• Journal of Korean Society of Transportation
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• v.30 no.6
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• pp.81-92
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• 2012

The current Korean preliminary feasibility study guidebook provides a simple method for determining the impact area of a transportation project without taking its characteristics into account, which often leads travelers to switch their travel modes. Hence, this study develops a comprehensive methodology for defining the impact area when evaluating railroad projects, which can significantly affect travel mode choice behaviors. To develop the methodology, a hypothetical project was devised. The analysis results show that the convergence of mode-diverted trip rates is improved from 76% to 93% by implementing the proposed method. In addition, there was a significant difference in benefits (about 10.9 billion won) between adopting the current method and the proposed method.

• Journal of Distribution Science
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• v.8 no.2
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• pp.5-12
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• 2010

The allotment rate for railway transportation keeps an yearly 6% in Korea. However, the railway logistics will cause the opposite result according to the continuous investment and logistics rationalization. The study on railway logistics as well as inter-Korean railway that might highly contribute to the development of railway logistics is not enough at all. The purpose of this paper is to study the revitalization strategy for inter-Korean railway by forecasting the demand and the scale of railway logistics depot. The revitalization strategies for inter-Korean railway through railway logistics depot are as followings. First, it is necessary to strengthen the partnership with coal user in the logistics depot. Second, it is encouraged to provide the financial assistance that are needed in the maintenance of the decrepit North Korea's track as well as the establishment of Donghae northern line that is from Gangneung to Jejin. Third, the railway cost on long/short transportation and large sized shipper is needed to apply in a flexible way. Fourth, it is necessary to obtain the railway traffic right by involving the foreign mining development. Fifth, it is encouraged to constantly find the small sized shipper like cement company.

• Korean Medical Education Review
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• v.18 no.2
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• pp.51-57
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• 2016

This literature review explores artificial intelligence (AI) technology trends and IBM Watson health and medical references. This study explains how healthcare will be changed by the evolution of AI technology, and also summarizes key technologies in AI, specifically the technology of IBM Watson. We look at this issue from the perspective of 'information overload,' in that medical literature doubles every three years, with approximately 700,000 new scientific articles being published every year, in addition to the explosion of patient data. Estimates are also forecasting a shortage of oncologists, with the demand expected to grow by 42%. Due to this projected shortage, physicians won't likely be able to explore the best treatment options for patients in clinical trials. This issue can be addressed by the AI Watson motivation to solve healthcare industry issues. In addition, the Watson Oncology solution is reviewed from the end user interface point of view. This study also investigates global company platform business to explain how AI and machine learning technology are expanding in the market with use cases. It emphasizes ecosystem partner business models that can support startup and venture businesses including healthcare models. Finally, we identify a need for healthcare company partnerships to be reviewed from the aspect of solution transformation. AI and Watson will change a lot in the healthcare business. This study addresses what we need to prepare for AI, Cognitive Era those are understanding of AI innovation, Cloud Platform business, the importance of data sets, and needs for further enhancement in our knowledge base.

• Journal of the Korea Society for Simulation
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• v.25 no.4
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• pp.65-75
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• 2016

Unused spare parts of military equipments which have been kept in the warehouse for a long term have been recognized as wasting of defense budget which also decreased people's confidence in the field of national defence. It is known that overstock is caused by information distortion and bullwhip effect from downstream to upstream in the logistic support as is like business cases. After upgrading the logistic information system, it is possible for Army Logistics Command(ALC) to know demand information of end-user which is the key factor to reduce bullwhip effect. However, inventory is still overstocked and stock-out at the same time. Previous studies have not accounted for these phenomenons and have mainly focused on forecasting inventory level instead of budgetary process(budget period, PROLT, ASL/N-ASL). Thus, this study focuses on the information distortion, overstock and stock-out which caused by budgetary process in the logistic support with system dynamics and simulation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.64-73
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• 2019

