Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network

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.

基于时频域融合和ECA-1DCNN的航空串联故障电弧检测

为了快速准确地检测航空交流线路中出现的串联故障电弧,提出了一种基于时频域融合和加入高效注意力机制(efficient channel attention, ECA)的一维卷积神经网络(one-dimensional convolutional neural network, 1DCNN)的故障检测算法。首先,搭建航空交流电弧故障实验平台,负载选择多类型、多参数值进行电流信号的采集;其次,为了保留更多的故障信息,分析其特征频段,经过大量数据验证,航空串联电弧在发生时,1 000~4 000 Hz分量具有一定的占比,因此将原始信号与特征频段进行融合,融合后的一维数据作为模型输入;最后,搭建ECA-1DCNN检测模型,进行训练,并通过K折交叉验证模型的有效性,得到测试集平均准确率为97.96%。该方法网络层数较少,计算快速,避免了复杂时频域计算过程,较为智能,对航空串联电弧检测装置的研究提供了理论参考。

基于BS-1DCNN的海缆振动信号识别

光纤振动信号是非线性的,传统的非线性振动信号识别方法通常需要信号分析和特征选择,既耗时又复杂。本文提出一种光纤振动信号识别新方法,可以直接提取特征,对原始信号进行分类,简化识别过程。本方法用支持向量机代替Softmax分类器,优化一维卷积神经网络(one-dimensional convolution neural network,1DCNN),以提高1DCNN结果在小样本条件下的稳定性。采用鸟群算法(bird swarm algorithm,BSA)对支持向量机(support vector machine,SVM)参数进行了优化,有效地提高识别精度。将本文提出的BS-1DCNN方法与1DCNN、VMD-GA-SVM、VMD-PSO-SVM、VMD-BSA-SVM共4种方法进行比较,结果表明,BS-1DCNN在识别准确率和测试时间方面性能表现良好。该算法能有效提高海缆振动信号识别率,且在不同样本比例下均能达到较好的识别效果。

Individual Dairy Cattle Recognition Based on Deep Convolutional Neural Network

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.

Lightweight and highly robust memristor-based hybrid neural networks for electroencephalogram signal processing

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.

Predicting Concrete Compressive Strength Using Deep Convolutional Neural Network Based on Image Characteristics

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.

MSSTNet:Multi-scale facial videos pulse extraction network based on separable spatiotemporal convolution and dimension separable attention

Background The use of remote photoplethysmography(rPPG)to estimate blood volume pulse in a noncontact manner has been an active research topic in recent years.Existing methods are primarily based on a singlescale region of interest(ROI).However,some noise signals that are not easily separated in a single-scale space can be easily separated in a multi-scale space.Also,existing spatiotemporal networks mainly focus on local spatiotemporal information and do not emphasize temporal information,which is crucial in pulse extraction problems,resulting in insufficient spatiotemporal feature modelling.Methods Here,we propose a multi-scale facial video pulse extraction network based on separable spatiotemporal convolution(SSTC)and dimension separable attention(DSAT).First,to solve the problem of a single-scale ROI,we constructed a multi-scale feature space for initial signal separation.Second,SSTC and DSAT were designed for efficient spatiotemporal correlation modeling,which increased the information interaction between the long-span time and space dimensions;this placed more emphasis on temporal features.Results The signal-to-noise ratio(SNR)of the proposed network reached 9.58dB on the PURE dataset and 6.77dB on the UBFC-rPPG dataset,outperforming state-of-the-art algorithms.Conclusions The results showed that fusing multi-scale signals yielded better results than methods based on only single-scale signals.The proposed SSTC and dimension-separable attention mechanism will contribute to more accurate pulse signal extraction.

