• Journal of Life Science
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• v.21 no.1
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• pp.102-109
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• 2011

The genioglossus muscle plays an important role in maintaining upper airway patency during inspiration; if this muscle does not contract normally, breathing disorders occur due to closing of the upper airway. These occur because of disorders of synaptic input to the genioglossus motoneurons, however, little is known about it. In this study, the distribution of GABA-, glycine-, and glutamate-like immunoreactivity in axon terminals on dendrites of the rat genioglossus motoneurons, stained intracellularly with horseradish peroxidase (HRP), was examined by using postembedding immunogold histochemistry in serial ultrathin sections. The motoneurons were divided into four compartments: the soma, and primary (Pd), intermediate (Id), and distal dendrites (Dd). Quantitative analysis of 157, 188, 181, and 96 boutons synapsing on 3 soma, 14 Pd, 35 Id, and 28 Dd, respectively, was performed. 71.9% of the total number of studied boutons had immunoreactivity for at least one of the three amino acids. 32.8% of the total number of studied boutons were immunopositive for GABA and/or glycine and 39.1% for glutamate. Among the former, 14.2% showed glycine immunoreactivity only and 13.3% were immunoreactive to both glycine and GABA. The remainder (5.3%) showed immunoreactivity for GABA only. Most boutons immunoreactive to inhibitory amino acids contained a mixture of flattened, oval, and round synaptic vesicles. Most boutons immunoreactive to excitatory amino acids contained clear and spherical synaptic vesicles with a few dense-cored vesicles. When comparisons of the inhibitory and excitatory boutons were made between the soma and three dendritic segments, the proportion of the inhibitory to the excitatory boutons was high in the Dd (23.9% vs. 43.8%) but somewhat low in the soma (35.7% vs. 38.2%), Pd (34.6% vs. 37.8%) and Id (33.1% vs. 38.7%). The percentage of synaptic covering of the inhibitory synaptic boutons decreased in the order of soma, Pd, Id, and Dd, but this trend was not applicable to the excitatory boutons. The present study provides possible evidence that the spatial distribution patterns of inhibitory and excitatory synapses are different in the soma and dendritic tree of the rat genioglussus motoneurons.

• Korean Journal of Environmental Agriculture
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• v.9 no.2
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• pp.133-141
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• 1990

For the purpose of evaluating nutrient loadings into rivers and lakes from agricultural land, especially from paddy fields and also nutrient degradation in drainage channels, the Total Kjeldahl Nitrogen(TKN) and the Total Phosphorus(TP) were investigated in 29.5 ha. paddy fields in Hwa-Sung, Kyong-Ki, Korea, during the period from May 8, 1989 to Sep. 27, 1989. The results of the study can be su㎜arized as follows : 1. Annual inputs into paddy fields were 180 N-kg/ha 46 P-kg/ha. by fertilization, and 15.0 TKN-kg/ha. 10.0 TP-kg/ha. by irrigation, 8.0 TKN-kg/ha. 0.34 TP-kg/ha. by rainfall respectively. The amount of nutrient involved in surface runoff from paddies was 39.0 TKN-kg/ha. 9.2 TP-kg/ha. and in seepage 7.5 TKN-kg/ha. 2.1 TP-kg/ha. respectively 2. In WS1 stream(reach length equals 950m), nutrients decreased 0.31 TKN-mg/L/km, 0.01 TP-mg/L/km and in WS2 stream (reach length equals 750m) which are more meandering and undulating than WS1, the nutrients decreased 0.84 TKN-mg/L/km, 0.11 TP-mg/L/km. From these results, it was concluded that low stream velocity due to meandering and undulation promotes more degradation of nutrient concentrations. 3. For the purpose of decreasing nutrient loads from paddy fields, the amount of fertilizer used needs to be controlled, irrigation weirs need to be constructed in the drainage channels to delay the transportation of nutrients by decelerating the stream velocity and plants such as plantain-lily need to be cultivated in the channel to consume nutrients and therefore enlarge chances of self-purification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.1
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• pp.77-88
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• 2000

In this paper, the 3-mode variable gain high power amplifier for a transmitter of INMARSAT-B operating at L-band(1626.5-1646.5 MHz) was developed. This SSPA can amplify 42 dBm in high power mode, 38 dBm in medium power mode and 36 dBm in low power mode for INMARSAT-B. The allowable errol sets +1 dBm as the upper limit and -2 dBm as the lower limit, respectively. To simplify the fabrication process, the whole system is designed by two parts composed of a driving amplifier and a high power amplifier. The HP's MGA-64135 and Motorola's MRF-6401 were used for driving amplifier, and the ERICSSON's PTE-10114 and PTF-10021 for the high power amplifier. The SSPA was fabricated by the RP circuits, the temperature compensation circuits and 3-mode variable gain control circuits and 20 dB parallel coupled-line directional coupler in aluminum housing. In addition, the gain control method was proposed by digital attenuator for 3-mode amplifier. Then il has been experimentally verified that the gain is controlled for single tone signal as well as two tone signals. In this case, the SSPA detects the output power by 20 dB parallel coupled-line directional coupler and phase non-splitter amplifier. The realized SSPA has 41.6 dB, 37.6 dB and 33.2 dB for small signal gain within 20 MHz bandwidth, and the VSWR of input and output port is less than 1.3:1. The minimum value of the 1 dB compression point gets more than 12 dBm for 3-mode variable gain high power amplifier. A typical two tone intermodulation point has 36.5 dBc maximum which is single carrier backed off 3 dB from 1 dB compression point. The maximum output power of 43 dBm was achieved at the 1636.5 MHz. These results reveal a high power of 20 Watt, which was the design target.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.5
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• pp.577-583
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• 2007

