http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
산촌개발사업 조성이후의 마을시설현황에 따른 주민만족도 평가에 관한 연구
이상홍,신용호,서희숙,Lee Sang-Hong,Shin Yong-Ho,Seo Hee-Sook 한국주거학회 2006 한국주거학회 논문집 Vol.17 No.3
The purpose of this study is to grasp effectiveness of Mountain Village Package Development Work. Mountain Village Package Development Work was started for the welfare and economic activation plan of residents of village from 1995. We want to grasp a practical economic and welfare of residents of village through the village that is enforced Mountain Village Package Development Work after two-three years. The process of this study is as follows. First, we select the village that Mountain Village Package Development Work finished. Second, we grasp the status of Mountain Village Package Development Work through a field study on destination village. Third, we investigate the resident satisfaction.
이상홍,Lee, Sang-Hong 한국과학기술정보연구원 2002 지식정보인프라 Vol.9 No.-
세계가 하나의 시장으로 통합되면서 국제표준을 의무적으로 수용할 것을 요구하는 등 어느때 보다는 기술표준의 역할 및 중요성이 부가되고 있는 현실이다. 따라서 세계 각국 및 기업들은 이에 발맞추어 통신시장을 선점하는 전략적 도구로서 표준화 활동을 보다 강화하기 위해 국제표준화기구 혹은 포럼 등에 보다 전략적으로 표준화활동을 하고 있는 실정이다.
이상홍 ( Sang-hong Lee ),임준식 ( Joon S. Lim ),신동근 ( Dong-kun Shin ) 한국인터넷정보학회 2010 인터넷정보학회논문지 Vol.11 No.6
본 논문은 걸음걸이 분석 기반의 특징 추출과 NEWFM(Neural Network with Weighted Fuzzy Membership Functions)을 이용하여 건강한 사람의 족압(foot pressure)과 파킨슨병 환자의 족압으로부터 건강한 사람과 파킨슨병 환자를 분류하는 방안을 제시하고 있다. NEWFM에서 사용할 입력을 추출하기 위해서 첫 번째 단계에서는 PhysioBank에서 제공하는 족압 데이터와 시간에 따른 족압의 변화를 이용하여 각각 4개의 특징을 추출하였다. 두 번째 단계에서는 웨이블릿 변환(wavelet transform, WT)을 이용하여 이전 단계에서 추출한 8개의 특징으로부터 웨이블릿 계수를 추출하였다. 마지막 단계에서는 추출된 웨이블릿 계수들을 이용하여 통계적 기법인 주파수 분포와 주파수 변동량을 이용하여 40개의 입력을 추출하였다. NEWFM은 족압 데이터로부터 8개의 특징을 추출하여 건강한 사람과 파킨슨병 환자를 분류하였을 때 왼쪽 족압과 오른쪽 족압의 차를 이용한 특징과 시간에 따른 족압의 변화에 대한 차를 이용한 특징의 경우에 높은 정확도(accuracy)가 나타났다. 이러한 결과를 통하여 걸음걸이에 있어서 질질 끄는 특징을 보이는 파킨슨병 환자의 양쪽 족압의 차가 건강한 사람의 양쪽 족압의 차보다는 상대적으로 적다는 특징을 본 실험을 통해 확인할 수 있었다. This paper presents a measure to classify healthy persons and Parkinson disease patients from the foot pressure of healthy persons and that of Parkinson disease patients using gait analysis based characteristics extraction and Neural Network with Weighted Fuzzy Membership Functions (NEWFM). To extract the inputs to be used in NEWFM, in the first step, the foot pressure data provided by the PhysioBank and changes in foot pressure over time were used to extract four characteristics respectively. In the second step, wavelet coefficients were extracted from the eight characteristics extracted from the previous stage using the wavelet transform (WT). In the final step, 40 inputs were extracted from the extracted wavelet coefficients using statistical methods including the frequency distribution of signals and the amount of variability in the frequency distribution. NEWFM showed high accuracy in the case of the characteristics obtained using differences between the left foot pressure and the right food pressure and in the case of the characteristics obtained using differences in changes in foot pressure over time when healthy persons and Parkinson disease patients were classified by extracting eight characteristics from foot pressure data. Based on these results, the fact that differences between the left and right foot pressures of Parkinson disease patients who show a characteristic of dragging their feet in gaits were relatively smaller than those of healthy persons could be identified through this experiment.
