• Smart Structures and Systems
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• v.30 no.4
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• pp.371-383
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• 2022

A novel impact localization method is presented based on impact induced elastic waves in sensorized composite structure subjected to temperature fluctuations. In real practices, environmental and operational conditions influence the acquired signals and consequently make the feature (particularly Time of Arrival (TOA)) extraction process, complicated and troublesome. To overcome this complication, a robust TOA estimation method is proposed based on the times in which the absolute amplitude of the signal reaches to a specific amplitude value. The presented method requires prior knowledge about the normalized wave velocity in different directions of propagation. To this aim, a finite element model of the plate was built in ABAQUS/CAE. The impact location is then highlighted by calculating an error value at different points of the structure. The efficiency of the developed impact localization technique is experimentally evaluated by dropping steel balls with different energies on a carbon fiber composite plate with different temperatures. It is demonstrated that the developed technique is able to localize impacts with different energies even in the presence of noise and temperature fluctuations.

• Journal of Practical Agriculture & Fisheries Research
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• v.20 no.1
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• pp.55-66
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• 2018

Judicious control of high temperature is the most important task for the successful intensive-cultivation of vegetables in glasshouses during the hot summer. Estimation of cooling load and wise selection of suitable equipments and facilities based upon the environmental conditions are essential for the efficient temperature control. A series of experiments were carried out to investigate VETH(ventilation, evapotranspiration, temperature and humidity) plot was prepared for the possible practical application in designing some evaporated cooling methods for the following 9 locations; Seoul, Seosan, Taejeon, Pusan, Cheju, Kwangju, Taegu, Chonju, and Chinju.

• Journal of the Korean earth science society
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• v.27 no.6
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• pp.677-686
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• 2006

Surface evapotranspiration is one of the most important factors to determine the surface energy budget, and its estimation is strongly related with the accuracy of weather forecasting. Surface evapotranspiration over Daegu Metropolitan was estimated using high resolution LANDSAT TM data. The estimation of surface evapotranspiration is based on the relationship between surface radiative temperature and vegetation index provided by a TM sensor. The distribution of NDVI (Normalized Difference of Vegetation Index) corresponds well with that of land-used in Deagu Metropolitan. The temperature of several part of downtown in Deagu metropolitan is lower in comparison with the averaged radiative temperature. This is caused by the high evapotranspiration from dense vegetation like DooRyu Park in Deagu Metropolitan. But, weak evapotranspiration availability is distinguished over the central part of downtown and the difference of evapotranspiration availability on industrial complexes and residential area is also clear.

• Korean Chemical Engineering Research
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• v.48 no.6
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• pp.695-699
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• 2010

The propellant KM10, a single propellant manufactured from nitrocellulose, was known to cause natural degradation phenomena at long term storage. In this study, the self-life was estimated using high temperature acceleration aging tests and stockpile analysis test. For the life expectancy estimation, Arrhenius equation and Berthelot equation were used in the high temperature acceleration tests, and the first order regression was used in the Stockpile analysis test. The self-life of propellant KM10 using the Arrhenius equation and Berthelot equation showed significantly different results as 43.73, 16.53 years in the high temperature acceleration test, and it showed 42.94 years in the Stockpile analysis test. The value of self-life predicted by Arrhenius equation was reasonable when compared with the result of E. R. Bixon.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1491-1495
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• 2023

This study utilized automated machine learning (AutoML) to calculate Arctic ice surface temperature (IST). AutoML-derived IST exhibited a strong correlation coefficient (R) of 0.97 and a root mean squared error (RMSE) of 2.51K. Comparative analysis with deep neural network (DNN) models revealed that AutoML IST demonstrated good accuracy, particularly when compared to Moderate Resolution Imaging Spectroradiometer (MODIS) IST and ice mass balance (IMB) buoy IST. These findings underscore the effectiveness of AutoML in enhancing IST estimation accuracy under challenging polar conditions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.349-350
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• 2008

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2004.06a
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• pp.205-208
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• 2004

• Proceedings of the Korean Nuclear Society Conference
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• 1999.05a
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• pp.135-135
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• 1999

• 한국가스학회:학술대회논문집
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• no.5
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• pp.111-118
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• 2002

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1114-1114
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• 2003