• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.925-936
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• 2020

To ascertain the characteristics of pool sloshing behavior that might be encountered during a core disruptive accident of sodium-cooled fast reactors, in our earlier work several series of experiments were conducted under various scenarios including the condition with mono-sized solid particles. It is found that under the particle-bed condition, three typical flow regimes (namely the bubble-impulsion dominant regime, the transitional regime and the bed-inertia dominant regime) could be identified and a flow-regime model (base model) has been even successfully established to estimate the regime transition. In this study, aimed to further understand this behavior at more realistic particle-bed conditions, a series of simulated experiments is newly carried out using mixed-size particles. Through analyses, it is verified that for present scenario, by applying the area mean diameter, our previously-developed base model can provide the most appropriate predictive results among the various effective diameters. To predict the regime transition with a form of extension scheme, a correction factor which is based on the volume-mean diameter and the degree of convergence in particle-size distribution is suggested and validated. The conducted analyses in this work also indicate that under certain conditions, the potential separation between different particle components might exist during the sloshing process.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.83-89
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• 2022

Internet of Things technology is rapidly developing due to the recent development of information and communication technology. IoT technology utilizes various sensors to generate unique data from each sensor, enabling diagnosis of system status. However, the equipment management system currently in effect is a post-preservation concept in which administrators must deal with the problem after the problem occurs, which could mean system reliability and availability problems due to system errors, and could result in economic losses due to negative productivity disruptions. Therefore, this study confirmed that edge controller control decision algorithms for more efficient operation of rectifiers in the factory by applying intelligent IoT (AIoT) technology and domain knowledge-based modeling for each sensor data collected based on this, outputting appropriate status messages for each scenario.

• Fisheries and Aquatic Sciences
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• v.25 no.9
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• pp.463-472
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• 2022

In this study, a microbial risk assessment was performed for the bacteria Vibrio parahaemolyticus, which causes a foodborne illness following the consumption of Jeotgal, a fermented seafood in South Korea. The assessment comprised of six stages: product, market, home, consumption, dose-response, and risk. The initial contamination level (IC) was calculated based on the prevalence of V. parahaemolyticus in 90 Jeotgal samples. The kinetic behavior of V. parahaemolyticus was described using predictive models. The data on transportation conditions from manufacturer to market and home were collected through personal communication and from previous studies. Data for the Jeotgal consumption status were obtained, and an appropriate probability distribution was established. The simulation models responding to the scenario were analyzed using the @RISK program. The IC of V. parahaemolyticus was estimated using beta distribution [Beta (1, 91)]. The cell counts during transportation were estimated using Weibull and polynomial models [δ = 1 / (0.0718 - 0.0097 × T + 0.0005 × T²)], while the probability distributions for time and temperature were estimated using Pert, Weibull, Uniform, and LogLogistic distributions. Daily average consumption amounts were assessed using the Pareto distribution [0.60284,1.32,Risk Truncate(0,155)]. The results indicated that the risk of V. parahaemolyticus infection through Jeotgal consumption is low in South Korea.

• KNOM Review
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• v.24 no.1
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• pp.1-12
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• 2021

VM (Virtual Machine) live migration is a server virtualization technique for deploying a running VM to another server node while minimizing downtime of a service the VM provides. Currently, in cloud data centers, VM live migration is widely used to apply load balancing on CPU workload and network traffic, to reduce electricity consumption by consolidating active VMs into specific location groups of servers, and to provide uninterrupted service during the maintenance of hardware and software update on servers. It is critical to use VMlive migration as a prevention or mitigation measure for possible failure when its indications are detected or predicted. In this paper, we propose two VNF live migration methods; one for predictive load balancing and the other for a proactive measure in failure. Both need machine learning models that learn periodic monitoring data of resource usage and logs from servers and VMs/VNFs. We apply the second method to a vEPC (Virtual Evolved Pakcet Core) failure scenario to provide a detailed case study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.480-488
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• 2018

The development of information and communication technology has been carried out actively in the field of agriculture to generate valuable information from large amounts of data and apply big data technology to utilize it. Crops and their varieties are determined by the influence of the natural environment such as temperature, precipitation, and sunshine hours. This paper derives the climatic factors affecting the production of crops using the garlic growth process and daily meteorological variables. A prediction model was also developed for the production of garlic per unit area. A big data analysis technique considering the growth stage of garlic was used. In the exploratory data analysis process, various agricultural production data, such as the production volume, wholesale market load, and growth data were provided from the National Statistical Office, the Rural Development Administration, and Korea Rural Economic Institute. Various meteorological data, such as AWS, ASOS, and special status data, were collected and utilized from the Korea Meteorological Agency. The correlation analysis process was designed by comparing the prediction power of the models and fitness of models derived from the variable selection, candidate model derivation, model diagnosis, and scenario prediction. Numerous weather factor variables were selected as descriptive variables by factor analysis to reduce the dimensions. Using this method, it was possible to effectively control the multicollinearity and low degree of freedom that can occur in regression analysis and improve the fitness and predictive power of regression analysis.

