• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.1
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• pp.52-58
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• 2019

Due to icing and snow, power transmission lines have asymmetric cross sections, and their motion becomes unstable. At this time, the vibration caused by the wind is called galloping. If galloping is continuous, short circuits or ground faults may occur. It is possible to prevent galloping by installing spacers between transmission lines. In this study, the transmission line is modeled as a mass-spring-damper system by using RecurDyn. To analyze the dynamic behavior of the transmission line, the damping coefficient is derived from the free vibration test of the transmission line and Rayleigh damping theory. The drag and lift coefficient for modeling the wind load are calculated from the flow analysis by using ANSYS Fluent. Galloping simulations according to spacer stiffness and interval are carried out. It is found that when the stiffness is 100 N/m and the interval around the support is dense, the galloping phenomenon is reduced the most.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1235-1238
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• 1987

In this paper, a new focusing method, to be called the sampled delay focusing (SDF), is proposed. This method improves the lateral resolution in ultrasound imaging system. In SDF, the analog delay lines are no longer necessary because sampling sum process can replace the conventional delay sum process. Also, this method offers continuous dynamic focusing on the resolution pixel basis if the maximum delay time is less than the sampling interval. Second order sampling is adopted in order to extend the sampling interval.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Journal of Power Electronics
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• v.16 no.5
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• pp.1781-1790
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• 2016

This study focuses on the coordination control problem of multiple electrical excited synchronous motor systems. A robust coordination controller is designed on the basis of cross coupling and an interval matrix. The proposed control strategy can deal with load uncertainty. In addition, the proposed control strategy is applied to a high-power metal-rolling system. Simulation and experiment results demonstrate that the proposed control strategy achieves good dynamic and static performance. It also shows better coordination performance than traditional proportional-integral controllers.

• Journal of the Korean Solar Energy Society
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• v.25 no.2
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• pp.71-79
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• 2005

In this study, the ventilation performance of mechanical ventilation system in apartment House was analyzed through model test according to characteristics of air-vent. Then adequate interval of air-vent was suggested using computer simulation which will create comfort environment through improvement of ventilation performance in apartment house. The result of experiment with separation plate to prevent mixture of contaminated exhaust air with fresh supply air, the ventilation efficiency improved about 10%. The result of simulation with horizontal location of exterior air-vent, contaminated exhaust air is mixed regardless of interval variation. Consequently, mixture of the exhaust air can be prevented through locating the supply air vent on the top side and exhaust air vent on the lower side.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.577-583
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• 2019

A dynamic system is called positive if any trajectory of the system starting from non-negative initial states remains forever non-negative for non-negative controls. In this paper, we consider the new stability condition for the positive time-varying linear discrete interval systems with time-varying delay and unstructured uncertainty. The delay time is considered as time-varying within certain interval having minimum and maximum values and the system is subjected to nonlinear unstructured uncertainty which only gives information on uncertainty magnitude. The proposed stability condition is an improvement of the previous results which can be applied only to time-invariant systems or had no consideration of uncertainty, and they can be expressed in the form of a very simple inequality. The stability conditions are derived using the Lyapunov stability theory and have many advantages over previous results using the upper solution bound of the Lyapunov equation. Through numerical example, the proposed stability conditions are proven to be effective and can include the existing results.

• Journal of Power Electronics
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• v.13 no.1
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• pp.139-160
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• 2013

