• Management Science and Financial Engineering
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• v.14 no.2
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• pp.89-104
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• 2008

Growth curves including Bass, Logistic and Gompertz functions are widely used in forecasting the market demand. Nonlinear least square method is often adopted for estimating the model parameters but it is difficult to set up the starting value for each parameter. If a wrong starting point is selected, the result may lead to erroneous forecasts. This paper proposes a method of selecting starting values for model parameters in estimating some growth curves by nonlinear least square method through grid search and transformation into linear regression model. Resealing the market data using the national economic index makes it possible to figure out the range of parameters and to utilize the grid search method. Application to some real data is also included, where the performance of our method is demonstrated.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1524-1524
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• 2001

A previous paper(Ito et al., 2000) has described the improvement of the standard error(SEC and SEP) of the predicted soluble solids(Brix) in a melon cultivar by non-contact mode with a fiber optic probe. Then we examined the immature and mature fruits. The objective of this study was to determine if non-contact mode could improve the standard error of the predicted Brix of matured melon fruits from cross progeny as well as the contact mode(usual method). The optical absorption spectrum was measured using a NIR Systems model 6500 spectrophotometer. A commercial spectral program(NSAS ver. 3.27) was used for multiple linear regression analysis. Absorbances of 902 and in the vicinity of 877 nm were included as the independent variables in both multiple regression equations. These wavelengths are key wavelengths for non-destructive Brix determination. When the results for the contact mode and non-contact mode are compared, the latter mode improved the former standard error(SEP and RMS).

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.147-157
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• 2009

Recently, there has been continued research on UCP from the development effort estimation method to a software development project applying object oriented development methodology. Current research proposes a linear model estimating the developmenteffort by multiplying a constant to AUCP which applies technical and environmental factors. However, the fact that a non-linear regression model is more appropriate as the software size increases, the development period increases exponentially. In addition, in the UCP calculation process the occurrence of FP errors due to the application of TCF and EF, it is unrealistic to estimate the size with AUCP. This paper presents the issue of current research based on UCP without considering problems of the research, for example, TCF and EF and expresses the models (linear, logarithmic, polynomial, power and exponential type) estimating the development effort directly from UUCP. Consequently, the exponential model within non-linear models exhibit more accurate results than the current linear model. Therefore, after calculating the UUCP of the developing software system, using the proposed model to estimate the development effort, it is possible to estimate the direct cost required in development.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.935-941
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• 2017

This paper proposes a method for estimating the distance betweeen vehicles in a moving situation using the image ratio of the distance between the tail lamps of a front vehicle. The actual distance between the tail lamps of a front vehicle was transmitted by LED tail lamps using visible light communication. As the distance between the front vehicle and the rear vehicle changes, it calculates the ratio of the pixel width between the tail lamps of the front vehicle projected on the image. The calculated values are used to derive a distance-mapping function through non-linear regression technique. Then, the distance between vehicles in the moving situation is estimated based on this function.

• The Korea Journal of Herbology
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• v.28 no.4
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• pp.1-6
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• 2013

Objectives : In other to determine the optimal extraction conditions, the various Bojungikgi-tang (BJIGT) decoctions prepared by different pressure levels and different extraction times were compared and evaluated in terms of the extract yield and the total soluble solid content. Methods : Decoctions were prepared by the pressure levels of 0 (non-pressurized) and 1 $kgf/cm^2$ (pressurized) for 60, 120 and 180 min. The extract yield and the total soluble solids content of decoctions were measured, and the amounts of the reference compounds in decoctions were investigated by the analysis using high performance liquid chromatography. Results : The extract yield and the total soluble solid content were higher in decoctions extracted by the pressurized method than those from decoction with non-pressurized method. The patterns of yield and contents showed a proportional increase to the extraction time. In analysis of the linear regression for four reference compounds such as liquiritin, nodakenin, hesperidin, and glycyrrhizin, the good linearity with the correlation coefficient more than 0.9999 was observed. The highest contents for four reference compounds were observed at 180 min of both the pressurized method and the non-pressurized method. Conclusions : This study suggests that the pressure in extraction method and the extraction time affect the compositional constituents in BJIGT decoctions. The extraction time of 180 min could be chosen in both pressurized and non-pressurized method as optimal extraction condition.

