• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.875-886
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• 2018

This study evaluated drought severity by bivariate frequency analysis using drought magnitude and precipitation deficit. A drought event was defined by Standardized Precipitation Index (SPI) and the precipitation deficit was estimated using reference precipitation corresponding to the SPI -1. In previous studies, drought magnitude and duration were used for bivariate frequency analysis. However, since these two variables have a largely linear relationship, extensibility of drought information is not great compared to the univariate frequency analysis for each variable. In the case of drought in 2015, return periods of 'drought magnitude-precipitation deficit' in the Seoul, Yangpyeong, and Chungju indicated severe drought over 300 years. However, the result of 'drought magnitude-duration' showed a significant difference by evaluating the return period of about 10, 50, and 50 years. Although a drought including the rainy season was seriously lacking in precipitation, drought magnitude did not adequately represent the severity of the absolute lack of precipitation. This showed that there is a limit to expressing the actual severity of drought. The results of frequency analysis for 'drought magnitude-precipitation deficit' include the absolute deficit of precipitation information, so which could consider being a useful indicator to cope with drought.

• Journal of Korean Society on Water Environment
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• v.32 no.2
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• pp.142-148
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• 2016

The characterization of stormwater runoff from mix land-use catchments with an inadequate sewer network is a challenge. This study focused on characterizing stormwater runoff from the Paldang watershed area based on land-use type and sewer system coverage. A total of 76 sites were monitored during wet weather from seven different counties within Paldang watershed. Public sewer system (PSS) was installed at 48 sites, while 28 sites had no or individual sewer system (ISS) coverage. The results indicated that the sites included in the ISS group with higher forest and paddy land-use percentage exhibit higher values of average event mean concentrations (EMCs) and first flush intensity for suspended solids (SS), total nitrogen (TN), and total phosphorous (TP). In addition, upgrading runoff interception system can capture 59 % of the TP load in the first 43% of runoff within these sites. Similarly, rainfall depth and storm duration showed a positive correlation (R > 0.6) with nutrient loads within ISS group sites, as compared to PSS group. Therefore, these sites are likely to contribute higher TP and TN loads during heavier storm events and should be selected as priority management areas to combat the problem of eutrophication in Paldang reservoir.

• Journal of Preventive Medicine and Public Health
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• v.22 no.3 s.27
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• pp.355-367
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• 1989

This study examined the physical and mental health factors affecting the industrial accidents of 142 injured and 1,212 uninjured workers in the shipbuilding industry from 1986 to 1988. The results acquired from the Todai Health Index (THI) and from analysis of the health examination were as follows: 1. Among the personal characteristics of the workers, the educational level of injured workers was significantly lower than that of the uninjured workers. 2. Among the physical characteristics, vision and $R\ddot{o}hrer$ Index of the injured workers were lower than those of the uninjured workers, and the difference was statistically significant. On the other hand, the differences in height, weight, hearing function, hematocrit, blood pressure, urine test, and X-ray findings were not statistically significant between the injured and uninjured workers. 3. The score of the THI questionnaire on the physical and mental health of the injured workers was higher than that of the uninjured workers, and the difference was statistically significant. 4. Form the THI score, the industrial workers had complained more about mental health than physical health and there was a statistically singinficant relation with the industrial accidents. 5. The relative risk expressed in terms of the odds ratio was 2.9 for poorer vision, 2.7 for a lower educational level, 2.2 for a higher THI score and 1.6 for overdrinking. 6 Educational level, vision, and the THI score were selected as significant factors influencing industrial accidents based on a log-linear model. According to the results of this model by logistic analysis, the odds ratio of industrial accidents was 1.8 for a lower educational level, 1.7 for poorer vision, and 1.6 for a higher THI score. 7 By event history analysis with the dependent variable as the duration of work at the time of the industrial accident, educational level, age, $R\ddot{o}hrer$ Index and THI score were the statistically significant variables selected, and the hazard rate of industrial accident occurrence was 0.24 for a lower educational level, 0.92 for age, 0.99 for a lower $R\ddot{o}hrer$ Index and 2.72 for a higher THI score. As we have seen, educational level and THI score were the most significant factors affecting the hazard rate of industrial accidents. Vision, $R\ddot{o}hrer$ Index, age, and drinking behavior were also statistically significant variables influencing industrial accidents. Therefore, in order to prevent industrial accidents, it is necessary to establish a health management plan for industry which can objectively evaluate not only the physical but also the mental health of the workers. If we use this type of study as a prospective study design, we can determine the relative risk of physical and mental health factors on industrial accidents. Furthermore, it is expected that this type of study will provide workers at high risk with more precise basic data for a health managment plan for industrial accident prevention.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.599-607
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• 2017

Since 2015, small and medium domestic enterprises that treat more than a certain quantity of chemical substances in accordance with the Chemical Substance Control Act are obliged to submit an off-site impact assessment and risk management plan. In order to reduce the administrative and economic burden of the risk assessment, its impact was determined. Toxic leaks of nitric acid, methanol, and acetic acid were estimated and the correlations (between them?) were calculated. In addition, the correlations of this study were used to compare the KORA results according to the accident scenarios of the actual workplace and the extent of the damage as a function of distance in the case of toxic leaks. In this study, the correlation formula of the materials can be used to quickly determine the damage distance in the event of the accidental leakage of materials in the road or workplace, and to prepare emergency plans and respond to emergencies more quickly.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.722-728
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• 2012

