• Korean Journal of Ecology and Environment
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• v.54 no.4
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• pp.315-326
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• 2021

The clear-water phase (CWP) is a notable limnological phenomenon in freshwater systems caused by predatory interactions between large filter-feeding zooplankton and phytoplankton. However, the mechanisms and factors that influence the extent of CWP, particularly in complex water systems with both fluvial and lacustrine characteristics, remain poorly understood. The present study evaluated CWP occurrence patterns at different sites in a large reservoir located in a temperate monsoon region (Lake Paldang, Korea); the relationships among factors associated with CWP occurrence, such as transparency, zooplankton diversity, and chlorophyll a concentration were investigated. Transparency exhibited significant correlations with precipitation and retention time, as well as the relative abundance of zooplankton (p<0.01), suggesting that a change in the retention time due to precipitation can alter CWP. Data collected before and after CWP occurrence were analyzed using paired t-test to determine variations in CWP occurrence based on the water system characteristics. The results demonstrated that various factors were associated with CWP occurrence in the fluvial-type and lacustrine-type sites. The correlation between zooplankton biomass and transparency was stronger in the lacustrine-type sites than in the fluvial-type sites. The lacustrine-type sites, where cladoceran emergence is common and is associated with long retention times, favored CWP occurrence. The results suggest that lacustrine-type sites, which are conducive to zooplankton development and have relatively long retention times, enhance CWP occurrence. Furthermore, CWP occurrence was notable in spring, and the present study revealed that site-specific CWP could occur throughout the year, regardless of the season.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.2
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• pp.171-187
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• 2023

Objectives: Aircraft cabin cleaning work is characterized by being performed within a limited time in a narrow and enclosed space. The objective of this study was to evaluate the exposure levels to dust, ultra fine dust(PM2.5) and black carbon(BC) among aircraft cabin cleaners. Methods: Active personal air sampling for respirable dust(n=73) and BC(n=47) was conducted during quick transit cleaning(cabin general and vacuum-specific) and seat cover replacement and total dust and PM2.5 were area-air-sampled as well. Also, size distribution of particle was identified with the cleaning workers targeted. Dusts were collected with PVC filters using gravimetric analysis. The concentration of PM2.5 and the particle size distribution were measured with real-time direct reading portable equipment using light scattering analysis. The concentration of BC was measured by aethalometer(filter-based real-time light absorption analysis instrument). Results: The geometric mean of respirable dust was the highest at vacuum cleaning as 74.4 ㎍/m³, following by replacing seat covers as 49.3 ㎍/m³ and cabin general cleaning as 47.8 ㎍/m³ . The arithmetic mean of PM2.5 was 4.83 ~ 9.89 ㎍/m³ inside the cabin, and 28.5~44.5 ㎍/m³ outside the cabin(from bus and outdoor waiting space). From size distribution, PM_2.5/PM₁₀ ratio was 0.54 at quick transit cleaning and 0.41 at replacing seat covers. The average concentration of BC was 2~7 ㎍/m³, showing a high correlation with the PM2.5 concentration. Conclusions: The hazards concentration levels of aircraft cabin cleaners were very similar to those of roadside outdoor workers. As the main source of pollution is estimated to be diesel vehicles operating at airports, and it is necessary to replace older vehicles, strengthen pollutant emission control regulations, and introduce electric vehicles. In addition, it is necessary to provide as part of airport-inftastructure a stable standby waiting space for aircraft cabin cleaners and introduce a systematic safety and health management system for all workers in the aviation industry.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.119-138
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• 2024

Text indicators are increasingly valuable in economic forecasting, but are often hindered by noise and high dimensionality. This study aims to explore post-processing techniques, specifically noise filtering and dimensionality reduction, to normalize text indicators and enhance their utility through empirical analysis. Predictive target variables for the empirical analysis include monthly leading index cyclical variations, BSI (business survey index) All industry sales performance, BSI All industry sales outlook, as well as quarterly real GDP SA (seasonally adjusted) growth rate and real GDP YoY (year-on-year) growth rate. This study explores the Hodrick and Prescott filter, which is widely used in econometrics for noise filtering, and employs sufficient dimension reduction, a nonparametric dimensionality reduction methodology, in conjunction with unstructured text data. The analysis results reveal that noise filtering of text indicators significantly improves predictive accuracy for both monthly and quarterly variables, particularly when the dataset is large. Moreover, this study demonstrated that applying dimensionality reduction further enhances predictive performance. These findings imply that post-processing techniques, such as noise filtering and dimensionality reduction, are crucial for enhancing the utility of text indicators and can contribute to improving the accuracy of economic forecasts.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1777-1785
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• 2021

