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Bayesian finite element model updating with a variational autoencoder and polynomial chaos expansion

ENGINEERING STRUCTURES 2024年 316卷

作者： Li, Qiang Ni, Pinghe Du, Xiuli Han, Qiang Xu, Kun Bai, Yulei Natl Key Lab Bridge Safety & Resilience Beijing Peoples R China

The quantification of uncertainty in civil structures poses a significant challenge in contemporary research due to the substantial computational demands involved. This study introduces an innovative approach for updating the finite element model (FEM) and quantifying uncertainties in civil structures through the synergistic use of variational autoencoder (VAE) and polynomial chaos expansion (PCE). Within this framework, the unknown parameters inherent to the structural FEM are represented as latent variables and can be effectively inferred through the VAE. These latent variables are modeled using a multivariate Gaussian distribution. In the proposed methodology, the PCE serves to approximate the log-likelihood function associated with the latent variables, facilitating the derivation of the analytic expression for the variational lower bound. By maximizing this variational lower bound, both the mean and standard deviation can be readily determined. To assess the accuracy and computational efficiency of the proposed technique, numerical analyses are performed on a cantilever beam and a steel pedestrian bridge. Furthermore, the effectiveness of the proposed approach is validated through its application to damage identification within a benchmark model. Significantly, the results indicate that the proposed method offers superior computational efficiency compared to the conventional VAE approach. Notably, the findings reveal that employing a high-order PCE is unnecessary;rather, a low-order PCE suffices for precise parameter identification. Consequently, the proposed methodology necessitates only a limited dataset for training to ascertain the PCE coefficients, thereby enhancing its practical applicability and efficiency.

关键词： Bayesian model updating variational autoencoder variational inference Uncertainty quantification Polynomial chaos expansion

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Multiscale variational autoencoder regressor for production prediction and energy saving of industrial processes

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CHEMICAL ENGINEERING SCIENCE 2024年 284卷

作者： Han, Yongming Wang, Yue Chen, Zhiwei Lu, Yi Hu, Xuan Chen, Liangchao Geng, Zhiqiang Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China Beijing Univ Chem Technol High Tech Res Inst Beijing 100029 Peoples R China Beijing Jiaboyunshu Technol Co Ltd Beijing 100089 Peoples R China

As modern industries increase in scale and integration, the industrial process data show more complex dynamic time variability. To solve the dynamic time-varying problem of industrial process data, a novel multiscale variational autoencoder (MSVAE) based Regressor (REG) (MSVAE-REG) is proposed for production prediction and energy saving. The encoder of the MSVAE is constructed by multiscale convolutional neural network (MSCNN) to extract the multiscale temporal dynamic features, including the overall and local trend features in the Gaussian latent space. Then, the decoder of the MSVAE recovers the input data to enhance the dynamic adaptability of multiscale features. Moreover, the REG utilizes the gated recurrent unit (GRU) to build a dynamic relationship between multiscale temporal dynamic features and the predicted output. Finally, the proposed model is verified on the propylene and ethylene production datasets. Compared with the back propagation (BP) network, the extreme learning machine (ELM), the radial basis function (RBF), the convolutional neural network (CNN) and the variational autoencoder (VAE), the MSVAE-REG achieves state-of-the-art results, with the prediction accuracy respectively reaching about 95% and 96%, providing a new judgment basis for optimizing the production process and saving energy.

关键词： variational autoencoder Multiscale convolutional neural network Energy saving Production prediction Propylene industrial production Ethylene industrial production

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Fast variational autoencoder with Inverted Multi-Index for Collaborative Filtering 22

Fast Variational AutoEncoder with Inverted Multi-Index for C...

