• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.582-593
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• 2015

For military training and course-of-action analysis, OneSAF Int'l version being used in ROK Army has a limited capability to simulate NBC(nuclear, biological, and chemical) damages. For high-fidelity NBC combat simulation, it is required to visualize NBC contamination dispersion in consideration of weather conditions and terrain characteristics. However, OneSAF itself handling interaction among thousands of combat entities cannot carry out a simulation of NBC contamination dispersion because it brings about an excess burden. To resolve this problem, this research aims to design simulation federation for analysis on NBC operational effects. After examining design consideration to connect OneSAF and a NBC contamination dispersion model, we design a federation architecture that facilitates the interaction between OneSAF and a NBC contamination dispersion model. Afterwards, we implement a federation interface to share simulation data by publish-subscribe pattern and to translate them into the proprietary format for each model. We prove the possibility of federation between both models, as showing that dispersion of NBC contaminated cloud and changes in concentration are reflected in OneSAF-based engagement simulation.

• Journal of the Korea Society for Simulation
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• v.30 no.2
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• pp.49-64
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• 2021

M&S techniques are widely used as scientific means to systematically develop response plans to chemical and biological (CB) hazards. However, while the theoretical area of hazard dispersion modeling has achieved remarkable practical results, the operational analysis area to simulate CB hazard response plans is still in an early stage. This paper presents a model to simulate CB hazard response plans such as detection, protection, and decontamination. First, we present a possible way to display high-fidelity hazard dispersion in a combat simulation model, taking into account weather and terrain conditions. We then develop an improved vulnerability model of the combat simulation model, in order to simulate CB damage of combat simulation entities based on other casualty prediction techniques. In addition, we implement tactical behavior task models that simulate CB hazard response plans such as detection, reconnaissance, protection, and decontamination. Finally, we explore its feasibility by analyzing contamination detection effects by distributed CB detectors and decontamination effects according to the size of the {contaminated, decontamination} unit. We expect that the proposed model will be partially utilized in disaster prevention and simulation training area as well as analysis of combat effectiveness analysis of CB protection system and its operational concepts in the military area.

• Korean Journal of Adult Nursing
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• v.27 no.2
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• pp.146-155
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• 2015

Purpose: The aim of this descriptive study was to explore the relationship between non-technical skills (NTSs) and cardiopulmonary resuscitation (CPR) performance of nurses' teams in simulated cardiac arrest in the hospital. Methods: The sample was 28 teams of nurses in one university hospital located in Seoul. A high fidelity simulator was used to enact simulated cardiac arrest. The nurse teams were scored by raters using both the CPR performance checklist and the NTSs checklist. Specifically the CPR performance checklist included critical actions; time elapsed to initiation of critical actions, and quality of cardiac compression. The NTSs checklist was comprised of leadership, communication, mutual performance monitoring, maintenance of guideline, and task management. Data were collected directly from manikin and video recordings. Results: There was a significant difference between the medians of the NTSs and CPR performance (Mann Whitney U=43.5, p=.014). In five subcategories, communication (p=.026), mutual performance monitoring (p=.005), and maintenance of guideline (p=.003) differed significantly with CPR performance in medians. Leadership (p=.053) and task management (p=.080) were not significantly different with CPR performance. Conclusion: The findings indicate that NTSs of teams in addition to technical skills of individual rescuers affect the outcome of CPR. NTSs development and assessment should be considered an integral part of resuscitation training.

• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.293-302
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• 2012

The purpose of this study was to identify the effect of human patient simulator(HPS)-based education on self-directed learning(SDL) and collective efficacy(CE) for nursing students. This study design was one group pre-posttest. The subjects were 2nd grade 92 students enrolling in the integrated practice. They have no previous experience of HPS-based education. HPS-based education included team based learning, skill training, taking a high-fidelity simulation with Medical Education Technologies, Inc (METI) simulator and being debriefed during 12 weeks. The pretest and posttest were conducted to understand the improvement in SDL and CE. After the subjects had participated in the HPS-based education, they showed statistically significant higher SDL(t=4.24, p=0.000) than before. However, there was no significant change in CE. Based on the results, this study suggests that SDL for nursing students were significantly improved by HPS-base education.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.223-231
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• 2020

The aim of this study was to develop and apply a nursing process simulation scenario for cerebral infarction patients using HPS for nursing students. The effects of this simulation were evaluated and compared with the effects of traditional teaching methods. This study was conducted on 3rd grade students at the College of Nursing from December 7 to December 26, 2018. This study examined 38 subjects in the experimental group and 39 in the control group. This research went through the analysis phase, design phase, and development phase for the development and application of the scenario. The confidence, knowledge, and performance before and after the intervention in the experimental and control groups were analyzed using a t-test. This study developed a simulation scenario based on a nursing process for cerebral infarct patients and was designed to infer three nursing diagnoses. Both groups showed significant pre-post scores except for the clinical performance. The experimental group had higher post-score scores and greater difference between the post-test and pre-test. This study is meaningful in that a standardized scenario of nursing process of infarct patients was developed. In addition, the developed scenario shows the possibility of developing teaching and learning methods that can integrate theoretical learning and practice.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.641-653
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• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.