• Transactions of the Korean Society of Mechanical Engineers
• /
• v.18 no.10
• /
• pp.2640-2652
• /
• 1994

The stiffness of 6-node isotropic element is stiffer than that of 8-node isotropic element of same configuration. This phenomenon was called 'Relative Stiffness Stiffening Phenomenon'. In this paper, an equation of sampling point modification which correct this phenomenon was derived for the composite plate, as well as an equation for an isotropic plate. The relative stiffness stiffening phenomena of an isotropic plate element could be corrected by modifying Gauss sampling points in the numerical integration of stiffness matrix. This technique could also be successfully applied to the static analyses of composite plate modeled by the 3-dimensional 16-node elements. We predicted theoretical errors of stiffness versus the number of layers that result from the reduction of numerical integration order. These errors coincide very well with the actual errors of stiffness. Therefore, we can choose full integration of reduced integration based upon the permissible error criterion and the number of layers by using the thoretically predicted error.

• Journal of Ocean Engineering and Technology
• /
• v.38 no.3
• /
• pp.103-114
• /
• 2024

Remote sensing wave observation data are crucial when analyzing ocean waves, the main external force of coastal disasters. Nevertheless, it has limitations in accuracy when used in low-wind environments. Therefore, this study collected the raw data from MIROS Wave and Current Radar (MWR) and wave radar at the Ieodo Ocean Research Station (IORS) and applied the optimal filter by combining filters provided by MIROS software. The data were validated by a comparison with South Jeju ocean buoy data. The results showed it maintained accuracy for significant wave height, but errors were observed in significant wave periods and extreme waves. Hence, this study used an artificial neural network (ANN) to improve these errors. The ANN was generalized by separating the data into training and test datasets through stratified sampling, and the optimal model structure was derived by adjusting the hyperparameters. The application of ANN effectively improved the accuracy in significant wave periods and high wave conditions. Consequently, this study reproduced past wave data by enhancing the reliability of the MWR, contributing to understanding wave generation and propagation in storm conditions, and improving the accuracy of wave prediction. On the other hand, errors persisted under high wave conditions because of wave shadow effects, necessitating more data collection and future research.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.2
• /
• pp.600-619
• /
• 2015

We propose a cost-efficient hybrid algorithm for online location updates that efficiently combines feature point detection with the online trajectory-based sampling algorithm. Our algorithm is designed to minimize the average trajectory error with the minimal number of sample points. The algorithm is composed of 3 steps. First, we choose corner points from the map as sample points because they will most likely cause fewer trajectory errors. By employing the online trajectory sampling algorithm as the second step, our algorithm detects several missing and important sample points to prevent unwanted trajectory errors. The final step improves cost efficiency by eliminating redundant sample points on straight paths. We evaluate the proposed algorithm with real GPS trajectory data for various bus routes and compare our algorithm with the existing one. Simulation results show that our algorithm decreases the average trajectory error 28% compared to the existing one. In terms of cost efficiency, simulation results show that our algorithm is 29% more cost efficient than the existing one with real GPS trajectory data.

• Nuclear Engineering and Technology
• /
• v.31 no.2
• /
• pp.192-201
• /
• 1999

This paper proposes an improved signal processing strategy for accurate feedwater flowrate estimation in nuclear power plants. It is generally known that ∼2% thermal power errors occur due to fouling Phenomena in feedwater flowmeters. In the strategy Proposed, the noises included in feedwater flowrate signal are classified into rapidly varying noises and gradually varying noises according to the characteristics in a frequency domain. The estimation precision is enhanced by introducing a low pass filter with the wavelet analysis against rapidly varying noises, and an autoassociative neural network which takes charge of the correction of only gradually varying noises. The modified multivariate stratification sampling using the concept of time stratification and MAXIMIN criteria is developed to overcome the shortcoming of a general random sampling. In addition the multi-stage robust training method is developed to increase the quality and reliability of training signals. Some validations using the simulated data from a micro-simulator were carried out. In the validation tests, the proposed methodology removed both rapidly varying noises and gradually varying noises respectively in each de-noising step, and 5.54% root mean square errors of initial noisy signals were decreased to 0.674% after de-noising. These results indicate that it is possible to estimate the reactor thermal power more elaborately by adopting this strategy.

