• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.3
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• pp.394-400
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• 2012

In this paper, charging modes of series resonant converter for a high voltage energy storage capacitor are compared in terms of charging time, peak resonant current, normalized peak resonant current and voltage in each operation mode. Operating principles of the full bridge series resonant converter with capacitor load are explained and analyzed in discontinuous and continuous operation mode. Based on the analysis and simulation result, $0.6{\omega}_r$ < ${\omega}_s$ < $0.75{\omega}_r$ and $1.3{\omega}_r$ < ${\omega}_s$ < $1.4{\omega}_r$ are evaluated to the best range of switching frequency for charging of an high voltage energy storage capacitor. 1.8 kJ/s SRC prototype is assembled with TI 28335 DSP controller and 40 kJ, 7 kV energy storage capacitor. Design rules based on the comparative analysis are verified by experiment.

• Journal of Information Display
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• v.10 no.4
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• pp.188-194
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• 2009

Conventional stereoscopic (3D) displays using binocular parallax generate unnatural conflicts between convergence and accommodation. These conflicts can affect the observer's ability to fuse binocular images and may cause visual fatigue. In this study, time series changes in visual fatigue and depth sensation when viewing stereoscopic images with changing parallax were examined. In particular, the physiological changes, including the subjective symptoms of visual fatigue, when viewing five parallax conditions, were examined. Then a comparative analysis of the 2D and 3D conditions was performed based on the visual function. To obtain data regarding the visual function, the time series changes in the spontaneous-blinking rate before and during the viewing of 3D images were measured. The time series change results suggest that 2D and 3D images cause significantly different types of visual fatigue over the range of binocular disparity.

• The Pure and Applied Mathematics
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• v.11 no.4
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• pp.275-285
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• 2004

In this paper, the time series models for the number of reported death claims of compulsory automobile liability insurance in Korea are studied. We found that IMA${(0, 1, 1)}\;{\times}\;{(0, 1, 1)}_{12}$ would the most appropriate model for the number of reported claims by the Box-Jenkins method.

• Korea Trade Review
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• v.45 no.5
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• pp.75-89
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• 2020

The purpose of this paper was to analyze the trend of container volume using the Seasonal Autoregressive Intergrated Moving Average (SARIMA) model. To this end, this paper used monthly time-series data of the East Sea Rim from 2001 to 2019. As a result, the SARIMA(2,1,1)12 model was identified as the most suitable model, and the superiority of the SARIMA model was demonstrated by comparative analysis with the ARIMA model. In addition, to confirmed forecasting accuracy of SARIMA model, this paper compares the volume of predict container to the actual volume. According to the forecast for 24 months from 2020 to 2021, the volume of containaer increased from 60,100,000Ton in 2020 to 64,900,000Ton in 2021

• International conference on construction engineering and project management
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• 2011.02a
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• pp.1-6
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• 2011

As large-scale building projects have recently increased for the residential, commercial and office facilities, construction costs for these projects have become a matter of great concern, due to their significant construction cost implications, as well as unpredictable market conditions and fluctuations in the rate of inflation during the projects' long-term construction periods. In particular, recent volatile fluctuations of construction material prices fueled such problems as cost forecasting. This research develops a time series model using the Box-Jenkins approach and material price time series data in Korea in order to forecast trends in the unit prices of required materials. Building information modeling (BIM) approaches are also used to analyze injection times of construction resources and to conduct quantity take-off so that total material prices can be forecast. To determine an optimal time series model for forecasting price trends, comparative analysis of predictability of tentative autoregressive integrated moving average (ARIMA) models is conducted. The proposed BIM-based time series forecasting model can help to deal with sudden changes in economic conditions by estimating material prices that correspond to resource injection times.

