• Journal of agriculture & life science
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• v.45 no.3
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• pp.43-51
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• 2011

This project was conducted to measure actual temperature, relative humidity (RH), and light intensity at different apartment floors and to suggest suitable indoor plants by investigating morphological changes of Treophelosparmum asiatioum and Fittonia verchaffeltii var. argyroneura as affected by light intensity. Temperature and RH in apartment verandas were measured in three different (2nd, 9th, and 16th) floors on three different buildings for 30 days seasonally. The light intensity, temperature, and RH were recorded outside (parking area) and inside apartment verandas for 24 hours on a selected sunny summer day (between Aug. 19 and Sept. 14, 2008). Based on the first study, we investigated effect of simulated light intensity (40, 70, 100, and $600{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$) on changes in growth and development of T. asiatioum and F. verchaffeltii var. argyroneurain growth chambers. However, daily mean light intensity of 2nd, 9th, and 16th floors was different each other as it was about 40, 70, and $100{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$, respectively. In the growth chamber experiment, plant height, internode length, and leaf length and width were not affected significantly by light intensity, but changes in leaf color were apparent in the new leaves with the increasing light intensity. The results suggest that T. asiatioum could be best fit to high, and F. verchaffeltii var. argyroneura to lower floor verandas, considering their aesthetic values.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.5
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• pp.657-664
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• 2019

Objective: This study was performed to evaluate whether cold ambient temperature and dietary rumen-protected fat (RPF) supplementation affect growth performance, rumen fermentation, and blood parameters in Korean cattle steers. Methods: Twenty Korean cattle steers (body weight [BW], $550.6{\pm}9.14kg$; age, $19.7{\pm}0.13months$) were divided into a conventional control diet group (n = 10) and a 0.5% RPF supplementation group (n = 10). Steers were fed a concentrate diet (1.6% BW) and a rice straw diet (1 kg/d) for 16 weeks (January 9 to February 5 [P1], February 6 to March 5 [P2], March 6 to April 3 [P3], and April 4 to May 2 [P4]). Results: The mean and minimum indoor ambient temperatures in P1 ($-3.44^{\circ}C$, $-9.40^{\circ}C$) were lower (p<0.001) than those in P3 ($5.87^{\circ}C$, $-1.86^{\circ}C$) and P4 ($11.18^{\circ}C$, $4.28^{\circ}C$). The minimum temperature in P1 fell within the moderate cold-stress (CS) category, as previously reported for dairy cattle, and the minimum temperatures of P2 and P3 were within the mild CS category. Neither month nor RPF supplementation affected the average daily gain or gain-to-feed ratio (p>0.05). Ruminal ammonia nitrogen concentrations were higher (p<0.05) in cold winter than spring. Plasma cortisol concentrations were lower (p<0.05) in the coldest month than in the other months. Serum glucose concentrations were generally higher in colder months than in the other months but were unaffected by RPF supplementation. RPF supplementation increased both total cholesterol (p = 0.004) and high-density lipoprotein (HDL) concentrations (p = 0.03). Conclusion: Korean cattle may not be significantly affected by moderate CS, considering that the growth performance of cattle remained unchanged, although variations in blood parameters were observed among the studied months. RPF supplementation altered cholesterol and HDL concentrations but did not affect growth performance.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.5
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• pp.22-30
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• 2014

The purpose of this study was to compare the Solar Radiation(SR) and the Mean Radiant Temperature(MRT) under the shades of the three landscaping trees in clear summer daytimes. The trees were Lagerstroemia indica, Quercus palustris and Ulmus parvifolia. The solar radiation, the globe temperature and the air temperature were recorded every minute from the $1^{st}$ of April to the $30^{th}$ of September 2013 at a height of 1.1m above on the four monitoring stations, with four same measuring system consisting of a solar radiation sensor, two resistance temperature detectors(Pt-100), a black brass globe (${\phi}50mm$) and data acquisition systems. At the same time, the sky view photos were taken automatically hourly by three scouting cameras(lens angle: $60^{\circ}$) fixed at each monitoring station. Based on the 258 daily sky view photos and 6,640 records of middays(10 A.M.~2 P.M.) from the $1^{st}$ of June to the $30^{th}$ of August, the time serial differences of SR and MRT under the trees were analysed and compared with those of open sky, The major findings were as follows; 1. The average ratio of sky views screened by the canopies of Quercus palustris, Lagerstroemia indica and Ulmus parvifolia were 99%, 98% and 97%, and the SR were $106W/m^2$, $163W/m^2$ and $202W/m^2$ respectively, while the SR of open sky was $823W/m^2$. Which shows the canopies blocked at least 70% of natural SR. 2. The average MRT under the canopies of Quercus palustris, Lagerstroemia indica and Ulmus parvifolia were $30.34^{\circ}C$, $33.34^{\circ}C$ and $34.77^{\circ}C$ respectively, while that of open sky was $46.0^{\circ}C$. Therefore, it can be said that the tree canopies can reduce the MRT around $10{\sim}16^{\circ}C$. 3. The regression test showed significant linear relationship between the SR and MRT. In summary, the performances of the landscaping shade trees were very good at screening the SR and reducing the MRT at the outdoor of summer middays. Therefore, it can be apparently said that the more shade trees or forest at the outdoor, the more effective in conditioning the outdoor space reducing the MRT and the useless SR for human activities in summertime.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.707-723
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• 2022

Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R² of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.274-284
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• 2014

Crops and varieties are mostly affected by temperature, the amount of precipitation, and duration of sunshine. This study aims to identify the weather factors that directly influence to apple yield among the series of daily measured weather variables during growing seasons. In order to identify them, 1) a priori natural scientific knowledge with respect to the growth stage of apples and 2) pure statistical approaches to minimize bias due to the subject selection of variables are considered. Each result estimated by the Panel regression using fixed/random effect models is evaluated through suitability (i.e., Akaike information criterion and Bayesian information criterion) and predictability (i.e., mean absolute error, root mean square error, mean absolute percentage). The Panel data of apple yield and weather factors are collected from fifteen major producing areas of apples from 2006 to 2013 in Korea for the case study. The result shows that variable selection using factor analysis, which is one of the statistical approaches applied in the analysis, increases predictability and suitability most. It may imply that all the weather factors are important to predict apple yield if statistical problems, such as multicollinearity and lower degree of freedom due to too many explanatory variables used in the regression, can be controlled effectively. This may be because whole growth stages, such as germination, florescence, fruit setting, fatting, ripening, coloring, and harvesting, are affected by weather.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.206-216
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• 2013

The emotion plays a critical role in human's daily life including learning, action, decision and communication. In this paper, emotion discrimination classifier is designed to reduce system complexity through reduced selection of dominant features from biosignals. The photoplethysmography(PPG), skin temperature, skin conductance, fontal and parietal electroencephalography(EEG) signals were measured during 4 types of movie watching associated with the induction of neutral, sad, fear joy emotions. The genetic algorithm with support vector machine(SVM) based fitness function was designed to determine dominant features among 24 parameters extracted from measured biosignals. It shows maximum classification accuracy of 96.4%, which is 17% higher than that of SVM alone. The minimum error features selected are the mean and NN50 of heart rate variability from PPG signal, the mean of PPG induced pulse transit time, the mean of skin resistance, and ${\delta}$ and ${\beta}$ frequency band powers of parietal EEG. The combination of parietal EEG, PPG, and skin resistance is recommendable in high accuracy instrumentation, while the combinational use of PPG and skin conductance(79% accuracy) is affordable in simplified instrumentation.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.65 no.3
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• pp.182-191
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• 2020

A field test and a phytotron study were performed over two years to examine whether rice varieties originated from higher altitude areas have lower optimum grain filling temperatures for yield improvement than the varieties from South Korea. Three varieties originated from North Korea and three varieties from northern China were compared to the same number of varieties from South Korea. In a field study, the optimum grain filling temperatures over 40 days after heading were 22.6 - 23.0℃, 21.5 - 22.3℃, and 21.5 - 23.6℃ for the varieties from North Korea, northern China, and South Korea, respectively, resulting in no significant difference among varietal groups. Meanwhile, the heading dates of the early maturing varieties from North Korea and China were 7 - 12 days earlier than that of the early maturing Odae variety from South Korea during the first transplant of 2017. The phytotron study, in which different temperature regimes were imposed from flowering/fertilization to harvest with constant daily mean temperatures, revealed that milled rice weight did not decrease under low temperatures, even at 16℃, compared to that at 22℃. At the fourth transplant in the field study, mean temperature lower than 10℃ appeared before rice grains were fully developed, resulting in yield reductions. It was concluded that rice varieties adaptable to high-altitude areas do not have lower optimum grain filling temperatures but, instead, possess shorter growth durations. It was further suggested that the optimum grain filling temperature of rice observed under natural conditions could be attributed to the lowering temperature at the late filling stage under temperate climatic conditions.

