• Journal of the Korean Operations Research and Management Science Society
• /
• v.29 no.4
• /
• pp.41-60
• /
• 2004

A stream data is a data set that is accumulated to the data storage from a data source over time continuously. The size of this data set, in many cases. becomes increasingly large over time. To mine information from this massive data. it takes much resource such as storage, memory and time. These unique characteristics of the stream data make it difficult and expensive to use this large size data accumulated over time. Otherwise. if we use only recent or part of a whole data to mine information or pattern. there can be loss of information. which may be useful. To avoid this problem. we suggest a method that efficiently accumulates information. in the form of rule sets. over time. It takes much smaller storage compared to traditional mining methods. These accumulated rule sets are used as prediction models in the future. Based on theories of ensemble approaches. combination of many prediction models. in the form of systematically merged rule sets in this study. is better than one prediction model in performance. This study uses a customer data set that predicts buying power of customers based on their information. This study tests the performance of the suggested method with the data set alone with general prediction methods and compares performances of them.

• The KIPS Transactions:PartB
• /
• v.9B no.4
• /
• pp.421-428
• /
• 2002

The local prediction method utilizing embedding vector loses the prediction power when the parameter r estimation is not exact for predicting the chaos time series induced from the high order differential equation. In spite of the fact that there have been a lot of suggestions regarding how to estimate the delay time ($\tau$), no specific method is proposed to apply to any time series. The inclinded linear model, which utilizes inclinded netter, yields satisfying degree of prediction power without estimating exact delay time ($\tau$). The usefulness of this approach has been indicated not only theoretically but also in practical situation when the method w8s applied to economical time series analysis.

• Special Issue of the Society of Naval Architects of Korea
• /
• 2011.09a
• /
• pp.75-81
• /
• 2011

An indirect concept and method is proposed to predict the paint dry time at the inside wall of ship block. To implement this concept on computer program, optimal hot air supply-exhaust system of paint dry facility was designed by CFD simulation and experiment was performed to get the paint dry time curve according to various paint dry conditions. After combining the block inside environment from the simulation results and the paint dry time prediction curve from the curve-fitting of experimental result, the GUI program which can be executed in general PC OS has been finally developed.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.41 no.1
• /
• pp.84-93
• /
• 2018

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of power energy consumption. To this end, we consider the volatility of the time series and apply the sample variance and the detrended fluctuation analyses to the volatilities. We demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the power consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the power consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of "context units" in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the power consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. To further confirm the experimental results, we performed two types of the cross validations designed for the time series data. We also support the validity of the model by analyzing the multi-step forecasting. We found that the prediction errors tend to be saturated although they increase as the prediction time step increases. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10b
• /
• pp.223-227
• /
• 1987

This paper presents the extended interaction prediction method for large scale discrete-time systems with interconnected state and control. Feedback gain is obtained from decentralized calculation without solving Riccati equation. Hence, Computer storage and calculation time is reduced.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.25 no.4
• /
• pp.124-129
• /
• 2017

Clouds are an important factor in aircraft flight. In particular, a significant impact on small aircraft flying at low altitude. Therefore, we have verified and characterized the low level cloud prediction data of the Unified Model(UM) - based Local Data Assimilation and Prediction System(LDAPS) operated by KMA in order to develop cloud forecasting service and contents important for safety of low-altitude aircraft flight. As a result of the low level cloud test for seven airports in Korea, a high correlation coefficient of 0.4 ~ 0.7 was obtained for 0-36 leading time. Also, we found that the prediction performance does not decrease as the lead time increases. Based on the results of this study, it is expected that model-based forecasting data for low-altitude aviation meteorology services can be produced.

• Environmental and Resource Economics Review
• /
• v.11 no.3
• /
• pp.447-463
• /
• 2002

The heat demand prediction is an essential issue in management of district heating system. Without an accurate prediction through the lead-time period, it might be impossible to make a rational decision on many issues such as heat production scheduling and heat exchange among the plants which are very critical for the district heating company. The heat demand varies with the temperature as well as the time nonlinearly. And the parametric specification of the heat demand model would cause a misspecification bias in prediction. A nonparametric model for the short-term heat demand prediction has been developed as an alternative to avoiding the misspecification error and tested with the actual data. The prediction errors are reasonably small enough to use the model to predict a few hour ahead heat demand.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.10
• /
• pp.1770-1782
• /
• 2011

A brain-computer interface (BCI) is a communication system that translates brain activity into commands for computers or other devices. In other words, BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways consisting of nerves and muscles. This is particularly useful for facilitating communication for people suffering from paralysis. Due to the low bit rate, it takes much more time to translate brain activity into commands. Especially it takes much time to input characters by using BCI-based typewriters. In this paper, we propose a brain-operated typewriter which is accelerated by a language prediction model. The proposed system uses three kinds of strategies to improve the entry speed: word completion, next-syllable prediction, and next word prediction. We found that the entry speed of BCI-based typewriter improved about twice as much through our demonstration which utilized the language prediction model.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2003.05a
• /
• pp.329-337
• /
• 2003

Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using artificial neural networks in this area. Recently, support vector machines (SVMs) are regarded as promising methods for the prediction of financial time-series because they me a risk function consisting the empirical ewer and a regularized term which is derived from the structural risk minimization principle. In this study, I apply SVM to predicting the Korea Composite Stock Price Index (KOSPI). In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.523-523
• /
• 2000

We have developed a method to build time series prediction models by Genetic Programming (GP). Our proposed CP includes two new techniques. One is the parameter optimization algorithm, and the other is the new mutation operator. In this paper, the sunspot prediction experiment by our proposed CP was performed. The sunspot prediction is good benchmark, because many researchers have predicted them with various kinds of models. We make three experiments. The first is to compare our proposed method with the conventional methods. The second is to investigate about the relation between a model-building period and prediction precision. In the first and the second experiments, the long-term data of annual sunspots are used. The third is to try the prediction using monthly sunspots. The annual sunspots are a mean of the monthly sunspots. The behaviors of the monthly sunspot cycles in tile annual sunspot data become invisible. In the long-term data of the monthly sunspots, the behavior appears and is complicated. We estimate that the monthly sunspot prediction is more difficult than the annual sunspot prediction. The usefulness of our method in time series prediction is verified by these experiments.