• Nuclear Engineering and Technology
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• 제42권2호
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• pp.219-230
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• 2010

In nuclear power plants (NPPs), periodic sensor calibrations are required to assure that sensors are operating correctly. By checking the sensor's operating status at every fuel outage, faulty sensors may remain undetected for periods of up to 24 months. Moreover, typically, only a few faulty sensors are found to be calibrated. For the safe operation of NPP and the reduction of unnecessary calibration, on-line instrument calibration monitoring is needed. In this study, principal component-based auto-associative support vector regression (PCSVR) using response surface methodology (RSM) is proposed for the sensor signal validation of NPPs. This paper describes the design of a PCSVR-based sensor validation system for a power generation system. RSM is employed to determine the optimal values of SVR hyperparameters and is compared to the genetic algorithm (GA). The proposed PCSVR model is confirmed with the actual plant data of Kori Nuclear Power Plant Unit 3 and is compared with the Auto-Associative support vector regression (AASVR) and the auto-associative neural network (AANN) model. The auto-sensitivity of AASVR is improved by around six times by using a PCA, resulting in good detection of sensor drift. Compared to AANN, accuracy and cross-sensitivity are better while the auto-sensitivity is almost the same. Meanwhile, the proposed RSM for the optimization of the PCSVR algorithm performs even better in terms of accuracy, auto-sensitivity, and averaged maximum error, except in averaged RMS error, and this method is much more time efficient compared to the conventional GA method.

• 정보처리학회 논문지
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• 제13권5호
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• pp.209-216
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• 2024

부동산 시장은 다양한 요인에 의해 가격이 결정되며 거시경제 변수뿐 만 아니라 뉴스 기사, SNS 등 다양한 텍스트 데이터의 영향을 받는다. 특히 뉴스 기사는 국민들이 느끼는 경제 심리를 반영하고 있으므로 부동산 매매 가격 예측에 있어 중요한 요인이다. 본 연구에서는 뉴스 기사를 감성 분석하여 그 결과를 뉴스 감성 지수로 점수화 한 후 부동산 가격 예측 모델에 적용하였다. 먼저 기사 본문을 요약 후 요약된 내용을 바탕으로 생성 AI를 활용하여 긍정, 부정, 중립으로 분류한 다음 총 점수를 산출하였고 이를 부동산 가격 예측 모델에 적용하였다. 부동산 가격 예측 모델로는 Multi-head attention LSTM 모델과 Vector Auto Regression 모델을 사용하였다. 제안하는 뉴스 감성 지수를 적용하지 않은 LSTM 예측 모델은 1개월, 2개월, 3개월 예측에서 각각 0.60, 0.872, 1.117의 Root Mean Square Error (RMSE)을 보였으며, 뉴스 감성 지수를 적용한 LSTM 예측 모델은 각각 0.40, 0.724, 1.03의 RMSE값을 나타낸다. 또한 뉴스 감성 지수를 적용하지 않은 Vector Auto Regression 예측 모델은 1개월, 2개월, 3개월 예측에서 각각 1.6484, 0.6254, 0.9220, 뉴스 감성 지수를 적용한 Vector Auto Regression 예측 모델은 각각 1.1315, 0.3413, 1.6227의 RMSE 값을 나타낸다. 앞선 아파트 매매가격지수 예측 모델을 통해 사회/경제적 동향을 반영한 부동산 시장 가격 변동을 예측할 수 있을 것으로 보인다.

• 산업공학
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• 제24권4호
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• pp.351-361
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• 2011

Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others.

• 지역연구
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• 제40권2호
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• pp.21-38
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• 2024

지역 인구와 경지면적 간의 상호작용을 이해하는 것은 지역 경제 활성화와 농업의 지속가능성 향상을 위해 중요하다. 기존의 연구들은 지역의 인구이동이나 경지면적 변화 각각의 주제에 중점을 두었으며, 이들의 상호작용에 관한 연구는 미흡했다. 또한, 새로운 노동 공급원인 외국인 근로자의 증가를 대상으로 한 지역 단위의 정량적 연구도 부족했다. 이에 본 연구에서는 지역의 인구 변화, 경지면적, 그리고 외국인 근로자 수 변화의 상호작용을 패널 자기 상관 모형(Panel Vector Auto Regression Model)을 활용하여 분석하였다. 분석 결과, 지역의 인구유입률과 경지면적은 서로에게 부정적인 영향을 발생시키지만, 농업부문의 외국인 근로자 증가는 경지면적을 증가시키는 것으로 나타났다. 또한, 밭 면적의 증가는 외국인 근로자를 증가시키는 것으로 나타났다. 이러한 결과는 지역의 인구감소와 경지면적 감소 현상이 상호영향을 미치고 있으며 외국인 근로자의 유입이 농촌지역의 구조적 문제해결에 긍정적인 영향을 미칠 가능성이 있음을 시사한다.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 하계종합학술대회논문집
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• pp.129-132
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• 2000

