• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.12-15
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• 1996

In this paper, a new shape control law is derived as a result of introducing the parametric curve representation. This control alw is based on the estimation of the curve parameters corresponding to the target joint positions and the target tip position. Estimating target curve parameters makes it possible to find, easily, a simple shape control law by the Lyapunov design method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권4호
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• pp.247-253
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• 2011

A sustainability of electricity supply has emerged as a critical issue for low carbon green growth in South Korea. Wind power is the fastest growing source of renewable energy. However, due to its own intermittency and volatility, the power supply generated from wind energy has variability in nature. Hence, accurate forecasting of wind speed and power plays a key role in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. This paper presents a short-term wind speed prediction method based on support vector regression. Moreover, particle swarm optimization is adopted to find an optimum setting of hyper-parameters in support vector regression. An illustration is given by real-world data and the effect of model regularization by particle swarm optimization is discussed as well.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2018년도 제30회 한글 및 한국어 정보처리 학술대회
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• pp.510-513
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• 2018

본 논문은 다양한 워드 임베딩 모델(word embedding model)들과 하이퍼 파라미터(hyper parameter)들을 조합하였을 때 특정 영역에 어떠한 성능을 보여주는지에 대한 연구이다. 3 가지의 워드 임베딩 모델인 Word2Vec, FastText, Glove의 차원(dimension)과 윈도우 사이즈(window size), 최소 횟수(min count)를 각기 달리하여 총 36개의 임베딩 벡터(embedding vector)를 만들었다. 각 임베딩 벡터를 Fast and Accurate Dependency Parser 모델에 적용하여 각 모들의 성능을 측정하였다. 모든 모델에서 차원이 높을수록 성능이 개선되었으며, FastText가 대부분의 경우에서 높은 성능을 내는 것을 알 수 있었다.

• Journal of the Korean Data and Information Science Society
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• 제20권5호
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• pp.925-931
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• 2009

In this paper we propose a variance function estimation method for replicated data based on averages of squared residuals obtained from estimated mean function by the least squares support vector machine. Newton-Raphson method is used to obtain associated parameter vector for the variance function estimation. Furthermore, the cross validation functions are introduced to select the hyper-parameters which affect the performance of the proposed estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.

• Communications for Statistical Applications and Methods
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• 제16권2호
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• pp.383-388
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• 2009

In this paper we propose a variance function estimation method based on kernel trick for replicated data or data consisted of sample variances. Newton-Raphson method is used to obtain associated parameter vector. Furthermore, the generalized approximate cross validation function is introduced to select the hyper-parameters which affect the performance of the proposed variance function estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2542-2547
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• 2005

In this paper, we propose a support vector machine (SVM) hyper and kernel parameter optimization method which is based on minimizing radius/margin bound which is a kind of estimation of leave-one-error. This method uses dynamic encoding algorithm for search (DEAS) and gradient information for better optimization performance. DEAS is a recently proposed optimization algorithm which is based on variable length binary encoding method. This method has less computation time than genetic algorithm (GA) based and grid search based methods and better performance on finding global optimal value than gradient based methods. It is very efficient in practical applications. Hand-written letter data of MNI steel are used to evaluate the performance.

• 한국전자통신학회논문지
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• 제11권11호
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• pp.1129-1134
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• 2016

본 논문은 영상 복원 문제에 대한 정규화 모수를 찾는 새로운 방법을 제시한다. 사전 정보가 없으면 티코노프(Tikhonov) 정규화 모수를 선택하기 위한 일반화된 교차 검증법이나 L자형 곡선 검정 등의 별도의 최적화 함수가 필요하다. 본 논문에서는 티코노프 정규화에 대한 통합된 베이즈 해석을 소개하고 영상 복원 문제에 적용한다. 티코노프 정규화 모수와 베이즈 하이퍼 모수들의 관계를 정립하고 최대 사후 확률과 근거 프레임워크를 사용한 정규화 모수를 구하는 공식을 제시한다. 실험결과는 제안하는 방법의 효능을 보여준다.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1353-1360
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• 2008

An estimating procedure is introduced for the nonlinear mixed-effect Poisson regression, for longitudinal study, where data from different subjects are independent whereas data from same subject are correlated. The proposed procedure provides the estimates of the mean function of the response variables, where the canonical parameter is related to the input vector in a nonlinear form. The generalized cross validation function is introduced to choose optimal hyper-parameters in the procedure. Experimental results are then presented, which indicate the performance of the proposed estimating procedure.

• 대한원격탐사학회지
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• 제34권4호
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• pp.661-670
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• 2018

This study aims to develop a methodology of convolutional neural networks (CNNs) to produce thematic maps from remote sensing data. Optimizing the image size for CNNs was studied, since the size of the image affects to accuracy, working as hyper-parameter. The selected study area is Mt. Ung, located in Dangjin-si, Chungcheongnam-do, South Korea, consisting of both coniferous forest and deciduous forest. Spatial structure analysis and the classification of forest type using CNNs was carried in the study area at a diverse range of scales. As a result of the spatial structure analysis, it was found that the local variance (LV) was high, in the range of 7.65 m to 18.87 m, meaning that the size of objects in the image is likely to be with in this range. As a result of the classification, the image measuring 15.81 m, belonging to the range with highest LV values, had the highest classification accuracy of 85.09%. Also, there was a positive correlation between LV and the accuracy in the range under 15.81 m, which was judged to be the optimal image size. Therefore, the trial and error selection of the optimum image size could be minimized by choosing the result of the spatial structure analysis as the starting point. This study estimated the optimal image size for CNNs using spatial structure analysis and found that this can be used to promote the application of deep-learning in remote sensing.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제12권6호
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• pp.251-258
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• 2023

안정적으로 전력을 공급하고 전력계통을 운영하기 위해서는 최대전력을 정확히 예측해야 한다. 특히, 최대전력이 높게 발생하는 겨울과 여름에는 그 중요성이 매우 커진다. 최대전력을 실제 수요보다 높게 예측하면 발전소 기동 비용이 증가하여 경제적 손실이 발생하고, 최대전력을 실제 수요보다 낮게 예측하면 기동이 가능한 발전소가 부족하여 정전이 발생할 수 있다. 최대전력의 예측 오차를 최소화함으로써 경제적 손실과 정전을 예방할 수 있다. 본 논문에서는 최대전력 예측의 오차를 최소화하기 위하여 최신 딥러닝 모델인 TCN을 이용한다. 딥러닝 모델은 하이퍼 파라미터를 어떻게 설정하느냐에 따라 성능 차이가 발생하므로, TCN의 하이퍼 파라미터를 최적화하는 방법을 제안한다. 2006년부터 2021년까지의 데이터를 입력하여 모델을 훈련하고, 2022년의 데이터를 이용하여 예측 오차를 실험하였다. 실험을 수행한 결과 본 논문에서 제안한 최적화 방법을 이용한 TCN 모델의 성능이 다른 딥러닝 모델보다 성능이 우수한 것을 확인하였다.