• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.253-259
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• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.4
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• pp.581-589
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• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.748-750
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• 2005

This paper approaches a methodology for deciding the optimal reliability criteria for an optimal composite power system expansion planning considering generation and transmission systems simultaneously. A probabilisticreliability criterion, $LOLE_R$(Loss of Load Expectation), is used in this study. The optimalreliability criterion $LOLE_R*$ is decided at minimum cost point of total cost curve which is the sum of the utility cost associated with construction cost and the customer outage cost associated with supply interruptions for load considering forced outage rates of elements(generators and lines) in long term forecasting. The characteristicsand effectiveness of this methodology are illustrated by the case study using MRBTS size system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.3
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• pp.144-151
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• 2005

This paper presents a forecasting technique of the short-term marginal price (SMP) using an Artificial Neural Network (ANN). The SW forecasting is a very important element in an electricity market for the optimal biddings of market participants as well as for market stabilization of regulatory bodies. Input data are organized in two different approaches, time-axis and day-axis approaches, and the resulting patterns are used to train the ANN. Performances of the two approaches are compared and the better estimate is selected by a composition rule to forecast the SMP. By combining the two approaches, the proposed composition technique reflects the characteristics of hourly, daily and seasonal variations, as well as the condition of sudden changes in the spot market, and thus improves the accuracy of forecasting. The proposed method is applied to the historical real-world data from the Korea Power Exchange (KPX) to verify the effectiveness of the technique.

• Journal of the Korean Society for Marine Environment & Energy
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• v.8 no.1
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• pp.46-52
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• 2005

A marine environmental information system (MEIS) useful for optimal route planning of ships running in the ocean was developed. Utilizing the simulated marine environmental data produced by the European Center for Medium-Range Weather Forecasts based on global environmental data observed by satellites, the real-time forecast and long-term statistics of marine environments around planned and probable ship routes are provided. The MEIS consists of a land-based data acquisition and analysis system(MEIS-Center) and a onboard information display system(MEIS-Ship) for graphic description of marine information and optimal route planning of ships. Also, it uses of satellite communication system for data transfer. The marine environmental components of winds, waves, air pressures and storms are provided, in which winds are described by speed and direction and waves are expressed in terms of height, direction and period for both of wind waves and swells. The real-time information is characterized by 0.5° resolution, 10 day forecast in 6 hour interval and daily update. The statistic information of monthly average and maximum value expected for a return period is featured by 1.5° resolution and based on 15 year database. The MEIS-Ship include an editing tool for route simulation and the forecasting and statistic information on planned routes can be displayed in graph or table. The MEIS enables for navigators to design an optimal navigational route that minimizes probable risk and operational cost.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.578-580
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• 1995

In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time, For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor font mentioned above. The one day ahead forecast errors are about 1.4% of absolute percentage average error.

• Water for future
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• v.25 no.4
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• pp.109-119
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• 1992

The purpose of this study is to develop real-time streamflow forecasting models in order to manage effectively the flood warning system and water resources during the storm. The stochastic system models of the rainfall-runoff process using in this study are constituted and applied the Recursive Least Square and the Instrumental Variable-Approximate Maximum Likelihood algorithm which can estimate recursively the optimal parameters of the model. Also, in order to improve the performance of streamflow forecasting, initial values of the model parameter and covariance matrix of parameter estimate errors were evaluated by using the observed historical data of the hourly rainfall-runoff, and the accuracy and applicability of the models developed in this study were examined by the analysis of the I-step ahead streamflow forecasts.

• Journal of Korea Water Resources Association
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• v.44 no.7
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• pp.537-551
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• 2011

Based on optimal input data combination selected in the earlier study, Neuro-Fuzzy flood forecasting model linked Takagi-Sugeno fuzzy inference theory with neural network in Wangsukcheon and Gabcheon is established. The established model was applied to Wangsukcheon and Gabcheon and water levels for lead time of 0.5 hr, 1 hr, 1.5 hr, 2.0 hr, 2.5 hr, 3.0 hr are forecasted. For the verification of the model, the comparisons between forecasting floods and observation data are presented. The forecasted results have shown good agreements with observed data. Additionally to evaluate quantitatively for applicability of the model, various statistical errors such as Root Mean Square Error are calculated. As a result of the flood forecasting can be simulated successfully without large errors in all statistical error. This study can greatly contribute to the construction of a high accuracy flood information system that secure lead time in medium and small streams.

• Journal of Korean Institute of Industrial Engineers
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• v.11 no.2
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• pp.173-187
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• 1985

A computer simulation model for optimum design and determination of optimal operational parameter values for modern container terminal systems was developed through the use of GASP-IV, a subset of SLAM. Input data reflecting current system configuration and operational practices at Pusan container terminal was used to test the model, which resulted in its validation. Possibilities for application of the model in areas of candidate system comparisons, operational parameter testing and forecasting operational performance under future traffic situations, are explained.