• Journal of applied mathematics & informatics
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• v.9 no.1
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• pp.101-124
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• 2002

The convergence rate of a numerical procedure barred on Schwarz Alternating Method (SAM) for solving elliptic boundary value problems (BVP's) depends on the selection of the interface conditions applied on the interior boundaries of the overlapping subdomains. It hee been observed that the Robin condition(mixed interface condition), controlled by a parameter, can optimize SAM's convergence rate. Since the convergence rate is very sensitive to the parameter, Tang[17] suggested another interface condition called over-determined interface condition. Based on the over-determined interface condition, we formulate the two-layer multi-parameterized SAM. For the SAM and the one-dimensional elliptic model BVP's, we determine analytically the optimal values of the parameters. For the two-dimensional elliptic BVP's , we also formulate the two-layer multi-parameterized SAM and suggest a choice of multi-parameter to produce good convergence rate .

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.446-448
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• 2021

In training a deep learning model, it is crucial to tune various hyperparameters and gain speed and accuracy. While hyperparameters that mathematically induce convergence impact training speed, system parameters that affect host-to-device transfer are also crucial. Therefore, it is important to properly tune and select parameters that influence the data loader as a system parameter in overall time acceleration. We propose an automated framework called Num Worker Tuner (NWT) to address this problem. This method finds the appropriate number of multi-processing subprocesses through the search space and accelerates the learning through the number of subprocesses. Furthermore, this method allows memory efficiency and speed-up by tuning the system-dependent parameter, the number of multi-process spawns.

• Proceedings of the ESK Conference
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• 1993.10a
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• pp.124-132
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• 1993

This study compared two prediction methods-regression and artificial neural network (ANN) on the visual search performance when monitoring a multi-parameter screen with different occurrence frequencies. Under the highlighting condition for the highest occurrence frequency parameter as a search cue, it was found from the requression analysis that variations of mean search time (MST) could be expained almost by three factors such as the number of parameters, the target occurrence frequency of a highlighted parameter, and the highlighted parameter size. In this study, prediction performance of ANN was evaluated as an alternative to regression method. Backpropagation method which was commonly used as a pattern associator was employed to learn a search behavior of subjects. For the case of increased number of parameters and incresed target occurrence frequency of a highlighted parameter, ANN predicted MST's moreaccurately than the regression method (p<0.000). Only the MST's predicted by ANN did not statistically differ from the true MST's. For the case of increased highlighted parameter size. both methods failed to predict MST's accurately, but the differences from the true MST were smaller when predicted by ANN than by regression model (p=0.0005). This study shows that ANN is a good predictor of a visual search performance and can substitute the regression method under certain circumstances.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.4
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• pp.193-199
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• 2011

The next generation domestic high speed railway system is a power distributed type and uses vector control method for motor speed control. Nowadays, inverter driven induction motor system is widely used. However, recently PMSM drives are deeply considered as a alternative candidate instead of an induction motor driven system due to their advantages in efficiency, noise reduction and maintenance. The next-generation high-speed train is composed of 2 converter units, 4 inverter units, and 4 Traction Motor units. Each motor is connected to the inverter directly. In this paper, the effects of IPMSM parameter variation to the system operation characteristics of the multi inverter driven high speed train system are investigated. The parallel connected inverter input-output characteristics are analyzed to the parameter mismatches of the IPMSM in 1C1M control using Matlab/Simulink, then the reliability of the simulation results are verified through experimental results.

• Journal of Power Electronics
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• v.18 no.2
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• pp.332-342
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• 2018

This paper proposes a parameter design method for multi-element resonant converters (MERCs) with a unique resonant zero point (RZP). This method is mainly composed of four steps. These steps include program filtration, loss comparison, 3D figure fine-tuning and priority compromise. It features easy implementation, effectiveness and universal applicability for almost all of the existing RZP-MERCs. Meanwhile, other design methods are always exclusive for a specific topology. In addition, a novel dual-CTL converter is also proposed here. It belongs to the RZP-MERC family and is designed in detail to explain the process of parameter selection. The performance of the proposed method is verified experimentally on a 500W prototype. The obtained results indicate that with the selected parameters, an extensive dc voltage gain is obtained. It also possesses over-current protection and minimal switching loss. The designed converter achieves high efficiencies among wide load ranges, and the peak efficiency reaches 96.9%.

• Geomechanics and Engineering
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• v.20 no.2
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• pp.113-120
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• 2020

Rock damage is the main cause of accidents in underground engineering. It is difficult to predict rock damage accurately by using only one parameter. In this study, a rock failure prediction model was established by using stress, energy, and damage. The prediction level was divided into three levels according to the ratio of the damage threshold stress to the peak stress. A classification predicting model was established, including the stress, energy, damage and AE impact rate using Bayesian method. Results show that the model is good practicability and effectiveness in predicting the degree of rock failure. On the basis of this, a multi-parameter classification predicting deterioration model of rock failure was established. The results provide a new idea for classifying and predicting rockburst.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.185-188
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• 2005

This paper suggests a control method for efficient topology/parameter evolution in a bond-graph-based GP design framework that automatically synthesizes designs for multi-domain, lumped parameter dynamic systems, We adopt a hierarchical breeding control mechanism with fitness-level-dependent differences to obtain better balancing of topology/parameter search - biased toward topological changes at low fitness levels, and toward parameter changes at high fitness levels. As a testbed for this approach, an eigenvalue assignment problem, which is to find bond graph models exhibiting minimal distance errors from target sets of eigenvalues, was tested and showed improved performance for various sets of eigenvalues.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.08a
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• pp.145-145
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• 2011

Recently, multi-hole electrode RF capacitively coupled plasma discharge is being used in the deposition of microcrystalline silicon for thin film solar cell to increase the speed of deposition. To make efficient multi-hole electrode RF capacitively coupled plasma discharge, the hole diameter is to be designed concerning the plasma parameters. In past studies, the relationship between plasma parameters such as pressures and gas species, and hole diameter for efficient plasma density enhancement is experimentally shown. In the presentation, the relationship between plasma deriving frequency and hole diameter for efficient multi-hole electrode RF capacitively coupled plasma discharge is shown. In usual capacitively coupled plasma discharge, plasma parameter, such as plasma density, plasma impedence and plasma temperature, change as frequency increases. Because of the change, the optimum hole diameter of the multi-hole electrode RF capacitively coupled plasma for high density plasma is thought to be modified when the plasma deriving frequency changes. To see the frequency effect on the multi-hole RF capacitively coupled plasma is discharged and one of its electrode is changed from a plane electrode to a variety of multi-hole electrodes with different hole diameters. The discharge is derived by RF power source with various frequency and the plasma parameter is measured with RF compensated single Langmuir probe. The shrinkage of the hole diameter for efficient discharge is observed as the plasma deriving frequency increases.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.353-356
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• 2000

Multi-bias parameter extraction technique for HEMT small signa] equivalent circuits is presented in this paper. The technique in this paper uses S-parameters measured at various bias points in the active region to construct one optimization problem, of which the vector of unknowns contains only a set of bias-independent elements. Tests are peformed on measured S-parameters of a pHEMT at 30 bias points. Results indicate that the calculated S-parameters is similar to the measured data.