• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.250-255
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• 2003

The position of a 3-dimensional(3D) point can be measured by using calibrated stereo camera. To obtain more accurate measurement ,more accurate camera calibration is required. There are many existing methods to calibrate camera. The simple linear methods are usually not accurate due to nonlinear lens distortion. The nonlinear methods are accurate more than linear method, but it increase computational cost and good initial guess is needed. The multi step methods need to know some camera parameters of used camera. Recent years, these explicit model based camera calibration work with the development of more precise camera models involving correction of lens distortion. But these explicit model based camera calibration have disadvantages. So implicit camera calibration methods have been derived. One of the popular implicit camera calibration method is to use neural network. In this paper, we propose implicit stereo camera calibration method for 3D reconstruction using support vector machine. SVM can learn the relationship between 3D coordinate and image coordinate, and it shows the robust property with the presence of noise and lens distortion, results of simulation are shown in section 4.

• Journal of the Ergonomics Society of Korea
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• v.23 no.2
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• pp.37-54
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• 2004

Fuzzy theory provides an intelligence treatment model for judgement about information when it needs a solution or a decision making about vague problems. Therefore, fuzzy theory is used for appropriate evaluation and decision on obscure information as human's emotion in human factors, In previous study, fuzzy membership function is defined for judgement infOlmation as human's emotion then ultimate results are deducted through fuzzy inference model. This method uses general CWTent through literature review or max, min and average as representative statics value about considering variables. But, this method makes away with nonlinear's or inegular's factors of human sensibility. Accordingly, application of this method leads to considerable loss of information in the ultimate evaluation. For that reason, this method has a limitation in objective evaluation of human factors. So, this study focuses on development of fuzzy membership function, which evaluates human's emotion or feeling accurately and objectively. We used the regression analysis and reasoned a fuzzy membership function about the relation of the variables. Then we verified the adequacy with the reliability through the experiment after this.

• Nuclear Engineering and Technology
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• v.42 no.2
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• pp.163-174
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• 2010

As pointed out in the OECD BEMUSE Program, when a high computation time is taken to obtain the relevant output values of a complex physical model (or code), the number of statistical samples that must be evaluated through it is a critical factor for the sampling-based uncertainty analysis. Two alternative methods have been utilized to avoid the problem associated with the size of these statistical samples: one is based on Wilks' formula, which is based on simple random sampling, and the other is based on the conventional nonlinear regression approach. While both approaches provide a useful means for drawing conclusions on the resultant uncertainty with a limited number of code runs, there are also some unique corresponding limitations. For example, a conclusion based on the Wilks' formula can be highly affected by the sampled values themselves, while the conventional regression approach requires an a priori estimate on the functional forms of a regression model. The main objective of this paper is to assess the feasibility of the ACE-RSM approach as a complementary method to the Wilks' formula and the conventional regression-based uncertainty analysis. This feasibility was assessed through a practical application of the ACE-RSM approach to the LOFT L2-5 LBLOCA PCT uncertainty analysis, which was implemented as a part of the OECD BEMUSE Phase III program.

• Journal of Korean Society of Transportation
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• v.31 no.1
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• pp.65-76
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• 2013

Traffic accidents at signalized intersections have been increased annually so that it is required to examine the causation to reduce the accidents. However, the current existing accident models were developed mainly by using non-linear regression models such as Poisson methods. These non-linear regression methods lack to reveal the complicated causation for traffic accidents, though they are the right choice to study randomness and non-linearity of accidents. Therefore, it is required to utilize another statistical method to make up for the lack of the non-linear regression methods. This study developed accident prediction models for 4 legged signalized intersections with Poisson methods and compared them with structural equation models. This study used structural equation methods to reveal the complicated causation of traffic accidents, because the structural equation method has merits to explain more causational factors for accidents than others.

