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Fault Diagnosis for Rolling Bearings with Stacked Denoising Auto-encoder of Information Aggregation 认领 引用
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作者 Li Zhang Xin Gao Xiao Xu 《Journal of Harbin Institute of Technology(New Series)》 CAS 2019年第4期69-77,共9页
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 rollin... 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. 展开更多
关键词 deep learning stacked denoising auto-encoder fault diagnosis PCA classification
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Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder 认领 引用 被引量：10
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作者 Xiaoping Zhao Jiaxin Wu +2 位作者 Yonghong Zhang Yunqing Shi Lihua Wang 《Computers, Materials & Continua》 SCIE EI 2018年第11期223-242,共20页
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 ... 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. 展开更多
关键词 Big data deep learning stacked de-noising auto-encoder fourier transform
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Data Cleaning Based on Stacked Denoising Autoencoders and Multi-Sensor Collaborations 认领 引用 被引量：1
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作者 Xiangmao Chang Yuan Qiu +1 位作者 Shangting Su Deliang Yang 《Computers, Materials & Continua》 SCIE EI 2020年第5期691-703,共13页
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 prop... 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. 展开更多
关键词 Data cleaning wireless sensor networks stacked denoising autoencoders multi-sensor collaborations
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Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders 认领 引用 被引量：2
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作者 Samah Ibrahim Alshathri Desiree Juby Vincent V.S.Hari 《Computers, Materials & Continua》 SCIE EI 2022年第4期1371-1386,共16页
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 ... 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. 展开更多
关键词 Stacked denoising autoencoder(SDAE) optical character recognition(OCR) signal to noise ratio(SNR) universal image quality index(UQ1)and structural similarity index(SSIM)
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Predicting the Antigenic Variant of Human Influenza A(H3N2) Virus with a Stacked Auto-Encoder Model 认领 引用
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作者 Zhiying Tan Kenli Li +1 位作者 Taijiao Jiang Yousong Peng 《国际计算机前沿大会会议论文集》 EI 2017年第2期71-73,共3页
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 ... 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 展开更多
关键词 Stacked auto-encoder Antigenic variation nfluenza Machine learning
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SNP site-drug association prediction algorithm based on denoising variational auto-encoder 认领 引用 被引量：2
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作者 SONG Xiaoyu FENG Xiaobei +3 位作者 ZHU Lin LIU Tong WU Hongyang LI Yifan 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2022年第3期300-308,共9页
