• Journal of the Korean earth science society
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• v.43 no.5
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• pp.591-603
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• 2022

The color and optical properties of seawater are determined by the interaction between dissolved organic and inorganic substances and plankton contained in it. The Ieodo - Ocean Research Institute (I-ORS), located in the East China Sea, is affected by the low salinity of the Yangtze River in the west and the Tsushima Warm Current in the south. Thus, it is a suitable site for analyzing the fluctuations in circulation and optical properties around the Korean Peninsula. In this study, seawater surrounding the I-ORS was classified according to its optical characteristics using the satellite remote reflectance observed with Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua and National Aeronautics and Space Administration (NASA) bio-Optical Marine Algorithm Dataset (NOMAD) from January 2016 to December 2020. Additionally, the variation characteristics of optical water types (OWTs) from different seasons were presented. A total of 59,532 satellite match-up data (d ≤ 10 km) collected from seawater surrounding the I-ORS were classified into 23 types using the spectral angle mapper. The OWTs appearing in relatively clear waters surrounding the I-ORS were observed to be greater than 50% of the total. The maximum OWTs frequency in summer and winter was opposite according to season. In particular, the OWTs corresponding to optically clear seawater were primarily present in the summer. However, the same OWTs were lower than overall 1% rate in winter. Considering the OWTs fluctuations in the East China Sea, the I-ORS is inferred to be located in the transition zone of seawater. This study contributes in understanding the optical characteristics of seawater and improving the accuracy of satellite ocean color variables.

• Korean Chemical Engineering Research
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• v.60 no.4
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• pp.535-543
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• 2022

A CPFD (Computational particle fluid dynamics) model of solar fluidized bed receiver of silicon carbide (SiC: average d_p=123 ㎛) particles was established, and the model was verified by comparing the simulation and experimental results to analyze the effect of particle behavior on the performance of the receiver. The relationship between the heat-absorbing performance and the particles behavior in the receiver was analyzed by simulating their behavior near bed surface, which is difficult to access experimentally. The CPFD simulation results showed good agreement with the experimental values on the solids holdup and its standard deviation under experimental condition in bed and freeboard regions. The local solid holdups near the bed surface, where particles primarily absorb solar heat energy and transfer it to the inside of the bed, showed a non-uniform distribution with a relatively low value at the center related with the bubble behavior in the bed. The local solid holdup increased the axial and radial non-uniformity in the freeboard region with the gas velocity, which explains well that the increase in the RSD (Relative standard deviation) of pressure drop across the freeboard region is responsible for the loss of solar energy reflected by the entrained particles in the particle receiver. The simulation results of local gas and particle velocities with gas velocity confirmed that the local particle behavior in the fluidized bed are closely related to the bubble behavior characterized by the properties of the Geldart B particles. The temperature difference of the fluidizing gas passing through the receiver per irradiance (∆T/I_DNI) was highly correlated with the RSD of the pressure drop across the bed surface and the freeboard regions. The CPFD simulation results can be used to improve the performance of the particle receiver through local particle behavior analysis.

• 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 Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.567-574
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• 2022

Coronary artery disease (CAD) and atrial fibrillation (AF) are known to share many risk factors. In particular, in the case of acute coronary syndrome, it may be difficult to clearly determine the diameter of the vessel due to complete occlusion of the vessel and thrombus. Thus, the relationship between the diameter of the coronary arteries was evaluated to be used as a reference data before the treatment of coronary arteries and drug selection in patients with AF. From January 2020 to August 2022, images of coronary angiography (CAG) with AF and normal sinus rhythm (NSR) on electrocardiography were target. In both subjects, images of normal coronary artery without lesions as a result of CAG were used. For all vessels, the diameters of the vessels were measured by dividing them into proximal, middle, and distal parts, and the measured diameters were divided by the average for evaluation. As a result of analyzing the left anterior descending artery diameter, the vessel diameter of the AF patient was 2.24±0.26 mm, which was smaller than that of the NSR patient, 2.86±0.38 mm, and was statistically significant. (p<0.001) As a result of analyzing the left circumflex artery diameter, the vessel diameter of the AF patient was 2.34±0.28 mm, which was smaller than the vessel diameter of the NSR patient, 2.87±0.29 mm, and was statistically significant. (p<0.001) As a result of analyzing the diameter of the right coronary artery, the vessel diameter of the AF patient was 2.68±0.5 mm, which was smaller than the vessel diameter of the NSR patient, 3.35±0.4 mm, and was statistically significant. (p<0.001) Considering that the coronary artery size of AF patients is significantly smaller than the coronary vessel size of NSR patients, it is considered as a useful study to be used as a reference for evaluating coronary artery diameter when the arrhythmia is AF. In particular, it is considered to be a study that can be helpful in diagnosing lesions, using drugs before and after surgery, and choosing to use auxiliary devices such as intravascular ultrasound.

