• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1003-1006
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• 2004

In this paper have developed a system for monitoring and processing the real time sensor data in remote site through network. For realizing this system, measurement equipment and protocol are used to transmit the measurement data to remote server and to process measurement data. In server part, the received data from remote site sensor is converted to text or graphic charts for user. The measurement device in sensor part receives the sensor data form sensor and store the received data to its internal memory for transmitting data to server part through Internet. Also the measurement device can receive data form server. The temperature sensor is connected to the measurement device located in laboratory and the measurement device measures temperature of laboratory which can be confirmed by user through Internet. We have developed a server programworking on the Linux to store measurement data from measurement device to server memory. The program is use for SNMP(Simple Network Management Protocol) to exchange data with measurement device. Also the program changes the measurement data into text and graphic charts for user display. The program is use apache PHP program for user display and inquiry. The real time temperature measurement system can be apply for many parts of industry and living.

• Ocean and Polar Research
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• v.34 no.1
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• pp.73-83
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• 2012

Antarctica is very sensitive to climate change but the number of stations is not sufficient to accurately analyze climate change in this regoin. Model reanalysis data supplements the lack of observation and can be used as long term data to verify climate change. In this study, the 20CR (Twentieth Century Reanalysis) Project data from NCEP/NCAR and monthly mean data (temperature, solar radiation and longwave radiation) from 1871 to 2008, was used to analyze the temperature trend and change in radiation. The 20CR data was used to validate the observation data from Antarctica since 1950 and the correlation coefficients between these data were determined to be over 0.95 at all stations. The temperature increased by approximately $0.23^{\circ}C$/decade during the study period and over $0.20^{\circ}C$/decade over all of the months. This increasing trend was observed throughout the Antarctica and a slight increase was observed in the Antarctic Peninsula. In addition, solar radiation (surface) and longwave radiation (surface and top of atmosphere) trends correlated with the increase in temperature. As a result, outgoing longwave radiation at the surface is attenuated by atmospheric water vapor or clouds and radiation at the top of the atmosphere was reduced. In addition, the absorbed energy in the atmosphere increases the temperature of the atmosphere and surface, and then the heated surface emits more longwave radiation. Eventually these processes are repeated in a positive feedback loop, which results in a continuous rise in temperature.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.43 no.2
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• pp.113-119
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• 1998

Spatial patterns of soil temperature on sloping lands are related to the amount of solar irradiance at the surface. Since soil temperature is a critical determinant of many biological processes occurring in the soil, an accurate prediction of soil temperature distribution could be beneficial to agricultural and environmental management. However, at least two problems are identified in soil temperature prediction over natural sloped surfaces. One is the complexity of converting solar irradiances to corresponding soil temperatures, and the other, if the first problem could be solved, is the difficulty in handling large volumes of geo-spatial data. Recent developments in geographic information systems (GIS) provide the opportunity and tools to spatially organize and effectively manage data for modeling. In this paper, a simple model for conversion of solar irradiance to soil temperature is developed within a GIS environment. The irradiance-temperature conversion model is based on a geophysical variable consisting of daily short- and long-wave radiation components calculated for any slope. The short-wave component is scaled to accommodate a simplified surface energy balance expression. Linear regression equations are derived for 10 and 50 cm soil temperatures by using this variable as a single determinant and based on a long term observation data set from a horizontal location. Extendability of these equations to sloped surfaces is tested by comparing the calculated data with the monthly mean soil temperature data observed in Iowa and at 12 locations near the Tennessee - Kentucky border with various slope and aspect factors. Calculated soil temperature variations agreed well with the observed data. Finally, this method is applied to a simulation study of daily mean soil temperatures over sloped corn fields on a 30 m by 30 m resolution. The outputs reveal potential effects of topography including shading by neighboring terrain as well as the slope and aspect of the land itself on the soil temperature.

• International Journal of Highway Engineering
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• v.19 no.2
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• pp.35-44
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• 2017

PURPOSES: This study suggests a specific methodology for the prediction of road surface temperature using vehicular ambient temperature sensors. In addition, four kind of models is developed based on machine learning algorithms. METHODS : Thermal Mapping System is employed to collect road surface and vehicular ambient temperature data on the defined survey route in 2015 and 2016 year, respectively. For modelling, all types of collected temperature data should be classified into response and predictor before applying a machine learning tool such as MATLAB. In this study, collected road surface temperature are considered as response while vehicular ambient temperatures defied as predictor. Through data learning using machine learning tool, models were developed and finally compared predicted and actual temperature based on average absolute error. RESULTS : According to comparison results, model enables to estimate actual road surface temperature variation pattern along the roads very well. Model III is slightly better than the rest of models in terms of estimation performance. CONCLUSIONS : When correlation between response and predictor is high, when plenty of historical data exists, and when a lot of predictors are available, estimation performance of would be much better.

