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Optimized Quantum Autoencoder 认领 引用
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作者 Yibin Huang Muchun Yang D.L.Zhou 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第2期117-130,共14页
Quantum autoencoder(QAE)compresses a bipartite quantum state into its subsystem using a self-checking mechanism.How to characterize and minimize the lost information in this process is essential for understanding the ... Quantum autoencoder(QAE)compresses a bipartite quantum state into its subsystem using a self-checking mechanism.How to characterize and minimize the lost information in this process is essential for understanding the compression mechanism of QAE.Here,we investigate how to minimize the lost information in QAE for any input mixed state.We theoretically show that the lost information is the quantum mutual information between the remaining subsystem and the discarded one;the encoding unitary transformation is designed to minimize this mutual information.Furthermore,we show that the optimized unitary transformation can be decomposed as the product of a permutation unitary transformation and a disentanglement unitary transformation,and the permutation unitary transformation can be searched by a regular Young tableau algorithm.When the search can be made exhaustive in lower-dimensional systems,the lost information is minimized numerically,which is shown theoretically to be a global minimum.When the dimension of the system becomes larger such that an exhaustive search is impossible,we adopt an approximate search algorithm to numerically identify that our compression scheme gives lower lost information than that from the quantum variational circuit-based QAE. 展开更多
关键词 quantum autoencoder qae compresses bipartite quantum state quantum autoencoder characterize minimize lost information quantum mutual information encoding unitary transformation compression mechanism permutation unitary transformation
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Long-range masked autoencoder for pre-extraction of trajectory features in within-visual-range maneuver recognition 认领 引用
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作者 Feilong Jiang Hutao Cui +2 位作者 Yuqing Li Minqiang Xu Rixin Wang 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第1期301-315,共15页
In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,... In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems. 展开更多
关键词 Within-visual-range maneuver recognition Trajectory feature pre-extraction Long-range masked autoencoder Kalman filter constraints Intelligent air combat
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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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Diffusional magnetic resonance imaging anonymizing with variational autoencoder 认领 引用
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作者 Yunheng Shen Ling Zheng +2 位作者 Ruohan Liu Haoran Feng Hairong Lv 《Quantitative Biology》 CAS CSCD 2026年第2期119-133,共15页
Anonymization is a crucial de-identification technique that protects data privacy while ensuring its utility for model building.Current generative models such as generative adversarial networks and variational autoenc... Anonymization is a crucial de-identification technique that protects data privacy while ensuring its utility for model building.Current generative models such as generative adversarial networks and variational autoencoders(VAEs)have been applied to medical image anonymization but mainly focus on general image features,lacking specificity in regions of interest such as lesions.This study proposes a novel framework for brain magnetic resonance imaging anonymization,enabling the handling of lesion region prediction while preserving patient privacy.The framework consists of three stages:pre-training VAEs to represent lesion and non-lesion regions in latent space;fine-tuning these latent representations using a diffusion model conditioned on spatial and temporal features;and generating medical image substitutions through joint decoding of lesion and nonlesion latent representations.The comparative investigation has highlighted the benefits of our proposed methods,achieving a promising privacy-utility balance.In a small number of real sample scenarios,using synthetic samples with an 86%anonymity rate still enhanced the downstream segmentation task by 4.60%and the classification task by 8.75%.Our proposed framework offers significant improvements over existing methods in preserving privacy and maintaining data utility for lesion prediction tasks,which holds potential implications for enhanced privacy practices in medical imaging. 展开更多
关键词 anonymization data privacy diffusion model medical image processing variational autoencoder
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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CG-MAE:BEV Masked Autoencoders Based on Cross-Modal Guidance for 3D Object Detection in Autonomous Driving 认领 引用
