• Korean Journal of Materials Research
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• v.16 no.6
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• pp.360-366
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• 2006

A series of ZnO thin films were grown by radio-frequency (RF) magnetron sputtering with various RF powers on $SiO_2/Si$(100) substrates at $500^{\circ}C$. Thicknesses of the investigated ZnO films were fixed to about 250nm by changing the growth time based on the changes of growth rates with RF powers. All the ZnO thin films were grown with <0001> preferred orientation. Average grain sizes of about 250nm-thick ZnO films evaluated by FE-SEM, AFM, and TEM were increased by decreasing the RF power. Structural properties addressed by FWHM values of XRD (0002) omega rocking curves and their intensities were better for the smaller grain sized ZnO films grown with high RF powers, which implies small values of tilt for smaller grain sized ZnO films. However, optical properties addressed by intensities of band edge emissions from room temperature and low temperature photoluminescence were better for the larger grain sized ZnO films with low RF power, which implies grain boundaries acted as nonradiation recombination centers.

• Smart Structures and Systems
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• v.18 no.3
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• pp.485-500
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• 2016

Stay cables in some cable-stayed bridges suffer large amplitude vibrations under the simultaneous occurrence of rain and wind. This phenomenon is called rain-wind-induced vibration (RWIV). The upper rivulet oscillating circumferentially on the inclined cable surface plays an important role in this phenomenon. However, its small size and high sensitivity to wind flow make measuring rivulet size and its movement challenging. Moreover, the distribution of the rivulet along the entire cable has not been measured. This paper applies the videogrammetric technique to measure the movement and geometry dimension of the upper rivulet along the entire cable during RWIV. A cable model is tested in an open-jet wind tunnel with artificial rain. RWIV is successfully reproduced. Only one digital video camera is employed and installed on the cable during the experiment. The camera records video clips of the upper rivulet and cable movements. The video clips are then transferred into a series of images, from which the positions of the cable and the upper rivulet at each time instant are identified by image processing. The thickness of the upper rivulet is also estimated. The oscillation amplitude, equilibrium position, and dominant frequency of the rivulet are presented. The relationship between cable and rivulet variations is also investigated. Results demonstrate that this non-contact, non-intrusive measurement method has good resolution and is cost effective.

• Journal of Animal Science and Technology
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• v.63 no.3
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• pp.651-661
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• 2021

This study aimed to investigate whether breed, sex, and age affected temperament differently (more or less neophobic) in mature horses during a novel object test. The study included Jeju crossbred (n = 12, age = 9.42 ± 4.57 y), Thoroughbred (n = 15, age = 10.73 ± 3.09 y), and Warmblood horses (n = 12, age = 13.08 ± 3.55 y) with the females (n = 22, age = 11.36 ± 4.24 y) and geldings (n = 17, age = 10.65 ± 3.66 y). Jeju crossbreds (Jeju horse × Thoroughbred) are valuable considering their popular usage in Korea, but limited studies have explored temperament of Jeju crossbred horses. A trained experimenter touched the left side of the neck with a white plastic bag (novel object). The test ended when the horse stopped escape response and heart rate (HR) dropped to baseline. Behavioral score and escape duration were measured as behavioral variables. Multiple variables related to HR and heart rate variability (HRV) were measured to reflect emotional state. These included basal HR (BHR), maximum HR (MHR), delay to reach maximum heart rate (Time to MHR), standard deviation of beat-to-beat intervals (SDNN), root mean square of successive differences (RMSSD), and ratio of low to high frequency components of a continuous series of heartbeats (LF/HF). Statistics revealed that Thoroughbreds had significantly higher behavioral scores, and lower RMSSD than Jeju crossbreds (p < 0.05), suggesting greater excitement and fear to the novel object in Thoroughbreds. None of the behavioral or cardiac parameters exhibited sex differences (p < 0.05). Age was negatively correlated with SDNN and RMSSD (p < 0.05), indicating that older horses felt more anxiety to the novelty than younger horses. Thoroughbreds and females had distinct correlations between behavioral and HRV variables in comparison with other groups (p < 0.05), implying that escape duration might be a good indicator of stress, especially in these two groups. These results are expected to improve equine welfare, safety and utility, by providing insights into the temperament of particular horse groups, to better match reactivity levels with specific functions.

• Journal of Korean Society on Water Environment
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• v.40 no.3
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• pp.121-129
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• 2024

Harmful cyanobacterial blooms (HCBs) are caused by the rapid proliferation of cyanobacteria and are believed to be exacerbated by climate change. However, the extent to which HCBs will be stimulated in the future due to increased temperature remains uncertain. This study aims to predict the future occurrence of cyanobacteria in the Nakdong River, which has the highest incidence of HCBs in South Korea, based on temperature rise scenarios. Representative Concentration Pathways (RCPs) were used as the basis for these scenarios. Data-driven model simulations were conducted, and out of the four machine learning techniques tested (multiple linear regression, support vector regressor, decision tree, and random forest), the random forest model was selected for its relatively high prediction accuracy. The random forest model was used to predict the occurrence of cyanobacteria. The results of boxplot and time-series analyses showed that under the worst-case scenario (RCP8.5 (2100)), where temperature increases significantly, cyanobacterial abundance across all study areas was greatly stimulated. The study also found that the frequencies of HCB occurrences exceeding certain thresholds (100,000 and 1,000,000 cells/mL) increased under both the best-case scenario (RCP2.6 (2050)) and worst-case scenario (RCP8.5 (2100)). These findings suggest that the frequency of HCB occurrences surpassing a certain threshold level can serve as a useful diagnostic indicator of vulnerability to temperature increases caused by climate change. Additionally, this study highlights that water bodies currently susceptible to HCBs are likely to become even more vulnerable with climate change compared to those that are currently less susceptible.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.632-645
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• 2021

