• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.117-120
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• 1996

The objective of this study is to suggest modified Covariance Structure Analysis that combine with existing Multivariate Statistical Method which is used Casual Analysis Method in Management Information. For this purpose, we'll consider special feature and limitation about Correlation Analysis, Regression Analysis, Path Analysis and connect Covariance Structure Analysis with Statistical Factor Analysis so that theoretical casual model compare with variables structure in collecting data. A example is also presented to show the practical applicability of this approach.

• 대한간호학회지
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• 제33권6호
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• pp.705-712
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• 2003

Purpose: The purpose of this study was to explore factors that influence care dependency of institutionalized patients with dementia. Method: This study utilized descriptive correlational design. The convenience sample was composed of 110 residents with dementia of two long-term care facilities in Korea. Stepwise multiple regression was used to identify significant factors influencing care dependency in patients with dementia. Care dependency was measured using the Care Dependency Scale, Korean version(CDS-K). Cognition was measured by the MMSE-K. Functional disability was measured by the PULSES Profile. Behavioral dysfunction was measured by the modified E-BEHAVE AD. Result: Care dependency was significantly influenced by cognition, functional disability, behavioral dysfunction, and duration of dementia. This regression model explained 61 % of the variances in care dependency. Cognition explained 37% of the variances, and functional disability explained 21% of the variances. Conclusion: Results of this study suggest that professional caregivers intervene more effectively in caring for their patients with dementia by recognizing the patients cognitive, functional, behavioral disability, and its periodic change. Individually, remaining abilities-focused intervention should be applied to enhance patient to be dependent and to prevent unnecessary independency.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.800-802
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• 2000

In this paper, we propose the Self-Organizing Networks(SON) based on competitive Fuzzy Polynomial Neuron(FPN) for the optimal design of nonlinear process system. The SON architectures consist of layers with activation nodes based on fuzzy inference rules. Here each activation node is presented as FPN which includes either the simplified or regression Polynomial fuzzy inference rules. The proposed SON is a network resulting from the fusion of the Polynomial Neural Networks(PNN) and a fuzzy inference system. The conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as liner, quadratic and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are studied. Chaotic time series data used to evaluate the performance of our proposed model.

• 품질경영학회지
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• 제27권2호
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• pp.112-125
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• 1999

In a robust regression model, it is typically assumed that the errors are normally distributed. However, what if the error distribution is deviated from the normality and the response variables are not completely observable due to censoring? For complete data, Kim and Lai(1998) suggested a new adaptive M-estimator with an asymptotically efficient score function. The adaptive M-estimator is based on using B-splines to estimate the score function and simple cross validation to determine the knots of the B-splines, which are a modified version of Kun( 1992). We herein extend this method to right-censored data and study how well the adaptive M-estimator performs for various error distributions and censoring rates. Some impressive simulation results are shown.

• 한국물환경학회지
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• 제25권4호
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• pp.580-588
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• 2009

In this study daily flow rates and delivered pollutant loads of Nakdong river basin are simulated with modified TANK model and minimum variance unbiased estimator. Based on the simulation results, flow duration curves, load duration curves, and delivery ratio duration curves have been established. Then GIS analysis is performed to obtain several hydrological geomorphic characteristics such as watershed area, stream length, watershed slope and runoff curve number. Finally, multiple regression analysis is carried out to estimate empirical equations for pollutants delivery ratio. The results show that there is positive relation between the flow rates and delivery ratios, and the proposed empirical formulas for delivery ratio can predict well river pollutant loads.

• Advances in concrete construction
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• 제8권4호
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• pp.285-294
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• 2019

In the present paper, the fiber theory has been employed to model the reinforced concrete (RC) deep beams (DBs) considering the reinforcing steel bar-concrete interaction. To simulate numerically the behavior of materials, the uniaxial materials' constitutive laws have been employed for reinforcements and concrete and the bond stress-slip between the reinforcing steel bars and surrounding concrete are taken into account. Because of the high sensitivity of DBs to shear deformations, the Timoshenko beam theory has been applied. The shear stress-strain (S-SS) relationship has been defined by the modified compression field theory (MCFT) model. By modeling about 300 RC panels and employing a produced numerical database, a study has been carried out to show the sensitivity of the MCFT model. This is performed based on the multiple linear regression (MLR) models. The results of this research also illustrate how different parameters such as characteristic compressive strength of concrete, yield strength of reinforcements and the percentages of reinforcements in different directions get involved in the shear behavior of RC panels without applying complex theories. Based on the results obtained from the analysis of the MCFT S-SS model, a relatively simplified numerical S-SS model has been proposed. Application of the proposed S-SS model in modeling and analyzing the considered samples indicates that there is a good agreement between the simulated and the experimental test results. The comparison between the proposed S-SS model and the MCFT model indicates that in addition to the advantage of better accuracy, the main advantage of the proposed method is simplicity in application.

