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Quality related fault detection based on dynamic-inner convolutional autoencoder and partial least squares and its application to ironmaking process 认领 引用 被引量：1
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作者 Ping Wu Yuxuan Ni +4 位作者 Huaimin Wang Xuguang Hu Zhenquan Wu Jian Jiang Yaowu Hu 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第1期267-276,共10页
Partial least squares (PLS) model maximizes the covariance between process variables and quality variables,making it widely used in quality-related fault detection.However,traditional PLS methods focus primarily on li... Partial least squares (PLS) model maximizes the covariance between process variables and quality variables,making it widely used in quality-related fault detection.However,traditional PLS methods focus primarily on linear processes,leading to poor performance in dynamic nonlinear processes.In this paper,a novel quality-related fault detection method,named DiCAE-PLS,is developed by combining dynamic-inner convolutional autoencoder with PLS.In the proposed DiCAE-PLS method,latent features are first extracted through dynamic-inner convolutional autoencoder (DiCAE) to capture process dynamics and nonlinearity from process variables.Then,a PLS model is established to build the relationship between the extracted latent features and the final product quality.To detect quality-related faults,Hotelling's T2 statistic is employed.The developed quality-related fault detection is applied to the widely used industrial benchmark of the Tennessee. 展开更多
关键词 Partial least squares Dynamic-inner convolutional autoencoder Quality-related fault detection Neural networks Safety Dynamic modeling
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DOA estimation of array signals based on convolutional sparse autoencoder under sparse prior 认领 引用
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作者 REN Jing TAN Xiuhui +4 位作者 BAI Yanping WANG Peng CHENG Rong ZHANG Feng XU Ting 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2026年第2期254-266,共13页
The application of deep learning to direction of arrival(DOA)estimation is of great significance in the field of array signal processing.The use of deep learning for DOA estimation of vector hydrophone array usually d... The application of deep learning to direction of arrival(DOA)estimation is of great significance in the field of array signal processing.The use of deep learning for DOA estimation of vector hydrophone array usually directly inputs the covariance matrix of the signal as the signal feature into the network,but this method has limitations such as high data requirements and high computational complexity.This paper proposes a DOA estimation method for vector hydrophone array based on a convolutional sparse autoencoder under sparse prior conditions.This method adds an L1 norm regularization term to the convolutional layer of a convolutional autoencoder to achieve sparsity constraints,and establishes a convolutional sparse autoencoder.At the same time,a residual compensation mechanism is introduced to avoid overfitting and loss of details during the training process.Subsequently,the columns of the signal covariance matrix of the vector hydrophone array are treated as under-sampled noisy linear measurements of the spatial spectrum,and are input into a convolutional sparse autoencoder for feature extraction and reconstruction.Finally,the obtained features are used as inputs for training a convolutional neural network to achieve multi-source DOA estimation.Furthermore,to address the shortcomings of classification methods in off-grid situations,we propose a DOA regression estimation method based on the convolutional sparse autoencoder.The simulation results show that under complex conditions such as low signal-to-noise ratio and a small number of snapshots,the classification method proposed in this paper outperforms various deep learning algorithms and traditional algorithms mentioned in the literature in terms of estimation performance.In addition,the proposed regression method can further improve the DOA estimation performance in off-grid scenarios. 展开更多
