Deep Learning-Based Stacked Auto-Encoder with Dynamic Differential Annealed Optimization for Skin Lesion Diagnosis

Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models.On the other hand,skin lesionbased segregation and disintegration procedures play an essential role in earlier skin cancer detection.However,artefacts,an unclear boundary,poor contrast,and different lesion sizes make detection difficult.To address the issues in skin lesion diagnosis,this study creates the UDLS-DDOA model,an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder(UDLS)optimized by Dynamic Differential Annealed Optimization(DDOA).Pre-processing,segregation,feature removal or separation,and disintegration are part of the proposed skin lesion diagnosis model.Pre-processing of skin lesion images occurs at the initial level for noise removal in the image using the Top hat filter and painting methodology.Following that,a Fuzzy C-Means(FCM)segregation procedure is performed using a Quasi-Oppositional Elephant Herd Optimization(QOEHO)algorithm.Besides,a novel feature extraction technique using the UDLS technique is applied where the parameter tuning takes place using DDOA.In the end,the disintegration procedure would be accomplished using a SoftMax(SM)classifier.The UDLS-DDOA model is tested against the International Skin Imaging Collaboration(ISIC)dataset,and the experimental results are examined using various computational attributes.The simulation results demonstrated that the UDLS-DDOA model outperformed the compared methods significantly.

Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis

(Aim)COVID-19 is an ongoing infectious disease.It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021.Traditional computer vision methods have achieved promising results on the automatic smart diagnosis.(Method)This study aims to propose a novel deep learning method that can obtain better performance.We use the pseudo-Zernike moment(PZM),derived from Zernike moment,as the extracted features.Two settings are introducing:(i)image plane over unit circle;and(ii)image plane inside the unit circle.Afterward,we use a deep-stacked sparse autoencoder(DSSAE)as the classifier.Besides,multiple-way data augmentation is chosen to overcome overfitting.The multiple-way data augmentation is based on Gaussian noise,salt-and-pepper noise,speckle noise,horizontal and vertical shear,rotation,Gamma correction,random translation and scaling.(Results)10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06%±1.54%,a specificity of 92.56%±1.06%,a precision of 92.53%±1.03%,and an accuracy of 92.31%±1.08%.Its F1 score,MCC,and FMI arrive at 92.29%±1.10%,84.64%±2.15%,and 92.29%±1.10%,respectively.The AUC of our model is 0.9576.(Conclusion)We demonstrate“image plane over unit circle”can get better results than“image plane inside a unit circle.”Besides,this proposed PZM-DSSAE model is better than eight state-of-the-art approaches.

Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder

With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.

Fault Diagnosis for Rolling Bearings with Stacked Denoising Auto-encoder of Information Aggregation

Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rolling bearing faults, a prognostic algorithm consisting of four phases was proposed. Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise. Unsupervised pre-training method, which can greatly simplify the traditional manual extraction approach, was utilized to process the depth of the data automatically. Furthermore, the aggregation layer of stacked denoising auto-encoder(SDA) was proposed to get rid of gradient disappearance in deeper layers of network, mix superficial nodes’ expression with deeper layers, and avoid the insufficient express ability in deeper layers. Principal component analysis(PCA) was adopted to extract different features for classification. According to the experimental data of this method and from the comparison results, the proposed method of rolling bearing fault classification reached 97.02% of correct rate, suggesting a better performance than other algorithms.

Transfer learning with deep sparse auto-encoder for speech emotion recognition

In order to improve the efficiency of speech emotion recognition across corpora,a speech emotion transfer learning method based on the deep sparse auto-encoder is proposed.The algorithm first reconstructs a small amount of data in the target domain by training the deep sparse auto-encoder,so that the encoder can learn the low-dimensional structural representation of the target domain data.Then,the source domain data and the target domain data are coded by the trained deep sparse auto-encoder to obtain the reconstruction data of the low-dimensional structural representation close to the target domain.Finally,a part of the reconstructed tagged target domain data is mixed with the reconstructed source domain data to jointly train the classifier.This part of the target domain data is used to guide the source domain data.Experiments on the CASIA,SoutheastLab corpus show that the model recognition rate after a small amount of data transferred reached 89.2%and 72.4%on the DNN.Compared to the training results of the complete original corpus,it only decreased by 2%in the CASIA corpus,and only 3.4%in the SoutheastLab corpus.Experiments show that the algorithm can achieve the effect of labeling all data in the extreme case that the data set has only a small amount of data tagged.

