• 무역학회지
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• 제46권6호
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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• pp.78-101
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• 2017

High-quality industrial control is critical to ensuring production quality, reducing production costs, improving management levels and stabilizing equipment and long-term operations. WiFi-based Long Distance (WiLD) networks have been used as remote industrial control networks. Real-time performance is essential to industrial control. However, the original mechanism of WiLD networks does not minimize end-to-end delay and restricts improvement of real-time performance. In this paper, we propose two algorithms to obtain the transmitting/receiving phase cycle length for each node such that real time constraints can be satisfied and phase switching overhead can be minimized. The first algorithm is based on the branch and bound method, which identifies an optimal solution. The second is a fast heuristic algorithm. The experimental results show that the execution time of the algorithm based on branch and bound is less than that of the heuristic algorithm when the network is complex and that the performance of the heuristic algorithm is close to the optimal solution.

• 정보학연구
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• 제9권4호
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• pp.75-85
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• 2006

This study is to test the influence of stock option granting information on the firm's performance. The important issue in stock option is that agent cost is the important determinant factor for the long term performance. The agent cost arises between the manager and shareholders. So many study are concentrated in diminishing the agent cost, and develop some substitute tools to measure the agent cost. The event study about stock option analyzes returns around event date at a time. Event study provides estimation periods and cumulative returns. Announcements about stock option are generally associated with positive abnormal returns in short term period, but not showing positive effect in long term period. It is important to investigate the responses of stocks to new information contained in the announcements of stock option. Therefore it is important to study the long term performance in the case of stock option. The event time portfolio approach exists the CAR model, BHAR model and WR model. And the calendar time portfolio approach has the 3 factor model, 4 factor model, CTAR model, and RATS model. This study is forced to develop and arrange two approach method in evaluating the performance, the event time portfolio approach and calendar time portfolio approach.

• 한국농공학회지
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• 제35권3호
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• pp.130-139
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• 1993

In order to evaluate the long-term permeability performace of the geotextiles, for five different combination of geotextiles and soils the long-term column test method The results obtained are as follows; 1.The gradient range of the initial stage of the long-term permeability curves varied with respect to the soil types, while that of the final stage varied according to the interaction of the soil/geotextile system. 2.The time required for a given soil/geotextile system to reach a interactive stable stage was measured ahout 100 hours for the standard sand and 150 to 600 hours for the silty content soils, respectively. 3.There were no differences between the plain woven geotextile and the non-geotextile in the long-term permeability performance. 4.As the silt content increased, the long-term performance of the geotextiles decreased, and the limiting silt content was about 15%. 5.The thickness and area density of the geotextiles did not influence on the variation of the seepage quantities. 6.The ayerage slope and the transition time of the long-time flow curve were calculated. 7.In order to evaluate the mechanism of soil/geotextile system more perfectly, the gradient ratio test or the hydraulic conductivity test is required.

• 유통과학연구
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• 제17권3호
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• pp.69-83
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• 2019

Purpose - This paper investigates the long term impact of RFID investment on firm value in Korea. We wand to find out why the long term performance of some firm's RFID investment is better than others. To understand the dynamics of the long term returns from RFID investment announcements, we divide our events into groups for each of the independent firm characteristic variable such as investment time period, kind of markets, industries, solvency and growth potential. We composed portfolios based on the RFID investment announcement date for each group and evaluate the monthly abnormal excess returns. Research design, data, and methodology - Based on these calendar-time portfolios, we measure the long term returns from 86 RFID investment announcements of 46 firms from 2003 to 2017. We construct the calendar-time portfolio for 3, 6, 9, 12 months of holding periods. Using the weighted least squares method, we regress the raw monthly returns of the portfolios on the Fama-French model and Carhart(1997) model. As a result, we can get the estimated risk adjusted mean monthly abnormal excess return α_P for each of the calendar-time portfolio. Results - We found that early adopters, large firms, non-manufacturing firms have very significant excess returns. We also found modestly significant excess returns for financially stable firms and slow growing firms. Put together, top managers of the firms which plan to invest RFID should understand the strategic role of RFID adoption and the generalized business process of distribution information technology investment in Korea. Moreover, the findings of this paper provide useful trading strategies to the managers of large funds who are considering on investing in RFID adopting firms. Conclusions - Put together, the results of this paper give us a new insight into how the RFID and IT technology in general and other characteristic factors' interactions affect the long term performance of firms. Using the unbiased estimates of long term returns of the calendar-time portfolios, this paper extends the understandings on short term impact of RFID adoption of existing studies. This paper also extends the current understandings of firm characteristics that affect the long term performance of RFID adopting firms.