关键词 vector hydrophone array direction of arrival estimation sparse representation convolutional sparse autoencoder L1-norm regularization regression
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基于CAE-LSTM的航发轴承故障诊断方法 认领 引用
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作者 尹震宇 刘思宇 +2 位作者 张飞青 徐光远 宋丹 《小型微型计算机系统》 CSCD 北大核心 2026年第5期1041-1047,共7页
随着计算机深度学习理论的发展及航空领域对关键装备智能化故障诊断与运维需求的提升,基于深度学习的航空发动机运行状态的监测与评估方法成为了飞机安全运行的重要保障.由于航空发动机机械结构复杂,轴承在高温、高压等恶劣环境下高速运... 随着计算机深度学习理论的发展及航空领域对关键装备智能化故障诊断与运维需求的提升,基于深度学习的航空发动机运行状态的监测与评估方法成为了飞机安全运行的重要保障.由于航空发动机机械结构复杂,轴承在高温、高压等恶劣环境下高速运行,其故障特征信息存在多尺度、非线性等问题,使得故障信号难以有效识别及分析诊断.因此,本文提出了一种基于CAE-LSTM的航发轴承故障诊断方法,首先利用改进的卷积自编码器(Convolutional Autoencoder,CAE)对高维振动信号进行降维和特征提取,然后将提取到的特征输入到长短期记忆网络(Long Short-Term Memory,LSTM)分类器中进行故障类型识别,从而提升轴承故障分类的准确性和鲁棒性.实验结果表明本文提出的方法能够有效地学习航发轴承传感信号序列中的动态特征,提高航发轴承故障诊断的精确性和智能性. 展开更多
关键词 航空发动机 卷积自编码器 长短期记忆网络 特征提取 故障诊断
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Reconstruction of pile-up events using a one-dimensional convolutional autoencoder for the NEDA detector array 认领 引用
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作者 J.M.Deltoro G.Jaworski +15 位作者 A.Goasduff V.González A.Gadea M.Palacz J.J.Valiente-Dobón J.Nyberg S.Casans A.E.Navarro-Antón E.Sanchis G.de Angelis A.Boujrad S.Coudert T.Dupasquier S.Ertürk O.Stezowski R.Wadsworth 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2025年第2期62-70,共9页
Pulse pile-up is a problem in nuclear spectroscopy and nuclear reaction studies that occurs when two pulses overlap and distort each other,degrading the quality of energy and timing information.Different methods have ... Pulse pile-up is a problem in nuclear spectroscopy and nuclear reaction studies that occurs when two pulses overlap and distort each other,degrading the quality of energy and timing information.Different methods have been used for pile-up rejection,both digital and analogue,but some pile-up events may contain pulses of interest and need to be reconstructed.The paper proposes a new method for reconstructing pile-up events acquired with a neutron detector array(NEDA)using an one-dimensional convolutional autoencoder(1D-CAE).The datasets for training and testing the 1D-CAE are created from data acquired from the NEDA.The new pile-up signal reconstruction method is evaluated from the point of view of how similar the reconstructed signals are to the original ones.Furthermore,it is analysed considering the result of the neutron-gamma discrimination based on charge comparison,comparing the result obtained from original and reconstructed signals. 展开更多
关键词 1D-CAE Autoencoder CAE Convolutional neural network(CNN) Neutron detector Neutron-gamma discrimination(NGD) Machine learning Pulse shape discrimination Pile-up pulse
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An Integrated Approach to Condition-Based Maintenance Decision-Making of Planetary Gearboxes: Combining Temporal Convolutional Network Auto Encoders with Wiener Process 认领 引用 被引量：1
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作者 Bo Zhu Enzhi Dong +3 位作者 Zhonghua Cheng Xianbiao Zhan Kexin Jiang Rongcai Wang 《Computers, Materials & Continua》 SCIE EI 2026年第1期661-686,共26页
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s... With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes. 展开更多
关键词 Temporal convolutional network autoencoder full lifecycle degradation experiment nonlinear Wiener process condition-based maintenance decision-making fault monitoring
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Adapting Convolutional Autoencoder for DDoS Attack Detection via Joint Reconstruction Learning and Refined Anomaly Scoring 认领 引用
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作者 Seulki Han Sangho Son +1 位作者 Won Sakong Haemin Jung 《Computers, Materials & Continua》 SCIE EI 2025年第11期2893-2912,共20页
