• 한국항공운항학회지
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• 제26권2호
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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.

• 한국농공학회지
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• 제36권2호
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• pp.79-87
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• 1994

A real-time flood forecasting system(FLOFS) was developed for the real-time and predictive determination of flood discharges and stages, and to aid in flood management decisions in the Keum River Estuary Dam. The system consists of three subsystems : data subsystem, model subsystem, and user subsystem. The data subsystem controls and manages data transmitted from telemetering systems and simulated by models. The model subsystem combines various techniques for rainfall-runoff modeling, tidal-level forecasting modeling, one-dimensional unsteady flood routing, Kalman filtering, and autoregressivemovingaverage(ARMA) modeling. The user subsystem in a menu-driven and man-machine interface system.

• 한국해양공학회지
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• 제36권3호
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• pp.194-210
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• 2022

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.

• 한국대기환경학회지
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• 제22권1호
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• pp.43-51
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• 2006

Nonthermal plasma-driven catalysis (PDC) was investigated for the decomposition of benzene and toluene as model compounds of volatile organic compounds (VOCs) at atmospheric pressure and low temperature. Two types of catalysts Ag/$TiO_{2}$ and Pt/$\gamma-Al_{2}O_{3}$ were tested in this study. The amount of catalysts packed in the PDC reactor did not influence on the decomposition efficiency of benzene. The type of catalysts also had no influence on the decomposition efficiency of toluene and carbon balance. The Ag/$TiO_{2}$ catalyst showed constant $CO_{2}$ selectivity of about $73\%$ regardless of the specific input energy. However, the selectivity of $CO_{2}$ was greatly enhanced with the Pt/$\gamma-Al_{2}O_{3}$ catalysts, and reached $97\%$ at 205 J/L. Two test runs with 20 fold difference in the gas flow clearly indicated that lab-scale data can be successfully applied for the scaling-up of PDC system.

• 대한원격탐사학회지
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• 제36권4호
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• pp.609-626
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• 2020

Satellite-based land surface temperature (LST) has been used as one of the major parameters in various climate and environmental models. Especially, Moderate Resolution Imaging Spectroradiometer (MODIS) LST is the most widely used satellite-based LST product due to its spatiotemporal coverage (1 km spatial and sub-daily temporal resolutions) and longevity (> 20 years). However, there is an increasing demand for LST products with finer spatial resolution (e.g., 10-250 m) over regions such as urban areas. Therefore, various methods have been proposed to produce high-resolution MODIS-like LST less than 250 m (e.g., 100 m). The purpose of this review is to provide a comprehensive overview of recent research trends and challenges for the downscaling of MODIS LST. Based on the recent literature survey for the past decade, the downscaling techniques classified into three groups-kernel-driven, fusion-based, and the combination of kernel-driven and fusion-based methods-were reviewed with their pros and cons. Then, five open issues and challenges were discussed: uncertainty in LST retrievals, low thermal contrast, the nonlinearity of LST temporal change, cloud contamination, and model generalization. Future research directions of LST downscaling were finally provided.

• 대한기계학회논문집
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• 제19권3호
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• pp.741-750
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• 1995

The turbulent buoyancy-driven flow in 2-dimensional enclosed cavities heated from the vertical side is numerically calculated for both cases of a Rayleigh number of 5*10$^{10}$ for air and 2.5*10$^{10}$ for water. Three different turbulence models are considered : standard k-.epsilon. model of Ozoe and low-Reynolds-number model of Lam and Bremhorst, and another low-Reynolds-number model of Davidson. The results indicate that the use of low-Reynolds number models is recommended for the indoor airflow computation, and the results from Davidson model are reasonably close to the reported experimental data. A sensitivity study shows that the amounts of wall-heat transfer and the velocity profiles with the Lam and Bremhorst model largely depend on the choice of the wall function for .epsilon..

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1343-1346
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• 2005

In the cold rolling mill, coil breakage that generated in rolling process makes the various types of troubles such as the degradation of productivity and the damage of equipment. Recent researches were done by the mechanical analysis such as the analysis of roll chattering or strip inclining and the prevention of breakage that detects the crack of coil. But they could cover some kind of breakages. The prediction of Coil breakage was very complicated and occurred rarely. We propose to build effective prediction modes for coil breakage in rolling process, based on data mining model. We proposed three prediction models for coil breakage: (1) decision tree based model, (2) regression based model and (3) neural network based model. To reduce model parameters, we selected important variables related to the occurrence of coil breakage from the attributes of coil setup by using the methods such as decision tree, variable selection and the choice of domain experts. We developed these prediction models and chose the best model among them using SEMMA process that proposed in SAS E-miner environment. We estimated model accuracy by scoring the prediction model with the posterior probability. We also have developed a software tool to analyze the data and generate the proposed prediction models either automatically and in a user-driven manner. It also has an effective visualization feature that is based on PCA (Principle Component Analysis).

• 한국음향학회지
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• 제42권4호
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• pp.313-319
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• 2023

본 논문에서는 일부 소음 데이터만 알고 있을 때 결손된 데이터를 예측할 목적으로 수조에서 측정된 기포유동 소음 데이터와 수중 운동체 발사 소음 데이터를 시계열 기계학습 모델인 Long Short Term Memory(LSTM)에 적용해 보았다. 기포유동소음 데이터는 파이프에서 측정된 소음으로 기포소음, 유동소음, 유체기인소음이 혼합되어 있으며 유형별로 3가지로 분류할 수 있다. 수중 운동체 발사소음은 모형 발사튜브에서 수중 운동체가 사출될 때 발생하는 소음으로 순간소음이며 발사 이벤트마다 불규칙하게 변한다. 이러한 종류의 소음 생성을 위해서는 해석적인 모델보다는 데이터 기반 모델이 유용할 수 있다. 본 연구에서는 LSTM을 데이터 기반 모델을 만들었다. 모델에 영향을 주는 LSTM의 은닉유닛의 개수, 입력시퀸스의 개수, 데시메이션 인자에 따른 모델의 성능을 확인하고 최적의 LSTM 모델을 구성했다. 같은 유형은 새로운 데이터에 대해서도 잘 동작하는 것을 보였다.

• Journal of Information Technology Applications and Management
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• 제12권4호
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• pp.105-121
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• 2005

Ubiquitous computing(UC) is an emerging paradigm. Its arrival as a mainstream is expected to trigger innovative UC-driven business models (UCBMs). Currently, there is no Parsimonious methodology to analyze and provide diagnostics for UCBMs. With this research, we propose a analytical architecture that enables the assessment of an UCBM in its structural strengths and weaknesses. With value logic as the cornerstone, the architecture is composed of value actors, value assets, value context, business value Propositions, customer value propositions, value creation logics, and value assumptions. Dimensional variables are initially Identified based on the review of business model literature. Then, their significance is empirically examined through 14 UCBM scenarios, and variables that are expected to Play an important role in the UCBM assessment are decided. Finally, by analyzing the scenarios in terms of the dimensional variables, we attempted to summarize general characteristics of emerging UCBMs.

• Wind and Structures
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• 제15권3호
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• pp.247-261
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• 2012

Some numerical investigations are presented concerning the response of a given plate under turbulence driven excitations. Three different input loads are simulated according to the wall pressure distributions derived from the models proposed by Corcos, Efimtsov and Chase, respectively. Modal solutions (finite element based) are used for building the modal stochastic responses in the sub-critical aerodynamic frequency range. The parametric investigations concern two different values of the structural damping and three values of the boundary layer thickness. A final comparison with available experimental data is also discussed. The results demonstrate that the selection of the adequate TBL input model is still the most critical step in order to get a good prediction.