• 대한전기학회논문지:전력기술부문A
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• 제53권10호
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• pp.529-535
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• 2004

Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. A wide variety of techniques/algorithms for load forecasting has been reported in many literatures. These techniques are as follows: multiple linear regression, stochastic time series, general exponential smoothing, state space and Kalman filter, knowledge-based expert system approach (fuzzy method and artificial neural network). These techniques have improved the accuracy of the load forecasting. In recent 10 years, many researchers have focused on artificial neural network and fuzzy method for the load forecasting. In this paper, we propose an algorithm of a hybrid load forecasting method using fuzzy linear regression and general exponential smoothing and considering the sensitivities of the temperature. In order to consider the lower load of weekends and Monday than weekdays, fuzzy linear regression method is proposed. The temperature sensitivity is used to improve the accuracy of the load forecasting through the relation of the daily load and temperature. And the normal load of weekdays is easily forecasted by general exponential smoothing method. Test results show that the proposed algorithm improves the accuracy of the load forecasting in 1996.

• 한국정보처리학회논문지
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• 제7권11호
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• pp.3408-3416
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• 2000

본 논문에서는 독립변수들의 특징을 추출하여 패턴층 뉴런의 중앙값을 이용함으로써 일반회귀 신경망의 성능을 개선하는 방법을 제안하였다. 제안된 방법에서는 적응적 학습 알고리즘의 주요성분분석 기법을 이용하여 회귀분석에 이용되는 데이터의 각 독립변수들의 집합으로 변환시키는 특징을 살려 일반회귀 신경망의 성능을 더 개선하기 위함이다. 제안된 기법의 일반회귀 신경망을 2개의 독립변수 집합을 가진 Solow의 경제문제와 4개의 독립변수 집합을 가진 국내 유선전화문제에 각각 적용하여 시뮬레이션한 결과, 중심값을 각 독립변수들의 평균이나 가중평균을 이용하는 일반회귀 신경망에 의한 결과와 비교할 때 더욱 우수한 회귀성능이 있음을 확인할 수 있었다. 그리고 신경망의 뉴런 수나 평활요소의 설정 면에서도 우수한 특성이 있음을 확인할 수 있었다.

• 보건행정학회지
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• 제27권4호
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• pp.315-323
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• 2017

Background: This study was purposed to analyze the effect of spatial accessibility to the psychiatry department in general hospital on the outpatient visit of mental patients. Methods: Data was provided from the Statistics Korea and Statistical Geographic Information Service, National Health Insurance Service, Health Insurance Review and Assessment Service, and Korea Transport Institute in 2015. The study regions were 103 administrative regions such as Si and Gu. The 103 regions had at least one general hospitals with a psychiatry department. The number of outpatient visit of mental patients in regions was used as the dependent variable. Spatial accessibility to mental general hospital was used as the independent variable. Control variables included such as demographic, economic, and health medical factors. This study used network analysis and multi-variate regression analysis. Network analysis by ArcGIS ver. 10.0 (ESRI, Redlands, CA, USA) was used to evaluate the average travel time and travel distance in Korea. Multi-variate regression analysis was conducted by SAS ver. 9.4 (SAS Institute Inc., Cary, NC, USA). Results: Travel distance and time had significant effects on the number of outpatient visits in mental patients in general hospital. Average travel time and travel distance had negative effects on the number of visits. Variables such as (number of total population, percentage of aged population over 65, and number of mental general hospital) had significant effects on the number of visit in mental patients. Conclusion: Health policy makers will need to consider the spatial accessibility to the mental healthcare organization in conducting regional health planning.

• 보건교육건강증진학회지
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• 제19권2호
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• pp.73-98
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• 2002

This study was carried out to investigate the relationship between social support, social network and health behaviors as surveyed by cross-sectional study in 744 rural people aged above 30 of a community dwelling sample of one county for 6 days of July in 2000. Objectives of this study was in order to establish an effective health promotion. The sample was accrued by face to face interview of direct visiting from clustered sampling method. Interview was conducted by trained medical students with the questionnaire consisted of socio-demographic data, health behavior, social support and social network based on previous literature. The summarized results were as follows: 1. There were significant difference in the level of social support and social network by general characteristic variables except occupation and residency type(p〈0.05). 2. There were significant difference in knowledge about hypertension, smoking status, status of physical exercise, diet patterns by social support and social network in spite of variation of social support and social network subconcept(p〈0.05). And there were significant difference in alcohol drinking status, body weight control and diet pattern according to level of social network(p〈0.05). But smoking status by social support and network results opposite direction(p〈0.05). 3. There were no regular or consistent result in the relationship between social support, social network and health behavior. 4. Major predictors for health behavior on the multiple logistic regression that included general characteristic, social support and social network were age, instrumental social support and worry about health. Significant variables of multiple logistic regression for health behavior that included social support(instrumental and emotional) and social network were instrumental social support and social network. These results suggest that only a instrumental element and social network may be associated with health behavior. Inconsistent with prior research in these some item, a positive consistent relationship was not found between social support, social network and health behavior. So the study should be replicated to determined the reliability of our findings.

