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Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network
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作者 Shengkang Zong Sheng Wang +3 位作者 Zhitao Luo Xinkai Wu Hui Zhang Zhonghua Ni 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2023年第3期252-261,共10页
Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of ci... Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC. 展开更多
关键词 Ultrasonic guided waves Singular value decomposition Damage detection and localization Environmental and operational conditions one-dimensional convolutional neural network
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Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network 被引量:3
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作者 LYU Defeng HU Yuwen 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2021年第5期867-875,共9页
In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based... In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness. 展开更多
关键词 AEROENGINE remaining useful life(RUL) principal component analysis(PCA) one-dimensional convolution neural network(1D-CNN) time series prediction state parameters
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Hybrid 1DCNN-Attention with Enhanced Data Preprocessing for Loan Approval Prediction
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作者 Yaru Liu Huifang Feng 《Journal of Computer and Communications》 2024年第8期224-241,共18页
In order to reduce the risk of non-performing loans, losses, and improve the loan approval efficiency, it is necessary to establish an intelligent loan risk and approval prediction system. A hybrid deep learning model... In order to reduce the risk of non-performing loans, losses, and improve the loan approval efficiency, it is necessary to establish an intelligent loan risk and approval prediction system. A hybrid deep learning model with 1DCNN-attention network and the enhanced preprocessing techniques is proposed for loan approval prediction. Our proposed model consists of the enhanced data preprocessing and stacking of multiple hybrid modules. Initially, the enhanced data preprocessing techniques using a combination of methods such as standardization, SMOTE oversampling, feature construction, recursive feature elimination (RFE), information value (IV) and principal component analysis (PCA), which not only eliminates the effects of data jitter and non-equilibrium, but also removes redundant features while improving the representation of features. Subsequently, a hybrid module that combines a 1DCNN with an attention mechanism is proposed to extract local and global spatio-temporal features. Finally, the comprehensive experiments conducted validate that the proposed model surpasses state-of-the-art baseline models across various performance metrics, including accuracy, precision, recall, F1 score, and AUC. Our proposed model helps to automate the loan approval process and provides scientific guidance to financial institutions for loan risk control. 展开更多
关键词 Loan Approval Prediction Deep Learning one-dimensional convolutional neural network Attention Mechanism Data Preprocessing
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基于1DCNN-GRU的启闭机液压系统故障诊断
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作者 刘英杰 董詠依 +1 位作者 刘鹏鹏 葛孟伟 《现代制造技术与装备》 2024年第4期169-173,共5页
由于启闭机液压系统内部结构复杂,故障信号不易采集,使用AMESim软件搭建启闭机液压系统仿真模型,构建6种典型故障数据集。基于这些数据集,提出一维卷积神经网络(1 Dimensional Convolutional Neural Network,1DCNN)与门控循环单元(Gated... 由于启闭机液压系统内部结构复杂,故障信号不易采集,使用AMESim软件搭建启闭机液压系统仿真模型,构建6种典型故障数据集。基于这些数据集,提出一维卷积神经网络(1 Dimensional Convolutional Neural Network,1DCNN)与门控循环单元(Gated Recurrent Unit,GRU)相结合的故障诊断方法,利用1DCNN提取信号数据的空间特征和GRU提取信号数据的时间特征,实现对信号数据空间及时间特征的融合,并对融合特征进行分类识别。 展开更多
