• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.246-247
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• 2005

• Journal of the military operations research society of Korea
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• v.23 no.2
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• pp.135-154
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• 1997

This paper deals with the perishable inventory models with uncertainties of demand functions. The traditional perishable inventory costs of holding and stockout are incorporated into the cost function. The average expected cost will be minimized to find the optimal quantile estimator. After three candidate estimators are proposed on the basis of order statistics, they will be evaluated by the simulation results and statistical analysis. Then the transition procedure algorithm using this estimator will be proposed to make the optimal decision under uncertainty.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.87-106
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• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.

• Journal of Korean Society on Water Environment
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• v.29 no.5
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• pp.681-690
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• 2013

Watershed models have been increasingly used to support an integrated management of land and water, non-point source pollutants, and implement total daily maximum load policy. However, these models demand a great amount of input data, process parameters, a proper calibration, and sometimes result in significant uncertainty in the simulation results. For this reason, uncertainty analysis is necessary to minimize the risk in the use of the models for an important decision making. The objectives of this study were to evaluate three different uncertainty analysis algorithms (SUFI-2: Sequential Uncertainty Fitting-Ver.2, GLUE: Generalized Likelihood Uncertainty Estimation, ParaSol: Parameter Solution) that used to analyze the sensitivity of the SWAT(Soil and Water Assessment Tool) parameters and auto-calibration in a watershed, evaluate the uncertainties on the simulations of runoff and sediment load, and suggest alternatives to reduce the uncertainty. The results confirmed that the parameters which are most sensitive to runoff and sediment simulations were consistent in three algorithms although the order of importance is slightly different. In addition, there was no significant difference in the performance of auto-calibration results for runoff simulations. On the other hand, sediment calibration results showed less modeling efficiency compared to runoff simulations, which is probably due to the lack of measurement data. It is obvious that the parameter uncertainty in the sediment simulation is much grater than that in the runoff simulation. To decrease the uncertainty of SWAT simulations, it is recommended to estimate feasible ranges of model parameters, and obtain sufficient and reliable measurement data for the study site.

• Earthquakes and Structures
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• v.26 no.4
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• pp.311-326
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• 2024

Transmission tower structures are particularly susceptible to damage and even collapse under strong seismic ground motions. Conventional seismic analyses of transmission towers are usually performed by considering only ground motion uncertainty while ignoring structural uncertainty; consequently, the performance evaluation and failure prediction may be inaccurate. In this context, the present study numerically investigates the seismic responses and failure mechanism of transmission towers by considering multiple sources of uncertainty. To this end, an existing transmission tower is chosen, and the corresponding three-dimensional finite element model is created in ABAQUS software. Sensitivity analysis is carried out to identify the relative importance of the uncertain parameters in the seismic responses of transmission towers. The numerical results indicate that the impacts of the structural damping ratio, elastic modulus and yield strength on the seismic responses of the transmission tower are relatively large. Subsequently, a set of 20 uncertainty models are established based on random samples of various parameter combinations generated by the Latin hypercube sampling (LHS) method. An uncertainty analysis is performed for these uncertainty models to clarify the impacts of uncertain structural factors on the seismic responses and failure mechanism (ultimate bearing capacity and failure path). The numerical results show that structural uncertainty has a significant influence on the seismic responses and failure mechanism of transmission towers; different possible failure paths exist for the uncertainty models, whereas only one exists for the deterministic model, and the ultimate bearing capacity of transmission towers is more sensitive to the variation in material parameters than that in geometrical parameters. This research is expected to provide an in-depth understanding of the influence of structural uncertainty on the seismic demand assessment of transmission towers.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.4
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• pp.505-510
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• 2009

In this paper, building energy analysis and energy cost of power stand up and demand control over the power proposed to reduce power demand. Through analysis of the load power demand special day were able to apply the pattern. In addition, the existing rate of change of load forecasting to reduce the large errors were not previously available data. And daily schedules and special day for considering the exponential smoothing methods were used. Previous year's special day and the previous day due to the uncertainty of the load and the model components were considered. The maximum demand power control simulation using the fuzzy control of power does not exceed the contract. Through simulation, the benefits of the proposed energy-saving techniques were demonstrated.

