• YAKHAK HOEJI
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• v.48 no.1
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• pp.6-12
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• 2004

Near-infrared spectra of methanol-water mixtures and acetonitrile-water mixtures were acquired to find interactions between solvents widely used for reverse-phase liquid chromatography. Mixtures were prepared to give a series of increasing mole fractions of methanol or acetonitrile in water. Data matrices of acquired spectra were analyzed to determine the proper number of principal components of each mixture system using Malinowski's factor indicator function. Initial guess of score matrix and loading matrix were calculated by nonlinear iterative partial least squares (NIPALS) algorithm for faster computation. Iterative target transform factor analysis (ITTFA) was applied to convert the initial estimation of score matrix to true concentration profile and loading matrix to pure spectra of pure components of the mixtures. In case of methanol-water the number of principal components was found to be 4 and those initial guess of factors were converted to the pure spectra of water methanol and two kinds of complexes. In case of acetonitrile-water the number of pure components of the mixtures was found to be 3 and the pure spectrum of acetonitrile-water complex was found. The nonlinear characteristics of concentration profiles of complexes in the solvent mixtures may give a good criteria in understanding their elution characteristics in reverse-phase liquid chromatogrsphy.

• Nuclear Engineering and Technology
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• v.33 no.1
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• pp.25-33
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• 2001

To determine the pin power distribution without disassembling, HANARO fuel assemblies are gamma-scanned and then the distribution is reconstructed tv using the tomography method. The iterative least squares method (ILSM and the wavelet singular value decomposition method (WSVD) are chosen to solve the problem. An optimal convergence criterion is used to stop the iteration algorithm to overcome the potential divergence in ILSM. WSVD gives better results than ILSM , and the average values from the two methods give the best results. The RMSE (root mean square errors) to the reference data are 5.1, 6.6, 5.0, 6.5, and 6.4% and the maximum relative errors are 10.2, 13.7, 12.2, 13.6, and 14.3%, respectively. It is found that the effect of random positions of the pins is important. Although the effect can be accommodated by the iterative calculations simulating the random positions, the use of experimental equipment with a slit covering the whole range of the assembly horizontally is recommended to obtain more accurate results. We made a new apparatus using the results of this study and are conducting an experiment in order to obtain more accurate results.

• Geophysics and Geophysical Exploration
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• v.1 no.3
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• pp.155-160
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• 1998

EM tomography technique has been developed. The algorithm used the extended Born approximations for forward modeling and reconstructed a conductivity image by a smoothness constraint least squares inversion method. Observed data, the vertical components of secondary magnetic fields, were simulated with the 3-D integral equation code. The results showed that the location of anomalous body could be imaged very well, but conductivity of the body was lower than real one and the vertical resolution was much higher than the horizontal resolution.

• Bulletin of the Korean Chemical Society
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• v.24 no.3
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• pp.279-284
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• 2003

The One Pass Method (OPM) previously presented for the identification of single input single output systems is used to estimate the parameters of a Decentralized Control System (DCS). The OPM is a linear and therefore a simple estimation method. All of the calculations are performed in one pass, and no initial parameter guess, iteration, or powerful search methods are required. These features are of interest especially when the parameters of multi input-output model are estimated. The benefits of the OPM are revealed by comparing its results against those of two recently published methods based on pulse testing. The comparison is performed using two databases from the literature. These databases include single and multi input-output process transfer functions and relevant disturbances. The closed loop responses of these processes are roughly captured by the previous methods, whereas the OPM gives much more accurate results. If the parameters of a DCS are estimated, the OPM yields the same results in multi or single structure implementation. This is a novel feature, which indicates that the OPM is a convenient and practice method for the parameter estimation of multivariable DCSs.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2678-2685
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• 2020

The pipe bends and elbows in nuclear power plants (NPPs) are vulnerable to degradation mechanisms and can cause wall-thinning defects. As it is difficult to detect both the defects generated inside the wall-thinned pipes and the preliminary signs, the wall-thinning defects should be accurately estimated to maintain the integrity of NPPs. This paper proposes a deep fuzzy neural network (DFNN) method and estimates the collapse moment of wall-thinned pipe bends and elbows. The proposed model has a simplified structure in which the fuzzy neural network module is repeatedly connected, and it is optimized using the least squares method and genetic algorithm. Numerical data obtained through simulations on the pipe bends and elbows with extrados, intrados, and crown defects were applied to the DFNN model to estimate the collapse moment. The acquired databases were divided into training, optimization, and test datasets and used to train and verify the estimation model. Consequently, the relative root mean square (RMS) errors of the estimated collapse moment at all the defect locations were within 0.25% for the test data. Such a low RMS error indicates that the DFNN model is accurate in estimating the collapse moment for wall-thinned pipe bends and elbows.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.4
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• pp.353-361
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• 2011