To overcome certain disadvantages of past typical control techniques for toxic contaminants emitted from various industrial processes, the current study was conducted to establish a thermal catalytic system using mesh-type transition-metal platinum(Pt)/stainless steel(SS) catalyst and to evaluate catalytic thermal destruction of five chlorinated hydrocarbons[chlorobenzene(CHB), chloroform(CHF), perchloroethylene (PCE), 1,1,1-trichloroethane(TCEthane), trichloroethylene(TCE)]. In addition, this study evaluated the catalyst poison effect on the catalytic thermal destruction. Three operating parameters tested for the thermal catalyst system included the inlet concentrations, the incineration temperature, and the residence time in the catalyst system. The thermal decomposition efficiency decreased from the highest value of 100% to the lowest value of almost 0%(CHB) as the input concentration increased, depending upon the type of chlorinated compounds. The destruction efficiencies of the four target compounds, except for TCEthane, increased upto almost 100% as the reaction temperature increased, whereas the destruction efficiency for TCEthane did not significantly vary. For the target compounds except for TCEthane, the catalytic destruction efficiencies increased up to 30% to 97% as the residence time increased from 10 sec to 60 sec, but the increase of destruction efficiency for TCEthane stopped at the residence time of 30 sec, suggesting that long residence times are not always proper for thermal destruction of VOCs, when considering the destruction efficiency and operation costs of thermal catalytic system together. Conclusively, the current findings suggest that when applying the transition-metal catalyst for the better destruction of chlorinated hydrocarbons, VOC type should be considered, along with their inlet concentrations, and reaction temperature and residence time in catalytic system. Meanwhile, the addition of high methyl sulfide(1.8 ppm) caused a drop of 0 to 50% in the removal efficiencies of the target compounds, whereas the addition of low methyl sulfide (0.1 ppm), which is lower than the concentrations of sulfur compounds measured in typical industrial emissions, did not cause.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.8 no.4
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• pp.357-368
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• 2003

Time-series CTDT (Conductivity/Temperature/Depth/Transmissivity) were obtained at one point near tidewater glacier of Marian Cove (King George Islands, Antarctica) to present water column properties and SPM (suspended particulate matter) dispersal pattern in relation with tide, current, meteorological data, and SPM concentration. Four layers were divided from the water column characteristics measured in the interval of an hour for about 2 days: 1) cold, fresh, and turbid surface mixed layer between 0-20 m in water depth, 2) warm, saline, and relatively clean Maxwell Bay inflow between 20-40 m in water depth, 3) turbid/cold tongue of subglacial discharges compared with the ambient waters between 40-70 m in water depth, and 4) cold, saline, and clean bottom water beneath 70 m in water depth. Surface plume, turbid freshwater at coastal/cliff area in late summer (early February), had the characteristic temperature and SPM concentration according to morphology, glacial condition, and composition of sediments. The restrict dispersion only over the input source of meltwater discharges was due to calm wether condition. Due to strong wind-induced surface turbulence, fresh and turbid surface plume, englacial upwelling cold water, glacier-contact meltwater, and Maxwell Bay inflow was mixing at ice-proximal zone and the consequent mixed layer deepened at the surface. Large amount of precipitation, the major controlling factor for increasing short-term glacial discharges, was accompanied by the apparent development of subglacial discharge that resulted in the rapid drop of salinity below the mid depth. Although amount of subglacial discharge and englacial upwelling may be large, however, their low SPM concentration would have small influence on bottom deposition of terrigenous sediments.