关键词 启闭机 液压系统 一维卷积神经网络(1dcnn) 门控循环单元(GRU) 特征融合 故障诊断
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Individual Dairy Cattle Recognition Based on Deep Convolutional Neural Network 被引量:2
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作者 ZHANG Mandun SHAN Xinyuan +3 位作者 YU Jinsu GUO Yingchun LI Ruiwen XU Mingquan 《Journal of Donghua University(English Edition)》 EI CAS 2018年第2期107-112,共6页
Image based individual dairy cattle recognition has gained much attention recently. In order to further improve the accuracy of individual dairy cattle recognition, an algorithm based on deep convolutional neural netw... Image based individual dairy cattle recognition has gained much attention recently. In order to further improve the accuracy of individual dairy cattle recognition, an algorithm based on deep convolutional neural network( DCNN) is proposed in this paper,which enables automatic feature extraction and classification that outperforms traditional hand craft features. Through making multigroup comparison experiments including different network layers,different sizes of convolution kernel and different feature dimensions in full connection layer,we demonstrate that the proposed method is suitable for dairy cattle classification. The experimental results show that the accuracy is significantly higher compared to two traditional image processing algorithms: scale invariant feature transform( SIFT) algorithm and bag of feature( BOF) model. 展开更多
关键词 DEEP learning DEEP convolutional neural network(dcnn) DAIRY CATTLE INDIVIDUAL RECOGNITION
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Predicting Concrete Compressive Strength Using Deep Convolutional Neural Network Based on Image Characteristics 被引量:2
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作者 Sanghyo Lee Yonghan Ahn Ha Young Kim 《Computers, Materials & Continua》 SCIE EI 2020年第10期1-17,共17页
In this study,we examined the efficacy of a deep convolutional neural network(DCNN)in recognizing concrete surface images and predicting the compressive strength of concrete.A digital single-lens reflex(DSLR)camera an... In this study,we examined the efficacy of a deep convolutional neural network(DCNN)in recognizing concrete surface images and predicting the compressive strength of concrete.A digital single-lens reflex(DSLR)camera and microscope were simultaneously used to obtain concrete surface images used as the input data for the DCNN.Thereafter,training,validation,and testing of the DCNNs were performed based on the DSLR camera and microscope image data.Results of the analysis indicated that the DCNN employing DSLR image data achieved a relatively higher accuracy.The accuracy of the DSLR-derived image data was attributed to the relatively wider range of the DSLR camera,which was beneficial for extracting a larger number of features.Moreover,the DSLR camera procured more realistic images than the microscope.Thus,when the compressive strength of concrete was evaluated using the DCNN employing a DSLR camera,time and cost were reduced,whereas the usefulness increased.Furthermore,an indirect comparison of the accuracy of the DCNN with that of existing non-destructive methods for evaluating the strength of concrete proved the reliability of DCNN-derived concrete strength predictions.In addition,it was determined that the DCNN used for concrete strength evaluations in this study can be further expanded to detect and evaluate various deteriorative factors that affect the durability of structures,such as salt damage,carbonation,sulfation,corrosion,and freezing-thawing. 展开更多
