• International journal of advanced smart convergence
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• v.8 no.3
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• pp.78-86
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• 2019

Recently, IoT researches using sensor data based on embedded networks in various fields including healthcare and sports have been continuously attempted. This study analyzes golf ball mobility to support IoT application in golf sports field. Generally, since the difference in density occurs due to the condition of the inner material and the abnormal state at the time of the outer skin joining during the manufacturing of the golf ball, the weight of each subset is equal for any two points with the same radius in the sphere cannot be guaranteed. For this reason, the deflected weight of the sphere has the undesirable effect of hitting the ball in a direction in which the weight of the ball is heavy. In this study, it is assumed that there is a unique center of gravity of the ball, and even if the golf ball cannot be manufactured perfectly, it wants to establish the basic principle to accurately recognize or mark the putting line based on the center of gravity. In addition, it is evaluated how the mobility of the golf ball with a deviation from the center of gravity of the golf ball affects the progress path (or movement direction) and the moving distance (or carry distance) after the golfer hits. The basic model of the mobility of the golf ball can help the golfer exercise model and the correlation analysis. The basic model of the mobility of the golf ball can help the golfer exercise model and the correlation analysis.

• Atmosphere
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• v.32 no.4
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• pp.263-276
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• 2022

The observation error of satellite radiation data that assimilated into the Korean Integrated Model (KIM) was diagnosed by applying the Hollingsworth and Lönnberg and Desrozier techniques commonly used. The magnitude and correlation of the observation error, and the degree of contribution for the satellite radiance data were calculated. The observation errors of the similar device, such as Advanced Technology Microwave Sounder (ATMS) and Advanced Microwave Sounding Unit-A shows different characteristics. The model resolution accounts for only 1% of the observation error, and seasonal variation is not significant factor, either. The observation error used in the KIM is amplified by 3-8 times compared to the diagnosed value or standard deviation of first-guess departures. The new inflation value was calculated based on the correlation between channels and the ratio of background error and observation error. As a result of performing the model sensitivity evaluation by applying the newly inflated observation error of ATMS, the error of temperature and water vapor analysis field were decreased. And temperature and water vapor forecast field have been significantly improved, so the accuracy of precipitation prediction has also been increased by 1.7% on average in Asia especially.

• Journal of the Korean Geosynthetics Society
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• v.21 no.1
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• pp.33-48
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• 2022

This paper presents illustrative examples of the application of advanced digital image correlation (DIC) technology in the geotechnical laboratory tests, such as shallow footing test, trapdoor test, retaining wall test, and wide width tensile test on geogrid. The theoretical background of the DIC technique is first introduced together with fundamental equations. Relevant reduced-scale model tests were then performed using standard sand while applying the DIC technique to capture the movement of target materials during tests. A number of different approaches were tried to obtain optimized images that allow efficient tracking of material speckles based on the DIC technique. In order to increase the trackability of soil particles, a mix of dyed and regular sand was used during the model tests while specially devised painted speckles were applied to the geogrid. A series of images taken during tests were automatically processed and analyzed using software named VIC-2D that automatically generates displacements and strains. The soil deformation field and associated failure patterns obtained from the DIC technique for each test were found to compare fairly well with the theoretical ones. Also shown is that the DIC technique can also general strains appropriate to the wide width tensile test on geogrid, It is demonstrated in this study that the advanced DIC technique can be effectively used in monitoring the deformation and strain field during a reduced-scale geotechnical model laboratory test.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.69-69
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• 2021

From the unprecedented 2011 spring flood, the residens reside by Lake Champlain and Richelieu River encountered enormous damages. The International Joint Committee (IJC) released the Lake Champlain-Richelieu River (LCRR) Plan of Study (PoS). One of the major tasks for the PoS is to investigate the possible scenarios that might happen in the LCRR basin based on the stochastic simulation of the Net Basin Supplies that calculates the amount of flow into the lake and the river. Therefore, the current study proposed a novel apporach that simulate the annual NBS teleconnecting the climate index. The proposed model employed the bivariate empirical decomposition to contamporaneously model the long-term evolution of nonstationary oscillation embeded in the annual NBS and the climate signal (here, Artic Oscillation: AO). In order to represent the variational behavior of NBS correlation structure along with the temporal revolution of the climate index, a new nonstationary parameterization concept is proposed. The results indicate that the proposed model is superior performance in preserving long and short temporal correlation. It can even preserve the hurst coefficient better than any other tested models.

