• Journal of KSNVE
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• v.3 no.4
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• pp.383-394
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• 1993

An automotive suspension system generally behaves like a low frequency band-pass filter(0.5 - 10 Hz). Passengers are very sensitive to this frequency range in terms of ride quality and road holding ability. In this paper, a LQG/ LTR controller is suggested to improve the ride quality and road holding ability in the specified frequency rage. It has been found by numerical simulation that the ride quality and road holding ability can be improved in the frequency ranges of 0.5 - 3.0 Hz and 0.3 - 2.1 Hz respectively. In addition, a new approach using root locus to evaluate the stability robustness of the active suspension system is studied. It is shown that the stability robustness of the LQG/LTR controller designed in this paper is improved, compared to the passive system.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.55 no.4
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• pp.402-410
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• 2019

This paper deals with the disturbance observer (DOB) based sliding mode control (SMC) for a DC motor to control motor rotating speed precisely and to ensure strong robustness against disturbance including load torque and parameter variation. The reason of steady state error in speed on conventional SMC without DOB is analyzed in detail. Especially, the suggested DOB is designed to prevent measuring noise and harmonics caused by derivative operation on rotating speed. The control performance of the DOB based SMC is evaluated by the various simulations. The simulation results showed that the DOB based SMC had more robust performance than the SMC system without DOB. Especially, precise speed control was possible even though motor parameter variation and load torque was added to the system.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.247-248
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• 2006

In this paper, we propose a new sound localization algorithm for an active perception system. In an active perception system, an acquired sound is mixed with the sound of motors. So a sound localization algorithm for an active perception system requires a robustness for the noise and a computational efficiency. The proposed localization algorithm can achieve robustness and efficiency to use only sub-band channels that are contained harmonic structure of the target speech.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.55-57
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• 1996

This paper presents an implementation of the adaptive ANC (Active Noise Control) system improving robustness. The system using the proposed algorithm shows a good performance of control, when the adaptive filter well does not work. We construct a real duct system and use DSP chip for experiment. Experimental results of the proposed algorithm prove the system to be superior than the conventional filtered-x LMS algorithm when the adaptive filter is out of order.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4463-4482
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• 2020

Mesh model generated from 3D reconstruction usually comes with lots of noise, which challenges the performance and robustness of mesh simplification approaches. To overcome this problem, we present a novel method for mesh simplification which could preserve structure and improve the accuracy. Our algorithm considers both the planar structures and linear features. In the preprocessing step, it automatically detects a set of planar structures through an iterative diffusion approach based on Region Seed Growing algorithm; then robust linear features of the mesh model are extracted by exploiting image information and planar structures jointly; finally we simplify the mesh model with plane constraint QEM and linear feature preserving strategies. The proposed method can overcome the known problem that current simplification methods usually degrade the structural characteristics, especially when the decimation is extreme. Our experimental results demonstrate that the proposed method, compared to other simplification algorithms, can effectively improve the quality of mesh and yield an increased robustness on noisy input mesh.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1603-1623
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• 2020

Multiple Mobile Robots System (MMRS) has shown many attractive features in lots of real-world applications that motivate their rapid and wide diffusion. In MMRS, the Cooperative Localization (CL) is the basis and premise of its high-performance task. However, the statistical characteristics of the system noise should be already known in traditional CL algorithms, which is difficult to satisfy in actual MMRS because of the numerous of disturbances form the complex external environment. So the CL accuracy will be reduced. To solve this problem, an improved Adaptive Active Cooperative Localization (A²CL) algorithm based on information optimization strategy for MMRS is proposed in this manuscript. In this manuscript, an adaptive information fusion algorithm based on the variance component estimation under Extended Kalman filter (VCEKF) method for MMRS is introduced firstly to enhance the robustness and accuracy of information fusion by estimating the covariance matrix of the system noise or observation noise in real time. Besides, to decrease the effect of observation uncertainty on CL accuracy further, an observation optimization strategy based on information theory, the Model Predictive Control (MPC) strategy, is used here to maximize the information amount from observations. And semi-physical simulation experiments were carried out to verity the A²CL algorithm's performance finally. Results proved that the presented A²CL algorithm based on information optimization strategy for MMRS cannot only enhance the CL accuracy effectively but also have good robustness.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.95-101
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• 2009

Among the adaptive noise cancellers (ANC), the least mean square (LMS) algorithm has probably become the most popular algorithm because of its robustness, good tracking properties, and simplicity of implementation. However, it has non-uniform convergence and a trade-off between the rate of convergence and excess mean square error (EMSE). To overcome these shortcomings, a number of variable step size least mean square (VSSLMS) algorithms have been researched for years. These LMS algorithms use a complex variable step method approach for rapid convergence but need high computational complexity. A variable step approach can impair the simplicity and robustness of the LMS algorithm. The proposed instantaneous gain control (IGC) algorithm uses the instantaneous gain value of the original signal and the noise signal. As a result, the IGC algorithm can reduce computational complexity and maintain better performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2324-2330
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• 2015

In blind signal processing in impulsive noise environment the maximum cross-correntropy (MCC) algorithm shows superior performance compared to MSE-based algorithms. But optimum weight conditions of MCC algorithm and its properties related with robustness to impulsive noise have not been studied sufficiently. In this paper, through the analysis of the behavior of its optimum weight and the relationship with the MSE-based LMS algorithm, it is shown that the optimum weight of MCC and MSE-based LMS have an equal solution. Also the factor that keeps optimum weight of MCC undisturbed and stable under impulsive noise is proven to be the magnitude controlled input through simulation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.3
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• pp.85-94
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• 2011

Spectrum sensing is one of the most important technologies in cognitive radio. Although many studies have considered energy detection technique as the spectrum sensing technique, noise variance in practical systems is difficult to estimate accurately. Thus, in the real system, the probability of false alarm will not be maintained constant. In this paper, with considering that the cognitive radio does not know the primary user's signal, we propose a new spectrum sensing scheme which can operate without the information of noise variance. Through simulations, we show that the proposed scheme can detect spectrum with the condition of unknown noise information and have robustness for the change of noise variance.

• Journal of Mechanical Science and Technology
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• v.20 no.5
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• pp.652-657
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• 2006

Although many methods about the control of irregular external noise have been introduced and implemented, it is still necessary to design a controller that will be more effective and efficient methods to exclude for various noises. Accumulation of errors due to model tracking, internal noises (thermal noise, shot noise and 1/f noise) that come from elements such as resistor, diode and transistor etc. in the circuit system and numerical errors due to digital process often destabilize the system and reduce the system performance. New stochastic controller is adopted to remove those noises using conventional controller simultaneously. Design method of a model tracking dual controller is proposed to improve the stability of system while removing external and internal noises. In the study, design process of the model tracking dual stochastic controller is introduced that improves system performance and guarantees robustness under irregular internal noises which can be created internally. The model tracking dual stochastic controller utilizing F-P-K stochastic control technique developed earlier is implemented to reveal its performance via simulation.