Single nucletide polymorphism(SNP)is an important factor for the study of genetic variation in human families and animal and plant strains.Therefore,it is widely used in the study of population genetics and disease re... Single nucletide polymorphism(SNP)is an important factor for the study of genetic variation in human families and animal and plant strains.Therefore,it is widely used in the study of population genetics and disease related gene.In pharmacogenomics research,identifying the association between SNP site and drug is the key to clinical precision medication,therefore,a predictive model of SNP site and drug association based on denoising variational auto-encoder(DVAE-SVM)is proposed.Firstly,k-mer algorithm is used to construct the initial SNP site feature vector,meanwhile,MACCS molecular fingerprint is introduced to generate the feature vector of the drug module.Then,we use the DVAE to extract the effective features of the initial feature vector of the SNP site.Finally,the effective feature vector of the SNP site and the feature vector of the drug module are fused input to the support vector machines(SVM)to predict the relationship of SNP site and drug module.The results of five-fold cross-validation experiments indicate that the proposed algorithm performs better than random forest(RF)and logistic regression(LR)classification.Further experiments show that compared with the feature extraction algorithms of principal component analysis(PCA),denoising auto-encoder(DAE)and variational auto-encode(VAE),the proposed algorithm has better prediction results. 展开更多
关键词 association prediction k-mer molecular fingerprinting support vector machine(SVM) denoising variational auto-encoder(DVAE)
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Deep Learning-Based Stacked Auto-Encoder with Dynamic Differential Annealed Optimization for Skin Lesion Diagnosis 认领 引用
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作者 Ahmad Alassaf 《Computer Systems Science & Engineering》 SCIE EI 2023年第12期2773-2789,共17页
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 extra... 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. 展开更多
关键词 Intelligent diagnosis stacked auto-encoder skin lesion unsupervised learning parameter selection
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Hformer:highly efficient vision transformer for low-dose CT denoising 认领 引用 被引量：8
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作者 Shi-Yu Zhang Zhao-Xuan Wang +5 位作者 Hai-Bo Yang Yi-Lun Chen Yang Li Quan Pan Hong-Kai Wang Cheng-Xin Zhao 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第4期161-174,共14页
In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and trans... In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture.The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset.Compared with the former representative state-of-the-art(SOTA)model designs under different architectures,Hformer achieved optimal metrics without requiring a large number of learning parameters,with metrics of33.4405 PSNR,8.6956 RMSE,and 0.9163 SSIM.The experiments demonstrated designed Hformer is a SOTA model for noise suppression,structure preservation,and lesion detection. 展开更多
