基于SDAE的终端区气象场景模式识别方法

气象条件是影响终端区航空器运行安全及效率的主要因素之一。为提高终端区气象场景模式识别精度,采用基于堆叠降噪自编码(SDAE)的聚类模型,在输入层添加随机噪声、构建3层自编码、逐层贪婪训练,降维后的特征作为聚类的输入,实现气象场景的模式识别。以天津滨海国际机场2022年气象观测数据为例,基于SDAE与欧氏距离、汉明距离、曼哈顿距离等传统相似性距离度量方法,分别使用K-medoids与FCM两种聚类方法进行验证。结果表明:基于SDAE的相似性度量在K-medoids与FCM聚类中均表现最优,与其他相似性度量相比差异率分别达到22.4%,12%,17.7%与24.8%,10.7%,11.8%,且运算时间最短,证明了基于SDAE的度量、聚类效果最优,最终识别出8个气象场景,各场景分类清晰明确。

基于MRSDAE-KPCA结合Bi-LST的滚动轴承剩余使用寿命预测

针对现有滚动轴承剩余使用寿命预测方法在提取数据特征时没有充分考虑数据的内部分布,且在构建健康因子时还需要专家经验进行人工提取等问题,提出一种基于流形正则化堆栈去噪自编码器、核主成分分析并结合双向长短时记忆网络的滚动轴承剩余使用寿命预测方法。首先采用无监督的堆栈去噪自编码器网络对原始振动数据进行深层特征提取,并使用核主成分分析法进一步降维,以提高健康因子的指标稳定性;然后在堆栈去噪自编码器中加入流形正则化,最大程度保留编码器隐藏层内部的数据分布结构,提高模型提取数据特征的有效性。最后使用双向长短时记忆网络预测轴承的剩余使用寿命,并采用AdaMax优化算法对网络模型的超参数进行自适应寻优。分析结果表明,提出的滚动轴承剩余使用寿命预测方法具有更高的精度。

融合DAE-LSTM的认知物联网智能频谱感知算法

第五代(fifth-generation,5G)移动通信技术的兴起,推动了物联网(Internet of things,IoT)的发展。然而,随着物联网数据传输量的爆发式增长,频谱资源短缺问题越来越严重。频谱感知技术极大的提高了物联网频谱利用率。但是,物联网移动通信环境的复杂性高以及信号易畸变的特性,对现有的频谱感知算法提出了重大挑战。因此,提出了一种融合去噪自编码器(denoising autoencoder,DAE)和改进长短时记忆(long short term memory,LSTM)神经网络的智能频谱感知算法。DAE通过编码和解码过程挖掘移动信号的底层结构特征,改进的LSTM频谱感知分类器模型结合过去时刻信息特征对时序信号序列进行分类。与支持向量机(support vector machine,SVM)、循环神经网络(recurrent neural network,RNN)、LeNet5、学习矢量量化(learning vector quantization,LVQ)和Elman算法相比,该算法的感知性能提高了45%。

Reconstruction of time series with missing value using 2D representation-based denoising autoencoder

Time series analysis is a key technology for medical diagnosis,weather forecasting and financial prediction systems.However,missing data frequently occur during data recording,posing a great challenge to data mining tasks.In this study,we propose a novel time series data representation-based denoising autoencoder(DAE)for the reconstruction of missing values.Two data representation methods,namely,recurrence plot(RP)and Gramian angular field(GAF),are used to transform the raw time series to a 2D matrix for establishing the temporal correlations between different time intervals and extracting the structural patterns from the time series.Then an improved DAE is proposed to reconstruct the missing values from the 2D representation of time series.A comprehensive comparison is conducted amongst the different representations on standard datasets.Results show that the 2D representations have a lower reconstruction error than the raw time series,and the RP representation provides the best outcome.This work provides useful insights into the better reconstruction of missing values in time series analysis to considerably improve the reliability of timevarying system.

