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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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Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition 认领 引用 被引量：12
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作者 Yixin Wang Shuang Qiu +3 位作者 Dan Li Changde Du Bao-Liang Lu Huiguang He 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第9期1612-1626,共15页
Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer i... Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in practice.We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples.To solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal data.Our method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common space.Through adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge.Extensive experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed method.Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data. 展开更多
关键词 Cycle-consistency domain adaptation electroencephalograph(EEG) multi modality variational autoencoder
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Enhancing the Effectiveness of Trimethylchlorosilane Purification Process Monitoring with Variational Autoencoder 认领 引用 被引量：2
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作者 Jinfu Wang Shunyi Zhao +1 位作者 Fei Liu Zhenyi Ma 《Computer Modeling in Engineering & Sciences》 SCIE EI 2022年第8期531-552,共22页
In modern industry,process monitoring plays a significant role in improving the quality of process conduct.With the higher dimensional of the industrial data,the monitoring methods based on the latent variables have b... In modern industry,process monitoring plays a significant role in improving the quality of process conduct.With the higher dimensional of the industrial data,the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database.Nevertheless,these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices,especially the T2 on them.Variational AutoEncoders(VAE),an unsupervised deep learning algorithm using the hierarchy study method,has the ability to make the latent variables follow the Gaussian distribution.The partial least squares(PLS)are used to obtain the information between the dependent variables and independent variables.In this paper,we will integrate these two methods and make a comparison with other methods.The superiority of this proposed method will be verified by the simulation and the Trimethylchlorosilane purification process in terms of the multivariate control charts. 展开更多
关键词 Process monitoring variational autoencoders partial least square multivariate control chart
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An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination 认领 引用 被引量：7
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作者 Hakan Gunduz 《Financial Innovation》 2021年第1期585-608,共24页
In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)models.These models were trained with four different f... In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)models.These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics.While the first experiments directly used the own stock features as the model inputs,the second experiments utilized reduced stock features through Variational AutoEncoders(VAE).In the last experiments,in order to grasp the effects of the other banking stocks on individual stock performance,the features belonging to other stocks were also given as inputs to our models.While combining other stock features was done for both own(named as allstock_own)and VAE-reduced(named as allstock_VAE)stock features,the expanded dimensions of the feature sets were reduced by Recursive Feature Elimination.As the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model,the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of 0.675.Although the classification results achieved with both feature types was close,allstock_VAE achieved these results using nearly 16.67%less features compared to allstock_own.When all experimental results were examined,it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock features.It was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features. 展开更多
关键词 Stock market prediction Variational autoencoder Recursive feature elimination Long-short term memory Borsa Istanbul LightGBM
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Facial landmark disentangled network with variational autoencoder 认领 引用
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作者 LIANG Sen ZHOU Zhi-ze +3 位作者 GUO Yu-dong GAO Xuan ZHANG Ju-yong BAO Hu-jun 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2022年第2期290-305,共16页
Learning disentangled representation of data is a key problem in deep learning.Specifically,disentangling 2D facial landmarks into different factors(e.g.,identity and expression)is widely used in the applications of f... Learning disentangled representation of data is a key problem in deep learning.Specifically,disentangling 2D facial landmarks into different factors(e.g.,identity and expression)is widely used in the applications of face reconstruction,face reenactment and talking head et al..However,due to the sparsity of landmarks and the lack of accurate labels for the factors,it is hard to learn the disentangled representation of landmarks.To address these problem,we propose a simple and effective model named FLD-VAE to disentangle arbitrary facial landmarks into identity and expression latent representations,which is based on a Variational Autoencoder framework.Besides,we propose three invariant loss functions in both latent and data levels to constrain the invariance of representations during training stage.Moreover,we implement an identity preservation loss to further enhance the representation ability of identity factor.To the best of our knowledge,this is the first work to end-to-end disentangle identity and expression factors simultaneously from one single facial landmark. 展开更多