• Journal of the Korean Geotechnical Society
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• v.38 no.10
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• pp.61-74
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• 2022

Considering that the number of cases in which a structure foundation is located on weathered rock has been increasing recently, for adequate design bearing capacity of a foundation on weathered rock, allowable bearing capacities of such foundations in geotechnical investigation reports were studied. With reference to the study results, the allowable bearing capacity of a foundation on weathered rock was approximately 400-700 kN/m², with a large variation, and was considered a conservative value. Because the allowable bearing capacity of the foundation ground is an important index in determining the foundation type in the early design stage, it can have a significant influence on the construction cost and period according to the initial decision. Thus, in this study, six large-scale plate-bearing tests were conducted on weathered rock, and the bearing capacity and settlement characteristics were analyzed. According to the test results, the bearing capacities from the six tests exceeded 1,500 kN/m², and it shows that the results are similar with the one of bearing capacity formula by Pressuremeter tests when compared with the various bearing capacity formula. In addition, the elastic modulus determined by the inverse calculation of the load-settlement behavior from the large-scale plate-bearing tests was appropriate for applying the elastic modulus of the Pressuremeter tests. With consideration of the large-scale plate-bearing tests in this study and other results of plate-bearing tests on weathered rock in Korea, the allowable bearing capacity of weathered rock is evaluated to be over 1,000 kN/m². However, because the settlement of the foundation increases as the foundation size increases, the allowable bearing capacity should be restrained by the allowable settlement criteria of an upper structure. Therefore, in this study, the anticipated foundation settlements along the foundation size and the thickness of weathered rocks have been evaluated by numerical analysis, and the foundation size and ground conditions, with an allowable bearing capacity of over 1,000 kN/m², have been proposed as a table. These findings are considered useful in determining the foundation type in the early foundation design.

• Phonetics and Speech Sciences
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• v.14 no.3
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• pp.87-101
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• 2022

In addition to administering a questionnaire (J-survey), which questions individuals on subjective vocal fatigue, voice samples were collected before and after speech-language pathology sessions from 50 female speech-language pathologists in their 20s and 30s in the Daejeon and Chungnam areas. We identified significant differences in Korean Vocal Fatigue Index scores between the fatigue and non-fatigue groups, with the most prominent differences in sections one and two. Regarding acoustic phonetic characteristics, both groups showed a pattern in which low-frequency band energy was relatively low, and high-frequency band energy was increased after the treatment sessions. This trend was well reflected in the low-to-high ratio of vowels, slope LTAS, energy in the third formant, and energy in the 4,000-8,000 Hz range. A difference between the groups was observed only in the vowel energy of the low-frequency band (0-4,000 Hz) before treatment, with the non-fatigue group having a higher value than the fatigue group. This characteristic could be interpreted as a result of voice abuse and higher muscle tonus caused by long-term voice work. The perturbation parameter and shimmer local was lowered in the non-fatigue group after treatment, and the noise-to-harmonics ratio (NHR) was lowered in both groups following treatment. The decrease in NHR and the fall of shimmer local could be attributed to vocal cord hypertension, but it could be concluded that the effective voice use of speech-language pathologists also contributed to this effect, especially in the non-fatigue group. In the case of the non-fatigue group, the rhamonics-to-noise ratio increased significantly after treatment, indicating that the harmonic structure was more stable after treatment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.145-154
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• 2022

Due to abnormal weather conditions caused by climate change, natural disasters and damages are gradually increasing around the world. Global climate change as accompanied by warming is projected to exert adverse impact on production of potato, which is known as cool season crop. Even though, role of potato as a food security crop is expected to increase in the future, the climate change impacts on potato and adaption strategies are not sufficiently established. Therefore, this study was conducted to analyze the damage pattern of potatoes due to high temperature treatment and to evaluate the response of cultivars. T he high temperature treatment (35~38℃) induced heat stress by sealing the plastic house in midsummer (July), and the quantity and quality characteristics of potatoes were compared with the control group. T otal yield, marketable yield (>80 g) and the number of tubers per plants decreased when heat treatment was performed, and statistical significance was evident. In the heat treatment, 'Jayoung' cultivar suffered a high heat damage with an 84% reduction in yield of >80 g compared to the control group. However, in Jopung cultivar, the decrease was relatively small at 26%. Tuber physiological disturbances (Secondary growth, Tuber cracking, Malformation) tended to increase in the heat stress. Under heat conditions, the tubers were elongated overall, which means that the marketability of potatoes was lowered. T he tuber firmness and dry matter content tended to decrease significantly in the heat-treated group. T herefore, the yield and quality of tubers were damaged when growing potatoes in heat conditions. T he cultivar with high heat adaptability was 'Jopung'. T his result can be used as basic data for potato growers and breeding of heat-resistant cultivars.

• Korean Chemical Engineering Research
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• v.60 no.4
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• pp.606-612
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• 2022

Relatively high indoor CO₂ concentration (>1,000 ppm) has a negative impact on human health. In this work, indoor CO₂ adsorbent was developed by impregnating KOH or K₂CO₃ on commercial activated carbon, named as KOH/AC and K₂CO₃/AC. Commercial activated carbon (AC) showed relatively high BET surface area (929 m²/g) whereas KOH/AC and K₂CO₃/AC presented lower BET surface area of 13.6 m²/g and 289 m²/g. Two experimental methods of TGA (2,000 ppmCO₂, weight basis) and chamber test (initial concentration: 2,000 ppmCO₂, CO₂ IR analyzer) were used to investigate the adsorption capacity. KOH/AC and K₂CO₃/AC exhibited similar adsorption capacities (145~150 mg_CO2/g), higher than K₂CO₃/Al+Si supports adsorbent (84.1 mg_CO2/g_sample). Similarly, chamber test also showed similar trend. Both KOH/AC and K₂CO₃/AC represented higher adsorption capacities (KOH/AC: 93.5 mg_CO2/g K₂CO₃/AC: 94.5 mg_CO2/g_sample) K₂CO₃/Al+Si supports. This is due to the KOH or K₂CO₃ impregnation increased alkaline active sites (chemical adsorption), which is beneficial for CO₂ adsorption. In addition, the regeneration test results showed both K-based adsorbents pose a good regeneration and reusability. Finally, the current study suggested that both KOH/AC and K₂CO₃/AC have a great potential to be used as CO₂ adsorbent for indoor CO₂ adsorption.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.