• Korean Journal of Exercise Nutrition
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• v.25 no.1
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• pp.23-29
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• 2021

[Purpose] This preliminary study aimed to develop a regression model to estimate the non-exercise activity thermogenesis (NEAT) of Korean adults using various easy-to-measure dependent variables. [Methods] NEAT was measured in 71 healthy adults (male n = 29; female n = 42). Statistical analysis was performed to develop a NEAT estimation regression model using the stepwise regression method. [Results] We confirmed that age^A, weight^B, heart rate (HR)_average^C, weight × HR_average^D, weight × HR_sum^E, systolic blood pressure (SBP) × HR_rest^F, fat mass ÷ height^2G, gender × HR_average^H, and gender × weight × HR_sum^I were important variables in various NEAT activity regression models. There was no significant difference between the measured NEAT values obtained using a metabolic gas analyzer and the predicted NEAT. [Conclusion] This preliminary study developed a regression model to estimate the NEAT in healthy Korean adults. The regression model was as follows: sitting = 1.431 - 0.013 × (A) + 0.00014 × (D) - 0.00005 × (F) + 0.006 × (H); leg jiggling = 1.102 - 0.011 × (A) + 0.013 × (B) + 0.005 × (H); standing = 1.713 - 0.013 × (A) + 0.0000017 × (I); 4.5 km/h walking = 0.864 + 0.035 × (B) + 0.0000041 × (E); 6.0 km/h walking = 4.029 - 0.024 × (C) + 0.00071 × (D); climbing up 1 stair = 1.308 - 0.016 × (A) + 0.00035 × (D) - 0.000085 × (F) - 0.098 × (G); and climbing up 2 stairs = 1.442 - 0.023 × (A) - 0.000093 × (F) - 0.121 × (G) + 0.0000624 × (E).

• ETRI Journal
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• v.42 no.5
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• pp.761-772
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• 2020

This paper presents an automatic proficiency assessment method for a non-native Korean read utterance using bidirectional long short-term memory (BLSTM)-based acoustic models (AMs) and speech data augmentation techniques. Specifically, the proposed method considers two scenarios, with and without prompted text. The proposed method with the prompted text performs (a) a speech feature extraction step, (b) a forced-alignment step using a native AM and non-native AM, and (c) a linear regression-based proficiency scoring step for the five proficiency scores. Meanwhile, the proposed method without the prompted text additionally performs Korean speech recognition and a subword un-segmentation for the missing text. The experimental results indicate that the proposed method with prompted text improves the performance for all scores when compared to a method employing conventional AMs. In addition, the proposed method without the prompted text has a fluency score performance comparable to that of the method with prompted text.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.2
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• pp.108-113
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• 2016

The real-world problems usually show nonlinear and multi-variate characteristics, so it is difficult to establish concrete mathematical models for them. Thus, it is common to practice data-driven modeling techniques in these cases. Among them, most widely adopted techniques are regression model and intelligent model such as neural networks. Regression model has drawback showing lower performance when much non-linearity exists between input and output data. Intelligent model has been shown its superiority to the linear model due to ability capable of effectively estimate desired output in cases of both linear and nonlinear problem. This paper proposes modeling method of daily photovoltaic power systems using ELM(Extreme Learning Machine) based modular networks. The proposed method uses sub-model by fuzzy clustering rather than using a single model. Each sub-model is implemented by ELM. To show the effectiveness of the proposed method, we performed various experiments by dataset acquired during 2014 in real-plant.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.835-842
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• 2020

The study aims to investigate the impact of debt on corporate profitability in the context of Vietnam. The paper investigates the impact of debt on corporate profitability in non-finance listed companies on the Vietnam stock market. The panel data of the research sample includes 118 non-financial listed companies on the Vietnam stock market for a period of nine years, from 2009 to 2017. The Generalized Method of Moments (GMM) is employed to address econometric issues and to improve the accuracy of the regression coefficients. In this research, corporate profitability is measured as the return of EBIT on total assets. The debt ratio is a ratio that indicates the proportion of a company's debt to its total assets. Firm sizes, tangible assets, growth rate, and taxes are control variables in the study. The empirical results show that debt has a statistically significant negative effect on corporate profitability. The result also shows this effect is stronger in a non-linear (concave) way, we show that the debt ratio has nonlinear effects on corporate profitability. From this, experimental evidence shows that the optimal debt ratio is 38.87%. This evidence provides a new insight to managers of the non-finance companies on how to improve the firm's profitability with debt.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.453-470
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• 2018

We study how to distinguish the parameters of the sparse autoregressive (AR) process from zero using a non-convex penalized estimation. A class of non-convex penalties are considered that include the smoothly clipped absolute deviation and minimax concave penalties as special examples. We prove that the penalized estimators achieve some standard theoretical properties such as weak and strong oracle properties which have been proved in sparse linear regression framework. The results hold when the maximal order of the AR process increases to infinity and the minimal size of true non-zero parameters decreases toward zero as the sample size increases. Further, we construct a practical method to select tuning parameters using generalized information criterion, of which the minimizer asymptotically recovers the best theoretical non-penalized estimator of the sparse AR process. Simulation studies are given to confirm the theoretical results.