A precise embedded ultrasonic localization system is developed for autonomous mobile robots in indoor environments, which is essential for autonomous navigation of mobile robots with various tasks. Although ultrasonic sensors are more cost-effective than other sensors such as LRF (Laser Range Finder) and vision, they suffer inaccuracy and directional ambiguity. First, we apply the matched filter to measure the distance precisely. For resolving the computational complexity of the matched filter for embedded systems, we propose a new matched filter algorithm with fast computation in three points of view. Second, we propose an accurate ultrasonic localization system which consists of three ultrasonic receivers on the mobile robot and two or more transmitters on the ceiling. Last, we add an extended Kalman filter to estimate position and orientation. Various simulations and experimental results show the effectiveness of the proposed system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.445-450
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• 2015

In the moving target problem, the velocity information of the moving target is very important as well as the high accuracy position information. To solve this problem, active researches are being conducted recently with combine the Time Difference of Arrival (TDOA) and Frequency Delay of Arrival(FDOA) measurements. However, since the FDOA measurement is utilizing the Doppler effect due to the relative velocity between the target source and the receiver sensor, it may be difficult to use the FDOA measurement if the moving target speed is not sufficiently fast. In this paper, we propose a method for estimating the position and the velocities of the target by using only the TDOA measurements for the low speed moving target in the indoor environment with sensor network. First, the target position and heading angle are obtained from the estimated positions of two attached transmitters on the target. Then, the target angular and linear velocities are also estimated. In addtion, we apply the Instrumental Variable (IV) technique to compensate the estimation error of the estimated target velocity. In simulation, the performance of the proposed algorithm is verified.

• Journal of Electrical Engineering and Technology
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• v.8 no.2
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• pp.376-384
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• 2013

With the continuous aging of the populations in developed countries, the medical requirements of the aged are expected to increase. In this paper, a ring-type pulse oximeter finger sensor and a 24-hour ambulatory heart rate monitoring system for the aged are presented. We also demonstrate the feasibility of extracting accurate heart rate variability measurements from photoelectric plethysmography signals gathered using a ring-type pulse oximeter sensor attached to the finger. We designed the heart rate sensor using a CPU with built-in ZigBee stack for simplicity and low power consumption. We also analyzed the various distorted signals caused by motion artifacts using a FFT, and designed an algorithm using a least squares estimator to calibrate the signals for better accuracy.

• Journal of Power Electronics
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• v.15 no.2
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• pp.411-418
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• 2015

In this paper, a new method for the online estimation of equivalent series resistances (ESR) of the DC-link capacitors in induction machine (IM) drive systems with a front-end diode rectifier is proposed, where the ESR estimation is conducted during the regenerative operating mode of the induction machine. In the first place, a regulated AC current component is injected into the q-axis current component of the induction machine, which induces the current and voltage ripple components in the DC-link. By processing these AC signals through digital filters, the ESR can be estimated by a recursive least squares (RLS) algorithm. To acquire the AC voltage across the ESR, the DC-link voltage needs to be measured at a double sampling frequency. In addition, the ESR current is simply reconstructed from the stator currents and switching states of the inverter. Experimental results have shown that the estimation error of the ESR is about 1.2%, which is quite acceptable for condition monitoring of the capacitor.

• Structural Engineering and Mechanics
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• v.76 no.5
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• pp.653-661
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• 2020

The paper presents a study regarding rubber compressibility behavior. The objective is to analyze the effect of compression degree of rubber on its mechanical properties and propose a new methodology based on reverse engineering to predict compressibility degree based on uniaxial stretching test and Finite Element Analysis (FEA). In general, rubbers are considered to be almost incompressible and Poisson's ratio is close to 0.5. Since this property is intimately related to the rubber packing density, little changes in Poisson's ratio can lead to significant changes regarding mechanical behavior. The deviatory hyperelastic constants were obtained through experimental data fitting by least squares method for the most relevant constitutive models implemented in commercial software Abaqus, such as: Neo-Hooke, Mooney-Rivlin, Ogden, Yeoh and Arruda-Boyce, whereas the hydrostatic part was determined through an optimization algorithm implemented in the Abaqus environment by Python scripting. The simulation results presented great influence of the Poisson's ratio in the rubber specimen mechanical behavior mainly for high strain levels. A conventional pure volumetric compression test was also carried out in order to compare the results obtained by the proposed methodology.