• The Journal of the Acoustical Society of Korea
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• v.7 no.2
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• pp.20-25
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• 1988

This study is to explain the characteristic of excess attenuation on the ground through the outdoors experiment about noise propagation and the reduced model experiment of acoustic. The outdoors experiment on the attenuation of noise propagation was tried with the small engine that had large acoustic output, and then it was conformed that there was relationship between the excess attenuation calculated by measurement from distance attenuation and Log(D/(Hs+Hr)). As a result, it was found that the attenuation of noise propogation depended upon the direction of the wind and frequency and was regressed in a straight line. And the numerical values of excess attenuation on the ground could be calculated by regarding Log(D/(Hs+Hr)) as a parameter with an airing resistance $\sigma$. It was found that when the mean square error between the excess attenuation calculated by measurement and the value calculated by a fomula $L=-20Log\mid1+(r_1/r_2)Qexp(ik, \bigtriangleup r)\mid$ about optional $\sigma$ was least, the optimal decision of u was made. As the characteristic of model is the model experiment on a reduced scale of 1 to 40, It was conformed that it corresponds enough with the measurement value with measuring the distance attenuation in the large anecoic chamber.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.3
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• pp.53-61
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• 2006

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy nile as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive fuzzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.

• Structural Engineering and Mechanics
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• v.46 no.5
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• pp.673-693
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• 2013

This paper presents a systematic design and training procedure for the feed-forward back-propagation neural network (NN) modeling of both forward and inverse behavior of a rotary magnetorheological (MR) damper based on experimental data. For the forward damper model, with damper force as output, an optimization procedure demonstrates accurate training of the NN architecture with only current and velocity as input states. For the inverse damper model, with current as output, the absolute value of velocity and force are used as input states to avoid negative current spikes when tracking a desired damper force. The forward and inverse damper models are trained and validated experimentally, combining a limited number of harmonic displacement records, and constant and half-sinusoidal current records. In general the validation shows accurate results for both forward and inverse damper models, where the observed modeling errors for the inverse model can be related to knocking effects in the measured force due to the bearing plays between hydraulic piston and MR damper rod. Finally, the validated models are used to emulate pure viscous damping. Comparison of numerical and experimental results demonstrates good agreement in the post-yield region of the MR damper, while the main error of the inverse NN occurs in the pre-yield region where the inverse NN overestimates the current to track the desired viscous force.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.598-601
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• 2006

The radio signal in GNSS was intentionally designed with two frequencies in order to combat the dispersion error caused by trans-ionospheric propagation. By measuring the path delay independently at the two, widely spaced GPS frequencies, L1 & L2, the TEC along the path from satellite to receiver can be measured directly. The issue with dual frequency measurement of the ionosphere is the calibration of L1/L2 interfrequency biases. L1/L2 interfrequency biases are generated because physical electric signal paths of L1 and L2 circuits are different from each other for both satellites and receiver. Conventionally L1/L2 interfrequency bias is estimated and broadcasted by 2D ionospheric model. In this paper, we estimated IFB (interfrequency bias) by 2D & 3D ionospheric models including real time filter methods and compared the result of those and concluded the merit of 3D tomography model to recover the problem of 2D thin shell model. We confirmed our conclusion by experimental data.

• Journal of Navigation and Port Research
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• v.32 no.8
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• pp.589-596
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• 2008

In this paper, Artificial Neural Network(ANN) is applied to automatic berthing control for a ship. ANN is suitable for a maneuvering such as ship's berthing, because it can describe non-linearity of the system. Multi-layer perceptron which has more than one hidden layer between input layer and output layer is applied to ANN. Using a back-propagation algorithm with teaching data, we trained ANN to get a minimal error between output value and desired one. For the automatic berthing control of a containership, we introduced low speed maneuvering mathematical models. The berthing control with the structure of 8 input layer units in ANN is compared to 6 input layer units. From the simulation results, the berthing conditions are satisfied, even though the berthing paths are different.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.255-262
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• 2007

This paper proposes an enhanced skin color-based detection method to find a region of human face in color images. The proposed detection method combines three color spaces, RGB, $YC_bC_r$, YIQ and builds color union histograms of luminance and chrominance components respectively. Combined color union histograms are then fed in to the back-propagation neural network for training and Levenberg-Marquadt algorithm is applied to the iteration process of training. Proposed method with Levenberg-Marquadt algorithm applied to training process of neural network contributes to solve a local minimum problem of back-propagation neural network, one of common methods of training for face detection, and lead to make lower a detection error rate. Further, proposed color-based detection method using combined color union histograms which give emphasis to chrominance components divided from luminance components inputs more confident values at the neural network and shows higher detection accuracy in comparison to the histogram of single color space. The experiments show that these approaches perform a good capability for face region detection, and these are robust to illumination conditions.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2005.03a
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• pp.18-25
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• 2005

Stochastic ground-motion method is adopted to simulate horizontal PGA values for the offshore Uljin earthquake recorded at nationwide seismic stations. For this purpose, the Fourier spectra are calculated at every stations based on comprehensive results of wave propagation and site effect which were previously revealed through inversion process applied to large accumulated spectral D/B. In addition, the apparent source spectrum of the offshore Uljin earthquake is estimated by removing the path and site response from the observed spectra. The distance dependent time-duration model is revised by iteratively fitting the PGA values generated by using the raw spectra data to the observed PGA data. The stochastic ground-motion method predicts the observed PGA values within a error of ${\sigma}_{log10}=0.1$. Transfer functions of a site relative to another site are estimated based on the error residual of the inversion results and used to convert PGA values at multiple stations to expected PGA values at a reference station of TJN. The converted PGA values can be used as basic data to evaluate the ground-motion attenuation relations developed for seismic hazard analysis that concerns the large damaging earthquakes.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2949-2952
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• 2000

A new neural networks and learning algorithm are proposed in order to measure nonlinear heights of complexed road environments in realtime without pre-information. This new neural networks is Error Self Recurrent Neural Networks(ESRN), The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between the 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 back-propagation 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. We can estimate nonlinear models in realtime by ESRN and learning algorithm and control nonlinear models. To show the performance of this one. we control 7 degree of freedom full car model with several control method. From this simulation. this estimation and controller were proved to be effective to the measurements of nonlinear road environment systems.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.705-709
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• 1995

The objective of this study is to design a multi-layer neural network which controls the position of excavator's attachment. In this paper, a dynamic controller has been developed based on an error back-propagation(BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it was used as a commanded feedforward input generator. A PD feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the excavator as well as the PD feedback error. By using the BP network as a feedforward controller, no a priori knowledge on system dynamics is need. Computer simulation results demonstrate such powerful characteristics of the proposed controller as adaptation to changing environment, robustness to disturbancen and performance improvement with the on-line learning in the position control of excavator attachment.