• 의학교육논단
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• 제9권2호
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• pp.29-40
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• 2007

Purpose: This study analysed the intention of medical students career choice, educational programmes, and mentoring and counseling system for career planning at Yonsei medical school in Korea. Methods: The data were collected based on four separate graduation questionnaires at Yonsei medical school in the years of 2005, 2006, 2007, and 2008. The number of the survey was 130 in 2005, 130 in 2006, 153 in 2007, and that of the latest was 120 in 2008. We analysed the career intention on medical specialties and activities, and perceptions of important factors in choosing medical specialty. Results : The results which can be drawn from this study are these: firstly. students had more intention for choosing clinical medicine as university faculty than any other activities. While male students preferred to major in surgery, neurosurgery, orthopedic surgery, urology, otorhinolaryngology, female students in internal medicine, neurology, anesthesiology and pain medicine, diagnostic radiology, laboratory medicine. Secondly, students perceived that the most important factor which can influence on choosing a medical specialty was individual factor such as one's interests and concerns, values, and aptitudes. In stead, they relatively less perceived mentor and role model's effects on choosing a medical specialty compared to those of the United States of America. Third, the career planning at Yonsei medical school was evaluated well, especially educational programmes for career planning such as self assessment programme, elective(specialized) courses, and conversation with a senior programme. Conclusions: Unexpectedly, there are high demands for career planning by medical students. Therefore, we will reorganize systematic devices for career planning such as mentoring and counseling system at medical school.

• 한국전자통신학회논문지
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• 제16권5호
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• pp.859-866
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• 2021

영농형 태양광 발전은 농지 상부에 태양광 발전 설비를 설치하는 방식으로 농작물과 전기를 동시에 생산함으로써 농가 소득을 증대시키는 새로운 모델이다. 최근 영농형 태양광 발전을 활용하는 다양한 시도들이 이루어지고 있다. 영농형 태양광 발전은 기존의 태양광 발전과는 달리 비교적 높은 구조물 상부에 설치하게 되므로 유지 보수가 상대적으로 어렵다는 단점이 있다. 이러한 문제를 해결하기 위해 지능적이고 효율적인 운용 및 진단 기능이 요구된다. 본 논문에서는 영농형 태양광 발전 설비의 전력 생산량을 수집, 저장하여 지능적인 예측 모델을 구현하기 위한 예측 및 진단 시스템의 설계 및 구현에 대해 논한다. 제안된 시스템은 태양광 발전량과 환경 센서 데이터를 기반으로 발전량을 예측하여 설비의 이상 유무를 판별하며 설비의 노화 정도를 산출하여 사용자에게 제공한다.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• 제25권1호
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• pp.30-40
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• 2022

Purpose: Pediatric acute appendicitis has a stable incidence rate in Western countries with an annual change of -0.36%. However, a sharp increase was observed in the Asian region. The Indonesian Health Department reveals appendicitis as the fourth most infectious disease, with more than 64,000 patients annually. Hence, there is an urgent need to identify and evaluate the risk factors and diagnostic modalities for accurate diagnosis and early treatment. This study also clarifies the usage of pediatric appendicitis score (PAS) for children <5 years of age. Methods: The current study employed a cross-sectional design with purposive sampling through demographic and PAS questionnaires with ultrasound sonography (USG) results. The analysis was performed using the chi-square and Mann-Whitney tests and logistic regression. Results: This study included 21 qualified patients with an average age of 6.76±4.679 years, weighing 21.72±10.437 kg, and who had been hospitalized for 4.24±1.513 days in Siloam Teaching Hospital. Compared to the surgical gold standard, PAS and USG have moderate sensitivity and specificity. Bodyweight and stay duration were significant for appendicitis (p<0.05); however, all were confounders in the multivariate regression analysis. Incidentally, a risk prediction model was generated with an area under the curve of 72.73%, sensitivity of 100.0%, specificity of 54.5%, and a cut-off value of 151. Conclusion: PAS outperforms USG in the sensitivity of diagnosing appendicitis, whereas USG outperforms PAS in terms of specificity. This study demonstrates the use of PAS in children under 5 years old. Meanwhile, no risk factors were significant in multivariate pediatric acute appendicitis risk factors.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• Journal of Veterinary Science
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• 제21권1호
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• pp.10.1-10.12
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• 2020

