• Journal of Preventive Medicine and Public Health
• /
• 제41권4호
• /
• pp.225-231
• /
• 2008

Objectives: Bioterrorism (BT) preparedness and response plans are particularly important among healthcare workers who will be among the first involved in the outbreak situations. This study was conducted to evaluate the current status of education for BT preparedness and response in health care-related colleges/junior colleges and to develop learning objectives for use in their regular curricula. Methods: We surveyed all medical colleges/schools, colleges/junior colleges that train nurses, emergency medical technicians or clinical pathologists, and 10% (randomly selected) of them that train general hygienists in Korea. The survey was conducted via mail from March to July of 2007. We surveyed 35 experts to determine if there was a consensus of learning objectives among healthcare workers. Results: Only 31.3% of medical colleges/schools and 13.3% of nursing colleges/junior colleges had education programs that included BT preparedness and responses in their curricula. The most common reason given for the lack of BT educational programs was 'There is not much need for education regarding BT preparedness and response in Korea'. None of the colleges/junior colleges that train clinical pathologists, or general hygienists had an education program for BT response. After evaluating the expert opinions, we developed individual learning objectives designed specifically for educational institutions. Conclusions: There were only a few colleges/junior colleges that enforce the requirement to provide education for BT preparedness and response in curricula. It is necessary to raise the perception of BT preparedness and response to induce the schools to provide such programs.

• 디지털융복합연구
• /
• 제19권1호
• /
• pp.229-237
• /
• 2021

본 연구의 목적은 시뮬레이션 기반 흉관배액관리 간호교육이 간호학생의 시나리오 경험에 대한 반응, 학습에 대한 자신감 및 문제해결능력에 미치는 효과를 검증하는 것이다, 본 연구는 단일군 전후설계 실험 연구로 133명의 3학년 간호학생을 31개조로 시뮬레이션 교육 중재를 시행하였다. 연구결과, 시뮬레이션 실습교육 시행 전보다 시행 후에 시나리오 경험에 대한 반응은 긍정적인 결과로 나타났고, 학습에 대한 자신감 및 문제해결능력은 유의하게 향상된 것으로 나타났다. 이는 간호학 실습교육에 있어서 시뮬레이터를 이용한 시뮬레이션 교육이 효과적임을 보여주는 결과라고 할 수 있다. 본 연구는 현장중심의 실습교육 및 임상실무의 질을 향상시키는데 기여할 수 있을 것이다.

• 시스템엔지니어링학술지
• /
• 제18권2호
• /
• pp.75-93
• /
• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Smart Structures and Systems
• /
• 제31권5호
• /
• pp.485-500
• /
• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• International journal of advanced smart convergence
• /
• 제10권2호
• /
• pp.201-208
• /
• 2021

Recently, artificial intelligence(AI) has been used throughout society, and social interest in it is increasing. Accordingly, the necessity of AI education is becoming a big topic in the education field. As a response to this trend, the Korean education authorities have also announced plans for AI education, and various studies have been performed in academic field to revitalize AI education in the future. However, the curriculum research on what differentiates AI education from existing SW education and what and how to train AI is still in its infancy. In this paper, Therefore, we focused on the experiences of elementary school students in solving problems in their own lives, and developed a teaching-learning model based on design-based research so that students can design a problem-solving process and experience the process of feedback. We applied the developed teaching-learning model to the problem-solving process and confirmed that it increased students' understanding and satisfaction with AI education.

• Journal of Positioning, Navigation, and Timing
• /
• 제11권1호
• /
• pp.45-49
• /
• 2022

In this paper, we propose a method for estimating the error of the Ultra-Wideband (UWB) distance measurement using the channel impulse response (CIR) of the UWB signal based on machine learning. Due to the recent demand for indoor location-based services, wireless signal-based localization technologies are being studied, such as UWB, Wi-Fi, and Bluetooth. The constructive obstacles constituting the indoor environment make the distance measurement of UWB inaccurate, which lowers the indoor localization accuracy. Therefore, we apply machine learning to learn the characteristics of UWB signals and estimate the error of UWB distance measurements. In addition, the performance of the proposed algorithm is analyzed through experiments in an indoor environment composed of various walls.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2021년도 추계학술대회
• /
• pp.449-451
• /
• 2021

엔드포인트를 대상으로 하는 사이버 공격이 표적형, 지능형 공격으로 정교하게 진화하면서 산업용 사물인터넷(IIoT, Industrial Internet of Things)을 겨냥하는 지능형 지속 공격(APT, Advanced Persistent Threat)이 증가하고 있다. APT 공격을 효과적으로 방어하기 위하여 룰 기반으로 악성 행위를 탐지하는 기존의 보안 도구를 결합하고 보완하는 머신러닝 기반의 엔드포인트 탐지 및 대응(EDR, Endpoint Detection and Response) 솔루션이 주목을 받고 있다. 하지만 범용 EDR 솔루션은 오탐률이 높고, 높은 수준의 분석가가 방대한 양의 경보를 모니터링 및 분석해야 하는 문제점이 존재한다. 따라서, IIoT 특성과 취약성을 반영한 머신러닝 기반의 EDR 솔루션 최적화 과정이 필수적이다. 본 연구에서는 IIoT 대상의 APT 공격의 흐름과 영향을 분석하고 머신러닝 기반 APT 탐지 EDR 솔루션을 비교 분석한다.

• 한국융합학회논문지
• /
• 제9권10호
• /
• pp.363-370
• /
• 2018

본 연구는 초등학교 학생들을 대상으로 반응 중심 학습법을 통한 동기유발이 체육수업 전 후의 참여태도에 미치는 영향과 변화를 분석하여 체육수업의 실제를 파악할 수 있는 관점을 제공하고자 하는데 목적이 있다. 이를 위해 본 연구의 대상은 C지역 소재 N초등학교 5, 6학년 126명의 학생을 학급과 성별 구분 없이 선정하였으며, 탐색적 요인분석과 요인 간 유사성을 알아보기 위한 적률상관계수를 적용하여 분석하였다. 본 연구결과에서 제시된 것과 같이 일방적인 주입식 교육이 아닌 학생들을 중심으로 한 교육으로 추진해 나가는 것이 필요하며 학생들이 적극적이고 활발한 체육수업 참여에 기여할 수 있도록 수업 전 후의 학습요인을 고려하여 융합적으로 적용하는 것이 중요하다.

• Genomics & Informatics
• /
• 제19권1호
• /
• pp.10.1-10.7
• /
• 2021

Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC₅₀ and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC₅₀ and 0.81 for AUC. We identify common predictor genes for IC₅₀ and AUC, with which the performance was similar to those with genes separately found for IC₅₀ and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC₅₀, 0.7945 for AUC) with 321 predictor genes.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2022년도 춘계학술대회
• /
• pp.198-201
• /
• 2022

최근 신기술 대상 신종 사이버 위협이 증가하고 있으며, 해커의 공격 표적도 광범위해지고 지능화되고 있다. 이러한 공격에 대응하기 위해 주요 보안 기업들은 전통적인 EDR(Endpoint Detection and Response) 중심의 솔루션을 활용하고 있다. 하지만 종래 방식은 컨텍스트를 고려하지 않아서 지능형 공격에 대한 대응 정확도와 효율성에 한계가 있다. 이 문제를 개선하기 위해 최근 XDR(Extended Detection and Response) 중심의 보안 솔루션의 필요성이 대두되었다. 본 연구에서는 머신러닝 기반의 컨텍스트 분석을 활용한 XDR 동향과 발전 로드맵을 통해 변화하는 환경에 효율적인 위협탐지와 대응방안을 제시한다.