• Journal of Korean Society for Quality Management
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• v.36 no.4
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• pp.93-101
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• 2008

The rare metals, used for semiconductors, PDP-LCS and other specialized metal areas necessarily, has been playing a key role for the Korean economic development. Rare metals are influenced by exogenous variables, such as production quantity, price and supplied areas. Nowadays the supply base of rare metals is threatened by the sudden increase in price. For the stable supply of rare metals, a rational demand outlook is needed. In this study, focusing on the domestic demand for chromium, the uncertainty and probability materializing from demand and price is analyzed, further, a demand forecast model, which takes into account various exogenous variables, is suggested, differing from the previously static model. Also, through the OOS(out-of-sampling) method, comparing to the preexistence ARIMA model, ARMAX model, multiple regression analysis model and ECM(Error Correction Mode) model, we will verify the superiority of suggested model in this study.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1158-1164
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• 2007

Recently, the demand on the practical application of life-cycle cost effectiveness for design and rehabilitation of civil infrastructure is rapidly growing unprecedentedly in civil engineering practice. Accordingly, it is expected that the life-cycle cost in the 21st century will become a new paradigm for all engineering decision problems in practice. However, in spite of impressive progress in the researches on the LCC, so far, most researches in Koreahave only focused on roadway bridges, which are not applicable to railway bridges. Thus, this paper presents the formulation models and methods for uncertainty-based LCCA for railroad bridges consideringboth objective statistical data available in the agency database of railroad bridges management and subjective data obtained form interviews with experts of the railway agency, which are used to anew uncertainty-based expected maintenance/repair costs including lifetime indirect costs. For reliable assessment of the life-cycle maintenance/repair costs, statistical analysis considering maintenance history data and survey data including the subjective judgments of railway experts on maintenance/management of railroad bridges, are performed to categorize critical maintenance items and associated expected costs and uncertainty-based deterioration models are developed. Finally, the formulation for simulation-based LCC analysis of railway bridges with uncertainty-based deterioration models are applied to the design-decision problem, which is to select an optimal bridge type having minimum Life-Cycle cost among various railway bridges types such as steel plate girder bridge, and prestressed concrete girder bridge in the basic design phase.

• Korea Trade Review
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• v.44 no.3
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• pp.25-42
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• 2019

This research examined the mediation effect of Workforce Agility (WA) on the relationship between environmental uncertainty and operational performance. We manipulated the control variables that are known to be affected by employment flexibility. Employment flexibility is caused by idiosyncratic characteristics of Korean port system. The analysis was tested by Baron & Kenny's method. The result indicates that each path of the proposed model is significant. Furthermore, the mediation effect was checked with the Sobel Test. The research revealed that environment uncertainty poses an indirect effect on operational performance. Both supply/demand uncertainty and technological uncertainty affected operational performance through the mediation effect of WA. Most of the distribution centers located in Busan Newport Distripark are operated in a bimodal labor (human resource) system which includes both permanent employees (workers) and temporary employees (workers). This empirical research provides theoretical and managerial implications by suggesting ways to increase efficiency in distribution center operation through WA enhancement, and to improve the unloading labor system.

• Journal of Information Technology Services
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• v.14 no.4
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• pp.185-197
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• 2015

As the uncertainty of demand in B2B electronics market has increased, firms need a strong method to estimate the market demand. An accurate prediction on the market demand is crucial for a firm not to overproduce or underproduce its goods, which would influence the performance of the firm. However, it is complicated to estimate the demand in a B2B market, particularly for the private sector, because firms are very diverse in terms of size, industry, and types of business. This study proposes both qualitative and quantitative demand forecasting approaches for B2B PC products. Four different measures for predicting PC products in B2B market with consideration of the different PC uses-personal work, common work, promotion, and welfare-are developed as the qualitative model's input variables. These measures are verified by survey data collected from experts in 139 firms, and can be applied when individual firms estimate the demand of PC goods in a B2B market. As the quantitative approach, the multiple regression model is proposed and it includes variables of region, type of industry, and size of the firm. The regression model can be applied when the aggregated demand for overall domestic PC market needs to be estimated.