• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.417-430
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• 2014

We have found an error in the operational OMI HCHO columns, and corrected it by applying a background parameterization derived on a 4th order polynomial fit to the time series of monthly average OMI HCHO data. The corrected OMI HCHO agrees with this understanding as well as with the other sensors measurements and has no unrealistic trends. A new scientific approach, statistical analyses with EOF and SVD, was adapted to reanalyze the consistency of the corrected OMI HCHO with other satellite measurements of HCHO, CO, $NO_2$, and fire counts over Africa. The EOF and SVD analyses with MOPITT CO, OMI $NO_2$, SCIAMAHCY, and OMI HCHO show the overall spatial and temporal pattern consistent with those of biomass burning over these regions. However, some discrepancies were observed from OMI HCHO over northern equatorial Africa during the northern biomass burning seasons: The maximum HCHO was found further downwind from where maximum fire counts occur and the minimum was found in January when biomass burning is strongest. The statistical analysis revealed that the influence of biogenic activity on HCHO wasn't strong enough to cause the discrepancies, but it is caused by the error in OMI HCHO from using the wrong Air Mass Factor (AMF) associated with biomass burning aerosol. If the error is properly taken into consideration, the biomass burning is the strongest source of HCHO seasonality over the regions. This study suggested that the statistical tools are a very efficient method for evaluating satellite data.

• Journal of Trauma and Injury
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• v.24 no.1
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• pp.1-6
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• 2011

Purpose: Even though traumatic pancreatic injuries occur in only 0.2% to 4% of all abdominal injuries, the morbidity and the mortality rates associated with pancreatic injuries remain high. The aim of this study was to evaluate the clinical outcomes of traumatic pancreatic injuries and to identify predictors of mortality and morbidity. Methods: We retrospectively reviewed the medical records of 26 consecutive patients with a pancreatic injury who underwent a laparotomy from January 2000 to December 2010. The data collected included demographic data, the mechanism of injury, the initial vital signs, the grade of pancreatic injury, the injury severity score (ISS), the revised trauma score (RTS), the Glasgow Coma Scale (GCS), the number of abbreviated injury scales (AIS), the number of associated injuries, the initial laboratory findings, the amount of blood transfusion, the type of operation, the mortality, the morbidity, and others. Results: The overall mortality rate in our series was 23.0%, and the morbidity rate was 76.9%. Twenty patients (76.9%) had associated injuries to either intra-abdominal organs or extra-abdominal organs. Two patients (7.7%) underwent external drainage, and 18 patients (69.3%) underwent a distal pancreatectomy. Pancreaticoduodenectomies were performed in 6 patients (23.0%). Three patients underwent a re-laparotomy due to anastomosis leakage or postoperative bleeding, and all patients died. The univariate analysis revealed 11 factors (amount of transfusion, AAST grade, re-laparotomy, associated duodenal injury, base excess, APACHE 11 score, type of operation, operation time, RTS, associated colon injury, GCS) to be significantly associated with mortality (p<0.05). Conclusion: Whenever a surgeon manages a patient with traumatic pancreatic injury, the surgeon needs to consider the predictive risk factors. And, if possible, the patient should undergo a proper and meticulous, less invasive surgical procedure.

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.153-160
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• 2000

Crustal velocities around the Korea peninsula are estimated and investigated from eight IGS permanent stations in eastern Asia area. GPS data for the period of May 1995 to December 1991 were analyzed to estimate daily coordinates of each site relative to TAEJ site. The velocity vector of each site is estimated from linear regression analysis with time series of coordinates. As the result, horizontal velocity components for four stations(Tsukuba, Usuda,Taiwan and Shanghai) using thirty-two months data were estimated with the standard error less than 1 mm/year. Our GPS velocity of six sites on the interiors of the Eurasia plate are similar within 1 cm/year with small differences respectively. On the other hand, the velocities of Tsukuba and Usuda showed the great differences from the other six sites. This can be explained by the fact that these two sites are enforced by the surrounding four plates, such as the Pacific, Eurasia, North America and Philipine plate. This study showed that the distance between Korea and Japan is shortened with the rate of 3 cm per year.