关键词 Deep convolutional neural network(dcnn) non-destructive testing(NDT) concrete compressive strength digital single-lens reflex(DSLR)camera MICROSCOPE
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基于LSTM与1DCNN的导弹轨迹预测方法 被引量:5
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作者 宋波涛 许广亮 《系统工程与电子技术》 EI CSCD 北大核心 2023年第2期504-512,共9页
针对弹道导弹等超远程攻击目标的轨迹难以预测的问题,提出一种基于长短期记忆(long short-term memory,LSTM)网络与一维卷积神经网络(1-dimensional convolutional neural network,1DCNN)的目标轨迹预测方法。首先,建立三自由度导弹运... 针对弹道导弹等超远程攻击目标的轨迹难以预测的问题,提出一种基于长短期记忆(long short-term memory,LSTM)网络与一维卷积神经网络(1-dimensional convolutional neural network,1DCNN)的目标轨迹预测方法。首先,建立三自由度导弹运动模型,依据再入类型设计3种目标轨迹数据,构建机动数据库,解决轨迹数据的来源问题。其次,采用重复分割与滑动窗口的方法对轨迹数据进行预处理。然后,基于LSTM与1DCNN设计了一种目标类型分类网络,对目标进行初步分类。最后,基于1DCNN设计轨迹预测网络,对目标轨迹进行预测。仿真结果表明,提出的轨迹预测网络能够完成轨迹预测任务,预测误差在合理范围内。 展开更多
关键词 弹道导弹 目标分类 轨迹预测 长短期记忆网络 一维卷积神经网络
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Lightweight and highly robust memristor-based hybrid neural networks for electroencephalogram signal processing
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作者 童霈文 徐晖 +5 位作者 孙毅 汪泳州 彭杰 廖岑 王伟 李清江 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第7期582-590,共9页
Memristor-based neuromorphic computing shows great potential for high-speed and high-throughput signal processing applications,such as electroencephalogram(EEG)signal processing.Nonetheless,the size of one-transistor ... Memristor-based neuromorphic computing shows great potential for high-speed and high-throughput signal processing applications,such as electroencephalogram(EEG)signal processing.Nonetheless,the size of one-transistor one-resistor(1T1R)memristor arrays is limited by the non-ideality of the devices,which prevents the hardware implementation of large and complex networks.In this work,we propose the depthwise separable convolution and bidirectional gate recurrent unit(DSC-BiGRU)network,a lightweight and highly robust hybrid neural network based on 1T1R arrays that enables efficient processing of EEG signals in the temporal,frequency and spatial domains by hybridizing DSC and BiGRU blocks.The network size is reduced and the network robustness is improved while ensuring the network classification accuracy.In the simulation,the measured non-idealities of the 1T1R array are brought into the network through statistical analysis.Compared with traditional convolutional networks,the network parameters are reduced by 95%and the network classification accuracy is improved by 21%at a 95%array yield rate and 5%tolerable error.This work demonstrates that lightweight and highly robust networks based on memristor arrays hold great promise for applications that rely on low consumption and high efficiency. 展开更多
关键词 MEMRISTOR LIGHTWEIGHT ROBUST hybrid neural networks depthwise separable convolution bidirectional gate recurrent unit(BiGRU) one-transistor one-resistor(1T1R)arrays
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基于DCNN网络及Self-Attention-BiGRU机制的轴承剩余寿命预测
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作者 刘森 刘美 +2 位作者 贺银超 韩惠子 孟亚男 《机电工程》 CAS 北大核心 2024年第5期786-796,共11页