• Journal of Distribution Science
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• v.12 no.3
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• pp.25-32
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• 2014

Purpose - This study reviews changes in the steel export-import structure between Korea and Japan using a trade related index; it focuses on analyzing comparative advantage based on time-series analysis statistics data using the trade intensity index (TII), revealed comparative advantage index (RCA), and trade specialization index (TSI). Research design, data, and methodology - In terms of their economic phase, Korea and Japan have a mutually complementary character. Therefore, this study aims to understand each country's trade structure to strengthen Korea-Japan economic cooperation, examine trade drawbacks, analyze factors that affect trade, and identify ways to improve and expand trade. Results - The results indicate immense potential for mutual cooperation and complementariness, which will yield guaranteed adequate profits comparable to those of any regional economic integrated community. Conclusion - From our viewpoint, Northeast economic cooperation can facilitate industry technological cooperation with Japanese partners in the prevailing environment that is characterized by increasing competition among industries and the need to secure stable resource supplies as well as the expansion of the export market and diversification, which can have significant positive implications.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.125-130
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• 2022

In modern society, as many data are used for research or commercial purposes, the value of data is gradually increasing. In related fields, research is being actively conducted to collect valuable data, but it is difficult to collect proper data because the value of collection is determined according to the performance of existing sensors. To solve this problem, a method to effectively reduce noise has been proposed, but there is a point in which performance is degraded due to damage caused by noise. In this paper, a device capable of collecting time series data was designed to correct such data noise, and a correction technique was performed by giving an error value based on the representatively collected ultrafine dust data, and then comparing before and after Compare performance. For the correction method, Kalman, LPF, Savitzky-Golay, and Moving Average filter were used. Savitzky-Golay filter and Moving Average Filter showed excellent correction rate as an experiment. Through this, the performance of the sensor can be supplemented and it is expected that data can be effectively collected.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1120-1126
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• 2000

Recently, in order to analyze the time series problems that occur in the nature word, and analyzing method using a neural electric network is being studied more than a typical statistical analysis method. A neural electric network has a generalization performance that is possible to estimate and analyze about non-learning data through the learning of a population. In this paper, after collecting weather datum that was collected from 1987 to 1996 and learning a population established, it suggests the weather forecasting system for an estimation and analysis the future weather. The suggested weather forecasting system uses 28*30*1 neural network structure, raises the total learning numbers and accuracy letting the selecting concentration learning about the pattern, that is not collected, using the descending epsilon learning method. Also, the weather forecasting system, that is suggested through a comparative experiment of the typical time series analysis method shows more superior than the existing statistical analysis method in the part of future estimation capacity.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.1-9
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• 2024

In this paper, we propose a comparative analysis to evaluate the impact of activation functions and attention mechanisms on the performance of time-series models for Mars meteorological data. Mars meteorological data are nonlinear and irregular due to low atmospheric density, rapid temperature variations, and complex terrain. We use long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) architectures to evaluate the effectiveness of different activation functions and attention mechanisms. The activation functions tested include rectified linear unit (ReLU), leaky ReLU, exponential linear unit (ELU), Gaussian error linear unit (GELU), Swish, and scaled ELU (SELU), and model performance was measured using mean absolute error (MAE) and root mean square error (RMSE) metrics. Our results show that the integration of attentional mechanisms improves both MAE and RMSE, with Swish and ReLU achieving the best performance for minimum temperature prediction. Conversely, GELU and ELU were less effective for pressure prediction. These results highlight the critical role of selecting appropriate activation functions and attention mechanisms in improving model accuracy for complex time-series forecasting.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.243-255
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• 1997

Forecasting the future values of an observed time series is an important problem in many areas, including economics, traffic engineering, production planning, sales forecasting, and stock control. The purpose of this paper is aimed to discover the more efficient forecasting model through the parameter estimation and residual analysis among the quantitative method such as Winters' exponential smoothing model, Box-Jenkins' model, and Kalman filtering model. The mean of the time series is assumed to be a linear combination of known functions. For a parameter estimation and residual analysis, Winters', Box-Jenkins' model use Statgrap and Timeslab software, and Kalman filtering utilizes Fortran language. Therefore, this paper can be used in real fields to obtain the most effective forecasting model.