Time series prediction has always been an important problem in the field of machine learning.Among them,power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulati...Time series prediction has always been an important problem in the field of machine learning.Among them,power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies.Traditional power load forecasting often has poor feature extraction performance for long time series.In this paper,a new deep learning framework Residual Stacked Temporal Long Short-Term Memory(RST-LSTM)is proposed,which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences.The network framework of RST-LSTM consists of two parts:one is a stacked time convolutional memory unit module for global and local feature extraction,and the other is a residual combination optimization module to reduce model redundancy.Finally,this paper demonstrates through various experimental indicators that RST-LSTM achieves significant performance improvements in both overall and local prediction accuracy compared to some state-of-the-art baseline methods.展开更多
Recent advancements have established machine learning's utility in predicting nonlinear fluid dynamics,with predictive accuracy being a central motivation for employing neural networks.However,the pattern recognit...Recent advancements have established machine learning's utility in predicting nonlinear fluid dynamics,with predictive accuracy being a central motivation for employing neural networks.However,the pattern recognition central to the networks function is equally valuable for enhancing our dynamical insight into the complex fluid dynamics.In this paper,a single-layer convolutional neural network(CNN)was trained to recognize three qualitatively different subsonic buffet flows(periodic,quasi-periodic and chaotic)over a high-incidence airfoil,and a near-perfect accuracy was obtained with only a small training dataset.The convolutional kernels and corresponding feature maps,developed by the model with no temporal information provided,identified large-scale coherent structures in agreement with those known to be associated with buffet flows.Sensitivity to hyperparameters including network architecture and convolutional kernel size was also explored.The coherent structures identified by these models enhance our dynamical understanding of subsonic buffet over high-incidence airfoils over a wide range of Reynolds numbers.展开更多
The unloading relaxation caused by excavation for construction of high arch dams is an important factor influencing the foundation’s integrity and strength.To evaluate the degree of unloading relaxation,the long-shor...The unloading relaxation caused by excavation for construction of high arch dams is an important factor influencing the foundation’s integrity and strength.To evaluate the degree of unloading relaxation,the long-short term memory(LSTM)network was used to estimate the depth of unloading relaxation zones on the left bank foundation of the Baihetan Arch Dam.Principal component analysis indicates that rock charac-teristics,the structural plane,the protection layer,lithology,and time are the main factors.The LSTM network results demonstrate the unloading relaxation characteristics of the left bank,and the relationships with the factors were also analyzed.The structural plane has the most significant influence on the distribution of unloading relaxation zones.Compared with massive basalt,the columnar jointed basalt experiences a more significant unloading relaxation phenomenon with a clear time effect,with the average unloading relaxation period being 50 d.The protection layer can effectively reduce the unloading relaxation depth by approximately 20%.展开更多
Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models,SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection model.Th...Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models,SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection model.The dataset is first preprocessed using solo thermal encoding and normalization functions.Then the spiral convolution-Long Short-Term Memory Network model is constructed,which consists of spiral convolution,a two-layer long short-term memory network,and a classifier.It is shown through experiments that the model is characterized by high accuracy,small model computation,and fast convergence speed relative to previous deep learning models.The model uses a new neural network to achieve fast and accurate network traffic intrusion detection.The model in this paper achieves 0.9706 and 0.8432 accuracy rates on the NSL-KDD dataset and the UNSWNB-15 dataset under five classifications and ten classes,respectively.展开更多
针对商品包装文本检测任务中弯曲密集型文本导致的错检、漏检问题,提出了一种由2个子网络组成的基于链接关系预测的文本检测框架(text detection network based on relational prediction,RPTNet)。在文本组件检测网络中,下采样采用卷...针对商品包装文本检测任务中弯曲密集型文本导致的错检、漏检问题,提出了一种由2个子网络组成的基于链接关系预测的文本检测框架(text detection network based on relational prediction,RPTNet)。在文本组件检测网络中,下采样采用卷积神经网络和自注意力并行的双分支结构提取局部和全局特征,并加入空洞特征增强模块(DFM)减少深层特征图在降维过程中信息的丢失;上采样采用特征金字塔与多级注意力融合模块(MAFM)相结合的方式进行多级特征融合以增强文本特征间的潜在联系,通过文本检测器从上采样输出的特征图中检测文本组件;在链接关系预测网络中,采用基于图卷积网络的关系推理框架预测文本组件间的深层相似度,采用双向长短时记忆网络将文本组件聚合为文本实例。为验证RRNet的检测性能,构建了一个由商品包装图片组成的文本检测数据集(text detection dataset composed of commodity packaging,CPTD1500)。实验结果表明:RPTNet不仅在公开文本数据集CTW-1500和Total-Text上取得了优异的性能,而且在CPTD1500数据集上的召回率和F值分别达到了85.4%和87.5%,均优于当前主流算法。展开更多
基金funded by NARI Group’s Independent Project of China(Granted No.524609230125)the foundation of NARI-TECH Nanjing Control System Ltd.of China(Granted No.0914202403120020).