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31st ACM Web Conference (WWW)

作者： Chen, Jin Lian, Defu Jin, Binbin Huang, Xu Zheng, Kai Chen, Enhong Univ Elect Sci & Technol China Chengdu Peoples R China Uni Sci & Technol China Hefei Anhui Peoples R China Huawei Cloud Comp Technol Co Ltd Dongguan Peoples R China

ISBN: (纸本)9781450390965

variational autoencoder (VAE) has been extended as a representative nonlinear method for collaborative filtering. However, the bottleneck of VAE lies in the softmax computation over all items, such that it takes linear costs in the number of items to compute the loss and gradient for optimization. This hinders the practical use due to millions of items in real-world scenarios. Importance sampling is an effective approximation method, based on which the sampled softmax has been derived. However, existing methods usually exploit the uniform or popularity sampler as proposal distributions, leading to a large bias of gradient estimation. To this end, we propose to decompose the inner-product-based softmax probability based on the inverted multi-index, leading to sublinear-time and highly accurate sampling. Based on the proposed proposals, we develop a fast variational autoencoder (FastVAE) for collaborative filtering. FastVAE can outperform the state-of-the-art baselines in terms of both sampling quality and efficiency according to the experiments on three real-world datasets.

关键词： Sampling Inverted Multi-Index Recommender Systems variational autoencoder Collaborative Filtering

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variational autoencoder based bipartite network embedding by integrating local and global structure

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INFORMATION SCIENCES 2020年第0期519卷 9-21页

作者： Jiao, Pengfei Tang, Minghu Liu, Hongtao Wang, Yaping Lu, Chunyu Wu, Huaming Tianjin Univ Ctr Biosafety Res & Strategy Tianjin Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Tianjin Univ Ctr Appl Math Tianjin Peoples R China

As a powerful tool for machine learning on the graph, network embedding, which projects nodes into low-dimensional spaces, has a variety of applications on complex networks. Most current methods and models are not suitable for bipartite networks, which have two different types of nodes and there are no links between nodes of the same type. Furthermore, the only existing methods for bipartite network embedding ignore the internal mechanism and highly nonlinear structures of links. Therefore, in this paper, we propose a new deep learning method to learn the node embedding for bipartite networks based on the widely used autoencoder framework. Moreover, we carefully devise a node-level triplet including two types of nodes to assign the embedding by integrating the local and global structures. Meanwhile, we apply the variational autoencoder (VAE), a deep generation model with natural advantages in data generation and reconstruction, to enhance the node embedding for the highly nonlinear relationships between nodes and complex features. Experiments on some widely used datasets show the effectiveness of the proposed model and corresponding algorithm compared with some baseline network (and bipartite) embedding techniques. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Bipartite network embedding Local and global structure variational autoencoder Nonlinear structure

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variational autoencoder With Optimizing Gaussian Mixture Model Priors

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IEEE ACCESS 2020年 8卷 43992-44005页

作者： Guo, Chunsheng Zhou, Jialuo Chen, Huahua Ying, Na Zhang, Jianwu Zhou, Di Hangzhou Dianzi Univ Telecommun Engn Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Video Anal & Proc Team Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Technol Co Ltd Hangzhou 310051 Peoples R China

The latent variable prior of the variational autoencoder (VAE) often utilizes a standard Gaussian distribution because of the convenience in calculation, but has an underfitting problem. This paper proposes a variational autoencoder with optimizing Gaussian mixture model priors. This method utilizes a Gaussian mixture model to construct prior distribution, and utilizes the Kullback-Leibler (KL) distance between posterior and prior distribution to implement an iterative optimization of the prior distribution based on the data. The greedy algorithm is used to solve the KL distance for defining the approximate variational lower bound solution of the loss function, and for realizing the VAE with optimizing Gaussian mixture model priors. Compared with the standard VAE method, the proposed method obtains state-of-the-art results on MNIST, Omniglot, and Frey Face datasets, which shows that the VAE with optimizing Gaussian mixture model priors can learn a better model.

关键词： Gaussian mixture model Gaussian distribution Training Standards Neural networks Aggregates variational autoencoder Gaussian mixture model Kullback-Leibler distance

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Advanced collision risk estimation in terminal manoeuvring areas using a variational autoencoder for

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期133卷

作者： Krauth, Timothe Morio, Jerome Olive, Xavier Figuet, Benoit Zurich Univ Appl Sci Ctr Aviat CH-8400 Winterthur Switzerland Univ Toulouse ONERA DTIS F-31055 Toulouse France Technikumstr 71 CH-8400 Winterthur Switzerland

