• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.42 no.7
• /
• pp.616-621
• /
• 2014

Consumption of the fuel on the satellite operating in low earth orbit, is increased due to the air resistance and the amount of increase makes the satellite lifetime decrease or the satellite mass risen. Therefore the prediction of drag force of the satellite is important. In the paper, drag force and drag coefficient analysis of the parabolic antenna satellite in low earth orbit using direct simulation monte carlo method (DSMC) is conducted according to the mission altitude and angle of attack. To verify the DSMC simulated rarefied air movement, Starshine satellite drag coefficient according to the altitude and gas-surface interaction are compared with the flight data. Finally, from the analysis results, it leads to appropriate satellite drag coefficient for orbit lifetime calculation.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.13 no.2 s.29
• /
• pp.133-140
• /
• 2007

Since early times, captain have been sailing to select the optimum route considering the weather, ship loading status condition and operational scheduling empirically. However, it is rare to find digitalized onboard route support system whereas weather facsimile or wave and swell chart are utilized for the officer, based on captain's experience. In this paper, optimal route safety assessment system which is composed of voyage efficiency and safety component is introduced. Optimum route minimizea ETA(estimated time of arrival) and fuel consumption that shipping company and captain are requiring to evaluate for efficient voyage considering speed loss and power increase based on wave added resistance of ship. In the view point of safety, seakeeping prediction is performed based on 3 dimensional panel method. Finally, It is assistance measure for ship's optimum navigation route safety planning & assessment.

• Tribology and Lubricants
• /
• v.35 no.5
• /
• pp.267-273
• /
• 2019

Wheel bearings are important automotive parts that bear the vehicle weight and translate rotation motion; in addition, their seals are components that prevent grease leakage and foreign material from entering from the outside of the bearings. Recently, as the need for electric vehicles and eco-friendly vehicles has been emerging, the reduction in fuel consumption and $CO_2$ emissions are becoming the most important issues for automobile manufacturers. In the case of wheel bearings, seals are a key part of drag torque. In this study, we investigate the prediction of the drag torque taking into consideration the hyperelastic and viscoelastic material properties of automotive wheel bearing seals. Numerical analysis based on the finite element method is conducted for the deformation analyses of the seals. To improve the reliability of the rubber seal analysis, three types of rubber material properties are considered, and analysis is conducted using the hyperelastic material properties. Viscoelastic material property tests are also conducted. Deformation analysis considering the hyperelastic and viscoelastic material properties is performed, and the effects of the viscoelastic material properties are compared with the results obtained by the consideration of the hyperelastic material properties. As a result of these analyses, the drag torque is 0.29 Nm when the hyperelastic characteristics are taken into account, and the drag torque is 0.27 Nm when both the hyperelastic and viscoelastic characteristics are taken into account. Therefore, it is determined that the analysis considering both hyperelastic and viscoelastic characteristics must be performed because of its reliability in predicting the drag torque of the rubber seals.

• Communications for Statistical Applications and Methods
• /
• v.30 no.2
• /
• pp.119-133
• /
• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• KIPS Transactions on Software and Data Engineering
• /
• v.7 no.4
• /
• pp.129-134
• /
• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2004.11a
• /
• pp.61-70
• /
• 2004

This paper treats optimal route assessment system at seaway based on weather forecasting and wave measurement through observation. Since early times. captain & officer have been sailing to select the optimum route considering the weather ana ship status condition empirically. However. it is rare to find digitalized onboard route support system whereas weather fax or wave and swell chart are utilized for the officer. based on officer's experience. In this paper, optimal route assessment system which is composed of voyage efficiency and safety component is introduced. Optimum route minimized ETA (estimated time of arrival) ana fuel consumption is evaluated for efficient voyage considering speed loss and power increase based on wave added resistance of ship. In the view point of safety, seakeeping prediction is performed based on 3 dimensional panel method. Basically. the weather forecast is assumed to be prepared previously in order to operate this system.

• Journal of Navigation and Port Research
• /
• v.28 no.10 s.96
• /
• pp.833-841
• /
• 2004

This paper treats optimal route assessment system at seaway based on weather forecasting and wave measurement through observation Since early times, captain & officer have been sailing to select the optimum route considering the weather and ship status condition empirically. However, it is rare to find digitalized onboard route support system whereas weather fax or wave and swell chart are utilized for the officer, based on officer's experience. In this paper, optimal route assessment system which is composed of voyage efficiency and safety component is introduced. Optimum route minimized ETA(estimated time of arrival) and fuel consumption is evaluated for efficient voyage considering speed loss and power increase based on wave added resistance of ship. In the view point of safety, seakeeping prediction is performed based on 3 dimensional panel method. Basically, the weather forecast is assumed to be prepared previously in order to operate this system.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.41 no.7
• /
• pp.629-634
• /
• 2017

In the case of the UHP (Ultra high performance) tire that the demand has increased rapidly, compared with the commonly used tire, severe deformation has been observed because of the low aspect ratio. When repeated deformations are applied to the sidewall rubber, accumulated fatigue damage may cause fatigue failure. Thus, the evaluation of the durability of the tire sidewall rubber has become a very important issue to prevent accidents that occur while the vehicle is running. However, the research and design criteria for the durability performance of the tire sidewall rubber hardly exist. In this study, we suggest a lifetime prediction formula using strain energy density obtained by performing tensile tests and fatigue tests on two different kinds of the tire sidewall compounds. Additionally, the applicability of our findings for low fuel consumption tires was reviewed by converting the fatigue life of the sidewall rubber into the expected mileage of the tire.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.14 no.2
• /
• pp.14-18
• /
• 2015

In recent years, world is faced with a transportation energy dilemma, and the transportation is almost dependent on a single fuel - petroleum. However, Hybrid Electric Vehicle (HEV) technology holds more advantages to reduce the demand for petroleum in the transportation by efficiency improvements of petroleum consumption. Therefore, there is a trend that lower gear noise levels are demanded in HEV for drivers to avoid annoyance and fatigue during operation. And meshing transmission error (T.E.) is the excitation that leads to the tonal noise known as gear whine, and radiated gear whine is also the dominant source of noise in the whole gearbox. In this paper, the analysis of gear tooth profile and lead modification is firstly presented, and then, the different transmission error of no mesh misalignment and mesh misalignment under one loaded torque for the 1st gear pair of HEV gearbox was investigated and compared. At last, the appropriate tooth modification was used to minimize and compare the transmission error of the gear pair with mesh misalignment under the loaded torque.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.10 no.1
• /
• pp.47-54
• /
• 2015

Recently, rapid urbanization and industrialization had increased the air pollution in major cities by increasing the fuel consumption. Air pollution models have been widely used for air quality management in many countries. Also, a lot of related studies have been conducted using air dispersion models. In this study, The meteorological preprocessing program was developed to convert the korea meteorological data to the U.S. meteorological data and to expand the usability of air dispersion models of U.S. EPA. In addition, the usability evaluation was carried out through a case study. In the results of the evaluation of the program, this program was accurately convert the Korea meteorological data to the U.S. meteorological data, and the prediction was carried out without a error in air quality modeling. Therefore, the program showed a high utilization as meteorological data pre-processing tool.