• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.204-206
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• 2022

The global economy and the air transport business have been affected since the spread of the COVID-19 pandemic. As countries tighten restrictions on international movements, the growing emphasis on air cargo puts pressure on airports to maintain and upgrade their cargo policies, facilities, and operations. Hence, ensuring the competitiveness of cargo airports becomes pivotal for airports survival under the volatile global demand. The study aims to evaluate the importance of the competitiveness factors for cargo airports and identify areas for further improvement. The study applies the Best-Worst Method (BWM) to assess the cargo airports' competitiveness factors: 'Transport Capacity,' 'Airport Operations and Facility Capacity,' 'Economic Growth,' 'Financial Performance,' and 'Airport Brand Value.' The selected airports include Heathrow Airport, Aéroport de Paris-Charles de Gaulle, Hong Kong International Airport, and Incheon International Airport. The results identify 'Transport Capacity' as the most significant competitiveness factor, and Hong Kong International Airport the best performing cargo airport. This research forms a reference framework for evaluating cargo airports' competitive position, which may help identify airports' relative strengths and weaknesses. Moreover, this framework can also serve as a tool facilitating the strategic design of airports that may accommodate both air cargo and passenger demand flexibly under the demand uncertainty.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.104-111
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• 2023

The semiconductor industry, which relies on global supply chains, has recently been facing longer lead time for material procurement due to supply chain uncertainties. Moreover, since increasing customer satisfaction and reducing inventory costs are in a trade-off relationship, it is challenging to determine the appropriate safety stock level under demand and lead time uncertainties. In this paper, we propose a framework for determining safety stock levels by utilizing the optimization method to determine the optimal safety stock level. Additionally, we employ a linear regression method to analyze customer satisfaction scores and inventory costs based on variations in lead time and demand. To verify the effectiveness of the proposed framework, we compared safety stock levels obtained by the regression equations with those of the conventional method. The numerical experiments demonstrated that the proposed method successfully reduces inventory costs while maintaining the same level of customer satisfaction when lead time increases.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.279-285
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• 2007

The objective of the research is two fold. The first is to design and propose a stable and robust learning control algorithm. The controller is CMAC Learning Controller which consists of a model-based controller, such as LQR or PID, as a reference control and a CMAC. The second objective is to implement a reference control and CMAC at two different sampling rates. Generally, a conventional controller is designed based on a mathematical plant model. However, increasing complexity of the plant and accuracy requirement on mathematical models nearly prohibits the application of the conventional controller design approach. To avoid inherent complexity and unavoidable uncertainty in modeling, biology mimetic methods have been developed. One of such attempts is Cerebellar Model Articulation Computer(CMAC) developed by Albus. CMAC has two main disadvantages. The first disadvantage of CMAC is increasing memory requirement with increasing number of input variables and with increasing accuracy demand. The memory needs can be solved with cheap memories due to recent development of new memory technology. The second disadvantage is a demand for processing powers which could be an obstacle especially when CMAC should be implemented in real-time. To overcome the disadvantages of CMAC, we propose CMAC learning controller with multiple sampling rates. With this approach a conventional controller which is a reference to CMAC at high enough sampling rate but CMAC runs at the processor's unoccupied time. To show efficiency of the proposed method, an inverted pendulum controller is designed and implemented. We also demonstrate it's possibility as an industrial control solution and robustness against a modeling uncertainty.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.5
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• pp.1-12
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• 2015

The study aimed to project inflows and demmands for the agricultural reservoir watersheds in South Korea considering a variety of regional characteristics and the uncertainty of future climate information. The study bias-corrected and spatially downscaled retrospective daily Global Climate Model (GCM) outputs under Representative Concentration Pathways (RCP) 4.5 and 8.5 emission scenarios using non-parametric quantile mapping method to force Soil and Water Assessment Tool (SWAT) model. Using the historical simulation, the skills of un-calibrated SWAT model (without calibration process) was evaluated for 5 reservoir watersheds (selected as well-monitored representatives). The study then, evaluated the performance of 9 GCMs in reproducing historical upstream inflow and irrigation demand at the five representative reservoirs. Finally future inflows and demands for 58 watersheds were projected using 9 GCMs projections under the two RCP scenarios. We demonstrated that (1) un-calibrated SWAT model is likely applicable to agricultural watershed, (2) the uncertainty of future climate information from different GCMs is significant, (3) multi-model ensemble (MME) shows comparatively resonable skills in reproducing water balances over the study area. The results of projection under the RCP 4.5 and RCP 8.5 scenario generally showed the increase of inflow by 9.4% and 10.8% and demand by 1.4% and 1.7%, respectively. More importantly, the results for different seasons and reservoirs varied considerably in the impacts of climate change.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.135-150
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• 2002

The basic assumption of analytical Dynamic Traffic Assignment models is that traffic demand and network conditions are known as a priori and unchanging during the whole planning horizon. This assumption may not be realistic in the practical traffic situation because traffic demand and network conditions nay vary from time to time. The rolling horizon implementation recognizes a fact : The Prediction of origin-destination(OD) matrices and network conditions is usually more accurate in a short period of time, while further into the whole horizon there exists a substantial uncertainty. In the rolling horizon implementation, therefore, rather than assuming time-dependent OD matrices and network conditions are known at the beginning of the horizon, it is assumed that the deterministic information of OD and traffic conditions for a short period are possessed, whereas information beyond this short period will not be available until the time rolls forward. This paper introduces rolling horizon implementation to enable a multi-class analytical DTA model to respond operationally to dynamic variations of both traffic demand and network conditions. In the paper, implementation procedure is discussed in detail, and practical solutions for some raised issues of 1) unfinished trips and 2) rerouting strategy of these trips, are proposed. Computational examples and results are presented and analyzed.

• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.131-138
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• 2010

Due to the increased water demand and severe drought as an effect of the global warming, the effluent from wastewater treatment plants becomes considered as an alternative water source to supply agricultural, industrial, and public (gardening) water demand. The effluent from the wastewater treatment plant is a sustainable water source because of its good quality and stable amount of water discharge. In this study, the water reuse system was developed to minimize total construction cost to cope with the uncertain water demand in future using two-stage stochastic linear programming with binary variables. The pipes in the water reuse network were constructed in two stages of which in the first stage, the water demands of users are assumed to be known, while the water demands in the second stage have uncertainty in the predicted value. However, the water reuse system has to be designed now when the future water demands are not known precisely. Therefore, the construction of a pipe parallel with the existing one was allowed to meet the increased water demands in the second stage. As a result, the trade-off of construction costs between a pipe with large diameter and two pipes having small diameters was evaluated and the optimal solution was found. Three scenarios for the future water demand were selected and a hypothetical water reuse network considering the uncertainties was optimized. The results provide the information about the economies of scale in the water reuse network and the long range water supply plan.

• Korean Journal of Health Education and Promotion
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• v.22 no.2
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• pp.195-213
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• 2005

Objectives: This study aims to determine fostering stress and mental health state that mothers of handicapped children perceive as primary care givers and to analyze their demand for information assistance in order to release their stress so that it can provide materials that contribute to establishment of assistance system for families with handicapped children. Methods: The research subjects were 340 mothers whose children went to a nursery for special children, 3 general nurseries and 6 special schools in Daegu, and the data were collected using structures questionnaires. The survey analyzed mothers' fostering stress, their demand for fostering information assistance, children's daily activity abilities. Component concepts of each scale was validated by adopting confirmatory factor analysis, and factors affecting demand for fostering information assistance were analyzed by adopting covariance structural analysis. Results: Younger mothers tend to have higher demand for information, and mothers with younger children or children with double handicaps also have higher demand. Mothers under 30 have the lowest demand for public health and medical care assistance and for home and community life assistance, while mothers with children with physical handicaps have the highest. The validity of component concepts was verified by categorizing as cognitive structure models fostering stress, information demand, children's daily activity abilities, and their appropriateness was evaluated through confirmatory factor analysis using structural equation modelling. And then, GFI (more than 0.9), CFI (more than 0.9), TLI (more than 0.9) and RMSAE (less than 0.08) were used to evaluate the appropriateness. It was found that all the component concepts are valid, as every item is within appropriate range. The result of analyzing information demand demonstrated that children's handicap levels significantly affect their mothers' mental health, while fostering stress significantly affect mothers' metal health, information demand. As well, it was confirmed that mothers' mental health has a significant effect on information demand. Conclusions: Therefore, to reduce special children's mothers' uncertainty, helplessness and fostering burden, it is necessary to provide them with information on children's challenges, development and fostering and to offer them quality public health, medical care and welfare assistance along with family and local community life assistance.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.424-429
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• 2020

An Energy Storage System (ESS) is recently drawing an increasing attention as an efficient tool to cope with variation in the energy system. In order to take the best utilization of ESS, the inherent variation of energy supply and demand should be properly addressed. This paper is concerned with computing the stochastic capacity of ESS in the face of such variations by way of Monte Carlo simulation. The issue of uncertainty in energy systems will be given further focus. More works are expected to be followed to address the issues in academia and industry.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1472-1479
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• 2024

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED).

• Journal of Industrial Convergence
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• v.19 no.6
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• pp.21-35
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• 2021

The purpose of this paper is to examine how demand for labor affects the job seeker's decision on the level of investment in education. In the current paradigm of economic growth in which innovations and technological developments generally weaken the strength demand for labor and increases the uncertainty related to employment, this paper provides a theoretical framework that can be used as a basic framework in understanding the decision of investment in education in varying conditions of demand for labor. The following are the findings of this paper. First, the level of investment in education can generally be regarded to be higher as the demand for labor exacerbates but for the job seekers with a certain characteristic. Second, the Arrow-Pratt absolute risk-aversion measure is the characteristic of the job seeker that determines in what direction the job seeker changes in the level of investment in education, For an arbitrary level of demand for labor there exists a certain threshold which determines the minimum degree of risk-aversion required for the job seeker's Arrow-Pratt should go over to increase the level of education as demand for labor weakens. Third, the job seekers lower the level of education even though the demand condition in labor markets weakens if the compensation function does not depend on the level of education. This is surprising because it turns out that one of the reasons why job seekers invest in education is that they want to be recognized in their compensation for their level of education even when more education still raises the probability of employment.