• Journal of Korea Water Resources Association
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• v.37 no.1
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• pp.77-86
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• 2004

This paper presents a reservoir operation plan coupled with storage forecasting model to maintain a target storage and a critical storage. The observed storage data from 1990 to 2001 in the Geum-Gang agricultural reservoir in Korea have been applied to the low flow frequency analysis, which yields storage for each return period. Two year return period drought storage is then designated as the target storage and ten year return period drought storage as the critical storage. Storage in reservoir should be forecasted to perform reasonable reservoir operation. The predicted storage can be effectively utilized to establish a reservoir operation plan. In this study the autoregressive error (ARE) model and the ARIMA model are adopted to predict storage of reservoir. The ARIMA model poorly generated reservoir storage in series because only observed storage data were used, but the autoregressive error model made to enhance the reliability of the forecasted storage by applying the explanation variables to the model. Since storages of agricultural reservoir with respect to time have been affected by irrigation area, high or mean temperature, precipitation, previous storage and wind velocity, the autoregressive error model has been adopted to analyze the relationship between storage at a period and affecting factors for storage at the period. Since the equation for predicting storage at a period by the autoregressive error model is similar to the continuity equation, the predicting storage equation may be practical. The results from compared the actual storage in 2002 and the predicted storage in the Geum-Gang reservoir show that forecasted storage by the autoregressive error model is reasonable.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.101-113
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• 2021

This paper inquires the macro-economic factors that may affect the corporate venture capital (CVC) from an industrial organization theory perspective. Unlike existing studies focusing CVC investments related to parent corporates' strategic intention, we identified CVC firm as an independent financial investor affected by macro environment and industrial structure. Specifically, we empirically investigate whether and how industry's boom, exogenous crisis, economic growth, and competition intensity affect the CVC investment for a data set of investment in the U.S. based corporate venture capital industry, 1996-2017. The empirical data analyzed in the study contained a total of 84 U.S. based CVC firms and their 2,306 investments from 1996 until 2017. After conducting a time-series negative binomial analysis, our empirical analyses suggest that the CVC investments are affected negatively by exogenous crisis and competition intensity, and positively by industrial boom and economic growth. we found the significance and direction of our independent variables strongly supported all of our four hypotheses in a highly robust manner. The results of this study are expected to contribute the literatures of corporate venture capital and venture investment by illustrating which macro-economic and industrial structure factors affect CVC investment decision to adapt to dynamic environmental change beside strategic intention of CVC firm's parent corporates.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.4
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• pp.443-463
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• 2024

This study sought to improve the accuracy of precipitation prediction by utilizing monthly precipitation data for each region over the past 30 years. Using statistical models (ARIMA, SARIMA) and deep learning models (LSTM, GBM), we learned monthly precipitation data from 1983 to 2012 in Gangneung, Gwangju, Daegu, Daejeon, Busan, Seoul, Jeju, and Chuncheon. Based on this, monthly precipitation was predicted for 10 years from 2013 to 2022. As a result of the prediction, most models accurately predicted the precipitation trend, but showed a tendency to underpredict the actual precipitation. To solve these problems, appropriate models were selected for each region and season. The LSTM model showed suitable results in Gangneung, Gwangju, Daegu, Daejeon, Busan, Seoul, Jeju, and Chuncheon. When comparing forecasting power by season, the SARIMA model showed particularly suitable forecasting performance in winter in Gangneung, Gwangju, Daegu, Daejeon, Seoul, and Chuncheon. Additionally, the LSTM model showed higher performance than other models in the summer when precipitation is concentrated. In conclusion, closely analyzing regional and seasonal precipitation patterns and selecting the optimal prediction model based on this plays a critical role in increasing the accuracy of precipitation prediction.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.363-375
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• 2024

