• Korean Journal of Applied Biomechanics
• /
• v.28 no.2
• /
• pp.127-134
• /
• 2018

Objective: In a statistical linear model estimating the center of rotation of a human hip joint, which is the parameter related to the mean of response vectors, assumptions of homoscedasticity and independence of position vectors measured repeatedly over time in the model result in an inefficient parameter. We, therefore, should take into account the variance-covariance structure of longitudinal responses. The purpose of this study was to estimate the efficient center of rotation vector of the hip joint by using covariance pattern models. Method: The covariance pattern models are used to model various kinds of covariance matrices of error vectors to take into account longitudinal data. The data acquired from functional motions to estimate hip joint center were applied to the models. Results: The results showed that the data were better fitted using various covariance pattern models than the general linear model assuming homoscedasticity and independence. Conclusion: The estimated joint centers of the covariance pattern models showed slight differences from those of the general linear model. The estimated standard errors of the joint center for covariance pattern models showed a large difference with those of the general linear model.

• Bulletin of the Korean Space Science Society
• /
• 2008.10a
• /
• pp.30.4-31
• /
• 2008

Formation of longitudinally wave-like plasma density structure in the low-latitude F region is now a well-known phenomenon from the extensive studies in recent years. Observations of plasma density from multiple satellites have shown that the locations of the crests of the plasma density that are seen to be stationary during daytime are shifted after sunset. This phenomenon has been understood to be caused by eastward drift of the ionosphere at night. However, the eastward drift velocity of the ionosphere after sunset is not sufficiently large enough to explain the day-night difference in the longitudinal density structure. The just after sunset and the nighttime ionospheric morphologymay be affected by this drift after sunset. In this study, we will investigate the temporal variation of the phase of the longitudinal density structure and vertical plasma drift by analyzing the ROCSAT-1, TIMED/GUVI, and DMSP data and verify the role of the vertical drift after sunset in the change of the phase of the longitudinal density structure.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.24 no.6
• /
• pp.802-813
• /
• 2000

This paper represents numerical computations of the interaction between the longitudinal vortex and a flat plate 3-D turbulent boundary layer. In the present study, the main interest is in the behavior of longitudinal vortices introduced in turbulent boundary layers. The flow field behind vortex generator is modeled by the information that is available from studies on the delta winglet. Also, the Reynolds-averaged Navier-Stoke equations for three-dimensional turbulent flows, together with a two-layer turbulence model to resolve the near-wall flow, is solved by the method of pseudo compressibility. The present results show that the boundary layer is thinned in the regions where the secondary flow is directed toward the wall and thickened where it is directed away from the wall, and have a good agreement with the experimental data.

• Journal of Auto-vehicle Safety Association
• /
• v.12 no.2
• /
• pp.14-20
• /
• 2020

This paper presents a vehicle mass and road grade estimator for developing an automated bus. To consider the dynamic characteristics of a bus varying with the number of passengers, the longitudinal controller needs the estimation of the vehicle's mass and road grade in real-time and utilizes the information to adjust the control gains. Discrete Kalman filter is applied to estimate the time-varying road grade, and the recursive least squares algorithm is adopted to account for the constant mass estimation. After being implemented in MATLAB/Simulink, the estimators are evaluated with the dynamic model and experimental data of the target bus. The proposed estimators will be applied to complement the algorithm of the longitudinal controller and proceed with algorithm verification.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.11 no.5
• /
• pp.561-569
• /
• 1999

The effects of longitudinal heat conduction on the performance of heat transfer surfaces are investigated by using a single-blow method. In the transient testing method for determining the heat transfer characteristics, exponential inlet temperature variations are made by using screen-mesh heater with small time constant and low frontal velocities of the test section, and the experimentally determined inlet temperature profile is used as the inlet fluid temperature condition. The effects of longitudinal heat conduction are negligible only if $\gamma^\act<0.05\;and \;N_{tu}\le3$ and should be considered if $N_{tu}\le3$ The test results ate compared with the existing theoretical and experimental data and the validity of this technique is confirmed by the good agreement.