结合SE-VAE与M1DCNN的小样本数据下轴承故障诊断

针对轴承故障诊断中故障样本数量少导致诊断正确率低的问题,提出了一种基于注意力机制变分自编码器(SE-VAE)和多尺度一维卷积神经网络(M1DCNN)的轴承故障诊断方法。将轴承数据集的训练集输入到SE-VAE中进行训练,生成与训练样本分布相似的生成样本,并添加到训练集中增加训练集的样本数量。将扩充后的训练集输入到M1DCNN中进行训练,随后将训练好的模型应用于测试集,输出故障诊断结果。实验结果表明,所提方法能够在不同负载的小样本轴承故障数据集上取得较好的故障诊断准确率。

基于RIME和1DCNN-LSTM-Attention的无创血糖预测模型研究

实现无创血糖检测对于糖尿病患者来说具有重要意义,然而目前市面上的无创血糖仪存在检测精度不高的问题。为了提高无创血糖检测的准确度,基于近红外无创血糖检测仪,构建了1DCNN-LSTM-Attention混合预测模型,同时引入了霜冰优化算法(RIME)。该模型通过一维卷积神经网络(1DCNN)提取数据中的局部特征,将所提取的特征向量作为长短期记忆(LSTM)网络的输入,捕捉数据中的依赖关系;采用注意力机制(Attention)为LSTM的输出赋予不同的权重,增强关键信息提取;通过RIME算法优化模型参数,避免陷入局部最优解。结果表明,引入RIME的1DCNN-LSTM-Attention混合模型预测效果优于1DCNN、LSTM、1DCNN-LSTM、1DCNN-LSTM-Attention等模型,预测血糖值与有创血糖值的平均绝对误差为0.121 0,均方误差为0.018 6,相关系数达到了0.982 3。该模型有助于提高近红外无创血糖检测的精确度和可靠性。

基于太赫兹光谱及1DCNN-BiLSTM的黄蜀葵花金丝桃苷含量预测

太赫兹(THz)光谱具有信噪比高、光子能量低、穿透性强、安全和快速等优点,已广泛应用于食药检测.现有基于HPLC等方法的黄蜀葵花金丝桃苷含量检测耗时长、操作复杂.提出一种基于太赫兹光谱及1DCNN-BiLSTM的黄蜀葵花金丝桃苷含量预测方法:首先采集黄蜀葵花的太赫兹时域谱,并通过傅里叶等变换获取其7种光谱数据;然后利用主成分分析(PCA)对7种光谱数据降维,以获得维数一致的7元样本数据;接着将降维对齐后的7元样本数据输入设计好的1DCNN-BiLSTM网络,以获得金丝桃苷含量预测.与1DCNN、BiLSTM的对比实验结果表明,1DCNN-BiLSTM网络具有较高的预测精度,10次随机实验的平均决定系数达0.970 5.

Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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.

Non-Intrusive Load Identification Model Based on 3D Spatial Feature and Convolutional Neural Network

Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I trajectory characteristics to a large extent, so it is widely used in load identification. However, using single binary V-I trajectory feature for load identification has certain limitations. In order to improve the accuracy of load identification, the power feature is added on the basis of the binary V-I trajectory feature in this paper. We change the initial binary V-I trajectory into a new 3D feature by mapping the power feature to the third dimension. In order to reduce the impact of imbalance samples on load identification, the SVM SMOTE algorithm is used to balance the samples. Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D feature has better observability and the proposed model has higher identification performance compared with other classification models on the public data set PLAID.

基于1DCNN-BiLSTM-BiGRU的电能质量扰动分类方法

为了应对电能质量扰动(PQD)识别中噪声干扰导致的识别率下降问题,本文提出一种基于一维卷积神经网络(1DCNN)-双向长短期记忆(BiLSTM)网络-双向门控循环单元(BiGRU)的PQD分类方法。该方法首先借助1DCNN有效地提取原始信号的浅层局部特征,然后通过BiLSTM和BiGRU组合模块对时序信息和上下文关系进行深入处理,从而实现深层时序特征的提取。最后,将所提取的特征经分类模块用于PQD识别。仿真结果表明,与传统方法相比,本文所提方法在准确性方面更具优势,且抗噪声能力更强。

基于1DCNN-GRU的启闭机液压系统故障诊断

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。

Hybrid 1DCNN-Attention with Enhanced Data Preprocessing for Loan Approval Prediction

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.