As cyber threats become increasingly sophisticated,Distributed Denial-of-Service(DDoS)attacks continue to pose a serious threat to network infrastructure,often disrupting critical services through overwhelming traffic... As cyber threats become increasingly sophisticated,Distributed Denial-of-Service(DDoS)attacks continue to pose a serious threat to network infrastructure,often disrupting critical services through overwhelming traffic.Although unsupervised anomaly detection using convolutional autoencoders(CAEs)has gained attention for its ability to model normal network behavior without requiring labeled data,conventional CAEs struggle to effectively distinguish between normal and attack traffic due to over-generalized reconstructions and naive anomaly scoring.To address these limitations,we propose CA-CAE,a novel anomaly detection framework designed to improve DDoS detection through asymmetric joint reconstruction learning and refined anomaly scoring.Our architecture connects two CAEs sequentially with asymmetric filter allocation,which amplifies reconstruction errors for anomalous data while preserving low errors for normal traffic.Additionally,we introduce a scoring mechanism that incorporates exponential decay weighting to emphasize recent anomalies and relative traffic volume adjustment to highlight highrisk instances,enabling more accurate and timely detection.We evaluate CA-CAE on a real-world network traffic dataset collected using Cisco NetFlow,containing over 190,000 normal instances and only 78 anomalous instances—an extremely imbalanced scenario(0.0004% anomalies).We validate the proposed framework through extensive experiments,including statistical tests and comparisons with baseline models.Despite this challenge,our method achieves significant improvement,increasing the F1-score from 0.515 obtained by the baseline CAE to 0.934,and outperforming other models.These results demonstrate the effectiveness,scalability,and practicality of CA-CAE for unsupervised DDoS detection in realistic network environments.By combining lightweight model architecture with a domain-aware scoring strategy,our framework provides a robust solution for early detection of DDoS attacks without relying on labeled attack data. 展开更多
关键词 Anomaly detection DDoS attack detection convolutional autoencoder
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基于1D_CAE-LSTM-AM的船舶交通流量预测 认领 引用
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作者 江鹏飞 王玫 +2 位作者 神显豪 阚瑞祥 仇洪冰 《现代电子技术》 北大核心 2026年第14期135-141,共7页
在水上交通系统中,船舶交通流量预测发挥着越来越重要的作用。为了提高船舶交通流量预测精度,针对已有方法对于长程流量数据感知能力不足的弱点,提出一种基于一维卷积自编码器(1D_CAE)、长短期记忆(LSTM)网络和注意力机制(AM)的组合预... 在水上交通系统中,船舶交通流量预测发挥着越来越重要的作用。为了提高船舶交通流量预测精度,针对已有方法对于长程流量数据感知能力不足的弱点,提出一种基于一维卷积自编码器(1D_CAE)、长短期记忆(LSTM)网络和注意力机制(AM)的组合预测模型。其中,1D_CAE具有局部特征提取和数据降维能力,LSTM能够有效处理时序数据的长短期依赖关系,注意力机制则能依托长程预测实际情况自适应分配不同时间步长权重,三者协同作用以全面提升模型预测精度。基于韦兰运河的历史船舶流量的AIS数据,与其他几种预测模型进行对比实验,定量验证所提模型的实际效果。实验结果表明:所提1D_CAE-LSTM-AM模型在回归分析实测中,RMSE为0.0692,MSE为0.0048,MAE为0.0487,R2为0.8781;以调整输入时间窗口长度(即增加每次输入序列的时间步数)为建模策略时,RMSE为0.0700,MSE为0.0049,MAE为0.0493,R2为0.8749。实测中所得各项指标均优于其他对比模型,表明所提模型具备较好的预测性能与实际应用价值。 展开更多
关键词 船舶交通流量预测 1D卷积自编码器 LSTM 注意力机制 组合预测模型 AIS数据
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结合RFE与1DCAE的工业机器人异常检测与定位方法 认领 引用
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作者 齐浩男 柳小勤 +1 位作者 伍星 李健龙 《噪声与振动控制》 CSCD 北大核心 2026年第3期148-155,228,共8页
工业机器人的异常状态检测对保证其安全持续运行具有重要作用。针对机器人异常识别中的检测和定位问题,利用末端三维振动信号,提出一种结合递归特征消除法(Recursive Feature Elimination,RFE)与一维卷积自编码器(1D Convolutional Auto... 工业机器人的异常状态检测对保证其安全持续运行具有重要作用。针对机器人异常识别中的检测和定位问题,利用末端三维振动信号,提出一种结合递归特征消除法(Recursive Feature Elimination,RFE)与一维卷积自编码器(1D Convolutional Autoencoder,1DCAE)的工业机器人异常检测与定位方法。首先,设计一个基于卷积神经网络的自编码器模型,并引入Dropout函数以提升模型泛化能力;然后,利用编码器模型拟合正常样本的数据分布,从而实现对原始信号的重构;最后,引入递归特征消除法提取重构信号和原始信号的有效特征,并基于重构信号与原始信号间的特征误差实现机器人的异常检测与定位。基于六关节工业机器人的实验结果表明,所提方法可以有效重构原始样本信号,且基于递归特征消除法构建的特征误差能有效检测异常样本及定位异常位置。 展开更多
