• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.11-19
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• 2015

A certain number of products are transported to be sold each month and some of them are returned for repair. In this study we first assume that the transported products are the ones that have been sold, Then nonparametric approach is applied to the warranty returns data to see how the reliability decreases over time. Parametric approach such as Weibull distribution is applied to the same data and the results for both nonparametric and parametric approaches are compared. Next we assume that there is a time lag between shipment and sale. Then both nonparametric and parametric approaches are applied to the time-lag data and the results are compared.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.959-962
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• 2010

There are a lot of methods available in designing PID(Proportional-Integral-Derivative) and Lead/Lag controllers in the industrial field of technology because of their useful advantages such as simplicity and robustness. In an early stage of development process, a computational simulation approach is a very efficient tool for the designs of the controllers. Thus, in this paper we propose a cost-effective, and practically efficient. The PID and Lead/Lag controllers. To show the effectiveness of the proposed Lead/Lag controller, we compare and contrast of the simulation results of each controller with the Matlab simulator. Although we have only considered the DC motors for the controllers, but it could be extended in future developments to more complex plants. As a result, the proposed frameworks could be used to solve industrial problems such as a reduction in development cycle time and minimizing system errors.

• Journal of Korean Society of Steel Construction
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• v.24 no.1
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• pp.1-11
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• 2012

To overcome the limitations of current evaluation procedures for floor vibration under crowd loading, two kinds of uncertainties associated with individual time lag differences and the complex behavior of crowd should be taken into account. The complex behavior of crowds has yet to be fully described, even though individual differences can be dealt with statistically. This paper proposes time lag considered (TLC) crowd model based on a probabilistic approach. The load reduction factor, which reflects the effect of a general degree of synchronization among crowd, is proposed. Extensive Monte Carlo simulations were carried out to determine various crowd behaviors by using the TLC crowd model proposed. The TLC crowd model can rationally treat the energy loss of various crowd patterns. This indicates that it may be used as a theoretical basis in refining dynamic load factor of crowd loading.

• Journal of Navigation and Port Research
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• v.39 no.2
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• pp.115-123
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• 2015

This study focused on the alternative to estimate the demand of employment in Kenya logistics. First of all, it investigated the importance and necessity of search about the present circumstance of the country's industry. Next, it reviewed respectively the concept and limitation of several previous models for employment, including Bureau of Labor Statistics, USA; ROA, Netherlands; IER (Institute for Employment Research), UK; and IAB, Germany. In regard to the demand forecasting of employers in logistics, it could anticipate more realistically the future demand by the time-lag approach. According to the findings, if value of output record 733,080 KSH million in 2015 and 970,640 in 2020, compared to 655,222 in 2013, demand on wage employment in logistics industry would be reached up to 95,860 in 2015 and 104,329 in 2020, compared to about 89,600 in 2012. To conclude, this study showed the more rational numbers about the demand forecasting of employment than the previous researches and displayed the systematic approach to estimate industry manpower in logistics.

• Journal of Korea Water Resources Association
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• v.38 no.10 s.159
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• pp.801-810
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• 2005

The lag time is one of the most important factors for estimating a flood runoff from streams. It is well known to be under the influence of the morphometric properties of basins which could be expressed by catchment shape descriptors. In this paper, the notion of the geometric characteristics of an equivalent ellipse proposed by Moussa(2003) is applied for calculating the lag time of geomorphological instantaneous unit hydrograph(GIUH) at the basin outlet. The lag time is obtained from the observed data of rainfall and runoff by using the method of moments suggested by Nash(1957), and the procedure based on geomorphology is used for GIUH. The relationships between the basin morphometric properties and the hydrological response are discussed as applied to 3 catchments In Korea. Additionally, the shapes of equivalent ellipse are examined how then are transformed from upstream area to downstream one. As a result, the relationship between the hydrological response and descriptors is shown to be comparatively good, and the shape of ellipse is presented to approach a circle along the river downwards. These results may be expanded to the estimation of hydrological response of ungauged catchment.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.179-179
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• 2023

