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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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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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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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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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Securing VANETs: Multi-Objective Intrusion Detection With variational autoencoders

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3867-3874页

作者： Nissar, Nabil Naja, Najib Jamali, Abdellah Inst Natl Postes Telecommun RAISS Lab Rabat 10000 Morocco Inst Natl Postes Telecommun Rabat 10000 Morocco Hassan I Univ Settat 26002 Morocco

The rising prevalence of connected vehicles in Vehicular Ad-hoc Networks (VANETs) within Intelligent Transportation Systems (ITS) has introduced a heightened susceptibility to various cyber threats, particularly zero-day attacks. The magnitude of intercommunicating vehicles compounds the difficulty of identifying dynamic and spatially-distant anomalies that elude static rules. Existing solutions, often rule-bound and inflexible, face challenges in dealing with novel threats. Hence, the diverse and evolving nature of potential threats underscores the necessity for more adaptive and robust detection frameworks, especially when considering the end consumer's safety and privacy *** paper introduces an innovative anomaly detection framework designed for VANETs. It employs a variational autoencoder (VAE) and optimizes multiple objectives: divergence, KL-divergence, and reconstruction error, using the AGE-MOEA and R-NSGA-III algorithms. Deployable within Roadside Units, it captures and analyzes broadcast vehicular data sequences, classifying messages as anomalous or normal to address not only the technological intricacies but also the paramount concern of consumer safety within the vehicular ecosystem. Experiments involve exploring various hyperparameters, with performance assessed using key metrics. The proposed framework undergoes comprehensive benchmarking against prior research, considering accuracy, precision, recall, and ROC-AUC. This underscores the efficacy of fully unsupervised learning and multi-objective optimization in enhancing VANETs security against emerging threats.

关键词： Consumer electronics VANETs security intrusion detection systems anomaly detection NSL-KDD artificial intelligence deep learning unsupervised learning variational autoencoder AGE-MOEA R-NSGA-III

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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 016131-016131页

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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Machine Learning-Driven Discovery of Thermoset Shape Memory Polymers With High Glass Transition Temperature Using variational autoencoders

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JOURNAL OF POLYMER SCIENCE 2025年第5期63卷 1095-1107页

作者： Teimouri, Amir Li, Guoqiang Louisiana State Univ Dept Mech & Ind Engn Baton Rouge LA 70803 USA

The discovery of high-performance shape memory polymers (SMPs) with enhanced glass transition temperatures (Tg) is of paramount importance in fields such as geothermal energy, oil and gas, aerospace, and other high-temperature applications, where materials are required to exhibit shape memory effect at extremely high-temperature conditions. Here, we employ a novel machine learning framework that integrates transfer learning with variational autoencoders (VAE) to efficiently explore the chemical design space of SMPs and identify new candidates with high Tg values. We systematically investigate the effect of different latent space dimensions on the VAE model performance. Several machine learning models are then trained to predict Tg. We find that the SVM model demonstrates the highest predictive accuracy, with R2 values exceeding 0.87 and a mean absolute percentage error as low as 6.43% on the test set. Through systematic molar ratio adjustments and VAE-based fingerprinting, we discover novel SMP candidates with Tg values between 190 degrees C and 200 degrees C, suitable for high-temperature applications. These findings underscore the effectiveness of combining VAEs and SVM for SMP discovery, offering a scalable and efficient method for identifying polymers with tailored thermal properties.

关键词： latent space optimization polymer discovery shape memory polymer transfer learning variational autoencoder

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Mitigating the Impact of Temperature Variations on Ultrasonic Guided Wave-Based Structural Health Monitoring through variational autoencoders

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SENSORS 2024年第5期24卷 1494页

作者： Junges, Rafael Lomazzi, Luca Miele, Lorenzo Giglio, Marco Cadini, Francesco Politecn Milan Dept Mech Engn Via Masa n1 I-20156 Milan Italy

Structural health monitoring (SHM) has become paramount for developing cheaper and more reliable maintenance policies. The advantages coming from adopting such process have turned out to be particularly evident when dealing with plated structures. In this context, state-of-the-art methods are based on exciting and acquiring ultrasonic-guided waves through a permanently installed sensor network. A baseline is registered when the structure is healthy, and newly acquired signals are compared to it to detect, localize, and quantify damage. To this purpose, the performance of traditional methods has been overcome by data-driven approaches, which allow processing a larger amount of data without losing diagnostic information. However, to date, no diagnostic method can deal with varying environmental and operational conditions (EOCs). This work aims to present a proof-of-concept that state-of-the-art machine learning methods can be used for reducing the impact of EOCs on the performance of damage diagnosis methods. Generative artificial intelligence was leveraged to mitigate the impact of temperature variations on ultrasonic guided wave-based SHM. Specifically, variational autoencoders and singular value decomposition were combined to learn the influence of temperature on guided waves. After training, the generative part of the algorithm was used to reconstruct signals at new unseen temperatures. Moreover, a refined version of the algorithm called forced variational autoencoder was introduced to further improve the reconstruction capabilities. The accuracy of the proposed framework was demonstrated against real measurements on a composite plate.

关键词： generative artificial intelligence variational autoencoder temperature ultrasonic guided wave

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Leveraging variational autoencoders for Parameterized MMSE Estimation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 3731-3744页

作者： Baur, Michael Fesl, Benedikt Utschick, Wolfgang Tech Univ Munich TUM Sch Computat Informat & Technol D-80333 Munich Germany

In this manuscript, we propose to use a variational autoencoder-based framework for parameterizing a conditional linear minimum mean squared error estimator. The variational autoencoder models the underlying unknown data distribution as conditionally Gaussian, yielding the conditional first and second moments of the estimand, given a noisy observation. The derived estimator is shown to approximate the minimum mean squared error estimator by utilizing the variational autoencoder as a generative prior for the estimation problem. We propose three estimator variants that differ in their access to ground-truth data during the training and estimation phases. The proposed estimator variant trained solely on noisy observations is particularly noteworthy as it does not require access to ground-truth data during training or estimation. We conduct a rigorous analysis by bounding the difference between the proposed and the minimum mean squared error estimator, connecting the training objective and the resulting estimation performance. Furthermore, the resulting bound reveals that the proposed estimator entails a bias-variance tradeoff, which is well-known in the estimation literature. As an example application, we portray channel estimation, allowing for a structured covariance matrix parameterization and low-complexity implementation. Nevertheless, the proposed framework is not limited to channel estimation but can be applied to a broad class of estimation problems. Extensive numerical simulations first validate the theoretical analysis of the proposed variational autoencoder-based estimators and then demonstrate excellent estimation performance compared to related classical and machine learning-based state-of-the-art estimators.

关键词： Estimation Training Inverse problems Data models Bayes methods Noise measurement Vectors Parameter estimation variational autoencoder conditional mean estimator generative model inverse problem

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