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作者 Junchen Huo Song Wang +2 位作者 Enqing Chen Yingqiang Ding Shouyi Yang 《Computers, Materials & Continua》 SCIE EI 2026年第9期183-203,共21页
Multi-modal 3D object detection,which leverages the complementary strengths of LiDAR point clouds and camera RGB images,has emerged as a critical component of 3D perception in autonomous driving.As a critical challeng... Multi-modal 3D object detection,which leverages the complementary strengths of LiDAR point clouds and camera RGB images,has emerged as a critical component of 3D perception in autonomous driving.As a critical challenge in multi-modal learning,modality alignment aims to establish accurate semantic correspondences across distinct modalities.However,existing methods encounter significant difficulties in achieving robust alignment when data from one modality is obscured,such as in the presence of object occlusion or adverse environmental conditions,including illumination variations and inclement weather.To alleviate this issue,we present CG-MAE,a dual-branch Bird’s-Eye-View(BEV)masked autoencoder framework based on cross-modal guidance for 3D object detection in autonomous driving.Specifically,a cross-modal guided reconstruction module is developed to predict the representations of the obscure objects in the BEV space,lowering the difficulty of the modality alignment during the multi-modal fusion process.To mimic the object obscuration caused by occlusion or adverse environments,this paper proposes a Ground truth-based foreground masking strategy to cover up the objects,such as vehicles and pedestrians,thereby encouraging the reconstructionmodule to focus onmodality alignment in the foreground regions with high information density.Considering that all the modality data can be obscured,this paper builds a dual-branch BEV masked autoencoder to implement the reconstruction of the data from both the camera and LiDAR modalities.Extensive experiments on the nuScenes dataset with camera-LiDAR inputs demonstrate that the proposed framework achieves superior performance over existing state-of-the-art multi-modal learning methods. 展开更多
关键词 3D object detection cross-modal guided reconstruction ground truth-based foreground masking multi-modality fusion dual-branch BEV masked autoencoder
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Variational Graph Autoencoder–Based Timing-Driven Initialization Placement 认领 引用
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作者 Ziyi Ju Ping Yu +1 位作者 Rui Song Tonglin Chen 《Computers, Materials & Continua》 SCIE EI 2026年第9期204-224,共21页
In modern high-performance chip design,achieving timing closure is essential to design success.With the increasing scale and complexity of modern chips,timing-driven placement has become increasingly important.Traditi... In modern high-performance chip design,achieving timing closure is essential to design success.With the increasing scale and complexity of modern chips,timing-driven placement has become increasingly important.Traditional placement methods primarily focus on minimizing wirelength,but lack timing optimization,making it difficult to meet the strict timing closure requirements of modern designs.Therefore,developing an efficient timing-driven placement method has become a critical challenge in modern chip design.This paper presents a novel timing-driven placement framework that integrates a variational graph autoencoder(VGAE)with a nonlinear mixed-size placement optimizer.The framework identifies timing-violation paths through static timing analysis and dynamically adjusts interconnect weights based on pin-level timing slack,enabling the VGAE to generate an initial placement that prioritizes critical-path optimization.Experimental results on the ICCAD2015 benchmarks show that the proposed method achieves a 25.4%improvement in worst negative slack and a 18.1%improvement in total negative slack compared with DREAMPlace4.0.These results demonstrate its effectiveness in improving timing quality. 展开更多
关键词 Timing optimization timing-driven placement variational graph autoencoder weight updating nonlinear optimization
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A Composite Loss-Based Autoencoder for Accurate and Scalable Missing Data Imputation 认领 引用
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作者 Thierry Mugenzi Cahit Perkgoz 《Computers, Materials & Continua》 SCIE EI 2026年第1期1985-2005,共21页
Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel a... Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications. 展开更多
关键词 Missing data imputation autoencoder deep learning missing mechanisms
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Dual-Branch Autoencoder with Hybrid Attention Mechanisms for Fading Channel Blind Equalization 认领 引用
