• 전력전자학회논문지
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• 제23권6호
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• pp.403-407
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• 2018

This study proposes a predictive direct power control method in a modular multilevel converter (MMC) high-voltage direct-current (HVDC) system. The conventional proportional integral (PI)-based control method uses a cascaded connection and requires an optimal gain selection procedure and additional decoupling scheme. However, the proposed control method has a simple structure for active/reactive power control due to the direct power control scheme and exhibits a fast dynamic response by predicting the future status of system variables and considering time delay. The effectiveness of the proposed method is verified by simulation results.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.630-644
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• 2021

Educational data analysis is attracting increasing attention with the rise of the big data industry. The amounts and types of learning data available are increasing steadily, and the information technology required to analyze these data continues to develop. The early identification of potential dropout students is very important; education is important in terms of social movement and social achievement. Here, we analyze educational data and generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayes method, and a multilayer perceptron. The multilayer perceptron model using independent variables selected via the variance analysis showed better performance than the other models. In addition, we experimentally found that not only grades but also extracurricular activities were important in terms of preventing student dropout.

• 제어로봇시스템학회논문지
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• 제3권3호
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• pp.272-279
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• 1997

In this paper, a fuzzy generalized predictive control (FGPC) for non-linear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus may possess inherent good properties of the GPC. Computer simulations show that the performance of the FGPC is satisfactory.

• 품질경영학회지
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• 제48권3호
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• pp.511-520
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• 2020

Purpose: The purpose of this study was to devise an accurate machine learning model for predicting silica concentrations following the addition of impurities, through time series analysis of mining data. Methods: The mining data were preprocessed and subjected to time series analysis using the machine learning model. Through correlation analysis, valid variables were selected and meaningless variables were excluded. To reflect changes over time, dependent variables at baseline were treated as independent variables at later time points. The relationship between independent variables and the dependent variable after n point was subjected to Pearson correlation analysis. Results: The correlation (R2) was strongest after 3 hours, which was adopted as a dependent variable. According to root mean square error (RMSE) data, the proposed method was superior to the other machine learning methods. The XGboost algorithm showed the best predictive performance. Conclusion: This study is important given the current lack of machine learning studies pertaining to the domestic mining industry. In addition, using time series analysis in mining data will show further improvement. Before establishing a predictive model for the proposed method, predictions should be made using data with time series characteristics. After doing this work, it should also improve prediction accuracy in other domains.

• 인터넷정보학회논문지
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• 제23권3호
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• pp.125-135
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• 2022

본 연구는 데이터, 네트워크, 인공지능을 기반으로 산업 생태계 조성을 위해 구축된 정부통계지표가 약국 매출액에 영향을 미치는지 머신러닝을 이용하여 변인을 탐색하고 약국 매출액 예측에 적합한 분석 기법을 제공하고자 한다. 이에, 본 연구는 28개 정부통계지표와 소매업종인 약국을 대상으로 2016년 1월부터 2021년 12월까지의 분석 데이터를 활용하여 머신러닝 기법인 랜덤 포레스트, XGBoost, LightGBM, CatBoost을 통해 예측 변인 및 성능을 탐색하였다. 분석결과 경기관련 지표인 경제심리지수, 경기동행지수순환변동치, 소비자심리지수는 약국 매출액에 영향을 미치는 중요한 변인으로 나타났고, 회귀성능은 지표 MAE, MSE, RMSE를 살펴본 결과 랜덤 포레스트가 XGBoost, LightGBM, CatBoost 보다 성능이 가장 우수하게 나타났다. 이에, 본 연구는 머신러닝 결과를 토대로 약국 매출액에 영향을 미치는 변인과 최적의 머신러닝 기법을 제시하였으며, 여러 시사점과 후속연구를 제안하였다.

• Journal of Preventive Medicine and Public Health
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• 제34권3호
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• pp.237-243
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• 2001

Objectives : To examine the screening rate of cervical cancer in women and to find out the predictive factors for participation in cervical cancer screening programs within their life-time and within the last two years. Methods : The data was based on self-reported questionnaires from 1,613 women whose ages ranged from 26 to 60 years; this survey was peformed between December 1999 and January 2000. This study analyzed the predictive factors for participation in cervical cancer screening programs within their life-time and within the last two years. A logistic regression analysis was performed in order to derive the significant variables from the predisposing factors(demographic factor, health promotion behavior, reproductive factor), intervention factors(information channel, relation with medical stan, and proximal factors(attitude, social influence, self-efficacy). All analyses were peformed by the PC-SAS 6.12. Results : Our analyses showed that the screening rate for the women who received a cervical cancer screening(Pap smear) more than once within their life-time was 56.1% while those who had received one within the last two years was 34.5%. The significant factors for participation in cervical cancer screening program within their life-time were their income, married age, health promotion score, relation with medical staffs, social influence, and self-efficacy. On the other hand, age, number of pregnancies, menarche age, relation with medical staffs, social influences, and self-efficacy were significant factors for those being screened within the last two years. The predictive power of the logit model within their life-time was 68.8% and that within the last two years was 66.6%. Conclusion : The predictive factors for participation in cervical cancer screening program within their life-time are different from those for within the last two years. and that women's relations with medical staffs and social influences were the critical factors impacting on cervical cancer screening rates.

