• Journal of the Korea Safety Management & Science
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• v.11 no.4
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• pp.153-159
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• 2009

This study was conducted in search of the acceptance level of air traffic services from domestic airlines pilot's perspective in comparison with male controllers and female controllers. Pilots responded to the questionnaire that female ATC controllers are of significance to male controllers in terms of pronunciation, accuracy of English grammar, attitude and kindness. Besides, The ICAO aviation English proficiency level four test revealed that female controllers were found superior to male controllers in terms of rating scales of holistic descriptors.

• Aerospace Engineering and Technology
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• v.12 no.2
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• pp.186-192
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• 2013

Fabrication of human powered aircraft and flight test procedure for pilot training are included in this paper. To meet the weight requirement, very light materials such as carbon fiber and PVC foam are used and the final weight was 41.5kg. Ninety two times of flight test were done at Goheung Aviation Test Center from August to September 2012. When KARI were lack of know-how about human powered aircraft, damages on the aircraft were very frequent. After knowing how to fly and to control, one of two pilots was finally successful in flying further to 240m in the Human Powered Aircraft Contest.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.394-403
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• 2019

This research focuses on the perception of security screeners using a full body scanner at airport which had been newly introduced to terminal 2 of Incheon Int'l airport. To accomplish the purpose of research, this paper used UTAUT (Unified Theory of Acceptance and Use of Technology) model. Through an empirical analysis, it was proven the factors consisting of technological acceptance and how those factors affect both organizational innovation at airport and job satisfaction of security screeners. According to an empirical analysis, it was found out all the factors of technological acceptance have a significant effect on both organizational innovation and job satisfaction. However, only the effort expectation was shown to have a significant negative effect on the two dependant variables contrary to the other variables (performance expectation, behavioral intention and self efficacy. It was also proven organizational innovation had a moderating effect between technological acceptance and job satisfaction. Such results suggested organizational innovation at airport security division is necessary to enhance job satisfaction using a newly introduced full body scanner.

• Korean Journal of Radiology
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• v.23 no.3
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• pp.343-354
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• 2022

Objective: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. Materials and Methods: This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). Results: The AI model showed an AUROC of 0.922 (95% CI, 0.842-0.969) in the internal test set and 0.870 (95% CI, 0.785-0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%-92.0%) and specificity of 91.3% (95% CI, 79.2%-97.6%) for the internal test set and 78.9% (95% CI, 54.4%-93.9%) and 88.2% (95% CI, 78.7%-94.4%), respectively, for the external test set. With the model's assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020-0.168; p = 0.012) and 0.069 (95% CI, 0.002-0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074-0.090; p = 0.850). Conclusion: A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.