Predicting the Antigenic Variant of Human Influenza A(H3N2) Virus with a Stacked Auto-Encoder Model

The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic variants in time. Here, we built a stacked auto-encoder (SAE) model for predicting the antigenic variant of human influenza A(H3N2) viruses based on the hemagglutinin (HA) protein sequences. The model achieved an accuracy of 0.95 in five-fold cross-validations, better than the logistic regression model did. Further analysis of the model shows that most of the active nodes in the hidden layer reflected the combined contribution of multiple residues to antigenic variation. Besides, some features (residues on HA protein) in the input layer were observed to take part in multiple active nodes, such as residue 189, 145 and 156, which were also reported to mostly determine the antigenic variation of influenza A(H3N2) viruses. Overall,this work is not only useful for rapidly identifying antigenic variants in influenza prevention, but also an interesting attempt in inferring the mechanisms of biological process through analysis of SAE model, which may give some insights into interpretation of the deep learning

基于深度SSDAE网络的刀具磨损状态识别

针对刀具磨损状态识别过程中采集数据量大、干扰信号复杂且需人为选择特征参数的问题,为提高刀具磨损状态识别模型的鲁棒性与泛化性,提出了一种数据驱动下深度堆叠稀疏降噪自编码(stacking sparse denoising auto-encoder,简称SSDAE)网络的刀具磨损状态识别方法,实现隐藏在数据中深层次的数据特征自动挖掘。首先,将原始振动信号分解为一系列固有模态分量(intrinsic mode function,简称IMF),并采用皮尔逊相关系数法选取了最优固有模态来组合一个新的信号;其次,采用SSDAE网络自适应提取特征后对刀具磨损阶段进行了状态识别,识别精度达到98%;最后,对网络模型进行实验验证,并与最常用的刀具磨损状态识别方法进行了对比。实验结果表明,所提出的方法能够很好地处理非平稳振动信号,对不同刀具磨损阶段状态的识别效果良好,并具有较好的泛化性能和可靠性。

基于多特征优化的PolSAR数据农作物精细分类方法

农作物精细分类在农业资源调查、农作物种植结构监管等诸多领域具有重要意义。极化合成孔径雷达(Polarimetric synthetic aperture radar,PolSAR)能够有效探测伪装和穿透掩盖物,提取多种散射特征信息,获取覆盖农作物生长关键物候阶段的连续时序信息,有效提升表达作物遥感特征的丰富度,在农作物分类中独具优势。但多时相和多特征的引入必然导致模型运算量剧增,不利于工程应用。针对上述问题,本文提出了一种基于多特征优化的PolSAR数据农作物精细分类方法,首先对PolSAR数据进行多种极化目标分解及参数提取以获得多个散射特征;然后使用基于栈式稀疏自编码网络和ReliefF优选的方法进行特征增强与优化,获取最优特征集;最后构建具有2个分支结构的卷积神经网络,融合不同卷积深度输出的特征,完成农作物的高精度分类。通过对单时相数据的特征分析、单时相数据初步分类实验和多时相数据不同特征集结合分类器的对比实验,证明本文所提方法能够在低维特征输入的前提下,最大程度提取不同作物之间的差异性特征,准确高效地实现对农作物的精细分类,最高分类精度和Kappa系数分别达到97.69%和97.24%。

基于堆叠稀疏去噪自编码器的混合入侵检测方法

针对高维数据场景下传统入侵检测方法特征提取困难、检测准确率低等问题,提出一种集成多种深度学习模型的混合入侵检测方法.该方法由特征降维算法和混合检测模型2部分组成.首先,利用堆叠稀疏去噪自编码器对原始数据进行特征降维,从而剔除可能存在的噪声干扰和冗余信息.然后,采用一维卷积神经网络和双向门控循环单元学习数据中的空间维度特征和时序维度特征,将融合后的空时特征通过注意力分配不同的权重系数,从而使有用的信息得到更好表达,再经由全连接层训练后进行分类.为检验方案的可行性,在UNSW-NB15数据集上进行验证.结果表明,该模型与其他同类型入侵检测算法相比,拥有更优秀的检测性能,其准确率达到99.57%,误报率仅为0.68%.