Blood perfusion of skeletal muscle and callus was evaluated using contrast-enhanced ultrasonography (CEUS) in a canine osteotomy model to determine the applicability of CEUS in the assessment of neovascularization during fracture healing and to compare the vascular signals on CEUS between external skeletal fixation and cast-applied dogs. In 6 Beagle dogs, a simple transverse osteotomy was performed at the left tibial shaft and external skeletal fixation (n = 3) or a cast (n = 3) was applied. Radiography, power Doppler ultrasonography (power Doppler), and CEUS were performed until complete union was achieved. On CEUS, vascular changes were quantitatively evaluated by measuring peak intensity (PI) and time to PI in the soft tissue and callus and by counting the vascular signals. Vascular signals from the soft tissue were detected on power Doppler and CEUS on day 2. Significantly more vascular signals were detected by CEUS than by power Doppler. On CEUS, PI in the surrounding soft tissue was markedly increased after the fracture line appeared indistinctively changed on radiography in all dogs. In the cast-applied dogs, vascular signals from the periosteal and endosteal callus were detected on CEUS before mineralized callus was observed on radiography. CEUS was useful in assessing the vascularity of soft tissue and callus, particularly in indirect fracture healing, and provided indications of a normally healing fracture.

• 한국인터넷방송통신학회논문지
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• 제23권1호
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• pp.163-170
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• 2023

본 논문은 제조설비의 자율제어와 상태판별 알고리즘을 위한 시멘틱 모델링 기술에 대해 소개한다. 디지털 트윈 기술과 스마트 팩토리의 다양한 ICT 기술의 발전으로 제조업은 새로운 생산 관리모델이 구축되고 있다. 발전된 스마트 제조기술을 바탕으로 상태판별 알고리즘은 자율제어와 공장 내의 설비 문제를 빠르게 파악하고 대처하기 위한 방법론으로 제시되었다. 그러나 기존의 상태판별 알고리즘은 사용자 또는 관리자에게 그리드 맵을 통해 주요 정보를 알려주고, 이에 대처하는 방향으로 제시되었다. 하지만 스마트 제조기술의 고도화와 방향성은 유연 생산과 소비자 니즈에 맞춘 생산등으로 다변화 하고 있다. 이에 따라 본 논문에서는 시멘틱 기반의 Linked List 자료구조를 이용하여 공장을 설계 구축하고 그래프 기반 정보를 통해 사용자 또는 관리자에게 필요한 정보만을 제공하여 관리의 효율성을 높일 수 있는 기술을 소개한다. 이러한 방법론은 유연 생산과 다품종 소량 생산 등에 적합한 구조로 활용될 수 있다.

• 대한의용생체공학회:의공학회지
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• 제42권5호
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• pp.241-249
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• 2021

Recently, artificial intelligence for diagnosis system of obstetric diseases have been actively studied. Artificial intelligence diagnostic assist systems, which support medical diagnosis benefits of efficiency and accuracy, may experience problems of poor learning accuracy and reliability when inappropriate images are the model's input data. For this reason, before learning, We proposed an algorithm to exclude unread cervical imaging. 2,000 images of read cervical imaging and 257 images of unread cervical imaging were used for this study. Experiments were conducted based on the statistical method Radiomics to extract feature values of the entire images for classification of unread images from the entire images and to obtain a range of read threshold values. The degree to which brightness, blur, and cervical regions were photographed adequately in the image was determined as classification indicators. We compared the classification performance by learning read cervical imaging classified by the algorithm proposed in this paper and unread cervical imaging for deep learning classification model. We evaluate the classification accuracy for unread Cervical imaging of the algorithm by comparing the performance. Images for the algorithm showed higher accuracy of 91.6% on average. It is expected that the algorithm proposed in this paper will improve reliability by effectively excluding unread cervical imaging and ultimately reducing errors in artificial intelligence diagnosis.