인공 고관절 전치환술에서 Harris-Galant형 반구 포말형 비구컵의 방사선학적 분석
이상홍 ( Sang Hong Lee ),위윤철 ( Yuen Chul Wee ),표영배 ( Young Bae Pyo ),하상호 ( Sang Ho Ha ),김영철 ( Young Chul Kim ) 대한고관절학회 1996 Hip and Pelvis Vol.8 No.2
The results of fifty-five consecutive primary total hip arthroplasties in which a Harris-Galante porous coated acetabular component had been used were reviewed after a minimum of five years. Areas of gaps between the porous mesh at the periphery of the acetabular component and the bone were seen on the immediate postoperative radiographs of twenty-eight hips(50.9%). At the second year follow up, a radiolucent line was found in twenty-six hips(47.3%). These radiolucent lines were never wider than one milimeter and were most frequently located in zone I[. At the time of last follow-up evaluation, a progressive radiolucent line was identified around eight hips and a discontinous radiolucent line was present in all three zones around eleven. The continous radiolucent line was identified in two hips. A progressive radiolucent line developed around only two of twenty-seven hips that were not associated with an initial peripheral gap, but a progressive radiolucent line developed around six of the twenty-eight hips that were associated with such a gap. An initial peripheral gap will subsequently show a progressive radiolucent line. None of the acetabular components were loose, however, six acetabular component were revised due to dissociation of polyethylene liner, postoperative femoral fractures and revision of femoral stem in two hips each. In six cases of the author's experience with revision, bone ingrowth around the porous surface of acetabular components was well developed. Our experience with Harris-Galante porous coated acetabular components suggests that the tech- nique used for implantation may be important not only for initial fixation and ingrowth of bone, but also, more importantly, for the design of the liner-metal back locking mechanisms.
KOSPI 예측을 위한 NEWFM 기반의 특징입력 및 퍼지규칙 추출
이상홍 ( Sang-hong Lee ),임준식 ( Joon S. Lim ) 한국인터넷정보학회 2008 인터넷정보학회논문지 Vol.9 No.1
본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted Fuzzy Membership Functions, NEWFM)을 사용하여 생성된 퍼지규칙과 비중복면적 분산 측정법에 의해 추출된 최소의 특징입력을 이용하여, 1일 후의 KOSPI 예측을 하는 방안을 제안하고 있다. NEWFM은 KOSPI의 최근 32일 동안의 CPPn,m(Current Price Position of day n for n-1 to n-m days)을 이용하여 1일 후의 KOSPI 상승과 하락을 예측한다. 특징입력으로써 CPPn,m과 최근 32일간의 CPPn,m을 웨이블릿 변환한 38개의 계수들 중 비중복면적 분산 측정법을 적용하여 추출된 5개의 계수가 사용되었다. 제안된 방법으로 1991년부터 1998년까지의 실험군을 사용한 결과 평균 67.62%의 예측율을 나타내었다. This paper presents a methodology to forecast KOSPI index by extracting fuzzy rules based on the neural network with weighted fuzzy membership functions (NEWFM) and the minimized number of input features using the distributed non-overlap area measurement method. NEWFM classifies upward and downward cases of KOSPI using the recent 32 days of CPPn,m (Current Price Position of day n for n-1 to n-m days) of KOSPI. The five most important input features among CPPn,m and 38 wavelet transformed coefficients produced by the recent 32 days of CPPn,m are selected by the non-overlap area distribution measurement method. For the data sets, from 1991 to 1998, the proposed method shows that the average of forecast rate is 67.62%.