关键词 disentanglement representation deep learning facial landmarks variational autoencoder
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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
Power consumption prediction in warehouses using variational autoencoders and tree-based regression models 认领 引用
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作者 Zaid Allal Hassan N.Noura +1 位作者 Ola Salman Khaled Chahine 《Energy and Built Environment》 EI CSCD 2026年第2期295-316,共22页
Precise power consumption prediction is essential for efficient energy management,resource allocation,infras-tructure planning,and cost optimization.In this paper,power consumption prediction is addressed using machin... Precise power consumption prediction is essential for efficient energy management,resource allocation,infras-tructure planning,and cost optimization.In this paper,power consumption prediction is addressed using machine learning(ML)techniques.The CU-BEMS dataset is employed to represent power consumption and variations in ambient conditions within a seven-floor building.The dataset is explored,preprocessed,and analyzed.A theo-retical framework linking power consumption and floor features is developed using a variational autoencoder(VAE)to compress all features from all zones on all floors into a latent space of only 15 features.This latent space is then combined with a specific zone(Zone 2)on a particular floor(Floor 6)and fed into a tree-based regressor layer to predict all features in this zone.A windowing function is used to create lagged versions of past data to predict future feature values in a multi-output scenario(6 outputs).It is found that for 1-hour-ahead forecasting,the ExtraTree regressor achieves the highest accuracy with a coefficient of determination(R2)of 97.4%and Mean Absolute Error(MAE)of 0.46.The prediction of power consumption features reaches 99.2%,specifically for the air conditioning unit’s consumed power.The proposed solution outperforms previous works,with LightGBM as the best regressor for 10-minute-ahead forecasting(MAE=0.218,R2=98.8)and 20-minute-ahead forecasting(MAE=0.317,R2=97.4).The proposed framework demonstrates its power in linking all floors and consump-tion zones,making it generalizable,accurate,transferable,and efficient for predicting power consumption within industrial facilities and habitats. 展开更多
关键词 Power consumption Variational autoencoder Tree-based algorithms Explainable artificial intelligence Chulalongkorn University Building Energy Management System(CU-BEMS)
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 VARIATIONAL AUTOENCODER BASED DUAL-PATH MODEL FOR SPEAKER-INDEPENDENT ACOUSTIC-TO-ARTICULATORY INVERSION 认领 引用
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作者 FANG Qiang 《中国语音学报》 2025年第2期91-100,共10页
Acoustic-to-articulatory inversion(AAI)aims to estimate articulatory trajectory of speech organs from acoustic speech signals.Although numerous studies have been conducted in the speaker-dependent scenario,only a limi... Acoustic-to-articulatory inversion(AAI)aims to estimate articulatory trajectory of speech organs from acoustic speech signals.Although numerous studies have been conducted in the speaker-dependent scenario,only a limited number of studies have addressed the issue in the speaker-independent scenario.In this study,we propose a dual-path speaker-independent AAI approach based on a variational autoencoder(VAE).This approach independently inverts content and speaker embeddings from acoustic domain to articulatory domain and synthesizes the articulatory trajectory using a fine-tuned decoder of a VAE in the articulatory domain.The results indicate that the proposed dual-path model outperforms the best model with only MFCC input in terms of root-mean-square error(RMSE).The RMSE of the dual-path model is reduced by 0.15 mm,representing an approximately 5.5% improvement relative to the RMSE obtained by the SAFN. 展开更多
关键词 Acoustic-to-Articulatory Inversion Speaker-Independent Dual-Path Variational Autoencoder
VASC: Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder 认领 引用 被引量：11
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作者 Dongfang Wang Jin Gu 《Genomics, Proteomics & Bioinformatics》 SCIE CAS CSCD 2018年第5期320-331,共12页
Single-cell RNA sequencing(scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities at the single cell level. It is an important step for studying cell subpopulations and lineages, with an effe... Single-cell RNA sequencing(scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities at the single cell level. It is an important step for studying cell subpopulations and lineages, with an effective low-dimensional representation and visualization of the original scRNA-Seq data. At the single cell level, the transcriptional fluctuations are much larger than the average of a cell population, and the low amount of RNA transcripts will increase the rate of technical dropout events. Therefore, scRNA-seq data are much noisier than traditional bulk RNA-seq data. In this study, we proposed the deep variational autoencoder for scRNA-seq data(VASC), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. VASC can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on over 20 datasets, VASC shows superior performances in most cases and exhibits broader dataset compatibility compared to four state-of-the-art dimension reduction and visualization methods. In addition, VASC provides better representations for very rare cell populations in the 2D visualization. As a case study, VASC successfully re-establishes the cell dynamics in pre-implantation embryos and identifies several candidate marker genes associated with early embryo development. Moreover, VASC also performs well on a 10× Genomics dataset with more cells and higher dropout rate. 展开更多