关键词 振动与波 工业机器人 一维卷积自编码器 递归特征消除 特征误差 异常检测与定位
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应用CAE-MFN深度学习算法的柴油机主轴承故障诊断 认领 引用
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作者 谢琳 李聪聪 《机械管理开发》 2026年第6期82-84,共3页
为了进一步提高柴油机主轴承故障诊断能力,设计了一种基于卷积自编码记忆融合网络(CAE-MFN)。通过CAE实现振动信号的特征提取与重构,采用LSTM神经网络完成多模态数据的记忆性融合。结果表明:采用融合模型识别准确度明显优于非融合模型,... 为了进一步提高柴油机主轴承故障诊断能力,设计了一种基于卷积自编码记忆融合网络(CAE-MFN)。通过CAE实现振动信号的特征提取与重构,采用LSTM神经网络完成多模态数据的记忆性融合。结果表明:采用融合模型识别准确度明显优于非融合模型,可以更高效判断故障特征。损失函数和网络消融说明利用卷积自编码器进行损失评判方法设置网络结构是可行的。所提CAE-MFN模型对不同实验均达到很高的精度,展现强大泛化效果。 展开更多
关键词 柴油机 主轴承 故障诊断 卷积自编码 记忆融合网络
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基于CAE-ECA模型的铁路信号设备异常振动状态感知方法 认领 引用
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作者 吴志云 《国外电子测量技术》 2026年第2期391-398,共8页
针对铁路信号设备内部机械部件的微观磨损、装配间隙变化以及电磁驱动系统的瞬态响应特性的复杂耦合,导致异常振动信号特征微弱、感知状态有效性降低的有效性的难题,提出一种基于CAE-ECA模型的铁路信号设备异常振动状态感知方法。该方... 针对铁路信号设备内部机械部件的微观磨损、装配间隙变化以及电磁驱动系统的瞬态响应特性的复杂耦合,导致异常振动信号特征微弱、感知状态有效性降低的有效性的难题,提出一种基于CAE-ECA模型的铁路信号设备异常振动状态感知方法。该方法深度融合了卷积自编码器(Convolutional Autoencoder,CAE)的深层特征挖掘能力与高效通道注意力(Efficient Channel Attention,ECA)机制。CAE通过卷积与反卷积操作提取铁路信号设备振动信号的潜在特征,ECA依据特征重要性动态分配通道权重,以强化异常振动状态的敏感特征。基于增强后的特征,计算均方根值等时域参数,实现设备状态的多维度量化。运用统计学原理构建动态阈值机制,通过比对实时参数与正常工况数据,并依托CAE-ECA模型的权重反馈动态调整判别参数,有效平衡误报率与漏报率,最终实现铁路信号设备异常振动状态的精准感知和预警。实验结果表明,该模型对正常振动信号的重构相关系数达0.95,异常信号重构相关系数低于0.6且显著波动;模型异常感知准确率达98%,相较传统CAE方法提升约10%,收敛速度加快50%;动态工况适应指数始终高于0.95,优于深度迁移学习0.79~0.88与多传感器融合方法0.65~0.75。可高效识别微弱异常振动,满足铁路现场精准感知与预警需求。 展开更多
关键词 CAE ECA 铁路信号设备 异常振动 状态感知 高效通道注意力
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Bridge damage identification based on convolutional autoencoders and extreme gradient boosting trees 认领 引用 被引量：7
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作者 Duan Yuanfeng Duan Zhengteng +1 位作者 Zhang Hongmei Cheng J.J.Roger 《Journal of Southeast University(English Edition)》 EI CAS 2024年第3期221-229,共9页
To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the accele... To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios. 展开更多
关键词 structural health monitoring damage identification convolutional autoencoder(CAE) extreme gradient boosting tree(XGBoost) machine learning
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Aircraft engine fault detection based on grouped convolutional denoising autoencoders 认领 引用 被引量：13
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作者 Xuyun FU Hui LUO +1 位作者 Shisheng ZHONG Lin LIN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2019年第2期296-307,共12页
Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection abil... Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection ability, Aircraft Communications Addressing and Reporting System(ACARS) data can be used. However, owing to the characteristics of high dimension, complex correlations between parameters, and large noise content, it is difficult for existing methods to detect faults effectively by using ACARS data. To solve this problem, a novel engine fault detection method based on original ACARS data is proposed. First, inspired by computer vision methods, all variables were divided into separated groups according to their correlations. Then, an improved convolutional denoising autoencoder was used to extract the features of each group. Finally, all of the extracted features were fused to form feature vectors. Thereby, fault samples could be identified based on these feature vectors. Experiments were conducted to validate the effectiveness and efficiency of our method and other competing methods by considering real ACARS data as the data source. The results reveal the good performance of our method with regard to comprehensive fault detection and robustness. Additionally, the computational and time costs of our method are shown to be relatively low. 展开更多
关键词 Aircraft engines Anomaly detection Convolutional Neural Network(CNN) Denoising autoencoder Engine health management Fault detection
