• 한국정보통신학회논문지
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• 제18권12호
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• pp.2946-2952
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• 2014

표본 기반 초해상도(Super Resolution 이하 SR) 방법들 중 네이버 임베딩(Neighbor Embedding 이하 NE) 기법의 기본 원리는 지역적 선형 임베딩이라는 매니폴드 학습방법의 개념과 같다. 그러나, 네이버 임베딩은 국부 학습 데이터 집합의 크기가 너무 작기 때문에 이에 따른 빈약한 일반화 능력으로 인하여 알고리즘의 성능을 크게 저하시킨다. 본 논문에서는 이와 같은 문제점을 해결하기 위해서 일반화 능력이 뛰어난 Support Vector Regression(이하 SVR)을 이용한 Sparse-Neighbor 영상 표현 학습 방법에 기반한 새로운 알고리즘을 제안하였다. 저해상도 입력 영상이 주어지면 bicubic 보간법을 이용하여 확대된 영상을 얻고, 이 확대된 영상으로부터 패치를 얻은 후 저주파 패치인지 고주파 패치 인지를 판별한 후 각 영상 패치의 가중치를 얻은 후 두 개의 SVR을 훈련하였으며 훈련된 SVR을 이용하여 고해상도의 해당 화소 값을 예측하였다. 실험을 통하여 제안된 기법이 기존의 보간법 및 네이버 임베딩 기법 등에 비해 정량적인 척도 및 시각적으로 향상된 결과를 보여 주었다.

• 터널과지하공간
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• 제28권6호
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• pp.651-669
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• 2018

본 연구에서는 유전자 프로그래밍과 개체군집최적화기법을 이용하여 픽 커터의 비에너지를 예측하기 위한 모델을 제안하였다. 기계굴착장비의 굴진성능을 평가하는 것은 터널의 설계 초기 단계에서 매우 중요하며, 비에너지를 이용한 기계 굴착장비의 굴진성능평가방법은 모든 기계굴착공법에 적용될 수 있는 표준화된 방법이다. 본 연구에서는 코니컬형상의 픽 커터가 암석을 절삭할 때 요구되는 비에너지와 암석의 강도특성, 절삭조건 간의 상관관계를 분석하고자 하였으며, 선행연구를 통해 총46개의 선형절삭시험 결과를 수집하여 분석에 활용하였다. 본 연구에서 제안한 예측모델을 이용하여 산정된 픽 커터의 비에너지는 다중선형회귀분석에 비해 작은 평균제곱오차를 나타내었으며, 결정계수 또한 본 연구에서 제안한 모델이 다중선형회귀분석에 비해 우수한 예측결과를 나타내는 것을 확인할 수 있었다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
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• pp.701-702
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• 2021

The purpose of the study was to identify fiber changes induced by electrical stimulation of a certain neural substrate in the rat brain. In the stimulation group, the peripheral nerve was injured and the brain area associated to inhibit sensory information was electrically stimulated. There were sham and sham stimulation groups as controls. Then high-field diffusion tensor imaging (DTI) was acquired. 35 features were taken from the DTI measures from 7 different brain pathways. To compare the efficacy of the classification for 3 animal groups, the linear regression analysis (LDA) and the machine learning technique (MLP) were applied. It was found that the testing accuracy by MLP was about 77%, but that of accuracy by LDA was much higher than MLP. In conclusion, machine learning algorithm could be used to identify and predict the changes of the brain white matter in some situations. The limits of this study will be discussed.

• 한국임상약학회지
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• 제5권2호
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• pp.1-12
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• 1995

The purpose of this investigation was to determine pharmacokinetic parameters of gentamicin using linear least square regression(LLSR) and Bayesian analysis in Korean normal volunteers and appendicitis patients. Nonparametric expected maximum(NPEM) algorithm for population pharmacokinetic parameters was used. Gentamicin was administered every 8 hours for 3 days by infusion over 30 minutes. The volume of distribution(V) and elimination rate constant(K) of gentamicin were $0.215\pm0.0562,\;0.226\pm0.0325L/kg\;and\;0.339\pm0.0443,\;0.357\pm0.0243hr^{-1}$ for normal volunteers and appendicitis patients using LLSR analysis. Population pharmacokinetic parameters, VS and KS were $0.228\pm0.0614L/kg\;and\;0.00356\pm0.00041(hr{\cdot}mL/min/1.73m^2)^{-1}$ for appendicitis patients using NPEM algorithm. The V and K were $0.232\pm0.0568L/kg\;and\;0.337\pm0.0385hr^{-1}$ for appendicitis patients using Bayesian analysis. There were no differences in gentamicin pharmacokinetics between LLSR and Bayesian analysis.