• Korean journal of applied entomology
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• v.48 no.2
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• pp.203-209
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• 2009

This study was conducted to investigate the relationship between the climbing up event of Luciola lateralis larvae and water temperature. In the laboratory condition, the larvae did not come out of water at constant temperature condition of 19.3${\pm}$$0.3^{\circ}C$ and tried to climb up at varying temperature condition of 20${\sim}$$22^{\circ}C$ (mean temperature 20.9${\pm}$$0.9^{\circ}C$) without success. However, they climbed up at constant temperature condition of 20.8${\pm}$$0.6^{\circ}C$. The frequency of the larval climbing up was highest as 63.6% at 20${\sim}$$21^{\circ}C$. The most larvae climbed up at approximately $21^{\circ}C$ of average daily water temperature, In the natural condition, the larvae climbed up at 19.8${\sim}$$21.7^{\circ}C$ and the frequency of the larval climbing up was high as 80.4% at 20${\sim}$$21^{\circ}C$. The larval climbing up was also little observed at 19${\sim}$$20^{\circ}C$ at which no larval climbing up was found in the laboratory experiment. No larvae attempted to climb up when water temperature was below $19^{\circ}C$ and above $22^{\circ}C$. It took 18 days from climbing up of larvae to appearance of adults. In its habitat, the range of water temperature required for larvae to climb up is assumed to be 19.6${\sim}$$21.8^{\circ}C$ and the most suitable water temperature may be 20.4${\pm}$$2.3^{\circ}C$. Therefore, the range of water temperature required for larvae to climb up is assumed to be 19.8${\sim}$$21.7^{\circ}C$, and the suitable water temperature range for larvae may be 20${\sim}$$21^{\circ}C$, and the most suitable temperature is thought to be $21^{\circ}C$.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.613-623
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• 2019

Over the past decades, daily sea surface temperature (SST) composite data have been produced using periodically and extensively observed satellite SST data, and have been used for a variety of purposes, including climate change monitoring and oceanic and atmospheric forecasting. In this study, we evaluated the accuracy and analyzed the error characteristic of the SST composite data in the sea around the Korean Peninsula for optimal utilization in the regional seas. We evaluated the four types of multi-satellite SST composite data including OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis), OISST (Optimum Interpolation Sea Surface Temperature), CMC (Canadian Meteorological Centre) SST, and MURSST (Multi-scale Ultra-high Resolution Sea Surface Temperature) collected from January 2016 to December 2016 by using in-situ temperature data measured from the Ieodo Ocean Research Station (IORS). Each SST composite data showed biases of the minimum of 0.12℃ (OISST) and the maximum of 0.55℃ (MURSST) and root mean square errors (RMSE) of the minimum of 0.77℃ (CMC SST) and the maximum of 0.96℃ (MURSST) for the in-situ temperature measurements from the IORS. Inter-comparison between the SST composite fields exhibited biases of -0.38-0.38℃ and RMSE of 0.55-0.82℃. The OSTIA and CMC SST data showed the smallest error while the OISST and MURSST data showed the most obvious error. The results of comparing time series by extracting the SST data at the closest point to the IORS showed that there was an apparent seasonal variation not only in the in-situ temperature from the IORS but also in all the SST composite data. In spring, however, SST composite data tended to be overestimated compared to the in-situ temperature observed from the IORS.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.1
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• pp.50-58
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• 2013

This study was carried out to evaluate the geospatial characteristics of blooming date migration in three major spring flowers across North and South Korea as influenced by climate change. A thermal time-based phenology model driven by daily maximum and minimum temperature was adjusted for the key parameters (i.e., reference temperature, chilling requirement, heating requirement) used for predicting blooming of forsythia, azaleas, and Japanese cherry. The model was run by the RCP 8.5 projected temperature outlook over the Korean Peninsula and produced the mean booming dates for the three climatological normal years in the future (2011-2040, 2041-2070, and 2071-2100) at a 12.5 km grid spacing. Comparison against the observed blooming date patterns in the baseline climate (1971-2000) showed that there will be a substantial acceleration in blooming dates of the three species, resulting in cherry booming in February and flowers of azaleas and forsythia found at the top of mountain Baikdu by the 2071-2100 period. Flowering dates of the three species in the near future (2011-2040) may be accelerated by 3-5 days at minimum and 10-11 days at maximum compared with that in the baseline period (1971-2000). Those values corresponding to the middle future (2041-2070) can be from a minimum of 9-11 days to a maximum of 23-24 days. Blooming date of Japanese cherry can be accelerated by 26 days on average for the far future (2071-2100). The acceleration seems more prominent at islands and coastal plain areas than over inland mountainous areas.