• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.658-665
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• 2013

While the chirp signal is extensively used in radar and sonar systems for target decision in wireless communication systems, it has not been widely used for positioning in indoor environments. Recently, the IEEE 802.15.4a standard has adopted the chirp spread spectrum (CSS) as an underlying technique for low-power and low-complexity precise localization. Chirp signal based ranging solutions have been established and deployed but their ranging performance has not been analyzed in multipath environments. This paper presents a ranging performance analysis of a chirp signal and suggests a method to suppress multipath error by using a type of non-linear chirp signal. Multipath ranging performance is evaluated using a conventional linear chirp signal and the proposed non-linear chirp signal. We verify the feasibility of both methods using two-ray multipath model simulation. Our results demonstrate that the proposed non-linear chirp signal can successfully suppress the multipath error.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.5
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• pp.140-150
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• 2003

Observational methods such as the Asaoka's method and the hyperbolic method are widely applied on the settlement analysis using observed settlement. The most unreliable aspects in those methods is arose from the subjective discretion of initial non-linearity on linear regression. The initial non-linearity is inevitable due to the settlement behaviour itself. Therefore an objective method is essential to achieve more reliable results on settlement analysis. It was found that the initial non-linear data are statistical outliers. New automation algorithms of the hyperbolic and the Asaoka's method were developed based on outlier detection method. The methods are a successive detection of outliers and a searching method of suitable hyperbolic range for the Asaoka's and the hyperbolic method respectively. Applicability of the algorithms was verified through case studies.

• Biomaterials and Biomechanics in Bioengineering
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• v.2 no.4
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• pp.207-223
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• 2015

In this paper a method for simultaneous swift non-linear analysis and optimal design/posture of mechanical/biomechanical systems is presented. The method is developed to get advantages of iterations in non-linear analysis and/or generations in genetic algorithm (GA) for the purpose of efficient analysis within the optimal design/posture. The method is applicable for both size and geometry optimizations wherein material and geometry non-linearity are present. In addition to established mechanical systems, the method can solve biomechanical models of human musculoskeletal system. Optimization-based procedures are popular methods for resolving the redundancy at joints wherein the number of unknown muscle forces is far more than the number of equilibrium equations. These procedures involve optimization of a cost function(s) which is assumed to be consistent with the central nervous system's strategy when activating muscles to assure equilibrium. However, because of the complexity of biomechanical problems (i.e., due to non-linear biomaterial, large deformation, redundancy of the problem and so on) efficient analysis are required within optimization procedures as suggested in this paper.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.12 s.189
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• pp.72-79
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• 2006

Vibration of a non-linear system under random parametric excitations was evaluated by probabilistic methods. The non-linear characteristic terms of a system structure were quasi-linearized and excitation terms were remained as they were An analytical method where the square mean of error was minimized was used An alternative method was an energy method where the damping energy and restoring energy of the linearized system were equalized to those of the original non-linear system. The numerical results were compared with those obtained by Monte Carlo simulation. The comparison showed the results obtained by Monte Carlo simulation located between those by the analytical method and those by the energy method.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.785-790
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• 2008

Vibration of a non-linear system under random parametric excitations was evaluated by probablistic methods. The non-linear characteristic terms of a system were quasi-linearized and excitation terms were remained as they were given. An analytical method where the square mean of error was minimized was ysed. An alternative method was an energy method where the damping energy and rstoring energy of the linearized system were equalized to those of the original non-linear system. The numerical results were compared with those obtained by Monte Carlo simulation. The comparison showed the results obtained by Monte Carlo simulation located between those by the analytical method and those by the energy method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.113-114
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• 2006

Vibration of a non-linear system under random excitations was evaluated by probabilistic methods. The non-linear characteristic terms of a system structure were quasi-linearized and excitation terms were remained as they were. An analytical method where the square mean of error was minimized was used. An alternative method was an energy method where the damping energy and restoring energy of the linearized system were equalized to those of the original non-linear system. The numerical results were compared with those obtained by Monte Carlo simulation. The comparison showed the results obtained by Monte Carlo simulation located between those by the analytical method and those by the energy method.

• Structural Engineering and Mechanics
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• v.54 no.3
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• pp.597-605
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• 2015

In this study, linear vibrations of an axially moving beam under non-ideal support conditions have been investigated. The main difference of this study from the other studies; the non-ideal clamped support allow minimal rotations and non-ideal simple support carry moment in minimal orders. Axially moving Euler-Bernoulli beam has simple and clamped support conditions that are discussed as combination of ideal and non-ideal boundary with weighting factor (k). Equations of the motion and boundary conditions have been obtained using Hamilton's Principle. Method of Multiple Scales, a perturbation technique, has been employed for solving the linear equations of motion. Linear equations of motion are solved and effects of different parameters on natural frequencies are investigated.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.797-803
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• 2001

This paper obtained Bayes prediction density for the spatial linear model with non-informative prior. It showed the results that predictive inferences is completely unaffected by departures from the normality assumption in the direction of the elliptical family and the structure of prediction density is unchanged by more than one additional future observations.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.499-505
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• 2006

Regularlization approach to regression can be easily found in Statistics and Information Science literature. The technique of regularlization was introduced as a way of controlling the smoothness properties of regression function. In this paper, we have presented a new method to evaluate linear and non-linear fuzzy regression model based on Tanaka's model using the idea of regularlization technique. Especially this method is a very attractive approach to model non -linear fuzzy data.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.2
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• pp.71-83
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• 2013

Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.