Air Traffic Management aims at ensuring safety during aircraft operations, particularly within Terminal Manoeuvring Areas where traffic density is high. The challenge lies in balancing safety and efficiency by closely managing the likelihood of mid-air collisions regarding the airport movements. Traditional models like the Reich and Anderson -Hsu models have been influential, but they fall short in representing the complex reality of Terminal Manoeuvring Areas. Data -driven approaches are emerging, with Monte Carlo simulations offering a more flexible methodology for collision risk estimation. This paper introduces a framework for assessing Mid -Air Collision likelihood resulting from Terminal Manoeuvring Area procedures by combining the field of Deep Generative Modelling using a variational autoencoder with the domain of rare event statistics through Subset Simulation. By incorporating disentanglement into the variational autoencoders model, we create a latent space that aligns dimensions with distinctive trajectory traits. Then, Subset Simulation is employed to gauge Mid -Air Collision probability, utilizing latent representations as input. Finally, sensitivity analysis reveals pivotal factors influencing collision risk, correlated with trajectory attributes via disentanglement. The methodology is applied to traffic around Zurich Airport: it evaluates the risk arising from go -around and take -off procedures using Automatic Dependent Surveillance -Broadcast data.

关键词： Air traffic management Rare event probability estimation Deep generative models variational autoencoder Uncertainty quantification

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Unleashing the Power of Hierarchical variational autoencoder for Predicting Breast Cancer

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IEEE ACCESS 2024年 12卷 195658-195670页

作者： Sreelekshmi, V. Pavithran, K. Nair, Jyothisha J. Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Amritapuri 690525 India Amrita Inst Med Sci Dept Med Oncol Kochi 682041 Kerala India

Breast cancer continues to be a major health concern worldwide. Early and accurate prediction is crucial for effective treatment and improving survival rates. Computer Aided Diagnosis system serves as an invaluable tool for radiologists, aiming to reduce diagnostic errors and enhance the accuracy of diagnosis. These systems incorporate various processing techniques, including pre-processing, segmentation, feature extraction, and classification. Moreover, deep learning methods frequently suffer from sub optimal performance and demand substantial computational resources. This study focuses on developing an automated classification model for mammography images to aid in breast cancer diagnosis. Our proposed model initiates with noise removal using median filters, followed by the removal of the pectoral muscle in images through the Canny-edge detection method. On these preprocessed images, we applied data augmentation using a two-point crossover technique, addressing issues of small datasets and class imbalances common in medical image analysis. The images then undergo multi-scale representation via the fourth-order complex diffusion algorithm. Feature extraction is conducted on these multi-scaled images using a Hierarchical variational Auto-encoders and then classified using a Support Vector Machine. Employing fourth-order complex diffusion for initial multi-scale representation significantly enhances the accuracy of feature extraction resulting in robust classification performance. The training process involves two different datasets like MIAS and the KAU-BCMD. Test results for the KAU-BCMD dataset include: accuracy of 99.80%, Area Under the Curve of 99.30%, F1-score of 99.20%, balanced accuracy of 99.80%, and Matthews correlation coefficient of 99.20%. For the MIAS dataset, test results show accuracy of 99.30%, Area Under the Curve of 99.10%, F1-score of 98.30%, balanced accuracy of 99.00%, and Matthews correlation coefficient of 99.00%. Our validation results clearl

关键词： Feature extraction Breast cancer Muscles Accuracy Data augmentation Mammography Image segmentation Noise Convolutional neural networks Classification algorithms convolutional neural network data augmentation edge detection multi-scale representation variational autoencoder

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A topic modeling and image classification framework: The Generalized Dirichlet variational autoencoder

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PATTERN RECOGNITION 2024年 146卷

作者： Ojo, Akinlolu Oluwabusayo Bouguila, Nizar Concordia Univ 1455 Maisonneuve Blvd W Montreal PQ H3G 1M8 Canada

