• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.127-134
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• 2021

In this study, an anomaly detection (AD) algorithm was implemented to detect the failure of a marine air compressor. A lab-scale experiment was designed to produce fault datasets (time-series electric current measurements) for 10 failure modes of the air compressor. The results demonstrated that the temporal pattern of the datasets showed periodicity with a different period, depending on the failure mode. An AD model with a convolutional autoencoder was developed and trained based on a normal operation dataset. The reconstruction error was used as the threshold for AD. The reconstruction error was noted to be dependent on the AD model and hyperparameter tuning. The AD model was applied to the synthetic dataset, which comprised both normal and abnormal conditions of the air compressor for validation. The AD model exhibited good detection performance on anomalies showing periodicity but poor performance on anomalies resulting from subtle load changes in the motor.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.313-322
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• 2022

Seed-borne pathogens in crops reduce the seed germination rate and hamper seedling growth, leading to significant yield loss. Due to the growing concerns about environmental damage and the development of resistance to agrochemicals among pathogen populations, there is a strong demand for eco-friendly alternatives to synthetic chemicals in agriculture. It has been well established during the last few decades that plant seeds harbor diverse microbes, some of which are vertically transmitted and important for plant health and productivity. In this study, we isolated culturable endophytic bacteria and fungi from soybean seeds and evaluated their antagonistic activities against common bacterial and fungal seed-borne pathogens of soybean. A total of 87 bacterial isolates and 66 fungal isolates were obtained. Sequencing of 16S rDNA and internal transcribed spacer amplicon showed that these isolates correspond to 30 and 15 different species of bacteria and fungi, respectively. Our antibacterial and antifungal activity assay showed that four fungal species and nine bacterial species have the potential to suppress the growth of at least one seed-borne pathogen tested in the study. Among them, Pseudomonas koreensis appears to have strong antagonistic activities across all the pathogens. Our collection of soybean seed endophytes would be a valuable resource not only for studying biology and ecology of seed endophytes but also for practical deployment of seed endophytes toward crop protection.

• Geophysics and Geophysical Exploration
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• v.24 no.4
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• pp.171-179
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• 2021

We outline a process for estimating Cole-Cole parameters from time-domain induced polarization (IP) data. The IP transients are all inverted to 2D Cole-Cole earth models that include resistivity, chargeability, relaxation time, and the frequency exponent. Our inversion algorithm consists of two stages. We first convert the measured voltage decay curves into time series of current-on time apparent resistivity to circumvent the negative chargeability problem. As a first step, a 4D inversion recovers the resistivity model at each time channel that increases monotonically with time. The desired intrinsic Cole-Cole parameters are then recovered by inverting the resistivity time series of each inversion block. In the second step, the Cole-Cole parameters can be estimated readily by setting the initial model close to the true value through a grid search method. Finally, through inversion procedures applied to synthetic data sets, we demonstrate that our algorithm can image the Cole-Cole earth models effectively.

• Geophysics and Geophysical Exploration
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• v.24 no.4
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• pp.149-157
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• 2021

Given that induced polarization (IP) and direct current (DC) resistivity surveys are similar in terms of data acquisition, most DC resistivity systems are equipped with a time-domain IP data acquisition function. In addition, the time-domain IP data include the DC resistivity values. As such, IP and DC resistivity data are intimately linked, and the inversion of IP data is a two-step process based on DC resistivity inversions. Nevertheless, IP surveys are rarely applied, in contrast to DC resistivity surveys, as proper inversion software is unavailable. In this study, through numerical modeling and inversion experiments, we analyze the problems with the conventional inverse mapping technique used to invert time-domain IP data. Furthermore, we propose a modified inverse mapping technique that can effectively suppress inversion artifacts. The performance of the technique is confirmed through inversions applied to synthetic IP data.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.1
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• pp.116-123
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• 2023

Flow-through aquaculture systems generate large amounts of wastewater containing compounds such as solids that can settle near aquafarms and cause eutrophication. The settled solids are often reintroduced into flow-through systems, and aquatic animals can be affected by the solids and pathogens associated with these solids. For a sustainable aquaculture operation, adequate wastewater treatment is required. Hydrocyclones are one of the most promising technologies for the removal of solids in aquaculture wastewater. In this study, a model for performance prediction of hydrocyclones was investigated under three different operating conditions: water temperature, solids concentration, and water inlet velocity. The synthetic solids solution was prepared using settled solids from abalone aquaculture farms. The daily solids removal rates of the tested hydrocyclones ranged from 0.18 to 26.0 g solids-m^-3-day^-1, and removal efficiency ranged from 5.1 to 34.4%. The inlet water velocity had the greatest effect on solids removal and hydrocyclone efficiencies. The following multiregression model equation was derived from the daily solids removal rate (g solids-m^-3-day^-1) results for water temperature (T, ℃), solids concentration (SS, mg-L^-1), and tangential inlet water velocity (TIV, m-sec^-1): daily solids removal rate: f(z)=4.465+0.809TIV-0.375T+0.217SS (r²=0.976).

