• Korean Journal of Construction Engineering and Management
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• v.9 no.5
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• pp.86-94
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• 2008

Productivity is one of key management indexes for evaluating soundness of a manufacturing organization and its efficiency. In many aspects of productivity management in the construction industry, however, intuition of an experienced field manager still plays a greater role while productivity data is not utilized efficiently for the construction management purposes, because the collection and analysis of the productivity-related information are not systematic. Lack of systematic method in collecting and analyzing the productivity data results in such problems. The existing WBS should therefore be improved to solve them. The authors developed a new WBS for productivity data collection and analysis by following the research direction that was determined by literature reviews, overseas cases, and interviews with field engineers. The new breakdown structure was then evaluated for its feasibility as a productivity analysis framework. It is expected that the productivity data collected by the WBS will be used for OLAP and mining for future productivity forecast.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.50-58
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• 2018

Intermittent demand is a demand with a pattern in which zero demands occur frequently and non-zero demands occur sporadically. This type of demand mainly appears in spare parts with very low demand. Croston's method, which is an initiative intermittent demand forecasting method, estimates the average demand by separately estimating the size of non-zero demands and the interval between non-zero demands. Such smoothing type of forecasting methods can be suitable for mid-term or long-term demand forecasting because those provides the same demand forecasts during the forecasting horizon. However, the smoothing type of forecasting methods aims at short-term forecasting, so the estimated average forecast is a factor to decrease accuracy. In this paper, we propose a forecasting method to improve short-term accuracy by improving Croston's method for intermittent demand forecasting. The proposed forecasting method estimates both the non-zero demand size and the zero demands' interval separately, as in Croston's method, but the forecast at a future period adjusted by binomial weight according to occurrence probability. This serves to improve the accuracy of short-term forecasts. In this paper, we first prove the unbiasedness of the proposed method as an important attribute in forecasting. The performance of the proposed method is compared with those of five existing forecasting methods via eight evaluation criteria. The simulation results show that the proposed forecasting method is superior to other methods in terms of all evaluation criteria in short-term forecasting regardless of average size and dispersion parameter of demands. However, the larger the average demand size and dispersion are, that is, the closer to continuous demand, the less the performance gap with other forecasting methods.

• Journal of Korea Port Economic Association
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• v.29 no.3
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• pp.1-23
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• 2013

This paper estimates and forecasts the container throughput of Busan port using the monthly data for years 1992-2011. To do this, this paper uses the several seasonal multiplicative ARIMA models. Among several ARIMA models, the seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$ is selected as the best model by AIC, SC and Hannan-Quin information criteria. According to the forecasting values of the selected seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$, the container throughput of Busan port for 2013-2020 will increase steadily annually, but there will be some volatile variations monthly due to the seasonality and other factors. Thus, to forecast the future container throughput of Busan port and to develop the Busan port efficiently, we need to use and analyze the seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$.

• Archives of design research
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• v.17 no.1
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• pp.253-264
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• 2004

The final goal of this research is development of graphic design evaluation methodology in elevation of a usability at the mobile internet services and of measurement model which can forecast user needs in interface design, and systemize evaluation basis. For this, we systemize core contents of GUI design evaluation methodology and embodied UI design support system that supports prototype layout and evaluation process directly. The sight language that can inform flow of controled information by the quick and implicated method so that user may complete task in a short time without overload of recognition in limited display environment of Small Screen device it must improve objectivity in the reflection of UI design with image. Thus evaluation methodology that can evaluate usability of mobile internet systematically is important and specially, graphic design evaluation model which can forecast user's design need and trend is meaningful because of special quality that can reflect sensitive aspect of user in interface design. Mobile internet GUI was done by the result of this design evaluation, and I hope this result can be utilized for the GUI development of Ubiquitous environment for the future research.

• Bulletin of the Korean Space Science Society
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• 2010.04a
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• pp.38.2-38.2
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• 2010

We have examined the occurrence probability of solar proton events (SPEs) and their peak fluxes depending three flare parameters (X-ray peak flux, longitude, and impulsive time). For this we used NOAA SPEs from 1976 to 2006, and their associated X-ray flare data. As a result, we selected 166 proton events that were associated with major flares; 85 events associated with X-class flares and 81 events associated with M-class flares. Especially the occurrence probability strongly depends on these three parameters. In addition, the relationship between X-ray flare peak flux and proton peak flux as well as its correlation coefficient are strongly dependent on longitude and impulsive time. Among NOAA SPEs from 1997 to 2006, most of the events are related to both flares and CMEs but a few fraction of events (5/93) are only related with CMEs. We carefully identified the sources of these events using LASCO CME catalog and SOHO MDI data. Specifically, we examined the directions of CMEs related with the events and the history of active regions. As a result, we were able to determine active regions which are likely to produce SPEs without ambiguity as well as their longitudes at the time of SPEs by considering solar rotation rate. From this study, we found that the longitudes of five active regions are all between $90^{\circ}W$ and $120^{\circ}W$. When the flare peak time is assume to be the CME event time, we confirmed that the dependence of their rise times (proton peak time - flare peak time) on longitude are consistent with the previous empirical formula. These results imply that five events should be also associated with flares which were not observed because they occurred from back-side. Now we are examining the occurrence probability of SPEs depending on CME parameters. Finally, we will discuss the future prospects on the development of an empirical SPE forecast model based on the information of flares and CMEs.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.67-75
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• 2021

Despite the increase in sales of imported vehicles in Korea, research on the sales forecast of parts logistics centers is very limited. This study aims to perform a sales prediction on bestselling goods in the automobile part logistics center. System dynamics was adopted as a methodology for the prediction method, which considered causal relationship of variables that affected the dynamic characteristics and feedback loops. The analysis results showed that the consumable sales amount of oil increased over time. As a result of conducting the MAPE, the model was assessed to be a reasonable predictive model of 31.3%. In addition, the sales of battery products increased from every October in both of actual and predicted data followed by the peak sales in December and then decrease from next February. This study has academic implications that it secured actual data of specific imported automobile part logistics center, which has not done before in previous studies and quantitatively analyzed the prediction of the quantity of released goods of future sales through system dynamics.

