• 의학교육논단
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• 제24권2호
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• pp.79-92
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• 2022

With increasing demands for medical care by society, the medical system, and general citizens and rapid changes in doctor's awareness, the competencies required of doctors are also changing. The goal of this study was to develop a doctor's competency framework from the patient's perspective, and to make it the basis for the development of milestones and entrustable professional activities for each period of medical student education and resident training. To this end, a big data analysis using topic modeling was performed on domestic and international research papers (2011-2020), domestic newspaper articles (2016-2020), and domestic social networking service data (2016-2020) related to doctor's competencies. Delphi surveys were conducted twice with 28 medical education experts. In addition, a survey was conducted on doctor's competencies among 1,000 citizens, 407 nurses, 237 medical students, 361 majors, and 200 specialists. Through the above process, six core competencies, 16 sub-competencies, and 47 competencies were derived as subject-oriented doctor's competencies. The core competencies were: (1) competency related to disease and health as an expert; (2) competency related to patients as a communicator; (3) competency related to colleagues as a collaborator; (4) competency related to society as a health care leader (5) competency related to oneself as a professional, and (6) competency related to academics as a scholar who contributes to the development of medicine.

• International Journal of Computer Science & Network Security
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• 제23권2호
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• pp.173-182
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• 2023

The UAE government has set its sights on creating a smart, electronic-based government system that utilizes AI. The country's collaboration with India aims to bring substantial returns through AI innovation, with a target of over $20 billion in the coming years. To achieve this goal, the UAE launched its AI strategy in 2017, focused on improving performance in key sectors and becoming a leader in AI investment. To ensure public safety as the role of AI in government grows, the country is working on developing integrated cyber security solutions for SCADA systems. A questionnaire-based study was conducted, using the AI IQ Threat Scale to measure the variables in the research model. The sample consisted of 200 individuals from the UAE government, private sector, and academia, and data was collected through online surveys and analyzed using descriptive statistics and structural equation modeling. The results indicate that the AI IQ Threat Scale was effective in measuring the four main attacks and defense applications of AI. Additionally, the study reveals that AI governance and cyber defense have a positive impact on the resilience of AI systems. This study makes a valuable contribution to the UAE government's efforts to remain at the forefront of AI and technology exploitation. The results emphasize the need for appropriate evaluation models to ensure a resilient economy and improved public safety in the face of automation. The findings can inform future AI governance and cyber defense strategies for the UAE and other countries.

• 한국시뮬레이션학회논문지
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• 제18권2호
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• pp.1-8
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• 2009

이종의 분산환경에서 다양한 보안시스템에 대한 효율적인 보안 관리를 위해서 관리자는 보안 시스템들이 설치된 네트워크 환경에 대한 사전에 전문적인 보안 지식을 갖고 있어야하며, 개방형 네트워크 환경의 경우 새로운 보안시스템이 추가되면 새로운 보안 정책과 기술을 적용해야 한다. 이는 전산망 운영 기관의 보안 관리 비용을 가중시키며 체계적이고 일괄적인 보안 정책 및 기술 구현을 불가능하게 하여 오히려 보안 문제를 야기시키는 역기능을 초래할 수 있다. 그리고, 보안 제품의 개발과 공급이 다수의 공급자에 의해서 공급되므로 서로 상이한 특성을 갖는 보안 시스템들로 구성된 보안 관리 구조의 효율적인 운용과 유지에 상당한 어려움이 있다. 이에 본 논문에서는 이종의 보안시스템을 관리하는 통합보안시스템의 보안정책을 Z-Notation을 통해서 정의하고 통합관리에서 발생되는 정책 충돌 문제를 대표적인 보안시스템인 침입차단시스템(Firewall : 방화벽)을 대상으로 모델링하고 이를 해결하는 알고리즘을 제시하고자 한다.

• 디지털정책학회지
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• 제2권2호
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• pp.1-6
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• 2023

본 논문은 3차원 모델을 제작하고 Unity를 활용하여 사용자가 보다 더 쉽게 지능형 관광서비스를 접할 수 있도록 하는 방법을 연구했다. 본 연구의 핵심기능 및 환경은 게임 제작용 툴인 Unity를 사용하여, 가상공간을 만들고 안에서 제어와 카메라 시점을 적용한 NPC를 통해 다양한 각도와 위치에서 관광 서비스를 이용할 수 있도록 하는 방법을 연구했다. 본 프로젝트는 가상현실 기술을 활용하여 관광명소를 현장에 가지 않고도 가상 세상에서 둘러 볼 수 있는 지능형 서비스 콘텐츠이다. 본 지능 서비스는 UI/UX 도구를 사용해 게임 형태로 만들고 재미요소를 넣기 위해 간단한 게임 형태로 융합하여 관광지 홍보를 위한 게이미피케이션을 적용했으며, 가상현실 관광지 체험을 실행할 수 있게 하는 것이 연구의 목적이다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.157-157
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• 2022

