Images are complex multimedia data which contain rich semantic information.Most of current image description generator algorithms only generate plain description,with the lack of distinction between primary and second...Images are complex multimedia data which contain rich semantic information.Most of current image description generator algorithms only generate plain description,with the lack of distinction between primary and secondary object,leading to insufficient high-level semantic and accuracy under public evaluation criteria.The major issue is the lack of effective network on high-level semantic sentences generation,which contains detailed description for motion and state of the principal object.To address the issue,this paper proposes the Attention-based Feedback Long Short-Term Memory Network(AFLN).Based on existing codec framework,there are two independent sub tasks in our method:attention-based feedback LSTM network during decoding and the Convolutional Block Attention Module(CBAM)in the coding phase.First,we propose an attentionbased network to feedback the features corresponding to the generated word from the previous LSTM decoding unit.We implement feedback guidance through the related field mapping algorithm,which quantifies the correlation between previous word and latter word,so that the main object can be tracked with highlighted detailed description.Second,we exploit the attention idea and apply a lightweight and general module called CBAM after the last layer of VGG 16 pretraining network,which can enhance the expression of image coding features by combining channel and spatial dimension attention maps with negligible overheads.Extensive experiments on COCO dataset validate the superiority of our network over the state-of-the-art algorithms.Both scores and actual effects are proved.The BLEU 4 score increases from 0.291 to 0.301 while the CIDEr score rising from 0.912 to 0.952.展开更多
In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dime...In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dimensional batch normalization visual geometry group(3D-BN-VGG)and long short-term memory(LSTM)network is designed.In this network,3D convolutional layer is used to extract the spatial domain features and time domain features of video sequence at the same time,multiple small convolution kernels are stacked to replace large convolution kernels,thus the depth of neural network is deepened and the number of network parameters is reduced.In addition,the latest batch normalization algorithm is added to the 3-dimensional convolutional network to improve the training speed.Then the output of the full connection layer is sent to LSTM network as the feature vectors to extract the sequence information.This method,which directly uses the output of the whole base level without passing through the full connection layer,reduces the parameters of the whole fusion network to 15324485,nearly twice as much as those of 3D-BN-VGG.Finally,it reveals that the proposed network achieves 96.5%and 74.9%accuracy in the UCF-101 and HMDB-51 respectively,and the algorithm has a calculation speed of 1066 fps and an acceleration ratio of 1,which has a significant predominance in velocity.展开更多
Monitoring and predicting of urban surface subsidence are important for urban disaster prevention and mitigation.In this paper,the Long Short-Term Memory(LSTM)network was used to predict the surface subsidence process...Monitoring and predicting of urban surface subsidence are important for urban disaster prevention and mitigation.In this paper,the Long Short-Term Memory(LSTM)network was used to predict the surface subsidence process of Changchun City from 2018 to 2020 based on PS-InSAR monitoring data.The results show that the prediction error of 57.89% of PS points in the LSTM network was less than 1mm with the average error of 1.8 mm and the standard deviation of 2.8 mm.The accuracy and reliability of the prediction were better than regression analysis,time series analysis and grey model.展开更多
Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,w...Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.展开更多
针对飞行器飞行试验中外测级间段数据缺失和精度不高的问题,提出了基于长短期记忆(long-short term memory,LSTM)网络的外测级间段数据预测方法。利用遥测视速度数据和外测融合数据建立LSTM网络回归模型,将外测级间段数据作为缺失数据...针对飞行器飞行试验中外测级间段数据缺失和精度不高的问题,提出了基于长短期记忆(long-short term memory,LSTM)网络的外测级间段数据预测方法。利用遥测视速度数据和外测融合数据建立LSTM网络回归模型,将外测级间段数据作为缺失数据进行预测插值,可将制导工具系统误差以及飞行器初始误差,包括遥外测时间对不准误差,一并利用回归网络表示,从而将遥测视速度数据作为网络输入,得到外测级间段的预测数据。试验数据处理结果证明,基于LSTM网络获得的外测级间段预测数据满足精度要求,所提方法具有实际应用价值。展开更多
针对现有基于深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法的再入制导方法计算精度较差,对强扰动条件适应性不足等问题,在DDPG算法训练框架的基础上,提出一种基于长短期记忆-DDPG(long short term memory-DDPG,LST...针对现有基于深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法的再入制导方法计算精度较差,对强扰动条件适应性不足等问题,在DDPG算法训练框架的基础上,提出一种基于长短期记忆-DDPG(long short term memory-DDPG,LSTM-DDPG)的再入制导方法。该方法采用纵、侧向制导解耦设计思想,在纵向制导方面,首先针对再入制导问题构建强化学习所需的状态、动作空间;其次,确定决策点和制导周期内的指令计算策略,并设计考虑综合性能的奖励函数;然后,引入LSTM网络构建强化学习训练网络,进而通过在线更新策略提升算法的多任务适用性;侧向制导则采用基于横程误差的动态倾侧反转方法,获得倾侧角符号。以美国超音速通用飞行器(common aero vehicle-hypersonic,CAV-H)再入滑翔为例进行仿真,结果表明:与传统数值预测-校正方法相比,所提制导方法具有相当的终端精度和更高的计算效率优势;与现有基于DDPG算法的再入制导方法相比,所提制导方法具有相当的计算效率以及更高的终端精度和鲁棒性。展开更多
建立了北方苍鹰算法优化长短期记忆神经网络(northern goshawk optimization-long short term memory,NGO-LSTM)的预测模型。以深圳市共享单车为例,首先对共享单车数据进行预处理,以Geohash算法为基础将骑行的时变数据作为特征输入;然...建立了北方苍鹰算法优化长短期记忆神经网络(northern goshawk optimization-long short term memory,NGO-LSTM)的预测模型。以深圳市共享单车为例,首先对共享单车数据进行预处理,以Geohash算法为基础将骑行的时变数据作为特征输入;然后采用Canopy算法结合K-means聚类算法将深圳市地铁站进行聚类分析,以此发掘不同类型站点骑行规律;最后在此基础上建立了NGO-LSTM预测模型对站点的需求量进行预测分析,并与其他模型进行对比。实验结果表明,NGO-LSTM模型的决定系数达到0.90。展开更多
基金This research study is supported by the National Natural Science Foundation of China(No.61672108).
文摘Images are complex multimedia data which contain rich semantic information.Most of current image description generator algorithms only generate plain description,with the lack of distinction between primary and secondary object,leading to insufficient high-level semantic and accuracy under public evaluation criteria.The major issue is the lack of effective network on high-level semantic sentences generation,which contains detailed description for motion and state of the principal object.To address the issue,this paper proposes the Attention-based Feedback Long Short-Term Memory Network(AFLN).Based on existing codec framework,there are two independent sub tasks in our method:attention-based feedback LSTM network during decoding and the Convolutional Block Attention Module(CBAM)in the coding phase.First,we propose an attentionbased network to feedback the features corresponding to the generated word from the previous LSTM decoding unit.We implement feedback guidance through the related field mapping algorithm,which quantifies the correlation between previous word and latter word,so that the main object can be tracked with highlighted detailed description.Second,we exploit the attention idea and apply a lightweight and general module called CBAM after the last layer of VGG 16 pretraining network,which can enhance the expression of image coding features by combining channel and spatial dimension attention maps with negligible overheads.Extensive experiments on COCO dataset validate the superiority of our network over the state-of-the-art algorithms.Both scores and actual effects are proved.The BLEU 4 score increases from 0.291 to 0.301 while the CIDEr score rising from 0.912 to 0.952.
基金the National Natural Science Foundation of China(No.61772417,61634004,61602377)Key R&D Program Projects in Shaanxi Province(No.2017GY-060)Shaanxi Natural Science Basic Research Project(No.2018JM4018).