• 한국융합학회논문지
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• 제11권6호
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• pp.203-208
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• 2020

이 연구는 첫째, 경도인지장애(MCI)가 언어 기억 및 시공간 기억 등 특정 기억의 저하와 관련이 있는지를 파악하고, 둘째, 정상 노인으로부터 MCI를 감별하는 데 예측력이 우수한 지표를 탐색하였다. 표준화 된 기억검사를 수행한 189명(정상 노인 103 명, MCI 86 명)을 분석하였다. 언어 기억은 Seoul Verbal Learning Test를 이용하였고. 시공간 기억은 Rey Complex Figure Test를 이용해서 측정하였다. 다항 로지스틱 회귀모형을 이용하여 기억 검사의 예측력을 분석한 결과, 언어 기억과 시공간 기억은 정상 노인으로부터 MCI를 감별하는 데 예측 성능이 유의미하였다. 반면, 각 기억 검사의 수행결과를 포함하여 모든 혼란변수를 보정했을 때, 언어 기억의 즉시 회상만 정상 노인으로부터 MCI를 감별하는 데 예측력이 유의미하였으며, 시공간 기억의 즉시 회상은 예측력이 유의미하지 않았다. 이 결과는 MCI를 선별할 때 언어 기억과 시공간 기억의 지연 회상, 언어 기억의 즉시 회상이 MCI의 기억능력을 감별할 수 있는 최상의 조합임을 시사한다.

• 대한한의학회지
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• 제40권4호
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• pp.49-60
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• 2019

Objectives: To develop a standardized diagnostic pattern identification equation for stroke patients, our group conducted a study to derive the predictive logistic equations. However, the sample size was relatively small. In the current study, we aimed to derive new predictive logistic equations for each diagnostic pattern using an expanded number of subjects. Methods: This study was a hospital-based multi-center trial recruited stroke patients within 30 days of symptom onset. Patients' general information, and the variables related to diagnostic pattern identification were measured. The diagnostic pattern of each patient was identified independently by two Korean Medicine Doctors. To derive a predictive model for pattern identification, binary logistic regression analysis was applied. Results: Among the 1,251 patients, 385 patients (30.8%) had the Fire Heat Pattern, 460 patients (36.8%) the Phlegm Dampness Pattern, 212 patients (16.9%) the Qi Deficiency Pattern, and 194 patients (15.5%) the Yin Deficiency Pattern. After the regression analysis, the predictive logistic equations for each pattern were determined. Conclusion: The predictive equations for Fire Heat, Phlegm Dampness, Qi Deficiency, and Yin Deficiency would be useful to determine individual stroke patients' pattern identification in the clinical setting. However, further studies using objective measurements are necessary to validate these data.

• 대한지역사회영양학회지
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• 제19권6호
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• pp.568-580
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• 2014

Objectives: The purpose of this study was to measure the resting metabolic rate (RMR) and to assess the accuracy of RMR predictive equations for Korean farmers. Methods: Subjects were 161 healthy Korean farmers (50 males, 111 females) in Gangwon-area. The RMR was measured by indirect calorimetry for 20 minutes following a 12-hour overnight fasting. Selected predictive equations were Harris-Benedict, Mifflin, Liu, KDRI, Cunningham (1980, 1991), Owen-W, F, FAO/WHO/UNU-W, WH, Schofield-W, WH, Henry-W, WH. The accuracy of the equations was evaluated on the basis of bias, RMSPE, accurate prediction and Bland-Altman plot. Further, new RMR predictive equations for the subjects were developed by multiple regression analysis using the variables highly related to RMR. Results: The mean of the measured RMR was 1703 kcal/day in males and 1343 kcal/day in females. The Cunningham (1980) equation was the closest to measured RMR than others in males and in females (males Bias -0.47%, RMSPE 110 kcal/day, accurate prediction 80%, females Bias 1.4%, RMSPE 63 kcal/day, accurate prediction 81%). Body weight, BMI, circumferences of waist and hip, fat mass and FFM were significantly correlated with measured RMR. Thus, derived prediction equation as follow : males RMR = 447.5 + 17.4 Wt, females RMR = 684.5 - 3.5 Ht + 11.8 Wt + 12.4 FFM. Conclusions: This study showed that Cunningham (1980) equation was the most accurate to predict RMR of the subjects. Thus, Cunningham (1980) equation could be used to predict RMR of Korean farmers studied in this study. Future studies including larger subjects should be carried out to develop RMR predictive equations for Korean farmers.

• Communications for Statistical Applications and Methods
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• 제30권6호
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• pp.577-587
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• 2023

Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.