• The Journal of the Acoustical Society of Korea
• /
• v.16 no.5
• /
• pp.26-35
• /
• 1997

This paper deals with establishing correspondent points of feature pints and sampling period when we estimate object motion parameters from image information of freely moving objects in space of gravity-free state. Replacing the inertial coordinate system with the camera coordinate system which is equipped within a space robot, it is investigated to be able to analyze a problem of correspond points from image information, and to obtain sequence of angular velocity $\omega$ which determine a motion of object by means of computer simulation. And if a sampling period ${\Delta}t$ is shortened, the relative errors of angular velocity are increased because the relative errors against moving distance of feature points are increased by quantization. In reverse, if a sampling period ${\Delta}t$ is lengthened too much, the relative error are likewise increased because a sampling period is long for angular velocity to be approximated, and we confirmed the precision that grows according to ascending of resolution.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.43 no.12 s.354
• /
• pp.61-67
• /
• 2006

This paper proposes the algorithm for tracking of the residual phase errors incurred by carrier frequency offset and sampling frequency offset in the orthogonal frequency division multiplexing (OFDM) systems which are suitable for high data rate wireless communications. In the OFDM systems the subcarriers which are orthogonal to each other are modulated by digital data and transmitted simultaneously. The carrier frequency offset causes degradation of signal to noise ratio(SNR) performance and interference between the adjacent subcarriers. The errors in the sampling timing caused by the sampling frequency difference between the transmitter and the receiver sides also cause a major performance degradation in the OFDM systems. The residual error tracking and compensation mechanism is essential in the OFDM system since the carrier and the sampling frequency offset cause the loss of orthogonality resulting in the system performance loss. This paper proposes the scheme where the channel gain and the payload data information are reflected in the residual error tracking process which results in the reduction of the estimation error and the tracking performance improvements under the frequency selective fading wireless channels.

• Proceedings of the Optical Society of Korea Conference
• /
• 2005.02a
• /
• pp.346-347
• /
• 2005

• Journal of Preventive Medicine and Public Health
• /
• v.46 no.2
• /
• pp.96-104
• /
• 2013

Objectives: The inherent nature of the Korean National Health and Nutrition Examination Survey (KNHANES) design requires special analysis by incorporating sample weights, stratification, and clustering not used in ordinary statistical procedures. Methods: This study investigated the proportion of research papers that have used an appropriate statistical methodology out of the research papers analyzing the KNHANES cited in the PubMed online system from 2007 to 2012. We also compared differences in mean and regression estimates between the ordinary statistical data analyses without sampling weight and design-based data analyses using the KNHANES 2008 to 2010. Results: Of the 247 research articles cited in PubMed, only 19.8% of all articles used survey design analysis, compared with 80.2% of articles that used ordinary statistical analysis, treating KNHANES data as if it were collected using a simple random sampling method. Means and standard errors differed between the ordinary statistical data analyses and design-based analyses, and the standard errors in the design-based analyses tended to be larger than those in the ordinary statistical data analyses. Conclusions: Ignoring complex survey design can result in biased estimates and overstated significance levels. Sample weights, stratification, and clustering of the design must be incorporated into analyses to ensure the development of appropriate estimates and standard errors of these estimates.

• The KIPS Transactions:PartD
• /
• v.11D no.6
• /
• pp.1269-1276
• /
• 2004

The Parameter Estimation for software existing reliability models, Goel-Okumoto, Yamada-Ohba-Osaki model was reviewed and Rayleigh model based on Rayleigh distribution was studied. In this paper, we discusses comparison of parameter estimation using maximum likelihood estimator and Bayesian estimation based on Gibbs sampling to analysis of the estimator' pattern. Model selection based on sum of the squared errors and Braun statistic, for the sake of efficient model, was employed. A numerical example was illustrated using real data. The current areas and models of Superposition, mixture for future development are also employed.

• Journal of the Korean Statistical Society
• /
• v.13 no.2
• /
• pp.107-113
• /
• 1984

In a linear regression model the idependent variables are frequently subject to measurement errors. For this case, the problem of estimating unknown parameters has been extensively discussed in the literature while very few has been concerned with the effect of measurement errors on prediction. This paper investigates the behavior of the predicted values of the dependent variable in terms of the average mean square error of prediction (AMSEP). AMSEP may be used as a criterion for selecting an appropriate estimation method, for designing an estimation experiment, and for developing cost-effective future sampling schemes.