• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1261-1272
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• 2018

It is important to measure the Arctic surface air temperature because it plays a key-role in the exchange of energy between the ocean, sea ice, and the atmosphere. Although in-situ observations provide accurate measurements of air temperature, they are spatially limited to show the distribution of Arctic surface air temperature. In this study, we proposed machine learning-based models to estimate the Arctic surface air temperature in summer based on buoy data and Advanced Microwave Scanning Radiometer 2 (AMSR2)satellite data. Two machine learning approaches-random forest (RF) and support vector machine (SVM)-were used to estimate the air temperature twice a day according to AMSR2 observation time. Both RF and SVM showed $R^2$ of 0.84-0.88 and RMSE of $1.31-1.53^{\circ}C$. The results were compared to the surface air temperature and spatial distribution of the ERA-Interim reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). They tended to underestimate the Barents Sea, the Kara Sea, and the Baffin Bay region where no IABP buoy observations exist. This study showed both possibility and limitations of the empirical estimation of Arctic surface temperature using AMSR2 data.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.55-63
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• 2014

Up to date, numerous studies on wireless sensor networks have been performed to overcome the Energy-Constraint of the sensor system. Existing schemes for estimating the residual energy have considered only voltage of sensor system. However battery performance in the real is affected by temperature and load. In this paper we introduce more accurate scheme, for the use in wireless sensor node, based on the interpolation of lookup tables which allow for temperature and load characteristics, as well as battery voltage.

• Journal of the Korea Institute of Building Construction
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• v.12 no.1
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• pp.87-97
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• 2012

This paper presents a method of estimating the setting time of cement mortar incorporating recycled aggregate powder (RP) and cement kiln dust (CKD) at various curing temperatures by applying an equivalent age method. To estimate setting time, the equivalent age using apparent activation energy (Ea) was applied. Increasing RP and CKD leads to a shortened initial and final set. Ea at the initial set and final set obtained by Arrhenius function showed differences in response to mixture type. These were estimated to be from 10~19 KJ/mol in all mixtures, which is smaller than those of conventional mixture ranging from 30~50 KJ/mol. Based on the application of Ea to Freisleben Hansen and Pederson's equivalent age function, equivalent age is nearly constant, regardless of curing temperature and RP contents. This implies that the concept of maturity is applicable in estimating the setting time of concrete containing RP and CKD. A high correlation was observed between estimated setting time and measured setting time. A multiregression model was provided to determine setting time reflecting RP and CKD. Thus, the setting time estimation method studied herein can be applicable to concrete incorporating RP and CKD in the construction field.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.5
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• pp.525-531
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• 2004

In this paper, the concept of infrared thermography(IRT), the principle of measurement of IRT and how to set up the IR camera for the nondestructive testing are described in detail. Also, its utilization and non-destructive testing(NDT) diagnosis are reviewed. By performing the periodic non-touched WDT through the estimation of thermal patterns related with the temperature for the surface targeted, IRT can be applied to the early prevention of the device failure. For the diagnosis utilization, thermal imaging patterns obtained from IRT for heated blocks with internal defects were estimated through the lion-destructive method and discussed the way of IRT estimation from the analysis of characteristics between material defects and thermal imaging patterns.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.86-89
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• 2005

Design and estimation of a spindle system which was composed of grinding spindle and regulating spindle for the centerless grinding of ferrule was performed and prototypes of each spindle were manufactured. Loop stiffness of the spindle system was 130 N/${\mu}m$. Although the value was lower than the target value of 150 N/${\mu}m$, as there included 20% of the safety factor, it was enough to machine the ferrule. Rotational accuracies of each spindle were about 0.2${\mu}m$ at the primary speed of 2,300 rpm(grinding spindle) and 300 rpm(regulating spindle). Temperature rises at the same speed were about $4.4\;\~\;4.7^{\circ}C$ in the case of grinding spindle and $1.8^{\circ}C$in the case of regulating spindle, which were well agreed with the designed value. From these results, it was estimated that the prototype of spindle system had a enough performances for the centerless grinding machine to machine the ferrule.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.5
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• pp.1-9
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• 2010

Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, artificial neural network (ANN) models for reference crop evapotranspiration ($ET_0$) estimation were developed on a monthly basis (May~October). The models were trained and tested for Suwon, Korea. Four climate factors, daily maximum temperature ($T_{max}$), daily minimum temperature ($T_{min}$), rainfall (R), and solar radiation (S) were used as the input parameters of the models. The target values of the models were calculated using Food and Agriculture Organization (FAO) Penman-Monteith equation. Future climate data were generated using LARS-WG (Long Ashton Research Station-Weather Generator), stochastic weather generator, based on HadCM3 (Hadley Centre Coupled Model, ver.3) A1B scenario. The evapotranspirations were 549.7 mm/yr in baseline period (1973-2008), 558.1 mm/yr in 2011-2030, 593.0 mm/yr in 2046-2065, and 641.1 mm/yr in 2080-2099. The results showed that the ANN models achieved good performances in estimating future reference crop evapotranspiration.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.2
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• pp.128-136
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• 2008

It is necessary to estimate the cooling load of the next day for effective control of ice thermal storage system. In this paper, new methodology is proposed to estimate the cooling load using design parameters of building and predicted weather data. Only six input parameters such as sensible heat coefficient and constant, latent heat coefficient and constant, maximum and minimum temperature are necessary to obtain hourly distribution of cooling load for the next day. Two benchmarking buildings(hospital and research institute) are selected to validate the performance of the proposed method, and the estimated cooling loads in hourly and daily bases are calculated and compared with the measured data for E hospital. The estimated results show fairly good agreement with the measured data for both buildings.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.12
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• pp.74-83
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• 2014

This paper proposes a study on the configuration of balancing of plant(BOP) and implementation of battery management system(BMS) functions for vanadium redox flow battery(VRFB) and propose a method consists of sensor and required design specifications BOP system configuration. And it proposes an method of the functions implementation and control algorithm of the BMS for flow battery. Functions of BMS include temperature control, the charge and discharge control, flow control, level control, state of charge(SOC) estimation and a battery protection through the sensor signal of BOP. Functions of BMS is implemented by the sensor signal, so it is recognized as a very important factor measurement accuracy of the data. Therefore, measuring a mechanical signal(flow rate, temperature, level) through the BOP test model, and the measuring an electrical signal(cell voltage, stack voltage and stack current) through the VRFB charge-discharge system and analyzes the precision of data in this paper. Also it shows a good charge-discharge test results by the SOC estimation algorithm of VRFB. Proposed BOP configuration and BMS functions implementation can be used as a reference indicator for VRFB system design.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1927-1934
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• 2018

In this paper, we studied the state of charge (SOC) estimation algorithm of a high-capacity lithium secondary battery for electric vehicles (EVs) considering temperature characteristics. Nonlinear characteristics of high-capacity lithium secondary batteries are represented by differential equations in the mathematical form and expressed by the state space equation through battery modeling to extract the characteristic parameters of the lithium secondary battery. Charging and discharging equipment were used to perform characteristic tests for the extraction of parameters of lithium secondary batteries at various temperatures. An extended Kalman filter (EKF) algorithm, a state observer, was used to estimate the state of the battery. The battery capacity and internal resistance of the high-capacity lithium secondary battery were investigated through battery modeling. The proposed modeling was applied to the battery pack for EVs to estimate the state of the battery. We confirmed the feasibility of the proposed study by comparing the estimated SOC values and the SOC values from the experiment. The proposed method using the EKF is expected to be highly applicable in estimating the state of the high-capacity rechargeable lithium battery pack for electric vehicles.