Перегляд вихідного коду сторінки A-1210477 Principles Explained

By taking a flood in 1982 as an example, one can depict the flood process as shown in Fig. 2(a and b). Although there were three floods in 1982, flood series II has the biggest peak flood (2520?m3/s), which is much bigger than that of flood series I and III (Fig. 2a). Second, the beginning and end times are identified as following: the beginning (or end) time is the nearest bottom point on the left (or right) of the peak flow, as shown in Fig. 2(b). At the same time, the duration of this flood event can be measured as the time difference between points A and B. It can be seen that the flood [http://www.selleckchem.com/products/ldn193189.html LDN-193189 price] duration of this event is 330?h, spanning from May 10th, 8:00 (point A) to May 24th, 2:00 (point B) in Fig. 2(b). We only take the calculation of the maximum 72-h flood volume as an example. For a hypothetical flow process given in Fig. 3, the 72-h flood volume ((V��72)Ts(V��72)Ts) is determined by equation(1) (V��72)Ts=��TsTeQj?dt=��j=TsTeQjwhere QjQj is the observed discharge of the j ?th hour for a flood event, Ts=1,2,��,t?71Ts=1,2,��,t?71 and t ? is no more than the flood duration. Te?TsTe?Ts is 72?h. So, the maximum 72-h flood volume (V ?72) is defined by V72=max(V��72)TsV72=max(V��72)Ts. The calculation of V24, the maximum 24-h flood volume, is similar to that of V72. The clustering method can be used to detect [http://en.wikipedia.org/wiki/Diglyceride diglyceride] the similar groups among [http://www.selleckchem.com/products/a-1210477.html A-1210477 clinical trial] observed floods and therefore the results exhibit high internal (within-cluster) homogeneity of the flood. The fuzzy c-means algorithm (FCM), originally introduced by Dunn (1973) and improved by Bezdek (1981), is one of the most widely used fuzzy clustering algorithms. The algorithm is based on minimization of the following objective function equation(2) Jm(U,V)=��i=1n��j=1kuijmdij2,?for???1��m�ܡ�with dij2=||xj?vi||2 and U=uij;v=(v1,v2,��,vn)U=uij;v=(v1,v2,��,vn), where uijuij is the degree of membership of xjxj in the i ?th cluster, vivi is the ith cluster center, ||*|| is a norm expression of the similarity between any measured data and the center. m is any real number of greater than 1 Fuzzy partition is carried out through an iterative optimization of Eq. (2) by updating membership uijuij and the cluster center vivi via equation(3) uij=��j=1kdikdjk2/(m?1)?1?(i=1,2,��,n;??????j=1,2,��,k) equation(4) vi=��i=1nuijmxj��i=1nuijm The iteration stops when maxijuijk+1?uijk