• Journal of Korean Society of Water and Wastewater
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• v.25 no.4
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• pp.581-589
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• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2490-2495
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• 2005

The scheduling of plant should be determined based on the product demands correctly forecasted by reasonable methods. However, because most existing forecasting packages need user's knowledge about forecasting, it has been hard for plant engineers without forecasting knowledge to apply forecasted demands to scheduling. Therefore, a forecasting module has been developed for plant engineers without forecasting knowledge. In this study, for the development of the forecasting module, an automatic method using the ARIMA model that is framed from the modified Box-Jenkins process is proposed. And a new method for safety inventory determination is proposed to reduce the penalty cost by forecasting errors. Finally, using the two proposed methods, the web-based automatic module has been developed.

• Journal of the Ergonomics Society of Korea
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• v.30 no.5
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• pp.589-595
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• 2011

Objective: This study compares two visual occlusion methods for the evaluation of in-vehicle interfaces. Background: Visual occlusion is a visual demand measuring technique which uses periodic vision/occlusion cycle to simulate a driving(or mobile) environment. It has been widely used for the evaluation of in-vehicle interfaces. There are two major implementation methods for this technique: (1) occlusion using PLATO(portable liquid crystal apparatus for tachistoscopic occlusion) goggles; (2) occlusion using a software application on a touchscreen device. Method: An experiment was conducted to examine the visual demand of an in-vehicle interface prototype using the goggle-based and the touchscreen-based occlusion methods. Address input and radio tuning tasks were evaluated in the experiment. Results: The results showed that, for the radio tuning task, there were no significant differences in total shutter open time and resumability ratio between the two occlusionconditions. However, it took longer for the participants to input addresses with the touchscreen-based occlusion. Conclusion & Application: The results suggest that touchscreen-based method could be used as an alternative to traditional, gogglebased visual occlusion especially in less demanding visual tasks such as radio tuning.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.3
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• pp.189-194
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• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method based on hybrid type composed of AR and Neuro-Fuzzy model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.4
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• pp.169-174
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• 2013

Due to the increase of power consumption, it is difficult to construct an accurate prediction model for daily peak power demand. It is very important work to know power demand in next day to manage and control power system. In this research, we develop a daily peak power demand prediction method based on Extreme Learning Machine(ELM) with fast learning procedure. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• Earthquakes and Structures
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• v.10 no.1
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• pp.105-123
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• 2016

In this paper, using the probabilistic methods, the seismic demand of buckling restrained braced frames subjected to earthquake was evaluated. In this regards, 4, 6, 8, 10, 12 and 14-storybuildings with different buckling restrained brace configuration (including diagonal, split X, chevron V and Inverted V bracings) were designed. Because of the inherent uncertainties in the earthquake records, incremental dynamical analysis was used to evaluate seismic performance of the structures. Using the results of incremental dynamical analysis, the "capacity of a structure in terms of first mode spectral acceleration", "fragility curve" and "mean annual frequency of exceeding a limit state" was determined. "Mean annual frequency of exceeding a limit state" has been estimated for immediate occupancy (IO) and collapse prevention (CP) limit states using both Probabilistic Seismic Demand Analysis (PSDA) and solution "based on displacement" in the Demand and Capacity Factor Design (DCFD) form. Based on analysis results, the inverted chevron (${\Lambda}$) buckling restrained braced frame has the largest capacity among the considered buckling restrained braces. Moreover, it has the best performance among the considered buckling restrained braces. Also, from fragility curves, it was observed that the fragility probability has increased with the height.

• Computers and Concrete
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• v.32 no.3
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• pp.313-326
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• 2023

The seismic safety of the shear wall structure can be assessed through seismic fragility analysis, which requires high computational costs in estimating seismic demands. Accordingly, machine learning methods have been applied to such fragility analyses in recent years to reduce the numerical analysis cost, but it still remains a challenging task. Therefore, this study uses the ensemble machine learning method to present an improved framework for developing a more accurate seismic demand model than the existing ones. To this end, a rank-based selection method that enables determining an excellent model among several single machine learning models is presented. In addition, an index that can evaluate the degree of overfitting/underfitting of each model for the selection of an excellent single model is suggested. Furthermore, based on the selected single machine learning model, we propose a method to derive a more accurate ensemble model based on the bagging method. As a result, the seismic demand model for which the proposed framework is applied shows about 3-17% better prediction performance than the existing single machine learning models. Finally, the seismic fragility obtained from the proposed framework shows better accuracy than the existing fragility methods.

• Health Policy and Management
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• v.27 no.1
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• pp.18-29
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• 2017

Background: In Korea, the problem of physician workforce imbalances has been a debated issue for a long time. This study aimed to draw key lessons and policy implications to Korea by analyzing projection models of physician demand/supply among five countries. Methods: We adopted theoretical framework and analyzed detail indicators used in projection models of demand/supply comparatively among countries. A systematic literature search was conducted using PubMed and Google Scholar with key search terms and it was complimented with hand searching of grey literature in Korean or English. Results: As a results, Korea has been used a supply-based traditional approach without taking various variables or environmental factors influencing on demand/supply into consideration. The projection models of USA and Netherlands which considered the diversity of variables and political issues is the most closest integrated approach. Based on the consensus of stakeholder, the evolved integrated forecasting approach which best suits our nation is needed to minimize a wasteful debate related to physician demand/supply. Also it is necessary to establish the national level statistics indices and database about physician workforce. In addition, physician workforce planning will be discussed periodically. Conclusion: We expect that this study will pave the way to seek reasonable and developmental strategies of physician workforce planning.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.167-176
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• 2009

Several travel demand management schemes have been used for controlling overloaded traffics on urban area. To maximize efficiency of the travel management, traffic manager has to set target level that we try to arrive in advance, and then to find optimal variable to attain this goal. In this regard, this paper presents two travel demand management models, expressed by mathematical program, and also presents their solution algorithms. The first is to find optimal travel demand for origin-destination (OD) pair, based on average travel time between the OD pair, and the second is based on the ratio of volume over capacity on congested area. An example is given to test the models.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.406-415
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• 2024

As the equipment of the military has become more advanced and expensive, the cost of securing spare parts is also constantly increasing along with the increase in equipment assets. In particular, forecasting demand for spare parts one of the important management tasks in the military, and the accuracy of these predictions is directly related to military operations and cost management. However, because the demand for spare parts is intermittent and irregular, it is often difficult to make accurate predictions using traditional statistical methods or a single statistical or machine learning model. In this paper, we propose a model that can increase the accuracy of demand forecasting for irregular patterns of spare parts demanding by using a combination of statistical and machine learning algorithm, and through experiments on Cheonma spare parts demanding data.