• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.513-521
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• 2020

In this paper, we have developed a Fama - French five factor model (FF5 model) from Fama & French (2015) by using concept of time-varying coefficient. For a data set, we have used monthly data form Kenneth R. French home page, it include Japan portfolios (classified by using size and book-to-market) and 5 factors from July 1990 to April 2020. The first analysis, we used Augmented Dickey-Fuller test (ADF test) for the stationary test, from the result, all Japan portfolios and 5 factors are stationary. Next analysis, we estimated a coefficient of Fama - French five factor model by using a generalized additive model with a thin-plate spline to create the time-varying coefficient Fama - French five factor model (TV-FF5 model). The benefit of this study is TV-FF5 model which can capture a different effect at different times of 5 factors but the traditional FF5 model can't do it. From the result, we can show a time-varying coefficient in all factors and in all portfolios, for time-varying coefficients of Rm-Rf, SMB, and HML are significant for all Japan portfolios, time-varying coefficients of RMW are positively significant for SM, and SH portfolio and time-varying coefficients of CMA are significant for SM, SH, and BM portfolio.

• The Transactions of the Korea Information Processing Society
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• v.3 no.6
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• pp.1646-1657
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• 1996

This paper presents a color transformation method based on a uniform color image model. Firstly, color variation factors are grouped into identical (multiplicative) factor and independent(additive) one for the color model, and they are modelled by the Gaussian function. The shape of a color cluster in (R, G, B) feature space is an ellipsoid whose elongated major axis correspond to the direction of mean vector. Secondly, the transformation of a color cluster using the model is studied. A transformation method for three dimensional coordinated is described. The proposed method is applied to artificial and natural color images. By the result of experiments, the elongated major axis of each cluster making up the transformed color image aggress with the direction of its mean vector.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.509-518
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• 2018

Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.1821-1825
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• 2015

Background: Associations between the NQO1 C609T polymorphism and hepatocellular carcinoma (HCC) risk are a subject of debate. We therefore performed the present meta-analysis to evaluate links with HCC susceptibility. Materials and Methods: Several major databases (PubMed, EBSCO), the Chinese national knowledge infrastructure (CNKI) and the Wanfang database were searched for eligible studies. Crude odds ratios (ORs) with 95% confidence intervals (CIs) were used to measure the strength of associations. Results: A total of 4 studies including 1,325 patients and 1,367 controls were identified. There was a significant association between NQO1 C609T polymorphism and HCC for all genetic models (allelic model: OR=1.45, 95%CI=1.23-1.72, p<0.01; additive model: OR=1.96, 95%CI=1.57-2.43, p<0.01; dominant model: OR=1.62, 95%CI=1.38-1.91, p<0.01; and recessive model: OR=1.53, 95%CI=1.26-1.84, p<0.01). On subgroup analysis, similarly results were identified in Asians. For Asians, the combined ORs and 95% CIs were (allelic model: OR=1.50, 95%CI=1.24-1.82, p<0.01; additive model: OR=2.11, 95%CI=1.48-3.01, p<0.01; dominant model: OR=1.69, 95%CI=1.42-2.02, p<0.01; and recessive model: OR=1.59, 95%CI=1.16-2.19, p<0.01). Conclusions: The current meta-analysis suggested that the NQO1 C609T polymorphism could be a risk factor for developing HCC, particularly in the Chinese population.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.483-488
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• 1998

A pressure drop model for the PWR grids with and without mixing device is proposed at single phase based on the fluid mechanistic approach. Total pressure loss is expressed in additive way for form and frictional losses. The general friction factor correlations and form drag coefficients available in the open literatures are used to the model. As the results, the model shows better predictions than the existing ones for the non-mixing grids, and reasonable agreements with the available experimental data for mixing grids. Therefore it is concluded that the proposed model for pressure drop can provide sufficiently good approximation for grid optimization and design calculation in advanced grid development.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1215-1224
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• 2016

Climate information of the high resolution grid units is an important factor to explain the phenomenon in a variety of research field. Statistical linear interpolation models are computationally inexpensive and applicable to any climate data compared to the dynamic simulation method at regional scales. In this paper, we considered four different linear-based statistical interpolation models: general linear model, generalized additive model, spatial linear regression model, and Bayesian spatial linear regression model. The climate variable of interest was the daily mean temperature, where the spatial variability was explained using geographic terrain information: latitude, longitude, elevation. The data were collected by weather stations in January from 2003 and 2012. In the sense of RMSE and correlation coefficient, Bayesian spatial linear regression model showed better performance in reflecting the spatial pattern compared to the other models.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1133-1145
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• 2000

In this paper a model following flight control system design using the discrete time quasi-sliding mode control method is described. The quasi-sliding mode is represented as the sliding mode band, not as the sliding surface. The quasi-sliding mode control is composed of the equivalent control for the nominal system without uncertainties and disturbances and the additive control compensating the uncertainties and disturbances. The linearized plant on the equilibrium point is used in designing a flight control system and the stability conditions are proposed for the model uncertainties. Pseudo-state feedback control which uses the model variables for the unmeasured states is proposed. The proposed method is applied to the design of the roll attitude and pitch load factor control of a bank-to-turn missile. The performance is verified through the nonlinear six degrees of freedom flight simulation.

• KSBB Journal
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• v.15 no.5
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• pp.452-457
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• 2000

Due to the advantage of RP-HPLC with a variety of compositions of mobile phases, experiments on water-soluble charged species were examined. The samples were mononucleotides (5-CMP, 5-UMP, 5-GMP, 5-IMP, 5-AMP), and the buffers used were sodium phosphate monobasic and acetic acid. The concentrations of buffers ranged from 0.01 to 10 mM, while that of the methanol, an additive to the mobile phase was 5 to 20 vol.%. To predict the retention factor of a sample in terms of its methanol composition (M, vol.%) and buffer(C(sub)B, mM), the following nonlinear equation is suggested, k= $\frac{a+b C_B}{(1+c C_B) M^d}($ where a, b, c, and d were experimentally determined constants. The regression coefficients were above 0.96, and the agreement between experimental and calculated retention factors were relatively good.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.109-125
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• 1996

Biases and errors in the human being's reasoning process have been studied continuously by the researchers, especially psychlogists and social scientists. These bias phenomenon is classified on the basis of the origin, i. e. motivation and cognition. Furthermore the necessity of research on the bias in the management and management information system areas in increased more and more recently, which have their academic backgrounds in the psychology and social science. The biased information stream is transformed into the systematic error due to the motivation and cognitive bias of human-being, then its resulting phenomena are as follows; 1. the availability of salient information 2. preconceived ideas or theories about peoples and event 3. anchoring and perseverence phenomena. In order to reduce the information errors, Satty suggested the Analytic Hierarchy Process (AHP) that is the subject of this paper and that is widely used for evaluation of complex decision making alternatives. THerefore this paper studies AHP's effects and its limitations in applying to the management area. Thus this paper compared the performances of the 3 models : 1 the traditional additive regression model. 2 regression model using the factor score, and 3 the regression model with AHP. As a result, 3 models produce the different outcomes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.7
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• pp.1689-1698
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• 1998

In this paper, we present a new method for estimating the parameters of transient-type signal in additive white Gaussian noise. This method makes use of the truncated singular value decomposition of an extended-order auto-correlation-like matrix based on the linear-prediction model. The method is tested on data consisting of two exponentially dampled sinusoidal signals with the same damping factor and different damping factor. Simulation results are illustrated to demonstrate the better performance of the method applied to the auto-correlation-like matrix than that applied to the data matrix.