• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.747-763
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• 2022

Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.327-339
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• 2022

The purpose of this study is to present the effective smoke control flow rate and mode for securing safety through quantitative risk assessment according to the smoke control flow rate and mode (supply or exhaust) of the platform when a train fire occurs at the subway platform. To this end, a fire outbreak scenario was created using a side platform with a central staircase as a model and fire analysis was performed for each scenario to compare and analyze fire propagation characteristics and ASET, evacuation analysis was performed to predict the number of deaths. In addition, a fire accident rate (F)/number of deaths (N) diagram (F/N diagram) was prepared for each scenario to compare and evaluate the risk according to the smoke control flow rate and mode. In the ASET analysis of harmful factors, carbon monoxide, temperature, and visible distance determined by performance-oriented design methods and standards for firefighting facilities, the effect of visible distance is the largest, In the case where the delay in entering the platform of the fire train was not taken into account, the ASET was analyzed to be about 800 seconds when the air flow rate was 4 × 833 m³/min. The estimated number of deaths varies greatly depending on the location of the vehicle of fire train, In the case of a fire occurring in a vehicle adjacent to the stairs, it is shown that the increase is up to three times that of the vehicle in the lead. In addition, when the smoke control flow rate increases, the number of fatalities decreases, and the reduction rate of the air supply method rather than the exhaust method increases. When the supply flow rate is 4 × 833 m³/min, the expected number of deaths is reduced to 13% compared to the case where ventilation is not performed. As a result of the risk assessment, it is found that the current social risk assessment criteria are satisfied when smoke control is performed, and the number of deaths is the flow rate 4 × 833 m³/min when smoke control is performed at 29.9 people in 10,000 year, It was analyzed that it decreased to 4.36 people.

• Tuberculosis and Respiratory Diseases
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• v.45 no.1
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• pp.140-152
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• 1998

Background: Prone position improves oxygenation in patients with ARDS probably by reducing shunt Reduction of shunt in prone position is thought to be effected by lowering of the critical opening pressure (COP) of the dorsal lung because the pleural pressure becomes less positive in prone position compared to supine position. It can then be assumed that prone position would bring about greater improvement in oxygenation when PEEP applied in supine position is just beneath COP than when PEEP is above COP. Hemodynamically, prone position is expected to attenuate the lifting of cardiac fossa induced by PEEP. Based on these backgrounds, we investigated whether the effect of prone position on oxygenation differs in magnitude according to the level of PEEP applied in supine position, and whether impaired cardiac output in supine position by PEEP can be restored in prone position. Methods: In seven mongrel dogs, $PaO_2/F_1O_2$(P/F) was measured in supine position and at prone position 30 min. Cardiac output (CO), stroke volume (SV), pulse rate (PR), and pulmonary artery occlusion pressure (PAOP) were measured in supine position, at prone position 5 min, and at prone position 30 min. After ARDS was established with warmed saline lavage(P/F ratio $134{\pm}72$ mm Hg), inflection point was measured by constant flow method($6.6{\pm}1.4cm$ $H_2O$), and the above variables were measured in supine and prone positions under the application of Low PEEP($5.0{\pm}1.2cm$ $H_2O$), and Optimal PEEP($9.0{\pm}1.2cm$ $H_2O$)(2 cm $H_2O$ below and above the inflection point, respectively) consecutively. Results : P/F ratio in supine position was $195{\pm}112$ mm Hg at Low PEEP and $466{\pm}63$ mm Hg at Optimal PEEP(p=0.003). Net increase of P/F ratio at prone position 30 min, however, was far greater at Low PEEP($205{\pm}90$ mm Hg) than at Optimal PEEP($33{\pm}33$ mm Hg)(p=0.009). Compared to CO in supine position at Optimal PEEP($2.4{\pm}0.5$ L/min), CO in prone improved to $3.4{\pm}0.6$ L/min at prone position 5 min (p=0.0180) and $3.6{\pm}0.7$ L/min at prone position 30 min (p=0.0180). Improvement in CO was attributable to the increase in SV: $14{\pm}2$ ml in supine position, $20{\pm}2$ ml at prone position 5 min (p=0.0180), and $21{\pm}2$ ml at prone position 30 min (p=0.0180), but not to change in PR or PAOP. When the dogs were turned to supine position again, MAP ($92{\pm}23$ mm Hg, p=0.009), CO ($2.4{\pm}0.5$ L/min, p=0.0277) and SV ($14{\pm}1$ ml, p=0.0277) were all decreased compared to prone position 30 min. Conclusion: Prone position in a dog with saline-lavaged acute lung injury appeared to augment the effect of relatively low PEEP on oxygenation, and also attenuate the adverse hemodynamic effect of relatively high PEEP. These findings suggest that a PEEP lower than Optimal PEEP can be adopted in prone position to achieve the goal of alveolar recruitment in ARDS avoiding the hemodynamic complications of a higher PEEP at the same time.

