• Proceedings of the KSME Conference
• /
• 2008.11a
• /
• pp.1063-1068
• /
• 2008

A lens system of a camera module for mobile phones is comprised of the composition and design of various shapes of lens. To improve responses such as the modular transfer function (MTF), a lens system should always be constructed by considering uncertainty that can be caused by manufacturing and assembly error. In this study, tolerance optimization using the Latin Hypercube Sampling (LHS) technique is performed. In order to reduce the computational burden of the tolerance optimization process and decrease the influence from numerical noise effectively, we use the Progressive Quadratic Response Surface Modeling (PQRSM), which is one of Sequential Approximate Optimization (SAO) techniques. Using this method, we achieved optimal tolerance for each lens and obtained reliability for satisfying user‘s requirements. In addition, through the design process the manufacturing and assembly cost of a lens system was reduced.

• Journal of Communications and Networks
• /
• v.14 no.3
• /
• pp.225-229
• /
• 2012

We examine spectrum sensing in a situation of uncertain channel model. In particular, we assume that, besides additive noise, the observed signal contains an interference term whose probability distribution is unknown, and only its range and maximum power are known. We discuss the evaluation of the detector performance and its design in this situation. Although this paper specifically deals with the design of spectrum sensors, its scope is wider, as the applicability of its results extends to a general class of problems that may arise in the design of receivers whenever there is uncertainty about how to model the environment in which one is expected to operate. The theory expounded here allows one to determine the performance of a receiver, by combining the available (objective) probabilistic information with (subjective) information describing the designer's attitude.

• Transactions of The Korea Fluid Power Systems Society
• /
• v.5 no.1
• /
• pp.1-5
• /
• 2008

Pneumatic driving systems have hard non-linear characteristic and large friction force compared with driving power. Hence, it cannot be robust against parameter uncertainties, modelling error, disturbance and noise. In this study, we apply a mixed $H_2/H_{\infty}$ control to the generalized plant for a pneumatic driving apparatus system including parameter uncertainty and disturbance. In order to design the $H_2/H_{\infty}$ controller, we use the LMI technique. To evaluate control performance and robust stability of the designed controller, we compare it with a conventional controller such as PVA(Position-Velocity-Acceleration state controller) using the simulation results. As a result, it can be known that designed controller shows better robust stability than the conventional controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.11a
• /
• pp.228-233
• /
• 1997

An efficient corner matching algorithm is developed to minimize the amount of calculation. To reduce the amount of calculation, all available information from a corner detector is used to make model. This information has uncertainties due to discretization noise and geometric distortion, and this is represented by fuzzy rule base which can represent and handle the uncertainties. Form fuzzy inference procedure, a matched segment list is extracted, and resulted segment list is used to calculate the transformation between object of model and scene. To reduce the false hypotheses, a vote and re-vote method is developed. Also an auto tuning scheme of the fuzzy rule base is developed to find out the uncertainties of features from recognized results automatically. To show the effectiveness of the developed algorithm, experiments are conducted for images of real electronic components.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.05a
• /
• pp.144-147
• /
• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.14 no.1
• /
• pp.41-48
• /
• 2014

This paper analyzes the Monte Carlo localization (MCL) method, which estimates the pose of an indoor mobile robot. A mobile robot must know where it is to navigate in an indoor environment. The MCL technique is one of the most influential and popular techniques for estimation of robot position and orientation using a particle filter. For the analysis, we perform experiments in an indoor environment with a differential drive robot and ultrasonic range sensor system. The analysis uses MATLAB for implementation of the MCL and investigates the effects of the control parameters on the MCL performance. The control parameters are the uncertainty of the motion model of the mobile robot and the noise level of the measurement model of the range sensor.

• Smart Structures and Systems
• /
• v.15 no.3
• /
• pp.735-749
• /
• 2015

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which nms multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 1999.04a
• /
• pp.169-177
• /
• 1999

The base shear force and the overturning moment are important factors for the earthquake design of a structure. These should be predicted exactly especially when the nonlinear seismic isolation bearings are used against earthquake motions. Generally these are derived by the acceleration responses of a structure with the he assumed masses. However these can be contaminated by the noise in the measured responses and the uncertainty of assumed masses. This paper presents the results of the derived base shear force and overturning moment compared with the measured results by multi-axis load cells. Also discussions are made on the cross-coupling effects of the multi-axis load cell.

• International journal of advanced smart convergence
• /
• v.7 no.1
• /
• pp.15-23
• /
• 2018

Spectrum allocation is a key operation in cognitive radio networks (CRNs), where secondary users (SUs) are usually selfish - to achieve itself utility maximization. In view of this context, much prior lit literature proposed spectrum allocation base on non-cooperative game models. However, the most of them proposed non-cooperative game models based on complete information of CRNs. In practical, primary users (PUs) in a dynamic wireless environment with noise uncertainty, shadowing, and fading is difficult to attain a complete information about them. In this paper, we propose a non-cooperative game joint hidden markov model scheme for spectrum allocation in CRNs. Firstly, we propose a new hidden markov model for SUs to predict the sensing results of competitors. Then, we introduce the proposed hidden markov model into the non-cooperative game. That is, it predicts the sensing results of competitors before the non-cooperative game. The simulation results show that the proposed scheme improves the energy efficiency of networks and utilization of SUs.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.8
• /
• pp.482-491
• /
• 2003

In this paper are presented kinematic and dynamic modeling and path-tracking of four-wheeled mobile robots with 2 d.o.f haying the limited drive-torques. Controllability of wheeled-mobile robots is revealed by the kinematic model. Instantaneously coincident coordinate system, force/torque propagation and Newton's equilibrium law are used to drive the dynamic model. When drive-torques generated by inverse dynamics exceed the limitation, we make wheeled-mobile robots follow the reference path by modifying the planned reference trajectory with time-scaling. The controller is introduced to compensate for error owing to modeling uncertainty and measurement noise. And simulation results prove that method proposed by this paper is efficient.