Latent Dirichlet allocation model (LDA) has been widely used in topic modeling. Recent works have shown the effectiveness of integrating neural network mechanisms with this generative model for learning text representation. However, one of the significant setbacks of LDA is that it is based on a Dirichlet prior that has a restrictive covariance structure. All its variables are considered to be negatively correlated, which makes the model restrictive. In a practical sense, topics can be positively or negatively correlated. To address this problem, we proposed a generalized Dirichlet variational autoencoder (GD-VAE) for topic modeling. The Generalized Dirichlet (GD) distribution has a more general covariance structure than the Dirichlet distribution because it takes into account both positively and negatively correlated topics in the corpus. Our proposed model leverages rejection sampling variational inference using a reparameterization trick for effective training. GDVAE compares favorably to recent works on topic models on several benchmark corpora. Experiments show that accounting for topics' positive and negative correlations results in better performance. We further validate the superiority of our proposed framework on two image data sets. GD-VAE demonstrates its significance as an integral part of a classification architecture. For reproducibility and further research purposes, code for this work can be found at https://***/hormone03/GD-VAE.

关键词： Generalized Dirichlet distribution Correlation variational autoencoder Topic models Reparameterization Image classification

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A multimodal dynamical variational autoencoder for audiovisual speech representation learning

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NEURAL NETWORKS 2024年 172卷 106120页

作者： Sadok, Samir Leglaive, Simon Girin, Laurent Alameda-Pineda, Xavier Seguier, Renaud CentraleSupelec UMR CNRS 6164 IETR Gif Sur Yvette France Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab Grenoble France Univ Grenoble Alpes Inria LJK CNRS Grenoble France

High -dimensional data such as natural images or speech signals exhibit some form of regularity, preventing their dimensions from varying independently. This suggests that there exists a lower dimensional latent representation from which the high -dimensional observed data were generated. Uncovering the hidden explanatory features of complex data is the goal of representation learning, and deep latent variable generative models have emerged as promising unsupervised approaches. In particular, the variational autoencoder (VAE) which is equipped with both a generative and an inference model allows for the analysis, transformation, and generation of various types of data. Over the past few years, the VAE has been extended to deal with data that are either multimodal or dynamical (i.e., sequential). In this paper, we present a multimodal and dynamical VAE (MDVAE) applied to unsupervised audiovisual speech representation learning. The latent space is structured to dissociate the latent dynamical factors that are shared between the modalities from those that are specific to each modality. A static latent variable is also introduced to encode the information that is constant over time within an audiovisual speech sequence. The model is trained in an unsupervised manner on an audiovisual emotional speech dataset, in two stages. In the first stage, a vector quantized VAE (VQ-VAE) is learned independently for each modality, without temporal modeling. The second stage consists in learning the MDVAE model on the intermediate representation of the VQ-VAEs before quantization. The disentanglement between static versus dynamical and modality -specific versus modality -common information occurs during this second training stage. Extensive experiments are conducted to investigate how audiovisual speech latent factors are encoded in the latent space of MDVAE. These experiments include manipulating audiovisual speech, audiovisual facial image denoising, and audiovisual speech emotion

关键词： Deep generative modeling Disentangled representation learning variational autoencoder Multimodal and dynamical data Audiovisual speech processing

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A multi-channel fusion variational autoencoder-based RUL prediction approach for multi-sensor systems

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第11期35卷 116129-116129页

作者： Wang, Yuxiao Suo, Chao Zhao, Yuyu Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China

Deep learning (DL)-based approaches have demonstrated remarkable performance in predicting the remaining useful life (RUL) of complex systems, which is beneficial for making timely maintenance decisions. However, the majority of these DL methods suffer from a lack of interpretability, and it is difficult to mine the degradation features in the presence of significant measurement noises. To remedy the deficiency, a multi-channel fusion variational autoencoder (MCFVAE)-based approach is proposed. A feature fusion module is designed to capture and fuse the multi-channel features, which facilitates the disclosure of the degradation information from the multi-sensor data. A variational inference module is further introduced to generate the compressive representations and project them into a latent space as an interpretable component, which can display the degradation degree of the multi-sensor systems. A regressor module is finally utilized to establish the relationship between the compressive representations and the RUL. The superior feature fusion and distribution characteristics learning abilities of the MCFVAE contribute to achieving robust and interpretable RUL prediction. The effectiveness and superiority of the proposed method are experimentally validated through a publicly available Commercial modular aero propulsion system simulation dataset and compared with the existing methods.

关键词： multi-sensor systems variational autoencoder interpretable remaining useful life prediction

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