• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.3
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• pp.493-499
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• 2017

This paper proposes a combined very-short-term and short-term multi-variate time-series model for 24 hour load forecasting. First, the best model for very-short-term and short-term load forecasting is selected by considering the least error value, and then they are combined by the optimal forecasting time. The actual load data of industry complex is used to show the effectiveness of the proposed model. As a result the load forecasting accuracy of the combined model has increased more than a single model for 24 hour load forecasting.

• Journal of Korea Water Resources Association
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• v.39 no.7 s.168
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• pp.617-626
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• 2006

In this study 3-month SPI data from 59 stations over the Korean peninsula are analyzed by deriving and spatially characterizing the EOFs. Also, the coefficient time series of EOF are applied to the multi-variate time series model to generate the time series of 10,000 years, to average them to estimate the areal average, and to decide the maximum drought severity for given return periods. Finally, the drought prevention ability is evaluated by considering the effective storage of dam within the basin and the size of agricultural area. Especially for the return period of 30 years, only the Han river basin has the potential to overcome the drought. Other river basins like the Youngsan river basin, which has a large portion of agricultural area but less water storage, are found to be very vulnerable to the rainfall-sensitive agricultural drought.

• Journal of Technology Innovation
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• v.27 no.4
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• pp.21-48
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• 2019

In the basic research field, quantitative expansion is carried out with active support from the government, but there is no research and policy data suggesting systematic investment plans or data-based financial requirements yet. Therefore, this study predicted future financial requirements of basic research support programs by using time series prediction model. In order to consider various factors including the characteristics of the basic research field, we selected the ARIMAX model which can reflect the effect of multi valuable factors rather than the ARIMA model which predicts the value of single factor over time. We compared the predictions of ARIMAX and ARIMA models for model suitability and found that the ARIMAX model improves the prediction error rate. Based on the ARIMAX model, we predicted the fiscal spending of basic research support programs for five years from 2017 to 2021. This study has significance in that it considers the financial requirements of the basic research support programs as a pilot research conducted by applying a time series model, which is a statistical approach, and multi-variate rather than single-variate. In addition, considering the policy trends that emphasize the importance of basic research investment such as 'the expansion of basic research budget twice', which is the current government's national policy task, it can be used as reference data in establishing basic research investment strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1431-1445
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• 2022

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.

• Journal of Agricultural Extension & Community Development
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• v.7 no.2
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• pp.257-277
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• 2000

The objectives of this study are to investigate the relationship between the growth of the horticultural sector and horticultural research and extension and to examine the socioeconomic returns to investment on research and extension in Korean horticulture. Data for horticultural production values, producer price indices and research and extension budgets for horticultural sector from 1965 to 1998 are collected from various sources. Multi-variate time series analysis technique with vector auto-regression model and Akino-Hayami Formula were employed for the analysis. This study finds (1) horticultural production responds about seven years later to the horticultural research investment shock. the magnitude of the impacts increases to a peak in seventeen years from the initial expenditures and then declines slowly thereafter until twenty years. and this peak gives a tip that horticultural research impact lasts much longer than grain's or agriculture's: (2) the social surplus from research investment benefits more to the consumer rather than to the horticultural producer: (3) B/C ratios in horticultural research are quite high with the range of 9 to 55 from 1965 to 1998. but these have been decreased since the early 1990s: (4) the socioeconomic returns to horticultural research is quite high with 56 percents of internal rate of return. It remains to be analyzed returns to investment on extension in horticulture because of no statistic significance in this study.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.3
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• pp.377-386
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• 2014

This study analyzed how alternative investment goods would affect a market in a neighboring shopping area in order to provide parties involved in the investment market of this neighboring shopping area with standards which would help them when they try to make a reasonable determination. The study estimated forms and explanation power of the effects of a bid rate of a neighboring shopping area, and came up with those results as follows. Increases in the representative macro economic indicators, the composite stock price index and the fluctuation rate of land price, including the real estate business would have a positive influence on the market of the neighboring shopping area as playing a circumstantial evidence of market recovery and yet, the increase in interest rate, the alternative investment goods, would reduce the relative price-earnings ratio which would, eventually, negatively affect the charm of the investment in the market of the neighboring shopping area. The study, now, understands that housing with a feature of consumers' goods and neighboring shopping area with a feature of investment goods would not have great concern with each other as they are observed to be two different markets from an aspect of interactionism.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.1-15
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• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.