• Korean Journal of Ecology and Environment
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• v.34 no.4 s.96
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• pp.298-309
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• 2001

The purpose of this study was to evaluate the filtering-feeding effect of a freshwater mussel (Corbicula leana) on the phytoplankton communities in two lakes with different trophic conditions between June and September, 2000. Manipulation experiments were conducted with two treatments (the control and mussel addition), and each established in duplicate 10 l chambers. Both ambient nutrient (TN, TP) and chlorophyll-a concentrations were significantly (p<0.01) higher in Lake Ilgam than Lake Soyang. Cyanophytes (Microcystis, Oscillatoria, Lyngbya and Dactylococcopis) consistently dominated algal community in Lake llgam, while flagellated algae (Dinobryon divergence, Mallomonas, Rhodomonas) and cyanophytes (Microcystis)dominated in Lake Soyang. The net exponential death rate ($R\;=\;day^{-1}$) of total phytoplankton in the mussel treatment ranged $1.70{\sim}7.39$ and $0.38{\sim}1.64$ in Lakes Soyang and Ilgam, respectively. Mean filtering rate standardized by mussel AFDW ($ml\;mgAFDW^{1}\;h^{-1}$) was much higher in Lake Soyang ($1.70{\sim}3.06$) than in Lake Ilgam($0.24{\sim}0.88$0.24~o.88). Estimating FR per mussel, 1 mussel filtered $1.6{\sim}7.8\;l$ per day and $1.7{\sim}3.0\;l$ per day in Lakes Soyang and Ilgam, respectively. Based on tile C-flux tobiomass ratio, Corbicula leana consumed $0.8{\sim}4.4$ fold of phytoplankton standing stock in Lake Soyang, and $0.4{\sim}1.6$ fold in Lake Ilgam per day. Mussel feeding resulted in increase of SRP concentration by $30{\sim}50%$, compared with the control. The results of this study suggest that filter-feeding activity of Corbicula leana varies depending on the phytoplankton density and community composition. The high seston consumption rate of Corsicuja Jeaua even in a eutrophic lake suggests that biomanipulation approach using filter-feeding mussels can be used far wate rquality management in small eutrophic reservoirs.

• International Journal of Vascular Biomedical Engineering
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• v.4 no.2
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• pp.19-24
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• 2006

Background: Pulse wave velocity (PWV), which is inversely related to the distensibility of an arterial wall, offers a simple and potentially useful approach for an evaluation of cardiovascular diseases. In spite of the clinical importance and widespread use of PWV, there exist no standard either for pulse sensors or for system requirements for accurate pulse wave measurement. Objective of this study was to assess the reproducibility of PWV values using a newly developed PWV measurement system in healthy subjects prior to a large-scale clinical study. Methods: System used for the study was the PP-1000 (Hanbyul Meditech Co., Korea), which provides regional PWV values based on the measurements of electrocardiography (ECG), phonocardiography (PCG), and pulse waves from four different sites of arteries (carotid, femoral, radial, and dorsalis pedis) simultaneously. Seventeen healthy male subjects with a mean age of 33 years (ranges 22 to 52 years) without any cardiovascular disease were participated for the experiment. Two observers (observer A and B) performed two consecutive measurements from the same subject in a random order. For an evaluation of system reproducibility, two analyses (within-observer and between-observer) were performed, and expressed in terms of mean difference ${\pm}2SD$, as described by Bland and Altman plots. Results: Mean and SD of PWVs for aorta, arm, and leg were $7.07{\pm}1.48m/sec,\;8.43{\pm}1.14m/sec,\;and\;8.09{\pm}0.98m/sec$ measured from observer A and $6.76{\pm}1.00m/sec,\;7.97{\pm}0.80m/sec,\;and\;\7.97{\pm}0.72m/sec$ from observer B, respectively. Between-observer differences ($mean{\pm}2SD$) for aorta, arm, and leg were $0.14{\pm\}0.62m/sec,\;0.18{\pm\}0.84m/sec,\;and\;0.07{\pm}0.86m/sec$, and the correlation coefficients were high especially 0.93 for aortic PWV. Within-observer differences ($mean{\pm}2SD$) for aorta, arm, and leg were $0.01{\pm}0.26m/sec,\;0.02{\pm}0.26m/sec,\;and\;0.08{\pm}0.32m/sec$ from observer A and $0.01{\pm}0.24m/sec,\;0.04{\pm}0.28m/sec,\;and\;0.01{\pm}0.20m/sec$ from observer B, respectively. All the measurements showed significantly high correlation coefficients ranges from 0.94 to 0.99. Conclusion: PWV measurement system used for the study offers comfortable and simple operation and provides accurate analysis results with high reproducibility. Since the reproducibility of the measurement is critical for the diagnosis in clinical use, it is necessary to provide an accurate algorithm for the detection of additional features such as flow wave, reflection wave, and dicrotic notch from a pulse waveform. This study will be extended for the comparison of PWV values from patients with various vascular risks for clinical application. Data acquired from the study could be used for the determination of the appropriate sample size for further studies relating various types of arteriosclerosis-related vascular disease.

• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.845-859
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• 2020

Climate change and recent heat waves have drawn public attention toward other environmental issues, such as water pollution in the form of algal blooms, chemical leaks, and oil spills. Water pollution by the leakage of chemicals may severely affect human health as well as contaminate the air, water, and soil and cause discoloration or death of crops that come in contact with these chemicals. Chemicals that may spill into water streams are often colorless and water-soluble, which makes it difficult to determine whether the water is polluted using the naked eye. When a chemical spill occurs, it is usually detected through a simple contact detection device by installing sensors at locations where leakage is likely to occur. The drawback with the approach using contact detection sensors is that it relies heavily on the skill of field workers. Moreover, these sensors are installed at a limited number of locations, so spill detection is not possible in areas where they are not installed. Recently hyperspectral images have been used to identify land cover and vegetation and to determine water quality by analyzing the inherent spectral characteristics of these materials. While hyperspectral sensors can potentially be used to detect chemical substances, there is currently a lack of research on the detection of chemicals in water streams using hyperspectral sensors. Therefore, this study utilized remote sensing techniques and the latest sensor technology to overcome the limitations of contact detection technology in detecting the leakage of hazardous chemical into aquatic systems. In this study, we aimed to determine whether 18 types of hazardous chemicals could be individually classified using hyperspectral image. To this end, we obtained hyperspectral images of each chemical to establish a spectral library. We expect that future studies will expand the spectral library database for hazardous chemicals and that verification of its application in water streams will be conducted so that it can be applied to real-time monitoring to facilitate rapid detection and response when a chemical spill has occurred.

• Korean Chemical Engineering Research
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• v.43 no.2
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• pp.206-214
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• 2005

Heterogeneously-catalyzed oxidation of aqueous phase trichloroethylene (TCE) over supported metal oxides has been conducted to establish an approach to eliminate ppm levels of organic compounds in water. A continuous flow reactor system was designed to effect predominant reaction parameters in determining catalytic activity of the catalysts for wet TCE decomposition as a model reaction. 5 wt.% $CoO_x/TiO_2$ catalyst exhibited a transient period in activity vs. on-stream time behavior, suggesting that the surface structure of the $CoO_x$ might be altered with on-stream hours; regardless, it is probable to be the most promising catalyst. Not only could the bare support be inactive for the wet decomposition reaction at $36^{\circ}C$, but no TCE removal also occurred by the process of adsorption on $TiO_2$ surface. The catalytic activity was independent of all particle sizes used, thereby representing no mass transfer limitation in intraparticle diffusion. Very low TCE conversion appeared for $TiO_2$-supported $NiO_x$ and $CrO_x$ catalysts. Wet oxidation performance of supported Cu and Fe catalysts, obtained through an incipient wetness and ion exchange technique, was dependent primarily on the kinds of the metal oxides, in addition to the acidic solid supports and the preparation routes. 5 wt.% $FeO_x/TiO_2$ catalyst gave no activity in the oxidation reaction at $36^{\circ}C$, while 1.2 wt.% Fe-MFI was active for the wet decomposition depending on time on-stream. The noticeable difference in activity of the both catalysts suggests that the Fe oxidation states involved to catalytic redox cycle during the course of reaction play a significant role in catalyzing the wet decomposition as well as in maintaining the time on-stream activity. Based on the results of different $CoO_x$ loadings and reaction temperatures for the decomposition reaction at $36^{\circ}C$ with $CoO_x/TiO_2$, the catalyst possessed an optimal $CoO_x$ amount at which higher reaction temperatures facilitated the catalytic TCE conversion. Small amounts of the active ingredient could be dissolved by acidic leaching but such a process gave no appreciable activity loss of the $CoO_x$ catalyst.

