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정선우 한국정보과학회 1984 한국정보과학회 학술발표논문집 Vol.11 No.2
This paper introduce the Fourth-Generation language(4GL) RAMIS-Ⅱ and its use as an Information Systems "Prototyping" tool, to probe the gap between demands and supply of software. Information Center concept only is not sufficient without information generation tool like RAMIS-Ⅱ which is suitable for consumer computing. Information engineering is needed to build a computerized enterprise based on today's data systems. To minimize maintenance costs in commercial data processing, an organization should minimize the need for the software engineering it performs and maximize information engineering.
정선우,김인택,박중석,이동준 7개 국립대학교 환경연구 논문집 공동발행 위원회 2004 공업기술연구 Vol.4 No.-
The community structure of benthic macroinvertebratcs in the stream of Mt. Kajisan was studied. The surveyed sites were streams of Seoknamsa area and Backyconsa area. The Collection was per-formed from May to October of 2004. as a result, total macroinvertabates collecter in the stream of Mt. Kajisan were 4 phyla, 5 classes, 15 orders, 42 families, 62 genera, 79 species and 806 individuals. From the Seoknamsa area, 4 phyla, 5 classes 14 orders, 38 families 54 genaera, 69 species and 429 individuals were identified. From the Backyconsa area, 4 phyla, 5 classes,14 order, 36 families 48 genera, 58 species and 377 individuals were found. The community analysis showed higher species diversity and species richness in Seaoknamsa area than those of Backyeonsa area. The dominant species of both areas were Semisulco-spira libertina and it occupied 10% of total individuals or more. The total species richness and species diversity of benthic macroivertcbratcs in the stream of Mt. Kajisan was 26.98 and 1.68 respectively.
정선우 昌原大學校 基礎科學硏究所 1998 基礎科學硏究所論文集 Vol.10 No.-
The distribution of aquatic and terrestrial insects form Junam reservoir was surveyed. Junam reservoir locate at Dong-Up, Changwon-city, and various insects were collected from 10 serveying sites. Specimens were collected from May to October of 1997 by various collecting methods. Total insect taxa collected from reservoirs were 10 orders, 63 families, 143 genera, 170 species and 4295 individuals. In Odonata, 6 families, 16 genera, 25 species were identified. From order Mentodea, 1 families, 2 species were identified. From Dermaptera, 2 families, 3 species were recorded. In Orthoptera, 4 families, 8 genera, 10 species were identified, and 13 families, 29 genera, 35 species were collected in Hemiptera. On Homoptera, 6 families, 8 species were collected, and 13 families, 37 species were contained in Coleoptera. In Hymenoptera, 6 families, 19 species were classified, and 8 families, 20 species were identified in Diptera. In Lepidoptera, 4 families, 10 species were classified. From the faunal data, diversity index(H′), richness index(RI), relative density(RD), and dominance to the species(D) were calculated respectively. Ischnura asiatica showed the highest dominance index, and richness index of Junam reservoir was 46.52. Diversity index was highest at the site of D.
정선우,신지원,민순재,허장욱 한국산학기술학회 2022 한국산학기술학회논문지 Vol.23 No.12
Thermal imaging cameras are mainly used at night, in bad weather, and in high-temperature environments. Their durability due to the intended long-term use is very important as failure can cause fatal damage. Recently, the demand for thermal imaging cameras used for various purposes, such as communicable disease control, fire, and failure diagnostics, has been increasing. There are many studies on failure diagnostics in systems using thermal imaging cameras. However, few studies diagnose faults specifically in thermal imaging cameras. Therefore, this study extracted data values at a temperature range of 70~90°C for the printed circuit board (PCB) and infrared (IR) lenses with high risk priority number (RPN) for the failure mode and effect analysis (FMEA) of thermal imaging cameras and confirmed that the resistance increased during the failure of the PCB module. The acquired time series data were analyzed using long short-term memory (LSTM), which is one of the deep learning techniques. The results showed that when three data accuracy errors were obtained by temperature, 0.028~4.208% higher accuracy was obtained compared to models of other systems. 열화상카메라는 주로 야간, 악천후 및 고온의 환경에서 사용되며, 고장이 발생하면 치명적인 손상을 입을 수있어 장기간 사용에 따른 내구성이 매우 중요하다. 최근, 방역, 화재 및 고장진단 등 다양한 용도로 사용되는 열화상카메라의 수요가 증가하고 있다. 열화상카메라를 이용하여 다른 시스템의 고장을 진단하는 연구는 많지만, 열화상카메라 자체의 고장진단을 연구하는 사례는 거의 없다. 따라서 본 연구에서는 열화상카메라의 FMEA(Failure Mode and Effect Analysis)에 대해 RPN(Risk Priority Number)이 높은 PCB(Printed Circuit Board) 및 적외선 렌즈(IR Lens)를 대상으로 70~90°C 온도에 따른 데이터 값을 추출하였으며, PCB 모듈의 고장 시 저항이 증가하는 것을 확인하였다. 획득한시계열 데이터를 딥러닝 기법 중 하나인 LSTM(Long Short-Term Memory)을 사용하여 분석을 진행하였다. 그 결과, 3가지의 데이터 정확도 오차를 온도별로 구하여 보았을 때, 다른 시스템의 모델에 비해 0.028~4.208%의 높은 정확도를얻었다.