深度神经网络在剩余寿命预测(RUL)领域得到了广泛的应用。传统的滚动轴承寿命预测模型存在预测精确度较低、鲁棒性较弱的问题。为了进一步提升预测模型的精确度以及鲁棒性,提出了一种融合深度卷积神经网络(DCNN)、双向门控循环单元(BiG... 深度神经网络在剩余寿命预测(RUL)领域得到了广泛的应用。传统的滚动轴承寿命预测模型存在预测精确度较低、鲁棒性较弱的问题。为了进一步提升预测模型的精确度以及鲁棒性,提出了一种融合深度卷积神经网络(DCNN)、双向门控循环单元(BiGRU)以及自注意力机制(Self-Attention)三种模块的滚动轴承剩余使用寿命预测模型。首先,利用DCNN网络对原始振动信号的时域特征、频域特征进行了提取;然后,使用不确定量化的方法对提取到的特征进行了评价和筛选,利用筛选过后的特征构建了新的替代特征集;最后,利用Self-Attention-BiGRU网络对轴承的剩余使用寿命进行了预测,并在IEEE PHM2012数据集上进行了验证。实验结果表明:相较于BiGRU、GRU和BiLSTM三种模型的预测结果,基于DCNN及Self-Attention-BiGRU方法的预测结果最优,两项误差值:平均绝对误差(MAE)、均方根误差(RMSE)最低,其中工况一的一号轴承RUL预测的MAE值相较于BiGRU、GRU以及BiLSTM网络分别下降了7.0%、7.4%和6.5%,RMSE值相较于其他三种模型分别下降了7.6%、8.4%和6.9%,预测的Score值最高,分值为0.985。通过不同数据集的划分,证明了该方法在轴承RUL预测时的强鲁棒性。实验结果验证了基于DCNN网络及Self-Attention-BiGRU模型在轴承剩余使用寿命预测中的有效性。 展开更多
关键词 滚动轴承 剩余使用寿命 双向门控循环单元 不确定量化 自注意力机制 深度卷积神经网络 预测与健康管理
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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization
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作者 Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi +3 位作者 Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan 《Computers, Materials & Continua》 SCIE EI 2024年第9期4791-4812,共22页
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
关键词 Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units
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Bearings Intelligent Fault Diagnosis by 1-D Adder Neural Networks
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作者 Jian Tang Chao Wei +3 位作者 Quanchang Li Yinjun Wang Xiaoxi Ding Wenbin Huang 《Journal of Dynamics, Monitoring and Diagnostics》 2022年第3期160-168,共9页
Integrated with sensors,processors,and radio frequency(RF)communication modules,intelligent bearing could achieve the autonomous perception and autonomous decision-making,guarantying the safety and reliability during ... Integrated with sensors,processors,and radio frequency(RF)communication modules,intelligent bearing could achieve the autonomous perception and autonomous decision-making,guarantying the safety and reliability during their use.However,because of the resource limitations of the end device,processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network(CNN),which involves a great amount of multiplicative operations.To minimize the computation cost of the conventional CNN,based on the idea of AdderNet,a 1-D adder neural network with a wide first-layer kernel(WAddNN)suitable for bearing fault diagnosis is proposed in this paper.The proposed method uses the l1-norm distance between filters and input features as the output response,thus making the whole network almost free of multiplicative operations.The whole model takes the original signal as the input,uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise,and then uses two layers of small kernels for nonlinear mapping.Through experimental comparison with CNN models of the same structure,WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost.The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources. 展开更多
关键词 adder neural network convolutional neural network fault diagnosis intelligent bearings l1-norm distance
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Audiovisual speech recognition based on a deep convolutional neural network
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作者 Shashidhar Rudregowda Sudarshan Patilkulkarni +2 位作者 Vinayakumar Ravi Gururaj H.L. Moez Krichen 《Data Science and Management》 2024年第1期25-34,共10页
