• 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 Korea Robotics Society
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• v.8 no.1
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• pp.20-28
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• 2013

Large workspace and strong grasping force are required when a robot manipulates big and/or heavy objects. In that situation, bimanual manipulation is more useful than unimanual manipulation. However, the control of both hands to manipulate an object requires a more complex model compared to unimanual manipulation. Learning by human demonstration is a useful technique for a robot to learn a model. In this paper, we propose an imitation learning method of bimanual object manipulation by human demonstrations. For robust imitation of bimanual object manipulation, movement trajectories of two hands are encoded as a movement trajectory of the object and a force trajectory to grasp the object. The movement trajectory of the object is modeled by using the framework of dynamic movement primitives, which represent demonstrated movements with a set of goal-directed dynamic equations. The force trajectory to grasp an object is also modeled as a dynamic equation with an adjustable force term. These equations have an adjustable force term, where locally weighted regression and multiple linear regression methods are employed, to imitate complex non-linear movements of human demonstrations. In order to show the effectiveness our proposed method, a movement skill of pick-and-place in simulation environment is shown.

• Nutrition Research and Practice
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• v.15 no.4
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• pp.468-478
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• 2021

BACKGROUND/OBJECTIVES: This study aimed to analyze the association between dietary omega-3 fatty acid intake and depression in postmenopausal women using data from the Korea National Health and Nutrition Examination Survey (KNHANES) VI. SUBJECTS/METHODS: The KNHANES is a cross-sectional nationwide health and nutrition survey. Dietary data, including omega-3 fatty acids, were assessed using the 24-h recall method. Depression was evaluated using a survey questionnaire. The association between dietary omega-3 fatty acids and depression was evaluated using multivariate logistic regression analysis. Depression, according to the dietary omega-3 fatty acid intake, was expressed as the odds ratio (OR) with a 95% confidence interval (CI). A total of 4,150 postmenopausal women were included in the analysis. RESULTS: In the fully-adjusted model, the group with the highest dietary omega-3 fatty acid intake significantly showed lower prevalence of depression than the group with the lowest intake (OR, 0.52; 95% CI, 0.33-0.83); a significant linear trend was detected (P for trend = 0.04). According to the dose-response analysis using cubic restricted spline regression, this association was linear and monotonic (P for non-linearity = 0.32). CONCLUSIONS: In this study, the dietary omega-3 fatty acid intake in postmenopausal women was inversely proportional to depression in a dose-response manner. Large cohort studies are needed to verify the causality between omega-3 fatty acids and depression in Korean postmenopausal women.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.1017-1023
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• 2021

This research aims to determine the effect of customers' threat emotion and price on the decision to purchase a certain smartphone product. This study uses a quantitative method with a type of descriptive and causal research. It employs non-probability sampling with purposive sampling, with 385 respondents to answer the questionnaires. Data analysis techniques used descriptive analysis and multiple linear regression analysis. Based on the results of descriptive analysis of emotion, price and purchasing decisions are in sync with each other. The results of multiple linear regression analysis techniques indicate the threat emotion and brand trust are influential against the positive decision to purchase smartphone products. The magnitude of the influence of emotions and price have simultaneous effect on purchasing decisions and other decision variables, which are not included in this study, also play minor role in determining purchase intention, such as product quality, brand image and others. Partially, threat emotion and brand trust have a positive effect toward purchasing decisions. The magnitude of the highest influence was the one of price, then followed by emotional threats. The findings of this study suggest that psychological and behavioral effects also play important roles in determining customers' purchase decision.

• Science of Emotion and Sensibility
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• v.7 no.3
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• pp.15-25
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• 2004

The regression analysis and Adaptive -Network based Fuzzy-inference system (ANFIS) were applied to the explanation on human's visual texture of cotton fabrics with 7 mechanical properties. The ANFIS uses the structure with fuzzy membership function and neural network. The results obtained by the statistical analysis through the coefficient of correlation and regression analysis showed that subjective texture had a linear relationship with mechanical properties. But It had a relatively low coefficient of determination and was difficult that the statistical analysis explained other relationship with the exception of a lineality and interaction among mechanical properties. Comparing the statistical analysis, the ANFIS was an effective tool to explain human's non-linear perceptions and their interactions. But to apply ANFIS to human's perceptions more effectively, it is necessary to discriminate effective input variables through controlling the properties of samples.