The manufacturing companies under Make-To-Order (MTO) production environment face highly variable requirements of the customers. It makes them difficult to establish preemptive production strategy through inventory management and demand forecasting. Therefore, the ability to establish an optimal production schedule that incorporates the various requirements of the customers is emphasized as the key success factor. In this study, we suggest a process of designing the simulation model for establishing production schedule and apply this model to the case of a flat glass processing company. The flat glass manufacturing industry is under MTO production environment. Academic research of flat glass industry is focused on minimizing the waste in the cutting process. In addition, in the practical view, the flat glass manufacturing companies tend to establish the production schedule based on the intuition of production manager and it results in failure of meeting the due date. Based on these findings, the case study aims to present the process of drawing up a production schedule through simulation modeling. The actual data of Korean flat glass processing company were used to make a monthly production schedule. To do this, five scenarios based on dispatching rules are considered and each scenario is evaluated by three key performance indicators for delivery compliance. We used B2MML (Business To Manufacturing Markup Language) schema for integrating manufacturing systems and simulations are carried out by using SIMIO simulation software. The results provide the basis for determining a suitable production schedule from the production manager's perspective.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.108-118
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• 2018

Recently, agricultural sector apply ICT technology such as Smart Farm to pursue innovation in the changing situation that is emerging as the fourth industrial revolution. However, this innovation requires techniques for forecasting and analyzing in various data bases and spatial information provides such infrastructure data. In this study, the cultivation area of Chinese cabbage, radish, garlic, onion, and red pepper were calculated and analyzed by year. The purpose of this analysis is to cope with sudden changes in vegetable crops and changes in cultivated area caused by weather changes to supply and demand of major vegetables and price instability. As a result of this study, spatial information based on time series information of vegetable complex will be used as efficient agricultural environment observation data, as well as interpretation of various spatial ranges such as the estimation of cultivation area using remote sensing.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.27-35
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• 2019

There is a growing interest in various microgrid solutions that supply electricity 24 hours a day to off-grid areas where are not connected with the main grid, and Korea has many positive effects by constructing overseas microgrids as a country operating the emission trading scheme. Since it is not clear how to obtain load curves that is one of the inputs of the HOMER used to design a microgrid optimization plan, or it is necessary to examine whether electricity is supplied to the peak load level of the areas where have not received the electricity benefits from the viewpoint of the demand management, a methodology should be developed to know the load composition ratio and the shape of the daily load curve. In this paper, the relative coefficient and average load information for each load group obtained from the survey are used besides peak load and total average load. A mathematical model is proposed to derive the load composition ratio in the form of a Quadratic Programming and the load forecasting is performed using simple linear regression with future indicators. The effectiveness of the proposed method is confirmed for the Philippine island region supported by Korea Energy Agency and the Asian Development Bank.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.409-414
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• 2021

The growing concerns on the emission of particulate matter has prompted a demand for highly reliable particulate matter forecasting. Currently, several studies on particulate matter prediction use various deep learning algorithms. In this study, we compared the predictive performances of typical neural networks used for particulate matter prediction. We used deep neural network(DNN), recurrent neural network, and long short-term memory algorithms to design an optimal predictive model on the basis of a hyperparameter search. The results of a comparative analysis of the predictive performances of the models indicate that the variation trend of the actual and predicted values generally showed a good performance. In the analysis based on the root mean square error and accuracy, the DNN-based prediction model showed a higher reliability for prediction errors compared with the other prediction models.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.39-48
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• 2020

This study intends to the estimate the determinants and state dependence of long-term care services in Korea. For this purpose, we analyzed the transition patterns among three states of long-term care service utilization over time by using the Korea Welfare Panel Study data with the random effect multinomial logit model. It is found that the result showed a strong state dependence in long-term care service utilization. Especially, long-term care insurance for the elderly showed a strong state dependence among others. Among the individual demographic characteristics, the higher the age, the higher the probability of using long-term care insurance for the elderly, while the lower the probability when married. The characteristics of the residential region showed that the residents of the urban-rural integrated region had a significantly higher probability of using long-term care insurance than the reference region. The results of this study suggest that the long-term care service users have a strong state dependence, which means that it is important to take into account the increase in the utilization period of existing users in future demand forecasting.