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Plant Disease Detection and Classification Using Hybrid Model Based on Convolutional Auto Encoder and Convolutional Neural Network 认领 引用 被引量：1
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作者 Tajinder Kumar Sarbjit Kaur +4 位作者 Purushottam Sharma Ankita Chhikara Xiaochun Cheng Sachin Lalar Vikram Verma 《Computers, Materials & Continua》 SCIE EI 2025年第6期5219-5234,共16页
During its growth stage,the plant is exposed to various diseases.Detection and early detection of crop diseases is amajor challenge in the horticulture industry.Crop infections can harmtotal crop yield and reduce farm... During its growth stage,the plant is exposed to various diseases.Detection and early detection of crop diseases is amajor challenge in the horticulture industry.Crop infections can harmtotal crop yield and reduce farmers’income if not identified early.Today’s approved method involves a professional plant pathologist to diagnose the disease by visual inspection of the afflicted plant leaves.This is an excellent use case for Community Assessment and Treatment Services(CATS)due to the lengthy manual disease diagnosis process and the accuracy of identification is directly proportional to the skills of pathologists.An alternative to conventional Machine Learning(ML)methods,which require manual identification of parameters for exact results,is to develop a prototype that can be classified without pre-processing.To automatically diagnose tomato leaf disease,this research proposes a hybrid model using the Convolutional Auto-Encoders(CAE)network and the CNN-based deep learning architecture of DenseNet.To date,none of the modern systems described in this paper have a combined model based on DenseNet,CAE,and ConvolutionalNeuralNetwork(CNN)todiagnose the ailments of tomato leaves automatically.Themodelswere trained on a dataset obtained from the Plant Village repository.The dataset consisted of 9920 tomato leaves,and the model-tomodel accuracy ratio was 98.35%.Unlike other approaches discussed in this paper,this hybrid strategy requires fewer training components.Therefore,the training time to classify plant diseases with the trained algorithm,as well as the training time to automatically detect the ailments of tomato leaves,is significantly reduced. 展开更多
关键词 Tomato leaf disease deep learning DenseNet-121 convolutional autoencoder convolutional neural network
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Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder 认领 引用
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作者 Habib Dhahri Besma Rabhi +3 位作者 Slaheddine Chelbi Omar Almutiry Awais Mahmood Adel M.Alimi 《Computers, Materials & Continua》 SCIE EI 2021年第12期3259-3274,共16页
The exponential increase in new coronavirus disease 2019(COVID-19)cases and deaths has made COVID-19 the leading cause of death in many countries.Thus,in this study,we propose an efficient technique for the automatic ... The exponential increase in new coronavirus disease 2019(COVID-19)cases and deaths has made COVID-19 the leading cause of death in many countries.Thus,in this study,we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images.A stacked denoising convolutional autoencoder(SDCA)model was proposed to classify X-ray images into three classes:normal,pneumonia,and COVID-19.The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images.The proposed model’s architecture mainly composed of eight autoencoders,which were fed to two dense layers and SoftMax classifiers.The proposed model was evaluated with 6356 images from the datasets from different sources.The experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting,respectively.The metrics used for the SDCA model were the classification accuracy,precision,sensitivity,and specificity for both schemes.Our results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%.Therefore,this model can help physicians accelerate COVID-19 diagnosis. 展开更多
关键词 Stacked autoencoder augmentation multiclassification COVID-19 convolutional neural network