In this paper, an intelligent robust control system (IRCS) for precision tracking control of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The IRCS comprises a recurrent wavelet-based interval type-2 fuzzy-neural-network controller (RWIT2FNNC), an RWIT2FNN estimator (RWIT2FNNE) and a compensated controller. The RWIT2FNNC combines the merits of a self-constructing interval type-2 fuzzy logic system, a recurrent neural network and a wavelet neural network. Moreover, it performs the structure and parameter-learning concurrently. The RWIT2FNNC is used as the main tracking controller to mimic the ideal control law (ICL) while the RWIT2FNNE is developed to approximate an unknown dynamic function including the lumped parameter uncertainty. Furthermore, the compensated controller is designed to achieve $L_2$ tracking performance with a desired attenuation level and to deal with uncertainties including approximation errors, optimal parameter vectors and higher order terms in the Taylor series. Moreover, the adaptive learning algorithms for the compensated controller and the RWIT2FNNE are derived by using the Lyapunov stability theorem to train the parameters of the RWIT2FNNE online. A computer simulation and an experimental system are developed to validate the effectiveness of the proposed IRCS. All of the control algorithms are implemented on a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the IRCS grants robust performance and precise response regardless of load disturbances and PMSM parameters uncertainties.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.519-519
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• 2023

Estuarine dams are a recent and global phenomenon. While estuarine dams can provide the benefit of improved freshwater resources, they can also alter estuarine processes. Due to the wide range of estuarine types and estuarine dam configurations, the effect of estuarine dams on estuaries is not well understood in general. To develop a systematic understanding of the effect of estuarine dam location and freshwater discharge interval on a range of estuarine types (strongly stratified, partially mixed, periodically stratified, and well-mixed), this study used a coupled hydrodynamic-sediment dynamic numerical model (COAWST) and compared flow, sediment transport, and morphological conditions in the pre- and post-dam estuaries. For each estuarine type, scenarios with dam locations at 20, 55 and 90 km from the mouth and discharge intervals of a discharge every 0.5, 3, and 7 days were investigated. The results were analyzed in terms of change in tide, river discharge, estuarine classification, and sediment flux mechanism. The estuarine dam location primarily affected the tide-dominated estuaries, and the resonance length was an important length scale affecting the tidal currents and Stokes return flow. When the location was less than the resonance length, the tidal currents and Stokes return flow were most reduced due to the loss of tidal prism, the dead-end channel, and the shift from mixed to standing tides. The discharge interval primarily affected the river-dominated estuaries, and the tidal cycle period was an important time scale. When the interval was greater than the tidal cycle period, notable seaward discharge pulses and freshwater fronts occurred. Dams located near the mouth with large discharge interval differed the most from their pre-dam condition based on the estuarine classification. Greater discharge intervals, associated with large discharge magnitudes, resulted in scour and seaward sediment flux in the river-dominated estuaries, and the dam located near the resonance length resulted in the greatest landward tidal pumping sediment flux and deposition in the tide-dominated estuaries.

• International Journal of Fluid Machinery and Systems
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• v.8 no.2
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• pp.102-112
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• 2015

In the Nordic grid, a trend observed the recent years is the increase in grid frequency variations, which means the frequency is outside the normal range (49.9-50.1 Hz) more often. Variations in the grid frequency leads to changes in the speed of rotation of all the turbines connected to the grid, since the speed of rotation is closely related to the grid frequency for synchronous generators. When the speed of rotation changes, this implies that the net torque acting on the rotating masses are changed, and the material of the turbine runners must withstand these changes in torque. Frequency variations thus leads to torque oscillations in the turbine, which become dynamical loads that the runner must be able to withstand. Several new Francis runners have recently experienced cracks in the runner blades due to fatigue, obviously due to the runner design not taking into account the actual loads on the runner. In this paper, the torque oscillations and dynamic loads due to the variations in grid frequency are simulated in a 1D MATLAB program, and measured grid frequency is used as input to the simulation program. The maximum increase and decrease in the grid frequency over a 440 seconds interval have been investigated, in addition to an extreme event where the frequency decreased far below the normal range within a few seconds. The dynamic loading originating from grid frequency variations is qualitatively found by a constructed variable $T_{stress}$, and for the simulations presented here the variations in $T_{stress}$ are found to be around 3 % of the mean value, which is a relatively small dynamic load. The important thing to remember is that these dynamic loads come in addition to all other dynamic loads, like rotor-stator interaction and draft tube surges, and should be included in the design process, if not found to be negligible.