关键词 Single cell RNA sequencing Deep variational autoencoder Dimension reduction Visualization Dropout
Seismic labeled data expansion using variational autoencoders 认领 引用 被引量：3
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作者 Kunhong Li Song Chen Guangmin Hu 《Artificial Intelligence in Geosciences》 2020年第1期24-30,共7页
Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method ... Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method based on deep variational Autoencoders(VAE),which are made of neural networks and contains two partsEncoder and Decoder.Lack of training samples leads to overfitting of the network.We training the VAE with whole seismic data,which is a data-driven process and greatly alleviates the risk of overfitting.The Encoder captures the ability to map the seismic waveform Y to latent deep features z,and the Decoder captures the ability to reconstruct high-dimensional waveform Yb from latent deep features z.Later,we put the labeled seismic data into Encoders and get the latent deep features.We can easily use gaussian mixture model to fit the deep feature distribution of each class labeled data.We resample a mass of expansion deep features z*according to the Gaussian mixture model,and put the expansion deep features into the decoder to generate expansion seismic data.The experiments in synthetic and real data show that our method alleviates the problem of lacking labeled seismic data for supervised seismic facies analysis. 展开更多
关键词 Deep learning Variational autoencoders Data expansion
VAEFL: Integrating variational autoencoders for privacy preservation and performance retention in federated learning 认领 引用 被引量：1
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作者 Zhixin Li Yicun Liu +4 位作者 Jiale Li Guangnan Ye Hongfeng Chai Zhihui Lu Jie Wu 《Security and Safety》 2024年第4期44-60,共17页
Federated Learning(FL) heralds a paradigm shift in the training of artificial intelligence(AI) models by fostering collaborative model training while safeguarding client data privacy. In sectors where data sensitivity... Federated Learning(FL) heralds a paradigm shift in the training of artificial intelligence(AI) models by fostering collaborative model training while safeguarding client data privacy. In sectors where data sensitivity and AI model security are of paramount importance, such as fintech and biomedicine, maintaining the utility of models without compromising privacy is crucial with the growing application of AI technologies. Therefore, the adoption of FL is attracting significant attention. However, traditional FL methods are susceptible to Deep Leakage from Gradients(DLG) attacks, and typical defensive strategies in current research, such as secure multi-party computation and diferential privacy, often lead to excessive computational costs or significant decreases in model accuracy. To address DLG attacks in FL, this study introduces VAEFL, an innovative FL framework that incorporates Variational Autoencoders(VAEs) to enhance privacy protection without undermining the predictive prowess of the models. VAEFL strategically partitions the model into a private encoder and a public decoder. The private encoder, remaining local, transmutes sensitive data into a latent space fortified for privacy, while the public decoder and classifier, through collaborative training across clients, learn to derive precise predictions from the encoded data. This bifurcation ensures that sensitive data attributes are not disclosed, circumventing gradient leakage attacks and simultaneously allowing the global model to benefit from the diverse knowledge of client datasets. Comprehensive experiments demonstrate that VAEFL not only surpasses standard FL benchmarks in privacy preservation but also maintains competitive performance in predictive tasks. VAEFL thus establishes a novel equilibrium between data privacy and model utility, ofering a secure and efficient FL approach for the sensitive application of FL in the financial domain. 展开更多
关键词 Federated learning variational autoencoders deep leakage from gradients AI model security privacy preservation
Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder 认领 引用
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作者 Ruimin Ma Ruitao Xie +5 位作者 Yanlin Wang Jintao Meng Yanjie Wei Yunpeng Cai Wenhui Xi Yi Pan 《Big Data Mining and Analytics》 EI CSCD 2024年第3期781-793,共13页
Autism Spectrum Disorder(ASD)is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients,making early screening and intervention of ASD critical.With the deve... Autism Spectrum Disorder(ASD)is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients,making early screening and intervention of ASD critical.With the development of the machine learning and neuroimaging technology,extensive research has been conducted on machine classification of ASD based on structural Magnetic Resonance Imaging(s-MRI).However,most studies involve with datasets where participants'age are above 5 and lack interpretability.In this paper,we propose a machine learning method for ASD classification in children with age range from 0.92 to 4.83 years,based on s-MRI features extracted using Contrastive Variational AutoEncoder(CVAE).78 s-MRIs,collected from Shenzhen Children's Hospital,are used for training CVAE,which consists of both ASD-specific feature channel and common-shared feature channel.The ASD participants represented by ASD-specific features can be easily discriminated from Typical Control(TC)participants represented by the common-shared features.In case of degraded predictive accuracy when data size is extremely small,a transfer learning strategy is proposed here as a potential solution.Finally,we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions,which discloses potential biomarkers that could help target treatments of ASD in the future. 展开更多
