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공고 『2 · 1체제』교육과정 체제 개선 방안 : 공업고등학교 교사들의 인식을 중심으로
김기수,우연재,이창훈 충남대학교 공업교육연구소 2005 論文集 Vol.27 No.1
The purposes of this study was subjected a scheme to improve technical highschcol “2+lsystem” curriculum. The method of the study for carrying out the study is as follows. First, the realities of management were analyzed through the preceding study related with technical highschool “2+lsystem” and the plan of school education of technical highschool “2+1 system” management school. Second, the conference of the teacher in charge of technical highschool management school and the person in charge of the department related with Education City and Province Hall was held. Third, the question search was practiced to all school subjects in the whole country through the electronic document with the help of ministry of education & human resources development (N=930). The effect of the study is as follows. First, the thing which is formed in the schools and industries of technical highschool “2+lsystem” curriculum formation must be made to manage according to the seventh curriculum. Second, the field practice of the present type must be made to carry out in technical highschool as well during the third grade (mid term, final term). Third, the curriculum formation by system improvement and the completed unit (216 unit) are drawn up according to the seventh curriculum.
문수연,최영실,박미연,이정아,정미경,정혜숙,정두련,송재훈,백경란 대한감염학회 2009 감염과 화학요법 Vol.41 No.3
Q fever is a zoonosis caused by Coxiella burnetii, Presenting as acute and chronic illness and it has been reported worldwide. Acute Q fever is usually asymptomatic or mild and self-limiting, but infective endocarditis is one of the most serious complications of chronic Q fever and can be fatal. Known risk factors for Q fever endocarditis are valvular heart disease, immunocompromised hosts, and pregnancy. There have been some reports on Q fever in Korea but there exists no report on Q fever endocarditis. We have experienced 2 cases of Q fever with underlying valvular heart disease; both Patients came to the hospital for evaluation of prolonged fever. Although Q fever and Q fever endocarditis are rare in Korea, Q fever endocarditis should be considered in the differential diagnosis of patient with infective endocarditis when causative microorganism cannot be identified.
김연수,이철현,권재술 한국교원대학교 과학교육연구소 2000 청람과학교육연구논총 Vol.10 No.1
이 연구에서는 ICT 활용이 다른 어떤 교과보다도 과학교과의 교수-학습을 촉진시키는데 다양한 도움을 줄 수 있다는 것을 탐구영역 별로 확인하고, ICT 활용 내용 분석 모형을 제안하였다. 과학수업에서 ICT를 수업전략과 연계시킬 때, 본문에서 제시한 ICT 활용 내용 분석 모형 기준을 적용하면 도움을 받을 수 있을 것이다. 또한 분석 모형 기준을 적용할 때. 연구자가 조사한 물리개념을 중심으로 한 인터넷 사이트를 적절히 구조화시켜 융합시키면 과학탐구활동을 촉진시킬 수 있을 것이다. 과학교사가 이러한 분석모형적용을 숙달시킴으로써 과학 교수-학습에서 ICT 활용이 언제 효과적이고, 효과적이지 않은지 또는 유익한지 유익하지 않은지 판단할 수 있을 것이다.
반사성 교감신경계 기능장애 환자 치료시 방사성 동위원소 혈관주사술을 이용한 효과 판정
이주행,김종래,김진수,이윤우,이영석,이경민,김수연 대한마취과학회 1989 Korean Journal of Anesthesiology Vol.22 No.2
Reflex sympathetic dystrophy syndrome is characterized by variable complex of the following symptoms, such as pain, tenderness, vasomotor instability, and trophic changes in distal extremities resulting from injury to either central or peripheral nervous tissue. We measured blood flow using radioisotope angiography with agent containing technecium-99m in one patient with reflex sympathetic dystrophy syndrome. Blood flow were significantly lower in affected side than normal side. After epidural block with 0.5% lidocaine and left lumbar sympathetic block with 100% alcohol, burning pain was disappeared and blood flow was increased to near or above in relation to the normal side. Radioisotope angiography is a noninvasive study and not so expensive. And we thought that radioisotope angiography may be a useful aid not only for diagnosis, but also for evaluating therapeu-tic response.
이수현(Soo-Hyeon Lee),이해연(Hae-Yeoun Lee) 대한전자공학회 2019 대한전자공학회 학술대회 Vol.2019 No.11
Camera model discrimination technology has occupied a major position in the digital forensic technique. Research has been conducted in a variety of ways, most with over 95% accuracy. In this paper, we focused on the fact that the subject is in the center of the image and the edges of the image contain contents that are difficult to find feature of image, such as mountains, sky and sea. Therefore, performance analysis was performed for both the central area of image and all area of image. As a result, it was confirmed that using the center portion of the image shows a higher performance.
이수현(Soo-Hyeon Lee),김준수(Junsu Kim),황현욱(Hyunuk Hwang),이해연(Hae-Yeoun Lee) 한국디지털포렌식학회 2021 디지털 포렌식 연구 Vol.15 No.1
IT 기술의 발전으로 많은 이미지 데이터들이 생산 및 유통되고 있어서, 디지털포렌식에서 처리해야 하는 데이터의 양이 급증하고 있어서 활용할 수 있는 인적 및 물적 자원의 한계에 도달하고 있다. 문서 스캔 이미지는 포렌식 분석의 주요한 대상으로서, 방대한 양의 이미지 데이터에서 문서 스캔 이미지들만을 탐색하는 것은 중요하지만, 범용적인 딥 러닝 기술의 경우 포렌식에서 요구하는 정확도를 충족하지 못하는 문제가 있다. 본 논문에서는 딥 러닝을 이용하여 문서 스캔 이미지를 탐색하는 기술에 대하여 설명하고, 실제 수집된 문서 스캔 이미지를 대상으로 탐색한 결과를 제시한다. 영상 인식과 분류에서 범용적으로 사용되는 컨볼루셔널 뉴럴 네트워크 기반의 딥 러닝 모델을 도입하였고, 문서 스캔 이미지 탐색에 적합하도록 컨볼루셔널 및 전연결 계층에 대한 설계를 하였으며, 활성화 함수 및 풀링 함수 등의 설정을 하였다. MSCOCO 데이터셋에서 가져온 일반 이미지들과 5종으로 정의하여 수집한 문서 스캔 이미지를 이용하여 데이터베이스를 구축하였고, 이를 바탕으로 제시한 딥 러닝 모델에 대하여 학습을 수행한 후에 탐색 성능에 대한 분석을 수행하였고 평균적으로 98.89%의 탐색 정확도를 달성하였다. As many images are created and distributed with advances in IT technology, the amount of data that must be processed with digital forensics is rapidly increasing and available human and material resources are reaching their limits. Document scan images are a major target of forensic analysis and it is important to search these images from a vast amount of images. However, general-purpose deep learning techniques do not meet the accuracy required for forensics. In this paper, we describe a document scan image searching technique using deep learning, and present the results of searching the actually collected document scan images as targets. A convolutional neural network-based deep learning model, which is commonly used in image recognition and classification, was adapted. To be suitable for document scan image searching, convolutional and fully-connected layers were designed and activation functions and pooling functions, etc. were set. A database was constructed using general images of MSCOCO dataset and document scan images collected as 5 types. After training the presented deep learning model using this database, the searching performance was analyzed and the average searching accuracy of 98.89% was achieved.