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variational autoencoders-LSTM based fault detection of time-dependent high dimensional processes

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第4期62卷 1092-1107页

作者： Maged, Ahmed Lui, Chun Fai Haridy, Salah Xie, Min City Univ Hong Kong Dept Adv Design & Syst Engn Kowloon Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China Benha Univ Benha Fac Engn Dept Mech Engn Banha Egypt Univ Sharjah Coll Engn Dept Ind Engn & Engn Management Sharjah U Arab Emirates City Univ Hong Kong Kowloon Tong Kowloon Hong Kong Peoples R China

In modern large-scale industrial processes, data are often high dimensional time-dependent due to the frequent sampling, dynamic nature and large number of variables. Appropriate monitoring of such processes allows for efficient decision-making that can improve the baseline of manufacturing companies either through decreasing production costs or enhancing production efficiency. Various latent variable-based control charts have been proposed for addressing high dimensional data;however, many of these methods assume that the data are independent and normally distributed. The violation of these assumptions results in an increased false alarm rate, in addition to the deterioration in the performance of such methods. In this study, we propose a variational autoencoder-Long Short Term Memory (VAE-LSTM) deep learning based T(2 )chart that integrates the unique features of both VAE and LSTM for intelligent fault detection of time-dependent high dimensional processes. The effectiveness and applicability of the proposed model are demonstrated through extensive simulations, an open-source online dataset, and a real case study.

关键词： Anomaly detection multivariate process monitoring deep learning variational autoencoder LSTM

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variational autoencoders for 3D data processing

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第2期57卷 42-42页

作者： Molnar, Szilard Tamas, Levente Tech Univ Cluj Napoca Automat Dept Memorandumului St 25 Cluj Napoca 400114 Romania

variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representation with the power of recent deep learning techniques. The main advantages of these types of generators lie in their ability to encode the information with the possibility to decode and generalize new samples. This capability was heavily explored for 2D image processing;however, only limited research focuses on VAEs for 3D data processing. In this article, we provide a thorough review of the latest achievements in 3D data processing using VAEs. These 3D data types are mostly point clouds, meshes, and voxel grids, which are the focus of a wide range of applications, especially in robotics. First, we shortly present the basic autoencoder with the extensions towards the VAE with further subcategories relevant to discrete point cloud processing. Then, the 3D data specific VAEs are presented according to how they operate on spatial data. Finally, a few comprehensive table summarizing the methods, codes, and datasets as well as a citation map is presented for a better understanding of the VAEs applied to 3D data. The structure of the analyzed papers follows a taxonomy, which differentiates the algorithms according to their primary data types and application domains.

关键词： variational autoencoder Survey 3D data

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variational autoencoders for Biomedical Signal Morphology Clustering and Noise Detection

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第1期28卷 169-180页

作者： Nowroozilarki, Zhale Mortazavi, Bobak J. Jafari, Roozbeh Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77840 USA Texas A&M Univ Dept Biomed Engn Elect & Comp Engn Comp Sci & Engn College Stn TX 77840 USA

Accurate estimation of physiological biomarkers using raw waveform data from non-invasive wearable devices requires extensive data preprocessing. An automatic noise detection method would offer significant utility for various domains. As data labeling is onerous, having a minimally supervised abnormality detection method for input data, as well as an estimation of the severity of the signal corruptness, is essential. We propose a model-free, time-series biomedical waveform noise detection framework using a variational autoencoder coupled with Gaussian Mixture Models, which can detect a range of waveform abnormalities without annotation, providing a confidence metric for each segment. Our technique operates on biomedical signals that exhibit periodicity of heart activities. This framework can be applied to any machine learning or deep learning model as an initial signal validator component. Moreover, the confidence score generated by the proposed framework can be incorporated into different models' optimization to construct confidence-aware modeling. The result confirms that our approach removes noisy cardiac cycles and the remaining signals, classified as clean, exhibit a 59.92% reduction in the standard deviation of DTW distances. Using a dataset of bio-impedance data of 97885 cardiac cycles, we further demonstrate a significant improvement in the downstream task of cuffless blood pressure estimation, with an average reduction of 2.67 mmHg root mean square error (RMSE) of Diastolic Blood pressure and 2.13 mmHg RMSE of systolic blood pressure, with increases of average Pearson correlation of 0.28 and 0.08, with a statistically significant improvement of signal-to-noise ratio respectively in the presence of different synthetic noise sources.