• Health Policy and Management
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• v.30 no.3
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• pp.376-385
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• 2020

Background: The purpose of this study is to investigate the factors affecting the healthy living practice rate such as non-smoking, moderate drinking, walking, and low-salt diet by elementary municipality (so called, 'si-gun-hu'). Methods: The 2016 Korean Community Health Survey was used for the analysis. The theoretical model is founded upon the Anderson model, and both the multiple linear regression analysis and the beta regression analysis was performed for estimation. Results: As a result of the beta regression analysis, healthy living practice rate was found to be significantly higher in the areas with a less number of cigarette retailers, participating in healthy city projects, a low proportion of people who perceive their body type as obesity, a higher proportion of women, and a lower proportion of spouses. Conclusion: In order to improve healthy living practices, the regulations on health risk businesses, the spread of Healthy City project, and policy efforts awaring obesity are recommended.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• Journal of Forest and Environmental Science
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• v.30 no.4
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• pp.351-361
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• 2014

Leaf area ($A_0$) and leaf biomass ($M_0$) estimation are significant prerequisites to studying tree physiological processes and modeling in the forest ecosystem. The objective of this study was to develop allometric models for estimating $A_0$ and $M_0$ of Swietenia mahagoni L. from different tree parameters such as DBH and tree height of mahogany plantations in the northeastern region of Bangladesh. A total of 850 healthy and well formed trees were selected randomly for sampling in the five study sites. Then, twenty two models were developed based on different statistical criteria that propose reliable and accurate models for estimating the $A_0$ and $M_0$ using non-destructive measurements. The results exposed that model iv and xv were selected on a single predictor of DBH and showed more statistically accuracy than other models. The selected models were also validated with an additional test data set on the basis of linear regression and t-test for mean difference between observed and predicted values. After that, a comparison between the best logarithmic and non-linear allometric model shows that the non-linear model produces systematic biases and underestimates $A_0$ and $M_0$ for larger trees. As a result, it showed that the bias-corrected logarithmic model iv and xv can be used to help quantify forest structure and functions, particularly valuable in future research for estimating $A_0$ and $M_0$ of S. mahagoni in this region.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.2
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• pp.128-135
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• 2007

In response to anthropogenic global warming due to a buildup greenhouse gas, the effect of the air temperature on water temperature has been noticed. Therefore, effects have been made to build an air/water temperature relationship at three study regions using the data collected by the Ministry of the Maritime Affairs and Fisheries (MOMAF). The air/water relationship varies with time-scale and weekly time-scale was chosen for the study. The data were fitted to the S-shaped non-linear relationship, and the parameters for the S-curve were derived using a single-criteria multi-parameter optimization scheme. Separate regression curves were fitted to consider seasonal hysteresis at the Masan site. The study results support the S-shaped non-linear relationship is the best fit for the air/water relationship at the Korean coastal zone. This study will contribute to determine the future policy regarding water quality and ecosystem for the decision-driving organization.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.233-254
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• 2023

Purpose - This study aims to verify whether the effect of tax avoidance on corporate value is non-linear in the Korean financial markets. Design/methodology/approach - This study believes that the cause of the inconsistent empirical analysis results of previous studies that verified the relationship between tax avoidance and firm value may be an error in assuming linearity, and verifies whether a nonlinear relationship exists. The sample company in this study is a December settlement corporation listed on the Korean stock market, and the analysis period is from 2000 to 2021. In the empirical analysis model, Tobin's Q is used as a proxy for corporate value, tax avoidance is used as the main independent variable, and a regression model is designed with corporate size, growth rate, and debt ratio set as control variables. Findings - As a result of the empirical analysis, it can be confirmed that there is an inverted U-shaped nonlinear relationship between tax avoidance and corporate value. In the additional analysis using Ohlson (1995) firm valuation model for the robustness of the results of the empirical analysis, the same nonlinear value relationship between tax avoidance can be confirmed. Research implications or Originality - This study is considered to be meaningful in that it verifies the non-linear relationship of tax avoidance, which has not been attempted in previous studies. The meaning of the inverted U-shaped nonlinear relationship presented in this study is that corporate tax avoidance acts as a factor that increases corporate value up to a certain level, but rather becomes a factor that decreases corporate value when it exceeds a critical point. These results are expected to provide new perspectives and perspectives on tax avoidance to companies belonging to the Korean capital market.