• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.3
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• pp.244-249
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• 2008

In this paper, the stable region for missile guidance loop employing an integrated proportional navigation guidance law is derived. The missile guidance loop is formulated as a closed-loop control system consisting of a linear time-invariant feed-forward block and a time-varying feedback gain. By applying the circle criterion to the system, a bound for the time of flight up to which stability can be assured is established as functions of flight time. Less conservative results, as compared to the result by Popov criterion, are obtained.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.5
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• pp.94-105
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• 2010

To detect faults in an inverter-fed permanent magnet synchronous motor (PMSM) drive under the circumstance having faults in a stator winding and inverter switch, an on-line basis fault detecting scheme during operation is presented. The proposed scheme is achieved by monitoring the second-order harmonic component in q-axis current and the fault is detected by comparing these components with those in normal conditions. The linear interpolation method is employed to determine the harmonic data in normal operating conditions. As soon as the fault is detected, the operating mode is changed to identify a fault type using the phase current waveform. To verify the effectiveness of the proposed fault detecting scheme, a test motor to allow inter-turn short in the stator winding has been built. The entire control algorithm is implemented using DSP TMS320F28335. Without requiring an additional hardware, the fault can be effectively detected by the proposed scheme during operation so long as the steady-state condition is satisfied.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.163-171
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• 2009

Classical linear models have been generally used to analyze and forecast hydrologic time series. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. In recent, the BDS (Brock-Dechert-Scheinkman) statistic instead of conventional techniques has been used for detecting nonlinearity of time series. The BDS statistic was derived from the statistical properties of the correlation integral which is used to analyze chaotic system and has been effectively used for distinguishing nonlinear structure in dynamic system from random structures. DVS (Deterministic Versus Stochastic) algorithm has been used for detecting chaos and stochastic systems and for forecasting of chaotic system. This study showed the DVS algorithm can be also used for detecting nonlinearity of the time series. In this study, the stochastic and hydrologic time series are analyzed to detect their nonlinearity. The linear and nonlinear stochastic time series generated from ARMA and TAR (Threshold Auto Regressive) models, a daily streamflow at St. Johns river near Cocoa, Florida, USA and Great Salt Lake Volume (GSL) data, Utah, USA are analyzed, daily inflow series of Soyang dam and the results are compared. The results showed the BDS statistic is a powerful tool for distinguishing between linearity and nonlinearity of the time series and DVS plot can be also effectively used for distinguishing the nonlinearity of the time series.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.299-304
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• 2002

본 연구에서는 자궁 내 태아의 발육지연이 주산기 사망률 및 이환율을 증가시키는 고위험 임신의 한 예로써, 태아 발육제한증과 관련한 비선형적인 자료를 통계적인 방법으로 접근하는데 초점을 두었다. 이에 정상태아와 발육제한증 태아를 판별하기 위한 분석을 실시함에 있어 신경망 이론 중 하나인 다층 퍼셉트론 모형으로 예측하고자 하였다.

• Proceedings of the KIPE Conference
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• 2004.07a
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• pp.456-460
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• 2004

본 논문은 HVDC시스템의 안정도와 제어 게인과의 상관관계를 다루고 있다. HVDC와 같은 시스템은 비선형적인 요소를 많이 가지고 있기 때문에 수학적인 알고리즘으로 안정도를 판별하고, 제어게인을 구하는 것이 매우 어렵기 때문에 축약된 모델링을 이용하여 제어 게인을 구하였고, 안정도를 분석하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.631-634
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• 2019