关键词： Biomarker estimation biomedical signal processing deep learning Gaussian mixture models variational autoencoder

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AI-driven music composition: Melody generation using Recurrent Neural Networks and variational autoencoders

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ALEXANDRIA ENGINEERING JOURNAL 2025年 120卷 258-270页

作者： Zhao, Hanbing Min, Siran Fang, Jianwei Bian, Shanshan Beihua Univ Conservatory Mus Jilin 132100 Peoples R China Russian State Pedag Univ St Petersburg 191186 Russia Lubeck Conservatory Mus D-2355212 Lubeck Schleswig Holst Germany China Unicom Software Res Inst Beijing 100176 Peoples R China

Automatic melody generation has recently gained significant attention in music creation and artificial intelligence. However, existing models often lack accuracy in emotional expression, coherence, and diversity. To address these issues, we propose a melody generation model based on Recurrent Neural Networks (RNN) and variational autoencoders (VAE), integrating emotional consistency loss and generative adversarial loss. This approach enhances melody diversity via VAE and captures long- and short-term dependencies using RNNs for better structural coherence. Emotional consistency loss helps maintain target emotions during generation, while generative adversarial loss improves naturalness and fluency. Experimental results show that our model outperforms traditional models like Music Transformer, MuseNet, and DeepBach in fluency, creativity, emotional expression, and harmony. The generated melodies are more expressive and innovative, providing anew method and perspective in melody generation, improving emotional expression and diversity, and laying a foundation for advancing automatic music creation technology.

关键词： Melody generation variational autoencoder Recurrent neural network Emotional expression Automated music composition Diversity and coherence

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variational autoencoderS FOR UNSUPERVISED OBJECT COUNTING FROM VHR IMAGERY: APPLICATIONS IN DWELLING EXTRACTION FROM FORCIBLY DISPLACED PEOPLE SETTLEMENT AREAS

VARIATIONAL AUTOENCODERS FOR UNSUPERVISED OBJECT COUNTING FR...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Gella, Getachew Workineh Gangloff, Hugo Wendt, Lorenz Tiede, Dirk Lang, Stefan Paris Lodron Univ Salzburg PLUS Christian Doppler Lab Geospatial & EO Based Human Dept Geoinformat Z GIS Salzburg Austria Univ Paris Saclay AgroParisTech INRAE UMR MIA Paris Saclay F-91120 Palaiseau France

ISBN: (纸本)9798350320107

Even though computer vision models are excellent for automatic scene segmentation and object identification from remotely sensed imagery, they demand a huge corpus of annotated data for the training and validation which is a huge challenge in humanitarian emergency response. To tackle this problem, we propose unsupervised dwelling object counting combining variational autoencoder (VAE) with an anomaly detection approach. The approach is tested in six Forcibly Displaced People (FDP) settlement areas situated in different parts of the world. Using an anomaly map computed with the VAE model, we demonstrated the possibility of properly locating dwelling objects using anomaly maps. Dwelling counts are obtained by further segmenting anomaly maps. Results show that, though it has strong spatio-temporal variation, the VAE model exhibits promising potential for locating and counting dwellings. It is also observed that in FDP settlements with dense buildings and extremely low contrast between buildings and ground or environment, the performance is relatively lower than the performance achieved in settlement areas with regularly spaced and less complex building structures.

关键词： Anomaly detection Dwelling extraction Emergency response variational autoencoder

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variational autoencoders for Localized Mesh Deformation Component Analysis

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第10期44卷 6297-6310页

作者： Tan, Qingyang Zhang, Ling-Xiao Yang, Jie Lai, Yu-Kun Gao, Lin Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing 100864 Peoples R China Univ Maryland College Pk MD 20742 USA Univ Chinese Acad Sci Beijing 100049 Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff CF10 3AT Wales

Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale nonlinear deformations, and may not always be able to identify important deformation components. In this paper we propose a mesh-based variational autoencoder architecture that is able to cope with meshes with irregular connectivity and nonlinear deformations, assuming that the analyzed dataset contains meshes with the same vertex connectivity, which is common for deformation analysis. To help localize deformations, we introduce sparse regularization in this framework, along with spectral graph convolutional operations. Through modifying the regularization formulation and allowing dynamic change of sparsity ranges, we improve the visual quality and reconstruction ability of the extracted deformation components. Our system also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. As an important application of localized deformation components and a novel approach on its own, we further develop a neural shape editing method, achieving shape editing and deformation component extraction in a unified framework, and ensuring plausibility of the edited shapes. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations. We also demonstrate the effectiveness of our method for neural shape editing.

关键词： Strain Shape Three-dimensional displays Principal component analysis Geometry Convolution Solid modeling 3D meshes variational autoencoder graph convolution sparsity regularization

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Failure Detection in Sensors via variational autoencoders and Image-Based Feature Representation

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SENSORS 2025年第7期25卷 2175-2175页

作者： Moreno Haro, Luis Miguel Oliveira-Filho, Adaiton Agard, Bruno Tahan, Antoine Polytech Montreal Dept Math & Ind Engn Lab Intelligence Donnees CIRRELT Montreal PQ H3T 0A3 Canada Ecole Technol Super Dept Mech Engn Montreal PQ H3C 1K3 Canada

