传统的石油化工过程建模中仅使用静态数据,而未能充分考虑连续生产过程中时序信息对建模指标的影响。本文提出了一种静态与时序数据组合网络(CNSS)模型,使用前馈神经网络提取静态数据的信息,使用Bi-LSTM(Bidirectional-Long Short Term ...传统的石油化工过程建模中仅使用静态数据,而未能充分考虑连续生产过程中时序信息对建模指标的影响。本文提出了一种静态与时序数据组合网络(CNSS)模型,使用前馈神经网络提取静态数据的信息,使用Bi-LSTM(Bidirectional-Long Short Term Memory)和自注意力机制提取操作变量时序数据中的信息,其中Bi-LSTM提取操作变量在时序逻辑上的信息,自注意力机制提取操作变量之间的交叉信息,通过静态和时序数据信息的组合以获得更好的模型预测性能;并使用CNSS模型分别对S Zorb装置精制汽油辛烷值(RON)、催化裂化烟气脱硝系统氮氧化物(NO_(x))的出口质量浓度进行预测,结果表明:CNSS模型的预测精度明显高于仅使用静态数据的机器学习模型,其对精制汽油RON预测的平均绝对误差和平均绝对百分比误差分别为0.1091、0.12%,对NO_(x)出口质量浓度预测的平均绝对误差和平均绝对百分比误差分别为2.4430 mg/m3、5.60%。对于因工艺参数波动较大而需要考虑时序信息的石油化工过程,CNSS模型可以为其建立机器学习模型提供重要参考。展开更多
针对目前网络谣言鉴别研究,文本学习往往会受到文本读入内容过长导致长距离信息丢失或者是为了捕捉局部信息而依赖于长期输入表示从而影响鉴别结果。通过提出S-LSTM(sentence-state long short term memory networks)算法在保留字词节...针对目前网络谣言鉴别研究,文本学习往往会受到文本读入内容过长导致长距离信息丢失或者是为了捕捉局部信息而依赖于长期输入表示从而影响鉴别结果。通过提出S-LSTM(sentence-state long short term memory networks)算法在保留字词节点信息的同时对句子进行聚合,从而保留句子的局部和全局信息,进而提升网络谣言鉴别的精确性和有效性。与TextGCN、Bi-GCN、Att_BiLSTM等几种深度网络谣言鉴别方法的对比中,该方法在两组模型测试上的准确率分别达到78.87%、90.30%,均取得了不错的效果,在考虑句子全局信息的情况下,其对谣言鉴别效果会有不错的提升。展开更多
A long short-term memory(LSTM)neural network has excellent learning ability applicable to time series of nuclear pulse signals.It can accurately estimate parameters associated with amplitude,time,and so on,in digitall...A long short-term memory(LSTM)neural network has excellent learning ability applicable to time series of nuclear pulse signals.It can accurately estimate parameters associated with amplitude,time,and so on,in digitally shaped nuclear pulse signals—especially signals from overlapping pulses.By learning the mapping relationship between Gaussian overlapping pulses after digital shaping and exponential pulses before shaping,the shaping parameters of the overlapping exponential nuclear pulses can be estimated using the LSTM model.Firstly,the Gaussian overlapping nuclear pulse(ONP)parameters which need to be estimated received Gaussian digital shaping treatment,after superposition by multiple exponential nuclear pulses.Secondly,a dataset containing multiple samples was produced,each containing a sequence of sample values from Gaussian ONP,after digital shaping,and a set of shaping parameters from exponential pulses before digital shaping.Thirdly,the Training Set in the dataset was used to train the LSTM model.From these datasets,the values sampled from the Gaussian ONP were used as the input data for the LSTM model,and the pulse parameters estimated by the current LSTM model were calculated by forward propagation.Next,the loss function was used to calculate the loss value between the network-estimated pulse parameters and the actual pulse parameters.Then,a gradient-based optimization algorithm was applied,to feedback the loss value and the gradient of the loss function to the neural network,to update the weight of the LSTM model,thereby achieving the purpose of training the network.Finally,the sampled value of the Gaussian ONP for which the shaping parameters needed to be estimated was used as the input data for the LSTM model.After this,the LSTM model produced the required nuclear pulse parameter set.In summary,experimental results showed that the proposed method overcame the defect of local convergence encountered in traditional methods and could accurately extract parameters from multiple,severely overlapping Gaussian pulses,to achieve optimal estimation of nuclear pulse parameters in the global sense.These results support the conclusion that this is a good method for estimating nuclear pulse parameters.展开更多