• 설비공학논문집
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• 제28권4호
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• pp.165-170
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• 2016

Long-term performance of the enthalpy exchange element is a topic of current interest due to the concern of possible performance degradation over time. In this study, a 350 CMH enthalpy recovery ventilator equipped with an enthalpy exchange element was installed in an office room, and the performance has been traced over the past 5 years. The appearance, overall dimension, thermal performance, leakage ratio and anti-bacterial performance were checked annually. Results showed that the change in thermal performance (sensible, latent and enthalpy efficiency) was negligible with periodic cleaning with an air gun. However, the leakage ratio increased with time, measuring 7.3% after 5 years. Anti-bacterial test revealed that no bacteria were found during the test period. The largest change in the dimension occurred at the middle location of the element, although the change was less than 2% of the initial value.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.237-237
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• 2000

In this paper, The modified dead-time compensator for plants with an integrator and long dead time is proposed. The design procedure takes account of the closed-loop performance and robustness. The tuning of the controller can be done using some information about the plant and its uncertainties. The proposed controller is compared to others recently presented in the literature. Some simulation results verify good closed-performance and robustness of the proposed DTC.

• 정보학연구
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• 제9권3호
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• pp.77-89
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• 2006

The event study analyzes returns around event date at a time. Event study provides estimation periods and cumulative returns. Stock split announcements are generally associated with positive abnormal returns. It is important to investigate the responses of stocks to new information contained in the announcements of stock splits. So It is important to study the long term performance in the case of Stock Split. This Study forced to two approach method in evaluating the performance, the event time portfolio approach and calendar time portfolio approach. The event time portfolio approach exists the CAR model, BHAR model and WR model. And the calendar time portfolio approach has the 3 factor model, 4 factor model, CTAR model, and RATS model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2089-2092
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• 2002

The PID controller have the simple structure and show the comparatively good control performance. Crystal Grower(FF-CZ150) melt polycrystalline silicon at the temperature of about 1450$^{\circ}C$, then grow it into a single crystalline ingot. The automatic diameter control system of the Crystal Grower has a good performance with only PD control. But it contain the integrator in the plant which has a long time delay. In this paper, we show the secondary approximate model and applies time delay controller which has good performance for the plant with long time delay. It will be able to improve the response characteristic against a standard input and a load disturbance.

• 응용통계연구
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• 제32권1호
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• pp.69-81
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• 2019

본 논문에서는 딥러닝을 이용한 이변량 장기종속시계열(long-range dependent time series) 예측을 고려하였다. 시계열 데이터 예측에 적합한 LSTM(long short-term memory) 네트워크를 이용하여 이변량 장기종속시계열을 예측하고 이를 이변량 FARIMA(fractional ARIMA) 모형인 FIVARMA 모형과 VARFIMA 모형과의 예측 성능을 실증 자료 분석을 통해 비교하였다. 실증 자료로는 기능적 자기공명 영상(fMRI) 및 일일 실현 변동성(daily realized volatility) 자료를 이용하였으며 표본외 예측(out-of sample forecasting) 오차 비교를 통해 예측 성능을 측정하였다. 그 결과, FIVARMA 모형과 VARFIMA 모형의 예측값에는 미묘한 차이가 존재하며, LSTM 네트워크의 경우 초매개변수 선택으로 복잡해 보이지만 계산적으로 더 안정되면서 예측 성능도 모수적 장기종속시계열과 뒤지지 않은 좋은 예측 성능을 보였다.