• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.21 no.7
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• pp.629-637
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• 2008

We report several experimental data capable of evaluating the amorphous-to-crystalline (a-c) phase transformation in $(Ag)_x(Ge_2Sb_2Te_5)_{1-x}$ (x = 0, 0.05, 0.1) thin films prepared by a thermal evaporation. The isothermal a-c structural phase changes were evaluated by XRD, and the optical transmittance was measured in the wavelength range of $800{\sim}3000$ nm using a UV-vis-IR spectrophotometer. A speed of the a-c transition was evaluated by detecting the reflection response signals using a nano-pulse scanner with 658 nm laser diode (power P = $1{\sim}17$ mW, pulse duration t = $10{\sim}460$ ns). The surface morphology and roughness of the films were imaged by AFM. It was found that the crystallization speed was so enhanced with an increase of Ag content. While the sheet resistance of c-phase $(Ag)_x(Ge_2Sb_2Te_5)_{1-x}$ was similar to that of c-phase $Ge_2Sb_2Te_5$ (i.e., $R_c{\sim}10{\Omega}/{\square}$), the sheet resistance of a-phase $(Ag)_x(Ge_2Sb_2Te_5)_{1-x}$ was found to be lager than that of a-phase $Ge_2Sb_2Te_5$, $R_a{\sim}5{\times}10^6{\Omega}{/\square}$. For example, the ratios of $R_a/R_c$ for $Ge_2Sb_2Te_5$ and $(Ag)_{0.1}(Ge_2Sb_2Te_5)_{0.9}$ were approximately $5{\times}10^5$ and $5{\times}10^6$, respectively.

• Journal of the Korean Institute of Gas
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• v.5 no.2 s.14
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• pp.43-51
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• 2001

ln this study, a natural gas pipeline network model was established using STONER. First a map of natural gas pipeline network was drawn on STONER and then the length and diameter of the pipe were inputted. And as the specific gravity of gas flowing in the pipeline which is the value of natural gas was inputted. Finally in order to decide the pipeline variables and gas temperature, through the verification with observed real data, the possible error was minimized. For the verification, the pipeline variables and gas temperature were assumed and the pipeline network analysis was accomplished with real demand data. The square deviation of analysed pressure from observed pressure was calculated and the minimum case was selected for the optimum pipeline variables and gas temperature. Thus a proper natural gas pipeline network model for real network was established.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.6
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• pp.7-14
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• 1989

A trajectory control method being capable of singularity avoidance is proposed for a robot and positoner (R-P) system. In the proposed method, the damping factor of the Damped Least Square (DLS) method is adjusted by gradients of trajectory following errors so that the singularity avoidance can be achieved while mimimizing the errors. Two numerical examples are given by employing a Rhino robot with five degrees-of-freedom (d.o.f.) and two d.o.f's, where the method of maximizing the manipulagility the DLS method with a fixd damping factor and the proposed method are compared in terms of trajectory following errors, manipulabilities and joint velocities.

• Journal of Integrative Natural Science
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• v.15 no.1
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• pp.9-18
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• 2022

This research examines the power of bootstrap two-sample test, and compares it with the powers of two-sample t-test and Wilcoxon rank sum test, through simulation. For simulation work, a positively skewed and heavy tailed distribution was selected as a population distribution, the chi-square distributions with three degrees of freedom, χ²₃. For two independent samples, the fist sample was selected from χ²₃. The second sample was selected independently from the same χ²₃ as the first sample, and calculated d+ax for each sampled value x, a randomly selected value from χ²₃. The d in d+ax has from 0 to 5 by 0.5 interval, and the a has from 1.0 to 1.5 by 0.1 interval. The powers of three methods were evaluated for the sample sizes 10,20,30,40,50. The null hypothesis was the two population medians being equal for Bootstrap two-sample test and Wilcoxon rank sum test, and the two population means being equal for the two-sample t-test. The powers were obtained using r program language; wilcox.test() in r base package for Wilcoxon rank sum test, t.test() in r base package for the two-sample t-test, boot.two.bca() in r wBoot pacakge for the bootstrap two-sample test. Simulation results show that the power of Wilcoxon rank sum test is the best for all 330 (n,a,d) combinations and the power of two-sample t-test comes next, and the power of bootstrap two-sample comes last. As the results, it can be recommended to use the classic inference methods if there are widely accepted and used methods, in terms of time, costs, sometimes power.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.487-494
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• 2023