• Conservation Science in Museum
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• v.29
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• pp.111-132
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• 2023

Terahertz radiation cannot pass through metal and therefore reflect and return most signals. Utilizing this property, this study analyzed information on paintings to verify the usage of metal materials on paintings and the scope of their application. First, the study tested specimens of metal-based pigments and synthetic pearl pigments with metallic colors and textures in order to compare basic characteristics of terahertz images, such as signal severance caused by metallic substances, traits reflected in cross-section images, and high degree of reflection. Subsequently, based on the collected information, the study diagnosed various types of paintings including Korean traditional paintings and oil paintings using the terahertz imaging technique to confirm the usage of metal-based pigments in the inner layers of paintings and their scope of application. The terahertz imaging technique could has the potential to provide scientific evidence for previously-undiscovered information and art-historical records about various types of paintings that used metalbased pigments, thereby rendering significant utility for the conservation and authentication of paintings.

• The Korean Journal of Physiology and Pharmacology
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• v.27 no.4
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• pp.383-398
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• 2023

Dihydroaustrasulfone alcohol (DA), the synthetic precursor of a natural compound (austrasulfone) isolated from the coral species Cladiella australis, has shown cytotoxic effects against cancer cells. However, it is unknown whether DA has antitumor effects on nasopharyngeal carcinoma (NPC). In this study, we determined the antitumor effects of DA and investigated its mechanism of action on human NPC cells. The MTT assay was used to determine the cytotoxic effect of DA. Subsequently, apoptosis and reactive oxygen species (ROS) analyses were performed by using flow cytometry. Apoptotic and PI3K/AKT pathway-related protein expression was determined using Western blotting. We found that DA significantly reduced the viability of NPC-39 cells and determined that apoptosis was involved in DA-induced cell death. The activity of caspase-9, caspase-8, caspase-3, and PARP induced by DA suggested caspase-mediated apoptosis in DA-treated NPC-39 cells. Apoptosis-associated proteins (DR4, DR5, FAS) in extrinsic pathways were also elevated by DA. The enhanced expression of proapoptotic Bax and decreased expression of antiapoptotic BCL-2 suggested that DA mediated mitochondrial apoptosis. DA reduced the expression of pPI3K and p-AKT in NPC-39 cells. DA also reduced apoptosis after introducing an active AKT cDNA, indicating that DA could block the PI3K/AKT pathway from being activated. DA increased intracellular ROS, but N-acetylcysteine (NAC), a ROS scavenger, reduced DA-induced cytotoxicity. NAC also reversed the chances in pPI3K/AKT expression and reduced DA-induced apoptosis. These findings suggest that ROS-mediates DA-induced apoptosis and PI3K/AKT signaling inactivation in human NPC cells.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1373-1387
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• 2021

Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy conditions. Therefore, in this study, Sentinel-1 Synthetic Aperture Radar (SAR) data from Europe Space Agency, which can be collected in all weather conditions, were used to identify forest fire damaged area based on a series of processes including Principal Component Analysis (PCA) and K-means clustering. Four forest fire cases, which occurred in Gangneung·Donghae and Goseong·Sokcho in Gangwon-do of South Korea and two areas in North Korea on April 4, 2019, were examined. The estimated burned areas were evaluated using fire reference data provided by the National Institute of Forest Science (NIFOS) for two forest fire cases in South Korea, and differenced normalized burn ratio (dNBR) for all four cases. The average accuracy using the NIFOS reference data was 86% for the Gangneung·Donghae and Goseong·Sokcho fires. Evaluation using dNBR showed an average accuracy of 84% for all four forest fire cases. It was also confirmed that the stronger the burned intensity, the higher detection the accuracy, and vice versa. Given the advantage of SAR remote sensing, the proposed statistical processing and K-means clustering-based approach can be used to quickly identify forest fire damaged area across the Korean Peninsula, where a cloud cover rate is high and small-scale forest fires frequently occur.

• 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.