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作者 Pu Sihui Duan Ruifeng +1 位作者 Sun Guodong LiuWanchun 《China Communications》 SCIE EI CSCD 2026年第5期113-134,共22页
Modulation signals are likely to experience channel fading when transmitted over wireless channels,and channel blind equalization is a powerful method to combat fading and guarantee high transmission quality and effic... Modulation signals are likely to experience channel fading when transmitted over wireless channels,and channel blind equalization is a powerful method to combat fading and guarantee high transmission quality and efficiency because no pilot information is required.In this paper,we propose a dual-branch blind equalization autoencoder with hybrid attention mechanisms,referred to as DBeA-HA,to restore various modulation signal waveforms.In the main branch,we employ multi-layer depthwise separable convolutions(DSC)with rich residual connections and squeeze-and-excitation(SE)mechanism in our encoder to extract channel features of faded signals at different resolutions,while reducing complexity.The auxiliary branch is constructed by incorporating a lightweight residual temporal dilated convolution to capture temporal correlations of faded signals.Additionally,the convolutional block attention module(CBAM)and multi-head self-attention(MHSA)mechanisms are applied within and between branches,respectively,to further enhance feature extraction and fusion capabilities.By integrating two branches effectively,the proposed method achieves high equalization performance and keeps low complexity.Experimental results reveal that in signal-to-noise ratio(SNR)ranging from–12–8 dB with six-path fading,our DBeAHA effectively compensates for the effects of fading channels and surpasses existing equalization methods.For demodulation,compared with“ResNet+De”and the least mean square(LMS)methods,our DBeA-HA reduces the BER of QPSK by an average of 32.66%and 72.98%,and reduces the BER of 16-QAM by an average of 19.41%and 55.33%,respectively,with a moderate level of complexity. 展开更多
关键词 autoencoder channel blind equalization dual-branch fading channel hybrid attention mechanisms
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A Hybrid Wasserstein GAN and Autoencoder Model for Robust Intrusion Detection in IoT 认领 引用 被引量：3
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作者 Mohammed S.Alshehri Oumaima Saidani +4 位作者 Wajdan Al Malwi Fatima Asiri Shahid Latif Aizaz Ahmad Khattak Jawad Ahmad 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第6期3899-3920,共22页
The emergence of Generative Adversarial Network(GAN)techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems(IDS).However,conventional GAN-based IDS ... The emergence of Generative Adversarial Network(GAN)techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems(IDS).However,conventional GAN-based IDS models face several challenges,including training instability,high computational costs,and system failures.To address these limitations,we propose a Hybrid Wasserstein GAN and Autoencoder Model(WGAN-AE)for intrusion detection.The proposed framework leverages the stability of WGAN and the feature extraction capabilities of the Autoencoder Model.The model was trained and evaluated using two recent benchmark datasets,5GNIDD and IDSIoT2024.When trained on the 5GNIDD dataset,the model achieved an average area under the precisionrecall curve is 99.8%using five-fold cross-validation and demonstrated a high detection accuracy of 97.35%when tested on independent test data.Additionally,the model is well-suited for deployment on resource-limited Internetof-Things(IoT)devices due to its ability to detect attacks within microseconds and its small memory footprint of 60.24 kB.Similarly,when trained on the IDSIoT2024 dataset,the model achieved an average PR-AUC of 94.09%and an attack detection accuracy of 97.35%on independent test data,with a memory requirement of 61.84 kB.Extensive simulation results demonstrate that the proposed hybrid model effectively addresses the shortcomings of traditional GAN-based IDS approaches in terms of detection accuracy,computational efficiency,and applicability to real-world IoT environments. 展开更多
关键词 Autoencoder cybersecurity generative adversarial network Internet of Things intrusion detection system
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Multi-scale feature fused stacked autoencoder and its application for soft sensor modeling 认领 引用 被引量：3
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作者 Zhi Li Yuchong Xia +2 位作者 Jian Long Chensheng Liu Longfei Zhang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第5期241-254,共14页
Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE... Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively. 展开更多
关键词 Multi-scale feature fusion Soft sensors Stacked autoencoders Computational chemistry Chemical processes Parameter estimation
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A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries 认领 引用
