In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the e...In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.展开更多
With limited number of labeled samples,hyperspectral image(HSI)classification is a difficult Problem in current research.The graph neural network(GNN)has emerged as an approach to semi-supervised classification,and th...With limited number of labeled samples,hyperspectral image(HSI)classification is a difficult Problem in current research.The graph neural network(GNN)has emerged as an approach to semi-supervised classification,and the application of GNN to hyperspectral images has attracted much attention.However,in the existing GNN-based methods a single graph neural network or graph filter is mainly used to extract HSI features,which does not take full advantage of various graph neural networks(graph filters).Moreover,the traditional GNNs have the problem of oversmoothing.To alleviate these shortcomings,we introduce a deep hybrid multi-graph neural network(DHMG),where two different graph filters,i.e.,the spectral filter and the autoregressive moving average(ARMA)filter,are utilized in two branches.The former can well extract the spectral features of the nodes,and the latter has a good suppression effect on graph noise.The network realizes information interaction between the two branches and takes good advantage of different graph filters.In addition,to address the problem of oversmoothing,a dense network is proposed,where the local graph features are preserved.The dense structure satisfies the needs of different classification targets presenting different features.Finally,we introduce a GraphSAGEbased network to refine the graph features produced by the deep hybrid network.Extensive experiments on three public HSI datasets strongly demonstrate that the DHMG dramatically outperforms the state-ofthe-art models.展开更多
N^(6)-Methyladenine is a dynamic and reversible post translational modification,which plays an essential role in various biological processes.Because of the current inability to identify m6A-containing mRNAs,computati...N^(6)-Methyladenine is a dynamic and reversible post translational modification,which plays an essential role in various biological processes.Because of the current inability to identify m6A-containing mRNAs,computational approaches have been developed to identify m6A sites in DNA sequences.Aiming to improve prediction performance,we introduced a novel ensemble computational approach based on three hybrid deep neural networks,including a convolutional neural network,a capsule network,and a bidirectional gated recurrent unit(BiGRU)with the self-attention mechanism,to identify m6A sites in four tissues of three species.Across a total of 11 datasets,we selected different feature subsets,after optimized from 4933 dimensional features,as input for the deep hybrid neural networks.In addition,to solve the deviation caused by the relatively small number of experimentally verified samples,we constructed an ensemble model through integrating five sub-classifiers based on different training datasets.When compared through 5-fold cross-validation and independent tests,our model showed its superiority to previous methods,im6A-TS-CNN and iRNA-m6A.展开更多
针对具有时频特性的雷达信号,传统的雷达信号识别方法已经无法满足对信号类型精准识别的需求,因此需要通过采集并分析雷达信号脉内的时频特征实现对目标雷达的具体信息进行有效评估。设计了一种卷积-双向长短时记忆(Convolution-Bidirec...针对具有时频特性的雷达信号,传统的雷达信号识别方法已经无法满足对信号类型精准识别的需求,因此需要通过采集并分析雷达信号脉内的时频特征实现对目标雷达的具体信息进行有效评估。设计了一种卷积-双向长短时记忆(Convolution-Bidirectional Long Short-Term Memory,CNN-BiLSTM)混合神经网络模型,主要通过BiLSTM的时序记忆特性深度挖掘雷达信号的时域特征,结合权值共享特性和CNN层捕获雷达信号的时频特征,再利用二者信号特征联合完成对雷达信号调制方式的识别。通过对比实验验证,所提方法对若干种雷达信号的识别具有较高的准确度,平均值达到95.349%;优于只使用单一特征的网络和传统算法,具有良好的抗噪声能力。展开更多
文摘In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.
文摘With limited number of labeled samples,hyperspectral image(HSI)classification is a difficult Problem in current research.The graph neural network(GNN)has emerged as an approach to semi-supervised classification,and the application of GNN to hyperspectral images has attracted much attention.However,in the existing GNN-based methods a single graph neural network or graph filter is mainly used to extract HSI features,which does not take full advantage of various graph neural networks(graph filters).Moreover,the traditional GNNs have the problem of oversmoothing.To alleviate these shortcomings,we introduce a deep hybrid multi-graph neural network(DHMG),where two different graph filters,i.e.,the spectral filter and the autoregressive moving average(ARMA)filter,are utilized in two branches.The former can well extract the spectral features of the nodes,and the latter has a good suppression effect on graph noise.The network realizes information interaction between the two branches and takes good advantage of different graph filters.In addition,to address the problem of oversmoothing,a dense network is proposed,where the local graph features are preserved.The dense structure satisfies the needs of different classification targets presenting different features.Finally,we introduce a GraphSAGEbased network to refine the graph features produced by the deep hybrid network.Extensive experiments on three public HSI datasets strongly demonstrate that the DHMG dramatically outperforms the state-ofthe-art models.
