answer vector generation based on question上引入了 Attention Model。 QA representation independently [×] -> attention for ans generation 当biLSTM需要在长距离QA中传递依赖时, fixed width of hidden vectors 成为了瓶颈 Attention model针对这个问题,采用 使更多对回答问题 有信息价值的部分 动态排列起来。 3.4...
Then, the LSTM model was used to train the differences between the Tm values obtained by discrete integration of the ERA5 data and Tm values calculated by the ERATM model to enhance the accuracy of the ERATM model. We use the ERA5 and sounding data from 2021 to 2022 to analyze the ...
(2) Road features encoded by the LSTM model are designed to address the interaction with the road, static obstacles, etc. (3) The focal attention mechanism is employed to improve LSTM by focusing on more relevant features. (4) HNU dataset is constructed to evaluate the applicability of the ...
CLKA with the predicted settlement in good agreement with measured data. The model is verified generic and can thus be applied to similar projects. A Graphical User Interface is finally developed to make the LSTM-based model available for engineering practice. Author information Authors and Affiliati...
1 (ASM1) model. Liu et al. [11] demonstrated the effectiveness of ASM1-based MPC in controlling ammonia nitrogen in WWTPs. However, MPC requires a detailed model whose modeling process is complex due to the nonlinear and time-varying nature of the bioreaction system and the influence of ...
In all these processes in which model predictive control is applied, mathematical models describing the relationship between manipulated inputs and process outputs are essential for building model-based control systems for industrial applications [9]. However, due to the complexity of the physical and ...
(=1) * n_hidden]59model = torch.mm(output, self.W) + self.b#model : [batch_size, n_class]60returnmodel61else:62X = X.transpose(0, 1)#X : [n_step, batch_size, n_class]63outputs, hidden =self.rnn(X, hidden)64#outputs : [n_step, batch_size, num_directions(=1) * n_...
We used the advanced models in trajectory prediction as the comparison models, such as LSTM, support vector machine (SVM), back propagation (BP) neural network, Hidden Markov Model (HMM), and convolutional long-term memory neural network (CNN-LSTM). The model we proposed is superior to the ...
For validation, all image blocks of the year 2017 were given to the model, and the resultant images were compared with the image blocks of the year 2019, as shown in Fig. 6. In this study, experimental results were conducted using Amazon Discussions In this paper, we have proposed a ...
The first layers for spatial feature learning are similar to the 3DCNN_C-LSTM model. After the 3D convolutional layers, a global average pooling layer is added to yield a 1D vector with the length of the input time-series. One 1D Convolution is applied on this vector with the learned ...