关键词 Low-dose CT Deep learning Medical image Image denoising Convolutional neural networks Selfattention Residual network Auto-encoder
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基于能量-熵特征和改进堆叠降噪自编码器的水轮机空化状态识别方法 认领 引用 被引量：2
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作者 刘圳 刘忠 +2 位作者 邹淑云 周泽华 乔帅程 《发电技术》 CSCD 2026年第1期176-184,共9页
【目的】针对混流式水轮机空化声发射(acoustic emission,AE)信号受背景噪声干扰、故障难以识别的问题,提出一种基于能量-熵特征和哈里斯鹰优化(Harris hawks optimization,HHO)算法联合3折交叉验证(3-fold crossvalidation,3Fold)优化... 【目的】针对混流式水轮机空化声发射(acoustic emission,AE)信号受背景噪声干扰、故障难以识别的问题,提出一种基于能量-熵特征和哈里斯鹰优化(Harris hawks optimization,HHO)算法联合3折交叉验证(3-fold crossvalidation,3Fold)优化堆叠降噪自编码器(stacked denoising auto encoder,SDAE)的状态识别方法。【方法】首先,利用变分模态分解算法对信号进行分解,得到一系列固有模态函数。其次,提取相关系数最大的2个固有模态函数的能量和熵特征,构建12维特征向量,输入识别模型。再次,利用HHO算法联合3Fold,对SDAE的超参数进行优化。最后,将HHO-3Fold-SDAE算法与其他算法寻优得到的最优参数分别输入模型中运行,并进行对比分析。【结果】与其他算法相比,HHO-3Fold-SDAE算法具有更小的准确率方差、损失率以及更高的平均准确率;相较于SDAE,其测试集平均准确率提高了6%;相较于HHO-SDAE,其测试集平均准确率提高了4%,准确率方差降低了17%。【结论】所提方法可用于水轮机空化AE信号的分类识别,可为水力机械状态监测提供参考。 展开更多
关键词 水力发电 水轮机 空化状态识别 哈里斯鹰优化(HHO)算法 堆叠降噪自编码器(SDAE) 熵
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融合CEEMDAN与堆叠降噪自编码器的爆破振动速度信号降噪方法 认领 引用
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作者 徐琛 王超 +4 位作者 邱浪 蒋相秋 刘驰 李海涛 穆鹏宇 《振动与冲击》 EI CSCD 北大核心 2026年第15期224-236,共13页
在矿山开采与隧道掘进等工程爆破作业中,实测爆破振动信号易受到复杂环境噪声干扰,导致信号特征提取与爆破效果评估精度下降。针对传统降噪方法难以兼顾噪声抑制与信号保真度的问题,提出了一种融合自适应噪声完备集合经验模态分解(compl... 在矿山开采与隧道掘进等工程爆破作业中,实测爆破振动信号易受到复杂环境噪声干扰,导致信号特征提取与爆破效果评估精度下降。针对传统降噪方法难以兼顾噪声抑制与信号保真度的问题,提出了一种融合自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN)与堆叠降噪自编码器(stacked denoising auto encoder, SDAE)的爆破振动信号联合降噪方法。该方法首先利用CEEMDAN对非平稳振动信号进行自适应分解,通过方差贡献率筛选有效本征模态函数(intrinsic mode function, IMF)分量;随后引入SDAE对筛选后的IMF进行深层特征学习与重构,实现多尺度噪声识别与智能去除。仿真信号分析结果表明,与EMD-WT、EEMD-WT、EMD-SDAE及EEMD-SDAE等方法相比,CEEMDAN-SDAE方法的信噪比最高可达9.39,均方误差最低为0.167 5,在噪声抑制与信号特征保持方面表现最优。工程应用结果进一步验证了该方法在复杂现场环境下的适用性:降噪后信号在200~1 000 Hz高频噪声区间内的杂散成分得到显著削弱,同时0~200 Hz主频能量保持良好;相较于传统方法,实测信号信噪比提高至9.45,均方误差降低至2.06×10-4,能量比提高为0.82,均值曲率降低为1.32×10-3。研究结果表明,CEEMDAN-SDAE联合降噪方法能够实现爆破振动信号的高保真降噪与关键特征保持,为爆破振动精细分析及工程安全评估提供了可靠的数据支撑。 展开更多
关键词 爆破振动信号 自适应噪声完备集合经验模态分解(CEEMDAN) 堆叠降噪自编码器(SDAE) 降噪
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基于SDAE-SVM的高压电缆局部放电类型识别 认领 引用
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作者 杨帆 程琛 +1 位作者 黄乐 彭小圣 《高压电器》 CAS CSCD 北大核心 2026年第6期90-96,共7页
提出一种基于改进堆栈去噪自编码器(SDAE-SVM)的深度学习方法,用于高压电缆不同绝缘缺陷局部放电(PD)信号的模式识别。首先在高压实验室中对5种类型的人工缺陷进行PD测试,并提取3500组PD瞬时脉冲,构建了34种特征参数。其次,详细介绍了SD... 提出一种基于改进堆栈去噪自编码器(SDAE-SVM)的深度学习方法,用于高压电缆不同绝缘缺陷局部放电(PD)信号的模式识别。首先在高压实验室中对5种类型的人工缺陷进行PD测试,并提取3500组PD瞬时脉冲,构建了34种特征参数。其次,详细介绍了SDAE-SVM的原理和网络架构。然后,使用所提模型识别不同缺陷类型的PD信号,获得了93.56%的识别精度。接着,使用t分布随机邻接嵌入(t-SNE)对SDAE-SVM逐层输出进行了可视化,说明了深度神经网络SDAE-SVM逐层优化的本质。最后,将所提方法与反向传播神经网络(BPNN)、支持向量机(SVM)和堆栈去噪自编码器(SDAE)进行了对比。结果表明,相比BPNN、SVM和SDAE、SDAE-SVM的总体识别精度分别提高了7.46%、6.70%、1.37%,具备较高的工程应用价值。 展开更多
关键词 高压电缆 局部放电 模式识别 深度学习 堆栈去噪自编码器
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基于SAT-SDAE的成品汽油在线调和质量自适应预测方法 认领 引用