• Journal of Chest Surgery
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• v.43 no.6
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• pp.596-601
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• 2010

Background: This study was designed to evaluate the efficacy of a mixture of hyaluronic acid and sodium carboxymethyl cellulose ($Guardix-sol^{(R)}$) on experimental pericardial adhesion. Material and Method: Thirty rats were divided into 2 groups of 15 rats each and pericardial mesothelial injury was induced during surgery by abrasion. In the control group, blood and normal saline were administered into pericardium; in the test group, blood and HA-CMC solution were administered. Pericardial adhesions were evaluated at 2 weeks (n=5), 4 weeks (n=5), and 6 weeks (n=5) after surgery. The severity of adhesions was graded by macroscopic examination, and the adhesion tissue thickness was analyzed microscopically with Masson trichrome stain and an image processing program. Result: The test group had significantly lower macroscopic adhesion scores ($2.9{\pm}0.6$ : $3.9{\pm}0.4$, p<0.000) compared with the control group. For microscopic adhesion tissue thickness, the test group had lower scores compared with the control group, but this difference was not statistically significant ($91.73{\pm}49.91$ : $117.67{\pm}46.4$, p=0.106). Conclusion: We conclude that an HA-CMC solution ($Guardix-sol^{(R)}$) reduces the formation of pericardial adhesions in this animal model.

• Korean Journal of Applied Biomechanics
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• v.12 no.2
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• pp.159-177
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• 2002

The purpose of this study was to investigate the relations between the segments of the body, the three dimensional anatomical angle and the angular velocity of the air born phase and understand the control mechanism of the high-bar movement, the somersault, the double somersault, the double somersault with full twist. For this study seven well trained university gymnastic volunteered, Zatsiorky and Seluyanov(1983, 1985)'s sixteen segment system anatomical model was used for this study. For the movement analysis three dimensional cinematographical method(Arial Performance Analysis System : APAS) was used and for the calculation of the kinematic variables a self developed program was used with the LabVIEW 5.1 graphical profromming(Johnson, 1999) program. By using Eular's equations the three dimensional anatomical Cardan angles of the joint and angular velocity were defined. As a result of this study 1. As the rotation of the body increased in the air born phase the projection angle of the CM of the total increased, this resulted the increased of the max hight of the CM. 2. In three dimensional angular velocity the Z axis(vertical direction) projection angular velocity increased as the rotation of the body increased in the airborn phase, but the Y axis and the X axis projection angular velocity did not show significant differences. 3. As the rotation of the body increased in the air born phase the angular movement of the shoulder and the hip showed significant change. These movement act as the starter in the preparation phase. 4. The somersault angle, the twist angle, the tilt angle of the upper body related to the global reference frame in the releas phase the average somersault angle of the three types of high-bar movement was $57.7^{\circ}$, $38.8^{\circ}$, $39.7^{\circ}$, the average tilt angle was $-1.5^{\circ}$, $-5.4^{\circ}$, $-8.4^{\circ}$, the average twist angle was $13.4^{\circ}$, $10.6^{\circ}$, $23.3^{\circ}$. This result showed that the somersault with full twist had the largest movement.

• Journal of Life Science
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• v.27 no.10
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• pp.1168-1175
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• 2017

Probiotics are usually defined as intestinal bacteria that provide healthy benefit to the host and may offer new therapeutic materials for the treatment of inflammatory diseases. Lactobacillus, Bifidobacterium and Enterococcus are known as typical probiotics. But, these bacteria have mostly a weak viability and thus decreased probiotics-mediated effects in the intestinal tract. Asthma is an inflammatory airway disease, which is characterized by the releases of inflammatory mediators including cytokine and IgE. They are mainly associated with the recruitment, activation and disregulation of specific inflammatory cells, especially mast cells, monocytes, T cells, eosinophils and neutrophils in asthma. We performed these studies as in vitro and in vivo test the human inflammatory cell lines and ovalbumin (OVA)-induced asthma mouse model. And then the inhibitory effects of Enterococcus faecalis whole extract on inflammatory responses were examined. For our examinations, the E. faecalis whole extract (Ef extract) was acquired from whole bacteria of E. faecalis using freeze/thawing after ultrasonication method. As results, OVA-mediated THP-1 cell viability was decreased by the treatment of Ef extract. In the asthmatic mouse model, Ef extract inhibited the infiltration of inflammatory cells into the inflammatory sites and blood. This whole extract may have anti-asthmatic effects associated with the regulation of IL-5 and IgE expression. It may also be a promising candidate in anti-allergic medicine for the treatment of asthma.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.567-582
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• 2022

Chainsaw use for motor-manual timber harvesting in South Korea is associated with worker safety issues. However, forestry operations such as timber harvesting have already been mechanized to reduce hazards to workers and increase productivity. This study analyzed the productivities and costs of felling and processing, felling and processing using an excavator-based stroke harvester for Pinus rigida and Quercus mongolica stands. To efficiently operate the stroke harvester, we developed a regression equation to estimate the productivities of felling and processing, felling, and processing operations,and we conducted sensitivity analysis of the operation costs using DBH and machine utilization. The felling and processing productivity was 6.53 and 4.02 m³/SMH for P. rigida a nd Q. mongolica, respectively, and the cost was 17,983 and 29,210 won/m³, respectively. The felling productivity for P. rigida a nd Q. mongolica wa s 40.9 and 23.0 m³/SMH, respectively, and the cost was 2,667 and 4,743 won/m³, respectively. The processing productivity for P. rigida and Q. mongolica was 8.25 and 7.75 m³/SMH, respectively, and the cost was 15,296 and 16,283 won/m³, respectively. In the developed regression equation, the DBH, traveling distance, and number of cuttings were found to be important factors (p<0.05). Therefore, it is necessary to construct a DB considering the various conditions and species associated with harvester operations, and further research is needed to increase the accuracy of predicting operation productivity and costs.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.