• 문화기술의 융합
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• 제9권3호
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• pp.845-850
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• 2023

본 논문에서는 전이학습을 이용하여 볼베어링의 진동진단을 수행하는 방법을 제안한다. 고장을 진단하기 위해 진동신호를 시간-주파수로 분석할 수 있는 STFT을 CNN의 입력으로 이용하였다. CNN 기반의 딥러닝 인공신경망을 빠르게 학습하고 진단 성능을 높이기 위해 전이학습 기반의 딥러닝 학습 기법을 제안하였다. 전이학습은 VGG 기반의 영상 분류 모델을 이용하여 특징 추출기와 분류기를 선택적으로 학습하였고, 학습에 사용한 데이터 세트는 Case Western Reserve University 대학에서 제공하는 공개된 볼베어링 진동 데이터를 사용하였으며, 성능평가는 기존의 CNN 모델과 비교하는 방법으로 수행하였다. 실험 결과 전이학습이 볼베어링 진동 데이터에서 상태 진단에 유용하다는 것을 증명할 수 있을 뿐만 아니라 이를 통해 다른 산업에서도 전이학습을 사용하여 상태 진단을 개선할 수 있다.

• 한국항행학회논문지
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• 제27권5호
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• pp.528-533
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• 2023

항공사에서는 보다 효과적인 교육훈련을 실시하고자 실무와 연계한 훈련 프로그램을 도입하고자 하였다. 이를 위해 항공사들은 항공 인력의 실무 역량 강화 및 안전 문화 증진을 위해 증거 기반 훈련(EBT)을 시행해 오고 있다. 항공사들은 효과적인 EBT 모델 개발을 위해 운항 데이터 및 사례 연구를 분석하여 항공 인력의 역량 및 실무 능력을 체계적으로 평가할 수 있다. 또한 승무원 자원 관리(CRM)와 같은 기술적 방법 및 인적 요인을 포함하는 전체적인 접근법을 적용하여 EBT 모델을 구성할 수 있다. EBT 도입으로 인해 항공사들은 조종사의 실무 업무에 대한 진단 및 피드백 시스템을 구축하게 되며 개인 맞춤형 교육을 제공할 수 있고 교육 성과를 거쳐 교육 효과를 검증하는 교육훈련 체계를 확립하게 된다.

• 공학교육연구
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• 제27권2호
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• pp.35-50
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• 2024

The purpose of this study is to identify the core competencies of graduate students at A research university in the context of graduate education in science and engineering, and to develop and validate a diagnostic tool to measure them. To achieve the research objectives, first, 6 factors and 18 sub-competencies of core competencies were derived based on a review of domestic and foreign studies, cases of excellent research-centered overseas universities, and interviews with members of A University. Second, a theoretical model was constructed by deriving behavioral indicators based on the core competencies and sub-competencies, and a preliminary survey was conducted on 188 graduate students of University A to verify the statistical validity of the theoretical model. Results of exploratory and confirmatory factor analysis, the core competencies of graduate students at A research university consisted of 6 factors, 16 sub-competencies, and 77 items. Specifically, it included "Independent research capability(13 items)", "Social Entrepreneurship(10 items)", "Academic agility(15 items)", "Ingenious Challenges(15 items)", "Collegial Collaboration(9 items)", and "Mueunjae leadership(15 items)". This study contributes to the development of theories related to core competencies of graduate students in science and engineering, and has practical significance as a basis for a data-driven competency-based graduate education system.