• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1029-1037
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• 2010

If an internal combustion engine is operated by consolidated control, the minimum fuel consumption is achieved and the demanded objectives are satisfied. For this, it is necessary that the engine is operated on the ideal operating line which satisfies minimum fuel consumption. In this context of view, there are many tries to achieve given object. However, the parameters in the internal combustion engines are variable and depend on the operating points. Therefore, it is necessary to cope with the uncertainties such that the optimal operating may be possible. From this point of view, this paper gives a controller design method and a robust stability condition for engine torque control which satisfies the given control performance and robust stability in the presence of physical parameter perturbation. Exactly, in this paper, we consider the robust stability problem of this 2DOF servosystem with nonlinear type uncertainty in the engine system, and a robust stability condition for the servosystem is shown. This result guarantees that if the plant uncertainty is in the permissible set defined by the given condition, then a gain tuning can be carried out to suppress the influence of the plant uncertainties.

• Journal of Power Electronics
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• v.14 no.5
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• pp.1008-1027
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• 2014

An electric scooter with a V-belt continuously variable transmission (CVT) driven by a permanent magnet synchronous motor (PMSM) has a lot of nonlinear and time-varying characteristics, and accurate dynamic models are difficult to establish for linear controller designs. A PMSM servo-drive electric scooter controlled by a novel hybrid modified recurrent Legendre neural network (NN) control system is proposed to solve difficulties of linear controllers under the occurrence of nonlinear load disturbances and parameters variations. Firstly, the system structure of a V-belt CVT driven electric scooter using a PMSM servo drive is established. Secondly, the novel hybrid modified recurrent Legendre NN control system, which consists of an inspector control, a modified recurrent Legendre NN control with an adaptation law, and a recouped control with an estimation law, is proposed to improve its performance. Moreover, the on-line parameter tuning method of the modified recurrent Legendre NN is derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, two optimal learning rates for the modified recurrent Legendre NN are derived to speed up the parameter convergence. Finally, comparative studies are carried out to show the effectiveness of the proposed control scheme through experimental results.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.4
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• pp.593-608
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• 1995

In this study two issues are considered, one is to develop a pneumatic servomechanism using a direct-connected circuit between inlet and outlet, the other is to design two kinds of advanced controllers such as fuzzy and PID controllers for a fingering system. Besides, the application of the advanced controllers to the newly proposed servomechanism is presented. The procedure of this study is composed of following 6 steps : [Step 1] Structuring of a control system; [Step 2] Development of a pneumatic circuit for the servomechanism ; [Step 3] Characteristic analysis of the valve and cylinder systems ; [Step 4] Determination of optimal parameters of the PID controller ; [Step 5] Design of a fuzzy controller and parameter tuning; and, [Step 6] Experimental analysis of fuzzy and PID controllers. Experimental results show that the newly proposed pneumatic servomechanism has good performance and, not only the performance of the fuzzy controller is better than that of the PID controller but also the fuzzy controller fits well to the control of the pneumatic servomechanism.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.475-482
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• 2018

In this paper, we present a study on data augmentation methods for DNN (Deep Neural Network)-based audio tagging. In this system, an audio signal is converted into a mel-spectrogram and used as an input to the DNN for audio tagging. To cope with the problem associated with a small number of training data, we augment the training samples using time stretching, pitch shifting, dynamic range compression, and block mixing. In this paper, we derive optimal parameters and combinations for the augmentation methods through audio tagging simulations.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.353-364
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• 2022

This study presents a hybrid algorithm for classifying the rock joints, where the improved artificial bee colony (IABC) and the fuzzy C-means (FCM) clustering algorithms are incorporated to take advantage of the artificial bee colony (ABC) algorithm by tuning the FCM clustering algorithm to obtain the more reasonable and stable result. A coefficient is proposed to reduce the amount of blind random searches and speed up convergence, thus achieving the goals of optimizing and improving the ABC algorithm. The results from the IABC algorithm are used as initial parameters in FCM to avoid falling to the local optimum in the local search, thus obtaining stable classifying results. Two validity indices are adopted to verify the rationality and practicability of the IABC-FCM algorithm in classifying the rock joints, and the optimal amount of joint sets is obtained based on the two validity indices. Two illustrative examples, i.e., the simulated rock joints data and the field-survey rock joints data, are used in the verification to check the feasibility and practicability in rock engineering for the proposed algorithm. The results show that the IABC-FCM algorithm could be applicable in classifying the rock joint sets.