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锂离子电池健康状态的DCAE-Transformer预测方法研究 认领 引用 被引量：5
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作者 李浩平 于波涛 +3 位作者 孟荣华 金朱鸿 杜昕毅 李景瑞 《三峡大学学报（自然科学版）》 CAS 北大核心 2025年第1期106-112,共7页
提出了一种基于Transformer的DCAE-Transformer模型,旨在改善健康状态(SOH)估计的准确性.该方法通过Pearson相关系数筛选关键特征,利用去噪自编码器(DAE)和卷积神经网络(CNN)相结合进行数据预处理和特征提取,再将数据输入Transformer框... 提出了一种基于Transformer的DCAE-Transformer模型,旨在改善健康状态(SOH)估计的准确性.该方法通过Pearson相关系数筛选关键特征,利用去噪自编码器(DAE)和卷积神经网络(CNN)相结合进行数据预处理和特征提取,再将数据输入Transformer框架完成预测.使用NASA和CALCE提供的数据集进行验证,DCAE-Transformer模型在NASA电池样本上的误差指标(EMA、EMAP和ERMS)均低于1%,R2值超过99.5%;在CALCE样本上,误差指标低于5%,R2值超过98%.结果表明,该模型在锂电池SOH估计方面具有较高的精确性和泛化性. 展开更多
关键词 锂电池 健康状态估计 卷积去噪自编码器 Transformer 预测性能
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A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout 认领 引用 被引量：2
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作者 Chen Chen Xingqiu Li +2 位作者 Kai Huang Zhongwei Xu Meng Mei 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第7期471-485,共15页
Railway turnout is one of the critical equipment of Switch&Crossing(S&C)Systems in railway,related to the train’s safety and operation efficiency.With the advancement of intelligent sensors,data-driven fault ... Railway turnout is one of the critical equipment of Switch&Crossing(S&C)Systems in railway,related to the train’s safety and operation efficiency.With the advancement of intelligent sensors,data-driven fault detection technology for railway turnout has become an important research topic.However,little research in the literature has investigated the capability of data-driven fault detection technology for metro railway turnout.This paper presents a convolutional autoencoder-based fault detection method for the metro railway turnout considering human field inspection scenarios.First,the one-dimensional original time-series signal is converted into a twodimensional image by data pre-processing and 2D representation.Next,a binary classification model based on the convolutional autoencoder is developed to implement fault detection.The profile and structure information can be captured by processing data as images.The performance of our method is evaluated and tested on real-world operational current data in themetro stations.Experimental results show that the proposedmethod achieves better performance,especially in terms of error rate and specificity,and is robust in practical engineering applications. 展开更多
关键词 Convolutional autoencoder fault detection metro railway turnout
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基于CAE和改进式VGGNet的心电身份识别算法 认领 引用 被引量：2
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作者 严洁 张烨菲 张显飞 《计算机工程》 CAS CSCD 北大核心 2025年第1期295-303,共9页
随着物联网技术和人工智能技术的不断发展,生物识别技术面临着信息泄露的风险。心电图(ECG)信号因其活体识别的高防伪性在生物识别领域具有一定的优势。针对传统ECG识别算法不能适应多变的采集环境、识别稳定性不高以及基于深度神经网络... 随着物联网技术和人工智能技术的不断发展,生物识别技术面临着信息泄露的风险。心电图(ECG)信号因其活体识别的高防伪性在生物识别领域具有一定的优势。针对传统ECG识别算法不能适应多变的采集环境、识别稳定性不高以及基于深度神经网络的ECG识别算法模型参数量较大与难以实现快速响应等问题,提出一种基于卷积自动编码器(CAE)和改进式VGGNet的ECG身份识别算法。首先设计了结合小波阈值去噪和单心拍分割的预处理方法,得到干净的单周期ECG信号作为模型输入。其次构建了基于CAE的信号模态特征提取与降维处理模块,学习得到输入数据更小维度的潜在表示。最后基于VGGNet优化模型设计,进一步深入学习特征表示,得到个体识别的结果。实验结果表明,该算法在MIT-BIH Arrhythmia Database、European ST-T Database和ECG-ID等数据库的189位测试者中实现了96%以上的识别精度,其中European ST-T Database的识别精度高达99.82%,可实现准确率较高、泛化能力较强的个体身份识别。 展开更多
关键词 心电图 ECG识别 卷积自动编码器 残差网络 信号预处理
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Wavelet Transform-Based Bayesian Inference Learning with Conditional Variational Autoencoder for Mitigating Injection Attack in 6G Edge Network 认领 引用 被引量：1
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作者 Binu Sudhakaran Pillai Raghavendra Kulkarni +1 位作者 Venkata Satya Suresh kumar Kondeti Surendran Rajendran 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第10期1141-1166,共26页
Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies... Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies. 展开更多
