• Journal of Korea Water Resources Association
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• 제53권2호
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• Journal of Chest Surgery
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• 제43권3호
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• pp.246-253
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• 2010

Background: Our objective was to review the long-term prognosis of patients with preoperative mild to moderate ischemic mitral regurgitation who underwent off-pump coronary artery bypass grafting. Material and Method: We prospectively followed 1,000 consecutive and systematic off-pump coronary artery bypass grafting patients who were operated on between September 1996 and March 2004; follow-up was achieved for 97%. Sixty-seven patients (6.7%) had mild to moderate ischemic mitral regurgitation at the time of surgery. Operative mortality, actuarial survival and major adverse cardiac event free survival were compared to assess the effect of ischemic mitral regurgitation. Result: Average follow-up was $66{\pm}22$ months. Patients with ischemic mitral regurgitation were older (p<0.001), had lower ejection fractions (p<0.001) and more comorbidities. Significantly more female patients presented with ischemic mitral regurgitation (p=0.002). There was no significant difference in operative mortality and perioperative myocardial infarction in ischemic mitral regurgitation patients (p=0.25). Eight-year survival was decreased in ischemic mitral regurgitation patients ($39.6{\pm}11.8%$ vs $76.7{\pm}2.2$, p<0.001). However, after correcting for risk factors, mild to moderate ischemic mitral regurgitation was not found to be a significant independent risk factor for long-term mortality (p=0.42). Major adverse cardiac event free survival at 8 years was significantly lower in ischemic mitral regurgitation patients ($53.12{\pm}12%$ vs $77{\pm}2%$, p<0.001). After correction for risk factors, ischemic mitral regurgitation remained a significant independent cause of major adverse cardiac events (HR: 2.31), especially congestive heart failure and recurrent myocardial infarction. Conclusion: In our series, patients with preoperative mild to moderate ischemic mitral regurgitation had a higher prevalence of preoperative risk factors than patients without ischemic mitral regurgitation. They had comparable perioperative mortality and morbidity, but, in the long term, were found to be at elevated risk for recurrent cardiac events.

• The Journal of the Korea Contents Association
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• 제19권12호
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• pp.535-545
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• 2019

If the real estate auction market indicators are relevant and meaningful, they can be meaningful information to the real estate market in connection with general real estate. The purpose of this study is to examine whether time-supply logic is applied in auction market by identifying time series correlations for the number of auctions, the auction rate, and the auction price rate, which are major indicators of real estate auction market. The real estate types were classified into three categories: residential real estate, land, and commercial real estate. The monthly time series of auctions in the metropolitan real estate were compiled for 96 months. Based on this data, the auction market model for each type was established and the mutual influences between the indicators were analyzed. As a result, the supply and demand indicators, the number of auctions and the auction rate, showed the nature of supply and demand according to the supply and demand logic of the market. However, the correlation was high for residential real estate and relatively low for commercial real estate. the auction rate has a long-term impact on price indicators, especially residential real estate, which is quantitatively explanatory and significant. The three auction-related indicators differ in degree, but there is a correlation, especially for residential real estate, which can be useful information for policy making.

• Journal of the Korean earth science society
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• 제42권2호
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• pp.164-174
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• 2021

The study examines the shearing and crushing characteristics of land-derived subaqueous granular materials in a gravel-bed river. A series of large-sized ring shear tests were performed to examine the effect of shear time and shear velocity on the shear stress characteristics of aquarium gravels with a 6-mm mean grain size. Three different shear velocities (i.e., 0.01, 0.1, and 1 mm/sec) were applied to measure the shear stress under the drained (long-term shearing) and undrained (short-term shearing) conditions. Different initial shear velocities, i.e., 0.01→0.1→1 mm/sec and 0.1→0.01→1 mm/sec, were considered in this study. The test results show that the grain crushing effect is significant regardless of drainage conditions. The shear stress of coarse-grained materials is influenced by initial shear velocities, regardless of the drainage conditions. In particular, particle breakage increases as grain size increases. The shearing time and initial shear velocity are the primary influencing factors determining the shear stress of gravels. The granular materials may be broken easily into particles through frictional resistance, such as abrasion, interlocking and fracture due to the particle-particle interaction, resulting in the high mobility of granular materials in a subaqueous environment.

• Asian Pacific Journal of Cancer Prevention
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• 제15권22호
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• pp.9949-9953
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• 2014