Objective: To investigate the accuracy of the Agatston score obtained with the ultra-high-pitch (UHP) acquisition mode using tin-filter spectral shaping (Sn150 kVp) and a kVp-independent reconstruction algorithm to reduce the radiation dose. Materials and Methods: This prospective study included 114 patients (mean ± standard deviation, 60.3 ± 9.8 years; 74 male) who underwent a standard 120 kVp scan and an additional UHP Sn150 kVp scan for coronary artery calcification scoring (CACS). These two datasets were reconstructed using a standard reconstruction algorithm (120 kVp + Qr36d, protocol A; Sn150 kVp + Qr36d, protocol B). In addition, the Sn150 kVp dataset was reconstructed using a kVp-independent reconstruction algorithm (Sn150 kVp + Sa36d, protocol C). The Agatston scores for protocols A and B, as well as protocols A and C, were compared. The agreement between the scores was assessed using the intraclass correlation coefficient (ICC) and the Bland-Altman plot. The radiation doses for the 120 kVp and UHP Sn150 kVp acquisition modes were also compared. Results: No significant difference was observed in the Agatston score for protocols A (median, 63.05; interquartile range [IQR], 0-232.28) and C (median, 60.25; IQR, 0-195.20) (p = 0.060). The mean difference in the Agatston score for protocols A and C was relatively small (-7.82) and with the limits of agreement from -65.20 to 49.56 (ICC = 0.997). The Agatston score for protocol B (median, 34.85; IQR, 0-120.73) was significantly underestimated compared with that for protocol A (p < 0.001). The UHP Sn150 kVp mode facilitated an effective radiation dose reduction by approximately 30% (0.58 vs. 0.82 mSv, p < 0.001) from that associated with the standard 120 kVp mode. Conclusion: The Agatston scores for CACS with the UHP Sn150 kVp mode with a kVp-independent reconstruction algorithm and the standard 120 kVp demonstrated excellent agreement with a small mean difference and narrow agreement limits. The UHP Sn150 kVp mode allowed a significant reduction in the radiation dose.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.4 no.4
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• pp.113-125
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• 1984

Water supply has been mainly dependent on the construction of the dams in Korea. It is difficult, however, to continue to construct dams for many reasons, such as the decrease of construction sites, the increase of construction costs, the compensation of residents in flooded areas, and the environmental effects. Water demands have increased and are expected to continue increasing due to the concentration of people in the cities, the rise of the living standard, and rapid industrial growth. It is acutely important to find countermeasures such as development of ground water, desalination, and recycling of waste water to cope with increasing water demands. Recycling waste water includes all means of supplying non-potable water for their respective usages with proper water quality which is not the same quality as potable water. The usages of the recycled water include toilet flushing, air conditioning, car washing, yard watering, road cleaning, park sprinkling, and fire fighting, etc. Raw water for recycling is obtained from drainage water from buildings, toilets, and cooling towers, treated waste water, polluted rivers, ground water, reinfall, etc. The water quantity must be considered as well as its quality in selecting raw water for the recycling. The types of recycling may be classified roughly into closed recycle systems and open recycle systems, which can be further subdivided into individual recycle systems, regional recycle systems and large scale recycle system. The treatment methods of wastewater combine biochemical and physiochemical methods. The former includes activated sludge treatment, bio-disc treatment, and contact aeration treatment, and the latter contains sedimentation, sand filtration, activated carbon adsorption, ozone treatment, chlorination, and membrane filter. The recycling patterns in other countries were investigated and the effects of the recycling were divided into direct and indirect effects. The problems of water reuse in recycle patterns were also studied. The problems include technological, sanitary, and operational problems as well as cost and legislative ones. The duties of installation and administrative organization, structural standards for reuse of water, maintenance and financial disposal were also studied.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Korean Journal of Animal Reproduction
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• v.26 no.3
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• pp.245-252
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• 2002

This study was to evaluate the in vitro survival of bovine enucleated MII (eMII) oocytes according to minimum volume cooling (MVC) freezing method and activation timing, and their in vitro development after somatic cell nuclear transfer (SONT). in vitro matured bovine oocytes for 20 h were stained with 5 $\mu\textrm{g}$/$m\ell$ Hoechst, and their 1st polar body and MII plate were removed by enucleation micropipette under UV filter. Also, eMII oocytes were subjected to activation after (group II) and before (group III) vitrification in 5 ${\mu}{\textrm}{m}$ ionomycin added CRlaa medium for 5 min. For vitrification, eMll oocytes were pretreated with EG10 for 5 min, exposed to EG30 for 30 sec and then directly plunged into L$N_2$. Thawing was taken by 4-step procedures at 37$^{\circ}C$. Survived eMII oocytes were subjected to SONT with cultured adult bovine ear cells. Reconstructed oocytes were cultured in 10 $\mu\textrm{g}$/$m\ell$ of cycloheximide and 2.5 $\mu\textrm{g}$/$m\ell$ of cytochalasin D added CRlaa medium for 1 h, and then in 10 $\mu\textrm{g}$/$m\ell$ of cycloheximide added CRlaa medium for 4 h. Subsequently, the reconstructed oocytes were incubated for 2 days and cleaved embryos were further cultured on cumulus-cell monolayer drop in CRlaa medium for 6 days. Survival rates of bovine vitrified-thawed eMII oocytes in group II (activation after vitrification and thawing) and III (activation before vitrification) were 81.0% and 84.9%, respectively. Fusion rates of cytoplasts and oocytes in group II and III were 69.0% and 70.0%, respectively, and their results were not different with non-frozen NT group (control, 75.2%). Although their cleaved rates (53.4% and 58.4%) were not different, cytoplasmic fragment rate in group II (32.8%) was significantly higher than that in group III (15.6%)(P<0.05). Also, subsequent development rate into >morula in group II (8.6%) was low than that in group III(15.6%). However, in vitro development rate in group III was not different with that in control (24.8%). This result suggested that MVC method was appropriate freezing method for the bovine eMII oocytes and vitrified eMII oocytes after pre-activation could support in vitro embryonic development after SONT as equally well as fresh oocytes.