In this study, performance comparison was performed on R and TensorFlow, which are free deep learning tools. In the experiment, six types of deep neural networks were built using each tool, and the neural networks were trained using the 10-year Korean temperature dataset. The number of nodes in the input layer of the constructed neural network was set to 10, the number of output layers was set to 5, and the hidden layer was set to 5, 10, and 20 to conduct experiments. The dataset includes 3600 temperature data collected from Gangnam-gu, Seoul from March 1, 2013 to March 29, 2023. For performance comparison, the future temperature was predicted for 5 days using the trained neural network, and the root mean square error (RMSE) value was measured using the predicted value and the actual value. Experiment results shows that when there was one hidden layer, the learning error of R was 0.04731176, and TensorFlow was measured at 0.06677193, and when there were two hidden layers, R was measured at 0.04782134 and TensorFlow was measured at 0.05799060. Overall, R was measured to have better performance. We tried to solve the difficulties in tool selection by providing quantitative performance information on the two tools to users who are new to machine learning.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.2
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• pp.202-212
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• 1997

In this study, performance of the multi-stage explicit methods for numerical computation of the unsteady Navier-Stokes equations is investigated. Three methods under consideration are 1 st-, 2 nd-, and 4 th-order Runge-Kutta (R-K) methods. Compared in this estimation is stability, accuracy, and CPU time of each method. The computational codes developed are applied to the two-dimensional flow in a square cavity driven by an oscillating lid. It turned out that at Reynolds number 400, the 1 st-order R-K method is the best, while at 3200 the 2 nd-order R-K is recommended. At higher Reynolds numbers, it is conjectured that the 4 th-order R-K method will be the best algorithm among three due to its highest stability.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.298-303
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• 1998

In the speed sensorless control of the induction motor, the machine parameters (especially the secondary resistance R2) have a strong influence to the speed estimation. It is known that the simultaneous estimation of the speed and R2 is impossible in the slip frequency type vector control, because the secondary flux is constant. But the secondary flux is not always constant in the speed transient state. In this paper the R2 estimation in the transient state without adding any additional signal to the stator current is proposed. This algorithm uses the least mean square algorithm and the adaptive algorithm, and it is possible to estimate the R2 exactly. This algorithm is verified by the digital simulations and the experiments.

• International Journal of Naval Architecture and Ocean Engineering
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• v.9 no.5
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• pp.537-551
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• 2017

In this paper, a two-phase flow solver $HiFoam^{(R)}$ has been developed based on the $OpenFOAM^{(R)}$ to predict resistance of a ship in calm water. The VOF method of $OpenFOAM^{(R)}$ was reviewed and a simple flux limiter was introduced to enhance the robustness of the solver. The procedure for predicting ship motion was modified by introducing Quasi-Steady Fluid-Body Interaction (QS-FBI) with least square regression to improve the efficiency. Other minor factors were considered as well in terms of the efficiency and robustness. The HiFoam was applied to KCS and JBC simulations to validate its efficiency and accuracy by comparing the results to experimental data and STAR-CCM+. The $HiFoam^{(R)}$ was also applied to various ships and it showed good agreements to the experimental data.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.146-151
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• 2019

This paper reports a sliding window filtering approach for ground moving targets with cross-correlated sensor noise and uncertainty. In addition, the effect of uncertain parameters during a tracking error on the model performance is considered. A distributed fusion sliding window filter is also proposed. The distributed fusion filtering algorithm represents the optimal linear combination of local filters under the minimum mean-square error criterion. The derivation of the error cross-covariances between the local sliding window filters is the key to the proposed method. Simulation results of the motion of the ground moving target a demonstrate high accuracy and computational efficiency of the distributed fusion sliding window filter.

• Journal of Sensor Science and Technology
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• v.28 no.2
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• pp.88-93
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• 2019

This paper is concerned with distributed fusion moving average prediction for continuous-time linear stochastic systems with multiple sensors. A distributed fusion with the weighted sum structure is applied to the optimal local moving average predictors. The distributed fusion prediction algorithm represents the optimal linear fusion by weighting matrices under the minimum mean square criterion. The derivation of equations for error cross-covariances between the local predictors is the key of this paper. Example demonstrates effectiveness of the distributed fusion moving average predictor.