단백질의 구조적 동등성을 평가를 위한 형태 기반의 기술자에 대한 연구는 제한적으로 이루어지고 있으며 대부분 지역적 특성 값으로 표현된 지역적 접근 방법이 다수를 이루고 있다. 지역적 특성과 전역적 특성을 포함하는 형태기술자의 경우 각 특성들이 동등한 중요도로 결합되어 있다. 본 연구에서는 선형 회귀분석을 적용하여 각 특성에 대한 중요도를 최적화하여 형태기술자를 재정의 하였다. 최적화된 형태기술자를 단백질의약품인 인슐린 모델에 적용하여 구조적 동등성을 평가할 수 있는 방법론을 제시하였다. 최적화된 형태기술자는 동일한 그룹에 속한 인간 인슐린 단백질 모델과 지역적으로 다른 구조를 가지는 인슐린 아날로그 그룹을 명확히 구분할 수 있음을 확인하였고 이러한 성능은 이전 연구의 형태기술자와 3D 저니크 기술자보다 더 좋은 성능을 보였다. 또한 제안한 방법은 고해상도 단백질 3차 구조 정보를 활용하여 유사성을 판별한 RMSD 방법과 유사하게 서로 다른 표면 구조를 가지는 단백질을 구별할 수 있음을 확인하였다. 이러한 결과로부터 본 연구에서 제시하는 형태기술자 및 최적화된 동등성 평가 함수는 SAXS 분석과 같이 저해상도 단백질 표면 모델을 확보할 수 있는 분석에 적용하여 단백질의 구조적 동등성을 판별할 수 있는 기반을 제공할 수 있을 것으로 판단된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.5
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• pp.986-994
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• 2005

This Paper proposes the likelihood-based nonlinear dimension reduction method of the speech feature parameters in order to construct the voice activity detecter adaptable in noisy environment. The proposed method uses the nonlinear values of the Gaussian probability density function with the new parameters for the speec/nonspeech class. We adapted Likelihood Ratio Test to find speech part and compared its performance with that of Linear Discriminant Analysis technique. In experiments we found that the proposed method has the similar results to that of Gaussian Mixture Models.

• Journal of KIISE
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• v.43 no.12
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• pp.1396-1403
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• 2016

In streaming data analysis where underlying data distribution may be changed or the concept of interest can drift with the progress of time, the ability to adapt to concept drift can be very powerful especially in the process of incremental learning. In this paper, we develop a general framework for an adaptive incremental classifier on data stream with concept drift. A distribution, representing the performance pattern of a classifier, is constructed by utilizing the distance between the confidence score of a classifier and a class indicator vector. A hypothesis test is then performed for concept drift detection. Based on the estimated p-value, the weight of outdated data is set automatically in updating the classifier. We apply our proposed method for two types of linear discriminant classifiers. The experimental results on streaming data with concept drift demonstrate that the proposed adaptive incremental learning method improves the prediction accuracy of an incremental classifier highly.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.343-348
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• 2014

Face recognition system has advanctage to automatically recognize a person without causing repulsion at deteciton process. However, the face recognition system has a drawback to show lower perfomance according to illumination variation unlike the other biometric systems using fingerprint and iris. Therefore, this paper proposed a robust face recogntion method against illumination varition by slective fusion technique using both visible and infrared faces based on fuzzy linear disciment analysis(fuzzy-LDA). In the first step, both the visible image and infrared image are divided into four bands using wavelet transform. In the second step, Euclidean distance is calculated at each subband. In the third step, recognition rate is determined at each subband using the Euclidean distance calculated in the second step. And then, weights are determined by considering the recognition rate of each band. Finally, a fusion face recognition is performed and robust recognition results are obtained.

• The KIPS Transactions:PartD
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• v.19D no.2
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• pp.147-150
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• 2012

While LDA is a supervised dimension reduction method which finds projective directions to maximize separability between classes, the performance of LDA is severely degraded when the number of labeled data is small. Recently semi-supervised dimension reduction methods have been proposed which utilize abundant unlabeled data and overcome the shortage of labeled data. However, matrix computation usually used in statistical dimension reduction methods becomes hindrance to make the utilization of a large number of unlabeled data difficult, and moreover too much information from unlabeled data may not so helpful compared to the increase of its processing time. In order to solve these problems, we propose an ensemble approach for semi-supervised dimension reduction. Extensive experimental results in text classification demonstrates the effectiveness of the proposed method.