http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
김현명,강세병,정승호,Kim, Hyeon Myeong,Gang, Se Byeong,Jeong, Seung Ho Korean Chemical Society 2001 Bulletin of the Korean Chemical Society Vol.22 No.9
Thermoanaerobacter ethanolicus is a strictly anaerobic and thermophilic bacterium whose optimum temperature ranges over $65-68^{\circ}C.$ T. ethanolicus was known to contain a bipolar very long chain fatty acyl component such as $\alpha$, $\omega-1316-dimethyloctacosanedioate$, as one of the major membrane components. However, exact physiological role of this unusual component in the membrane remains unknown. Such a very long chain fatty acyl component, $\alpha$, ${\omega}-1316-dimethyloctacosanedioate$, dimethyl ester (DME C30), was isolated, and purified from the membrane of T. ethanolicus. As a function of added concentrations of the $\alpha$, $\omega-1316-dimethyloctacosanedioate$, dimethyl ester (DME C30) or cholesterol into the standard liposomes, the acyl chain ordering effect was investigated by the steady-state anisotropy with 1,6-diphenyl-1,3,5-hexatriene (DPH) as a fluorescent probe. Acyl chain order parameter (S) of vesicles containing DME C30 is higher comparing with phosphatidylcholine (PC) only vesicles. This result was discussed thermodynamically with the aid of the simulated annealing molecular dynamics simulations. Through the investigation of all the possible conformational changes of DME C30 or cholesterol, we showed that DME C30 is very flexible and its conformation is variable depending on the temperature comparing with cholesterol, which is rigid and restricted at overall temperature. We propose that the conformational change of DME C30, not the configurational change, may be involved in the regulation of the membrane fluidity against the changes of external temperature.
예보인자의 효과적 추출을 위한 다항식 방사형 기저 함수 신경회로망 기반 초단기 강수예측 분류기의 설계
김현명(Hyun-Myung Kim),오성권(Sung-Kwun Oh),김현기(Hyun-Ki Kim) 대한전기학회 2015 전기학회논문지 Vol.64 No.1
In this study, we develop the very short-term precipitation forecasting model as well as classifier based on polynomial radial basis function neural networks by using AWS(Automatic Weather Station) and KLAPS(Korea Local Analysis and Prediction System) meteorological data. The polynomial-based radial basis function neural networks is designed to realize precipitation forecasting model as well as classifier. The structure of the proposed RBFNNs consists of three modules such as condition, conclusion, and inference phase. The input space of the condition phase is divided by using Fuzzy C-means(FCM) and the local area of the conclusion phase is represented as four types of polynomial functions. The coefficients of connection weights are estimated by weighted least square estimation(WLSE) for modeling as well as least square estimation(LSE) method for classifier. The final output of the inference phase is obtained through fuzzy inference method. The essential parameters of the proposed model and classifier such ad input variable, polynomial order type, the number of rules, and fuzzification coefficient are optimized by means of Particle Swarm Optimization(PSO) and Differential Evolution(DE). The performance of the proposed precipitation forecasting system is evaluated by using KLAPS meteorological data.