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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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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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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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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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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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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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Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network 认领 引用 被引量：6
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作者 LYU Defeng HU Yuwen 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2021年第5期867-875,共9页
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... 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. 展开更多
关键词 aeroengine remaining useful life(RUL) principal component analysis(PCA) one-dimensional convolution neural network(1D-CNN) time series prediction state parameters
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Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network 认领 引用 被引量：1
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作者 Shengkang Zong Sheng Wang +3 位作者 Zhitao Luo Xinkai Wu Hui Zhang Zhonghua Ni 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2023年第3期252-261,共10页
Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of ci... Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC. 展开更多
关键词 Ultrasonic guided waves Singular value decomposition Damage detection and localization Environmental and operational conditions One-dimensional 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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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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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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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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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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Unsupervised Electric Motor Fault Detection by Using Deep Autoencoders 认领 引用 被引量：20
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作者 Emanuele Principi Damiano Rossetti +1 位作者 Stefano Squartini Francesco Piazza 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2019年第2期441-451,共11页
Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literatu... Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literature for detecting faults automatically. Deep neural networks have been successfully employed for this task, but, up to the authors' knowledge, they have never been used in an unsupervised scenario. This paper proposes an unsupervised method for diagnosing faults of electric motors by using a novelty detection approach based on deep autoencoders. In the proposed method, vibration signals are acquired by using accelerometers and processed to extract LogMel coefficients as features. Autoencoders are trained by using normal data only, i.e., data that do not contain faults. Three different autoencoders architectures have been evaluated: the multilayer perceptron(MLP) autoencoder, the convolutional neural network autoencoder, and the recurrent autoencoder composed of long short-term memory(LSTM) units. The experiments have been conducted by using a dataset created by the authors, and the proposed approaches have been compared to the one-class support vector machine(OC-SVM) algorithm. The performance has been evaluated in terms area under curve(AUC) of the receiver operating characteristic curve, and the results showed that all the autoencoder-based approaches outperform the OCSVM algorithm. Moreover, the MLP autoencoder is the most performing architecture, achieving an AUC equal to 99.11 %. 展开更多
关键词 Autoencoder convolutional neural networks electric motor fault detection long short-term memory neural networks novelty detection
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Multi-Source Data with Laplacian Eigenmaps and Denoising Autoencoder for Predicting Microbe-Disease Associations via Convolutional Neural Network 认领 引用
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作者 Xiu-Juan Lei Ya-Li Chen Yi Pan 《Journal of Computer Science & Technology》 SCIE EI CSCD 2025年第2期588-604,共17页
1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,Ch... 1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China 3 Shenzhen Key Laboratory of Intelligent Bioinformatics,Shenzhen Institute of Advanced Technology,Chinese Academy of Science,Shenzhen 518055,China E-mail:xjlei@snnu.edu.cn;yalichen@snnu.edu.cn;yi.pan@siat.ac.cn Received December 9,2022;accepted July 29,2024.Abstract Identifying microbes associated with diseases is important for understanding the pathogenesis of diseases as well as for the diagnosis and treatment of diseases.In this article,we propose a method based on a multi-source association network to predict microbe-disease associations,named MMHN-MDA.First,a heterogeneous network of multimolecule associations is constructed based on associations between microbes,diseases,drugs,and metabolites.Second,the graph embedding algorithm Laplacian eigenmaps is applied to the association network to learn the behavior features of microbe nodes and disease nodes.At the same time,the denoising autoencoder(DAE)is used to learn the attribute features of microbe nodes and disease nodes.Finally,attribute features and behavior features are combined to get the final embedding features of microbes and diseases,which are fed into the convolutional neural network(CNN)to predict the microbedisease associations.Experimental results show that the proposed method is more effective than existing methods.In addition,case studies on bipolar disorder and schizophrenia demonstrate good predictive performance of the MMHN-MDA model,and further,the results suggest that gut microbes may influence host gene expression or compounds in the nervous system,such as neurotransmitters,or metabolites that alter the blood-brain barrier. 展开更多