Objective: To evaluate the long term results among patients with soft tissue sarcoma of the thoracic wall. Materials and Methods: Twenty-six patients who were treated with pre-or postoperative radiotherapy between December 1980-December 2007, with a diagnosis of soft tissue sarcoma of the thoracic wall were retrospectively evaluated. Results: The median age was 44 years (14-85 years) and 15 of them were male. A total of 50% of patients were grade 3. The most common histologic type of tumor was undifferentiated pleomorphic sarcoma (26.9%). Tumor size varied between 2-25 cm (median 6.5 cm). Seventeen of the cases had marginal and 9 had wide local resection. Four cases received preoperative radiotherapy and 22 postoperative radiotherapy. Six of the patients with large and high grade tumors received chemotherapy. Median follow-up time was 82 months (9-309 months). Local recurrence and metastasis was detected in 34.6% and 42.3% of patients, respectively. Five-year local control (LC), disease-free survival (DFS), overall survival (OS), and disease-specific survival (DSS) were 62%, 38%, 69%, and 76% respectively. On univariate analysis, the patients with positive surgical margins had a markedly lower 5-year LC rate than patients with negative surgical margin, but the difference was not significant (43% vs 78%, p=0.1). Five-year DFS (66% vs 17%) and DSS (92% vs 60%) rates were significantly worse for the patients who had high grade tumors (p=0.01, p=0.008 respectively). Conclusions: Tumor grade and surgical margin are essential parameters for determining the prognosis of thoracic wall soft tissue sarcoma both in our series and the literature.

• Korean Journal of Remote Sensing
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• 제33권2호
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• pp.249-255
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• 2017

Antarctica isimportant area in order to understand climate change. In addition, this area is complex region where indicate warming and cooling trend according to previous studies. Therefore, it is necessary to understand the long-term variability of Antarctic energy budget. Net radiation, one of energy budget factor, is affected by albedo, and albedo cause negative radiative forcing. It is necessary to analyze a relationship between albedo and net radiation in order to analyze relationship between two factors in Antarctic climate changes and ice-albedo feedback. In thisstudy, we calculated net radiation using satellite data and performed an analysis of long-term variability of net radiation over Antarctica. In addition we analyzed correlation between albedo. As a results, net radiation indicates a negative value in land and positive value in ocean during study periods. As an annual changes, oceanic trend indicates an opposed to albedo. Time series pattern of net radiation is symmetrical with albedo. Correlation between the two factors indicate a negative correlation of -0.73 in the land and -0.32 in the ocean.

• Journal of IKEEE
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• 제22권3호
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• pp.822-828
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• 2018

Recently, energy issues such as massive blackout due to increase in power consumption have been emerged, and it is necessary to improve the accuracy of prediction of power consumption as a solution for these problems. In this study, we investigate the difference between the actual power consumption and the predicted power consumption through the deep learning- based power consumption forecasting experiment, and the possibility of adjusting the power reserve ratio. In this paper, the prediction of the power consumption based on the deep learning can be used as a basis to reduce the power reserve ratio so as not to excessively produce extra power. The deep learning method used in this paper uses a learning model of long-short-term-memory (LSTM) structure that processes time series data. In the computer simulation, the generated power consumption data was learned, and the power consumption was predicted based on the learned model. We calculate the error between the actual and predicted power consumption amount, resulting in an error rate of 21.37%. Considering the recent power reserve ratio of 45.9%, it is possible to reduce the reserve ratio by 20% when applying the power consumption prediction algorithm proposed in this study.

• Journal of the Korean Society of Marine Environment & Safety
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• 제27권5호
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.

• Journal of Korea Water Resources Association
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• 제51권12호
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• pp.1229-1235
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• 2018

We have examined dynamic relationships among water-efficiency, economic growth, electricity generation, and $CO_2$ emissions in Korea using various time-series analysis methods for 1990-2014. While previous studies have been limited to economic growth, $CO_2$ emissions, and electricity generation, this study contributed to explain the relationship between existing variables and water-efficiency. We find that the four variables reach a balanced state in the long run through short-term adjustment, $CO_2$ emissions and economic growth are responsible for water efficiency, and that $CO_2$ emissions, economic growth and water efficiency are the causes of electricity generation. The long-term impact coefficient estimates on water-efficiency show that the increase in electricity generation and the decrease in $CO_2$ emissions increase water-efficiency. Although economic growth has increased water-efficiency, moreover, we have identified an inverted U-shaped relationship between economic growth and water-efficiency, which suggests that economic growth above a certain level reduces the rate of increase in water-efficiency.

• Journal of the Korean Society of Marine Environment & Safety
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• 제27권1호
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• pp.22-28
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• 2021

This is a basic study on the development of deep learning-based algorithms to detect smoke before the smoke detector operates in the event of a ship fire, analyze and utilize the detected data, and support fire suppression and evacuation activities by predicting the spread of smoke before it spreads to remote areas. Proposed algorithms were reviewed in accordance with the following procedures. As a first step, smoke images obtained through fire simulation were applied to the YOLO (You Only Look Once) model, which is a deep learning-based object detection algorithm. The mean average precision (mAP) of the trained YOLO model was measured to be 98.71%, and smoke was detected at a processing speed of 9 frames per second (FPS). The second step was to estimate the spread of smoke using the coordinates of the boundary box, from which was utilized to extract the smoke geometry from YOLO. This smoke geometry was then applied to the time series prediction algorithm, long short-term memory (LSTM). As a result, smoke spread data obtained from the coordinates of the boundary box between the estimated fire occurrence and 30 s were entered into the LSTM learning model to predict smoke spread data from 31 s to 90 s in the smoke image of a fast fire obtained from fire simulation. The average square root error between the estimated spread of smoke and its predicted value was 2.74.