• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.99-104
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• 1998

본 논문은 효과적인 시계열 예측을 위한 새로운 퍼지 학습방법을 제안한다. 기존의 학습방법에서는 입력 데이터를 F(y(t),y(t-1),y(t-2)..)의 형태로 주어 예측을 수행했으나 본 논문에서 제안한 방법에서는 입력 데이터를 F(y(t)-y(t-1),y(t-1)-y(t-2)..)로 설정한다. 이것은 각 입력값의 차이를 새로운 입력으로 사용함으로써 유사한 시계열 분포를 좀더 능동적인 퍼지 규칙으로 만들기 때문에 Non-stationary한 데이터뿐만 아니라 기존의 시계열 데이터 예측방법 보다 나은 결과를 나타낸다. 알고리즘의 수행능력을 살펴보기 위해 Mackey-Glass time series와 Lorenz data를 사용하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• Economic and Environmental Geology
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• v.50 no.4
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• pp.267-276
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• 2017

Seawater intrusion into coastal fractured rock aquifer, resulting in groundwater contamination, is of serious concern in coastal areas of Jeolla Namdo, Korea, which heavily depends on groundwater resources. Time series analysis and forecasting were carried out to analyze and predict EC which is a major indicator of seawater intrusion. Two time series models of autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) were tested for suggesting appropriate time series model. Time series data of EC measured over one year showed a increasing trend with short periodic fluctuations, due to tidal effect and pumping, which indicated that EC time series data tended to be non-stationary. SARIMA model was found better fitted to observed EC than any other time series model. Time series analysis and modeling was found to be a useful tool to analyze EC at coastal fractured rock aquifer subject to seawater intrusion.

• Journal of Wetlands Research
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• v.12 no.2
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• pp.13-23
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• 2010

According to recent researches on climate change, the global warming is obvious to increase rainfall intensity. Damage caused by extreme hydrologic events due to global change is steadily getting bigger and bigger. Recently, frequently occurring heavy rainfalls surely affect the trend of rainfall observations. Probability precipitation estimation method used in designing and planning hydrological resources assumes that rainfall data is stationary. The stationary probability precipitation estimation method could be very weak to abnormal rainfalls occurred by climate change, because stationary probability precipitation estimation method cannot reflect increasing trend of rainfall intensity. This study analyzed temporal variation of trend in rainfall time series at 51 stations which are not significant for statistical trend tests. After modeling rainfall time series with maintaining observed statistical characteristics, this study also estimated whether rainfall data is significant for the statistical trend test in near future. It was found that 13 stations among sample stations will have trend within 10 years. The results indicate that non-stationary probability precipitation estimation method must be applied to sufficiently consider increase trend of rainfall.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.9-15
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• 2009

In the case of traditional binary encoding technique, it takes long time to converge the optimal solutions and brings about complexity of the systems due to encoding and decoding procedures. However, the ROGAs (real-coded genetic algorithms) do not require these procedures, and the k-means clustering algorithm can avoid global searching space. Thus, this paper proposes a new approach by using their advantages. The proposed method constructs the multiple predictors using the optimal differences that can reveal the patterns better and properties concealed in non-stationary time series where the k-means clustering algorithm is used for data classification to each predictor, then selects the best predictor. After selecting the best predictor, the cluster centers of the predictor are tuned finely via RCGKA in secondary tuning procedure. Therefore, performance of the predictor can be more enhanced. Finally, we verifies the prediction performance of the proposed system via simulating typical time series examples.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1295-1304
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• 2004

In this paper, the Bayesian recurrent neural network is proposed to predict time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one needs to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, the weights vector is set as a state vector of state space method, and its probability distributions are estimated in accordance with the particle filtering process. This approach makes it possible to obtain more exact estimation of the weights. In the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent neural network with Bayesian inference, what we call Bayesian recurrent neural network (BRNN), is expected to show higher performance than the normal neural network. To verify the proposed method, the time series data are numerically generated and various kinds of neural network predictor are applied on it in order to be compared. As a result, feedback structure and Bayesian learning are better than feedforward structure and backpropagation learning, respectively. Consequently, it is verified that the Bayesian reccurent neural network shows better a prediction result than the common Bayesian neural network.