• Smart Structures and Systems
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• v.19 no.6
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• pp.665-678
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• 2017

In this paper the tuned mass-damper-inerter (TMDI) is considered for passive vibration control and energy harvesting in harmonically excited structures. The TMDI couples the classical tuned mass-damper (TMD) with a grounded inerter: a two-terminal linear device resisting the relative acceleration of its terminals by a constant of proportionality termed inertance. In this manner, the TMD is endowed with additional inertia, beyond the one offered by the attached mass, without any substantial increase to the overall weight. Closed-form analytical expressions for optimal TMDI parameters, stiffness and damping, given attached mass and inertance are derived by application of Den Hartog's tuning approach to suppress the response amplitude of force and base-acceleration excited single-degree-of-freedom structures. It is analytically shown that the TMDI is more effective from a same mass/weight TMD to suppress vibrations close to the natural frequency of the uncontrolled structure, while it is more robust to detuning effects. Moreover, it is shown that the mass amplification effect of the inerter achieves significant weight reduction for a target/predefined level of vibration suppression in a performance-based oriented design approach compared to the classical TMD. Lastly, the potential of using the TMDI for energy harvesting is explored by substituting the dissipative damper with an electromagnetic motor and assuming that the inertance can vary through the use of a flywheel-based inerter device. It is analytically shown that by reducing the inertance, treated as a mass/inertia-related design parameter not considered in conventional TMD-based energy harvesters, the available power for electric generation increases for fixed attached mass/weight, electromechanical damping, and stiffness properties.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.106-118
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• 2019

In recent years, research and development on image object recognition using artificial intelligence have been actively carried out, and it is expected to be used for road maintenance. Among them, artificial intelligence models for object detection of road surface are continuously introduced. In order to develop such object recognition algorithms, a backbone network that extracts feature maps is essential. In this paper, we will discuss how to select the appropriate neural network. To accomplish it, we compared with 4 different deep neural networks using 6,000 road surface damage images. Based on three evaluation methods for analyzing characteristics of neural networks, we propose a method to determine optimal neural networks. In addition, we improved the performance through optimal tuning of hyper-parameters, and finally developed a light backbone network that can achieve 85.9% accuracy of road surface damage classification.

• Nuclear Engineering and Technology
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• v.52 no.2
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• pp.279-286
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• 2020

In this article, we develop a reactive distillation (RD) column configuration for the production of hydrogen. This RD column is in the HI decomposition section of the sulphur - iodine (SI) thermochemical cycle, in which HI decomposition and H₂ separation take place simultaneously. The section plays a major role in high hydrogen production efficiency (that depends on reaction conversion and separation efficiency) of the SI cycle. In the column simulation, the rigorous thermodynamic phase equilibrium and reaction kinetic model are used. The tuning parameters involved in phase equilibrium model are dependent on interactive components and system temperature. For kinetic model, parameter values are adopted from the Aspen flowsheet simulator. Interestingly, there is no side reaction (e.g., solvation reaction, electrolyte decomposition and polyiodide formation) considered aiming to make the proposed model simple that leads to a challenging prediction. The process parameters are determined on the basis of optimal hydrogen production as reflux ratio = 0.87, total number of stages = 19 and feeding point at 8th stage. With this, the column operates at a reasonably low pressure (i.e., 8 bar) and produces hydrogen in the distillate with a desired composition (H₂ = 9.18 mol%, H₂O = 88.27 mol% and HI = 2.54 mol%). Finally, the results are compared with other model simulations. It is observed that the proposed scheme leads to consume a reasonably low energy requirement of 327 MJ/kmol of H₂.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1297-1300
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• 2008

A conventional linear accelerator system requires a flat-topped pulse with less than ${\pm}$ 0.5% ripple to meet the beam energy spread requirements and to improve pulse efficiency of RF systems. A pulse transformer is one of main determinants on the output pulse voltage shape. The pulse transformer was investigated and analyzed with the pulse response characteristics using a simplified equivalent circuit model. The damping factor ${\sigma}$ must be >0.86 to limit the overshoot to less than 0.5% during the flat-top phase. The low leakage inductance and distributed capacitance are often limiting factors to obtain a fast rise time. These parameters are largely controlled by the physical geometry and winding configuration of the transformer. A rise time can be improved by reducing the number of turns, but it produces larger pulse droop and requires a larger core size. By tradeoffs among these parameters, the high-voltage pulse transformer with a pulse width of 10 ${\mu}s$, a rise time of 0.84 ${\mu}s$, and a pulse droop of 2.9% has been designed and fabricated to drive a klystron which has an output voltage of 284 kV, 30-MW peak and 60-kW average RF output power. This paper describes design optimization of a high-voltage pulse transformer for high-power pulsed applications. The experimental results were analyzed and compared with the design. The design and optimal tuning parameter of the system was identified using the model simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.297-299
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• 2021

Predicting future productivity of tight oil is an important task for analyzing residual oil recovery and reservoir behavior. In general, productivity prediction is made using the decline curve analysis(DCA). In this study, we intend to propose an effective model for predicting future production using deep learning-based recurrent neural networks(RNN), LSTM, and GRU algorithms. As input variables, the main parameters are oil, gas, water, which are calculated during the production of tight oil, and the type curve calculated through various cluster analyzes. the output variable is the monthly oil production. Existing empirical models, the DCA and RNN models, were compared, and an optimal model was derived through hyperparameter tuning to improve the predictive performance of the model.