• Journal of Industrial Convergence
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• v.18 no.6
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• pp.27-32
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• 2020

Recently, as the business using IT has increased rapidly, a lot of budget is required to operate and manage the complex IT infrastructure. There is a need for a new IT infrastructure technology that can effectively and reduce costs. In this paper, we introduce CI(Converge Infrastructure) and HCI(Hyper Converge Infrastructure) methods, which are IT infrastructure methods for simplifying information systems and reducing operational management efficiency and cost. In addition, it proposes an information system using the HCI method, a new IT infrastructure technology, and a plan to establish a disaster recovery system to provide continuity of service in the event of a disaster or failure. In addition to the introduction of major technologies for using the HCI method, the area, power consumption, and operational efficiency of the information system before and after the introduction of HCI are compared and analyzed.

• Journal of Korean Society on Water Environment
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• v.27 no.1
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• pp.73-87
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• 2011

An abnormal mono-specific bloom of the cyanobacterium Microcystis aeruginosa had developed at a specific location (transitional zone, monitoring station of Hoenam) in Daecheong Reservoir from middle of July to early August, 2001. The maximum cell counts during the peak bloom reached 1,477,500 cells/mL, which was more than 6~10 times greater than those at other monitoring sites. The hypothesis of this study is that the timing and location of the algal bloom was highly correlated with the local environmental niche that was controled by physical processes such as hydrodynamic mixing and pollutant transport in the reservoir. A three-dimensional, coupled hydrodynamic and ecological model, ELCOM-CAEDYM, was applied to the period of development and subsequent decline of the bloom. The model was calibrated against observed water temperature profiles and water quality variables for different locations, and applied to reproduce the algal bloom event and justify the limiting factor that controled the Microcystis bloom at R3. The simulation results supported the hypothesis that the phosphorus loading induced from a contaminated tributary during several runoff events are closely related to the rapid growth of Microcystis during the period of bloom. Also the physical environments of the reservoir such as a strong thermal stratification and weak wind velocity conditions provided competitive advantage to Microcystis given its light adaptation capability. The results show how the ELCOM-CAEDYM captures the complex interactions between the hydrodynamic and biogeochemical processes, and the local environmental niche that is preferable for cyanobacterial species growth.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.53-59
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• 2018

Due to the recent developments in computer vision technology, complex actions can be recognized with reasonable accuracy in smart cities. In contrast, violence recognition such as events related to fight and knife, has gained less attention. The capability of visual surveillance can be used for detecting fight in streets or in prison centers. In this paper, we proposed a deep learning-based violence recognition method for surveillance cameras. A convolutional neural network (CNN) model is trained and fine-tuned on available benchmark datasets of fights and knives for violence recognition. When an abnormal event is detected, an alarm can be sent to the nearest police station to take immediate action. Moreover, when the probabilities of fight and knife classes are predicted very low, this situation is considered as normal situation. The experimental results of the proposed method outperformed other state-of-the-art CNN models with high margin by achieving maximum 99.21% accuracy.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.113-122
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• 2009

Software is getting more complex due to a variety of requirements that include desired functions and properties. Therefore, verifying and testing the software are complicated problems. Moreover, if the software is already implemented, inserting and deleting tracing/logging code into the source code may cause several problems, such as the code tangling and the code scattering problems. This paper proposes the Aspect DEVS Verification Framework which supports the verification and testing process. The Aspect DEVS Verification Framework utilizes Aspect Oriented Programming features to handle the code tangling and the code scattering problems. By applying aspect oriented features, a user can find and fix the inconsistency between requirement and implementation of a software without suffering the problems. The first step of the verification process is the building aspect code to make a software act as a generator. The second step is developing a requirement specification using DEVS diagrams and implementing it using the DEVSIM++. The final step is comparing the event traces from the software with the possible execution sequences from DEVS model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.275-276
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• 2021

In the event of a fire in a complex, the identification of isolated people's location information is delayed, resulting in many casualties. In order to prevent such an accident, research on estimating the location of the requesters by detecting the wireless signal of the mobile phone at fire sites is in progress. The main concept is to use a wireless signal scanner to detect the wireless signal of a mobile phones at fire sites, and then position the mobile phone based on this. However, it is difficult to secure visibility at the fire site due to the smoke, and there is a difficulty in rescuing requesters in need compared with general disaster sites. Therefore, it will be one of the important issues to be solved to determine the minimum number of rescuers to be deployed according to the number and condition of the requesters. In this study, we propose a method to calculate the number of rescuers put to fire sites by using the radio signal generated from mobile phones and the information generated from the inertial sensor of the mobile phones.