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作者 李炜 崔伟伟 +2 位作者 鲁春燕 李亚洁 王成文 《石油学报（石油加工）》 EI CAS CSCD 北大核心 2026年第3期936-948,共13页
针对成品汽油在线调和中质量指标预测效果难以达到预期的问题,基于自注意力机制(SAT)与堆叠去噪自编码机(SDAE)建立成品汽油在线调和质量指标自适应预测模型SAT-SDAE-DNN。首先,在调和组分比例的基础上,通过新添调和组分中影响产品质量... 针对成品汽油在线调和中质量指标预测效果难以达到预期的问题,基于自注意力机制(SAT)与堆叠去噪自编码机(SDAE)建立成品汽油在线调和质量指标自适应预测模型SAT-SDAE-DNN。首先,在调和组分比例的基础上,通过新添调和组分中影响产品质量指标的关键属性,并兼顾产品性能与环保指标,确定了契合实际工程的12输入1输出的单一指标独立预测方案。其次,通过堆叠多个去噪自编码机构建深度网络,实现了对成品汽油质量相关复杂特征的深层提取和高噪声工业数据的鲁棒表征。再者,将SAT模块嵌入SDAE之前,以实时关注并适应输入特征变量变化及之间的相互关联对输出质量指标的动态影响,自动应对原油产地差异和工况变化等因素导致的批次效应和波动;同时引入粒子群算法(PSO)对深度模型的超参数进行寻优,以进一步提升模型预测性能并避免超参数获取的繁复性。最后,经某炼油化工企业实际工业数据实验验证结果表明,基于SAT-SDAE-DNN模型,对成品汽油在线调和的研究法辛烷值、抗爆指数、烯烃含量、芳烃含量和苯含量5项质量指标的预测性能均优于其他模型,能够为成品汽油在线调和高效能生产提供可靠依据。 展开更多
关键词 汽油在线调和 神经网络 堆叠去噪自编码 自注意力机制 自适应预测
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数据不平衡下的航空发动机气路故障诊断方法 认领 引用
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作者 戴郎杰 蔡开龙 +1 位作者 王阿久 刘正扬 《兵器装备工程学报》 CAS CSCD 北大核心 2026年第2期279-288,共10页
针对航空发动机气路故障诊断中气路数据的类不平衡性和故障的耦合性,提出了一种基于过采样技术(Borderline-SMOTE)与开普勒算法(KOA)优化堆叠降噪自编码器(SDAE)的航空发动机气路故障诊断模型。采用Borderline-SMOTE对训练集中的故障样... 针对航空发动机气路故障诊断中气路数据的类不平衡性和故障的耦合性,提出了一种基于过采样技术(Borderline-SMOTE)与开普勒算法(KOA)优化堆叠降噪自编码器(SDAE)的航空发动机气路故障诊断模型。采用Borderline-SMOTE对训练集中的故障样本进行扩充,缓解了数据的类不平衡问题。利用KOA算法优化SDAE的关键参数(隐含层神经元数目、网络学习率和噪声覆盖率),构建基于KOA-SDAE的航空发动机气路故障诊断模型。并将该模型与反向传播神经网络(BPNN)、长短期记忆网络(LSTM)、卷积神经网络(CNN)和原始SDAE等模型在不同采样方法下进行对比实验。实验结果表明,所提出方法的F1分数(F1 score)最高(97.82%),且在噪声干扰下的F1分数下降范围(1.67%~7.20%)显著小于对比模型(3.62%~16.08%),展现出更强的抗干扰能力和鲁棒性,为解决航空发动机气路故障诊断中气路数据的类不平衡问题提供了潜在的方法。 展开更多
关键词 航空发动机 故障诊断 过采样算法 堆叠降噪自编码器 开普勒优化算法
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基于参数自适应FMD和SDAE的变负载下轴承故障诊断 认领 引用
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作者 何勇 刘晓玲 《振动与冲击》 EI CSCD 北大核心 2026年第2期189-200,共12页
针对堆叠降噪自编码器(stacked denoisingauto-encoder,SDAE)网络在强噪声干扰及变负载工况下难以准确识别滚动轴承故障特征这一难题,提出一种基于特征模态分解(feature mode decomposition,FMD)与SDAE相结合的滚动轴承故障诊断方法。首... 针对堆叠降噪自编码器(stacked denoisingauto-encoder,SDAE)网络在强噪声干扰及变负载工况下难以准确识别滚动轴承故障特征这一难题,提出一种基于特征模态分解(feature mode decomposition,FMD)与SDAE相结合的滚动轴承故障诊断方法。首先,采用信号自相关函数对传统基尼系数进行改进;其次,以改进基尼系数作为模态分量评价指标,建立了参数自适应FMD方法,并采用该方法对SDAE网络输入信号进行降噪;最后,将降噪后信号的包络谱输入到SDAE网络中并得到滚动轴承变负载工况下的故障类型诊断结果。基于3个开源数据集的算例分析表明,该方法能够有效提升SDAE网络的滚动轴承故障诊断准确率。通过与其他方法的对比,验证了该方法具有更好的稳定性和更高的故障诊断准确率。 展开更多
关键词 故障诊断 滚动轴承 堆叠降噪自编码器(SDAE) 参数自适应特征模态分解(FMD) 变负载工况
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Fault prediction of combine harvesters based on stacked denoising autoencoders 认领 引用 被引量：1
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作者 Zhaomei Qiu Gaoxiang Shi +3 位作者 Bo Zhao Xin Jin Liming Zhou Tengfei Ma 《International Journal of Agricultural and Biological Engineering》 SCIE 2022年第2期189-196,共8页