文摘In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dimensional batch normalization visual geometry group(3D-BN-VGG)and long short-term memory(LSTM)network is designed.In this network,3D convolutional layer is used to extract the spatial domain features and time domain features of video sequence at the same time,multiple small convolution kernels are stacked to replace large convolution kernels,thus the depth of neural network is deepened and the number of network parameters is reduced.In addition,the latest batch normalization algorithm is added to the 3-dimensional convolutional network to improve the training speed.Then the output of the full connection layer is sent to LSTM network as the feature vectors to extract the sequence information.This method,which directly uses the output of the whole base level without passing through the full connection layer,reduces the parameters of the whole fusion network to 15324485,nearly twice as much as those of 3D-BN-VGG.Finally,it reveals that the proposed network achieves 96.5%and 74.9%accuracy in the UCF-101 and HMDB-51 respectively,and the algorithm has a calculation speed of 1066 fps and an acceleration ratio of 1,which has a significant predominance in velocity.
基金Supported by the National Key Research and Development Program of China(No.2020YFA0714103).
文摘Monitoring and predicting of urban surface subsidence are important for urban disaster prevention and mitigation.In this paper,the Long Short-Term Memory(LSTM)network was used to predict the surface subsidence process of Changchun City from 2018 to 2020 based on PS-InSAR monitoring data.The results show that the prediction error of 57.89% of PS points in the LSTM network was less than 1mm with the average error of 1.8 mm and the standard deviation of 2.8 mm.The accuracy and reliability of the prediction were better than regression analysis,time series analysis and grey model.
基金This work is supported by the National Key Research and Development Program of China(No.2023YFB4203000)the National Natural Science Foundation of China(No.U22A20178)
文摘Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.
文摘针对飞行器飞行试验中外测级间段数据缺失和精度不高的问题,提出了基于长短期记忆(long-short term memory,LSTM)网络的外测级间段数据预测方法。利用遥测视速度数据和外测融合数据建立LSTM网络回归模型,将外测级间段数据作为缺失数据进行预测插值,可将制导工具系统误差以及飞行器初始误差,包括遥外测时间对不准误差,一并利用回归网络表示,从而将遥测视速度数据作为网络输入,得到外测级间段的预测数据。试验数据处理结果证明,基于LSTM网络获得的外测级间段预测数据满足精度要求,所提方法具有实际应用价值。
文摘针对现有基于深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法的再入制导方法计算精度较差,对强扰动条件适应性不足等问题,在DDPG算法训练框架的基础上,提出一种基于长短期记忆-DDPG(long short term memory-DDPG,LSTM-DDPG)的再入制导方法。该方法采用纵、侧向制导解耦设计思想,在纵向制导方面,首先针对再入制导问题构建强化学习所需的状态、动作空间;其次,确定决策点和制导周期内的指令计算策略,并设计考虑综合性能的奖励函数;然后,引入LSTM网络构建强化学习训练网络,进而通过在线更新策略提升算法的多任务适用性;侧向制导则采用基于横程误差的动态倾侧反转方法,获得倾侧角符号。以美国超音速通用飞行器(common aero vehicle-hypersonic,CAV-H)再入滑翔为例进行仿真,结果表明:与传统数值预测-校正方法相比,所提制导方法具有相当的终端精度和更高的计算效率优势;与现有基于DDPG算法的再入制导方法相比,所提制导方法具有相当的计算效率以及更高的终端精度和鲁棒性。
文摘建立了北方苍鹰算法优化长短期记忆神经网络(northern goshawk optimization-long short term memory,NGO-LSTM)的预测模型。以深圳市共享单车为例,首先对共享单车数据进行预处理,以Geohash算法为基础将骑行的时变数据作为特征输入;然后采用Canopy算法结合K-means聚类算法将深圳市地铁站进行聚类分析,以此发掘不同类型站点骑行规律;最后在此基础上建立了NGO-LSTM预测模型对站点的需求量进行预测分析,并与其他模型进行对比。实验结果表明,NGO-LSTM模型的决定系数达到0.90。