• Journal of Korea Water Resources Association
• /
• v.48 no.4
• /
• pp.299-308
• /
• 2015

The aim of this study is that a theoretical formula for estimating the one-dimensional longitudinal dispersion coefficient is derived based on a transverse distribution equation for the depth averaged stream-wise velocity in open channel. In "Part I. Theoretical equation for stream-wise velocity" which is the former volume of this article, the velocity distribution equation is derived analytically based on the Shiono-Knight Method (SKM). And then incorporating the velocity distribution equation into a triple integral formula which was proposed by Fischer (1968), the one-dimensional longitudinal dispersion coefficient can be derived theoretically in "Part II. Longitudinal dispersion coefficient" which is the latter volume of this article. The proposed equations for the velocity distribution and the longitudinal dispersion coefficient are verified by using observed data set. As a result, the non-dimensional longitudinal dispersion coefficient is inversely proportional to square of the Manning's roughness coefficient and the non-dimensional transverse dispersion coefficient, and is directly proportional to square of the aspect ratio (channel width to depth).

• Journal of Auto-vehicle Safety Association
• /
• v.10 no.1
• /
• pp.38-44
• /
• 2018

This paper presents an analysis on driving safety in lane change situation based on road driving data. Autonomous driving is a global trend in vehicle industry. LKAS technologies are already applied in commercial vehicle and researches about lane change maneuver have been actively studied. In autonomous vehicle, not only safety control issue but also imitating human driving maneuver is important. Driving data analysis in lane change situation has been usually dealt with ego vehicle information such as longitudinal acceleration, yaw rate, and steering angle. For this reason, developing safety index according to surrounding vehicle information based on human driving data is needed. In this research, driving data is collected from perception module using LIDAR, radar and RT-GPS sensors. By analyzing human driving pattern in lane change maneuver, safety index that considers both ego vehicle and surrounding vehicle state by using relative velocity and longitudinal clearance has been designed.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.6
• /
• pp.1409-1419
• /
• 2013

We analyzed the data from KYPS using the latent growth model which has been widely studied as an analysis method of longitudinal data. In this study, we applied repeated measures ANOVA to unconditional model in order for faster decision of the unconditional model of the latent growth model. Also, we compared the six-type models, the quadratic model and the model of which repeated measures ANOVA is applied.

• Journal of the Korea Convergence Society
• /
• v.10 no.2
• /
• pp.217-225
• /
• 2019

The Latent Growth Modeling(LGM) is known as the typical analysis method of longitudinal data and it could be classified into unconditional model and conditional model. It is common to assume that the growth trajectory of unconditional model of LGM is linear. In the case of quasi-linear, the methodology for improving the model fitness using Sequential Pattern of Association Rule Mining is suggested. To do this, we divide longitudinal data into quintiles and extract periodic changes of the longitudinal data in each quintiles and make sequential pattern based on this periodic changes. To evaluate the effectiveness, the LGM module in SPSS AMOS was used and the dataset of the Youth Panel from 2001 to 2006 of Korea Employment Information Service. Our methodology was able to increase the fitness of the model compared to the simple linear growth trajectory.

• The Journal of Bigdata
• /
• v.5 no.1
• /
• pp.11-27
• /
• 2020

In this paper, we developed a big data platform to store, process, and analyze effectively on such education longitudinal study data. And it was applied to the Seoul Education Longitudinal Study(SELS) to confirm its usefulness. The developed platform consists of data preprocessing unit and data analysis unit. The data preprocessing unit 1) masking, 2) converts each item into a factor 3) normalizes / creates dummy variables 4) data derivation, and 5) data warehousing. The data analysis unit consists of OLAP and data mining(DM). In the multidimensional analysis, OLAP is performed after selecting a measure and designing a schema. The DM process involves variable selection, research model selection, data modification, parameter tuning, model training, model evaluation, and interpretation of the results. The data warehouse created through the preprocessing process on this platform can be shared by various researchers, and the continuous accumulation of data sets makes further analysis easier for subsequent researchers. In addition, policy-makers can access the SELS data warehouse directly and analyze it online through multi-dimensional analysis, enabling scientific decision making. To prove the usefulness of the developed platform, SELS data was built on the platform and OLAP and DM were performed by selecting the mathematics academic achievement as a measure, and various factors affecting the measurements were analyzed using DM techniques. This enabled us to quickly and effectively derive implications for data-based education policies.