• 설비공학논문집
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• 제24권12호
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• pp.830-836
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• 2012

In this study, the effects of heating control methods on heating control performance and energy consumption in the floor radiant heating control system of residential apartments were research by computer simulation. A general regression neural network(GRNN) control method for reducing indoor temperature overshoot and saving energy in floor radiant heating system is suggested. The GRNN control method shows good responses in comparison with the conventional and outdoor reset control methods for improving indoor thermal environment and reducing energy consumption.

• 한국지열·수열에너지학회논문집
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• 제16권4호
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• pp.16-23
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• 2020

In this study, the control characteristics and effects of control methods on heating performance and energy consumption for the hot water floor radiant heating control system of a residential apartment were research by experiment. As a control method, On-Off control and outdoor reset control methods with GRNN(General Regression Neural Network) and without GRNN are considered. Also, the control performances with regard to improvement of indoor thermal environment and reduction of energy consumption are compared, respectively. Experiment results show that the performance of the control method with GRNN is better than that of conventional on-off control method without GRNN in the responses of room set temperature and energy saving.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.44-50
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• 2024

This paper offers an overview of matrix formation and calculation techniques within the framework of General Linear Models (GLMs). It takes a sequential approach, beginning with a detailed exploration of matrix formation and calculation methods in regression analysis and univariate analysis of variance (ANOVA). Subsequently, it extends the discussion to cover multivariate analysis of variance (MANOVA). The primary objective of this study was to provide a clear and accessible explanation of the underlying matrices that play a crucial role in GLMs. Through linking, essentially different statistical methods, by fundamental principles and algebraic foundations that underpin the GLM estimation. Insights presented here aim to assist researchers, statisticians, and data analysts in enhancing their understanding of GLMs and their practical implementation in diverse research domains. This paper contributes to a better comprehension of the matrix-based techniques that can be extended to GLMs.

• Wind and Structures
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• 제2권1호
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• pp.25-40
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• 1999

In this paper, artificial neural networks, a new kind of intelligent method, are employed to model and predict amplitude dependent damping in buildings based on our full-scale measurements of buildings. The modelling method and procedure using neural networks to model the damping are studied. Comparative analysis of different neural network models of damping, which includes multi-layer perception network (MLP), recurrent neural network, and general regression neural network (GRNN), is performed and discussed in detail. The performances of the models are evaluated and discussed by tests and predictions including self-test, "one-lag" prediction and "multi-lag" prediction of the damping values at high amplitude levels. The established models of damping are used to predict the damping in the following three ways : (1) the model is established by part of the data measured from one building and is used to predict the another part of damping values which are always difficult to obtain from field measurements : the values at the high amplitude level. (2) The model is established by the damping data measured from one building and is used to predict the variation curve of damping for another building. And (3) the model is established by the data measured from more than one buildings and is used to predict the variation curve of damping for another building. The prediction results are discussed.

• 지능정보연구
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• 제14권3호
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• pp.25-39
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• 2008

본 연구는 인공신경망을 이용하여 대대급 방어 작전에서 임의시점에서의 작전지속능력을 예측하는 데 있다. 전투결과에 대한 수학적 모델링은 이를 위한 많은 요인들이 가지는 시?공간적 가변성으로 인해 전투력을 평가하는데 많은 문제점이 있었다. 따라서 이번 연구에서는 대대 전투지휘훈련간 각 부대의 생존률을 전방향 다층 신경망(Feed-Forward Multilayer Perceptrons, MLP)과 일반 회귀신경망(General Regression Neural Network, GRNN)모형에 적용하여 임무달성 여부를 예측하였다. 실험 결과 매개변수들의 비선형적인 관계에도 불구하고 각각 82.62%, 85.48%의 적중률을 보여 일반회귀신경망 모형이 지휘관이 상황을 인식하고 예비대 투입 우선순위 선정 등 실시간 지휘결심을 하는데 도움을 줄 수 있는 방법임을 보여준다.