Urban sewer systems has a limitation of capacity of rainwater storage and problem of occurrence of untreated sewage, so adopting a storage facility for sewer flooding prevention and urban non-point pollution reduction has a big attention. The Korea Ministry of Environment has recently introduced a new concept of "multi-functional storage facility", which is crucial not only in preventive stormwater management but also in dealing with combined sewer overflow and sanitary sewer discharge, and also has been promoting its adoption. However, reserving a space for a single large-scale storage facility might be difficult especially in urban areas. Thus, decentralized construction of small- and midium-sized storage facilities and its operation have been introduced as an alternative way. In this paper, we propose a model predictive control scheme for an optimized operation of distributed storage facilities and sewer networks. To this aim, we first describe the mathematical model of each component of networks system which enables us to analyze its detailed dynamic behavior. Second, overflow locations and volumes will be predicted based on the developed network model with data on the external inflow occurred at specific locations of the network. MPC scheme based on the introduced particle swarm optimization technique then produces the optimized the gate setting for sewer network flow control, which minimizes sewer flooding and maximizes the potential storage capacity. Finally, the operational efficacy of the proposed control scheme is demonstrated by simulation study with virtual rainstorm event.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.671-680
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• 2019

Many researches illustrated that the magnitude and frequency of hydrological event would increase in the future due to changes of hydrological cycle components according to climate change. However, few studies performed quantitative analysis and evaluation of future rainfall in North Korea, where the damage caused by extreme precipitation is expected to occur as in South Korea. Therefore, this study predicted the extreme precipitation change of North Korea in the future (2020-2060) compared to the current (1981-2017) using stationary and nonstationary frequency analysis. This study conducted nonstationary frequency analysis considering the external factors (mean precipitation of JFM (Jan.-Mar.), AMJ (Apr.-Jun.), JAS (Jul.-Sept.), OND (Oct.-Dec.)) of the HadGEM2-AO model simulated according to the Representative Concentration Pathway (RCP) climate change scenarios. In order to select external factors that have a similar tendency with extreme rainfall events in North Korea, the maximum annual rainfall data was obtained by using the ensemble empirical mode decomposition (EEMD) method. Correlation analysis was performed between the extracted residue and the external factors. Considering selected external factors, nonstationary GEV model was constructed. In RCP4.5, four of the eight stations tended to decrease in future extreme precipitation compared to the present climate while three stations increased. On the other hand, in RCP8.5, two stations decreased while five stations increased.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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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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• v.54 no.5
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• pp.321-334
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• 2021

A fixed reflectivity-rainfall relationship approach, such as the Marshall-Palmer relationship, for an entire year and different seasons, can be problematic in cases where the relationship varies spatially and temporally throughout a region. From this perspective, this study explores the use of long-term radar reflectivity for South Korea to obtain a nationwide calibrated Z-R relationship and the associated uncertainties within a Bayesian inference framework. A calibrated spatially structured pattern in the parameters exists, particularly for the wet season and parameter for the dry season. A pronounced region of high values during the wet and dry seasons may be partially associated with storm movements in that season. Overall, the radar rainfall fields based on the proposed modeling procedure are similar to the observed rainfall fields. In contrast, the radar rainfall fields obtained from the existing Marshall-Palmer relationship show a systematic underestimation. In the event of high impact weather, it is expected that the value of national radar resources can be improved by establishing an active watershed-level hydrological analysis system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.115-121
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• 2021

This paper used big data and artificial intelligence technology to predict the rapidly increasing internet traffic. There have been various studies on traffic prediction in the past, but they have not been able to reflect the increasing factors that induce huge Internet traffic such as smartphones and streaming in recent years. In addition, event-like factors such as the release of large-capacity popular games or the provision of new contents by OTT (Over the Top) operators are more difficult to predict in advance. Due to these characteristics, it was impossible for an ISP (Internet Service Provider) to reflect real-time service quality management or traffic forecasts in the network business environment with the existing method. Therefore, in this study, in order to solve this problem, an Internet traffic collection system was constructed that searches, discriminates and collects traffic data in real time, separate from the existing NMS. Through this, the flexibility and elasticity to automatically register the data of the collection target are secured, and real-time network quality monitoring is possible. In addition, a large amount of traffic data collected from the system was analyzed by machine learning (AI) to predict future traffic of OTT operators. Through this, more scientific and systematic prediction was possible, and in addition, it was possible to optimize the interworking between ISP operators and to secure the quality of large-scale OTT services.

• 교육공학연구
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• v.33 no.4
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• pp.1001-1024
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• 2017

Flipped classroom has used widely in university in that its unique structure can facilitate learners' higher-thinking skills and promote competencies. Learners are expected to extend knowledge through performing online and offline, but they have difficulty in understanding their roles and specific behaviors to achieve the learning goals in the flipped learning. Therefore, a guidance for students has been required to support learners' mastery learning. The purpose of this study is to identify successful learners' characteristics in terms of "competency". For this, three-phased competency modeling was employed. In Phase I, Behavioral Event Interviews were conducted with eight learners of the flipped classroom. In Phase II for identifying competencies and developing a competency model, the data was coded, followed by testing reliability of the coding. Based on the meaning codes, competencies and behavioral indexes were developed. The final competencies consist of learning orientation, learning management, feedback seeking, peer interaction, and knowledge extension. In Phase III, validation of the competency model was conducted by explanatory factor analysis. As last, competencies were aligned by the two-phase of the flipped classroom. The finding will be used as the guidance for the learners and instructors in the flipped classroom.