文摘Time series prediction has always been an important problem in the field of machine learning.Among them,power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies.Traditional power load forecasting often has poor feature extraction performance for long time series.In this paper,a new deep learning framework Residual Stacked Temporal Long Short-Term Memory(RST-LSTM)is proposed,which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences.The network framework of RST-LSTM consists of two parts:one is a stacked time convolutional memory unit module for global and local feature extraction,and the other is a residual combination optimization module to reduce model redundancy.Finally,this paper demonstrates through various experimental indicators that RST-LSTM achieves significant performance improvements in both overall and local prediction accuracy compared to some state-of-the-art baseline methods.
文摘Recent advancements have established machine learning's utility in predicting nonlinear fluid dynamics,with predictive accuracy being a central motivation for employing neural networks.However,the pattern recognition central to the networks function is equally valuable for enhancing our dynamical insight into the complex fluid dynamics.In this paper,a single-layer convolutional neural network(CNN)was trained to recognize three qualitatively different subsonic buffet flows(periodic,quasi-periodic and chaotic)over a high-incidence airfoil,and a near-perfect accuracy was obtained with only a small training dataset.The convolutional kernels and corresponding feature maps,developed by the model with no temporal information provided,identified large-scale coherent structures in agreement with those known to be associated with buffet flows.Sensitivity to hyperparameters including network architecture and convolutional kernel size was also explored.The coherent structures identified by these models enhance our dynamical understanding of subsonic buffet over high-incidence airfoils over a wide range of Reynolds numbers.
基金This work was supported by the National Key Research and Development Program of China(Grant No.2018YFC0407004)the Natural Science Foundation of China(Grants No.51939004 and 11772116).
文摘The unloading relaxation caused by excavation for construction of high arch dams is an important factor influencing the foundation’s integrity and strength.To evaluate the degree of unloading relaxation,the long-short term memory(LSTM)network was used to estimate the depth of unloading relaxation zones on the left bank foundation of the Baihetan Arch Dam.Principal component analysis indicates that rock charac-teristics,the structural plane,the protection layer,lithology,and time are the main factors.The LSTM network results demonstrate the unloading relaxation characteristics of the left bank,and the relationships with the factors were also analyzed.The structural plane has the most significant influence on the distribution of unloading relaxation zones.Compared with massive basalt,the columnar jointed basalt experiences a more significant unloading relaxation phenomenon with a clear time effect,with the average unloading relaxation period being 50 d.The protection layer can effectively reduce the unloading relaxation depth by approximately 20%.
基金the Gansu University of Political Science and Law Key Research Funding Project in 2018(GZF2018XZDLW20)Gansu Provincial Science and Technology Plan Project(Technology Innovation Guidance Plan)(20CX9ZA072).
文摘Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models,SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection model.The dataset is first preprocessed using solo thermal encoding and normalization functions.Then the spiral convolution-Long Short-Term Memory Network model is constructed,which consists of spiral convolution,a two-layer long short-term memory network,and a classifier.It is shown through experiments that the model is characterized by high accuracy,small model computation,and fast convergence speed relative to previous deep learning models.The model uses a new neural network to achieve fast and accurate network traffic intrusion detection.The model in this paper achieves 0.9706 and 0.8432 accuracy rates on the NSL-KDD dataset and the UNSWNB-15 dataset under five classifications and ten classes,respectively.
文摘针对商品包装文本检测任务中弯曲密集型文本导致的错检、漏检问题,提出了一种由2个子网络组成的基于链接关系预测的文本检测框架(text detection network based on relational prediction,RPTNet)。在文本组件检测网络中,下采样采用卷积神经网络和自注意力并行的双分支结构提取局部和全局特征,并加入空洞特征增强模块(DFM)减少深层特征图在降维过程中信息的丢失;上采样采用特征金字塔与多级注意力融合模块(MAFM)相结合的方式进行多级特征融合以增强文本特征间的潜在联系,通过文本检测器从上采样输出的特征图中检测文本组件;在链接关系预测网络中,采用基于图卷积网络的关系推理框架预测文本组件间的深层相似度,采用双向长短时记忆网络将文本组件聚合为文本实例。为验证RRNet的检测性能,构建了一个由商品包装图片组成的文本检测数据集(text detection dataset composed of commodity packaging,CPTD1500)。实验结果表明:RPTNet不仅在公开文本数据集CTW-1500和Total-Text上取得了优异的性能,而且在CPTD1500数据集上的召回率和F值分别达到了85.4%和87.5%,均优于当前主流算法。