• Management Science and Financial Engineering
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• v.14 no.1
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• pp.23-34
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• 2008

In this study, we consider stationary waiting times in a Poisson driven single-server m-node queues in series. We assume that service times at nodes are independent, and are either deterministic or non-overlapped. Each node excluding the first node has a finite waiting line and every node is operated under a FIFO service discipline and a communication blocking policy (blocking before service). By applying (max, +)-algebra to a corresponding stochastic event graph, a special case of timed Petri nets, we derive the explicit expressions for stationary waiting times at all areas, which are functions of finite buffer capacities. These expressions allow us to compute the performance measures of interest such as mean, higher moments, or tail probability of waiting time. Moreover, as applications of these results, we introduce optimization problems which determine either the biggest arrival rate or the smallest buffer capacities satisfying probabilistic constraints on waiting times. These results can be also applied to bounds of waiting times in more general systems. Numerical examples are also provided.

• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.5
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• pp.79-88
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• 2007

This study evaluated the statistical stationarity of rainfall quantiles as well as the rainfall itself. The conventional methodologies like the Cox-Stuart test for trend and Dickey-Fuller test for a unit root used for testing the stationarity of a time series were applied and evaluated their application to the rainfall quantiles. As results, first, no obvious increasing or decreasing trend was found for the rainfall in Seoul, which was also found to be a stationary time series based on the Dickey-Fuller test. However, the Cox-Stuart test for the rainfall quantiles show some trends but not in consistent ways of increasing or decreasing. Also, the Dickey-Fuller test for a unit root shows that the rainfall quantiles are non-stationary. This result is mainly due to the difference between the rainfall data and rainfall quantiles. That is, the rainfall is a random variable without any trend or non-stationarity. On the other hand, the rainfall quantiles are estimated by considering all the data to result in high correlation between their consecutive estimates. That is, as the rainfall quantiles are estimated by adding a stationary rainfall data continuously, it becomes possible for their consecutive estimates to become highly correlated. Thus, it is natural for the rainfall quantiles to be decided non-stationary if considering the methodology used in this study.

• Asia pacific journal of information systems
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• v.16 no.2
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• pp.111-122
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• 2006

This paper investigated the causal relationship between IT investment and economic performance with the office, computing and accounting machinery (OCAM) and gross domestic product (GDP) statistics from the United States for the period 1961 to 2001. Due to non-stationary aspects of the series, found by unit root tests, it was deemed applicable to apply growth models using the first difference of the series. The results indicate that IT investment growth at the country level do not only cause economic performance growth, but are also caused by economic performance growth. While IT investment growth affect economic performance growth over shorter time periods, economic performance growth affect IT investment growth over longer time periods. As a result, this study reveals IT investment growth have the preceding effect on economic performance growth, and then economic performance growth impact subsequently on IT investment growth.

• Journal of Korean Society of Water and Wastewater
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• v.12 no.4
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• pp.43-52
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• 1998

The purpose of this study is to establish a method of estimating the daily urban water demend using Backpropagation algorithm is part of ANN(Artificial Neural Network). This method will be used for the development of the efficient management and operations of the water supply facilities. The data used were the daily urban water demend, the population and weather conditions such as treperarture, precipitation, relative humidity, etc. Kwangju city was selected for the case study area. We adjusted the weights of ANN that are iterated the training data patterns. We normalized the non-stationary time series data [-1,+1] to fast converge, and choose the input patterns by statistical methods. We separated the training and checking patterns form input date patterns. The performance of ANN is compared with multiple-regression method. We discussed the representation ability the model building process and the applicability of ANN approach for the daily water demand. ANN provided the reasonable results for time series forecasting.