• Steel and Composite Structures
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• 제42권6호
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• pp.733-745
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• 2022

This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.543-556
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• 2015

We present a survey of contributions that defined the nature and extent of robust statistics for the last 50 years. From the pioneering work of Tukey, Huber, and Hampel that focused on robust location parameter estimation, we presented various generalizations of these estimation procedures that cover a wide variety of models and data analysis methods. Among these extensions, we present linear models, clustered and dependent observations, times series data, binary and discrete data, models for spatial data, nonparametric methods, and forward search methods for outliers. We also present the current interest in robust statistics and conclude with suggestions on the possible future direction of this area for statistical science.

• Journal of Korean Society for Atmospheric Environment
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• 제24권E2호
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• pp.92-101
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• 2008

The objective of this study is to evaluate and improve the capability of the Weather Research and Forecasting (WRF) model in simulating fog and visibility in local airports over Korea. The WRF model system is statistically evaluated for the 48-fog cases over Korea from 2003 to 2006. Based on the 4-yr evaluations, attempts are made to improve the simulation skill of fog and visibility over Korea by revising the statistical coefficients in the visibility algorithms of the WRF model. A comparison of four existing visibility algorithms in the WRF model shows that uncertainties in the visibility algorithms include additional degree of freedom in accuracy of numerical fog forecasts over Korea. A revised statistical algorithm using a linear-regression between the observed visibility and simulated hydrometeors and humidity near the surface exhibits overall improvement in the visibility forecasts.

• Molecular & Cellular Toxicology
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• 제4권2호
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• pp.170-176
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• 2008

The quantitative structure-activity relationship (QSAR) of a set of 35 flavonoid compounds presenting antioxidant activity was established by means of Genetic Algorithm-Multiple Linear Regression (GA-MLR) technique. Four-parametric models for two sets of data, the 1,1-diphenyl-2-picryl hydrazyl (DPPH) radical scavenging activity $(R^2=0.788,\;Q^2_{cv}=0.699\;and\;Q^2_{ext}=0.577)$ and scavenging activity of reactive oxgen species (ROS) induced by $H_2O_2 (R^=0.829,\;Q^2_{cv}=0.754\;and\;Q^2_{ext}=0.573)$ were obtained with low external predictive ability on a mass basis, respectively. Each model gave some different mechanistic aspects of the flavonoid compounds tested in terms of the radical scavenging activity. Topological charge, H-bonding complex and deprotonation processes were likely to be involved in the radical scavenging activity.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.694-696
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• 2003

The KOMPSAT/OSMI remote sending data of 800 km swath are collected by whisk broom method employing 96 charge coupled devices (CCDs). The stripping noise in the OSMI imageries, which arise mainly due to the non-uniform sensitivities of 96 CCDs, are the major hindrance for oceanographic applications of the OSMI data. The OSMI images are corrected by 'Ensemble Smoothness' method which is based on an assumption that the series of the averages and variances of digital numbers in each line should vary smoothly. The data of each line are corrected by linear regression model of which coefficients are obtained by Ensemble Smoothness method. Our algorithm can be applied not only to OSMI data but also for other remote sensing date collected by whisk broom or push broom.

• 제어로봇시스템학회논문지
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• 제18권4호
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• pp.314-320
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• 2012

This paper proposes a new robust motion deblurring filter using the inertial sensor measurements for strapdown image IR applications. With taking the PSF measurement error into account, the motion blurred image is modeled by the linear uncertain state space equation with the noise corrupted measurement matrix and the stochastic parameter uncertainty. This motivates us to solve the motion deblurring problem based on the recently developed robust least squares estimation theory. In order to suppress the ringing effect on the deblurred image, the robust least squares estimator is slightly modified by adoping the ridge-regression concept. Through the computer simulations using the actual IR scenes, it is demonstrated that the proposed algorithm shows superior and reliable motion deblurring performance even in the presence of time-varying motion artifact.