• Journal of Mechanical Science and Technology
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• v.17 no.4
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• pp.469-476
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• 2003

Despite of considerable research results or uniaxial tension creep available for superalloys, few studies have been made on high temperature creep using the Initial Stram Method (ISM) In this paper, the real-time prediction of high temperature creep strength and creep lift for the nickel-based superalloy Udimet 720 (high-temperature and high-pressure gas turbine engine materials) was performed on round-bar type specimens under pure static load at the temperatures of 538$^{\circ}C$. 649$^{\circ}C$, and 704$^{\circ}C$. The predictive equation derived from the ISM in creep tests showed better reliability than those from LMP (Larson-Miller Parameter) and LMP-lSM (Larson Miller Parameter-Initial Strain Method) specially for long time creep prediction (10$^3$∼10$\^$5/h).

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.05a
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• pp.253-254
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• 2012

In this study, it carried out measurement experiment Ca(OH)₂ and chemically bound water to verify Ca(OH)₂ and chemically bound water prediction model out of hydration model of cement incorporating blast furnace slag. It compared and analyzed prediction results using prediction model with measurement results of Ca(OH)₂ quantity using thermogravimetric differential temperature analysis and chemically bound water quantity using electronic furnace. It agrees well experiments results with prediction results.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.394-397
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• 2004

This paper presents a simple analytic method using 2D simulation program for predications of cutting force and machining temperature in dry type milling process. And also, comparison of cutting force and machining temperature obtained from experiment and simulation work is accomplished to distinguish of suitability.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.246-253
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• 2000

The optimal equations to predict the soil tempratures of twelve cities in the region of the southern part of the Korea such as Changhung, Cheju, Chinju, Kwangju, Masan, Miryang, Mokpo, Muan, Pusan, Sogwipo, Ulsan, Yoosu, were suggested as function of time and soil depth and the time dependent variation and soil depth dependent distribution of temperature were analyzed for the back data of the geothermal energy utilization system design and agricultural usages. The equation form is $T(x,\;t)\;=\;T_{m}\;-\;T_{so}{\cdot}Exp(-\xi){\cdot}cos{\omega}(t\;-\;t_{o}\;-\;x\;/\sqrt{2{\alpha}{\omega}}$) and it can predict the soil temperatures well with the correlation factor of 0.98 or upwards for most data. The range of mean soil temperature was $14.99~18.53^{\circ}C$ and soil surface temperature swing, 11.65~14.54 days, soil thermal diffusivity, $0.025~0.069\;m^2/day$ except Mokpo of $0.100\;m^2/day$, and phase shift, 19.66~27.81 days. During about thirty years from 1960s to 1990s, the mean soil temperature was increased by $0.04~1.25^{\circ}C$. The temperature difference depending on soil depth was not significant.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.67-72
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• 2006

Temperature prediction in asphalt pavements is the one of most important factors for estimating the pavement response and predicting the pavement performance in the mechanistic-empirical pavement design. A study on temperature prediction procedure with variation of time and depth in asphalt pavements was conducted using field monitoring data. After selecting the temperature monitoring sections, the temperature sensors have been installed in different depths and the temperature data have been collected in every one hour. The developed pavement temperature prediction model was calibrated using field monitoring temperature data. The predicted temperatures were compared with measured temperatures at different seasons in selected sections. The results showed that the solar absorptivity and emissivity values in the fall is different from the values in other seasons. The predicted temperatures agree well with the measured temperatures at a wide range of temperatures. The temperature differences between each other fall in the range of ${\pm}3^{\circ}C$. It is also found that the regional characteristics did not affect the temperature prediction procedure.

• Atmosphere
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• v.27 no.4
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• pp.385-398
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• 2017

Numerical weather prediction experiments were carried out by applying topographic effects to reduce or enhance the solar radiation by terrain. In this study, x and ${\kappa}({\phi}_o,\;{\theta}_o)$ are precalculated for topographic effect on high resolution numerical weather prediction (NWP) with 1 km spatial resolution, and meteorological variables are analyzed through the numerical experiments. For the numerical simulations, cases were selected in winter (CASE 1) and summer (CASE 2). In the CASE 2, topographic effect was observed on the southward surface to enhance the solar energy reaching the surface, and enhance surface temperature and temperature at 2 m. Especially, the surface temperature is changed sensitively due to the change of the solar energy on the surface, but the change of the precipitation is difficult to match of topographic effect. As a result of the verification using Korea Meteorological Administration (KMA) Automated Weather System (AWS) data on Seoul metropolitan area, the topographic effect is very weak in the winter case. In the CASE 1, the improvement of accuracy was numerically confirmed by decreasing the bias and RMSE (Root mean square error) of temperature at 2 m, wind speed at 10 m and relative humidity. However, the accuracy of rainfall prediction (Threat score (TS), BIAS, equitable threat score (ETS)) with topographic effect is decreased compared to without topographic effect. It is analyzed that the topographic effect improves the solar radiation on surface and affect the enhancements of surface temperature, 2 meter temperature, wind speed, and PBL height.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.2
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• pp.81-88
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• 2014

Synthetic products such as casting tape and splints are rapidly replacing conventional plaster casts to treat orthopedic patients. Most synthetic products are produced through a polymerization process with related chemical agents. In this study, the effect of the process parameters on the residual NCO content within a prepolymer for casting tape and the hardening temperature for casting tape were experimentally evaluated. In order to verify the effects of the process parameters, an experimental method was adopted. From an S/N ratio analysis, optimal parameter combinations were determined to produce a pre-polymer with a suitable residual NCO content and alower hardening temperature. Prediction models for the NCO content and the hardening temperature were developed and confirmed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1232-1245
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• 2021

In this study, prediction of product sales as they relate to changes in temperature is proposed. This model uses long short-term memory (LSTM), which has shown excellent performance for time series predictions. For verification of the proposed sales prediction model, the sales of short pants, flip-flop sandals, and winter outerwear are predicted based on changes in temperature and time series sales data for clothing products collected from 2015 to 2019 (a total of 1,865 days). The sales predictions using the proposed model show increases in the sale of shorts and flip-flops as the temperature rises (a pattern similar to actual sales), while the sale of winter outerwear increases as the temperature decreases.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.970-977
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• 2014

In this paper, the artificial neural network is used to predict the junction temperature of the IGBT power module, by measuring the temperature sensitive electrical parameters (TSEP) of the module. An experiment circuit is built to measure saturation voltage drop and collector current under different temperature. In order to solve the nonlinear problem of TSEP approach as a junction temperature evaluation method, a Back Propagation (BP) neural network prediction model is established by using the Matlab. With the advantages of non-contact, high sensitivity, and without package open, the proposed method is also potentially promising for on-line junction temperature measurement. The Matlab simulation results show that BP neural network gives a more accuracy results, compared with the method of polynomial fitting.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.27.1-27.1
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• 2005