• Journal of Korean Neurosurgical Society
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• 제66권4호
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• pp.382-392
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• 2023

Objective : The use of indocyanine green videoangiography (ICG-VA) to assess blood flow in the brain during cerebrovascular surgery has been increasing. Clinical studies on ICG-VA have predominantly focused on qualitative analysis. However, quantitative analysis numerical modelling for time profiling enables a more accurate evaluation of blood flow kinetics. In this study, we established a multiple exponential modified Gaussian (multi-EMG) model for quantitative ICG-VA to understand accurately the status of cerebral hemodynamics. Methods : We obtained clinical data of cerebral blood flow acquired the quantitative analysis ICG-VA during cerebrovascular surgery. Varied asymmetric peak functions were compared to find the most matching function form with clinical data by using a nonlinear regression algorithm. To verify the result of the nonlinear regression, the mode function was applied to various types of data. Results : The proposed multi-EMG model is well fitted to the clinical data. Because the primary parameters-growth and decay rates, and peak center and heights-of the model are characteristics of model function, they provide accurate reference values for assessing cerebral hemodynamics in various conditions. In addition, the primary parameters can be estimated on the curves with partially missed data. The accuracy of the model estimation was verified by a repeated curve fitting method using manipulation of missing data. Conclusion : The multi-EMG model can possibly serve as a universal model for cerebral hemodynamics in a comparison with other asymmetric peak functions. According to the results, the model can be helpful for clinical research assessment of cerebrovascular hemodynamics in a clinical setting.

• Communications for Statistical Applications and Methods
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• 제24권3호
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• pp.241-254
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• 2017

Drawing a sample can be costly or time consuming in some studies. However, it may be possible to rank the sampling units according to some baseline auxiliary covariates, which are easily obtainable, and/or cost efficient. Ranked set sampling (RSS) is a method to achieve this goal. In this paper, we propose a modified approach of the RSS method to allocate units into an experimental study that compares L groups. Computer simulation estimates the empirical nominal values and the empirical power values for the test procedure of comparing L different groups using modified RSS based on the regression approach in analysis of covariance (ANCOVA) models. A comparison to simple random sampling (SRS) is made to demonstrate efficiency. The results indicate that the required sample sizes for a given precision are smaller under RSS than under SRS. The modified RSS protocol was applied to an experimental study. The experimental study was designed to obtain a better understanding of the pathways by which positive experiences (i.e., goal completion) contribute to higher levels of happiness, well-being, and life satisfaction. The use of the RSS method resulted in a cost reduction associated with smaller sample size without losing the precision of the analysis.

• 한국자동차공학회논문집
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• 제22권3호
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• pp.114-122
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• 2014

Sensor fusion is the one of the main research topics. It offers the highly reliable estimation of vehicle movement by processing and mixing several sensor outputs. But unfortunately, every sensor has drift which degrades the performance of sensor. It means a single degraded sensor output may affect whole sensor fusion system. Drift in most research is ideally assumed to be zero because it's usually a nonlinear model and has sample variation. Plus, it's very difficult for the acceleration to separate drift from the output signal since it contains many contributors such as vehicle acceleration, slope angle, pitch angle, surface condition and so on. In this paper, modified spline interpolation is introduced as a dynamic temperature compensation method covering sample variation. Using the last known output and the first initial output is suggested to build and update compensation factor. When the system has more compensation data, the system will have better performance of compensated output because of the regression compensation model. The performance of the dynamic temperature compensation system is evaluated by measuring offset drift between with and without the compensation.

• 대한교통학회지
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• 제28권5호
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• pp.43-54
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• 2010

본 연구에서는 현행 버스 서비스 평가체계를 보완하고 선행연구에 비해 합리적인 분석결과를 도출하기 위해 유익 유해산출물을 모두 반영한 수정 BCC 모형을 이용하여 2009년 서울시 113개 간선버스노선의 효율성을 추정하였다. 분석대상은 총 보유비와 총 가동비, 중앙차로 정류장수 비율과 타노선과의 중복길이를 사용하여 유익산출물인 총 승객수와 서비스 만족도 점수, 그리고 유해산출물인 CO2 배출량을 산출하는 형태로 상정하였다. 분석결과 유해산출물을 함께 반영한 모형이 유익산출물만을 반영한 모형에 비해 합리적인 결과를 도출하는 것으로 나타났다. 서울시 간선버스노선은 총 보유비와 총 가동비, 그리고 타노선과의 중복길이를 평균적으로 약 10% 감소할 수 있으며, 중앙차로 정류장수 비율은 약 160% 증가시킬 수 있는 것으로 분석되었다. 한편 효율성에 영향을 미치는 결정요인을 분석하기 위한 토빗회귀분석을 수행한 결과 총 보유비와 중앙차로 정류장수 비율이 통계적으로 유의미성을 확보하였다.