• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.3-6
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• 2009

본 연구에서는 경주시 내남면 일대를 대상으로 KOMPSAT MSC(Multi Spectral Camera) 영상(2007.06.12)을 이용하여 TCT(Tasseled-Cap Transformation), NDVI(Normalized Difference Vegetation Index) 알고리즘을 적용하여 분포도를 작성 하였으며 TCT DN 값을 기초로 영상 강조 및 변환을 통한 임상분류에 적합한 밴드 추출과 NDVI 분포도에서의 DN값을 기초로 산림현장 조사 결과에서 취득된 결과와의 비교 분석을 통하여 알고리즘에 대한 임상분류에 있어서의 변별력 분석을 수행하였다. 본 연구를 통하여 KOMPSAT MSC 영상에서의 임상분류를 위한 식생 알고리즘 적용 가능성을 검토하고자 한다.

• Korean Journal of Clinical Laboratory Science
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• v.49 no.4
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• pp.477-488
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• 2017

This study presents a discussion on the biomedical laboratory science (formally clinical laboratory science or medical laboratory science) with the identity of biomedical laboratory science, as well as the academic classification system for systematic approach. The field of biomedical laboratory science is not registered in the academic research area classification system of the National Research Foundation of Korea. Since the inception of the first department of biomedical laboratory science in 1963, about 52 departments were since established. Despite the scientific identity, biomedical laboratory science have not been acknowledged professionally in most institutions. Observing the academic research area classification, the physical therapy, occupational therapy, and dental hygiene science are systematically classified and approved the identities by the authorities. This study is freshly academic area classification system of the biomedical laboratory science. The contents of this study are summarized as follows. The medical laboratory technologist's discipline is considered within the medical and science category, clinical pathology in class, and biomedical laboratory science in division. Sections of biomedical laboratory science include hematology, transfusionology, immunology, biochemistry, microbiology, parasitology, science, molecular biology, histology, cytology, cardiopulmonary physiology, and neurophysiology.

• Korean Journal of Remote Sensing
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• v.17 no.3
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• pp.275-284
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• 2001

This study was carried out to evaluate high resolution satellite imagery of IKONOS for classifying the land cover, especially forest type. The IKONOS imagery of 11km$\times$11km size was taken on April 24, 2000 in Bong-pyoung Myun Pyungchang-Gun, Kangwon Province. Land cover classes were water, coniferous evergreen, Larix leptolepis, broad-leaved tree, bare land, farm land, grassland, sandy soil and asphalted area. Supervised classification method with algorithm of maximum likelihood was applied for classification. The terrestrial survey was also carried out to collect the reference data in this area. The accuracy of the classification was analyzed with the items of overall accuracy, producer's accuracy, user's accuracy and k for test area through the error matrix. In the accuracy analysis of the test area, overall accuracy was 94.3%, producer's accuracy was 77.0-99.9%, user's accuracy was 71.9-100% and k and 0.93. Classes of bare land, sandy soil and farm land were less clear than other classes, whereas classification result of IKONOS in forest area showed higher performance than that of other resolution(5-30m) satellite data.

• Proceedings of the Korea Contents Association Conference
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• 2012.05a
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• pp.343-344
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• 2012

본 연구는 중증도가 높은 신경계중환자를 대상으로 중환자 중증도 분류도구와 Glasgow coma scale 적용의 유용성을 검정하고자 하는데 있다. 본 연구에서 대상자의 일반적 특성 및 임상 관련 특성에 따른 사망률 확인, 중환자 중증도 분류도구(CPSCS)의 일반적 특성, 임상관련 특성에 따른 중증도 차이, GCS의 일반적 특성과 임상관련 특성에 따른 중증도 차이를 파악하고, 임상적 유용성을 검정하고자 한다.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.219-226
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• 2014

Fractional values resulted from the spectral mixture analysis could be used to classify not only urban area with various materials but also forest area in more detailed spatial scale. Especially South Korea is largely consist of mixed forest, so the spectral mixture analysis is suitable as a classification method. For the successful classification using spectral mixture analysis, extraction of optimal endmembers is prerequisite process. Though geometric endmember selection has been widely used, it is barely suitable for forest area. Therefore, in this study, we modified Iterative Error Analysis (IEA), one of the most famous algorithms of image endmember selection which extracts pure pixel directly from the image. The endmembers which represent deciduous and coniferous trees are automatically extracted. The experiments were implemented on two sites of Compact Airborne Spectrographic Imager (CASI) and classified forest area into two types. Accuracies of each classification results were 86% and 90%, which mean proposed algorithm effectively extracted proper endmembers. For the more accurate classification, another substances like forest gap should be considered.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.359-364
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• 2006

