• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.2
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• pp.39-46
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• 2018

Departure Manager (DMAN) is a tool to optimize the departure sequence and to suggest appropriate take-off time and off-block time of each departure aircraft to the air traffic controllers. To that end, Variable Taxi Time (VTT), which is time duration of the aircraft from the stand to the runway, should be estimated. In this paper, a study for development of VTT prediction algorithm based on machine learning techniques is presented. The factors affecting aircraft taxi speeds were identified through the analysis of historical traffic data on the airport surface. The prediction model suggested in this study consists of several sub-models that reflect different types of surface maneuvers based on the analysis result. The prediction performance of the proposed method was evaluated using the actual operational data.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.3
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• pp.245-259
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• 1994

This paper is concerned with the development of a three-dimensional numerical model for coastal circulation and heat transport with improved prediction ability. The model uses fully nonlinear, time-dependent three-dimensional, $\sigma$-transformed equations of motion and equation of heat transport The model was verified with experimental data for wind-driven current in a one-dimensional channel and thermal jets flowing into stagnant waters and applied for unsteady flow induced by tide and thermal jets in coastal waters around Kori nuclear power plant. The model results were in good agreements with experimental data sets for wind-driven current and thermal jet, and field observed data sets in coastal waters. This study has shown that the $\kappa$-$\varepsilon$ turbulence model is applicable to various coastal conditions without any modification of turbulence constants.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.2
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• pp.73-73
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• 1988

Knapsack problems represent many business application such as cargo loading, project selection, and capital budgeting. In this research we developed a knapsack problem solver based on Martello-Toth algorithm using a relational database management system on the PC platform. The solver used the menu-driven user interface. The solver can be easily integrated with the database of decision support system because the solver can access the database to retrieve the data for the model and to store the result directly.

• Korean Management Science Review
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• v.13 no.2
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• pp.73-94
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• 1996

Knapsack problems represent many business application such as cargo loading, project selection, and capital budgeting. In this research we developed a knapsack problem solver based on Martello-Toth algorithm using a relational database management system on the PC platform. The solver used the menu-driven user interface. The solver can be easily integrated with the database of decision support system because the solver can access the database to retrieve the data for the model and to store the result directly.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.400-402
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• 2004

임베디드 시스템 개발의 성공여부는 정점 하드웨어에서 소프트웨어쪽으로 비중이 옮겨지고 있다. 그런데 임베디드 소프트웨어 개발은 하드웨어를 설계하고 구현하는 것에 드는 비용보다 더 않은 비용으로도 효과적이지 못한 실정이다. 또한 임베디드 소프트웨어 개발자들에게는 제한된 자원과 절러 가지 환경 변수를 고려해야 하는 부담이 따른다. 모델기반의 개발 방법은 비용-효율적이고 고품질의 소프트웨어를 개발하기 위한 연구이다. 본 논문은 임베디드 소프트웨어인 로봇 작동 프로그램을 모델기반으로 개발한 경험과 결과를 소개함으로써 임베디드 소프트웨어 개발자들이 쉽게 모델기반의 개발 방법을 적용할 수 있도록 한다.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.881-885
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• 2008

Wireless energy transmission system to a Micro Aerial Vehicle is now under development. A 5.8 GHz microwave phased array antenna and rectenna array receiver have been developed. An electric motor on a circling MAV model was driven by the transmitted power. In addition, 140GHz millimeter-waves of up to 1MW was beamed to a "Microwave Rocket" and its thrusting has been successfully demonstrated.

• International Journal of Fluid Machinery and Systems
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• v.7 no.1
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• pp.16-27
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• 2014

Double-blade vertical axis wind turbines (DB-VAWTs) can improve the self-starting performance of lift-driven VAWTs. We here propose the quadruple-multiple streamtube model (QMS), based on the blade element momentum (BEM) theory, for simulating DB-VAWT performance. Model validity is investigated by comparison to computational fluid dynamics (CFD) prediction for two kinds of two-dimensional DB-VAWT rotors for two rotor scales with three inner-outer radius ratios: 0.25, 0.5, and 0.75. The BEM-QMS model does not consider the effects of an inner rotor on the flow speed in the upwind half of the rotor, so we introduce a correction factor for this flow speed. The maximum power coefficient predicted by the modified BEM-QMS model for a DB-VAWT is thus closer to the CFD prediction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1413-1424
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• 2013

Recently, the frequency of severe storms increases in Korea. Severe storms occurring in a short time cause huge losses of both life and property. A considerable research has been performed for the flood control system development based on an accurate stream discharge prediction. A physical model is mainly used for flood forecasting and warning. Physical rainfall-runoff models used for the conventional flood forecasting process require extensive information and data, and include uncertainties which can possibly accumulate errors during modelling processes. ANFIS, a data driven model combining neural network and fuzzy technique, can decrease the amount of physical data required for the construction of a conventional physical models and easily construct and evaluate a flood forecasting model by utilizing only rainfall and water level data. A data driven model, however, has a disadvantage that it does not provide the mathematical and physical correlations between input and output data of the model. The characteristics of a data driven model according to functional options and input data such as the change of clustering radius and training data length used in the ANFIS model were analyzed in this study. In addition, the applicability of ANFIS was evaluated through comparison with the results of HEC-HMS which is widely used for rainfall-runoff model in Korea. The neuro-fuzzy technique was applied to a Cheongmicheon Basin in the South Han River using the observed precipitation and stream level data from 2007 to 2011.

• Journal of the Korean Institute of Gas
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• v.24 no.1
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• pp.66-75
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• 2020

Drilling operation is the most important and costly essential work in oil and gas exploration and development. Therefore, the studies about rate of penetration have been carried out continuously to improve drilling efficiency. In recent years, data-driven models have been developed by various researchers to overcome disadvantages of traditional mathematical models. For the data-driven models, selecting proper algorithms and parameters is very important. In addition, data-driven models should be retrained in real-time during continuous drilling operations in order to improve the model performance. In this paper, the latest studies are investigated to provide information about algorithms, drilling parameters and model retraining intervals that used in drilling optimization.

• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.1-12
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• 2013

Context-awareness is a computing technology that automatically delivers useful services to users based on their situation. Most previous studies on context-awareness adopted the view that the user simply is a consumer of what the developer creates. Few studies addressed catering to the need of personalized services for the user. They are either too complex for the user to grasp or unable to express many useful services due to their weak expressive power. To address these issues, we propose Dobby as a new model and architecture for user-driven context-aware service development. Dobby enables the user to create services that are more suited to his personal preferences. We argue that Dobby offers an enhanced method for defining personalized context-aware services over existing methods.