• The Journal of Bigdata
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• v.7 no.1
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• pp.75-87
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• 2022

This study investigates a dynamic flexible flow shop scheduling problem under uncertain rework operations for an automobile pipe production line. We propose a weighted dispatching rule (WDR) based on the multiple dispatching rules to minimize the weighted sum of average flowtime and tardiness. The set of weights in WDR should be carefully determined because it significantly affects the performance measures. We build a discrete-event simulation model and propose a genetic algorithm to optimize the set of weights considering complex and variant operations. The simulation experiments demonstrate that WDR outperforms the baseline dispatching rules in average flowtime and tardiness.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.128-137
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• 2024

Purpose: This study was conducted to investigate, through experimental methods, the efficiency of passenger movement and evacuation in the event of an emergency situation on an aircraft. Method: The experiment was conducted a total of 4 times, including 3 scenarios. The three situations were evacuation without luggage, evacuation with carry-on baggage, and evacuation with carry-on baggage and carrier. In the experiment, time was measured based on recorded video. Result: The total evacuation time was found to be approximately 1.5 times higher for the evacuation with luggage, and approximately 3.5 times higher for the evacuation with luggage and 3 carriers compared to the evacuation result in a situation where nothing was carried. As a result of applying the evacuation simulation, it was found that there was a difference from the experimental results. In particular, consideration of complex situations such as carrying out and moving carriers is considered to be a situation that requires more technical research. Conclusion: Quantitive data was obtained to determine how carry-on luggage and carrier affect evacuation.

• Korean Circulation Journal
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• v.52 no.4
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• pp.288-300
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• 2022

Background and Objectives: We compared real-world clinical outcomes of patients receiving intravascular lithotripsy (IVL) versus rotational atherectomy (RA) for heavily calcified coronary lesions. Methods: Fifty-three patients who received IVL from January 2017 to July 2020 were retrospectively compared to 271 patients who received RA from January 2017 to December 2018. Primary endpoints were in-hospital and 30-day major adverse cardiovascular events (MACE). Results: IVL patients had a higher prevalence of acute coronary syndrome (56.6% vs 24.4, p<0.001), multivessel disease (96.2% vs 73.3%, p<0.001) and emergency procedures (17.0% vs 2.2%, p<0.001) compared to RA. In-hospital MACE (11.3% vs 5.9%, p=0.152), MI (7.5% vs 3.3%, p=0.152), and mortality (5.7% vs 3.0%, p=0.319) were not statistically significant. 30-day MACE was higher in the IVL cohort vs RA (17.0% vs 7.4%, p=0.035). Propensity score adjusted regression using IVL was also performed on in-hospital MACE (odds ratio [OR], 1.677; 95% confidence interval [CI], 0.588-4.779) and 30-day MACE (OR, 1.910; 95% CI, 0.774-4.718). Conclusions: These findings represent our initial IVL experience in a high-risk, real-world cohort. Although the event rate in the IVL arm was numerically higher compared to RA, the small numbers and retrospective nature of this study preclude definitive conclusions. These clinical outcomes are likely to improve with greater experience and better case selection, allowing IVL to effectively treat complex calcified coronary lesions.

• Journal of Korean Neurosurgical Society
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• v.67 no.5
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• pp.493-509
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• 2024

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.4
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• pp.192-205
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• 2009

The unexpected wind over the Mt. Hwawang on 9 February 2009 was deadly when many spectators were watching a traditional event to burn dried grasses and the fire went out of control due to the wind. We analyzed the fatal wind based on wind flow simulations over a digitized complex terrain of the mountain with a localized heating area using a three dimensional computational fluid dynamics model, CFD_NIMR_SNU (Computational Fluid Dynamics_National Institute of Meteorological Research_Seoul National University). Three levels of fire intensity were simulated: no fire, $300^{\circ}C$ and $600^{\circ}C$ of surface temperature at the site on fire. The surface heat accelerated vertical wind speed by as much as $0.7\;m\;s^{-1}$ (for $300^{\circ}C$) and $1.1\;m\;s^{-1}$ (for $600^{\circ}C$) at the center of the fire. Turbulent kinetic energy was increased by the heat itself and by the increased mechanical force, which in turn was generated by the thermal convection. The heating together with the complex terrain and strong boundary wind induced the unexpected high wind conditions with turbulence at the mountain. The CFD_NIMR_SNU model provided valuable analysis data to understand the consequences of the fatal mountain fire. It is suggested that the place of fire was calm at the time of the fire setting due to the elevated terrain of the windward side. The suppression of wind was easily reversed when there was fire, which caused updraft of hot air by the fire and the strong boundary wind. The strong boundary wind in conjunction with the fire event caused the strong turbulence, resulting in many fire casualties. The model can be utilized in turbulence forecasting over a small area due to surface fire in conjunction with a mesoscale weather model to help fire prevention at the field.