• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• Clean Technology
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• v.28 no.2
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• pp.131-137
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• 2022

The semiconductor process currently emits various by-products and unused gases. Emissions containing pollutants are generally classified into categories such as organic, acid, alkali, thermal, and cabinet exhaust. They are discharged after treatment in an atmospheric prevention facility suitable for each exhaust type. The main components of organic exhaust are volatile organic compounds (VOC), which is a generic term for oxygen-containing hydrocarbons, sulfur-containing hydrocarbons, and volatile hydrocarbons, while the main components of alkali exhaust include ammonia and tetramethylammonium hydroxide. The purpose of this study was to determine the combustion characteristics and analyze the NO_X reduction rate by maintaining a direct combustion and temperature to process organic and alkaline exhaust gases simultaneously. Acetone, isopropyl alcohol (IPA), and propylene glycol methyl ether acetate (PGMEA) were used as VOCs and ammonia was used as an alkali exhaust material. Independent and VOC-ammonia mixture combustion tests were conducted for each material. The combustion tests for the VOCs confirmed that complete combustion occurred at an equivalence ratio of 1.4. In the ammonia combustion test, the NO_X concentration decreased at a lower equivalence ratio. In the co-combustion of VOC and ammonia, NO was dominant in the NO_X emission while NO₂ was detected at approximately 10 ppm. Overall, the concentration of nitrogen oxide decreased due to the activation of the oxidation reaction as the reaction temperature increased. On the other hand, the concentration of carbon dioxide increased. Flameless combustion with an electric heat source achieved successful combustion of VOC and ammonia. This technology is expected to have advantages in cost and compactness compared to existing organic and alkaline treatment systems applied separately.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.283-295
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• 2023

With the development of sensor and satellite technology, numerous high-resolution and multi-spectral satellite images have been available. Due to their wavelength-dependent reflection, transmission, and scattering characteristics, multi-spectral satellite images can provide complementary information for earth observation. In particular, the short-wave infrared (SWIR) band can penetrate certain types of atmospheric aerosols from the benefit of the reduced Rayleigh scattering effect, which allows for a clearer view and more detailed information to be captured from hazed surfaces compared to the visible band. In this study, we proposed a multi-resolution transform-based image fusion method to combine visible and SWIR satellite images. The purpose of the fusion method is to generate a single integrated image that incorporates complementary information such as detailed background information from the visible band and land cover information in the haze region from the SWIR band. For this purpose, this study applied the Laplacian pyramid-based multi-resolution transform method, which is a representative image decomposition approach for image fusion. Additionally, we modified the multiresolution fusion method by combining a haze-guided weight map based on the prior knowledge that SWIR bands contain more information in pixels from the haze region. The proposed method was validated using very high-resolution satellite images from Worldview-3, containing multi-spectral visible and SWIR bands. The experimental data including hazed areas with limited visibility caused by smoke from wildfires was utilized to validate the penetration properties of the proposed fusion method. Both quantitative and visual evaluations were conducted using image quality assessment indices. The results showed that the bright features from the SWIR bands in the hazed areas were successfully fused into the integrated feature maps without any loss of detailed information from the visible bands.

• Clean Technology
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• v.29 no.2
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• pp.126-134
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• 2023

Direct catalytic decomposition is a promising method for controlling the emission of nitrous oxide (N₂O) from the semiconductor and display industries. In this study, a γ-Al₂O₃ catalyst was developed to reduce N₂O emissions by a catalytic decomposition reaction. The γ-Al₂O₃ catalyst was prepared by an extrusion method using boehmite powder, and a N₂O decomposition test was performed using a catalyst reactor that was approximately 25.4 mm (1 in) in diameter packed with approximately 5 mm of catalysts. The N₂O decomposition tests were carried out with approximately 1% N₂O at 550 to 750 ℃, an ambient pressure, and a GHSV=1800-2000 h^-1. To confirm the N₂O decomposition properties and the effect of O₂ and steam on the N₂O decomposition, nitrogen, air, and air and steam were used as atmospheric gases. The catalytic decomposition tests showed that the 1% N₂O had almost completely disappeared at 700 ℃ in an N₂ atmosphere. However, air and steam decreased the conversion rate drastically. The long term stability test carried out under an N₂ atmosphere at 700 ℃ for 350 h showed that the N₂O conversion rate remained very stable, confirming no catalytic activity changes. From the results of the N₂O decomposition tests and long-term stability test, it is expected that the prepared γ-Al₂O₃ catalyst can be used to reduce N₂O emissions from several industries including the semiconductor, display, and nitric acid manufacturing industry.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.34.3-34.3
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• 2020

