• Journal of Welding and Joining
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• v.32 no.6
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• pp.14-21
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• 2014

High-temperature corrosion resistance of martensitic-hardfaced weldment is generally evaluated by the Cr content depending on dilution rate. Present study used a commercial program(SYSWELD) applying three-dimensional heat flow analysis to predict temperature distribution of weld. Configuration of weld bead can be determined by the contour of melting temperature and simultaneously dilution rate is calculated to predict Cr content. Experimental study also has been conducted to measure Cr content of harfaced surface welded by FCAW. Results indicated that computational results were well matched with those obtained from experiments.

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.5
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• pp.921-927
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• 1989

One of the most important problems in recent life prediction is to introduce the degradation effects into life prediction procedure. In the present paper, the effect of the material degradation on the fatigue surface crack growth and fatigue life prediction in a 2 1/4 Cr-1Mo steel were investigated. The 2 1/4 Cr-1Mo steel has been used in a plant having operated for over 60000hours and subjected to material degradation due to temper-embitterment. A Monte-Carlo simulation was made on the basis of the data obtained in the experiment in order to determine the P-S-N diagrams of surface crack growth for the degraded and recovered steels.

• Journal of the Korean Magnetics Society
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• v.25 no.2
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• pp.31-38
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• 2015

Recently, a first-principles study led to a prediction that quaternary Heusler compounds, CoFeCrZ (Z = Ga, Ge) are excellent half-metallic ferromagnets. In this study, we investigate the electronic and the magnetic properties at the (001) surfaces of CoFeCrGa and CoFeCrGe by means of the full-potential linearized augmented plane wave (FLAPW) method within generalized gradient approximation. We considered two types of surface termination: CoFe-terminated and CrZ-terminated surfaces, Z being either Ga or Ge. From the calculated total magnetic moments and the local density of states, we found that half-metallicity is not preserved for all the surfaces. But the calculated atomic density of states showed that CrGa-terminated surface of the CoFeCrGa is almost half-metallic. The magnetic moment of the Co, Fe, or Cr atoms at the surface or subsurface layers in each system had very different values.

• Elastomers and Composites
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• v.52 no.4
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• pp.237-241
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• 2017

The tensile strength (TS) and elongation-at-break (EB) loss of a CR/CB rubber composite sample prepared for the automotive parts were measured after accelerated thermal ageing at temperatures of 100, 120, 140, and $150^{\circ}C$. The change in TS was observed to be linear from the master curve prepared using the time-temperature superposition-principle (TTSP). An Arrhenius type of shift factor, $a_T$ was used to predict the life time of the sample, and a plot of ln $a_T$ vs. 1/T was also shown to be linear. The activation energy ($E_a$) of the sample was calculated as 70.30 kJ/mole from the Arrhenius plot. The expected life time of the sample was predicted at the given operating conditions by applying Arrhenius analysis. Assuming the $E_a$ value was constant at lower operating condition, life time of the sample was calculated as 2.3 years when the life limit was set as time to reach the 20% decrease of the initial TS value at operating temperature of $40^{\circ}C$.

• Journal of the Korean Society for Heat Treatment
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• v.31 no.4
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• pp.180-186
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• 2018

High temperature flow behaviors of Fe-Cr-Mo-V-W-C tool steel were investigated using isothermal compression tests on a Gleeble simulator. The compressive test temperature was varied from 850 to $1,150^{\circ}C$ with the strain rate ranges of 0.05 and $10s^{-1}$. The maximum height reduction was 45%. The dynamic softening related to the dynamic recrystallization was observed during hot deformation. The constitutive model based on Arrhenius-typed equation with the Zener-Hollomon parameter was proposed to simulate the hot deformation behavior of Fe-Cr-Mo-V-W-C steel. An artificial neural network (ANN) model was also developed to compare with the constitutive model. It was concluded that the ANN model showed more accurate prediction compared with the constitutive model for describing the hot compressive behavior of Fe-Cr-Mo-V-W-C steel.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.6
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• pp.312-321
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• 2020

In this study, machine learning techniques were applied to predict the residual resistance coefficient (Cr) of low-speed full ships. The used machine learning methods are Ridge regression, support vector regression, random forest, neural network and their ensemble model. 19 hull form variables were used as input variables for machine learning methods. The hull form variables and Cr data obtained from 139 hull forms of KRISO database were used in analysis. 80 % of the total data were used as training models and the rest as validation. Some non-linear models showed the overfitted results and the ensemble model showed better results than others.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.678-683
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• 2003

Ultrasonic method using SH(shear horizontal) wave has been developed to determine the surface damage in fatigued material. Fatigue damages based on propagation energy were analyzed by multiregression analysis and phase measurement in interrupted fatigue test specimen including CrMoV and 12Cr alloy steel. From the test results, as the fatigue damage increased the propagation time of the launched waves increased and amplitude of wavelet decreased. Also, analysis for the waveform modulation showed a reliable estimation, with confidence limit of 97% for 12Cr steel and 95% for CrMoV steel, respectively. Therefore, It is thought that SH ultrasonic wave technique can be applied to determine fatigue damage of in-service component nondestructively.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.153-158
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• 2019

In this paper, we analyze the infrared feature for the small coast targets according to the surrounding environment for autonomous flight device equipped with an infrared imaging sensor and we propose Cross Duality of Neural Network (CR-DuNN) method which can classify the target and clutter in coastal environment. In coastal environment, there are various property according to diverse change of air temperature, sea temperature, deferent seasons. And small coast target have various infrared feature according to diverse change of environment. In this various environment, it is very important thing that we analyze and classify targets from the clutters to improve target detection accuracy. Thus, we propose infrared feature learning algorithm through LSTM neural network and also propose CR-DuNN algorithm that integrate LSTM prediction network with Du-CNN classification network to classify targets from the clutters.

• Journal of Wetlands Research
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• v.4 no.1
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• pp.87-95
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• 2002

The objective of this study is to treat contaminated stream by using a UFBR(upflow fixed biofilm reactor) packed with waste-concrete media. This system was tested from June 1999 to January 2000. Over $20.0^{\circ}C$, $COD_{cr}$ removal efficiency did not affected with organic loading rate while, $COD_{cr}$ removal efficiency decreased about 7% with decrease of temperature from $27.0^{\circ}C$ to $8.7^{\circ}C$. Under $16^{\circ}C$, TKN removal efficiency was affected with TKN loading rate. The proposed model apply to mass balance equation of fixed biofilm reactor for predicting effluent was well satisfied with measured value($R^2=0.94$).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.597-598
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• 2011

In this paper, the spectrum hole prediction scheme was proposed for the cognitive radio networks using the primary user's traffic pattern. Using the channel prediction, the collision probability with primary users can be reduced and the system throuthput can be improved. Simulation result shows that the proposed method can enhance the throughput and reduce the interference to the primary user below the desired threshold.