• Fashion & Textile Research Journal
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• v.7 no.2
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• pp.196-202
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• 2005

The purposes of this study were to investigate and analyze the relationship between the elements of textile design and consumer emotion and to suggest effective design methods. In addition, the forecasting system for textile design based on the results of this study was developed. The database system of textile design was organized by installing Mysql database server and tomcat servlet container on windows NT. The user interface was utilized using jsp on the web. This study findings can provide textile design samples which were suitable for each emotional factor, and an evaluation basis for each design element by the descriptive system of textile design. The forecasting system based on this study findings can also provide specific design methods for the effectiveness of consumer emotion and can be applied in a practical design process. This study based on the results of the quantitative analysis on consumer emotion has presented an objective and an efficient design method. This will be a useful expedient to improve the existing textile design process and for the consumer design.

• IE interfaces
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• v.24 no.4
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• pp.373-378
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• 2011

High level of accuracy in forecasting heat demand of each district is required for operating and managing the district heating efficiently. Heat demand has a close connection with the demands of the previous days and the temperature, general demand forecasting methods may be used forecast. However, there are some exceptional situations to apply general methods such as the exceptional low demand in weekends or vacation period. We introduce a new method to forecast the heat demand to overcome these situations, using the linearities between the demand and some other factors. Our method uses the temperature and the past 7 days' demands as the factors which determine the future demand. The model consists of daily and hourly models which are multiple linear regression models. Appling these two models to historical data, we confirmed that our method can forecast the heat demand correctly with reasonable errors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.576-583
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• 2010

Wireless Internet is the network which provides wireless access in order to serve the Internet connections and data communication through the mobile handsets. To get efficient wireless access to the Internet, we need the WAP (Wireless Application Protocol) gateway that performs protocol translation and contents conversion between two different networks. We need the capacity dimensioning of the WAP gateway system in order to provide the wireless Internet service stably and cost-effectively. We also need the traffic engineering methods including traffic modelling and forecasting for the economical facility investment. The existing method of WAP gateway capacity dimensioning was intuitive and qualitative. But in this paper, we deal with the capacity dimensioning analytically and quantitatively on the basis of WAP traffic description parameters and traffic forecasting method.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.885-893
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• 2015

It is important to improve the forecasting accuracy of one-year-ahead seasonal factors in order to produce seasonally adjusted series of the following year. In this paper, seasonal factors of 8 monthly Korean economic time series are examined and forecast based on the functional principal component regression. One-year-ahead forecasts of seasonal factors from the functional principal component regression are compared with other forecasting methods based on mean absolute error (MAE) and mean absolute percentage error (MAPE). Forecasting seasonal factors via the functional principal component regression performs better than other comparable methods.

• Journal of IKEEE
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• v.26 no.4
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• pp.545-552
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• 2022

The number of patent application filing for a specific technology has a good relation with the technology's life cycle and future industry development on that area. So industry and governments are highly interested in forecasting the number of patent application filing in order to take appropriate preparations in advance. In this paper, a new method based on the bidirectional long short-term memory(LSTM), a kind of recurrent neural network(RNN), is proposed to improve the forecasting accuracy compared to related methods. Compared with the Bass model which is one of conventional diffusion modeling methods, the proposed method shows the 16% higher performance with the Korean patent filing data on the five selected technology areas.

• Proceedings of the Korea Society of Design Studies Conference
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• 1998.10a
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• pp.18-19
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• 1998

• International Journal of Industrial Entomology and Biomaterials
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• v.8 no.1
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• pp.17-26
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• 2004

Pest monitoring through field surveys and surveillance helps in forecasting the population build up of pest. It reduces the load of pesticides application and forms the basis of Integrated Pest Management in sericulture. Common sampling techniques for quantifying pest populations and damage caused by them are reviewed emphasizing the need for quick and simple sampling methods. Various direct and indirect sampling methods for establishing pest populations are discussed and methods have been discussed to use indirect sampling method under IPM programme in sericulture. The use of pheromone lures and traps forms one of the important ingredients of integrated pest management, which calls for integration of all available methods in a cost effective and environmental friendly manner offering consistent efficacy. Silk-worms feed on the variety of silk host plants and spin cocoons. Each silk host plant is attacked in the field by number of insect pest species. Several pests are common to mulberry, tasar, oak tasar, muga and eri host plant but pest status and seasonal abundance differs from each crop. The key pests are serious perennially occurring persistent species which cause considerable yield loss every year on large areas and require control measure. Regular occurrence of minor pest is noticed but sudden increase in its population is not known. The occasional pests are sporadic but potential causing sufficient damage. Silk losses due to attack of all the pests have not been calculated. However, information on pest biology and ecology, and control practices being practiced is available but the period of outbreak of major pests and predators on silkworms and its host plant needs to be reinvestigated. Pest and predators forecasting based on surveillance information may provide an opportunity to minimize the losses, particularly to reduce expenditure involved in pest management.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.103-123
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• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.451-454
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• 2014

Short-term load forecasting(STLF) for electric power demand is essential for stable power system operation and efficient power market operation. We improved STLF method by using hourly temperature as an input data. In order to using hourly temperature to STLF algorithm, we calculated temperature-electric power demand sensitivity through past actual data and combined this sensitivity to exponential smoothing method which is one of the STLF method. The proposed method is verified by case study for a week. The result of case study shows that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.543-556
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• 2001

This study suggests integrated neural network modes for he stock price index forecasting using change-point detection. The basic concept of this proposed model is to obtain significant intervals occurred by change points, identify them as change-point groups, and reflect them in stock price index forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in stock price index dataset. The second phase is to forecast change-point group with various data mining classifiers. The final phase is to forecast the stock price index with backpropagation neural networks. The proposed model is applied to the stock price index forecasting. This study then examines the predictability of integrated neural network models and compares the performance of data mining classifiers.