We propose a new method to deal with the optimized auto-tuning for the PID controller which is used to the process -control in various fields. First of all, in this method, initial values of DC motor are determined by the Ziegler-Nichols method. Finally, after studying the parameters of PID controller by input vector of multiple regression analysis, when we give new K, L, T values to multiple regression model, the optimized parameters of PID controller is found by multiple regression analysis program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• Asia Marketing Journal
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• 제18권3호
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• pp.1-18
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• 2016

The purpose of this paper is to examine empirically how to balance advertising expenditure before and after launch with regard to the direction of word of mouth in the motion picture industry. The vector auto-regression model is applied to assess the dynamic impact of advertising and word of mouth on sales. Empirical data, including advertising, word of mouth, and sales (the number of entries) of 83 movies are used for analysis. The research results show that for a movie having more positive word of mouth in the pre- and post-launch periods, it is worthwhile to spend the advertising budget in the pre-launch period only and to spare it in post-launch period. However, it is worthwhile to spare the advertising budget in the pre-launch period for movies having less positive word of mouth before and after launch, and to concentrate spending in post-launch period instead. Mangers who handle products and services facing shortened lifecycles, such as games, eBooks, and digital music contents, need to check the quality of pre-launch word of mouth for their advertising budget decisions in the pre- and post-launch periods and spend more of the advertising budget in the post- (pre-) launch period if pre-launch word of mouth is negative (positive). For products and services with a shortened lifecycle, it is recommended to spend more of the advertising budget in the post- (pre-) launch period if pre-launch word of mouth is negative (positive).

• 농촌지도와개발
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• 제7권2호
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• pp.257-277
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• 2000

The objectives of this study are to investigate the relationship between the growth of the horticultural sector and horticultural research and extension and to examine the socioeconomic returns to investment on research and extension in Korean horticulture. Data for horticultural production values, producer price indices and research and extension budgets for horticultural sector from 1965 to 1998 are collected from various sources. Multi-variate time series analysis technique with vector auto-regression model and Akino-Hayami Formula were employed for the analysis. This study finds (1) horticultural production responds about seven years later to the horticultural research investment shock. the magnitude of the impacts increases to a peak in seventeen years from the initial expenditures and then declines slowly thereafter until twenty years. and this peak gives a tip that horticultural research impact lasts much longer than grain's or agriculture's: (2) the social surplus from research investment benefits more to the consumer rather than to the horticultural producer: (3) B/C ratios in horticultural research are quite high with the range of 9 to 55 from 1965 to 1998. but these have been decreased since the early 1990s: (4) the socioeconomic returns to horticultural research is quite high with 56 percents of internal rate of return. It remains to be analyzed returns to investment on extension in horticulture because of no statistic significance in this study.

• 아태비즈니스연구
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• 제14권4호
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• pp.305-318
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• 2023

Purpose - This study analyzed the decoupling phenomenon between energy consumption and economic growth in Korea from 1990 to 2021. The main purpose of this study is to suggest policy implications for achieving a low-carbon society and decoupling that Korea must move forward in the face of the climate change crisis. Design/methodology/approach - This study investigated the relationship between energy consumption and economic growth by energy source and sector using the energy-EKC (EEKC) hypothesis which included the energy consumption on the traditional Environmental Kuznets Curve (EKC), and the impulse response function (IRF) model based on Bayesian vector auto-regression (BVAR). Findings - During the analysis period, the trend of decoupling of energy consumption and economic growth in Korea is confirmed starting from 1996. However, the decoupling tendency appeared differently depending on the differences in energy consumption by sources and fields. The results of the IRF model using data on energy consumption by source showed that the impact of GDP and renewable energy consumption resulted in an increase in energy consumption of bio and waste, but a decrease in energy consumption by sources, and the impact of trade dependence was found to increase the consumption of petroleum products. Research implications or Originality - According to the main results, efficient distribution by existing energy source is required through expansion of development of not only renewable energy but also alternative energy. Additionally, in order to increase the effectiveness of existing energy policies to achieve carbon neutrality, more detailed strategies by source and sector of energy consumption are needed.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2007년도 추계학술대회 및 정기총회
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• pp.477-481
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• 2007

The purpose of this study is to provide the ideal forecasting model of cadastral survey work load through the Economeatric Analysis of Time Series, Granger Causality and VAR Model Analysis, it suggested the forecasting reference materials for the total amount of cadastral survey general work load. The main result is that the derive of the environment variables which affect cadastral survey general work load and the outcome of VAR(vector auto regression) analysis materials(impulse response function and forecast error variance decomposition analysis materials), which explain the change of general work load depending on altering the environment variables. And also, For confirming the stability of time series data, we took a unit root test, ADF(Augmented Dickey-Fuller) analysis and the time series model analysis derives the best cadastral forecasting model regarding on general cadastral survey work load. And also, it showed up the various standards that are applied the statistical method of econometric analysis so it enhanced the prior aggregate system of cadastral survey work load forecasting.