• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.4
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• pp.261-266
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• 2010

In this paper, we report the changes of electrical, structural and optical characteristics in $Ge_2Sb_2Te_5$ thin films according to an increase of Si content. The Si-doped $Ge_2Sb_2Te_5$ thin films were prepared by rf-magnetron co-sputtering method. Isothermal annealing was carried out at $N_2$ atmosphere. The crystallization speed (v) of amorphous thin films was evaluated by detecting the reflection response signals using a nano-pulse scanner (wavelength = 658 nm) with illumination power of 1~17 mW and pulse duration of 10~460 ns. Structural phase changes were evaluated by XRD, and the optical transmittance was measured in the wavelength range of 300~3000 nm using UV-vis-NIR spectrophotometer. The sheet resistance (RS) of the thin films was measured using 4 point probe. Conclusivlely, the v-value decreased with an increase of Si content, while the RS-values of both crystalline and amorphous phases were increased. In particular, fcc-to-hexagonal transition was suppressed by the added Si atoms.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.1
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• pp.12-16
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• 2005

Chemical mechanical polishing (CMP) process is one of the most useful methods for improving the surface roughness of films. The effects of CMP on the surface morphology of WO$_3$ films prepared by RF sputtering system were investigated in this paper. A removal rate of films increased, and the uniformity performance of surface decreased with the addition of an oxidizer to the tungsten slurry. Non-uniformity performance of surface was superior as its value was below 5 % when oxidizers of 5.0 vol% and 2.5 vol%, respectively, were added to the tungsten slurry. The optimized oxidizer concentration, reflected both the improved roughness values and hillock-free surface with the good uniformity performance, was 5.0 vol% as an atomic force microscopy(AFM) analysis of thin film topographies. Our CMP results will be a useful reference for advanced technology of thin films for gas sensor applications in the near future.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.05c
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• pp.55-58
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• 2001

In this paper, we present the results of the design and fabrication of the VCO(Voltage controlled Oscillator) using RF circuit simulator GENESYS and electromagnetic field simulator EMpower Frequency range is fabricated VCO is 850 MHz ~ 950 MHz, which is used Colpitts Circuit. the fabricated VCO is consisted of resonator, oscillator and MSL(Microstrip Line) is used in LC tuning circuit.(operated by negative feedback) MSL(Microstrip Line), Varactor(Plastic package), low noise TR(SOT-23), chip inductor(1608), chip capacitor(1005), chip resistance(1005). 1005 type is used for sample fabrication of VCO. In the fabrication process, circuit pattern is screen printed on the alumina substrates of over 99.9% purity. Center frequency of the sample VCO is 850MHz at $V_T=1.5V$, while the simulated value was 1.0GHz at $V_T=1.5V$. Variable frequency range of the sample is 860~950MHz in contrast to the 1068~1100MHz of the simulated values.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.05c
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• pp.153-156
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• 2001

In this paper, we present the results of the design and fabrication of the VCO(Voltage controlled Oscillator) using RF circuit simulator GENESYS and electromagnetic field simulator EMpower Frequency range is fabricated VCO is 850 MHz ~ 950 MHz, which is used Colpitts Circuit. the fabricated VCO is consisted of resonator, oscillator and MSL(Microstrip Line) is used in LC tuning circuit.(operated by negative feedback) MSL(Microstrip Line), Varactor(Plastic package), low noise TR(SOT-23), chip inductor(1608), chip capacitor(1005), chip resistance(1005). 1005 type is used for sample fabrication of VCO. In the fabrication process, circuit pattern is screen printed on the alumina substrates of over 99.9% purity. Center frequency of the sample VCO is 850MHz at $V_T$=1.5V, while the simulated value was 1.0GHz at $V_T$=1.5V. Variable frequency range of the sample is 860~950MHz in contrast to the 1068~1100MHz of the simulated values.

• Journal of Ginseng Research
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• v.37 no.2
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• pp.219-226
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• 2013