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作者 Lei Cai Jing Yan +5 位作者 Haiyan Jin Jinhao Meng Jichang Peng Bin Wang Wei Liang Remus Teodorescu 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2025年第4期759-772,共14页
To predict the lithium-ion(Li-ion)battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great importance.However,under different operation conditions,Li-io... To predict the lithium-ion(Li-ion)battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great importance.However,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early cycles.Despite the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and expensive.In this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles'degradation trajectories.The two-stage method can properly predict the degradation from course to fine.The twin autoencoders serve as a feature extractor and a synthetic data generator,respectively.Ultimately,a learning procedure based on the long-short term memory(LSTM)network is designed to hybridize the learning process between the real and synthetic data.The performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors. 展开更多
关键词 Battery degradation trajectory Early prediction Autoencoder Synthetic data generation
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AESR3D:3D overcomplete autoencoder for trabecular computed tomography super resolution 认领 引用
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作者 Shuwei Zhang Yefeng Liang +3 位作者 Xingyu Li Shibo Li Xiaofeng Xiong Lihai Zhang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第3期652-665,共14页
Osteoporosis is a major cause of bone fracture and can be characterised by both mass loss and microstructure deterioration of the bone.The modern way of osteoporosis assessment is through the measurement of bone miner... Osteoporosis is a major cause of bone fracture and can be characterised by both mass loss and microstructure deterioration of the bone.The modern way of osteoporosis assessment is through the measurement of bone mineral density,which is not able to unveil the pathological condition from the mesoscale aspect.To obtain mesoscale information from computed tomography(CT),the super-resolution(SR)approach for volumetric imaging data is required.A deep learning model AESR3D is proposed to recover high-resolution(HR)Micro-CT from low-resolution Micro-CT and implement an unsupervised segmentation for better trabecular observation and measurement.A new regularisation overcomplete autoencoder framework for the SR task is proposed and theoretically analysed.The best performance is achieved on structural similarity measure of trabecular CT SR task compared with the state-of-the-art models in both natural and medical image SR tasks.The HR and SR images show a high correlation(r=0.996,intraclass correlation coefficients=0.917)on trabecular bone morphological indicators.The results also prove the effectiveness of our regularisation framework when training a large capacity model. 展开更多
关键词 overcomplete autoencoder segmentation super resolution trabecular CT
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A new maximum-a-posteriori-based gappy method for physical field reconstruction using proper orthogonal decomposition and autoencoder 认领 引用
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作者 Wenwei JIANG Chenhao TAN +2 位作者 Yuntao ZHOU Kai YANG Xiaowei GAO 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2025年第9期1729-1752,I0001-I0007,共24页
A novel gappy technology, gappy autoencoder with proper orthogonal decomposition(Gappy POD-AE), is proposed for reconstructing physical fields from sparse data. High-dimensional data are reduced via proper orthogonal ... A novel gappy technology, gappy autoencoder with proper orthogonal decomposition(Gappy POD-AE), is proposed for reconstructing physical fields from sparse data. High-dimensional data are reduced via proper orthogonal decomposition(POD),and low-dimensional data are used to train an autoencoder(AE). By integrating the POD operator with the decoder, a nonlinear solution form is established and incorporated into a new maximum-a-posteriori(MAP)-based objective for online reconstruction.The numerical results on the two-dimensional(2D) Bhatnagar-Gross-Krook-Boltzmann(BGK-Boltzmann) equation, wave equation, shallow-water equation, and satellite data show that Gappy POD-AE achieves higher accuracy than gappy proper orthogonal decomposition(Gappy POD), especially for the data with slowly decaying singular values,and is more efficient in training than gappy autoencoder(Gappy AE). The MAP-based formulation and new gappy procedure further enhance the reconstruction accuracy. 展开更多
关键词 data reconstruction gappy technology proper orthogonal decomposition(POD) autoencoder(AE) maximum-a-posteriori(MAP)
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ALSTNet:Autoencoder fused long-and short-term time-series network for the prediction of tunnel structure 认领 引用