基于粒子群算法和SDAE的采棉头故障诊断研究

针对采棉头故障诊断和故障预警缺失的问题,提出基于粒子群优化算法(PSO)优化堆叠降噪自编码器(SDAE)的采棉头故障诊断方法。将采棉滚筒转速与采棉头输入转速比和采棉头液压驱动压力作为输入,利用PSO算法对SDAE网络的超参数进行自适应选取,确定网络结构,然后将预处理后的数据输入PSO-SDAE网络进行深度特征提取,经过前向传播和反向微调,得到采棉头故障诊断模型。通过采棉头堵塞故障模拟试验对算法进行验证,试验结果表明:PSO-SDAE网络诊断方法在特征有效提取、故障诊断准确率方面均优于SDAE网络、支持向量机(SVM)、反向传播神经网络(BPNN)以及深度置信网络(DBN),可用于采棉头故障诊断和故障预警。

Offline Urdu Nastaleeq Optical Character Recognition Based on Stacked Denoising Autoencoder

Offline Urdu Nastaleeq text recognition has long been a serious problem due to its very cursive nature. In order to get rid of the character segmentation problems, many researchers are shifting focus towards segmentation free ligature based recognition approaches. Majority of the prevalent ligature based recognition systems heavily rely on hand-engineered feature extraction techniques. However, such techniques are more error prone and may often lead to a loss of useful information that might hardly be captured later by any manual features. Most of the prevalent Urdu Nastaleeq test recognition was trained and tested on small sets. This paper proposes the use of stacked denoising autoencoder for automatic feature extraction directly from raw pixel values of ligature images. Such deep learning networks have not been applied for the recognition of Urdu text thus far. Different stacked denoising autoencoders have been trained on 178573 ligatures with 3732 classes from un-degraded(noise free) UPTI(Urdu Printed Text Image) data set. Subsequently, trained networks are validated and tested on degraded versions of UPTI data set. The experimental results demonstrate accuracies in range of 93% to 96% which are better than the existing Urdu OCR systems for such large dataset of ligatures.

Improved Denoising Autoencoder for Maritime Image Denoising and Semantic Segmentation of USV

Unmanned surface vehicle(USV)is currently a hot research topic in maritime communication network(MCN),where denoising and semantic segmentation of maritime images taken by USV have been rarely studied.The former has recently researched on autoencoder model used for image denoising,but the existed models are too complicated to be suitable for real-time detection of USV.In this paper,we proposed a lightweight autoencoder combined with inception module for maritime image denoising in different noisy environments and explore the effect of different inception modules on the denoising performance.Furthermore,we completed the semantic segmentation task for maritime images taken by USV utilizing the pretrained U-Net model with tuning,and compared them with original U-Net model based on different backbone.Subsequently,we compared the semantic segmentation of noised and denoised maritime images respectively to explore the effect of image noise on semantic segmentation performance.Case studies are provided to prove the feasibility of our proposed denoising and segmentation method.Finally,a simple integrated communication system combining image denoising and segmentation for USV is shown.

Robust and Efficient Data Transmission over Noisy Communication Channels Using Stacked and Denoising Autoencoders

We study the effects of quantization and additive white Gaussian noise(AWGN) in transmitting latent representations of images over a noisy communication channel. The latent representations are obtained using autoencoders(AEs). We analyze image reconstruction and classification performance for different channel noise powers, latent vector sizes, and number of quantization bits used for the latent variables as well as AEs’ parameters. The results show that the digital transmission of latent representations using conventional AEs alone is extremely vulnerable to channel noise and quantization effects. We then propose a combination of basic AE and a denoising autoencoder(DAE) to denoise the corrupted latent vectors at the receiver. This approach demonstrates robustness against channel noise and quantization effects and enables a significant improvement in image reconstruction and classification performance particularly in adverse scenarios with high noise powers and significant quantization effects.

基于DAE和GRU组合的流量异常检测方法

流量异常检测能够有效识别网络流量数据中的攻击行为,是一种重要的网络安全防护手段。近年来,深度学习在流量异常检测领域得到了广泛应用,现有的深度学习模型进行流量异常检测存在两个问题:一是数据受噪声影响导致检测鲁棒性差、准确率低;二是数据特征维度高以及模型参数多导致训练和检测速度慢。为了在降低流量数据噪声影响的基础上提高检测速度和准确性,本文提出了一种基于去噪自编码器(Denoising Auto Encoder, DAE)和门控循环单元(Gated Recurrent Unit, GRU)组合的流量异常检测方法。首先设计了基于DAE的流量特征提取算法,采用小批量梯度下降算法对DAE进行训练,通过最小化含噪声数据的重构向量与原始输入向量间的差异,有效提取具有较强鲁棒性的流量特征,降低特征维度。然后设计了基于GRU的异常检测算法,利用提取的低维流量特征数据训练GRU,从而构建异常流量分类器,实现对攻击流量的准确检测。最后在NSL-KDD、UNSW-NB15、CICIDS2017数据集上的实验结果表明:与其他的机器学习、深度学习方法相比,本文所提方法的检测准确率最大提升了18.71%。同时,本文方法可以实现较高的精确率、召回率和检测效率,同时具有较低的误报率。在面对数据受到噪声破坏时,具有较强的检测鲁棒性。

