• Journal of Adhesion and Interface
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• v.14 no.1
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• pp.21-27
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• 2013

Cellulose-based rayon fibers or fabrics can be thermally decomposed very fast within a narrow temperature window during stabilization process. Therefore the stabilization stage is critically important for producing rayon-based carbon fibers. Consequently, in the present study the effects of isothermal stabilization and ultrasonic cleaning on the weight loss, chemical composition, microstructure, and fabric texture of cellulose-based rayon fabrics were explored. The temperature of the isothermal stabilization process performed in the range of $200{\sim}240^{\circ}C$ influenced the processing time, carbon and oxygen contents, cellulose structural change, and fabric texture. The ultrasonic cleaning, which was conducted prior to the stabilization process, played a role in shortening the stabilization time, increasing the carbon contents, decreasing the oxygen contents, and changing the XRD pattern. Also, it was considered that the ultrasonic cleaning contributed to retarding the weight loss, to reducing the thermal shrinkage, and further to reducing the fast physical change of rayon fabrics.

• Transactions of the Society of Information Storage Systems
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• v.6 no.2
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• pp.74-78
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• 2010

In this paper, 2-axis driving mechanism, which uses voice coil motor (VCM), is proposed to measure the friction force. The proposed VCM actuator consists of two parts; structural part and magnetic circuit part. Structural part is simplified to perform the friction experiments. Magnetic circuit part is composed of two parts. The experiments are accomplished by changing the mass of moving part. Through the experiments, optimal dimension is selected. Finally, the experimental results are verified and the optimal case is applied to the 2-axis driving mechanism, optical image stabilization actuator.

• Journal of the Korean Magnetic Resonance Society
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• v.23 no.3
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• pp.67-72
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• 2019

Caveolae are small plasma membrane invaginations that play many roles in signal transduction, endocytosis, mechanoprotection, lipid metabolism. The most important protein in caveolae is the integral membrane protein, caveolin, which is divided into three families such as caveolin 1, caveolin 2, and caveolin 3. Caveolin 1 and 3 are known to incorporate palmitate through linkage to three cysteine residues. Regulation of the protein palmitoylation cycle is important for the cellular processes such as intracellular localization of the target protein, membrane association, conformation, protein-protein interaction, and activity. However, the detailed aspect of individual palmitoylation has not been studied. In the present work, the role of each lipid modification at three cysteines was studied by NMR. Our results suggest that each lipid modification at the natively palmitoylation site has its own roles. For example, lipidations to C106 and C129 are play a role in structural stabilization, however, interestingly, lipid modification to C116 interrupts the structural stabilization.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.2
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• pp.383-394
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• 2001

The stabilization control of inverted pendulum (IP) system is difficult because of its nonlinearity and structural unstability. In this paper, an Evolving Neural Network Controller (ENNC) without Error Back Propagation (EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC)are compared with the one of conventional optimal controller and the conventional evolving neural network controller (CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.488-493
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• 2005

In gun stabilization systems, the torque comes from the unbalance mass of gun and the base acceleration is an important source of disturbance which degrades stabilization performance. Fatigue of gear train is another important factor affecting structural safety problems. In this paper, a feedback control gain is designed by optimal control weighting to difference between motor and gun velocity, and a feedforward controller using FXLMS algorithm is adopted to investigate those problems. Experimental results show that the feedforward compensator based on FXLMS can reduce the disturbance effects. The directional convergence property according to initial conditions of the FXLMS is also shown through experiments.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1327-1334
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• 1999

The stabilization control of ball-beam system is difficult because of its nonlinearity and structural unstability. Futhermore, a series of classical methods such as the PID and the full state feedback controller(FSFC) based on the local linearizations have narrow stabilizable regions. At the same time, the fine tunings of their gain parameters are also troublesome. Therefore, in this paper, three improved design techniques of stabilization controller for a ball-beam system were proposed. These parameter tuning methods in the double PID controller(DPIDC), the FSFC and the a sliding mode controller(SMC) were dependent upon the Real Value Elitist Genetic Algorithm (RVEGA). Finally, by applying the DPIDC, the FSFC and the Real Variable Elitist Genetic Algorithm based Sliding Mode Control(RVEGA SMC) to the stabilizations of a ball-beam system, the performances of the RVEGA SMC technique were showed to be superior to those of two other type controllers.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.551-558
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• 2008

This paper deals with the real-time stable walking for a humanoid robot, ISHURO-II, on uneven terrain. A humanoid robot necessitates achieving posture stabilization since it has basic problems such as structural instability. In this paper, a stabilization algorithm is proposed using the ground reaction forces, which are measured using FSR (Force Sensing Resistor) sensors during walking, and the ground conditions are estimated from these data. From this information the robot selects the proper motion pattern and overcomes ground irregularities effectively. In order to generate the proper reaction under the various ground situations, a fuzzy algorithm is applied in finding the proper angle of the joint. The performance of the proposed algorithm is verified by simulation and walking experiments on a 24-DOFs humanoid robot, ISHURO-II.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3013-3015
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• 2000

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Futhermore, a series of conventional techniques such as the pole placement and the optimal control based on the local linearizations have narrow stabilizable regions, At the same time, the fine tunings of their gain parameters are also troublesome, Thus, in this paper, an Evolving Neural Network ControlleY(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algorithm (RVEGA) was presented for stabilization of an IP system with nonlinearity, This proposed ENNC was described by a simple genetic chromosome. Through the simulation and experimental results, we showed that the finally acquired optimal ENNC was very useful in the stabilization control of IP system.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2001.05a
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• pp.243-248
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• 2001

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Thus, in this paper, an Evolving Neural Network Controller(ENNC) without Error Back Propagation(EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC) are compared with the ones of conventional optimal controller and the conventional evolving neural network controller(CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.37-60
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• 1992

This paper applies a Structural VAR approach to a 4 variable system in real GNP, M2, GNP deflator and nominal monthly earnings, disentangling 4 structural shocks, i.e., aggregate demand and supply shocks, wage pushes and various forms of regulations reinforced especially during stabilization process. Preliminary diagnostic tests confirm that the log level of each time series has at least one unit root, though the evidence is somewhat ambiguous for real GNP. One co-integration relationship is found among 4 variables, while no co-integration is found in a subsystem consisting of nomina) variables. The absence of co-integration among nominal variables strongly suggested that money is not neutral even in the long-run. The reduced form is estimated and the structural form is recovered using 6 additional identifying restrictions. Recovered structural shocks are able to capture main episodes of past 20 years, ranging from first and second oil shocks, to strong stabilization policy of early 80's and rapid wage hikes of late 80's. Overall responses of the economy to each structural shock are usually consistent with the standard Keynesian predictions, though some responses seem to be specific to Korean economic environment.