Audiovisual speech recognition is an emerging research topic.Lipreading is the recognition of what someone is saying using visual information,primarily lip movements.In this study,we created a custom dataset for India... Audiovisual speech recognition is an emerging research topic.Lipreading is the recognition of what someone is saying using visual information,primarily lip movements.In this study,we created a custom dataset for Indian English linguistics and categorized it into three main categories:(1)audio recognition,(2)visual feature extraction,and(3)combined audio and visual recognition.Audio features were extracted using the mel-frequency cepstral coefficient,and classification was performed using a one-dimension convolutional neural network.Visual feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural networks.Finally,integration was performed using a deep convolutional network.The audio speech of Indian English was successfully recognized with accuracies of 93.67%and 91.53%,respectively,using testing data from 200 epochs.The training accuracy for visual speech recognition using the Indian English dataset was 77.48%and the test accuracy was 76.19%using 60 epochs.After integration,the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67%and 91.75%,respectively. 展开更多
关键词 Audiovisual speech recognition Custom dataset 1D convolution neural network(CNN) Deep CNN(dcnn) Long short-term memory(LSTM) Lipreading Dlib Mel-frequency cepstral coefficient(MFCC)
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Fault Line Detection Using Waveform Fusion and One-dimensional Convolutional Neural Network in Resonant Grounding Distribution Systems 被引量:6
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作者 Jianhong Gao Moufa Guo Duan-Yu Chen 《CSEE Journal of Power and Energy Systems》 SCIE CSCD 2021年第2期250-260,共11页
Effective features are essential for fault diagnosis.Due to the faint characteristics of a single line-to-ground(SLG)fault,fault line detection has become a challenge in resonant grounding distribution systems.This pa... Effective features are essential for fault diagnosis.Due to the faint characteristics of a single line-to-ground(SLG)fault,fault line detection has become a challenge in resonant grounding distribution systems.This paper proposes a novel fault line detection method using waveform fusion and one-dimensional convolutional neural networks(1-D CNN).After an SLG fault occurs,the first-half waves of zero-sequence currents are collected and superimposed with each other to achieve waveform fusion.The compelling feature of fused waveforms is extracted by 1-D CNN to determine whether the fused waveform source contains the fault line.Then,the 1-D CNN output is used to update the value of the counter in order to identify the fault line.Given the lack of fault data in existing distribution systems,the proposed method only needs a small quantity of data for model training and fault line detection.In addition,the proposed method owns fault-tolerant performance.Even if a few samples are misjudged,the fault line can still be detected correctly based on the full output results of 1-D CNN.Experimental results verified that the proposed method can work effectively under various fault conditions. 展开更多
关键词 Fault line detection one-dimensional convolutional neural network resonant grounding distribution systems waveform fusion
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1D-CNN:Speech Emotion Recognition System Using a Stacked Network with Dilated CNN Features 被引量:6
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作者 Mustaqeem Soonil Kwon 《Computers, Materials & Continua》 SCIE EI 2021年第6期4039-4059,共21页
Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications,such as robotics,virtual reality,behavior assessments,and emergency call centers.Re... Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications,such as robotics,virtual reality,behavior assessments,and emergency call centers.Recently,researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches,but the recognition rate is still not convincing.Our main aim is to develop a new technique that increases the recognition rate with reasonable cost computations.In this paper,we suggested a new technique,which is a one-dimensional dilated convolutional neural network(1D-DCNN)for speech emotion recognition(SER)that utilizes the hierarchical features learning blocks(HFLBs)with a bi-directional gated recurrent unit(BiGRU).We designed a one-dimensional CNN network to enhance the speech signals,which uses a spectral analysis,and to extract the hidden patterns from the speech signals that are fed into a stacked one-dimensional dilated network that are called HFLBs.Each HFLB contains one dilated convolution layer(DCL),one batch normalization(BN),and one leaky_relu(Relu)layer in order to extract the emotional features using a hieratical correlation strategy.Furthermore,the learned emotional features are feed into a BiGRU in order to adjust the global weights and to recognize the temporal cues.The final state of the deep BiGRU is passed from a softmax classifier in order to produce the probabilities of the emotions.The proposed model was evaluated over three benchmarked datasets that included the IEMOCAP,EMO-DB,and RAVDESS,which achieved 72.75%,91.14%,and 78.01%accuracy,respectively. 展开更多
关键词 Affective computing one-dimensional dilated convolutional neural network emotion recognition gated recurrent unit raw audio clips
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基于VBGMM-DCNN的列车卫星定位欺骗干扰检测方法
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作者 王思琦 刘江 +1 位作者 蔡伯根 赵阳 《导航定位与授时》 CSCD 2023年第4期58-68,共11页
面向基于全球导航卫星系统的铁路列车定位实施欺骗干扰的主动检测,在卫星定位解算层次,运用深度学习建模学习方法的优势,提出一种基于变分贝叶斯高斯混合模型-深度卷积神经网络(variational Bayesian Gaussian mixture model-deep convo... 面向基于全球导航卫星系统的铁路列车定位实施欺骗干扰的主动检测,在卫星定位解算层次,运用深度学习建模学习方法的优势,提出一种基于变分贝叶斯高斯混合模型-深度卷积神经网络(variational Bayesian Gaussian mixture model-deep convolutional neural network,VBGMM-DCNN)的列车卫星定位欺骗干扰检测方法。该方法首先提取能够充分体现欺骗干扰对定位解算过程作用影响的卫星观测特征参数,构建干扰检测特征矢量;然后,采用VBGMM模型拟合经过预处理的特征向量的概率分布,得到二维概率密度图;最后,将概率密度图用于DCNN模型实施欺骗干扰的检测决策。结合现场实验所得运行场景数据,利用实验室搭建的欺骗干扰测试环境实施了干扰注入测试与检验,结果表明,欺骗干扰检测性能随着DCNN网络深度的增加而提升,相对于常规有监督决策方法F1值最高提升44.68%。基于VBGMM-DCNN的欺骗干扰检测能够适应测试验证中运用的列车运行特征及定位观测条件,所达到的检测性能优于对比算法。 展开更多
关键词 全球导航卫星系统 列车定位 欺骗攻击检测 变分贝叶斯高斯混合模型 深度卷积神经网络
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基于1-D CNN的二阶段OFDM系统定时同步方法 被引量:1
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作者 卿朝进 杨娜 +1 位作者 唐书海 饶川贵 《计算机应用研究》 CSCD 北大核心 2023年第2期565-570,共6页
针对存在多径干扰的正交频分复用系统的定时同步准确性低的问题,提出基于一维卷积神经网络(1-D CNN)的二阶段OFDM系统定时同步方法。在第一阶段,利用经典互相关方法实现路径特征初始抽取,捕获可分辨路径上的定时辅助同步点;基于定时辅... 针对存在多径干扰的正交频分复用系统的定时同步准确性低的问题,提出基于一维卷积神经网络(1-D CNN)的二阶段OFDM系统定时同步方法。在第一阶段,利用经典互相关方法实现路径特征初始抽取,捕获可分辨路径上的定时辅助同步点;基于定时辅助同步点构建1-D CNN网络学习第二阶段中的定时偏移;最后,结合两阶段处理,获得系统最终的定时同步偏移估计。相比于基于压缩感知的定时同步方法和基于极限学习机的定时同步方法,所研究的二阶段OFDM系统定时同步方法提高了定时同步准确性,并有效地降低计算复杂度与处理延迟。 展开更多
关键词 二阶段定时同步 一维卷积神经网络 正交频分复用
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基于DCNN算法的非均匀光照图像人脸识别系统研究
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作者 任金金 任敬敏 +1 位作者 张怡然 曹攀玲 《信息与电脑》 2023年第1期187-189,共3页
人脸识别系统的人脸图像特征识别失误较高,因此文章设计了基于DCNN算法的非均匀光照图像人脸识别系统。硬件方面设计,采用ARM720T处理器与S3C2440芯片。软件方面建立非均匀光照图像人脸识别功能模块,设计软件整体架构。利用动态卷积神... 人脸识别系统的人脸图像特征识别失误较高,因此文章设计了基于DCNN算法的非均匀光照图像人脸识别系统。硬件方面设计,采用ARM720T处理器与S3C2440芯片。软件方面建立非均匀光照图像人脸识别功能模块,设计软件整体架构。利用动态卷积神经网络(Dynamic Convolution Neural Network,DCNN)算法,设计人脸光照图像特征的识别程序,在非均匀光照条件下仍能有效地识别出人脸特征。测试结果表明,该能够准确识别出人脸特征,人脸系统识别得更加清晰,能够应用于实际生活中。 展开更多