基于LSTM与1DCNN的导弹轨迹预测方法

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。

基于1DCNN-LSTM和迁移学习的短期电力负荷预测

针对在短期电力负荷预测中,当某区域电力负荷数据较少时,负荷预测精度较差的问题,提出一种基于1DCNN-LSTM(1D Convolutional Neural-Long Short-Term Memory Networks)和参数迁移的短期负荷预测方法,并采用1DCNN-LSTM结合迁移学习针对性提高预测精度。使用美国某地区的实际负荷数据进行仿真分析,实验结果表明,该方法能有效提升区域电力负荷数据缺失时负荷预测的精度。

基于1DCNN-BiLSTM的电力电缆故障诊断

为了提升电力电缆故障诊断的准确率,解决电缆故障诊断中过程烦琐、效率低、识别精度不高等问题,使其能够在电缆故障发生时准确地诊断出故障类型,提出了一种基于连续卷积神经网络(CNN)和双向长短网络记忆(BiLSTM)的电缆故障检测方法。通过Simulink搭建仿真模型,提取单相接地短路、两相接地短路、两相相间短路、三相短路故障的电压信号,构建故障样本集。将信号输入到该网络模型,一维卷积神经网络提取电缆故障信号的局部特征,双向长短时记忆网络捕捉故障信号时序信息,基于自动提取的特征实现对电缆故障的诊断。经仿真结果验证,该方法能够对电力电缆的4种短路故障进行识别和分类,对单相接地短路故障和三相短路故障分类的正确概率达到97%,对两相接地短路和两相相间短路分类的正确概率达到92%,整体准确率达到98.37%。通过对损失函数曲线、准确率曲线的分析,证明该方法能够取得较好的电缆故障诊断效果。最后使用实际数据进行验证,结果表明该方法具有可行性。

基于1DCNN-GRU的网络入侵检测混合模型

针对现有网络入侵检测模型存在检测率低、误报率高的问题,提出一种基于一维卷积神经网络(1DCNN)与门控循环单元(GRU)网络的网络入侵检测混合模型,1DCNN以膨胀卷积的方式将输入的数据逐层卷积合并以提取输入特征,GRU进一步提取特征数据并挖掘时序信息,并且在训练模型的过程中加入高斯噪声作为数据增广手段以增强网络的鲁棒性,从而提高模型的泛化能力。采用UNSW-NB15数据集进行二分类实验,该模型与其他模型相比较,有效地提高了网络入侵检测的准确率,降低了误报率。

基于PCA-1DCNN的近红外光谱粮食作物主要成分检测方法

针对传统的近红外光谱定量技术难以选择合适的光谱预处理方法且模型预测精度低的问题,以3个谷物数据集的近红外光谱数据集为研究对象,构建了基于主成分分析光谱筛选算法的一维卷积神经网络模型。与传统的偏最小二乘回归和支持向量机模型的性能做了对比后,一维卷积神经网络构建的模型性能均为最优。其中在对玉米数据集的水分、油脂、蛋白质、淀粉的定量建模中,模型的决定系数分别为99.09%、98.15%、98.89%、99.60%;在对grain数据集的定量建模中,4种成分模型的决定系数分别为100%、100%、100%、99.99%;在对小麦数据集的定量建模中,小麦蛋白质模型的决定系数为99.80%。为了验证主成分分析光谱筛选算法对粮食作物主要成分定量回归模型的有效性,在3个光谱数据集上去除了主成分分析算法进行消融实验。研究结果表明:基于主成分分析算法与一维卷积神经网络的回归建模方法为粮食作物成分含量的检测提供一种快速无损精确的判定方式,研究结果对于粮食作物成分的含量检测具有促进作用。