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作者 Bowen Du Haohan Liang +3 位作者 Yuhang Wang Junchen Ye Xuyan Tan Weizhong Chen 《Deep Underground Science and Engineering》 EI CAS CSCD 2025年第1期72-82,共11页
It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and externa... It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions.Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring data.Then,the proposed model was formalized on multiple time series of strain monitoring data.Also,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model.As the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures.As a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future. 展开更多
关键词 autoencoder deep learning structural health monitoring time-series prediction
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Dynamic behavior recognition in aerial deployment of multi-segmented foldable-wing drones using variational autoencoders 认领 引用 被引量：2
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作者 Yilin DOU Zhou ZHOU Rui WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第6期143-165,共23页
The aerial deployment method enables Unmanned Aerial Vehicles(UAVs)to be directly positioned at the required altitude for their mission.This method typically employs folding technology to improve loading efficiency,wi... The aerial deployment method enables Unmanned Aerial Vehicles(UAVs)to be directly positioned at the required altitude for their mission.This method typically employs folding technology to improve loading efficiency,with applications such as the gravity-only aerial deployment of high-aspect-ratio solar-powered UAVs,and aerial takeoff of fixed-wing drones in Mars research.However,the significant morphological changes during deployment are accompanied by strong nonlinear dynamic aerodynamic forces,which result in multiple degrees of freedom and an unstable character.This hinders the description and analysis of unknown dynamic behaviors,further leading to difficulties in the design of deployment strategies and flight control.To address this issue,this paper proposes an analysis method for dynamic behaviors during aerial deployment based on the Variational Autoencoder(VAE).Focusing on the gravity-only deployment problem of highaspect-ratio foldable-wing UAVs,the method encodes the multi-degree-of-freedom unstable motion signals into a low-dimensional feature space through a data-driven approach.By clustering in the feature space,this paper identifies and studies several dynamic behaviors during aerial deployment.The research presented in this paper offers a new method and perspective for feature extraction and analysis of complex and difficult-to-describe extreme flight dynamics,guiding the research on aerial deployment drones design and control strategies. 展开更多
关键词 Dynamic behavior recognition Aerial deployment technology Variational autoencoder Pattern recognition Multi-rigid-bodydynamics
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Spatially Constrained Variational Autoencoder for Geochemical Data Denoising and Uncertainty Quantification 认领 引用 被引量：1
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作者 Dazheng Huang Renguang Zuo +1 位作者 Jian Wang Raimon Tolosana-Delgado 《Journal of Earth Science》 SCIE CAS CSCD 2025年第5期2317-2336,共20页
Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying... Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying prediction uncertainty is hence crucial for robust geoscientific decision-making.This study proposes a novel deep learning framework,the Spatially Constrained Variational Autoencoder(SC-VAE),for denoising geochemical survey data with integrated uncertainty quantification.The SC-VAE incorporates spatial regularization,which enforces spatial coherence by modeling inter-sample relationships directly within the latent space.The performance of the SC-VAE was systematically evaluated against a standard Variational Autoencoder(VAE)using geochemical data from the gold polymetallic district in the northwestern part of Sichuan Province,China.Both models were optimized using Bayesian optimization,with objective functions specifically designed to maintain essential geostatistical characteristics.Evaluation metrics include variogram analysis,quantitative measures of spatial interpolation accuracy,visual assessment of denoised maps,and statistical analysis of data distributions,as well as decomposition of uncertainties.Results show that the SC-VAE achieves superior noise suppression and better preservation of spatial structure compared to the standard VAE,as demonstrated by a significant reduction in the variogram nugget effect and an increased partial sill.The SC-VAE produces denoised maps with clearer anomaly delineation and more regularized data distributions,effectively mitigating outliers and reducing kurtosis.Additionally,it delivers improved interpolation accuracy and spatially explicit uncertainty estimates,facilitating more reliable and interpretable assessments of prediction confidence.The SC-VAE framework thus provides a robust,geostatistically informed solution for enhancing the quality and interpretability of geochemical data,with broad applicability in mineral exploration,environmental geochemistry,and other Earth Science domains. 展开更多