Data Cleaning Based on Stacked Denoising Autoencoders and Multi-Sensor Collaborations

Wireless sensor networks are increasingly used in sensitive event monitoring.However,various abnormal data generated by sensors greatly decrease the accuracy of the event detection.Although many methods have been proposed to deal with the abnormal data,they generally detect and/or repair all abnormal data without further differentiate.Actually,besides the abnormal data caused by events,it is well known that sensor nodes prone to generate abnormal data due to factors such as sensor hardware drawbacks and random effects of external sources.Dealing with all abnormal data without differentiate will result in false detection or missed detection of the events.In this paper,we propose a data cleaning approach based on Stacked Denoising Autoencoders(SDAE)and multi-sensor collaborations.We detect all abnormal data by SDAE,then differentiate the abnormal data by multi-sensor collaborations.The abnormal data caused by events are unchanged,while the abnormal data caused by other factors are repaired.Real data based simulations show the efficiency of the proposed approach.

基于DAE-iForest的燃气轮机排气温度异常检测

通过燃气轮机排气温度对燃烧室及涡轮前几级叶片等高温部件开展异常检测,早期可靠的检测异常对确保燃气轮机高效运行至关重要。随着机器学习的广泛应用,数据驱动的状态监测方法已经越来越流行。针对故障数据缺失场景下的的燃气轮机排气温度分布异常检测问题,使用深度自编码器(Deep Autoencoder,DAE)学习特征,并采用隔离森林(isolated Forset,iForset)学习特征数据的正常信息,从而实现异常检测。与其他单分类的异常检测方法对比,该方法具有最佳的检测性能指标,能实现有效灵敏的燃气轮机排气温度异常检测。

基于CDAE-LMSAF的水下目标辐射信号增强

针对远距离目标(如潜艇、鱼雷等)被动定位时存在海洋环境噪声、舰艇自身噪声等影响,从而导致定位精度降低的问题,本文提出了一种基于卷积去噪自编码器和自适应最小均方误差滤波(CDAE-LMSAF)的增强方法,通过提取水下目标辐射信号和含噪信号的时频谱图特征,作为卷积去噪自编码的输入进行训练和建模,再利用自适应滤波器对神经网络增强后的音频进行优化,实现对水下目标辐射信号的增强。仿真实验结果表明,在信噪比为-5 dB时,本文方法的信噪比为17.51 dB,相比于多窗谱谱减法的1.23 dB,卷积去噪自编码器的7.21 dB,自适应最小均方误差滤波的4.12 dB,本文方法具有更高的信噪比增益。

基于CNN+DAE集成模型的电机主轴轴承故障诊断研究

为了提高电机主轴轴承故障诊断效率,设计了一种优化降噪自编码器(DAE)轴承故障诊断模型,开发了一种高效数据预处理方法,并对网络结构实施了调整。研究结果表明:逐渐提高训练轮次后获得更高检测精确,在到达20个训练轮次时进入稳定阶段,准确率达到99.52%。CNN+DAE模型可以满足初始数据特征的准确识别,对故障达到高精度的分类性能。较小噪声信号下,采用DAE提取特征都表现出比10 dB噪声状态下更弱的泛化能力与鲁棒性,达到了较高的识别率。每种自编码器网络对于初始时域信号都没有达到良好性能,判断卷积神经网络进行特征提取时比全连接神经网络更优。该研究去除了网络结构中卷积神经网络(CNN)池化层,全面保留初始一维振动信号数据,实现模型鲁棒性与泛化能力的显著提升。

A Double-Weighted Deterministic Extreme Learning Machine Based on Sparse Denoising Autoencoder and Its Applications

Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. However, due to the traditional ELM shallow architecture, it requires a large number of hidden nodes when dealing with high-dimensional data sets to ensure its classification performance. The other aspect, it is easy to degrade the classification performance in the face of noise interference from noisy data. To improve the above problem, this paper proposes a double pseudo-inverse extreme learning machine (DPELM) based on Sparse Denoising AutoEncoder (SDAE) namely, SDAE-DPELM. The algorithm can directly determine the input weight and output weight of the network by using the pseudo-inverse method. As a result, the algorithm only requires a few hidden layer nodes to produce superior classification results when classifying data. And its combination with SDAE can effectively improve the classification performance and noise resistance. Extensive numerical experiments show that the algorithm has high classification accuracy and good robustness when dealing with high-dimensional noisy data and high-dimensional noiseless data. Furthermore, applying such an algorithm to Miao character recognition substantiates its excellent performance, which further illustrates the practicability of the algorithm.

