• The Korean Journal of Applied Statistics
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• v.5 no.1
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• pp.59-80
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• 1992

This paper is concerned with the development and application of a stochastic model for winter air-temperature of Seoul area. The annual and interannual flucturations of the regression trend, periodicity and dependence of the air-temperature are analyzed based on the data during the past 30 years(1959-1989). A statistical procedure for using the stochastic model to predict the air-temperature is proposed. Some statistical characteristics of winter air-temperature including unusual air-temperature and Samhansaon are also discussed.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.5
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• pp.861-868
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• 2022

The purpose of this study is to design an effective atmospheric environment system through the design of the dust collection in the air shot room being operated in a domestic shipyard. The ventilation system in the current air shot room mostly uses a dust collecting filter to filter internal particles and releases them in the atmosphere. A conventional design was made too much. In order to prevent an error and draw an optimal design, Computational fluid dynamics (CFD) tried to be applied only to air shot room. In the advanced design technique, computer simulation was conducted to secure basic design data. In order to find the basic design of the ventilation system and the flow field in the air shot room at propeller mold workplace of a shipyard, the CFD was conducted. In the case of Model-1 as a conventional workplace, where air flows in the inlet due to the subatmospheric pressure generated by inhalation of an air blower and flows out to the outlet, a discharge flow rate was somewhat low, and there was the holdup zone in the room. In the case of Model-2 as an improved model, the ventilation system was improved in the Push-Pull type, and the holdup of the internal flow field was improved.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.6
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• pp.386-394
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• 2016

In this research, in order to optimize the multi-objective function effectively, we suggested the optimization model to maximize the total destruction of ground targets and minimize the total damage of aircrafts and cost of air munitions by using goal programming. To satisfy the various variables and constraints of this mathematical model, the concept of air strike package is applied. As a consequence, effective attack can be possible by identifying the prior ground targets more quickly. This study can contribute to maximize the ROK air force's combat power and preservation of high value air asset in the war.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.157-160
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• 2010

Management of indoor air quality of underground subway station is an important issue since the limited natural ventilation, limited sunshine incoming, and highly moistured atmosphere. The improvement in IAQ of platform is expected because most stations were installed with platform screen door currently, however, the poor air quality in tunnel might be affecting subway cabin indoor. In this study, we developed the air quality assessment model based on computational fluid dynamics. The geometry of air ventilation unit, seat, LCD monitors, and passengers were modeled using commercial software (Design Modeler) and fluid pattern and pollutants trajectories were analyzed by using CFX. We predicted the thermal comfort by predicted mean vote (PMV), distribution of CO2 and PM10 concentration. It is expected that this model can be used for the performance test of air cleaners which are under development.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.1
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• pp.89-96
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• 2016

The mechanism for laminar dust flame propagation can only be elucidated from a comprehensive mathematical model which incorporates conduction and radiation, as well as the chemical kinetics of particle devolatilization and gas phase and char reaction. The mathematical model for a flat, laminar, premixed coal-air flame is applied to the atmospheric coal-air mixtures studied by Smoot and co-workers, and comparisons are made with their measurements and predictions. Here the principal parameter for comparison is the laminar burning velocity. The studies of Smoot and co-workers are first reviewed and compared with those predicted by the present model. The effects of inlet temperature and devolatilization rate constants on the burning velocities are studied with the present model, and compared with their measurements and predictions. Their measured burning velocities are approximately predicted with the present model at relatively high coal concentrations, with a somewhat increased inlet temperature. From the comparisons, their model might over-estimate particle temperature and rates of devolatilization. This would enable coal-air mixtures to be burned without any form of preheat and would tend to increase their computed values of burning velocity.

• KIEAE Journal
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• v.10 no.3
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• pp.3-10
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• 2010

As indoor air quality directly affects health and comforts of the residents, researchers from different countries have continued to explore criteria by which indoor air quality can be indicated in a scientific and quantitative manner over the past several decades. However, there are many possibilities that can deteriorate indoor air quality. Due to the uncertainty of influence factors, it is quite difficult to develop a correct evaluation model and quantitative method. Furthermore, the effects from the indoor air pollutants have different levels, leading to the difficulties to apply the regular standard. This study aims to propose evaluation criteria by using the FD-AHP analysis. Obtained findings will be beneficial to construct apartment buildings, commercial buildings and others health performance evaluation framework.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.3
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• pp.13-20
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• 2009

Development of a dynamic engine model is essential to predict and analyze of dynamic characteristics from a natural gas engine. Reducing the harmful exhaust emissions can be accomplished by a precise air-fuel ratio control. In this paper, the dynamic engine model was proposed and included mixture formation and intake process because the dynamic characteristics can be affected by the mixture components such as an air and a gaseous fuel. The air mass flow, the partial pressure ratio, and the gas constant are changed by variations of the components in the mixture formation and intake process. The dynamic engine model is applied to the natural gas engine for validation test. Experimental results show that the dynamic engine model is effective to predict the dynamic characteristics of the natural gas engine.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.4
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• pp.361-372
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• 2019

The flow pattern of air layers and skin-friction drag reduction by air injection are investigated to find the suitable multiphase flow model using unstructured finite-volume CFD solver for the Reynolds-averaged Navier-Stokes equations. In the present computations, two different multiphase flow modeling approaches, such as the Volume of Fluid (VOF) and the Eulerian Multi-Phase (EMP), are adopted to investigate their performances in resolving the two-phase flow pattern and in estimating the frictional drag reduction. First of all, the formation pattern of air layers generated by air injection through a circular opening on the bottom of a flat plate are investigated. These results are then compared with those of MMkiharju's experimental results. Subsequently, the quantitative ratios of skin-friction drag reduction including the behavior of air layers, within turbulent boundary layers in large scale and at high Reynolds number conditions, are investigated under the same conditions as the model test that has been conducted in the US Navy's William B. Morgan Large Cavitation Channel (LCC). From these results, it is found that both VOF and EMP models have similar capability and accuracy in capturing the topology of ventilated air cavities so called'air pockets and branches'. However, EMP model is more favorable in predicting quantitatively the percentage of frictional drag reduction by air injection.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.440-449
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• 2022

In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

• ETRI Journal
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• v.46 no.2
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• pp.250-262
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• 2024

Air-writing recognition is relevant in areas such as natural human-computer interaction, augmented reality, and virtual reality. A trajectory is the most natural way to represent air writing. We analyze the recognition accuracy of words written in air considering five features, namely, writing direction, curvature, trajectory, orthocenter, and ellipsoid, as well as different parameters of a hidden Markov model classifier. Experiments were performed on two representative datasets, whose sample trajectories were collected using a Leap Motion Controller from a fingertip performing air writing. Dataset D₁ contains 840 English words from 21 classes, and dataset D₂ contains 1600 English words from 40 classes. A genetic algorithm was combined with a hidden Markov model classifier to obtain the best subset of features. Combination ftrajectory, orthocenter, writing direction, curvatureg provided the best feature set, achieving recognition accuracies on datasets D₁ and D₂ of 98.81% and 83.58%, respectively.