关键词 geochemical data denoising spatially constrained variational autoencoder geostatistics bayesian optimization uncertainty analysis geochemistry
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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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Drive-by damage detection methodology for high-speed railway bridges using sparse autoencoders 认领 引用 被引量：1
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作者 Edson Florentino de Souza Cássio Bragança +2 位作者 Diogo Ribeiro Túlio Nogueira Bittencourt Hermes Carvalho 《Railway Engineering Science》 EI 2025年第4期614-641,共28页
High-speed railway bridges are essential components of any railway transportation system that should keep adequate levels of serviceability and safety.In this context,drive-by methodologies have emerged as a feasible ... High-speed railway bridges are essential components of any railway transportation system that should keep adequate levels of serviceability and safety.In this context,drive-by methodologies have emerged as a feasible and cost-effective monitor-ing solution for detecting damage on railway bridges while minimizing train operation interruptions.Moreover,integrating advanced sensor technologies and machine learning algorithms has significantly enhanced structural health monitoring(SHM)for bridges.Despite being increasingly used in traditional SHM applications,studies using autoencoders within drive-by methodologies are rare,especially in the railway field.This study presents a novel approach for drive-by damage detection in HSR bridges.The methodology relies on acceleration records collected from multiple bridge crossings by an operational train equipped with onboard sensors.Log-Mel spectrogram features derived from the acceleration records are used together with sparse autoencoders for computing statistical distribution-based damage indexes.Numerical simulations were performed on a 3D vehicle-track-bridge interaction system model implemented in Matlab to evaluate the robustness and effectiveness of the proposed approach,considering several damage scenarios,vehicle speeds,and environmental and operational variations,such as multiple track irregularities and varying measurement noise.The results show that the pro-posed approach can successfully detect damages,as well as characterize their severity,especially for very early-stage dam-ages.This demonstrates the high potential of applying Mel-frequency damage-sensitive features associated with machine learning algorithms in the drive-by condition assessment of high-speed railway bridges. 展开更多
关键词 Drive-by Indirect monitoring Damage detection High-speed railway bridges Autoencoders
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Point-MASNet:Masked Autoencoder-Based Sampling Network for 3D Point Cloud 认领 引用
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作者 Xu Wang Yi Jin +3 位作者 Hui Yu Yigang Cen Tao Wang Yidong Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第11期2300-2313,共14页
Task-oriented point cloud sampling aims to select a representative subset from the input,tailored to specific application scenarios and task requirements.However,existing approaches rarely tackle the problem of redund... Task-oriented point cloud sampling aims to select a representative subset from the input,tailored to specific application scenarios and task requirements.However,existing approaches rarely tackle the problem of redundancy caused by local structural similarities in 3D objects,which limits the performance of sampling.To address this issue,this paper introduces a novel task-oriented point cloud masked autoencoder-based sampling network(Point-MASNet),inspired by the masked autoencoder mechanism.Point-MASNet employs a voxel-based random non-overlapping masking strategy,which allows the model to selectively learn and capture distinctive local structural features from the input data.This approach effectively mitigates redundancy and enhances the representativeness of the sampled subset.In addition,we propose a lightweight,symmetrically structured keypoint reconstruction network,designed as an autoencoder.This network is optimized to efficiently extract latent features while enabling refined reconstructions.Extensive experiments demonstrate that Point-MASNet achieves competitive sampling performance across classification,registration,and reconstruction tasks. 展开更多
关键词 Autoencoder deep learning efficiency-enhanced point cloud task-oriented sampling
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