• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권9호
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• pp.2284-2298
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• 2013

In this paper, a fast and efficient signal reconstruction algorithm for solving the basis pursuit (BP) problem in compressed sensing (CS) is proposed. This fast linearized Bregman method (FLBM), which is inspired by the fast method of Beck et al., is based on the fact that the linearized Bregman method (LBM) is equivalent to a gradient descent method when applied to a certain formulation. The LBM requires $O(1/{\varepsilon})$ iterations to obtain an ${\varepsilon}$-optimal solution while the FLBM reduces this iteration complexity to $O(1/\sqrt{\varepsilon})$ and requiring almost the same computational effort on each iteration. Our experimental results show that the FLBM can be faster than some other existing signal reconstruction methods.

• 대한한의학방제학회지
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• 제18권2호
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• pp.251-257
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• 2010

Objectives : To develop and validate High-performance liquid chromatography-photodiode array methods for simultaneous determination of two constituents in Oryeong-san(ORS). Methods : Reverse-phase chromatography using a Gemini C18 column operating at $40^{\circ}C$, and photodiode array(PDA) detection at 280 nm, were used for quantification of the two marker components of ORS. The mobile phase using a gradient flow consisted of two solvent systems. Solvent A was $H_2O$ and solvent B was acetonitrile. Results : Calibration curves were acquired with correlation coefficient ($r^2$)>0.9999, and the relative standard deviation(RSD) values(%) for intra- and inter-day precision were not exceed 1.0%. The recovery rate of each compound was in the range of 93.01-104.16%, with an RSD less than 2.0%. The contents of two compounds in ORS were 1.10-3.72 mg/g. Conclusions : The established HPLC method will be helpful to improve quality control of ORS.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 정기총회 및 창립40주년기념 학술대회 학회본부
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• pp.421-424
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• 1987

Recently, the problem associated with the achievement of desired trajectories for non-linear robotic manipulatory systems are researched. The control system which is designed for this robot manipulator, poses a number of severe problem. The methods proposed to deal with the problem fall loosely into three main classes : "direct" "adaptive", "anthropomorphic". Besides there is an approach which is described based upon the application of optimal control theory. In this paper, using the optimal theory, we choose error-coordinate, between the desired trajectories and the practical as the state values, and determine the control law U which minimize a corresponding performance criterion. Let's consider the robotic arm proposed by Freund and set up the deviations of it's trajectory as a measure of performance. To find the optimal control law $U^*$ and the next state value which need to obtain $U^*$ here, we should introduced the conjugate gradient algorithm and the Runge Kutta method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2034-2036
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• 2002

In this paper, we propose the modeling of a chaotic nonlinear system using wavelet neural networks. In our modeling, we used the parameter adjusting method as the training method of a wavelet neural network. The difference between the actual output of a nonlinear chaotic system and that of a wavelet neural network adjusts the parameters of a wavelet neural network using the gradient-descent method. To verify the efficiency of this paper, we perform the simulation using Duffing system, which is a representative continuous time chaotic nonlinear system.

• 충청수학회지
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• 제27권4호
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• pp.675-688
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• 2014

In this paper, we present an efficient way to determine a suitable value of the regularization parameter using the global generalized cross validation and analyze the experimental results from preconditioned global conjugate gradient linear least squares(Gl-CGLS) method in solving image deblurring problems. Preconditioned Gl-CGLS solves general linear systems with multiple right-hand sides. It has been shown in [10] that this method can be effectively applied to image deblurring problems. The regularization parameter, chosen from the global generalized cross validation, with preconditioned Gl-CGLS method can give better reconstructions of the true image than other parameters considered in this study.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2013년도 제44회 동계 정기학술대회 초록집
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• pp.269-269
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• 2013

Usingvan der Waals (vdW) energy-corrected density-functional theory without or with self-consistent screening (SCS) effects, we calculate the adsorption energy of acetylene, ethylene and benzene on Si(100). We find that vdW interactions without SCS effects increase the adsorption energy by 0.23, 0.30, and 0.64 eV for adsorbed $C_2H_2$, $C_2H_4$, and $C_6H_6$ on Si(100), respectively. However, if SCS effects are included, this increase of the adsorption energy is reduced as 0.19, 0.24, and 0.54 eV for the three adsorption systems, respectively. The resulting adsorption energy for each system is between the values computed using the local-density approximation and the generalized-gradient approximation.

• Journal of Power Electronics
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• 제16권6호
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• pp.2150-2161
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• 2016

Mechanical and electrical parameter uncertainties cause dynamic and static estimation errors of the rotor speed and position, resulting in performance deterioration of sensorless control systems. This paper applies an extended nonlinear observer to interior permanent magnet synchronous motors (IPMSM) for the simultaneous estimation of the rotor speed and position. Two compensation methods are proposed to improve the observer performance against parameter uncertainties: an on-line rotational inertia adjustment approach that employs the gradient descent algorithm to suppress dynamic estimation errors, and an equivalent flux error compensation approach to eliminate static estimation errors caused by inaccurate electrical parameters. The effectiveness of the proposed control strategy is demonstrated by experimental tests.

• 한국철도학회:학술대회논문집
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• 한국철도학회 1998년도 창립기념 춘계학술대회 논문집
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• pp.596-603
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• 1998

The entry compression wave, which is generated at the entrance of the tunnel, is almost always associated with the pressure transients in the tunnel as well as the impulse noise at the exit of the tunnel. It is highly required to design the train nose shape that can minimize such undesirable phenomena. The objective of the current work is to investigate the effects of the train nose shape on the entry compression wave. Numerical computations were applied to one-dimensional unsteady compressible flow in high-speed railway train/tunnel systems. A various shape of train noses were tested for a wide range of train speeds. The results showed that the strength of the entry compression wave is not influenced by the train nose shape, but the time variation of pressure in the entry compression wavefront is strongly related to the train nose shape. The current method of the characteristics was able to represent a desirable nose shape for various train speeds. Optimum nose shape was found to considerably reduce the maximum pressure gradient of the entry compression wave.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1626-1629
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• 2002

This paper describes novel methods to construct fuzzy inference rules with gradient descent. The present methods have a constructive mechanism of the rule unit that is applicable in two parameters: the central value and the width of the membership function in the antecedent part. The first approach is to create the rule unit at the nearest position from the input space, for the central value of the membership function in the antecedent part. The second is to create the rule unit which has the minimum width, for the width of the membership function in the antecedent part. Experimental results are presented in order to show that the proposed methods are effective in difference on the inference error and the number of learning iterations.

• International journal of advanced smart convergence
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• 제7권3호
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• pp.15-22
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• 2018

Nowadays multiple control methods are used in robot control systems. A model, predictor or error estimator is often used as feedback controller to control a robot. While robots have become more and more intensive with algorithms capable to acquiring independent knowledge from raw data. This paper represents experimental results of real time machine learning control that does not require explicit knowledge about the plant. The controller can be applied on a broad range of tasks with different dynamic characteristics. We tested our controller on the balancing problem of a single propeller pendulum. Experimental results show that the use of a supervised machine learning algorithm in a single propeller pendulum allows the stable swing of a given angle.