基金supported by the National Natural Science Foundation of China(Nos.62071079 and 61803065).
文摘N^(6)-Methyladenine is a dynamic and reversible post translational modification,which plays an essential role in various biological processes.Because of the current inability to identify m6A-containing mRNAs,computational approaches have been developed to identify m6A sites in DNA sequences.Aiming to improve prediction performance,we introduced a novel ensemble computational approach based on three hybrid deep neural networks,including a convolutional neural network,a capsule network,and a bidirectional gated recurrent unit(BiGRU)with the self-attention mechanism,to identify m6A sites in four tissues of three species.Across a total of 11 datasets,we selected different feature subsets,after optimized from 4933 dimensional features,as input for the deep hybrid neural networks.In addition,to solve the deviation caused by the relatively small number of experimentally verified samples,we constructed an ensemble model through integrating five sub-classifiers based on different training datasets.When compared through 5-fold cross-validation and independent tests,our model showed its superiority to previous methods,im6A-TS-CNN and iRNA-m6A.
文摘随着大量直流电源和负荷的接入,交直流混合的配电网技术已成为未来配电网的发展趋势.然而,源荷不确定性及可调度设备的类型多样化给配电网调度带来了巨大的挑战.本文提出了基于分支决斗深度强化网络(branching dueling Q-network,BDQ)和软演员-评论家(soft actor critic,SAC)双智能体深度强化学习的交直流配电网调度方法.该方法首先将经济调度问题与两智能体的动作、奖励、状态相结合,建立经济调度的马尔可夫决策过程,并分别基于BDQ和SAC方法设置两个智能体,其中,BDQ智能体用于控制配电网中离散动作设备,SAC智能体用于控制连续动作设备.然后,通过集中训练分散执行的方式,两智能体与环境进行交互,进行离线训练.最后,固定智能体的参数,进行在线调度.该方法的优势在于采用双智能体能够同时控制离散动作设备电容器组、载调压变压器和连续动作设备变流器、储能,同时通过对双智能体的集中训练,可以自适应源荷的不确定性.改进的IEEE33节点交直流配电网算例测试验证了所提方法的有效性.
文摘针对具有时频特性的雷达信号,传统的雷达信号识别方法已经无法满足对信号类型精准识别的需求,因此需要通过采集并分析雷达信号脉内的时频特征实现对目标雷达的具体信息进行有效评估。设计了一种卷积-双向长短时记忆(Convolution-Bidirectional Long Short-Term Memory,CNN-BiLSTM)混合神经网络模型,主要通过BiLSTM的时序记忆特性深度挖掘雷达信号的时域特征,结合权值共享特性和CNN层捕获雷达信号的时频特征,再利用二者信号特征联合完成对雷达信号调制方式的识别。通过对比实验验证,所提方法对若干种雷达信号的识别具有较高的准确度,平均值达到95.349%;优于只使用单一特征的网络和传统算法,具有良好的抗噪声能力。
文摘针对现有超分辨率(super-resolution)重建算法重建出的图像存在高频细节丢失、结构化失真的问题,结合多尺度混合注意力网络,给出一种新的重建算法。首先,设计了一种多尺度残差模块(multi-scale residual module,MRM),提取不同尺度信息的特征并进行融合来获取包含更多信息的浅层特征;其次,采用残差混合注意力模块(residual hybrid attention module,RHAM),依次沿着通道和空间2个不同的维度增强网络特征提取能力,进行自适应的特征优化,提高高频特征的复用;最后,通过重建模块对提取的特征进行增强,获取相应的高分辨率图像。在基准数据集上进行测试,实验结果表明:文中提出的算法相较主流图像SR算法,在放大尺度为2、3、4倍时峰值信噪比(peak signal to noise ratio,PSNR)平均提高了0.104、0.224、0.146 dB,结构相似性(structural similarity index measure,SSIM)平均提高了0.0349、0.0276、0.0181。该算法能更有效地利用原始图像信息,重建出的图像在边缘和纹理细节等方面有一定的提高。