Recent decades have seen all over the world increasing drought in some regions and increasing flood in others. Climate change has been alarming in many regions resulting in degradation and diminution of available freshwater. The effect of global warming and overpopulation associated with increasing irrigated farming and valuable agricultural lands could be particularly disastrous for coastal areas like the one of Benin. The coastal region of Benin is under a heavy demographic pressure and was in the last decades the object of important urban developments. The present study aims to roughly study the general effect of climate change (Sea Level Rise: SLR) and groundwater pumping on Seawater intrusion (SWI) in Benin's coastal region. To reach the main goal of our study, the region aquifer system was built in numerical model using SEAWAT engine from Visual MODFLOW. The model is built and calibrated from 2016 to 2020 in SEAWAT, and using WinPEST the model parameters were optimized for a better performance. The optimized parameters are used for seawater intrusion intensity evaluation in the coastal region of Benin The simulation of the hydraulic head in the calibration period, showed groundwater head drawdown across the area with an average of 1.92m which is observed on the field by groundwater level depletion in hand dug wells mainly in the south of the study area. SWI area increased with a difference of 2.59km² between the start and end time of the modeling period. By considering SLR due to global warming, the model was stimulated to predict SWI area in 2050. IPCC scenario IS92a simulated SLR in the coastal region of Benin and the average rise is estimated at 20cm by 2050. Using the average rise, the model is run for SWI area estimation in 2050. SWI area in 2050 increased by an average of 10.34% (21.04 km²); this is expected to keep increasing as population grows and SLR.

• 대한원격탐사학회지
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• 제39권5_3호
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• 인터넷정보학회논문지
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• 제24권5호
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• pp.51-66
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• 2023

본 논문에서는 비즈니스 프로세스 모델의 생명주기관리를 지원하는 대표적인 지식발견기술인 프로세스 마이닝과 지식개선기술인 프로세스 리엔지니어링 접근방법을 기반으로 하는 새로운 유형의 프로세스 발견 프레임워크를 제안한다. 또한, 제안된 프레임워크를 기반으로 하는 프로세스 마이닝 시스템을 개발하고, 이를 통한 실험적 검증을 수행한다. 실험적 효과검증에 적용된 프로세스 실행 이벤트 로그를 특별히 프로세스 빅-로그(Process BIG-Logs)라고 정의하고, 분산 비즈니스 프로세스 관리 시스템의 로깅메커니즘과 연계된 조각-실행로그이력들을 클러스터링하는 전처리과정을 거친 마이닝의 입력데이터세트로 활용한다. 결과적으로, 본 논문에서는 구조적 정보제어넷기반 프로세스 마이닝 알고리즘인 ρ-알고리즘을 개선한 제어경로기반 프로세스 그룹 발견 알고리즘과 프레임워크를 설계 및 구현하고, 구현된 시스템을 이용하여 제안한 알고리즘과 프레임워크의 정확성을 실험적으로 검증한다.

• Advances in aircraft and spacecraft science
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• 제10권4호
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• pp.303-315
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• 2023

The primary goal of this research is to investigate flow separation phenomena using various turbulence models. Also investigated are the effects of free-stream turbulence intensity on the flow over a NACA 0018 airfoil. The flow field around a NACA 0018 airfoil has been numerically simulated using RANS at Reynolds numbers ranging from 100,000 to 200,000 and angles of attack (AoA) ranging from 0° to 18° with various inflow conditions. A parametric study is conducted over a range of chord Reynolds numbers for free-stream turbulence intensities from 0.1 % to 0.5 % to understand the effects of each parameter on the suction side laminar separation bubble. The results showed that increasing the free-stream turbulence intensity reduces the length of the separation bubble formed over the suction side of the airfoil, as well as the flow prediction accuracy of each model. These models were used to compare the modeling accuracy and processing time improvements. The K- SST performs well in this simulation for estimating lift coefficients, with only small deviations at larger angles of attack. However, a stall was not predicted by the transition k-kl-omega. When predicting the location of flow reattachment over the airfoil, the transition k-kl-omega model also made some over-predictions. The Cp plots showed that the model generated results more in line with the experimental findings.

• 한국BIM학회 논문집
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• 제13권4호
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• pp.96-105
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• 2023

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.