• Smart Structures and Systems
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• v.3 no.3
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• pp.341-356
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• 2007

A good understanding of normal modal variability of civil structures due to varying environmental conditions such as temperature and wind is important for reliable performance of vibration-based damage detection methods. This paper addresses the quantification of wind-induced modal variability of a cable-stayed bridge making use of one-year monitoring data. In order to discriminate the wind-induced modal variability from the temperature-induced modal variability, the one-year monitoring data are divided into two sets: the first set includes the data obtained under weak wind conditions (hourly-average wind speed less than 2 m/s) during all four seasons, and the second set includes the data obtained under both weak and strong (typhoon) wind conditions during the summer only. The measured modal frequencies and temperatures of the bridge obtained from the first set of data are used to formulate temperature-frequency correlation models by means of artificial neural network technique. Before the second set of data is utilized to quantify the wind-induced modal variability, the effect of temperature on the measured modal frequencies is first eliminated by normalizing these modal frequencies to a reference temperature with the use of the temperature-frequency correlation models. Then the wind-induced modal variability is quantitatively evaluated by correlating the normalized modal frequencies for each mode with the wind speed measurement data. It is revealed that in contrast to the dependence of modal frequencies on temperature, there is no explicit correlation between the modal frequencies and wind intensity. For most of the measured modes, the modal frequencies exhibit a slightly increasing trend with the increase of wind speed in statistical sense. The relative variation of the modal frequencies arising from wind effect (with the maximum hourly-average wind speed up to 17.6 m/s) is estimated to range from 1.61% to 7.87% for the measured 8 modes of the bridge, being notably less than the modal variability caused by temperature effect.

• Journal of Climate Change Research
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• v.6 no.2
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• pp.95-104
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• 2015

To discuss whether we have credible estimations about historical surface temperature evolution since industrial revolution or not, present study investigates consistencies and differences of averaged surface air temperature since 1900 between the multiple data sources: Hadley Center Climate Research Unit (HadCRU4) surface air temperature data, ECMWF 20 Century Reanalysis data (ERA20CR), and NCEP 20 Century Reanalysis data (NCEP20CR). Averaged surface temperatures are obtained for the global, polar (90S~60S, 60N~0N), midlatitude (60S~30S, 30N~60N), tropical (30S~30N) region, separately. From the analysis, we show that: 1) spatio-temporal inhomogenity and scarcity of HadCRU4 data are not major obstacles in the reliable estimation of global surface air temperature. 2) Globally averaged temperature variability is largely contributed by those of tropical and midlatitude, which occupy more than 70% of earth surface in area. 3) Both data show consistent temperature variability in tropical region. 4) ERA20CR does not capture warm period over Arctic region in early 1900s, which is obvious feature in HadCRU4 data. Discrepancies among datasets suggest that high-level caution is needed especially in the interpretation of large Arctic warming in the early 1900s, which is often regarded as a natural variability in the Arctic region.

• Journal of Environmental Science International
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• v.30 no.7
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• pp.585-595
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• 2021

We used seawater temperature data, measured in the Garolim Bay, to analyze temperature variation on an hourly and daily basis. Lagrange's interpolation using before and after data was applied to restore nonconsecutive missing temperature data. The estimated error of the data restoration was 0.11℃. Spectral analyses of seawater temperature showed significant periodicities of approximately 12.4 h (semidiurnal tide) and 15.0 d (long-period tide), which is close to those of M₂ and M_f partial tides. Variation in seawater temperature was correlated more with tidal height than with air temperature around the Garolim Bay. In June and December, when the seawater temperature difference between the inside and outside of the Garolim Bay was very large, the periodicities of 12.4 h and 15.0 d were highly prominent. These results indicate that the exchange of seawater between the inside and outside of the Garolim Bay induced variations in seawater temperature owing to tide. Understanding temperature variation because of tide helps to prevent abnormal mortality of cultured fish and to predict seawater temperature in the Garolim Bay.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2006.04a
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• pp.277-281
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• 2006

A study on stream-groundwater exchange was performed using head and temperature data of stream water, streambed, and groundwater. Groundwater level and temperature were obtained from multi-depth monitoring wells in small-scale watershed. In the summer time, time series of temperatrue data at streambed and groundwater were monitored for three months. In the winter time, we measured the temperature gradient between stream water and streambed. The observed data showed three typical types of temperature characteristics. First, the temperature of streambed was lower than that of stream water; second, the temperature of streambed and stream water was similar; and last, the temperature of streambed was higher than that of stream water. The interconnections between the stream and the streambed were not homogeneously distributed due to weakly developed sediments and heterogeneous bedrock exposed as bed of the stream. The temperature data may be used in formal solutions of the inverse problems to estimate groundwater flow and hydraulic conductivity.

• Journal of the Korean Solar Energy Society
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• v.29 no.5
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• pp.81-87
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• 2009

Computer simulation of buildings and solar energy systems is being used increasingly in energy assessments and design.. Building designers often now predict the performance of buildings simulation programmes that require hourly weather data. However, not all weather stations provide hourly data. Climate prediction models such as HadCM3 also provide the daily average dry bulb temperature as well as the maximum and minimum. Hourly temperature values are available for building thermal simulations that accounts for future changes to climate. In order to make full use of these predicted future weather data in building simulation programmes, algorithms for downscaling daily values to hourly values are required. This paper describes a more accurate method for generating hourly temperature values in the South Korea that uses all three temperature parameters from climate model. All methods were evaluated for accuracy and stability in terms of coefficient of determination and cumulative error. They were compared with hourly data collected in Seoul and Ulsan, South Korea.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.518-521
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• 2007

To retrieve Sea Surface Temperature(SST) from NOAA-AVHRR imagery the spilt window atmospheric correction algorithm is generally used. Recently, there have been various new algorithms developed to process these data, namely the variable-coefficient split-window, the R54 transmittance-ratio method, fixed-coefficient nonlinear algorithm, dynamic water vapour (DWV) correction method, Dynamic Water Vapour and Temperature algorithm (DWVT). We used MCSST (Multi-Channel Sea surface temperature) and NLSST(Non linear sea surface temperature) algorithms in this study. The study area is around the Korea sea area (Yellow Sea). We compared and analyzed with various methods by applying each Ocean in-situ data and satellite data. The primary aim of study is to verify and optimize algorithms. Finally, this study proposes an optimized algorithm for SST retrieval.