This study suggests a new approach of water level forecasting for extended lead times using original data preprocessing with variational mode decomposition (VMD). Here, two machine learning algorithms including light gradient boosting machine (LGBM) and random forest (RF) were considered to incorporate extended lead times (i.e., 5, 10, 15, 20, 25, 30, 40, and 50 days) forecasting of water levels. At first, the original data at two water level stations (i.e., SW173 and SW269 in Bangladesh) and their decomposed data from VMD were prepared on antecedent lag times to analyze in the datasets of different lead times. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the machine learning models in water level forecasting. As results, it represents that the errors were minimized when the decomposed datasets were considered to predict water levels, rather than the use of original data standalone. It was also noted that LGBM produced lower MAE, RMSE, and MSE values than RF, indicating better performance. For instance, at the SW173 station, LGBM outperformed RF in both decomposed and original data with MAE values of 0.511 and 1.566, compared to RF's MAE values of 0.719 and 1.644, respectively, in a 30-day lead time. The models' performance decreased with increasing lead time, as per the study findings. In summary, preprocessing original data and utilizing machine learning models with decomposed techniques have shown promising results for water level forecasting in higher lead times. It is expected that the approach of this study can assist water management authorities in taking precautionary measures based on forecasted water levels, which is crucial for sustainable water resource utilization.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.185-195
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• 2020

The study investigates the impact of globalization on coal consumption in Vietnam. This study employs an autoregressed distributed lag approach on time series data for the period of 1990 to 2017. The study tests the stationary, cointegration of time series data and utilizes autoregressed distributed lag modeling technique to determine the short-run and long-run relationship among coal consumption, globalization, income, population, and CO₂ emissions. The results show that globalization increases coal consumption in Vietnam in the long run. The results also show that rapid economic growth promotes more coal consumption in the short run as well as in the long run. Moreover, higher population reduces coal consumption, and CO₂ emissions decrease coal consumption both in the short run and the long run. The findings of the study suggest that globalization increases coal consumption in Vietnam in the long run. This result suggests that the increase in globalization level in Vietnam increases coal consumption. An interesting finding is that higher population reduces coal consumption, and population is an important factor towards the lessening in coal consumption. The findings confirm that environmental pollution decreases coal consumption in the short run and the long run. This implies that coal consumption may be green consumption in Vietnam.

• Journal of Information Technology Applications and Management
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• v.20 no.4
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• pp.221-234
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• 2013

In this paper, we report bankruptcy risk level forecasting result for automobile parts manufacturing industry. With the premise that upstream supply risk and downstream demand risk could impact on automobile parts industry bankruptcy level in advance, we draw upon industry input-output table to use the economic indicators which could reflect the extent of supply and demand risk of the automobile parts industry. To verify the validity of each economic indicator, we applied simple linear regression for each indicators by varying the time lag from one month (t-1) to 12 months (t-12). Finally, with the valid indicators obtained through the simple regressions, the composition of valid economic indicators are derived using stepwise linear regression. Using the monthly automobile parts industry bankruptcy frequency data accumulated during the 5 years, R-square values of the stepwise linear regression results are 68.7%, 91.5%, 85.3% for the 3, 6, 9 months time lag cases each respectively. The computational testing results verifies the effectiveness of our approach in forecasting bankruptcy risk forecasting of the automobile parts industry.

• Proceedings of the Korea Association of Information Systems Conference
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• 2000.11a
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• pp.36-44
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• 2000

The purpose of this study is to introduce a more efficient forecasting technique, which could help result the reduction of cost in removing the waste of airline in-flight meals. We will use a neural network approach known to many researchers as the “Outstanding Forecasting Technique”. We employed a multi-layer perceptron neural network using a backpropagation algorithm. We also suggested using other related information to improve the forecasting performances of neural networks. We divided the data into three sets, which are training data set, cross validation data set, and test data set. Time lag variables are still employed in our model according to the general view of time series forecasting. We measured the accuracy of our model by “Mean Square Error”(MSE). The suggested model proved most excellent in serving economy class in-flight meals. Forecasting the exact amount of meals needed for each airline could reduce the waste of meals and therefore, lead to the reduction of cost. Better yet, it could enhance the cost competition of each airline, keep the schedules on time, and lead to better service.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.1-9
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• 2021

This paper investigates the impact of the domestic and global outbreak of the coronavirus (COVID-19) pandemic on the trading size of the Malaysian stock (MS) market. The theoretical model posits that stock markets are affected by their response to disasters and events that arise in the international or local environments, as well as to several financial factors such as stock volatility and spread bid-ask prices. Using daily time-series data from 27 January to 12 May 2020, this paper utilizes the traditional Augmented Dickey and Fuller (ADF) technique and Zivot and Andrews with structural break' procedures for a stationarity test analysis, while the autoregressive distributed lag (ARDL) method is applied according to the trading size of the MS market model. The analysis considered almost all 789 listed companies investing in the main stock market of Malaysia. The results confirmed our hypotheses that both the daily growth in the active domestic and global cases of coronavirus (COVID-19) has significant negative effects on the daily trading size of the stock market in Malaysia. Although the COVID-19 has a negative effect on the Malaysian stock market, the findings of this study suggest that the COVID-19 pandemic may have an asymmetric effect on the market.