Faint z ~ 5 quasars with M1450 ~ -23 mag are known to be the potentially important contributors to the ultraviolet ionizing background in the post-reionization era. However, their number density has not been well determined, making it difficult to assess their role in the early ionization of the intergalactic medium (IGM). In this work, we present the updated results of our z ~ 5 quasar survey using the Infrared Medium-deep Survey (IMS), a near-infrared imaging survey covering an area of 85 square degrees. From our spectroscopic observations with the Gemini Multi-Object Spectrograph (GMOS) on the Gemini-South 8 m Telescope, we discovered eight new quasars at z ~ 5 with -26.1 ≤ M1450 ≤ -23.3. Combining our IMS faint quasars with the brighter Sloan Digital Sky Survey (SDSS) quasars, we derive, for the first time, the z ~ 5 quasar luminosity function (QLF) without any fixed parameters down to the magnitude limit of M1450 = -23 mag. We find that the faint-end slope of the QLF is very flat (-1.2) with a characteristic luminosity of -25.7 mag. The number density of z ~ 5 quasars from the QLF gives lower ionizing emissivity and ionizing photon density than those in previous works. These results imply that quasars are responsible for only 10-20% of the photons required to completely ionize the IGM at z ~ 5, disfavoring the idea that quasars alone could have ionized the IGM at z ~ 5.

• Economic and Environmental Geology
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• v.56 no.6
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• pp.715-728
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• 2023

Tracksite of pterosaurs, birds, and dinosaurs in Chungmugong-dong in Jinju was designated as a natural monument in 2011 and is known as the world's largest in terms of the number and density of pterosaur footprints. This site has been managed by installing protection buildings to conserve in 2018. About 17% of the footprints of pterosaur, theropod, and ornithopod in this site under management in the 2nd protection building are of great academic value, but observation of footprints has difficulties due to continuous physical and chemical damage. In particular, the accumulation of milk-white contaminants is formed by the gypsum and air pollutant complex. Gypsum remains evaporated with a plate or columnar shape in the process of water circulation around the 2nd protection building, and the dust is from through the inflow of the gallery windows. The aqueous solution of gypsum, consisting of calcium from the lower bed and sulfur from grass growth, is catchmented into the groundwater from the area behind the protection building. Pollen and a few minerals other constituents of contaminants, go through the gallery window, which makes it difficult to expel dust. To conserve the fossil-bearing beds from two contaminants of different origins, controlling the water and atmospheric circulation of the 2nd protection building and removing the contaminants continuously is necessary. When cleaning contaminants, the steam cleaning method is sufficiently effective for powder-shaped milk-white contaminants. The fossil-bearing bed consists of dark gray shale with high laser absorption power; the laser cleaning method accompanies physical loss to fossils and sedimentary structures; therefore, avoiding it as much as possible is desirable.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.655-667
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• 2023

Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R²), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R² of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.13-19
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• 2024

An analysis of the coastal water temperature in the Tongyeong waters, the eastern sea of the South Sea of Korea, revealed that the water temperature rose sharply before the typhoon made landfall. The water temperature rise occurred throughout the entire water column. An analysis of the sea surface temperature data observed by NOAA(National Oceanic and Atmospheric Administration) satellites, indicated that sea water with a temperature of 30℃ existed in the eastern waters of the eastern South Sea of Korea before the typhoon landed. The southeastern sea of Korea is an area where ocean currents prevail from west to east owing to the Tsushima Warm Current. However, an analysis of the satellite data showed that seawater at 30℃ moved from east to west, indicating that it was affected by the Ekman transport caused by the typhoon before landing. In addition, because the eastern waters of the South Sea are not as deep as those of the East Sea, the water temperature of the entire water layer may remain constant owing to vertical mixing caused by the wind. Because the rise in water temperature in each water layer occurred on the same day, the rise in the bottom water temperature can be considered as owing to vertical mixing. Indeed, the southeastern sea of Korea is a sea area where the water temperature can rise rapidly depending on the direction of approach of the typhoon and the location of high temperature formation.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.