关键词 multi-source data Laplacian eigenmap denoising autoencoder convolutional neural network microbe-disease association
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Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM 认领 引用
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作者 Yahang Qin Zhenni Li +3 位作者 Shengli Xie Bo Li Ming Liu Victor Kuzin 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2025年第1期68-86,共19页
Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite System(BDS).However,most existing approaches to this issue involve supervi... Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite System(BDS).However,most existing approaches to this issue involve supervised machine learning(ML)methods,and it is difficult to move to unsupervised multipath signal recognition because of the limitations in signal labeling.Inspired by an autoencoder with powerful unsupervised feature extraction,we propose a new deep learning(DL)model for BDS signal recognition that places a long short-term memory(LSTM)module in series with a convolutional sparse autoencoder to create a new autoencoder structure.First,we propose to capture the temporal correlations in long-duration BeiDou satellite time-series signals by using the LSTM module to mine the temporal change patterns in the time series.Second,we develop a convolutional sparse autoencoder method that learns a compressed representation of the input data,which then enables downscaled and unsupervised feature extraction from long-duration BeiDou satellite series signals.Finally,we add an l1/2 regularizer to the objective function of our DL model to remove redundant neurons from the neural network while ensuring recognition accuracy.We tested our proposed approach on a real urban canyon dataset,and the results demonstrated that our algorithm could achieve better classification performance than two ML-based methods(e.g.,11%better than a support vector machine)and two existing DL-based methods(e.g.,7.26%better than convolutional neural networks). 展开更多
关键词 convolutional sparse autoencoder BeiDou Navigation Satellite System(BDS) long short-term memory(LSTM) multipath classification
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An attention graph stacked autoencoder for anomaly detection of electro-mechanical actuator using spatio-temporal multivariate signals 认领 引用 被引量：2
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作者 Jianyu WANG Heng ZHANG Qiang MIAO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第9期506-520,共15页
Health monitoring of electro-mechanical actuator(EMA)is critical to ensure the security of airplanes.It is difficult or even impossible to collect enough labeled failure or degradation data from actual EMA.The autoenc... Health monitoring of electro-mechanical actuator(EMA)is critical to ensure the security of airplanes.It is difficult or even impossible to collect enough labeled failure or degradation data from actual EMA.The autoencoder based on reconstruction loss is a popular model that can carry out anomaly detection with only consideration of normal training data,while it fails to capture spatio-temporal information from multivariate time series signals of multiple monitoring sensors.To mine the spatio-temporal information from multivariate time series signals,this paper proposes an attention graph stacked autoencoder for EMA anomaly detection.Firstly,attention graph con-volution is introduced into autoencoder to convolve temporal information from neighbor features to current features based on different weight attentions.Secondly,stacked autoencoder is applied to mine spatial information from those new aggregated temporal features.Finally,based on the bench-mark reconstruction loss of normal training data,different health thresholds calculated by several statistic indicators can carry out anomaly detection for new testing data.In comparison with tra-ditional stacked autoencoder,the proposed model could obtain higher fault detection rate and lower false alarm rate in EMA anomaly detection experiment. 展开更多
关键词 Anomaly detection Spatio-temporal informa-tion Multivariate time series signals Attention graph convolution Stacked autoencoder
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基于Autoencoder-TCN的航空发动机排气温度预测 认领 引用 被引量：4
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作者 孔晨亦 李学仁 杜军 《空军工程大学学报（自然科学版）》 CSCD 北大核心 2020年第5期55-61,共7页
针对目前航空发动机排气温度预测模型精度不高、传统RNN类神经网络对飞行数据时间维度信息挖掘不充分的问题,提出了一种结合自编码器Autoencoder和时间卷积神经网络TCN的航空发动机排气温度预测模型。首先通过Autoencoder方法从飞行数... 针对目前航空发动机排气温度预测模型精度不高、传统RNN类神经网络对飞行数据时间维度信息挖掘不充分的问题,提出了一种结合自编码器Autoencoder和时间卷积神经网络TCN的航空发动机排气温度预测模型。首先通过Autoencoder方法从飞行数据中提取与排气温度相关的特征,以降维后的特征作为输入,建立TCN网络深度学习模型,以航空发动机排气温度作为输出,充分挖掘飞行数据的时间维度信息,从而提高模型精度。最后选取真实飞行数据进行实验,结果表明,与BP、LSTM神经网络模型相比,该模型的平均绝对百分比误差由13.035%和9.593%降低至3.369%,有效提高了模型预测精度。 展开更多
关键词 航空发动机 排气温度 自编码器 时间卷积神经网络
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