• Computational Structural Engineering : An International Journal
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• v.1 no.2
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• pp.117-130
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• 2001

Investigations of low-rice unbounded reinforced concrete shear walls with or without openings are performed with comparison of analytical and experimental results. Theoretical analysis is based on nonlinear finite element algorithm, which incorporates concrete failure criterion and nonlinear constitutive relationships. Studios focus on the effects of height-to-length ratio of shear walls, opening ratio, horizontal and vertical reinforcement radios, and diagonal reinforcement. Analytical solutions conform well with experimental results. Equations for cracking, yielding and ultimate loads with corresponding lateral displacements are derived by regression using analytical results and experimental data. Also, failure modes of low-rise unbounded shear walls are theoretically investigated. An explanation of change in failure mode is ascertained by comparing analytical results and ACI code equations. Shear-flexural failure can be obtained with additional flexural reinforcement to increase a wall's capacity. This concept leads to a design method of reducing flexural reinforcement in low-rise bounded solid shear wall's. Avoidance of shear failure as well as less reinforcement congestion leer these walls is expected.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.6
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• pp.848-859
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• 2004

Mechanical testing methods to determine the material constants for large deformation nonlinear finite element analysis were demonstrated for natural rubber. Uniaxial tension, uniaxial compression, equi-biaxial tension and pure shear tests of rubber specimens are performed to achieve the stress-strain curves. The stress-strain curves are obtained after between 5 and 10 cycles to consider the Mullins effect. Mooney and Ogden strain-energy density functions, which are typical form of the hyperelastic material, are determined and compared with each other. The material constants using only uniaxial tension data are about 20% higher than those obtained by any other test data set. The experimental equations of shear elastic modulus on the hardness and maximum strain are presented using multiple regression method. Large deformation finite element analysis of automotive transmission mount using different material constants is performed and the load-displacement curves are compared with experiments. The selection of material constant in large deformation finite element analysis depend on the strain level of component in service.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.831-839
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• 2010

Least squares support vector machine (LS-SVM) is a kernel trick gaining a lot of popularities in the regression and classification problems. We use LS-SVM to propose a iterative algorithm for a nonlinear generalized autoregressive conditional heteroscedasticity model in the mean (GARCH-M) model to estimate the mean and the conditional volatility of stock market returns. The proposed method combines a weighted LS-SVM for the mean and unweighted LS-SVM for the conditional volatility. In this paper, we show that nonlinear GARCH-M models have a higher performance than the linear GARCH model and the linear GARCH-M model via real data estimations.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1178-1183
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• 2006

This paper is concerned with the modeling and identification of pH neutralization process as nonlinear chemical system. The pH control has been applied to various chemical processes such as wastewater treatment, chemical, and biochemical industries. But the control of the pH is very difficult due to its highly nonlinear nature which is the titration curve with the steepest slope at the neutralization point. We apply SVM which have become an increasingly popular tool for machine teaming tasks such as classification, regression or detection to model pH process which has strong nonlinearities. Linear and radial basis function kernels are employed and each result has been compared. So SVH based on kernel method have been found to work well. Simulations have shown that the SVM based on the kernel substitution including linear and radial basis function kernel provides a promising alternative to model strong nonlinearities of the pH neutralization but also to control the system.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.5
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• pp.387-392
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• 2017

There is a constant demand for hull variation related to ship design. Various input variables are generally given to achieve the objective functions assigned by each variation process. When dealing with geometric shapes accompanied by nonlinear operations during the variation process, vague relationships or uncertainties among input variables are commonly observed. Therefore, it is strongly recommended to identify those uncertainty factors in advance. A method to modify the shape of a closed hull form with a new area and a centroid had been introduced as a new process of hull variation. Since uncertainty between input variables still existed in the method, however, it was not easy for the user to enter the area and the corresponding centroid. To overcome this problem, a method is presented in this paper to provide the feasible region of centroids for a given area. By utilizing the concept and techniques used in the statistics such as the number of samples, probability, margin error, and level of confidence, this method generates the distribution of possible centroids along the regression curve. The result shows that the method helps the user to choose an appropriate input value following his or her design intention.

• Journal of Biosystems Engineering
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• v.32 no.6
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• pp.403-407
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• 2007

This study was performed to determine desorption equilibrium moisture contents(EMC) of rapeseed grown in Korea. EMC values were measured by static method using saturated salt solutions at three temperature levels of 30, 40 and $50^{\circ}C$, and eight relative humidity levels in the range from 11.0 to 83.6%. The measured EMC values were fitted to Chung-Pfost, Modified Halsey, Modified Henderson and Modified Oswin models by using nonlinear regression analysis. The results of root mean square errors for four models showed that Halsey and Modified Oswin Models could serve as good models, but the Chung-Pfost and Modified Henderson models could not show reasonably good fitting.