• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1474-1482
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• 2004

In this paper, we propose an approach for extracting and filtering block motion vectors using an adaptive weight function. We first extract motion vectors from a sequence of images by using size-varibale block matching and then process them by adaptive robust estimation to filter out outliers (motion vectors out of concern). The proposed adaptive robust estimation defines a continuous sigmoid weight function. It then adaptively tunes the sigmoid function to its hard-limit as the residual errors between the model and input data are decreased, so that we can effectively separate non-outliers (motion vectors of concern) from outliers with the finally tuned hard-limit of the weight function. The experimental results show that the suggested approach is very effective in filtering block motion vectors.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1111-1117
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• 2003

The maximum Doppler frequency, or equivalently, the mobile speed is very useful information to optimize the performance of many wireless communication systems. However, the performance of a maximum Doppler frequency estimator is limited since it requires an estimate of the signal-to-noise ratio (SNR) of the channel environment. In this paper, the improved method for the maximum Doppler frequency estimations based on the decision feedback Doppler adaptive band-limit (DF-DABL) method is proposed. To reduce the effect of additive noise, the proposed algorithm uses a novel Doppler adaptive band-limit (DABL) technique. The distortion due to the additive noise is drastically removed by the proposed DF-DABL method. Especially, the DF-DABL method does not need any other channel information such as SNR.

• Journal of Drive and Control
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• v.15 no.3
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• pp.8-13
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• 2018

This paper presents a phase portrait analysis-based safety control algorithm for excavators, using adaptive sliding mode control. Since working postures and material types cause the excavator's rotational inertia to vary, the rotational inertia was estimated, and this estimation was used to design an adaptive sliding mode controller for collision avoidance of the excavator. In order to estimate the rotational inertia, the recursive least-squares estimation with multiple forgetting was applied with the information of the swing velocity of the excavator. For realistic evaluation, an actual working scenario-based performance evaluation was conducted. Based on the estimated rotational inertia and an analysis of estimation errors, sliding mode control inputs were computed. The actual working scenario-based performance evaluation of the designed safety algorithm was conducted, and the results showed that the developed safety control algorithm can efficiently avoid a collision with an object in consideration of rotational inertia variations.

• Structural Engineering and Mechanics
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• v.75 no.6
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• pp.771-784
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• 2020

Reliability analysis techniques combining with various surrogate models have attracted increasing attention because of their accuracy and great efficiency. However, they primarily focus on the structures with continuous response, while very rare researches on the reliability analysis for structures with discontinuous response are carried out. Furthermore, existing adaptive reliability analysis methods based on importance sampling (IS) still have some intractable defects when dealing with small failure probability, and there is no related research on reliability analysis for structures involving discontinuous response and small failure probability. Therefore, this paper proposes a novel reliability analysis method called AGPC-IS for such structures, which combines adaptive Gaussian process classification (GPC) and adaptive-kernel-density-estimation-based IS. In AGPC-IS, an efficient adaptive strategy for design of experiments (DoE), taking into consideration the classification uncertainty, the sampling uniformity and the regional classification accuracy improvement, is developed with the purpose of improving the accuracy of Gaussian process classifier. The adaptive kernel density estimation is introduced for constructing the quasi-optimal density function of IS. In addition, a novel and more precise stopping criterion is also developed from the perspective of the stability of failure probability estimation. The efficiency, superiority and practicability of AGPC-IS are verified by three examples.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.527-532
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• 2005

In this paper, we proposed an adaptive fuzzy sliding control for unknown nonlinear systems using estimation of bounds for approximation errors. Unknown nonlinearity of a system is approximated by the fuzzy logic system with a set of IF-THEN rules whose consequence parameters are adjusted on-line according to adaptive algorithms for the purpose of controlling the output of the nonlinear system to track a desired output. Also, using assumption that the approximation errors satisfy certain bounding conditions, we proposed the estimation algorithms of approximation errors by Lyapunov synthesis methods. The overall control system guarantees that the tracking error asymptotically converges to zero and that all signals involved in controller are uniformly bounded. The good performance of the proposed adaptive fuzzy sliding mode controller is verified through computer simulations on an inverted pendulum system.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.118-126
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• 2020

In this paper, we present an effective single image fog removal by using the Joint Bright and Dark Channel (JBDC) and pixel-based transmission estimation to enhance the visibility of outdoor images susceptible to degradation due to weather and environmental conditions. The conventional methods include refinement process of coarse transmission with heavy computational complexity. The proposed transmission estimation reveals excellent edge-preserving performance and does not require the refinement process. We estimate the atmospheric light in pixel-based fashion, which can improve the transmission estimation performance and visual quality of the restored image. Moreover, we propose an adaptive transmission estimation to enhance the visual quality specifically in sky regions. Comprehensive experiments on various fog images show that the proposed method exhibits reduced computational complexity and excellent fog removal performance, compared with the existing methods; thus, it can be applied to various fields including real-time devices.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.280-287
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• 2001

The studies to estimate the surface spectral reflectance of an object have received widespread attention using the multi-spectral camera system. However, the multi-spectral camera system requires the additional color filter according to increment of the channel and system complexity is increased by multiple capture. Thus, this paper proposes an algorithm to reduce the estimation error of surface spectral reflectance with the conventional 3-band RGB camera. In the proposed method, adaptive principal components for each pixel are calculated by renewing the population of surface reflectances and the adaptive principal components can reduce estimation error of surface spectral reflectance of current pixel. To evacuate performance of the proposed estimation method, 3-band principal component analysis, 5-band wiener estimation method, and the proposed method are compared in the estimation experiment with the Macbeth ColorChecker. As a result, the proposed method showed a lower mean square ems between the estimated and the measured spectra compared to the conventional 3-band principal component analysis method and represented a similar or advanced estimation performance compared to the 5-band wiener method.

• Journal of the Society of Naval Architects of Korea
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• v.33 no.1
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• pp.90-97
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• 1996

The optimal mesh refinement has a meaning that error of the every element is within an allowable level and in uniformly distributed. The adaptive mesh generation may be required to achieve the optimal mesh generation. For the purpose of optimal mesh generation, an error estimation and an adaptive mesh refinement are required. Using the adaptive mesh generation the second finite element analysis is performed with the result of the first analysis. In the process the error estimation is required. In this study the adaptive mesh generation program for triangular element is developed, and for a posteriori error estimation the stress projection approach is considered. It has been found the multigrid technique, where the error estimation and the mesh generation are combined in multi-step of analysis, may be used efficiently in the finite element analysis.

• The Journal of the Acoustical Society of Korea
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• v.35 no.5
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• pp.395-402
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• 2016

In this paper, a wideband adaptive beamforming approach for an acoustic vector sensor linear array is presented. It is a very important issue to estimate the stable covariance matrix for adaptive beamforming. In the conventional wideband adaptive beamforming based on coherent signal-subspace (CSS) processing, the error of bearing estimates is resulted from the focusing matrix estimation and the large number of data snapshot is necessary. To alleviate the estimation error and snapshot deficiency in estimating covariance matrix, the steered covariance matrix method in the pressure sensor is extended to the vector sensor array, and the subarray technique is incorporated. By this technique, more accurate azimuth estimates and a stable covariance matrix can be obtained with a small number of data snapshot. Through simulation, the azimuth estimation performance of the proposed beamforming method and a wideband adaptive beamforming based on CSS processing are assessed.