This paper presents a novel approach for detecting sensor failures using image-based feature representation and the Convolutional variational autoencoder (CVAE) model. Existing methods are limited when analyzing multiple failure modes simultaneously or adapting to diverse sensor data. This limitation may compromise decision-making and system performance, hence the need for more flexible and resilient models. The proposed approach transforms sensor data into image-based feature representations of statistics such as mean, variance, kurtosis, entropy, skewness, and correlation. The CVAE is trained on such image representations, and the corresponding reconstruction error leads to a Health Index (HI) for detecting multiple sensor failures. Moreover, the CVAE latent space is used to define a complementary HI and a convenient visualization tool, enhancing the interpretability of the proposed approach. The evaluation of the proposed detection approach with data comprising diverse configurations of faulty sensors showed encouraging results. The proposed approach is illustrated in an industrial case study emerging from the aeronautical domain, with data from a complex electromechanical system comprising nearly 80 sensor measurements at a 1 Hz sampling rate. The results demonstrate the potential of the proposed method in detecting multiple sensor failures.

关键词： sensor failure detection health index variational autoencoder feature representation aeronautical sensors

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Semi-supervised soft sensor method for fermentation processes based on physical monotonicity and variational autoencoders

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期137卷

作者： Cheng, Xinyue Yu, Zhenhua Wang, Guan Jiang, Qingchao Cao, Zhixing East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol State Key Lab Bioreactor Engn Shanghai 200237 Peoples R China

Data-driven models have shown broad application prospects in soft sensor modeling. However, numerous challenges persist. On the one hand, data-driven soft sensor methods have high requirements on data quality. On the other hand, models relying on limited experimental data often lack physical interpretability. To tackle these challenges, a semi-supervised soft sensor method (PMVAER) for fermentation processes based on physical monotonicity and variational autoencoders (VAEs) is introduced. First, physical monotonicity constraint is incorporated into the loss function of VAEs for regression to ensure that the model's predictions adhere to physical feasibility. Next, considering the disparate sampling frequencies for process and quality variables, this approach is extended to learn from unlabeled data, creating a semi-supervised soft sensor model. The proposed model is validated on simulation and real cases of penicillin fermentation. Comparisons with five other methods verify that the proposed method exhibits exceptional predictive accuracy along with enhanced generalization ability.

关键词： Soft sensor variational autoencoder Fermentation process Semi-supervised learning

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A few-shot object detection method for garbage via variational autoencoders and feature aggregation

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WASTE MANAGEMENT 2025年 200卷 114754页

作者： Xue, Shuya Song, Dian Chen, Wei Zhao, Lei Zhou, Qian Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Soochow Univ Sch Polit Sci & Publ Adm Suzhou Peoples R China

Outdoor waste detection plays a pivotal role in environmental monitoring and waste management systems. Traditional garbage detectors rely heavily on a large amount of labelled data for training. However, these approaches are resource-intensive and ill-suited for waste categories that evolve quickly or are uncommon. To address the limitation, we propose a novel few-shot object detection (FSOD) method, named Few-Shot Garbage Detection (FSGD), which is tailored to identify garbage with limited labelled data. In the context of garbage detection, the changes in waste shapes caused by human behaviours can result in situations where the support images fail to fully represent category information. To tackle the issue, we utilize variational autoencoders (VAEs) to infer class distributions and sample robust variational features, ensuring an accurate representation of the garbage categories. Moreover, we propose an advanced aggregation strategy to establish correlations between support and query features. This strategy addresses the common problem in FSOD where the Region Proposal Network (RPN) is insensitive to novel categories. Additionally, we separate the weight of backbone network shared by support and query branches, which improves performance in a simple yet efficient way. Extensive experiments demonstrate that our method outperforms existing state-of-the-art FSOD methods in all evaluated scenarios on garbage detection datasets. Furthermore, we evaluate the generalization ability of the proposed FSGD approach on the publicly available Pascal VOC dataset, and the results indicate that FSGD also performs better than compared methods on this dataset.

关键词： Garbage Detection Few-shot Object Detection Waste localization Waste recognition variational autoencoder Feature Aggregation

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Multi-head attention-based variational autoencoders ensemble for remaining useful life prediction of aero-engines

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第1期36卷

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

Accurate remaining useful life (RUL) prediction of aero-engines through condition monitoring (CM) data is of great significance for flight reliability and safety. Although deep learning (DL)-based approaches have been widely considered, individual DL models suffer from significant stochasticity and limited generalizability when predicting the RUL. To solve this issue, a novel multi-head attention-based variational autoencoders (MHAT-VAEs) ensemble model is proposed. Two distinct MHAT-VAEs are designed, employing linear and convolutional operations to capture global and temporal compressed representations of the CM data. Additionally, a dual-level ensemble strategy is introduced to adaptively fuse the outputs of the two base learners. A hyperparameter optimization method is also implemented to further enhance the efficiency and performance of the base learners. The effectiveness of the proposed method is validated using the C-MAPSS and N-CMAPSS datasets, with experimental results showing that it outperforms state-of-the-art approaches.

关键词： Remaining useful life multi-head attention mechanism variational autoencoder ensemble learning

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