• Atmosphere
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• v.33 no.4
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• pp.331-341
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• 2023

A numerical forecasting models usually predict future states by performing time integration considering fixed static time-steps. A time-step that is too long can cause model instability and failure of forecast simulation, and a time-step that is too short can cause unnecessary time integration calculations. Thus, in numerical models, the time-step size can be determined by the CFL (Courant-Friedrichs-Lewy)-condition, and this condition acts as a necessary condition for finding a numerical solution. A static time-step is defined as using the same fixed time-step for time integration. On the other hand, applying a different time-step for each integration while guaranteeing the stability of the solution in time advancement is called an adaptive time-step. The adaptive time-step algorithm is a method of presenting the maximum usable time-step suitable for each integration based on the CFL-condition for the adaptive time-step. In this paper, the adaptive time-step algorithm is applied for the Korean Integrated Model (KIM) to determine suitable parameters used for the adaptive time-step algorithm through the monthly verifications of 10-day simulations (during January and July 2017) at about 12 km resolution. By comparing the numerical results obtained by applying the 25 second static time-step to KIM in Supercomputer 5 (Nurion), it shows similar results in terms of forecast quality, presents the maximum available time-step for each integration, and improves the calculation efficiency by reducing the number of total time integrations by 19%.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.18-30
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• 2023

We compared empirically the forecast accuracies of the LSTM model, and the ARIMA model. ARIMA model used auto.arima function. Data used in the model is 100 days. We compared with the forecast results for 50 days. We collected the stock closing prices of the top 4 companies by market capitalization in Korea such as "Samsung Electronics", and "LG Energy", "SK Hynix", "Samsung Bio". The collection period is from June 17, 2022, to January 20, 2023. The paired t-test is used to compare the accuracy of forecasts by the two methods because conditions are same. The null hypothesis that the accuracy of the two methods for the four stock closing prices were the same were rejected at the significance level of 5%. Graphs and boxplots confirmed the results of the hypothesis tests. The accuracies of ARIMA are higher than those of LSTM for four cases. For closing stock price of Samsung Electronics, the mean difference of error between ARIMA and LSTM is -370.11, which is 0.618% of the average of the closing stock price. For closing stock price of LG Energy, the mean difference is -4143.298 which is 0.809% of the average of the closing stock price. For closing stock price of SK Hynix, the mean difference is -830.7269 which is 1.00% of the average of the closing stock price. For closing stock price of Samsung Bio, the mean difference is -4143.298 which is 0.809% of the average of the closing stock price. The auto.arima function was used to find the ARIMA model, but other methods are worth considering in future studies. And more efforts are needed to find parameters that provide an optimal model in LSTM.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.127-139
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• 2023

Numerous genuine issues, for example, financial exchange expectation, climate determining and so forth has inalienable arbitrariness related with them. Receiving a probabilistic system for forecast can oblige this dubious connection among past and future. Commonly the interest is in the contingent likelihood thickness of the arbitrary variable included. One methodology for expectation is with time arrangement and auto relapse models. In this work, liner expectation technique and approach for computation of forecast coefficient are given and likelihood of blunder for various assessors is determined. The current methods all need in some regard assessing a boundary of some accepted arrangement. In this way, an elective methodology is proposed. The elective methodology is to gauge the restrictive thickness of the irregular variable included. The methodology proposed in this theory includes assessing the (discretized) restrictive thickness utilizing a Markovian definition when two arbitrary factors are genuinely needy, knowing the estimation of one of them allows us to improve gauge of the estimation of the other one. The restrictive thickness is assessed as the proportion of the two dimensional joint thickness to the one-dimensional thickness of irregular variable at whatever point the later is positive. Markov models are utilized in the issues of settling on an arrangement of choices and issue that have an innate transience that comprises of an interaction that unfurls on schedule on schedule. In the nonstop time Markov chain models the time stretches between two successive changes may likewise be a ceaseless irregular variable. The Markovian methodology is especially basic and quick for practically all classes of classes of issues requiring the assessment of contingent densities.

• Proceedings of the KIEE Conference
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• 2005.05b
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• pp.16-18
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• 2005

Becoming more and more diversified and complicated, power quality management has focused on the electricity-failure duration(including the numbers), the appropriate rate of voltage(average voltage during 30 minutes), the stability rate of frequency etc. as a basic goal value. And recently the focus is moving into the instantaneous minute interruption factors such as voltage & current harmonics, surge occurring frequency, instantaneous voltage variation, voltage unbalance, instantaneous electricity failure, flicker etc. by the development of electricity & electronics and communication equipments, which had not been so big problems before. This paper will address the flow of analysis technology and forecast the desirable direction of power quality analysis technology in the future.