基于融合模型的网络安全态势感知方法

伴随着网络技术的飞速发展,网络安全面临的风险也日益增加,网络攻击呈现复杂化、多样化的特征,给现有网络攻击应对措施带来了巨大挑战。态势感知技术作为一种新兴概念,为网络安全领域带来了新的思路。针对现有网络安全态势感知方法存在数据特征提取及较长时间序列数据处理能力不足的问题,提出一种融合堆栈稀疏自编码器(SSAE)、卷积神经网络(CNN)、双向门控循环单元(BiGRU)和注意力机制(AM)的模型。通过SSAE和CNN提取数据特征,利用AM强化BiGRU对关键信息的关注度,实现对异常流量的攻击类别判定,并结合网络安全态势量化指标,对网络安全态势进行量化评分并划分等级。实验结果表明,融合模型在各项指标上均优于传统深度学习模型,能够准确感知网络态势。

基于堆叠稀疏自编码器的多缸喷油器堵塞定位算法

燃油喷射系统的工作质量直接影响柴油机工作过程及性能,针对多缸机不同喷油器发生堵塞故障且故障程度不一时,传统故障诊断方法难以精准定位故障喷油器的问题,提出一种基于堆叠稀疏自编码器(Stacked Sparse Autoencoder,SSAE)的故障定位算法。通过SSAE提取不同喷油器发生堵塞故障时轨压信号的深层特征,以softmax网络实现故障部件定位。以一维轨压信号为输入,故障喷油器定位为输出,并研究算法超参数对算法精度的影响。研究结果表明,此算法能精准定位发生堵塞故障的喷油器,且精度不受堵塞程度的影响,故障诊断正确率可达96.7%。

基于山区大气电场演变特征与雷电定位数据的雷电临近预警方法

由于高原山区雷暴活动具有尺度小、离散性强的特点,实现山区重点资源区域的雷电灾害准确预警存在较大困难。考虑到雷暴时空演变与地面大气电场特征的关联关系,提出了一种基于大气电场监测数据与实时雷电定位信息的山区雷电临近预警方法。通过分析典型高原山区不同雷暴发展情况下的大气电场演化特性,发现山区大气电场可作为雷电定位数据的补充源,充分表征雷云剧烈放电和雷暴临近发展的特征信息。在预警过程中,首先将大气电场形态学梯度提取的快速抖动、暂态突变特征与时空匹配的地闪活动特征输入堆叠稀疏自编码器网络模型,判断监测区域附近是否出现雷云放电迹象,再利用雷暴距离变化或者电场波形变化判断雷电活动的临近趋势,最后综合两者的结果完成半径15km监测区域的雷电活动短时预警。在2023年云南山区雷雨季节的雷暴算例分析中,通过双源数据共同提取的山区雷暴活动预警特征的有效识别,可以实现预警准确率为90%,约44%的警报提前时间不小于30 min。

南海深水少井区浅层气储层地震预测技术及应用

为应对南海深水区浅层/超浅层气藏勘探中因测井资料匮乏且可靠性低而导致的岩石物理建模、地震识别及储层预测的难题,针对该区域的地质特点,笔者构建了疏松浅层岩石物理模型,建立了地震响应识别模板,提出了少井区地震反演低频模型的构建方法,有效获取了浅层储层弹性参数。利用岩石物理模板,科学预测了储层含气性。结果表明,南海深水区浅层气藏的纵波速度受含气饱和度显著影响,但饱和度超过10%后影响减弱。浅层气层的纵波阻抗和密度较低,低阻抗区域指示潜在气藏,但在饱和度预测时准确性有限,纵横波速度比识别游离气具有较好的效果。

基于SCG优化SSAE-FFNN的电能质量复合扰动深度特征提取与分类

随着智能电网的发展,电能质量问题已遍布电网并威胁着电网的安全稳定,且电能质量监测数据日渐庞大,因此实现大规模系统中电能质量扰动(power quality disturbances,PQDs)的深度特征提取及智能分类识别对电力系统污染检测与管理具有重要意义。为此,文中提出一种基于堆叠稀疏自编码器(stacked sparse auto encoder,SSAE)和前馈神经网络(feedforward neural network,FFNN)的电能质量复合扰动分类方法。首先,基于IEEE标准构建PQDs仿真模型。然后,建立基于SSAE-FFNN的PQDs分类模型,并引入缩放共轭梯度(scaled conjugate gradient,SCG)算法对模型进行优化,以提高梯度下降速度和网络训练效率。接着,为有效降低堆叠网络的重构损失同时提取出深度的低维特征,构建SSAE的逐层训练集及微调策略。最后,通过算例分析验证文中方法的分类效果、鲁棒性、泛化性和适用场景规模。结果表明,文中方法能够有效识别电能质量复合扰动,对含误差扰动和某地市电网的21组实测扰动录波数据也有较高的分类准确率。

A Time Series Intrusion Detection Method Based on SSAE,TCN and Bi-LSTM

In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.