Accurate fault prediction is essential to ensure the safety and reliability of combine harvester operation.In this study,a combine harvester fault prediction method based on a combination of stacked denoising autoenco... Accurate fault prediction is essential to ensure the safety and reliability of combine harvester operation.In this study,a combine harvester fault prediction method based on a combination of stacked denoising autoencoders(SDAE)and multi-classification support vector machines(SVM)is proposed to predict combine harvester faults by extracting operational features of key combine components.In general,SDAE contains autoencoders and uses a deep network architecture to learn complex non-linear input-output relationships in a hierarchical manner.Selected features are fed into the SDAE network,deep-level features of the input parameters are extracted by SDAE,and an SVM classifier is then added to its top layer to achieve combine harvester fault prediction.The experimental results show that the method can achieve accurate and efficient combine harvester fault prediction.In particular,the experiments used Gaussian noise with a distribution center of 0.05 to corrupt the test data samples obtained by random sampling of the whole population,and the results showed that the prediction accuracy of the method was 95.31%,which has better robustness and generalization ability compared to SVM(77.03%),BP(74.61%),and SAE(90.86%). 展开更多
关键词 fault prediction combine harvester stacked denoising autoencoders support vector machines
应用ISDAE控制的冷连轧轧制力动态预测分析 认领 引用
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作者 罗珂珂 张璐瑶 刘正豪 《金属材料与冶金工程》 CAS 2026年第3期38-40,58,共3页
针对堆叠去噪自编码器(SDAE)在预测轧制力时存在重构误差不稳的情况,设计了一种基于维度特性与目标参数对重构偏差优化调整的改进堆叠去噪自编码器(ISDAE),并应用于冷连轧轧制力动态预测中。结果表明:相较于堆叠去噪自编码器(SDAE)与去... 针对堆叠去噪自编码器(SDAE)在预测轧制力时存在重构误差不稳的情况,设计了一种基于维度特性与目标参数对重构偏差优化调整的改进堆叠去噪自编码器(ISDAE),并应用于冷连轧轧制力动态预测中。结果表明:相较于堆叠去噪自编码器(SDAE)与去噪自编码器(DAE)方法,ISDAE展现出更精确预测能力,误差控制效果更优。在输入阶段就实现与目标值匹配效果,在模型训练前完成目标参数集成处理,经过100次迭代就达到收敛控制标准。该研究有助于提高轧制设备控制精度,为后续工艺优化奠定了理论基础。 展开更多
关键词 冷连轧 轧制力动态预测 堆叠去噪自编码器 误差分析
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基于堆叠去噪自编码器的胸片骨抑制 认领 引用
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作者 陈怡宁 王润平 +1 位作者 马小龙 买桂英 《新余学院学报》 2026年第2期41-50,共10页
针对胸片中骨骼遮挡病灶的问题,采用了一种基于堆叠去噪自编码器的胸片骨抑制模型。该模型采用深度卷积网络,集成去噪自编码器和堆叠自编码器,在编码-解码阶段进行特征降噪与特征重建,以平衡去噪性能与细节保留。为解决骨骼结构精准分... 针对胸片中骨骼遮挡病灶的问题,采用了一种基于堆叠去噪自编码器的胸片骨抑制模型。该模型采用深度卷积网络,集成去噪自编码器和堆叠自编码器,在编码-解码阶段进行特征降噪与特征重建,以平衡去噪性能与细节保留。为解决骨骼结构精准分离与软组织完整性保持的双重挑战,应用多目标损失函数:通过均方误差最小化重建误差,结合多尺度结构相似性指数优化影像结构保真度,实现影像结构特征的最优解耦。实验结果表明,该模型能有效抑制骨骼噪声,同时保留病灶边缘特征,为提升计算机辅助诊断系统的诊断性能提供技术支持。 展开更多
关键词 胸片骨抑制 堆叠去噪自编码器 深度卷积网络 损失函数 多尺度结构相似性指数
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基于胸片骨抑制与改进Cascade RCNN的肺结核病灶自动检测研究 认领 引用
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作者 陈怡宁 《兰州文理学院学报（自然科学版）》 2026年第3期61-70,共10页
当前计算机辅助诊断肺结核疾病面临两大挑战:一是胸片中骨骼与病灶重叠易遮挡病灶形态,干扰诊断;二是病灶面积小且形态复杂,导致误诊漏诊率升高.针对骨骼遮挡病灶问题,采用了一种基于堆叠去噪自编码器的胸片骨抑制模型,在编码解码阶段... 当前计算机辅助诊断肺结核疾病面临两大挑战:一是胸片中骨骼与病灶重叠易遮挡病灶形态,干扰诊断;二是病灶面积小且形态复杂,导致误诊漏诊率升高.针对骨骼遮挡病灶问题,采用了一种基于堆叠去噪自编码器的胸片骨抑制模型,在编码解码阶段进行特征降噪与特征重建,以平衡去噪性能与细节保留.结合均方误差与多尺度结构相似性指数组成的多目标损失函数,实现影像结构特征的最优解耦.针对小病灶问题,以Cascade RCNN为基础检测框架,提出融合多尺度空洞卷积与双向特征金字塔的肺结核检测模型.实验结果显示,骨抑制模型可将原始胸片转换为骨结构剔除彻底、软组织细节完整的影像,有效消除骨骼遮挡干扰.骨抑制处理后的胸片输入检测模型后,其平均精度提升了1.741%,改进后的肺结核检测模型与基础模型Cascade RCNN相比,其平均精度提升了2.649%.由此表明,本文提出的骨抑制模型和肺结核病灶检测模型的性能良好,能够为计算机辅助诊断的临床转化提供技术路径和实践参考. 展开更多
