• 한국전기화학회:학술대회논문집
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• 2001.10a
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• pp.41-41
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• 2001

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.536-538
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• 2023

수출의존도가 높은 한국은 코로나19 팬데믹, 우크라이나-러시아 전쟁 등 대외환경의 변화에 따른 수출 여건에 민감할 수 밖에 없는 환경이다. 이에 발 빠르게 대응하기 위해 정확한 수출증감률 예측이 필요하며 이를 가장 잘 수행할 수 있는 예측모델을 찾고자 한다. 수출에 영향을 끼치는 주요변수 선정 후, min-max 정규화를 시행하고 변수간 상관계수와 다중공선성 확인을 통해 변수를 축소했다. 그리고 머신러닝 예측모델로 많이 사용되는 Linear Regression, Decision Tree, Gradient Boost Regressor, Random Forest 4가지 모델에 대입하여 수출 증감률 예측 정확도를 비교했다. 그 결과, Linear Regression의 MSE가 0.087로 가장 낮아 제일 우수한 모델이라는 결론에 도달했다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.3
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• pp.183-189
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• 2013

In this paper, uncertainties and failure criteria of structure are mathematically expressed by random variables and a limit state equation. A limit state equation is approximated by Fleishman's 3rd order polynomials and the theoretical moments of an approximated limit state equation are calculated. Fleishman introduced a 3rd order polynomial in terms of only standard normal distiribution random variables. But, in this paper, Fleishman's polynomial is extended to various random variables including beta, gamma, uniform distributions. Cumulants and a normalized limit state equation are used to calculate a theoretical moments of a limit state equation. A cumulative distribution function of a normalized limit state equation is approximated by a Pearson system.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.127-136
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• 2013

This paper proposes a video deblurring algorithm which maintains uniform sharpness between frames. Unlike the previous algorithms using fixed parameters, the proposed algorithm keeps uniform sharpness by adjusting parameters for each frame. First, we estimate the initial blur kernel and perform deconvolution, then measure the sharpness of the deblurred image. In order to maintain uniform sharpness, we adjust the regularization parameter and kernel according to the examined sharpness, and perform deconvolution again. The experimental results show that the proposed algorithm achieves outstanding deblurring results while providing consistent sharpness.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.319-328
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• 2021

In this paper, we perform prediction of amount of electric power plant for complex of wind plant using multi-layer perceptron in order to calculate exact calculation of capacity of ESS to maximize profit through generation and to minimize generation cost of wind generation. We acquire wind speed, direction of wind and air density as variables to predict the amount of generation of wind power. Then, we merge and normalize there variables. To train model, we divide merged variables into data as train and test data with ratio of 70% versus 30%. Then we train model by using training data, and we alsouate the prediction performance of model by using test data. Finally, we present the result of prediction in amount of wind power.

• 대한핵의학회:학술대회논문집
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• 1997.05a
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• pp.176-176
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• 1997

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.489-492
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• 2003

In this paper, we propose an iterative mixed norm image restoration algorithm using multi regularization parameters. A functional which combines the regularized l$_2$ norm functional and the regularized l$_4$ functional is proposed. The smoothness of each functional is determined by the regularization parameters. Also, a regularization parameter is used to determine the relative importance between the regularized l$_2$ functional and the regularized l$_4$ functional. An iterative algorithm is utilized for obtaining a solution and its convergence is analyzed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.12
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• pp.1274-1285
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• 1991

This paper presents a statistical convergence analysis of the normalized least mean square(NLMS)algorithm that employs a single-pole lowpass filter, In this algorithm the lowpass filter is used to adjust its output towards the estimated value of the input signal power recursively. The estimated input signal power so obtained at each time is then used to normalize the convergence parameter. Under the assumption that the primary and reference inputs to the adaptive filter are zero mean wide sense stationary, and Gaussian random processes, and further making use of the independence assumption. we derive expressions that characterize the mean and maen squared behavior of the filter coefficients as well as the mean squared estimation error. Conditions for the mean and mean squared convergence are explored. Comparisons are also made between the performance of the NLMS algorithm and that of the popular least mean square(LMS) algorithm Finally, experimental results that show very good agreement between the analytical and emprincal results are presented.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.1
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• pp.73-82
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• 2008

In this paper, a new regularization method by parameter subset selection method is proposed based on the residual force vector for damage localization. Although subset selection using the fundamental modal characteristics as a residual function has been successful in detecting a single damage location, this method seems to have limited capabilities in the detection of multiple damage locations and typically requires cumbersome weighting values. The method is presented herein and considers cases in which damage detection must be achieved using incomplete measurements of the structural responses. Model expansion is incorporated to deal with this challenge. The unique advantage of employing the new regularization method is that it can reliably identify multiple damage locations. Through an illustrative example, the proposed damage detection method is demonstrated to be a reliable tool for identifying multiple damage locations for a planar truss structure.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.11a
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• pp.95-98
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• 2009

본 연구는 주관 감성에 따른 생리 데이터의 패턴을 분류하고, 임의의 생리 데이터의 패턴을 확인하여 각성-이완, 쾌-불쾌의 감성을 추론하기 위해 베이지안 이론(Bayesian learning)을 기반으로 한 추론 모델을 제안하는 것이 목적이다. 본 연구에서 제안하는 모델은 학습데이터를 분류하여 사전확률을 도출하는 학습 단계와 사후확률로 임의의 생리 데이터의 패턴을 분류하여 감성을 추론하는 추론 단계로 이루어진다. 자율 신경계 생리변수(PPG, GSR, SKT) 각각의 패턴 분류를 위해 1~7로 정규화를 시킨 후 선형 관계를 구하여 분류된 패턴의 사전확률을 구하였다. 다음으로 임의의 사전 확률 분포에 대한 사후 확률 분포의 계산을 위해 베이지안 이론을 적용하였다. 본 연구를 통해 주관적 평가를 실시하지 않고 다중 생리변수 인식을 통해 감성을 추론 할 수 있는 모델을 제안하였다.