• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.91-105
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• 2005

Using Campa and Chang's Expectations Hypothesis model, We test the expectations hypothesis in the term structure of volatilities in options on KOSPI 200 by using daily dosing prices from January 1999 to December 2003. In particular, it addresses whether long-dated volatilities are consistent with expected future short-dated volatilities, assuming rational expectation. Our results do not support the expectations hypothesis : long-term volatilities rise relative to short-term volatilities, but the increases are not matched as predicted by the expectations hypothesis. In addition, an increase in the current long-term volatilities relative to the current short-term volatilities is followed by at a random.

• International Journal of Advanced Culture Technology
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• v.5 no.4
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• pp.57-62
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• 2017

In this paper, we applied long short term memory(LSTM) for classifying political polarity in cyber public sphere. The data collected from the cyber public sphere is transformed into word corpus data through word embedding. Based on this word corpus data, we train recurrent neural network (RNN) which is connected by LSTM's. Softmax function is applied at the output of the RNN. We conducted our proposed system to obtain experimental results, and we will enhance our proposed system by refining LSTM in our system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.8
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• pp.1090-1094
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• 2013

In this paper, a method to cope with short-term interruption is proposed. The proposed method uses a capacitor bank consisting of supercapacitors. A supercapacitor is a good means for energy storage for short-term usage. The proposed circuit is simple and accordingly easy to construct and to control. A prototype of 360 W single-phase PFC ac-dc converter is constructed and experimental results are presented.

• International journal of advanced smart convergence
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• v.7 no.2
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• pp.73-85
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• 2018

The purpose of this study is to develop a short-term ICT training course that helps teachers from non-computing disciplines in developing countries acquire flipped-learning content creation skills. A field application is performed by applying the developed ICT training course to secondary school teachers of non-ICT subject specialisms in Laos. In the field study, participating teachers' teaching efficacy on ICT and satisfaction toward the training course are measured. The result of t-test on ICT teaching efficacy showed statistically significant increases in teachers' self-efficacy related to ICT use, both personal efficacy and outcome expectancy. The satisfaction survey performed after training showed that trainees were highly satisfied with the training course. The results of this field study could be used to propose a short-term teacher education model that could be applicable to teachers in other developing countries.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.832-837
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• 2015

The group method of data handling (GMDH) algorithm has proven to be a powerful and effective way to extract rules or polynomials from an electric load pattern. However, because it is nonstationary, the load pattern needs to be decomposed using a discrete wavelet transform. In addition, if a load pattern has a complicated curve pattern, GMDH should use a higher polynomial, which requires complex computing and consumes a lot of time. This paper suggests a method for short-term electric load forecasting that uses a wavelet transform and a GMDH algorithm. Case studies with the proposed algorithm were carried out for one-day-ahead forecasting of hourly electric loads using data during the years 2008-2011. To prove the effectiveness of our proposed approach, the results were evaluated and compared with those obtained by Holt-Winters method and artificial neural network. Our suggested method resulted in better performance than either comparison group.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.318-324
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• 2015

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

• Animal cells and systems
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• v.6 no.2
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• pp.153-157
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• 2002

To know whether a short-term exposure to a commonly used insecticide induces subtle negative toxic effects, female western mosquitofish, Gam-busia affinis, were exposed to 0.1, 0.5, and 1 pub endosulfan for one week and subsequently examined for their olfactory and sexual attractiveness to conspecific males. A short-term exposure to endosulfan did not impair the physical conditions investigated in this study nor did it disrupt olfactory attractiveness of female mosquitofish. However, 1 ppb endosulfan significantly reduced sexual attractiveness of exposed females. Test males showed significantly less copulation attempts with the exposed females. Our results suggest that in the field, a short term exposure of endosulfan may disrupt mating processes in non-targeted aquatic organisms.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.9
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• pp.399-399
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• 2001

This paper proposes TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. In the scatter diagram of daily peak load versus daily high or low temperature, we can find out that the load-temperature relationship has a negative slope in the lower regime and a positive slope in the upper regime due to the heating and cooling load, respectively. TAR model is adequate for analyzing these phenomena since TAR model is a piecewise linear autoregressive model. In this paper, we estimated and forecasted one day-ahead daily peak load by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.9
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• pp.309-405
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• 2001

This paper proposes TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. In the scatter diagram of daily peak load versus daily high or low temperature, we can find out that the load-temperature relationship has a negative slope in the lower regime and a positive slope in the upper regime due to the heating and cooling load, respectively. TAR model is adequate for analyzing these phenomena since TAR model is a piecewise linear autoregressive model. In this paper, we estimated and forecasted one day-ahead daily peak load by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.807-813
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• 2012

In this paper, we introduce data mining techniques for short-term load forecasting (STLF). First, we use the K-mean algorithm to classify historical load data by season into four patterns. Second, we use the k-NN algorithm to divide the classified data into four patterns for Mondays, other weekdays, Saturdays, and Sundays. The classified data are used to develop a time series forecasting model. We then forecast the hourly load on weekdays and weekends, excluding special holidays. The historical load data are used as inputs for load forecasting. We compare our results with the KEPCO hourly record for 2008 and conclude that our approach is effective.