• Journal of Korean Society of Transportation
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• v.34 no.4
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• pp.318-329
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• 2016

There has been a growing interest on the effect of volcanic eruption on the aviation safety, air travel and economy especially after the eruption of Eyjafjallajokull in Iceland. Since volcanic eruption is influential on a large geographic region, the effect usually extends to other neighboring countries. Korea also has an active volcano named Mountain Baekdu. Hence, the need to estimate in advance the quantitative impact of the potential eruption of Mt. Baekdu on South Korean air transportation system. However, previous studies with quantitative estimation were confined to the calculation of the direct economic loss from shut down of the airports, grounding of airlines, and trade deficits caused by the eruption. Therefore, this paper introduces a new approach to assess more accurate impact simultaneously considering volcanic ash dispersal and aviation routes. This approach is then applied to a virtual scenario to predict the damage to air traffic. With further development, this method can help estimate the damage in the air transportation industry in more accurate and faster ways. Prediction outcomes can also be utilized in setting up the emergency response plan for the air transportation industry and contribute to the creation of more proactive and predictive measures in the future.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.24.1-24.15
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• 2023

Background: The construction workers are vulnerable to fatigue due to high physical workload. This study aimed to investigate the relationship between overwork and heart rate in construction workers and propose a scheme to prevent overwork in advance. Methods: We measured the heart rates of construction workers at a construction site of a residential and commercial complex in Seoul from August to October 2021 and develop an index that monitors overwork in real-time. A total of 66 Korean workers participated in the study, wearing real-time heart rate monitoring equipment. The relative heart rate (RHR) was calculated using the minimum and maximum heart rates, and the maximum acceptable working time (MAWT) was estimated using RHR to calculate the workload. The overwork index (OI) was defined as the cumulative workload evaluated with the MAWT. An appropriate scenario line (PSL) was set as an index that can be compared to the OI to evaluate the degree of overwork in real-time. The excess overwork index (EOI) was evaluated in real-time during work performance using the difference between the OI and the PSL. The EOI value was used to perform receiver operating characteristic (ROC) curve analysis to find the optimal cut-off value for classification of overwork state. Results: Of the 60 participants analyzed, 28 (46.7%) were classified as the overwork group based on their RHR. ROC curve analysis showed that the EOI was a good predictor of overwork, with an area under the curve of 0.824. The optimal cut-off values ranged from 21.8% to 24.0% depending on the method used to determine the cut-off point. Conclusion: The EOI showed promising results as a predictive tool to assess overwork in real-time using heart rate monitoring and calculation through MAWT. Further research is needed to assess physical workload accurately and determine cut-off values across industries.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.1
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• pp.18-29
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• 2010

Offshore subsurface storage of $CO_2$ is regarded as one of the most promising options to response severe climate change. Marine geological storage of $CO_2$ is to capture $CO_2$ from major point sources, to transport to the storage sites and to store $CO_2$ into the offshore subsurface geological structure such as the depleted gas reservoir and deep sea saline aquifer. Since 2005, we have developed relevant technologies for marine geological storage of $CO_2$. Those technologies include possible storage site surveys and basic designs for $CO_2$ transport and storage processes. To design a reliable $CO_2$ marine geological storage system, we devised a hypothetical scenario and used a numerical simulation tool to study its detailed processes. The process of transport $CO_2$ from the onshore capture sites to the offshore storage sites can be simulated with a thermodynamic equation of state. Before going to main calculation of process design, we compared and analyzed the relevant equation of states. To evaluate the predictive accuracies of the examined equation of states, we compare the results of numerical calculations with experimental reference data. Up to now, process design for this $CO_2$ marine geological storage has been carried out mainly on pure $CO_2$. Unfortunately the captured $CO_2$ mixture contains many impurities such as $N_2$, $O_2$, Ar, $H_{2}O$, $SO_{\chi}$, $H_{2}S$. A small amount of impurities can change the thermodynamic properties and then significantly affect the compression, purification and transport processes. This paper analyzes the major design parameters that are useful for constructing onshore and offshore $CO_2$ transport systems. On the basis of a parametric study of the hypothetical scenario, we suggest relevant variation ranges for the design parameters, particularly the flow rate, diameter, temperature, and pressure.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.802-811
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• 2023

Marine spatial planning (MSP) is a crucial element for rational allocation and sustainable use of marine areas. Particularly, Fishing Activity Protected Areas constitute essential zones accounting for 45.6% designated for sustainable fishing activities. However, the current assessment of these zones does not adequately consider future demands and potential values, necessitating appropriate evaluation methods and predictive tools for long-term planning. In this study, we selected key fish species (Scomber japonicus, Trichiurus lepturus, Engraulis japonicus, and Larimichthys polyactis) within the Fishing Activity Protected Area to predict their distribution and compare it with the current designated zones for evaluating the ability of the prediction tool. Employing the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report scenarios (SSP1-2.6 and SSP5-8.5), we used species distribution models (such as MaxEnt) to assess the movement and distribution changes of these species owing to future variations. The results indicated a 30-50% increase in the distribution area of S. japonicus, T. lepturus, and L. polyactis, whereas the distribution area of E. japonicus decreased by approximately 6-11%. Based on these results, a species richness map for the four key species was created. Within the marine spatial planning boundaries, the overlap between areas rated "high" in species richness and the Fishing Activity Protected Area was approximately 15%, increasing to 21% under the RCP 2.6 scenario and 34% under the RCP 8.5 scenario. These findings can serve as scientific evidence for future evaluations of use zones or changes in reserve areas. The current and predicted distributions of species owing to climate change can address the limitations of current use zone evaluations and contribute to the development of plans for sustainable and beneficial use of marine resources.