关键词 Bayesian inference learning automaton convolutional wavelet transform conditional variational autoencoder malicious data injection attack edge environment 6G communication
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Robust Deep 3D Convolutional Autoencoder for Hyperspectral Unmixing with Hypergraph Learning 认领 引用
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作者 Peiyuan Jia Miao Zhang Yi Shen 《Journal of Harbin Institute of Technology(New Series)》 CAS 2021年第5期1-8,共8页
Hyperspectral unmixing aims to acquire pure spectra of distinct substances(endmembers)and fractional abundances from highly mixed pixels.In this paper,a deep unmixing network framework is designed to deal with the noi... Hyperspectral unmixing aims to acquire pure spectra of distinct substances(endmembers)and fractional abundances from highly mixed pixels.In this paper,a deep unmixing network framework is designed to deal with the noise disturbance.It contains two parts:a three⁃dimensional convolutional autoencoder(denoising 3D CAE)which recovers data from noised input,and a restrictive non⁃negative sparse autoencoder(NNSAE)which incorporates a hypergraph regularizer as well as a l2,1⁃norm sparsity constraint to improve the unmixing performance.The deep denoising 3D CAE network was constructed for noisy data retrieval,and had strong capacity of extracting the principle and robust local features in spatial and spectral domains efficiently by training with corrupted data.Furthermore,a part⁃based nonnegative sparse autoencoder with l2,1⁃norm penalty was concatenated,and a hypergraph regularizer was designed elaborately to represent similarity of neighboring pixels in spatial dimensions.Comparative experiments were conducted on synthetic and real⁃world data,which both demonstrate the effectiveness and robustness of the proposed network. 展开更多
关键词 deep learning unsupervised unmixing convolutional autoencoder hypergraph hyperspectral data
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Deep convolutional adversarial graph autoencoder using positive pointwise mutual information for graph embedding 认领 引用
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作者 MA Xiuhui WANG Rong +3 位作者 CHEN Shudong DU Rong ZHU Danyang ZHAO Hua 《High Technology Letters》 EI CAS 2022年第1期98-106,共9页
Graph embedding aims to map the high-dimensional nodes to a low-dimensional space and learns the graph relationship from its latent representations.Most existing graph embedding methods focus on the topological struct... Graph embedding aims to map the high-dimensional nodes to a low-dimensional space and learns the graph relationship from its latent representations.Most existing graph embedding methods focus on the topological structure of graph data,but ignore the semantic information of graph data,which results in the unsatisfied performance in practical applications.To overcome the problem,this paper proposes a novel deep convolutional adversarial graph autoencoder(GAE)model.To embed the semantic information between nodes in the graph data,the random walk strategy is first used to construct the positive pointwise mutual information(PPMI)matrix,then,graph convolutional net-work(GCN)is employed to encode the PPMI matrix and node content into the latent representation.Finally,the learned latent representation is used to reconstruct the topological structure of the graph data by decoder.Furthermore,the deep convolutional adversarial training algorithm is introduced to make the learned latent representation conform to the prior distribution better.The state-of-the-art experimental results on the graph data validate the effectiveness of the proposed model in the link prediction,node clustering and graph visualization tasks for three standard datasets,Cora,Citeseer and Pubmed. 展开更多
关键词 graph autoencoder(GAE) positive pointwise mutual information(PPMI) deep convolutional generative adversarial network(DCGAN) graph convolutional network(GCN) se-mantic information
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