关键词 动态卷积神经网络(dcnn)算法 非均匀光照 图像 人脸识别 图像特征 识别程序
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AR-MED共振特征增强的风电齿轮箱故障诊断
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作者 孙抗 史晓玉 +1 位作者 赵来军 杨明 《组合机床与自动化加工技术》 北大核心 2024年第8期163-167,174,共6页
针对风电齿轮箱故障时脉冲成分往往淹没在其他频率分量中,早期故障特征难以有效提取的问题,提出一种自回归最小熵解卷积(AR-MED)共振特征增强的风电齿轮箱故障诊断方法,并结合一维卷积神经网络(1DCNN),实现齿轮箱高精度故障诊断。首先,... 针对风电齿轮箱故障时脉冲成分往往淹没在其他频率分量中,早期故障特征难以有效提取的问题,提出一种自回归最小熵解卷积(AR-MED)共振特征增强的风电齿轮箱故障诊断方法,并结合一维卷积神经网络(1DCNN),实现齿轮箱高精度故障诊断。首先,使用共振稀疏分解算法(RSSD)将振动信号分解成含有噪声和谐波成分的高共振分量和含有故障冲击成分的低共振分量;其次,对低共振分量使用自回归最小熵解卷积运算,增强低共振分量中微弱的周期性冲击成分;最后,构建自回归最小熵解卷积共振特征增强的1DCNN模型,将分解得到的谐波分量和周期性冲击分量进行特征融合以及有针对的训练和分类。实验结果表明,与现有故障诊断模型相比,所提方法在提取风电齿轮箱的故障特征信息以及提高故障诊断精度方面具有有效性和优越性。 展开更多
关键词 共振稀疏分解 自回归最小熵解卷积 特征增强 一维卷积神经网络 风电齿轮箱
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基于深度学习的语音识别系统实现方法
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作者 窦亚珍 《电声技术》 2024年第10期74-76,共3页
研究基于深度学习的语音识别系统实现方法,首先探讨语音识别系统的总体框架,其次深入研究梅尔倒谱系数(MelFrequency Cepstral Coefficient,MFCC)的提取和深度卷积神经网络(Deep Convolutional Neural Network,DCNN)的基本原理,最后基于... 研究基于深度学习的语音识别系统实现方法,首先探讨语音识别系统的总体框架,其次深入研究梅尔倒谱系数(MelFrequency Cepstral Coefficient,MFCC)的提取和深度卷积神经网络(Deep Convolutional Neural Network,DCNN)的基本原理,最后基于Python和PyTorch框架进行系统测试。实验结果表明,所提方法在准确率、精确率及召回率方面均表现优异,能够较好地捕捉大多数样本。 展开更多
关键词 深度卷积神经网络(dcnn) 语音识别 PYTHON
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Marine aquaculture mapping using GF-1 WFV satellite images and full resolution cascade convolutional neural network 被引量:1
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作者 Yongyong Fu Shucheng You +6 位作者 Shujuan Zhang Kun Cao Jianhua Zhang Ping Wang Xu Bi Feng Gao Fangzhou Li 《International Journal of Digital Earth》 SCIE EI 2022年第1期2047-2060,共14页
Growing demand for seafood and reduced fishery harvests have raised intensive farming of marine aquaculture in coastal regions,which may cause severe coastal water problems without adequate environmental management.Ef... Growing demand for seafood and reduced fishery harvests have raised intensive farming of marine aquaculture in coastal regions,which may cause severe coastal water problems without adequate environmental management.Effective mapping of mariculture areas is essential for the protection of coastal environments.However,due to the limited spatial coverage and complex structures,it is still challenging for traditional methods to accurately extract mariculture areas from medium spatial resolution(MSR)images.To solve this problem,we propose to use the full resolution cascade convolutional neural network(FRCNet),which maintains effective features over the whole training process,to identify mariculture areas from MSR images.Specifically,the FRCNet uses a sequential full resolution neural network as the first-level subnetwork,and gradually aggregates higher-level subnetworks in a cascade way.Meanwhile,we perform a repeated fusion strategy so that features can receive information from different subnetworks simultaneously,leading to rich and representative features.As a result,FRCNet can effectively recognize different kinds of mariculture areas from MSR images.Results show that FRCNet obtained better performance than other classical and recently proposed methods.Our developed methods can provide valuable datasets for large-scale and intelligent modeling of the marine aquaculture management and coastal zone planning. 展开更多
关键词 Mariculture areas GaoFen-1 wide-field-of-view images fully convolutional neural networks deep learning
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