• Journal of Korea Water Resources Association
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• v.51 no.9
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• pp.827-837
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• 2018

The stabilities of sliding and overturning of caisson and bearing capacity of mound against eccentric and inclined loads, which possibly happen to a composite caisson breakwaters, have been analyzed by using the technique of multiple failure modes. In deterministic approach, mathematical functions have been first derived from the ultimate limit state equations. Using those functions, the minimum cross section of caisson can straightforwardly be evaluated. By taking a look into some various deterministic analyses, it has been found that the conflict between failure modes can be occurred, such that the stability of bearing capacity of mound decreased as the stability of sliding increased. Therefore, the multiple failure modes for the composite caisson breakwaters should be taken into account simultaneously even in the process of deterministically evaluating the design cross section of caisson. Meanwhile, the reliability analyses on multiple failure modes have been implemented to the cross section determined by the sliding failure mode. It has been shown that the system failure probabilities of the composite breakwater are very behaved differently according to the variation of incident waves. The failure probabilities of system tend also to increase as the crest freeboards of caisson are heightening. The similar behaviors are taken place in cases that the water depths above mound are deepening. Finally, the results of the first-order modal are quite coincided with those of the second-order modal in all conditions of numerical tests performed in this paper. However, the second-order modal have had higher accuracy than the first-order modal. This is mainly due to that some correlations between failure modes can be properly incorporated in the second-order modal. Nevertheless, the first-order modal can also be easily used only when one of failure probabilities among multiple failure modes is extremely larger than others.

• 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 the Korea Organic Resources Recycling Association
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• v.27 no.3
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• pp.81-89
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• 2019

The major problem encountered during the storage of pig slurry (PS) is the release of huge amounts of greenhouse gases (GHGs), which are dominated by methane ($CH_4$). To reduce this, concentrated sulfuric acid has been used as an additive to control the pH of pig slurry to 5.0-6.0. However, other low-risk substitutes have been developed due to some limitations to its use, such as corrosiveness, and hazards to animal and human health. In this study, sugar addition was proposed as an eco-friendly approach for limiting $CH_4$ emission from PS during storage. The pH of PS has been reduced from $7.1{\pm}0.1$ (control) to $5.8{\pm}0.1$, $4.6{\pm}0.1$, $4.4{\pm}0.1$, $4.1{\pm}0.1$, and $4.0{\pm}0.1$, by the addition of 10, 20, 30, 40, and 50 g sugar/L, respectively. Lactate, acetate, and propionate were detected as the dominant organic acids and at sugar concentration above 20 g/L, lactate concentration represented 42-72% (COD basis) of total organic acids. For 40 d of storage, $20.6{\pm}2.3kg\;CO_2\;eq./ton\;PS$ was emitted in the control. Such emission, however, was found to be reduced to $8.7{\pm}0.4$ and $0.4{\pm}0.1kg\;CO_2\;eq./ton\;PS$ at 10 and 20 g/L, respectively. Small amount of $CH_4$ from PS at 10 g/L was emitted until 30 d of storage, while for rest of storage period, it has increased to $8.7{\pm}0.4kg\;CO_2\;eq./ton\;PS$ ( 40% of the control) when methanogens have recovered by increasing pH to 7.0. By the end of storage, VS and COD removal in the control reached 24% and 27%, while their ranges reached 15-4% and 12-17% in the sugar added experiments, respectively. It was found that more than 90% of COD removal was done by aerobic biological process.

• Particle and aerosol research
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• v.14 no.4
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• pp.153-169
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• 2018

To identify a variety of organic compounds in the ambient aerosols, the two-dimensional gas chromatography-time of flight mass spectrometry (GCxGC) system (2DGC) has been applied. While 2DGC provides more peaks, the amount of the generated data becomes huge. A two-step approach has been proposed to efficiently interpret the organic aerosol analysis data. The two-dimensional 2DGC data were divided into 6 chemical groups depending on their volatility and polarity. Using these classification standards, all the peaks were subject to both qualitative and quantitative analyses and then classified into 8 classes. The aerosol samples collected in Seoul in summer 2013 and winter 2014 were used as the test case. It was found that some chemical classes such as furanone showed seasonal variation in the high polarity-volatile organic compounds (HP-VOC) group. Also, for some chemical classes, qualitative and quantitative analyses showed different trends. Limitations of the proposed method are discussed.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.