• Proceedings of the Korean Geotechical Society Conference
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• 2010.09c
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• pp.37-46
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• 2010

Marine clays are soft soil deposits having complicated mineralogy and formation characteristics. Thus, characterization of its geotechnical behavior has been a main issue for geotechnical engineers. Nowadays, the importance and applications of geophysical exploration on marine clays are increasing significantly according to the accuracy, efficiency, and reliability of geophysical survey technology. For marine clays, seismic survey is effective for density and elasticity characterization, while electro-magnetic wave provides the information about the fluid conductivity phenomena inside soil. For practical applications, elastic wave technology can evaluate the consolidation state of natural marine clay layers and estimate important geotechnical engineering parameters of artificially reclaimed marine deposits. Electrical resistivity can provide geophysical characteristics such as particle cementation, pore geometry shape, and pore material phase condition. Furthermore, nondestructive geophysical monitoring is applicable for risk management and efficiency enhancement during natural methane gas extraction from gas hydrate-bearing sediments.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.225-227
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper is proposed artificial intelligent-PI(AIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme. The validity of the proposed controller is verified by results at different dynamic operating conditions.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.32-34
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good Performance over a wide range of operation, even under ideal field oriented conditions. This paper is proposed model reference adaptive fuzzy control(MFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaption mechanism(FAM), MFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, MFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1453-1455
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• 2005

This paper presents self tuning PI controller of IPMSM drive using neural network. Self tuning PI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.497-519
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• 2024

In reliability analysis, the probability P(Y < X) is significant because it denotes availability and dependability in a stress-strength model where Y and X are the stress and strength variables, respectively. In reliability theory, the inverse Lomax distribution is a well-established lifetime model, and the literature is developing inference techniques for its reliability attributes. In this article, we are interested in estimating the stress-strength reliability R = P(Y < X), where X and Y have an unknown common scale parameter and follow the inverse Lomax distribution. Using Bayesian and non-Bayesian approaches, we discuss this issue when both stress and strength are expressed in terms of lower record values. The parametric bootstrapping techniques of R are taken into consideration. The stress-strength reliability estimator is investigated using uniform and gamma priors with several loss functions. Based on the proposed loss functions, the reliability R is estimated using Bayesian analyses with Gibbs and Metropolis-Hasting samplers. Monte Carlo simulation studies and real-data-based examples are also performed to analyze the behavior of the proposed estimators. We analyze electrical insulating fluids, particularly those used in transformers, for data sets using the stress-strength model. In conclusion, as expected, the study's results showed that the mean squared error values decreased as the record number increased. In most cases, Bayesian estimates under the precautionary loss function are more suitable in terms of simulation conclusions than other specified loss functions.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.174-180
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• 2015

In this paper, we propose the passive sonar signal processing technique for estimating target information using proximity sensor. This algorithm is performed by single sensor which is constituted underwater sensor network and has a hierarchical structure. The estimated parameter is the velocity, the depth, the distance and bearing at CPA situations and we can improve the accuracy of signal processing techniques through having a hierarchical structure. We verify the performance of the proposed method by computer simulation and then we check the result that 20% error can be occurred in maximum detectable range. We also confirm that proposed method has the reliability in the actual sea environment through the sea experiment.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.8
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• pp.57-66
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper proposes hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme, The validity of the proposed controller is verified by results at different dynamic operating conditions.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.11a
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• pp.210-215
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• 2002

This thesis is contents on the crystal grown by the solide phase method at $925^{\circ}C$ with orthorhombic structure that $LiMnO_{2}$ active material synthesised with precurse $Mn_{2}O_{3}$ and $LiOH.H_{2}O$ material to get three voltage level. The porosity analysis of the grown crystal in secondary batteries $LiMnO_{2}$ thin film is $1.323E+02\AA$ of the average pore diameter of powder particles and its structure to be taken the pore diameter was prepared. Adding voltage area to get properties of charge and discharge of which experiment result of $LiMnO_{2}$ thin film area 2.2V~4.3V, current and scan speed were 0.1mAh/g and $0.2mV/cm^{2}$ respectively, and properties of the charge and discharge to be got optimum experiment condition parameter and density rate of Li for analyze that unit discharge capacity with metal properties is 87mAh/g was 96.9[ppm] at 670.784[nm] wavelength, and density rate of Mn analyzed 837[ppm] at 257.610[nm]. It can be estimated the quality of thin film that wrong cell reject from the bottle of electrolyte. The results of SEM and XRD were the same that of original researchers.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.1
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• pp.86-93
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• 2003

This paper describes that the resonant tank type DC-DC converter consist of reactor and capacitor resonant tank circuit for increased the output current. This circuit configuration is composed of the resonant tank circuit used resonant capacitor and reactor and the capacitor connected in switch are a common using by resonance capacitor and ZVS(Zero Voltage Switching) capacitor. Therefore, the proposed converter can reduce a switching losses, noise, and voltage stress at turn-on and turn-on and has an advantage which is able to operating safely in load short, because DC reactor is connected with resonance reactor in order to supply a fixed current with low ripple from DC power supply. The analysis of proposed circuit uses normalized parameters and characteristic estimation is generally described the proposed circuit with the characteristics of power and output voltage etc. Also, design is based on the characteristic estmations in each step. Hence, We conform a rightfulness theoretical analysis by comparing a theoretical values and experimental values obtained from experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.635-643
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• 2018

This paper propose a method to identify the motor parameters and improve input voltage error which affect the low speed position error of the back-emf(back electromotive force) based sensorless algorithm and to secure the operation reliability and stability even in the case where the load fluctuation is severe and the start and low speed operation frequently occurs. In the model-based observer used in this paper, stator resistance, inductance, and input voltage are particularly influential factors on low speed performance. Stator resistance can cause resistance value fluctuation which may occur in mass production process, and fluctuation of resistance value due to heat generated during operation. The inductance is influenced by the fluctuation due to the manufacturing dispersion and at a low speed where the change of the current is severe. In order to find stator resistance and inductance which have different initial values and fluctuate during operation and have a large influence on sensorless performance at low speed, they are commonly measured through 2-point calculation method by 2-step align current injection. The effect of voltage error is minimized by offsetting the voltage error. In addition, when the command voltage is used, it is difficult to estimate the back-emf due to the relatively large distortion voltage due to the dead time and the voltage drop of the power device. In this paper, we propose a simple circuit and method to detect the voltage by measuring the PWM(Pulse Width Modulation) pulse width and compensate the voltage drop of the power device with the table, thereby minimizing the position error due to the exact estimation of the back-emf at low speed. The suitability of the proposed algorithm is verified through experiment.