Waterbody change detection using satellite images has recently been carried out in various regions in South Korea, utilizing multiple types of sensors. This study utilizes optical satellite images from Landsat and Sentinel-2 based on Google Earth Engine (GEE) to analyze long-term surface water area changes in four monitored small and medium-sized water supply dams and agricultural reservoirs in South Korea. The analysis covers 19 years for the water supply dams and 27 years for the agricultural reservoirs. By employing image analysis methods such as normalized difference water index, Canny Edge Detection, and Otsu'sthresholding for waterbody detection, the study reliably extracted water surface areas, allowing for clear annual changes in waterbodies to be observed. When comparing the time series data of surface water areas derived from satellite images to actual measured water levels, a high correlation coefficient above 0.8 was found for the water supply dams. However, the agricultural reservoirs showed a lower correlation, between 0.5 and 0.7, attributed to the characteristics of agricultural reservoir management and the inadequacy of comparative data rather than the satellite image analysis itself. The analysis also revealed several inconsistencies in the results for smaller reservoirs, indicating the need for further studies on these reservoirs. The changes in surface water area, calculated using GEE, provide valuable spatial information on waterbody changes across the entire watershed, which cannot be identified solely by measuring water levels. This highlights the usefulness of efficiently processing extensive long-term satellite imagery data. Based on these findings, it is expected that future research could apply this method to a larger number of dam reservoirs with varying sizes,shapes, and monitoring statuses, potentially yielding additional insights into different reservoir groups.

• Spatial Information Research
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• v.22 no.4
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• pp.39-47
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• 2014

The Yeongsan river estuary has a serious water quality problem due to the water stagnation and it is imperative to predict the changes of water quality for mitigating water pollution. EFDC(Environmental Fluid Dynamics Code) model was mainly utilized to predict the changes of water quality for the estuary. The EFDC modeling normally accompanies the large volume of modeling output. For checking the spatial distribution of the modeling results, post-processing for converting of the output is prerequisite and mainly post-processing program is EFDC_Explorer. However, EFDC_Explorer only shows the spatial distribution of the time series and this doesn't support overlay function with other thematic maps. This means the impossible to the connection analysis with a various GIS data and high dimensional analysis. Therefore, this study aims to develop a post-processing system of a EFDC output to use them as GIS layers. For achieving this purpose, a editing module for main input files, and a module for converting binary format into an ASCII format, and a module for converting it into a layer format to use in a GIS based environment, and a module for visualizing the reconfigured model result efficiently were developed. Using the developed system, result file is possible to automatically convert the GIS based layer and it is possible to utilize for water quality management.

• Journal of Bio-Environment Control
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• v.8 no.2
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• pp.136-146
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• 1999

A series of experiments were carried out in winter to investigate the indoor thermal environment in greenhouses with different kinds of heating systems, and characterize the energy consumption, heat transport and thermal energy efficiency of each system. By the Quantitative calculation of heat losses which transmit through the covers of greenhouse, the fundamental data of energy-saving of the particular heating system were obtained. And from the analysis of air temperature differences between indoor and outside, it was possible to select more effective energy-saving and comfortable heating system in greenhouses. The electric heater was more stable in thermal environment and cheaper in cost, since it could be used during the surplus time of electric power from 10：00 p.M. to 8：00 A.M. But the low air temperature in greenhouses besides these times resulted in a chilling problem of the crops. The heating system by hot air had the advantage to show nearly uniform temperature difference by the height above the ground. But the system had the disadvantage to require more energy consumption than the electric heating system.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.3
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• pp.20-38
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• 2014

Vegetation phenology is the most important indicator of ecosystem response to climate change. Therefore it is necessary to continuously monitor forest phenology. This paper analyzes the phenological characteristics of forests in South Korea using the MODIS vegetation index with error from clouds or other sources removed using the HANTS algorithm. After using the HANTS algorithm to reduce the noise of the satellite-based vegetation index data, we were able to confirm that phenological transition dates varied strongly with altitudinal gradients. The dates of the start of the growing season, end of the growing season and the length of the growing season were estimated to vary by +0.71day/100m, -1.33day/100m and -2.04day/100m in needleleaf forests, +1.50day/100m, -1.54day/100m and -3.04day/100m in broadleaf forests, +1.39day/100m, -2.04day/100m and -3.43day/100m in mixed forests. We found a linear pattern of variation in response to altitudinal gradients that was related to air temperature. We also found that broadleaf forests are more sensitive to temperature changes compared to needleleaf forests.