• Journal of Institute of Control, Robotics and Systems
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• v.8 no.10
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• pp.827-832
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• 2002

We propose a prediction method of the pollutant and a synchronous classification of the current state of SOx emission in the power plant. We use the auto-regressive with exogeneous (ARX) model as a predictor of SOx emission and use a radial basis function network (RBFN) as a pattem classifier. The ARX modeling scheme is implemented using recursive least squares (RLS) method to update the model parameters adaptively. The capability of SOx emission monitoring is utilized with the application of the RBFN classifier. Experimental results show that the ARX model can predict the SOx emission concentration well and ARX modeling parameters can be a good feature for the state monitoring. in addition, its validity has been verified through the power spectrum analysis. Consequently, the RBFN classifier in combination with ARX model is shown to be quite adequate for monitoring the state of SOx emission.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.240-240
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• 2000

This paper presents two algorithms based on the concept of the frequency domain adaptive filter(FDAF). First the frequency domain recursive least squares(FRLS) algorithm with the overlap-save filtering technique is introduced. This minimizes the sum of exponentially weighted square errors in the frequency domain. To eliminate discrepancies between the linear convolution and the circular convolution, the overlap-save method is utilized. Second, the sliding method of data blocks is studied Co overcome processing delays and complexity roads of the FRLS algorithm. The size of the extended data block is twice as long as the filter tap length. It is possible to slide the data block variously by the adjustable hopping index. By selecting the hopping index appropriately, we can take a trade-off between the convergence rate and the computational complexity. When the input signal is highly correlated and the length of the target FIR filter is huge, the FRLS algorithm based on the block processing technique has good performances in the convergence rate and the computational complexity.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.153-158
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• 2012

Noise maps are produced according to 'The Method of making a Noise Map' in order to noise control efficiently, and prediction model to predict road traffic noise which may apply to Korean situation, include CRTN, RLS 90, NMPB, Nord 2000 and ASJ 2003. Of them, ASJ 2003, Japan's prediction model has not been verified for the application to Korean situation according to the classification of vehicle. In addition, ASJ 2003 was revised to ASJ 2008 recently, a classification for motorcycle was added. This study attempts to check the classification of vehicle in ASJ 2008 and 'The Method of making a Noise Map' to confirm the suitability of the application of them to Korean situation.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.972-979
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• 1999

QFT(Quantitative Feedback Theory) is a very practical design technique that emphasizes the use of feedback for achieving the desired system performance tolerances in despite of plant uncertainty and disturbance. The fundamental concept of QFT is a loop shaping procedure that a suitable controller can be found by shaping a nominal loop transfer function. The loop shaping synthesis involves the identification of a structure and the specialization of parameter optimization of a desired system. This paper presents an improved loop shaping approach of QFT with model validation using GA(Genetic Algorithm). The method presented in this paper removes the problems of iterative operation, transformation error, and model validation in the conventional methods without consideration of frequency domain.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1113-1115
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• 1996

The servo system requires faster and more accurate dynamic responses. A new technique for the position control of DC servo motors is presented in this paper. The proposed technique employs a Self Tuning Regulator Proportional Integral Derivative(STR PID) position control systems in order to improve the dynamic performance of a DC servo motor. Recursive -least -squares (RLS) method is used in order to estimate the STR PID coefficients, $K_P$, $K_I$, and $K_D$. In order to consider dynamics such as voltage, angular velocity, and rotor angle, the above method was applied position control system.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1249-1253
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• 2003

License plate recognition is very important in an automobile society. This is because, since plate detection accuracy has large influence on subsequent number recognition, it is very important. However, it is very difficult to do it, because a background and a body color of cars are similar to that of the license plate. In this paper, we propose a new thresholds determination method in the various background by using the real-coded genetic algorithm (RGA). By using RGA, the most likely plate colors are decided under various lighting conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds)are obtained by RGA. The relationship between thresholds decided from RGA and brightness average is aproximate by using the recursive least squares (RLS) algorithm. In the case of plate detection, thresholds are decided from these functions.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1790-1791
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• 2011

In this paper, the diagnosis system for detecting and localizing the neutral wire defect in an energized XLPE power cable is proposed. The neutral wire defect is detectd and localized by the time-frequency domain reflectometry. To extract the reflected signal from the neutral wire defect, the adaptive RLS filtering is introduced. To verify the proposed method, the experiments for detecting and localizing neutral wire defect in active XLPE power cable is conducted.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1234-1238
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• 2004

A Controlled neural networks are proposed in order to measure nonlinear environments in adaptive and in realtime. The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between tile output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we have various experiments. And this controller call prove effectively to be control in the environments of various systems.

• Smart Structures and Systems
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• v.11 no.6
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• pp.569-587
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• 2013

This paper evaluates the closed loop performance of the reaching law based discrete sliding mode controller with multisensor data fusion (MSDF) in real time, by controlling the first two vibrating modes of a piezo actuated structure. The vibration is measured using two homogeneous piezo sensors. The states estimated from sensors output are fused. Four fusion algorithms are considered, whose output is used to control the structural vibration. The controller is designed using a model identified through linear Recursive Least Square (RLS) method, based on ARX model. Improved vibration suppression is achieved with fused data as compared to single sensor. The experimental evaluation of the closed loop performance of sliding mode controller with data fusion applied to piezo actuated structure is the contribution in this work.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.7
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• pp.499-507
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• 2003

This paper presents a method for active noise control (ANC) in a duct by using a ring-tyPe smart foam. The ring-type smart foam consists of an elastic porous material of lining shape and a PVDF film embedded In the material. The PVDF element acts as a secondary sound source to reduce the noise. Active noise control using a ring-type smart foam is only effective locally because of the way to excite radially. To enlarge the quiet zone, the duct Is lined with additional acoustic foam between the smart foam and the error microphone. When cancellation path ks optimized by the LMS/RLS algorithm, the computation power is reduced while control performance Is maintained. The filtered-x LMS algorithm is used to minimize the error signal.