• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.10a
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• pp.385-390
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• 1997

In recent years there has been an increasing emphasis on shortening design cycles for bringing products to market. This requires the development of computer aided engineering tools which allow analysts to quickly evaluate the effect of design changes on noise, vibration, and harshness. Statistical Energy Analysis (SEA) modeling is a valuable tool for predicting noise and vibration as SEA models are inherently simpler and more robust than deterministic models. SEA modeling can be combined with design sensitivity analysis (DSA) to identify design changes which give the largest performance benefit. This paper describes SEA modeling of an equipment cab. SEA predictions are compared to test data, showing good agreement. The use of design sensitivity analysis in improving cab design is then demonstrated.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.875-879
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• 2007

The effects of the statistical properties of the Coulomb friction coefficients on the dynamic responses of a galloping quadruped robot are investigated in this paper. In general, the Coulomb friction coefficients are assumed to be deterministic for a controller design to achieve required motion characteristics. However, the friction coefficients between the ground and the robot legs are not constant in reality. Therefore, statistical characteristics of the friction coefficients need to be considered for a multi-body modeling of the robot galloping on the ground. The effects of the statistical properties on the dynamic responses of the quadruped robots are investigated.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.87-96
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• 2000

Nonlinear time series prediction is derived and compared between statistic of modeling and neural network method. In particular mean squared errors of predication are obtained in generalized random coefficient model and generalized autoregressive conditional heteroscedastic model and compared with them by neural network forecasting.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.775-781
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• 2008

It is developed that a family of the semicircular Laplace distributions for modeling semicircular data by simple projection method. Mathematically it is simple to simulate observations from a semicircular Laplace distribution. We extend it to the l-axial Laplace distribution by a simple transformation for modeling any arc of arbitrary length. Similarly we develop the l-axial log-Laplace distribution based on the log-Laplace distribution. A bivariate version of l-axial Laplace distribution is also developed.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.85-93
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• 1995

Hannan and Rissanen proposed an innovation regression method of ARMA modeling, which is composed of three stages. Its second-stage is to choose orders of the ARMA model using the BIC, which needs a lot of calculation to estimate several regression models. We are going to present a simple and efficient algorithm for the second stage using a special property of triangular Toeplitz matrices.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.2
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• pp.107-111
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• 2003

In the power-line communication systems, there are many factors of noise and attenuation in the power-line channels, because they were designed for not the communication but the power transmission. Also the transfer function of the channels is highly changed with the topology and the load of the power-lines. To cope with these poor channel situation, channel modeling, one of the many studies in progress, is being studied hard. Channel modeling is essential to apply the active schemes to overcome the bad channel(e.g. modulation technique, channel coding, signal coupling & filtering, etc.) to the power-line communications. In this paper, we have realized the statistical model(this model is suggested as the channel modeling method for the power-line channels) that is combined the transfer function with the various noises. And we have compared and examined the results with the measured data. Also we have studied the plan which can effectively establish the channel data base for the channel information consisted of the parameters that are derived from this modeling, and we have studied the plan to utilize the data base.

• Composite Materials and Engineering
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• v.3 no.3
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• pp.221-239
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• 2021

This paper presents a multiscale modeling method for sheet molding compound (SMC) composites through a novel bundle packing reconstruction algorithm based on a micro-CT (Computed Tomography) image processing. Due to the complex flow pattern during the compression molding process, the SMC composites show a spatially varying orientation and overlapping of fiber bundles. Therefore, significant inhomogeneity and anisotropy are commonly observed and pose a tremendous challenge to predicting SMC composites' properties. For high-fidelity modeling of the SMC composites, the statistical distributions for the fiber orientation and local volume fraction are characterized from micro-CT images of real SMC composites. After that, a novel bundle packing reconstruction algorithm for a high-fidelity SMC model is proposed by considering the statistical distributions. A method for evaluating specimen level's strength and stiffness is also proposed from a set of high-fidelity SMC models. Finally, the proposed multiscale modeling methodology is experimentally validated through a tensile test.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.4
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• pp.284-290
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• 2015

Road traffic noise is closely related with urban forms and urban components, such as population, building, traffic and land-use, etc. Hence, it is possible to minimize the noise exposure problem depending on how to plan new town or urban planning alteration. This paper provides ways to apply for urban planning in consideration of noise exposure through road traffic noise estimation for alteration of small-scale urban form. Spatial autoregressive model from the former study is used as statistical model for noise simulation. The simulation results by the spatial statistical model are compared with those by the engineering program-based modeling for 5 scenarios of small-scale urban form alteration. The error from the limitation of containing informations inside the grid cell and the difficulties of reflecting acoustic phenomena exists. Nevertheless, in the stage of preliminary design, the use of the statistical models that have been estimated well could be useful in time and economically.

• Journal of Information Technology Services
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• v.10 no.2
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• pp.203-221
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• 2011

The purpose of this study is to investigate the applications of Structural Equation Modeling(SEM) into MIS area in recent years. Two methodologies, Lisrel and PLS, are adopted for the method comparison. A research model, based upon TAM(Technology Acceptance Model) is used for the analysis of the data set of a previous study. The research model includes six research variables that are composed of twenty-eight question items. 272 data are used for data analyses through Lisrel v.8.72 and Visual PLS v.1.04. This study shows the statistical results of Lisrel are the same to those of PLS. The contribution of this study can be suggested as the followings; (1) A theoretical comparison of two methodologies is shown, (2) A statistical analysis is done at a real-situated data set, and (3) Several implications are suggested.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.22 no.9
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• pp.741-746
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• 2009

Compare to conventional Indium Tin Oxide (ITO) film deposition methods, cesium assisted sputtering method has been shown superior electrical, mechanical, and optical film properties. However, it is not easy to use cesium assisted sputtering method since ITO film properties are very sensitive to Cesium assisted equipment condition but their mechanism is not yet clearly defined physically or mathematically. Therefore, to optimize deposited ITO film characteristics, development of accurate and reliable process model is essential. For this, in this work, we developed ITO film deposition process model using neural networks and design of experiment (DOE). Developed model prediction results are compared with conventional statistical regression model and developed neural process model has been shown superior prediction results on modeling of ITO film thickness, sheet resistance, and transmittance characteristics.