Satellite sea surface temperature (SST) composites provide important data for numerical forecasting models and for research on global warming and climate change. In this study, six types of representative SST composite database were collected from 2007 to 2018 and the characteristics of spatial structures of SSTs were analyzed in seas around the Korean Peninsula. The SST composite data were compared with time series of in-situ measurements from ocean meteorological buoys of the Korea Meteorological Administration by analyzing the maximum value of the errors and its occurrence time at each buoy station. High differences between the SST data and in-situ measurements were detected in the western coastal stations, in particular Deokjeokdo and Chilbaldo, with a dominant annual or semi-annual cycle. In Pohang buoy, a high SST difference was observed in the summer of 2013, when cold water appeared in the surface layer due to strong upwelling. As a result of spectrum analysis of the time series SST data, daily satellite SSTs showed similar spectral energy from in-situ measurements at periods longer than one month approximately. On the other hand, the difference of spectral energy between the satellite SSTs and in-situ temperature tended to magnify as the temporal frequency increased. This suggests a possibility that satellite SST composite data may not adequately express the temporal variability of SST in the near-coastal area. The fronts from satellite SST images revealed the differences among the SST databases in terms of spatial structure and magnitude of the oceanic fronts. The spatial scale expressed by the SST composite field was investigated through spatial spectral analysis. As a result, the high-resolution SST composite images expressed the spatial structures of mesoscale ocean phenomena better than other low-resolution SST images. Therefore, in order to express the actual mesoscale ocean phenomenon in more detail, it is necessary to develop more advanced techniques for producing the SST composites.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.131-142
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• 2015

In order to support the development of a cost-effective river bathymetric system, this research has focused on modeling GPS observables, which are obtained by array of five single-frequency receivers (i.e., two references and three rovers) to estimate the high accurate kinematic position, and the surveying vessel altitude. Also, by applying all GPS measurements as multiple-baselines with constraining rover baselines, we derived the socalled ‘kinematic network model.’ From the model, the integer-constrained least-squares (LS) for position estimation and the implicit LS for attitude determination were implemented, while a series of simulation tests with respect to the baseline lengths around 2km performed to demonstrate its accuracy analysis. The on-the-fly (OTF) ambiguity resolution tests revealed that ninety-nine percents of time-to-fix-first ambiguity (TTFF) can be decided in less than two seconds, when the positioning accuracy of ambiguity-fixed solutions was assessed as the greater than or equal to one and two centimeters in horizontal and vertical, respectively. Comparing to the GPS-derived attitudes, the achievable accuracy gradually descended in sequence of yaw, pitch and roll due to the antenna geometric configuration. Furthermore, the RMSE values for the baseline lengths of three to six meters were within ±1′for yaw, and less than ±10′and ±20′for pitch and roll, respectively, but those of between six to fifteen meters were less than ±1′for yaw, ±5′for pitch, and ±10′for roll.

• Science of Emotion and Sensibility
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• v.23 no.2
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• pp.3-12
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• 2020

The purpose of this work is to suggest the optimal color temperature, which induces a sense of comfort for autonomous vehicle users through the analysis of biosignal using electroencephalography (EEG) and photoplethysmography (PPG). To achieve this purpose, we applied lighting with a color temperature of 3000 K, 4000 K, 5000 K, and 6000 K to the autonomous driving environment. We experimented in a laboratory equipped with a graphic driving simulator. The experimental procedure is as follows: 1) stabilization (5 min). 2) Uchida-Kraepelin test (3 min). 3) Automatic driving + lighting (3 min). This procedure was repeated four times under different color temperatures. We performed frequency analysis on a collected time-series data and calculated the power value for each frequency band through power spectrum analysis. In the case of EEG, we analyzed α- and β-waves, which are indicators of stability and arousal, respectively. In the case of PPG, we analyzed the sympathetic nervous system activity. To reduce deviations between the subjects, we normalized the data before analysis. The result of the first analysis revealed that α-wave increased only at 5000 K, while the β-wave increased at almost all color temperatures. In addition, in the case of PPG, sympathetic nervous system activity (SNSA) increased under driving conditions. The result of the second analysis revealed that the difference between β-wave and SNSA is insignificant. In conclusion, the increase in α-waves showed that EEG was most stable at 5000 K. The results of this study can be applied to the upcoming autonomous driving era to induce high driver satisfaction. Furthermore, this approach could eventually lead to the acceptance of autonomous vehicles by suggesting a positive effect of autonomous driving.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.225-236
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• 2024

To prepare for the impending climate crisis, it is necessary to establish policies and strategies based on scientific predictions and analyses of climate change impacts. For this, climate change should be considered, however, in conventional scenario-led approach, researchers select and utilize representative climate change scenarios. Using the representative climate change scenarios makes prediction results high uncertain and low reliable, which leads to have limitations in applying them to relevant policies and design standards. Therefore, it is necessary to utilize scenario-neutral approach considering possible change ranges due to climate change. In this study, hydrologic risk was estimated for Boryeong after generating 343 time series of climate stress and calculating drought return period from bivariate drought frequency analysis. Considering 18 scenarios of SSP1-2.6 and 18 scenarios of SSP5-8.5, the results indicated that the hydrologic risks of drought occurrence with maximum return period ranged 0.15±0.025 within 20 years and 0.3125±0.0625 within 50 years, respectively. Therefore, it is necessary to establish drought policies and countermeasures in consideration of the corresponding hydrologic risks in Boryeong.