최근 GIS를 비롯한 공간정보기술의 눈부신 발전으로 국토전반에 대한 정보화와 과학적인 관리기법이 확산되고 있다. 이에 효율적인 산림관리를 위하여 위성영상과 지리정보시스템을 이용한 산림정보가 구축되고 있으나, 산림지역이라는 지리특성상 현장 출입이 제한적이고 위험하여 정보수집에 어려움이 있다. 본 연구에서는 효율적인 산림정보구축을 위하여 남제주군을 대상으로 고해상도 위성영상과 공간정보추출언어인 SML(Spatial Modeler Language)을 이용하여 다양한 모델별 산림지역의 임상정보를 추출, 임상분류를 위한 모델링 기법을 개발하였다. 또한 l/5,000도엽을 기준으로 표본점을 배치하고 임상, 하층식생, 입지 및 토양에 대한 현지조사를 수행하였으며, 추출자료와의 상관성 분석 및 검증을 통하여 공간정보 기술을 이용한 산림정보 구축의 기반을 마련하고자 하였다.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.4
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• pp.239-248
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• 2022

The taxonomy of bacteria in the field of clinical microbiology is in a state of constant flux. A large-scale revamping of the classification and nomenclature of anaerobic bacteria has taken place over the past few decades, mainly due to advances in molecular techniques such as 16S rRNA and whole genome sequencing (WGS). New genera and species have been added, and existing genera and species have been reclassified or renamed. A major role of the clinical microbiological laboratories (CMLs) is the accurate identification (ID) and appropriate antimicrobial susceptibility testing (AST) for clinically important bacteria, and rapid reporting and communication of the same to the clinician. Taxonomic changes in anaerobic bacteria could potentially affect the choice of appropriate antimicrobial agents and the antimicrobial breakpoints to use. Furthermore, current taxonomy is important to prevent treatment failures of emerging pathogenic anaerobes with antimicrobial resistance. Therefore, CMLs should periodically update themselves on the changes in the taxonomy of anaerobic bacteria and suitably inform clinicians of these changes for optimum patient care. This article presents an update on the taxonomy of clinically important anaerobic bacteria, together with the previous names or synonyms. This taxonomy update can help guide antimicrobial therapy for anaerobic bacterial infections and prevent treatment failure and can be a useful tool for both CMLs and clinicians.

• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.287-298
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• 2013

To develop a technique classifying patients based on computerized clinical data followed by validity verification by comparing with nurse's examination. Class scores were determined by nurses for a day on 348 resident patients in 7 wards of a general hospital according to KPCS-1. The class scores were simultaneously evaluated by reviewing the computerized clinical data acquired from the hospital management information system. These two class scores were both significantly different among different departments as well as disease patterns. Intraclass correlation analysis resulted a very high correlation coefficient of 0.96(p<0.01) between the two scoring methods, but the clinical data scores were somewhat higher. An automated patient classification system seemed possible to be developed in future with further enhancement of the present results based on computerized clinical data without manual scoring, which can be applied for performance evaluation as well as workforce planning.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.210-213
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• 2006

이 연구에서는 다중시기 Landsat 영상을 이용하여 강원도 일대 임상의 변화를 살펴보고 상록수의 영급을 구분하는 알고리즘을 개발하여 적용하였다. 1980년대에서 현재까지 축적된 Landsat-5와 Landsat-7영상 중에서, 대부분 지역에 활잡목 및 활엽수가 낙엽이 지고 눈이 아직 쌓이지 않을 시기인 11월에 촬영된 영상만을 이용하였다. 각 영상에서 양지바른 상록수, 활엽수, 그늘진 지역, 도시 및 바다 등을 클래스로 지정하여 감돌분류를 하였다. 분류 결과에서 양지바른 상록수만 추출하여 5개의 영상을 이진 분류체계로 조합한 후 임상의 시기적 변화 양상을 관찰한 결과, 강원대 연습림의 조림 기록 및 현황도와 상당히 일치함을 확인하였으며, Path 115, Row 34에 해당하는 강원도 일대로 연구지역을 확대하였다. 향후 Kompsat-2를 비롯한 고해상도 11월 영상이 지속적으로 촬영된다면, 이 연구에서 개발된 이진 분류체계 방법을 통하여 산림변화의 모니터링을 보다 용이하고 효율적으로 할 수 있을 것으로 기대된다.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.224-230
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• 2008

본 연구는 산림 내 임상을 구획하기 위해 고해상도 IKONOS 위성영상을 객체 지향기반으로 분할 및 분류하였다. 영상분할 시 분광정보와 공간정보를 동시에 이용하여 모양이나 분광정보에 있어서 동질한 영역이라고 정의되는 영상객체를 생성하였다. 분할된 영상을 분류계급(class)으로 분류하기 위하여 NDVI와 경사, 방위, 고도 등 지형인자를 새로운 레이어로 추가시키고, 분류개념을 형성하기 위하여 퍼지 규칙을 사용하였다. 영상의 획득시기가 5월초인 점을 감안하여 NDVI는 0.2, 경사 $^{\circ}5^{\circ}$ 그리고 고도 130m를 기준으로 산림과 비산림지역을 분류할 수 있었고, 지형인자에 영향을 많이 받는 굴참나무와 신갈나무 또한 효율적으로 분류할 수 있었다.