Accurate insight into the heat generation rate(HGR) of lithium-ion batteries(LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance.For this reason,this pap...Accurate insight into the heat generation rate(HGR) of lithium-ion batteries(LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance.For this reason,this paper proposes a novel physics-informed neural network(PINN) approach for HGR estimation of LIBs under various driving conditions.Specifically,a single particle model with thermodynamics(SPMT) is first constructed for extracting the critical physical knowledge related with battery HGR.Subsequently,the surface concentrations of positive and negative electrodes in battery SPMT model are integrated into the bidirectional long short-term memory(BiLSTM) networks as physical information.And combined with other feature variables,a novel PINN approach to achieve HGR estimation of LIBs with higher accuracy is constituted.Additionally,some critical hyperparameters of BiLSTM used in PINN approach are determined through Bayesian optimization algorithm(BOA) and the results of BOA-based BiLSTM are compared with other traditional BiLSTM/LSTM networks.Eventually,combined with the HGR data generated from the validated virtual battery,it is proved that the proposed approach can well predict the battery HGR under the dynamic stress test(DST) and worldwide light vehicles test procedure(WLTP),the mean absolute error under DST is 0.542 kW/m^(3),and the root mean square error under WLTP is1.428 kW/m^(3)at 25℃.Lastly,the investigation results of this paper also show a new perspective in the application of the PINN approach in battery HGR estimation.展开更多
In recent years,Deep Learning models have become indispensable in several fields such as computer vision,automatic object recognition,and automatic natural language processing.The implementation of a robust and effici...In recent years,Deep Learning models have become indispensable in several fields such as computer vision,automatic object recognition,and automatic natural language processing.The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field,especially for the Arabic language,which,compared to other languages,has a dearth of published works.In this work,we presented an efficient and new system for offline Arabic handwritten text recognition.Our new approach is based on the combination of a Convolutional Neural Network(CNN)and a Bidirectional Long-Term Memory(BLSTM)followed by a Connectionist Temporal Classification layer(CTC).Moreover,during the training phase of the model,we introduce an algorithm of data augmentation to increase the quality of data.Our proposed approach can recognize Arabic handwritten texts without the need to segment the characters,thus overcoming several problems related to this point.To train and test(evaluate)our approach,we used two Arabic handwritten text recognition databases,which are IFN/ENIT and KHATT.The Experimental results show that our new approach,compared to other methods in the literature,gives better results.展开更多
双向长短时记忆(bidirectional lorg short term memory,BLSTM)是一种特殊的递归神经网络(recurrent neural network,RNN),能够有效地对语音的长时上下文进行建模。该文提出一种基于深度BLSTM的语音驱动面部动画合成方法,利用说话人的...双向长短时记忆(bidirectional lorg short term memory,BLSTM)是一种特殊的递归神经网络(recurrent neural network,RNN),能够有效地对语音的长时上下文进行建模。该文提出一种基于深度BLSTM的语音驱动面部动画合成方法,利用说话人的音视频双模态信息训练BLSTM-RNN神经网络,采用主动外观模型(active appearance model,AAM)对人脸图像进行建模,将AAM模型参数作为网络输出,研究网络结构和不同语音特征输入对动画合成效果的影响。基于LIPS2008标准评测库的实验结果表明:具有BLSTM层的网络效果明显优于前向网络的,基于BLSTM-前向-BLSTM 256节点(BFB256)的三层模型结构的效果最佳,FBank、基频和能量组合可以进一步提升动画合成效果。展开更多
基金funded by the Artificial Intelligence Technology Project of Xi’an Science and Technology Bureau in China(No.21RGZN0014)。
文摘Accurate insight into the heat generation rate(HGR) of lithium-ion batteries(LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance.For this reason,this paper proposes a novel physics-informed neural network(PINN) approach for HGR estimation of LIBs under various driving conditions.Specifically,a single particle model with thermodynamics(SPMT) is first constructed for extracting the critical physical knowledge related with battery HGR.Subsequently,the surface concentrations of positive and negative electrodes in battery SPMT model are integrated into the bidirectional long short-term memory(BiLSTM) networks as physical information.And combined with other feature variables,a novel PINN approach to achieve HGR estimation of LIBs with higher accuracy is constituted.Additionally,some critical hyperparameters of BiLSTM used in PINN approach are determined through Bayesian optimization algorithm(BOA) and the results of BOA-based BiLSTM are compared with other traditional BiLSTM/LSTM networks.Eventually,combined with the HGR data generated from the validated virtual battery,it is proved that the proposed approach can well predict the battery HGR under the dynamic stress test(DST) and worldwide light vehicles test procedure(WLTP),the mean absolute error under DST is 0.542 kW/m^(3),and the root mean square error under WLTP is1.428 kW/m^(3)at 25℃.Lastly,the investigation results of this paper also show a new perspective in the application of the PINN approach in battery HGR estimation.
文摘In recent years,Deep Learning models have become indispensable in several fields such as computer vision,automatic object recognition,and automatic natural language processing.The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field,especially for the Arabic language,which,compared to other languages,has a dearth of published works.In this work,we presented an efficient and new system for offline Arabic handwritten text recognition.Our new approach is based on the combination of a Convolutional Neural Network(CNN)and a Bidirectional Long-Term Memory(BLSTM)followed by a Connectionist Temporal Classification layer(CTC).Moreover,during the training phase of the model,we introduce an algorithm of data augmentation to increase the quality of data.Our proposed approach can recognize Arabic handwritten texts without the need to segment the characters,thus overcoming several problems related to this point.To train and test(evaluate)our approach,we used two Arabic handwritten text recognition databases,which are IFN/ENIT and KHATT.The Experimental results show that our new approach,compared to other methods in the literature,gives better results.
文摘双向长短时记忆(bidirectional lorg short term memory,BLSTM)是一种特殊的递归神经网络(recurrent neural network,RNN),能够有效地对语音的长时上下文进行建模。该文提出一种基于深度BLSTM的语音驱动面部动画合成方法,利用说话人的音视频双模态信息训练BLSTM-RNN神经网络,采用主动外观模型(active appearance model,AAM)对人脸图像进行建模,将AAM模型参数作为网络输出,研究网络结构和不同语音特征输入对动画合成效果的影响。基于LIPS2008标准评测库的实验结果表明:具有BLSTM层的网络效果明显优于前向网络的,基于BLSTM-前向-BLSTM 256节点(BFB256)的三层模型结构的效果最佳,FBank、基频和能量组合可以进一步提升动画合成效果。