• Tuberculosis and Respiratory Diseases
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• v.43 no.4
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• pp.547-557
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• 1996

Background : An excessive accumulation of neutrophils in lung tissue has been known to play an important role in mediating the tissue injury among the adult respiratory distress syndrome, idiopathic pulmonary fibrosis and cystic fibrosis by releasing toxic oxygen radicals and proteolytic enzymes. Therefore, it is important to understand a possible mechanism of neutrophil accumulation in lung tissue. In many species, exposure to hyperoxic stimuli can cause changes of lung tissues very similar to human adult respiratory distress syndrome and neutrophils are also functioning as the main effector cells in hyperoxic lung injury. The purpose of the present study was to examine whether neutrophils function as a key effector cell and to study the nature of possible neutrophil chemotactic factors found in bronchoalveolar lavage fluid from the hyperoxia exposed rats. Methods : We exposed the rats to the more than 95% oxygen for 24, 48, 60 arid 72 hours and bronchoalveolar lavage(BAL) was performed. Neutrophil chemotactic activity was measured from the BAT- fluid of each experimental groups. We also evaluated the molecular weight of neutrophil chemotactic tractors using fast performance liquid chromatography and characterized the substances by dialyzer membrane and heat treatment. Results : 1) The neutrophil proportions in bronchoalveolar lavage fluid began to rise from 48 hours after oxygen exposure, and continued to be significantly increased with exposure times. 2) chemotactic index for neutrophils in lung lavages from rats exposed to hyperoxia was significantly higher in 48 hours group than in control group, and was significantly increased with exposure time. 3) No deaths occured until after 48 hours of exposure. However, mortality rates were increased to 33.3 % in 60 hours group and 81.3 % in 72 fours group. 4) Gel filtration using fast performance liquid chromatography disclosed two peaks of neutrophil chemotactic activity in molecular weight of 104,000 and 12,000 daltons. 5) Chemotactic indices of bronchoalveolar lavage fluid were significantly deceased when bronchoalveolar lavage fluid was treated with heat ($56^{\circ}C$ for 30 min or $100^{\circ}C$ for 10 min) or dialyzed (dialyzer membrane molecular weight cut off : 12,000 daltons). Conclusion : These results suggested that the generation of neutrophil chemotactic factor and subsequent neutrophil influx into the lungs are playing an important roles in hyperoxia-induced acute lung injury. Neutrophil chemotactic factor in the lung lavage fluids consisted of several distinct components having different molecular weight and different physical characteristics.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.3 no.2
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• pp.77-84
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• 2005

A voloxidizer for a hot cell demonstration, that handles spent fuels of a high radiation level in a limited space should be small and spent fuel powders should not be dispersed out of the equipment involved. In this study a density rate equation as well as the Stokes'equation has been proposed in order to obtain the theoretical terminal velocity of powders. The terminal velocity of U$_{3}$O$_{8}$ has been predicted by using the terminal velocity of SiO$_{2}$, and then determination has been the optimum air flow rate which is able to prevent powders from scattering. An equation which has shown a relationship between theoretical terminal velocities of U$_{3}$O$_{8}$ and SiO$_{2}$ has been derived with the help of the Stokes'equation, and then an experimental verification made for the theoretical Stokes' equation of SiO$_{2}$ by means of an experimental device made of acryl. The theoretical terminal velocity based on the proposed density rate equation has been verified by detecting U$_{3}$O$_{8}$ powders in a filter installed in the mock-up voloxidizer. As the results, the optimum air flow rates seem to be 20 LPM by the Stokes'equation while they are 14.5 L/min by the density rate equation. At the experiments with the mock-up voloxidizer, a trace amount of U$_{3}$O$_{8}$ seems to be detectable at the air flow rate of 14.5 L/min by the density rate equation, but U$_{3}$O$_{8}$ powders of 7$\mu$m diameter seem detectable at the air flow rate of 20 L/min by the Stokes'equation. It is revealed that 14.5 L/min is the optimum air flowe rate which is capable of preventing U$_{3}$O$_{8}$ powders from scattering in the UO$_{2}$ voloxidizer and the proposed density rate equation is proper to calculate the terminal velocity of U$_{3}$O$_{8}$ powders.