• Korean Journal of Ecology and Environment
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• v.42 no.1
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• pp.9-18
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• 2009

The objectives of this study were to evaluate seasonal and interannual variations of water quality and nutrient input (N, P) in Junam Reservoir, a nesting waterbody of migratory birds, over 10 years during 1998$\sim$2007 along with dynamic relations of trophic parameters using empirical models. Concentrations of COD averaged 7.8 mg $L^{-1}$ during the study, while TN and TP were $1.4\;mg\;L^{-1}$ and $83{\mu}g\;L^{-1}$, respectively, indicating an eutrophic-hypereutrophic state. Values of monthly COD had strong positive relations (r=0.669, p<0.001) with conductivity, indicating that summer rainfall resulted in an ionic dilution of the reservoir water by rainwater and contributed better water quality. One-way ANOVA tests showed significant differences (F=$5.2{\sim}12.9$, p<0.05) in TN and TP between the before and after the bird migration. In other words, nutrient levels were greater in the absence of migratory birds than in the presence of the migratory birds, suggesting a no-effect on nutrient inputs by the birds. Also, one-way ANOVA indicated no significant differences (F=$0.37{\sim}0.48$, p>0.05) in $NO_{3^-}N$ and $NH_{3^-}N$ between the before and after the birds migration. Linear empirical models using trophic parameters showed that algal biomass as CHL, had significant low correlations with TN ($R^2$=0.143, p<0.001, n=119) and TP ($R^2$=0.192, p<0.001, n=119). These results suggest that influences of nutrients on the CHL were evident, but the effect was weak. This fact was supported by analysis of Trophic State Index Deviation (TSID). Over 70% in the observed values of "TSI (CHL)-TSI (SD)" and "TSI (CHL)-TSI (TP)" were less than zero, suggesting a light limitation on the CHL by inorganic suspended solids.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1299-1310
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• 2018

In this study, we analyzed distribution and movement trends using in-situ observations and particle tracking methods to understand the movement of the drift ice in the Arctic Ocean. The in-situ movement data of the drift ice in the Arctic Ocean used ITP (Ice-Tethered Profiler) provided by NOAA (National Oceanic and Atmospheric Administration) from 2009 to 2018, which was analyzed with the location and speed for each year. Particle tracking simulates the movement of the drift ice using daily current and wind data provided by HYCOM (Hybrid Coordinate Ocean Model) and ECMWF (European Centre for Medium-Range Weather Forecasts, 2009-2017). In order to simulate the movement of the drift ice throughout the Arctic Ocean, ITP data, a field observation data, were used as input to calculate the relationship between the current and wind and follow up the Lagrangian particle tracking. Particle tracking simulations were conducted with two experiments taking into account the effects of current and the combined effects of current and wind, most of which were reproduced in the same way as in-situ observations, given the effects of currents and winds. The movement of the drift ice in the Arctic Ocean was reproduced using a wind-imposed equation, which analyzed the movement of the drift ice in a particular year. In 2010, the Arctic Ocean Index (AOI) was a negative year, with particles clearly moving along the Beaufort Gyre, resulting in relatively large movements in Beaufort Sea. On the other hand, in 2017 AOI was a positive year, with most particles not affected by Gyre, resulting in relatively low speed and distance. Around the pole, the speed of the drift ice is lower in 2017 than 2010. From seasonal characteristics in 2010 and 2017, the movement of the drift ice increase in winter 2010 (0.22 m/s) and decrease to spring 2010 (0.16 m/s). In the case of 2017, the movement is increased in summer (0.22 m/s) and decreased to spring time (0.13 m/s). As a result, the particle tracking method will be appropriate to understand long-term drift ice movement trends by linking them with satellite data in place of limited field observations.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.85-96
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• 2019

Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Korean Journal of Environmental Agriculture
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• v.38 no.1
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• pp.54-60
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• 2019

BACKGROUND: Hydrogen fluoride is one of the 97 accident preparedness substances regulated by the Ministry of Environment (Republic of Korea) and chemical accidents should be managed centrally due to continual occurrence. Especially, hydrogen fluoride has a characteristic of rapid diffusion and very toxic when leaking into the environment. Therefore, it is important to predict the impact range quickly and to evaluate the residual contamination immediately to minimize the human and environmental damages. METHODS AND RESULTS: In order to estimate the accident impact range, the off-site consequence analysis (OCA) was performed to the worst and alternative scenarios. Also, in order to evaluate the residual contamination of hydrogen fluoride in crop, the samples in accident site were collected from 15-divided regions (East direction from accident sites based on the main wind direction), and the concentration was measured by fluoride ($F^-$) ion-selective electrode potentiometer (ISE). As a result of the OCA, the affected distance by the worst scenario was estimated to be >10 km from the accident site and the range by the alternative scenario was estimated to be about 1.9 km. The residual contamination of hydrogen fluoride was highest in the samples near the site of the accident (E-1, 276.82 mg/kg) and tended to decrease as it moved eastward. Meanwhile, the concentrations from SE and NE (4.96~28.98 mg/kg) tended to be lower than the samples near the accident site. As a result, the concentration of hydrogen fluoride was reduced to a low concentration within 2 km from the accident site (<5 mg/kg), and the actual damage range was estimated to be around 2.2 km. Therefore, it is suggested that the results are similar to those of alternative accident scenarios calculated by OCA (about 1.9 km). CONCLUSION: It is difficult to estimate the chemical accident-affecting range/region by the OCA evaluation, because it is not possible to input all physicochemical parameters. However simultaneous measurement of the residual contamination in the environment will be very helpful in determining the diffusion range of actual chemical accident.