关键词 骨抑制 堆叠去噪自编码器 多尺度空洞卷积 双向特征金字塔 肺结核检测
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A new method for high resolution well-control processing of post-stack seismic data 认领 引用
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作者 Wu Dakui Wu Zongwei Wu Yijia 《Natural Gas Industry B》 2020年第3期215-223,共9页
Increasing the resolution of seismic data has long been a major topic in seismic exploration.Due to the effect of high-frequency noises,traditional methods could only improve the resolution limitedly.To end this,this ... Increasing the resolution of seismic data has long been a major topic in seismic exploration.Due to the effect of high-frequency noises,traditional methods could only improve the resolution limitedly.To end this,this paper newly proposed a high-resolution seismic data processing method based on welleseismic combination after summarizing the research status on high resolution.Synthetic record and seismogram are similar in effective signals but dissimilar in noises.Their effective signals are regular and noises are irregular.And they are similar in adjacent frequency.Based on these“three-regularity”characteristics,the relationship between synthetic record and seismogram was established using the neural network algorithm.Then,the corresponding extrapolation algorithm was proposed based on the self-adaptive geological and geophysical variation of multi-layer network structure.And a model was established by virtue of this method and the theoretical simulation was carried out.In addition,it was tested from the aspects of frequency component and amplitude energy recovery,phase correction,regularity elimination and stochastic noise.And the following research results were obtained.First,this new method can extract high-frequency information as much as possible and remain middle and low-frequency effective information while eliminating the noises.Second,in this method,the idea of traditional methods to denoisefirst and then expand frequency is changed completely and the limitation of traditional methods is broken.It establishes the idea of expanding frequency and denoising simultaneously and increases the resolution to the uttermost.Third,this new method has been applied to a variety of reservoir descriptions and the high-resolution processing results have been improved significantly in precision and accuracy. 展开更多
关键词 Synthetic record Seismogram Stack High resolution Neural network Denoising Frequency expanding Data processing
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BKD-136冷连轧轧制力SDAE动态预测及试验验证 认领 引用
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作者 赵冰洁 《山西冶金》 CAS 2026年第2期107-109,共3页
冷连轧机组作为冶金领域最关键的设备之一,轧制力控制精度直接影响冶金效率。为了更加精确预测冷连轧轧制力,设计了一种基于堆叠去噪自编码器(SDAE)的冷连轧轧制力动态预测方法。基于维度属性特征与目标值对重构误差实施修正,对重构损... 冷连轧机组作为冶金领域最关键的设备之一,轧制力控制精度直接影响冶金效率。为了更加精确预测冷连轧轧制力,设计了一种基于堆叠去噪自编码器(SDAE)的冷连轧轧制力动态预测方法。基于维度属性特征与目标值对重构误差实施修正,对重构损失函数进行最小化求解得到最优权值。结果表明:相比较DAE和ELM方法,SDAE法预测误差最小,消噪后能够更好提取高质量消噪特性,具备更优预测能力,只需100次迭代即可满足收敛要求,有助于提高轧制设备控制精度。 展开更多
关键词 冷连轧 轧制力动态预测 堆叠去噪自编码器 误差分析
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