• Journal of Mechanical Science and Technology
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• v.20 no.9
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• pp.1399-1409
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• 2006

An improved friction model was proposed with consideration of the effect of the sliding speed, the contact pressure and the temperature, and it was implemented into a user subroutine of a commercial FEM code, ABAQUS/Explicit. Then, a smooth tire was simulated for free rolling, driving, braking and cornering situations using the improved friction model and the Coulomb friction model, and the effect of the friction models on the slip, the frictional energy distribution and the cornering force and moment was analyzed. For the free rolling, the driving and the braking situations, the improved friction model and the Coulomb friction model resulted in similar profiles of the slip and the frictional energy distributions although the magnitudes were different. The slips obtained from the simulations were in a good correlation with experimental data. For the cornering situation, the Coulomb friction model with the coefficient of friction of 1 or 2 resulted in lower or higher cornering forces and moments than experimental data. In addition, in contrast to experimental data it did not result in a maximum cornering force and a decrease of the cornering moment for the increase of the speed. However, the improved friction model resulted in similar cornering forces and moments to experimental data, and it resulted in a maximum cornering force and a decrease of the cornering moment for the increase of the speed, showing a good correlation with experimental data.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.5
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• pp.421-430
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• 2017

In this study, a model was developed to predict for Disinfection By-Products (DBPs) generated in water supply networks and consumer premises, before and after the introduction of advanced water purification facilities. Based on two-way ANOVA, which was carried out to statistically verify the water quality difference in the water supply network according to introduce the advanced water treatment process. The water quality before and after advanced water purification was shown to have a statistically significant difference. A multiple regression model was developed to predict the concentration of DBPs in consumer premises before and after the introduction of advanced water purification facilities. The prediction model developed for the concentration of DBPs accurately simulated the actual measurements, as its coefficients of correlation with the actual measurements were all 0.88 or higher. In addition, the prediction for the period not used in the model development to verify the developed model also showed coefficients of correlation with the actual measurements of 0.96 or higher. As the prediction model developed in this study has an advantage in that the variables that compose the model are relatively simple when compared with those of models developed in previous studies, it is considered highly usable for further study and field application. The methodology proposed in this study and the study findings can be used to meet the level of consumer requirement related to DBPs and to analyze and set the service level when establishing a master plan for development of water supply, and a water supply facility asset management plan.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.281-296
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• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1295-1304
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• 2021

For the efficient use and management of water resources, a reliable rainfall-runoff analysis is necessary. Still, continuous hydrological data and rainfall-runoff data are insufficient to secure through measurements and models. In particular, as part of the reasonable improvement of a rainfall-runoff model in the case of an ungauged watershed, regionalization is being used to transfer the parameters necessary for the model application to the ungauged watershed. In this study, the GR4J model was selected, and the SCEM-UA method was used to optimize parameters. The rainfall-runoff model for the analysis of the correlation between watershed characteristics and parameters obtained through the model was regionalized by the Copula function, and rainfall-runoff analysis with the regionalized parameters was performed on the ungauged watershed. In the process, the intermediate state variables of the rainfall-runoff model were extracted, and the correlation analysis between water level and the ground water level was investigated. Furthermore, in the process of rainfall-runoff analysis, the Standardized State variable Drought Index (SSDI) was calculated by calculating and indexing the state variables of the GR4J model. and the calculated SSDI was compared with the standardized Precipitation index (SPI), and the hydrological suitability evaluation of the drought index was performed to confirm the possibility of drought monitoring and application in the ungauged watershed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.87-96
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• 2022

The traffic vulnerable, including elderly pedestrians, have a relatively low walking speed and slow cognitive response time due to reduced physical ability. Although a smart crossing system has been developed and operated to improve problem, it is difficult to operate a signal that reflects the appropriate walking speed for each pedestrian. In this study, a neural network model and a multiple regression model-based traversing speed estimation model were developed using image information collected in an area with a high percentage of traffic vulnerability. to support the provision of optimal walking signals according to real-time traffic weakness. actual traffic data collected from the urban traffic network of Paju-si, Gyeonggi-do were used. The performance of the model was evaluated through seven selected indicators, including correlation coefficient and mean absolute error. The multiple linear regression model had a correlation coefficient of 0.652 and 0.182; the neural network model had a correlation coefficient of 0.823 and 0.105. The neural network model showed higher predictive power.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.149-154
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• 2024

Research on particulate matter is advancing in real-time, and various methods are being studied to improve the accuracy of prediction models. Furthermore, studies that take into account various factors to understand the precise causes and impacts of particulate matter are actively being pursued. This paper trains an LSTM model using seasonal data and another LSTM model using concentration-based data. It compares and analyzes the PM_2.5 prediction performance of the two models. To train the model, weather data and air pollutant data were collected. The collected data was then used to confirm the correlation with PM_2.5. Based on the results of the correlation analysis, the data was structured for training and evaluation. The seasonal prediction model and the concentration-specific prediction model were designed using the LSTM algorithm. The performance of the prediction model was evaluated using accuracy, RMSE, and MAPE. As a result of the performance evaluation, the prediction model learned by concentration had an accuracy of 91.02% in the "bad" range of AQI. And overall, it performed better than the prediction model trained by season.