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오구균 ( Koo Kyoon Oh ),김도균 ( Do Gyun Kim ),김철의 ( Chul Eui Kim ) 한국환경생태학회 2008 한국환경생태학회지 Vol.22 No.2
북한산국립공원의 현존식생 및 녹지자연도와 외래식물 분포실태를 2007년에 조사하였다. 북한산국립공원의 현존식생은 총 8개의 식물군락과 기타 지역으로 구분 되었으며, 자연림은 신갈나무군락, 신갈나무-소나무군락, 낙엽활엽수림 등 5개 식물군락으로, 조림지는 잣나무림, 리기다소나무림, 아까시나무림 등 3개 산림유형으로 구분되었다. 북한산국립공원의 녹지자연도에서 가장 넓게 분포한 것은 8등급 지역이 약 92.8%였다. 북한산국립공원안에 식재된 수목류는 총 71과 212종류이었고 이 중 자생종은 37과 67종류(31.6%), 외래종은 58과 145종류(68.4%)이었다. 북한산국립공원의 자연환경 회복, 자생식물의 다양성 유지와 자연경관을 향상시키기 위해서는 외래수종에 대한 관리가 필요하다. This study was carried out to investigate the actual vegetation and Degree of Green Naturality(DGN) and distribution of exotic plants replanted in Bukhansan National Park. The actual vegetation of the surveyed site was classified into eight plant communities and crop land, etc. Substitutional forest were classified into five plant communities; Quercus mongolica community, Q. mongolica-Pinus densiflora community, Deciduous broadleaf forest, etc. Reforested lands were classified into three forest types: P. koraiensis forest, Robinia pseudoacacia forest, P. rigida forest, etc. The area of DGN 8 consisted of 92.80% in Bukhansan National Park. The replanted plants in Bukhansan National Park was enlisted as 212 taxa, 71 families. The indigenous native among the replanted species were enlisted as 67 taxa(31.6%), 37 families. The exotic plants were enlisted as 145 taxa(68.4%), 58 families. The exotic plants needs to management that for recovery of natural environment, improvement about maintenance of multiplicity and a site of scenery about natural plants in Bukhansan National Park.
Collapse moment estimation for wall-thinned pipe bends and elbows using deep fuzzy neural networks
Yun, So Hun,Koo, Young Do,Na, Man Gyun Korean Nuclear Society 2020 Nuclear Engineering and Technology Vol.52 No.11
The pipe bends and elbows in nuclear power plants (NPPs) are vulnerable to degradation mechanisms and can cause wall-thinning defects. As it is difficult to detect both the defects generated inside the wall-thinned pipes and the preliminary signs, the wall-thinning defects should be accurately estimated to maintain the integrity of NPPs. This paper proposes a deep fuzzy neural network (DFNN) method and estimates the collapse moment of wall-thinned pipe bends and elbows. The proposed model has a simplified structure in which the fuzzy neural network module is repeatedly connected, and it is optimized using the least squares method and genetic algorithm. Numerical data obtained through simulations on the pipe bends and elbows with extrados, intrados, and crown defects were applied to the DFNN model to estimate the collapse moment. The acquired databases were divided into training, optimization, and test datasets and used to train and verify the estimation model. Consequently, the relative root mean square (RMS) errors of the estimated collapse moment at all the defect locations were within 0.25% for the test data. Such a low RMS error indicates that the DFNN model is accurate in estimating the collapse moment for wall-thinned pipe bends and elbows.
Identification of LOCA and Estimation of Its Break Size by Multiconnected Support Vector Machines
Kwae Hwan Yoo,Young Do Koo,Ju Hyun Back,Man Gyun Na Professional Technical Group on Nuclear Science 2017 IEEE transactions on nuclear science Vol.64 No.10
<P>Nuclear power plants (NPPs) are composed of very large complex systems. During transient occurrences in NPPs, operators determine the transients of the NPP through information acquired from various measuring instruments. A support vector machine (SVM) based on serial and parallel connections, termed as a multiconnected SVM, is introduced in this paper. The loss of coolant accidents (LOCAs) was identified and their break sizes are estimated using the multiconnected SVM model. The optimal parameter values of the multiconnected SVM models are obtained using a genetic algorithm. In this paper, the modular accident analysis program code was used to simulate the severe accidents occurring due to a variety of design basis accidents. The proposed algorithm uses the short time-integrated simulated sensor signals just after the reactor trip. The results show that the multiconnected SVM model can identify LOCAs and estimate their break sizes accurately. It is expected that the LOCA identification and the accurate estimation of the break size are useful for NPP operators when they try to manage severe accidents.</P>
시뮬레이션 기반 수도권 리사이클링 센터 폐냉장고 전처리 공정 개선
김도균,강민구,최진영,박기진,공만식,Kim, Do Gyun,Kang, Min Koo,Choi, Jin Young,Park, Kiejin,Kong, Man-Sik 한국시뮬레이션학회 2013 한국시뮬레이션학회 논문지 Vol.22 No.4
본 논문에서는 수도권 리사이클링 센터 폐냉장고 전처리 공정의 성능 개선 방안에 관하여 연구하였다. 이를 위해 현행 전처리 공정을 기본 작업 단위로 구분하고 공정 별 작업 시간을 측정하여 ARENA를 이용한 모델링 및 성능 분석을 수행하였다. 또한 실무자의 경험과 선진 사례 등에 기반한 3가지 새로운 전처리 공정 대안을 생성한 후 ARENA를 이용한 모델링 및 시뮬레이션을 통해 평가하였다. 최종 선정된 대안은 현행 2-라인 공정을 1-라인으로 전환하고, 냉매를 회수하기 위한 별도의 셀라인을 도입하는 것을 포함한다. 본 연구 결과는 현재 새로운 전처리 공정 라인으로 설치되어 운영되고 있으며, 이전 공정에 비해 사이클 타임과 생산성 향상뿐만 아니라, 공간 활용률 측면에서도 크게 개선된 결과를 보였다. This paper studies the improvement of a pre-processing line for waste refrigerators in a metropolitan recycling center(MRC). We performed ARENA modeling and simulation by using work analysis and time measurement on the current processing line. Combined this result with some practical experiences from workers, we generated 3 alternatives to improve the current line and evaluated them by using ARENA simulation. The final decision selected consists of changing 2-line process into 1-line and having a separate cell-line for collecting refrigerant. Currently, the result of this study was applied to MRC by improving cycle time, throughput, and space utilization compared to the previous one.