关键词 Autism Spectrum Disorder(ASD)classification Contrastive Variational AutoEncoder(CVAE) transfer learning neuroanatomical interpretation
Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives 认领 引用
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作者 Marius Benkert Michael Heroth +2 位作者 Rainer Herrler Magda Gregorová Helmut C.Schmid 《Autonomous Intelligent Systems》 EI 2024年第1期297-306,共10页
The generation and optimization of simulation data for electrical machines remain challenging,largely due to the complexities of magneto-staticfinite element analysis.Traditional methodologies are not only resource-in... The generation and optimization of simulation data for electrical machines remain challenging,largely due to the complexities of magneto-staticfinite element analysis.Traditional methodologies are not only resource-intensive,but also time-consuming.Deep learning models can be used to shortcut these calculations.However,challenges arise when considering the unique parameter sets specific to each machine topology.Building on two recent studies(Parekh et al.in IEEE Trans.Magn.58(9):1-4,2022;Parekh et al.,Deep learning based meta-modeling for multi-objective technology optimization of electrical machines,2023,arXiv:2306.09087),that utilized a variational autoencoder to cohesively map diverse topologies into a singular latent space for subsequent optimization,this paper proposes a refined architecture and optimization workflow.Our modifications aim to streamline and enhance the robustness of both the training and optimization processes,and compare the results with the variational autoencoder architecture proposed recently. 展开更多
关键词 Deep learning Design optimization Electrical machines Variational autoencoder
An Evaluation of Variational Autoencoder in Credit Card Anomaly Detection 认领 引用
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作者 Faleh Alshameri Ran Xia 《Big Data Mining and Analytics》 EI CSCD 2024年第3期718-729,共12页
Anomaly detection is one of the many challenging areas in cybersecurity.The anomaly can occur in many forms,such as fraudulent credit card transactions,network intrusions,and anomalous imageries or documents.One of th... Anomaly detection is one of the many challenging areas in cybersecurity.The anomaly can occur in many forms,such as fraudulent credit card transactions,network intrusions,and anomalous imageries or documents.One of the most common challenges in anomaly detection is the obscurity of the normal state and the lack of anomalous samples.Traditionally,this problem is tackled by using resampling techniques or choosing models that approximate the distribution of the normal states.Variational AutoEncoder(VAE)has been studied in anomaly detections despite being more suitable in generative tasks.This study aims to explore the usage of VAE in credit card anomaly detection and evaluate latent space sampling techniques.In this study,we evaluate the usage of the convolutional network-based VAE model on a credit card transaction dataset.We train two VAE models,one with a large number of normal data and one with a small number of anomalous data.We compare the performance of both VAE models and evaluate the latent space of both VAE models by rescaling them with reconstruction error vectors.We also compare the effectiveness of the VAE model with other anomaly detection models when they are trained on imbalanced dataset. 展开更多
关键词 anomaly detection optimization imbalanced dataset generative modeling Convolutional Neural Network(CNN) Variational AutoEncoder(VAE) latent space scaling reconstruction error
Generative Semantic Communication:Architectures,Technologies,and Applications 认领 引用 被引量：4
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作者 Jinke Ren Yaping Sun +7 位作者 Hongyang Du Weiwen Yuan Chongjie Wang Xianda Wang Yingbin Zhou Ziwei Zhu Fangxin Wang Shuguang Cui 《Engineering》 SCIE EI CSCD 2026年第1期45-61,共17页
Semantic communication(SemCom)has emerged as a transformative paradigm for future wireless networks,aiming to improve communication efficiency by transmitting only the semantic meaning(or its encoded version)of the so... Semantic communication(SemCom)has emerged as a transformative paradigm for future wireless networks,aiming to improve communication efficiency by transmitting only the semantic meaning(or its encoded version)of the source data rather than the complete set of bits(symbols).However,traditional deep-learning-based SemCom systems present challenges such as limited generalization,low robustness,and inadequate reasoning capabilities,primarily due to the inherently discriminative nature of deep neural networks.To address these limitations,generative artificial intelligence(GAI)is seen as a promising solution,offering notable advantages in learning complex data distributions,transforming data between high-and low-dimensional spaces,and generating high-quality content.This paper explores the applications of GAI in SemCom and presents a comprehensive study.It begins by introducing three widely used SemCom systems enabled by classical GAI models:variational autoencoders,generative adversarial networks,and diffusion models.For each system,the fundamental concept of the GAI model,the corresponding SemCom architecture,and a literature review of recent developments are provided.Subsequently,a novel generative SemCom system is proposed,incorporating cutting-edge GAI technology—large language models(LLMs).This system features LLM-based artificial intelligence(AI)agents at both the transmitter and