拉曼光谱结合改进稀疏编码器特征优选的成品油混合浓度预测方法

成品油混合浓度的预测对成品油顺序输送过程中的安全监控、混油段分割具有重要的意义。本研究配制92#汽油-3#航煤以及3#航煤-0#车柴两组包含不同浓度的混合样品,并对其进行拉曼光谱采集;依次采用归一化、多元散射校正、BaselineWavelet基线校正3种光谱预处理方法进行优化;之后采用改进的栈式稀疏自编码器(Stacked Sparse Autoencoder,SSAE)模型对预处理之后的拉曼光谱进行稀疏特征提取,并结合全连接层进行回归预测;最后根据均方根误差(Root Mean Square Error,RMSE)和决定系数(R^(2))两项评价指标,与偏最小二乘回归(Partial Least Square Regression,PLSR)、最小二乘支持向量回归(Least Square Support Vector Machine,LSSVR)以及SSAE 3种模型进行对比。结果表明:改进的SSAE-FC模型表现出更优的预测精度和稳定性,92#汽油-3#航煤混油测试集的R^(2)和RMSEC指标分别为0.9952和0.8932,3#航煤-0#车柴混油测试集的R^(2)和RMSEC指标分别为0.9837和1.1967,且学习得到的稀疏特征的可解释性强。

基于梯度范数的暂态稳定评估模型的不平衡修正方法

为了解决电力系统中样本数量和质量不平衡造成的暂态稳定评估偏差问题,从评估模型的训练过程出发,通过预训练模型获得样本对模型参数修正的梯度范数,引入梯度范数均值比量化样本的不平衡程度,相较于先验信息,梯度范数均值比综合考虑了样本数量与样本质量的不平衡,并提出基于代价敏感法的不平衡修正方法,利用该方法改善模型的评估倾向性,以实现较好的修正效果。IEEE39节点系统和华东电网系统的仿真结果验证了所提方法的有效性。

一种面向能源工程数据评估的改进随机森林算法设计

传统电力工程数据稽核与评估方法的准确率偏低且效率较差,不适用于当前日益复杂的信息处理与分析工作。针对此,文中基于改进的随机森林算法提出了一种面向电力工程的异常数据检测算法。对于随机森林算法易受高维数据影响而导致信息特征提取能力不足的问题,该算法利用堆栈稀疏自编码器对高维数据进行降维,以提升数据检测的准确率。同时使用麻雀搜索算法对数据特征提取模型的参数加以优化,进一步提升了算法的性能和效率。在以电力工程造价数据为样本展开的实验测试中,所提算法的AUC与F1值领先于SSAE-RF算法2.73%及0.011,且异常数据识别率可达80%,运行时间也在对比算法中为最短,表明其具有较好的性能和计算效率。

基于堆叠稀疏自编码和谱聚类分析的带式输送机托辊故障诊断

针对煤矿带式输送机托辊故障数据庞大、可变性强等特点,以工业现场采集的音频数据为基础,对数据进程预处理,选用4个隐藏层,采用每层节点数分别为128、32、16、8的自编码模型进行特征值提取,计算出各特征值的相关性,提取偏度、均值、峭度、峰值、波数和过零率6个特征,采用K-means算法和谱聚类算法进行故障诊断对比分析,建立故障诊断分级标准。实验结果表明,堆叠稀疏自编码提取特征优于时域特征,能够有效过滤干扰信息;基于堆叠稀疏自编码提取的特征值的谱聚类算法对于故障分为4类时效果最佳,故障诊断准确率高达96%。

基于堆栈稀疏自编码器与XGBoost的电力变压器故障诊断

目的 针对变压器油中的H_(2)、CH_(4)、C_(2)H_(6)、C_(2)H_(4)、C_(2)H_(2)气体的浓度存在耦合性问题及电力变压器故障诊断精度较低的问题,提出了利用堆栈稀疏自编码器(Stacked Sparse Autoencoder,SSAE)和XGBoost模型结合的方法来提高电力变压器故障诊断的准确率。方法 首先利用堆栈稀疏自编码器(Stacked Sparse Autoencoder,SSAE)处理DGA数据;其次确定自编码器堆栈个数,确定隐含层数目;然后利用SSAE对原始数据进行数据转换,提取深层次特征信息;接着为了消除数据之间数量级差异较大的问题,对提取后的特征数据归一化进行处理;最后将处理之后得到的数据再输入XGBoost模型之中进行分类验证。结果 本文建立的基于堆栈稀疏自编码器与XGBoost的电力变压器故障诊断方法诊断准确率为91.11%,高于常用的其他机器学习模型。结论 实验结果验证了方法的有效性,表明基于堆栈稀疏自编码器与XGBoost的电力变压器故障诊断方法能够有效提高故障诊断的准确率。