• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1265-1274
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• 2018

This paper proposes a virtual flux (VF) and positive-sequence power based control strategy to improve the performance of grid-interfaced three-phase voltage source converters against unbalanced and distorted grid conditions. By using a second-order generalized integrator (SOGI) based VF observer, the proposed strategy achieves an AC voltage sensorless and grid frequency adaptive control. Aiming to realize a balanced sinusoidal line current operation, the fundamental positive-sequence component based instantaneous power is utilized as the control variable. Moreover, the fundamental negative-sequence VF feedforward and the harmonic attenuation ability of a sequence component generator are employed to further enhance the unbalance regulation ability and the harmonic tolerance of line currents, respectively. Finally, the proposed scheme is completed by combining the foregoing two elements with a predictive direct power control (PDPC). In order to verify the feasibility and validity of the proposed SOGI-VFPDPC, the scenarios of unbalanced voltage dip, higher harmonic distortion and grid frequency deviation are investigated in simulation and experimental studies. The corresponding results demonstrate that the proposed strategy ensures a balanced sinusoidal line current operation with excellent steady-state and transient behaviors under general grid conditions.

• 산업경영시스템학회지
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• 제43권4호
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• pp.107-115
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• 2020

Support vector regression (SVR) is devised to solve the regression problem by utilizing the excellent predictive power of Support Vector Machine. In particular, the ⲉ-insensitive loss function, which is a loss function often used in SVR, is a function thatdoes not generate penalties if the difference between the actual value and the estimated regression curve is within ⲉ. In most studies, the ⲉ-insensitive loss function is used symmetrically, and it is of interest to determine the value of ⲉ. In SVQR (Support Vector Quantile Regression), the asymmetry of the width of ⲉ and the slope of the penalty was controlled using the parameter p. However, the slope of the penalty is fixed according to the p value that determines the asymmetry of ⲉ. In this study, a new ε-insensitive loss function with p1 and p2 parameters was proposed. A new asymmetric SVR called GSVQR (Generalized Support Vector Quantile Regression) based on the new ε-insensitive loss function can control the asymmetry of the width of ⲉ and the slope of the penalty using the parameters p1 and p2, respectively. Moreover, the figures show that the asymmetry of the width of ⲉ and the slope of the penalty is controlled. Finally, through an experiment on a function, the accuracy of the existing symmetric Soft Margin, asymmetric SVQR, and asymmetric GSVQR was examined, and the characteristics of each were shown through figures.

• The Journal of Asian Finance, Economics and Business
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• 제6권4호
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• pp.37-43
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• 2019

The research aims to study the relationship between export performance and stock return of Vietnamese fishery companies. To conduct this study, quarterly data was collected for period from 2010-2018 of 13 fishery companies listing in Ho Chi Minh Stock Exchange (HOSE) and Ha Noi Stock Exchange (HNX). The export performance was measured by export intensity, export growth and export market coverage. In addition, interest rate, exchange rate, GDP, firm size, profitability, and financial leverage were considered as the control variables in the research model. Panel data analysis with Generalized Least Squares model was employed to estimate the predictive regression. The findings indicated that export intensity and export growth have a significant and positive relationship with stock returns. However, export market coverage has not a significant relationship with stock return at the 0.05 level. Profitability, financial leverage, and exchange rate have a positive relationship, while interest rate and GDP have no relation to stock return at the 0.05 significance level. The findings imply that investors should consider the export intensity instead of export growth and export market coverage as selecting stock of fishery exports firms to invest; managers should increase export intensity to increase company's stock price or firm market value.

• 환경영향평가
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• 제8권4호
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• pp.47-57
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• 1999

본 연구는 G7 과제의 일환으로서, Internet상에서 운영될 수 있는 광역적 환경 관리 시스템의 개발에 목표를 두었다. 이러한 목적을 달성하기 위해서는 아직 더 많은 연구가 수행되어야 하지만, 예비적인 개발 성과를 다음과 같이 요약할 수 있다 : 1) 통합적 환경 정보 관리 시스템, 2) web 기반의 제어 엔진, 3) 지표수환경 관리 시스템, 4) 지하수환경 관리 시스템, 5) 하수도 및 시설 관리 시스템. 본 엔진의 핵심 방법론은 다양한 편미분 방정식의 해석에 있어서 각 미분항을 서술하는 범용적 다차원 유한요소 행렬이다. GUI 지향적인 web 기반의 시스템을 구축하기 위해 공간 정보 관리 시스템(ArcView) 및 Visual Basic이 폭넓게 사용되었다. 개발된 시스템들은 환경 문제의 복합적인 관리의 필요성으로 인해 매우 집약적인 프로그램으로 구성되었다. web 기반의 엔진은 환경관련 사업의 통합적인 관리를 위한 정책 결정 도구로 제공될 수 있을 것이다.

• 한국학교보건학회지
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• 제36권3호
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• pp.113-125
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• 2023

Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescents based on health behavior information by employing a hybrid machine learning method. Methods: The study analyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Survey conducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stress perception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness, and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly, demographic factors (family economic status, gender, age), academic performance, physical health (body mass index, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleep time, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption of high-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smoking experience) data were input to develop a random forest model predicting mental health risk, using logistic and XGBoosting. The model and its prediction performance were compared. Results: First, the subjects were classified into two mental health groups using k-mean unsupervised learning, with the high mental health risk group constituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most of the adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictive performance of the random forest model for classifying mental health risk groups significantly outperformed that of the reference model (AUC=.94). Predictors of high importance were 'difficulty recovering from daytime fatigue' and 'subjective health perception'. Conclusion: Based on an understanding of adolescent health behavior information, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.