文摘传统的石油化工过程建模中仅使用静态数据,而未能充分考虑连续生产过程中时序信息对建模指标的影响。本文提出了一种静态与时序数据组合网络(CNSS)模型,使用前馈神经网络提取静态数据的信息,使用Bi-LSTM(Bidirectional-Long Short Term Memory)和自注意力机制提取操作变量时序数据中的信息,其中Bi-LSTM提取操作变量在时序逻辑上的信息,自注意力机制提取操作变量之间的交叉信息,通过静态和时序数据信息的组合以获得更好的模型预测性能;并使用CNSS模型分别对S Zorb装置精制汽油辛烷值(RON)、催化裂化烟气脱硝系统氮氧化物(NO_(x))的出口质量浓度进行预测,结果表明:CNSS模型的预测精度明显高于仅使用静态数据的机器学习模型,其对精制汽油RON预测的平均绝对误差和平均绝对百分比误差分别为0.1091、0.12%,对NO_(x)出口质量浓度预测的平均绝对误差和平均绝对百分比误差分别为2.4430 mg/m3、5.60%。对于因工艺参数波动较大而需要考虑时序信息的石油化工过程,CNSS模型可以为其建立机器学习模型提供重要参考。
文摘针对目前网络谣言鉴别研究,文本学习往往会受到文本读入内容过长导致长距离信息丢失或者是为了捕捉局部信息而依赖于长期输入表示从而影响鉴别结果。通过提出S-LSTM(sentence-state long short term memory networks)算法在保留字词节点信息的同时对句子进行聚合,从而保留句子的局部和全局信息,进而提升网络谣言鉴别的精确性和有效性。与TextGCN、Bi-GCN、Att_BiLSTM等几种深度网络谣言鉴别方法的对比中,该方法在两组模型测试上的准确率分别达到78.87%、90.30%,均取得了不错的效果,在考虑句子全局信息的情况下,其对谣言鉴别效果会有不错的提升。
基金supported by the National Natural Science Foundation of China(Nos.41774140 and 11675028)the Scientific Research Fund of Sichuan Provincial Education Department(No.18ZA0050)the Scientific Research Innovation Team of Chengdu University of Technology(No.10912-KYTD201701)
文摘A long short-term memory(LSTM)neural network has excellent learning ability applicable to time series of nuclear pulse signals.It can accurately estimate parameters associated with amplitude,time,and so on,in digitally shaped nuclear pulse signals—especially signals from overlapping pulses.By learning the mapping relationship between Gaussian overlapping pulses after digital shaping and exponential pulses before shaping,the shaping parameters of the overlapping exponential nuclear pulses can be estimated using the LSTM model.Firstly,the Gaussian overlapping nuclear pulse(ONP)parameters which need to be estimated received Gaussian digital shaping treatment,after superposition by multiple exponential nuclear pulses.Secondly,a dataset containing multiple samples was produced,each containing a sequence of sample values from Gaussian ONP,after digital shaping,and a set of shaping parameters from exponential pulses before digital shaping.Thirdly,the Training Set in the dataset was used to train the LSTM model.From these datasets,the values sampled from the Gaussian ONP were used as the input data for the LSTM model,and the pulse parameters estimated by the current LSTM model were calculated by forward propagation.Next,the loss function was used to calculate the loss value between the network-estimated pulse parameters and the actual pulse parameters.Then,a gradient-based optimization algorithm was applied,to feedback the loss value and the gradient of the loss function to the neural network,to update the weight of the LSTM model,thereby achieving the purpose of training the network.Finally,the sampled value of the Gaussian ONP for which the shaping parameters needed to be estimated was used as the input data for the LSTM model.After this,the LSTM model produced the required nuclear pulse parameter set.In summary,experimental results showed that the proposed method overcame the defect of local convergence encountered in traditional methods and could accurately extract parameters from multiple,severely overlapping Gaussian pulses,to achieve optimal estimation of nuclear pulse parameters in the global sense.These results support the conclusion that this is a good method for estimating nuclear pulse parameters.
