applied on the core increased as the voltage increased, the frequency decreased, and the core displacement increased.

• Journal of the Korean Society of Propulsion Engineers
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• v.26 no.3
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• pp.22-31
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• 2022

Existing Finocyl grain designs assume configurations and repeat the process of configuration modification and confirmation of the requirements through burn-back analysis. Such a design increases the design fatigue of workers and has a problem of different design completeness depending on capabilities. Therefore, this study devised an optimal design method that applied genetic algorithms to the Burn-back automation analysis program to solve the problem of existing design. For stable search, variable-offset and non-drawable configuration control techniques were developed. The program performance was verified through the searching neutral and double thrust grains.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.351-351
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• 2023

강우 자료는 수문 모델링에서 중요한 입력 요소 중 하나이다. 강우의 공간적 가변성은 모델링 불확실성의 중요한 원인으로 알려져 있다. 강우 관측자료는 많은 경우 유역을 대표하는 평균 면적강수량 (Mean Areal Precipitation, MAP)을 계산하여 수문모형에 입력된다. 선행 연구에서는MAP 예측 결과의 신뢰도를 개선하기 위하여 다양한 보간 방법이 개발되었다. 하지만, 강우특성의 동질성를 고려한 대표 기상 관측소 선정이 MAP 예측과 유출량 모의 결과에 미치는 연구는 아직 미흡한 실정이다. 본 연구에서는 유역의 MAP 예측에 있어 강우특성의 동실성을 고려한 강우 관측소 선정이 수문 모델링 성능 개선에 미치는 영향을 평가하고자 한다. 본 연구에서는 종관 기상관측(ASOS) 74개 지점과 방재기상관측(AWS) 400여개 지점에서 2003~2022년 기간에 대한 일강수량 자료를 수집하였고 강우특성이 동질한 지역을 구분하였다. 또한, 강우특성 동질성의 고려 유무에 따른 MAP를 계산하였다. 이후, 5개의 매개변수로 이루어진 개념적 강우-유출 모형FPHM을 사용하여 우리나라 전역 41개 유역을 대상으로 MAP 계산 결과가 모형 성능에 미치는 민감도를 조사하였다. 분석 결과, 강우특성의 동질성을 고려한 강우 관측소의 선택은 MAP 보간 방법 이상으로 중요한 요소임을 확인할 수 있었다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.146-147
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• 2023

해양사고는 도로교통과 달리 지속적으로 증가하고 있으며, 인명피해가 주로 발생하는 주요 사고의 치사율은 도로교통의 11.7배 이상이다. 해양사고는 외부 환경에 따라 사고 위치가 변하고 즉각적인 조치가 어려워 타 교통에 비해 대형 사고로 이어질 가능성이 매우 크다. 그러나 여전히 사고가 발생하고 난 후 대응하는 등 사후적 관리 단계에 무르고 있어 사고의 주요 요인을 사전에 식별·관리하는 선제적 관리단계로의 전환 필요성이 대두되고 있다. 따라서 본 연구에서는 해양사고 발생 지점 밀도 기반의 가변 공간 군집체계를 반영한 해양사고 예측모델을 개발하였다. 반복적인 공간 가산분석을 통해 밀도가 높을수록 작은 규모의 격자 체계를 가질 수 있도록 상세한 공간 군집체계를 구성하였으며, 단순 사고 위험도 예측뿐만 아닌 사고 인과관계를 설명할 수 있는 BN(Bayesian Network) 기반의 모형을 사용하여 해양사고 위험예측 모델을 개발하였다. 또한, Cost-of-Omission을 통해 해양사고 예측확률의 변화와 각 변수들의 영향력을 확인하였으며, 월별 해양사고예측 결과를 GIS를 활용하여 2D/3D 기반으로 시각화하였다.

• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.257-267
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• 2013

3-D ore modeling was performed to understand the configuration of ore bodies by integrated analysis of borehole and geophysical data in iron-mine area. Five representative indices of rocks were designated, which were obtained from geological survey and borehole. The five indices of rocks were geostatistically simulated by Sequential Indicator Simulation method to delineate boundary of the ore bodies. And Ordinary Kriging and Sequential Gaussian Simulation was applied to make secondary information using resistivity data from magnetotellurics and DC resistivity survey, and this information was used for simple kriging with local varying means, one of integrated kriging techniques. From the correlation analysis between each properties, it was found that high grade of ore is characterized by increased density, whereas the electrical resistivity decreases. With the integrated results of geophysical and borehole data, it was also found that the real configuration of ore body was similar to the modeled result and information about ore grade in 3-D space was obtained.

• The Journal of the Acoustical Society of Korea
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• v.21 no.5
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• pp.430-437
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• 2002

Having a variety of good characteristics against other pattern recognition techniques, the multilayer perceptron (MLP) has been widely used in speech recognition and speaker recognition. But, it is known that the error backpropagation (EBP) algorithm that MLP uses in learning has the defect that requires restricts long learning time, and it restricts severely the applications like speaker recognition and speaker adaptation requiring real time processing. Because the learning data for pattern recognition contain high redundancy, in order to increase the learning speed it is very effective to use the online-based learning methods, which update the weight vector of the MLP by the pattern. A typical online EBP algorithm applies the fixed learning rate for each update of the weight vector. Though a large amount of speedup with the online EBP can be obtained by choosing the appropriate fixed rate, firing the rate leads to the problem that the algorithm cannot respond effectively to different learning phases as the phases change and the number of patterns contributing to learning decreases. To solve this problem, this paper proposes a Changing rate and Omitting patterns in Instant Learning (COIL) method to apply the variable rate and the only patterns necessary to the learning phase when the phases come to change. In this paper, experimentations are conducted for speaker verification and speech recognition, and results are presented to verify the performance of the COIL.

• The KIPS Transactions:PartD
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• v.15D no.5
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• pp.713-720
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• 2008

Fingerprinting scheme uses digital watermarks to trace originator of unauthorized or pirated copies, however, multiple users may collude and escape identification by creating an average or median of their individually watermarked copies. Previous research works are based on ACC (anti-collusion code) for identifying each user, however, ACC are shown to be resilient to average and median attacks, but not to LCCA and cannot support large number of users. In this paper, we propose a practical SACC (scalable anti-collusion code) scheme and its angular decoding strategy to support a large number of users from basic ACC (anti-collusion code) with LCCA (linear combination collusion attack) robustness. To make a scalable ACC, we designed a scalable extension of ACC codebook using a Gaussian distributed random variable, and embedded the resulting fingerprint using human visual system based watermarking scheme. We experimented with standard test images for colluder identification performance, and our scheme shows good performance over average and median attacks. Our angular decoding strategy shows performance gain over previous decoding scheme on LCCA colluder set identification among large population.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.