receiver,which act as“brains”to enable advanced information understanding and content regeneration capabilities,respectively.Unlike traditional systems that focus on bitstream recovery,this design allows the receiver to directly generate the desired content from the coded semantic information sent by the transmitter.As a result,the communication paradigm shifts from“information recovery”to“information regeneration,”marking a new era in generative SemCom.A case study on point-to-point video retrieval is presented to demonstrate the effectiveness of the proposed system,showing a 99.98%reduction in communication overhead and a 53%improvement in average retrieval accuracy compared to traditional communication systems.Furthermore,four typical application scenarios for generative SemCom are described,followed by a discussion of three open issues for future research.In summary,this paper provides a comprehensive set of guidelines for applying GAI in SemCom,laying the groundwork for the efficient deployment of generative SemCom in future wireless networks. 展开更多
关键词 Semantic communication Generative artificial intelligence Large language model Variational autoencoder Generative adversarial network Diffusion model
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Inverse Design of Composite Materials Based on Latent Space and Bayesian Optimization 认领 引用 被引量：1
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作者 Xianrui Lyu Xiaodan Ren 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期1-25,共25页
Inverse design of advanced materials represents a pivotal challenge in materials science.Leveraging the latent space of Variational Autoencoders(VAEs)for material optimization has emerged as a significant advancement ... Inverse design of advanced materials represents a pivotal challenge in materials science.Leveraging the latent space of Variational Autoencoders(VAEs)for material optimization has emerged as a significant advancement in the field of material inverse design.However,VAEs are inherently prone to generating blurred images,posing challenges for precise inverse design and microstructure manufacturing.While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent,it simultaneously imposes a substantial burden on target optimization due to an excessively high search space.To address these limitations,this study adopts a Variational Autoencoder guided Conditional Diffusion Generative Model(VAE-CDGM)framework integrated with Bayesian optimization to achieve the inverse design of composite materials with targeted mechanical properties.The VAE-CDGM model synergizes the strengths of VAEs and Denoising Diffusion Probabilistic Models(DDPM),enabling the generation of high-quality,sharp images while preserving a manipulable latent space.To accommodate varying dimensional requirements of the latent space,two optimization strategies are proposed.When the latent space dimensionality is excessively high,SHapley Additive exPlanations(SHAP)sensitivity analysis is employed to identify critical latent features for optimization within a reduced subspace.Conversely,direct optimization is performed in the low-dimensional latent space of VAE-CDGM when dimensionality is modest.The results demonstrate that both strategies accurately achieve the targeted design of composite materials while circumventing the blurred reconstruction flaws of VAEs,which offers a novel pathway for the precise design of advanced materials. 展开更多
关键词 Variational autoencoder denoising diffusion generation model composite materials Bayesian opti-mization SHapley Additive exPlanations
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Industrial quality prediction via FVAE-WGAN:A data-driven approach 认领 引用
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作者 Shiwei Gao Jing Yan +1 位作者 Mengyi Chen Pengxue Yun 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第3期175-187,共13页
In industrial production,the acquisition of critical quality variables often faces significant challenges due to high costs and data scarcity,which not only limit the improvement of production efficiency but also incr... In industrial production,the acquisition of critical quality variables often faces significant challenges due to high costs and data scarcity,which not only limit the improvement of production efficiency but also increase the difficulty of quality control.With the advent of the industrial big data era,the availability and diversity of data have greatly increased,offering opportunities to address these issues.To address the problem of data scarcity,this paper proposes a novel data augmentation method for soft sensing—FVAE-WGAN,which generates high-quality synthetic data to expand the training dataset of soft sensors,thereby enhancing their prediction accuracy and generalization capability.This method integrates two stacked variational autoencoder(VAE)models with a Wasserstein generative adversarial network(WGAN),constructing a generator capable of learning from a broader data distribution.Additionally,an encoder is embedded in the discriminator,enhancing the model's ability to utilize late nt features of the data.By freezing specific layers of the discriminato r,the pro posed method reduces computational resource consumption during training and effectively mitigates overfitting.Experiments conducted on industrial process datasets show that the FVAE-WGAN model outperforms comparative models in terms of accuracy and robustness.This approach not only alleviates the impact of data scarcity,but also optimizes the efficiency and reliability of industrial processes,thereby bringing substantial economic benefits to industrial production. 展开更多
关键词 Variational autoencoder Frozen Soft sensor model Data-driven
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