N in Figure that is related to a multiPlace the obtained
N in Figure that is equivalent to a multiPlace the obtained load time history as shown in Figure 2,two, that is related to a multilayer roof.Assuming that raindrops flow down the multi-layer roof from point O, the layer roof. Assuming that raindrops flow down the multi-layer roof from point O, the counting result as well as the rain flow counting rule corresponding to the anxiety train recounting result and also the rain flow counting rule corresponding for the anxiety train response are as follows: sponse are as follows: (1) (1) InIn the method of raindrops flowing down the roof, if there’s noroof blocking, the the approach of raindrops flowing down the roof, if there’s no roof blocking, the raindrops will continue to flow down until it stops; raindrops will continue to flow down till it stops; (two) Raindrops that start the peak load point will end when they encounter a peak load (2) Raindrops that start off atat the peak load point will finish when they encounter a peak load point higher than it; point greater than it; (3) Raindrops that get started at the load valley may also finish when they encounter aa load valley Raindrops that begin in the load valley may also finish after they encounter load valley (three) decrease than it; reduced than it; (4) When raindrops flow, they quit when they encounter rain stream from the roof roof (4) When raindrops flow, they cease after they encounter CFT8634 site thethe rain stream from theabove. The path that the raindrops flow in the beginning point to the ending point represents above. the The path that the from o data sets in all load cycles can to the ending point repreload cycles. The raindrops flow in the beginning point be calculated to get the amplitude and mean worth information sets. The amplitude plus the mean worth can respectively sents the load cycles. The from o information sets in all load cycles might be calculated to acquire represent the load cycles extracted in the rain flow count. The amplitude (Sa ) and mean the amplitude and mean worth data sets. The amplitude along with the mean worth can respec(Sm ) value on the load cycle is often expressed as: tively represent the load cycles extracted in the rain flow count. The amplitude and mean worth in the load cycle can maxexpressed as: S be – Smin Sa = (1) two – (1) = 2 Smax Smin Sm = (2)Appl. Sci. 2021, 11, x FOR PEER REVIEWAppl. Sci. 2021, 11, x FOR PEER REVIEWAppl. Sci. 2021, 11,4 of== 4 of(two)Figure two. Load-time history.Figure Load-time history. Figure 2. 2. Load-time history.2.two. LSTM two.2. LSTMLSTM can predict the future by extracting the obvious qualities from the collected LSTM can predict the future by extracting the apparent traits from the collected information set, and can predict the futuremost feasible tools thethe task of data prediction. data set, LSTMit hasbecome probably the most extracting the clear qualities in the c and it has develop into among the by feasible tools in in process of data prediction. LSTM set, and it hasof recurrentneural the most feasible enhanced sort style of RNN information a unique form recurrent neural network, which can be an improved of RNN LSTM is is usually a specialtype ofbecome one of network, which is an tools in the process of data pr network.can be a particular type of recurrent neural network, which it could save far more type LSTM Via the deliberately designed neural LY294002 Epigenetics network structure, is it enhanced network. By way of the deliberately developed neural network structure,an can save extra long-term information, which solves the issue of model failure as a result of gradient explosion network. T.