采用SDAE-FFNN网络的PMSM逆变器开路故障诊断研究

针对电机驱动系统故障难以捕捉、特征精细刻画难和诊断准确性差等重难点问题,提出了一种融合堆叠降噪自编码器和前馈神经网络(stacked denoising autoencoder-feedforward neural network, SDAE-FFNN)模型。模拟仿真三相逆变器开路故障的不同类型;提取永磁同步电机输出的三相定子电流作为故障特征提取的对象;融合多种频域特征提取方法提取非线性特征并整合形成高维数据集;采用SDAE-FFNN模型实现对三相逆变器开路故障识别;对比传统深度网络模型,验证算法可行性。实验结果表明,SDAE-FFNN模型完成了有效故障分类识别,平均识别准确率高达98.8021%,优于传统深度学习方法。

基于DAE-BP神经网络的化工过程质量预测

BP神经网络因具有良好的非线性拟合能力,在建立预测模型中得到广泛应用;但化工过程数据不仅存在非线性特征,而且难以避免受噪声影响,造成数据波动从而影响预测模型准确性;为此,提出一种降噪自编码器融合反向传播算法(简称为,DAE-BP)的化工过程质量预测方法;首先,采用无监督学习模型降噪自编码器完成初始数据的噪声消除,其具有噪声鲁棒性的特点,在数据受到损坏的情况下可尽可能地恢复数据的原始状态,有利于进一步的质量预测;在此基础上,将获取的数据特征作为有监督学习模型BP神经网络的输入以获得可靠的预测结果;在脱丁烷塔化工过程实例上验证方法有效性;并与单一BP算法、主成分分析(PCA)及自编码器(AE)改进的BP算法作为对照;结果表明,经过DAE改进后的BP算法预测误差为1.2%,相比单一的BP算法提高了3.2%精度,较PCA-BP及AE-BP预测误差精度分别提高了2.3%、1.9%,表现出最好的预测性能。

Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.

Effective Denoising Architecture for Handling Multiple Noises

Object detection,one of the core research topics in computer vision,is extensively used in various industrial activities.Although there have been many studies of daytime images where objects can be easily detected,there is relatively little research on nighttime images.In the case of nighttime,various types of noises,such as darkness,haze,and light blur,deteriorate image quality.Thus,an appropriate process for removing noise must precede to improve object detection performance.Although there are many studies on removing individual noise,only a few studies handle multiple noises simultaneously.In this paper,we pro-pose a convolutional denoising autoencoder(CDAE)-based architecture trained on various types of noises.We also present various composing modules for each noise to improve object detection performance for night images.Using the exclusively dark(ExDark)Image dataset,experimental results show that the Sequentialfiltering architecture showed superior mean average precision(mAP)compared to other architectures.

基于QWDAE和HWMHGRU融合的电力系统短期负荷预测模型

为提升电力系统短期负荷预测精度,提出量子加权降噪自编码器和高速通道多层级门控循环单元神经网络融合的短期负荷预测模型。首先利用量子信息处理机制,采用量子加权神经元构建量子加权降噪自编码器,挖掘负荷序列中的有效信息作为输入特征;然后提出具有两级门控结构和高速通道结构的高速通道多层级门控循环单元,构成量子加权降噪自编码器和高速通道多层级门控循环单元融合的短期负荷预测模型。仿真结果表明,所提模型具有较好的预测精度和预测稳定性。

基于SWDAE-SVC的矿用齿轮箱自监督故障诊断方法

针对矿用齿轮箱振动数据易受噪声污染且故障类别标注困难问题,提出了一种基于栈式小波降噪自编码器(SWDAE)和支持向量聚类(SVC)的自监督故障诊断方法。首先,将小波映射函数引入栈式降噪自编码器(SDAE)模型,以实现强噪声下矿用齿轮箱的敏感故障特征提取。然后,利用所得高层抽象特征构建SVC模型,以实现无标签信息下的矿用齿轮箱故障诊断。实验结果表明,所提SWDAE-SVC方法具有优异的故障诊断性能。