This study was conducted to investigate the effect of extrusion conditions (moisture content 20% and 30%, screw speed 200 and 250 rpm, barrel temperature $115^{\circ}C$ and $130^{\circ}C$) on the acidic polysaccharide, ginsenoside contents and antioxidant properties of extruded Korean red ginseng (KRG). Extruded KRGs showed relatively higher amounts of acidic polysaccharide (6.80% to 9.34%) than non-extruded KRG (4.34%). Increased barrel temperature and screw speed significantly increased the content of acidic polysaccharide. The major ginsenosides (Rb1, Rb2, Rc, Rd, Re, Rf, Rg2s, Rg3s, Rh1, and Rg3r) of KRG increased through extrusion, while the ginsenoside (Rg1) decreased. The EX8 (moisture 30%, screw speed 250 rpm, and temperature $130^{\circ}C$) had more total phenolics and had a better scavenging effect on 2,2-diphenyl-1-picrylhydrazyl radicals than those of extruded KRG samples. The extrusion cooking showed a significant increase (6.8% to 20.9%) in reducing power. Increased barrel temperature significantly increased the values of reducing power, the highest value was 1.152 obtained from EX4 (feed moisture 20%, screw speed 250 rpm, and temperature $130^{\circ}C$). These results suggest that extrusion conditions can be optimized to retain the health promoting compounds in KRG products.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.08a
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• pp.138-141
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• 2002

The channel of the superconducting Flux Flow Transistor has been fabricated with plasma etching method using ICP. The ICP conditions were 700 W of ICP power, 150 W of rf chuck power, 5 mTorr of the pressure in chamber and 1:1 of Ar : $Cl_2$, respectively. The channel etched by plasma gas showed superconducting characteristics of over 77 K and superior surface morphology. The critical current of SFFT was altered by varying the external applied current. As the external applied current increased from 0 to 12 mA, the critical current decreased from 28 to 22 mA. Then the obtained $r_m$ values were smaller than $0.1\Omega$ at a bias current of 40 mA. The current gain was about 0.5. Output resistance was below $0.2\Omega$.

• Steel and Composite Structures
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• v.46 no.6
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• The Journal of Korean Physical Therapy
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• v.17 no.2
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• pp.38-45
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• 2005

This study was deigned to identify the effect of electromyographic activity of knee extensor during contralateral hip isometric adduction. six young adults who had not experienced any knee injuries were recruited. Their Q-angles were within a normal rage. They were asked to sit on the table. The EMG activities of the vastus lateralis (VL), vastus medialis oblique (VMO) were recorded in sitting by surface electrodes and normalized by MVC EMG values derived from manual muscle test. The EMG activity levels of the VL, RF, and VMO were the highest when foot was externally rotated. The EMG activity level of VMO/VL ratio did show significant differences(p<.05). The onset time of vastus lateralis (VL) and vastus medialis oblique (VMO) was similar in contralateral hip isometric adduction(p<.05).

• Advances in Computational Design
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• v.7 no.3
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• pp.253-279
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• 2022

This study investigated and predicted the Marshall stability of glass-fiber asphalt mix, carbon-fiber asphalt mix and glass-carbon-fiber asphalt (hybrid) mix by using machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest(RF), The data was obtained from the experiments and the research articles. Assessment of results indicated that performance of the Artificial Neural Network (ANN) based model outperformed applied models in training and testing datasets with values of indices as; coefficient of correlation (CC) 0.8492 and 0.8234, mean absolute error (MAE) 2.0999 and 2.5408, root mean squared error (RMSE) 2.8541 and 3.3165, relative absolute error (RAE) 48.16% and 54.05%, relative squared error (RRSE) 53.14% and 57.39%, Willmott's index (WI) 0.7490 and 0.7011, Scattering index (SI) 0.4134 and 0.3702 and BIAS 0.3020 and 0.4300 for both training and testing stages respectively. The Taylor diagram also confirms that the ANN-based model outperforms the other models. Results of sensitivity analysis show that Carbon fiber has a major influence in predicting the Marshall stability. However, the carbon fiber (CF) followed by glass-carbon fiber (50GF:50CF) and the optimal combination CF + (50GF:50CF) are found to be most sensitive in predicting the Marshall stability of fibrous asphalt concrete.

• Structural Engineering and Mechanics
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• v.84 no.5
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• pp.605-618
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• 2022

Glass fiber reinforced polymer (GFRP) elastic gridshells consist of long continuous GFRP tubes that form elastic deformations. In this paper, a method for the form-finding of gridshell structures is presented based on the interpretable machine learning (ML) approaches. A comparative study is conducted on several ML algorithms, including support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), AdaBoost, XGBoost, category boosting (CatBoost), and light gradient boosting machine (LightGBM). A numerical example is presented using a standard double-hump gridshell considering two characteristics of deformation as objective functions. The combination of the grid search approach and k-fold cross-validation (CV) is implemented for fine-tuning the parameters of ML models. The results of the comparative study indicate that the LightGBM model presents the highest prediction accuracy. Finally, interpretable ML approaches, including Shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions of the ML model since it is essential to understand the effect of various values of input parameters on objective functions. As a result of interpretability approaches, an optimum gridshell structure is obtained and new opportunities are verified for form-finding investigation of GFRP elastic gridshells during lifting construction.