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An explainable variational autoencoder model for three-dimensional acoustic emission source localization in hollow cylindrical structures

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年第3期24卷 1601-1618页

作者： Lee, Guan-Wei Livadiotis, Stylianos Salamone, Salvatore Univ Texas Austin Dept Civil Architectural & Environm Engn 301 DeanKeeton C1748 Room ECJ 4-710 Austin TX 78758 USA

We introduce an explainable variational autoencoder for three-dimensional (3D) localization of acoustic emission sources in hollow cylindrical structures, with an unsupervised approach. This research capitalizes on multi-arrival waveforms generated by helical path propagation in cylindrical geometries to enable efficient two-receiver localization. By integrating the modal characteristics of Lamb modes under multi-path conditions, we demonstrate that two sets of time-of-arrival differences and peak amplitudes extracted from one receiver can serve as effective localization features. This initial approach identifies four potential source locations, highlighting the feasibility of two-receiver source localization using traditional feature extraction methods. However, direct extraction can be challenging when mode overlaps occur, complicating the localization process. To address this, our work proposes a novel waveform-based method. This method leverages the consistent dispersion characteristics within isotropic materials, where each unique combination of mode arrival times and peak amplitudes constructs a distinct waveform. This distinctiveness overcomes the ambiguities associated with mode overlaps, significantly enhancing the method's precision and robustness. Our approach adopts a data-driven strategy for waveform-based localization using variational autoencoder (VAE). VAE discerns waveform patterns for localization, while also addressing data uncertainties. The VAE's encoder and decoder networks capture the localization process and the source's influence on waveform generation, respectively, guiding latent variables to segregate waveforms by source in the latent space. The design of the learning process focuses on specific localization characteristics to enhance result explainability. Localization predictions are generated by projecting test waveforms, not included in the training set, onto a trained latent space. The prediction is determined using a nearest-neighbo

关键词： Acoustic emission source localization Lamb waves variational autoencoder cylindrical structures explainable deep learning

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XIDINTFL-VAE: XGBoost-based intrusion detection of imbalance network traffic via class-wise focal loss variational autoencoder

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-38页

作者： Abdulganiyu, Oluwadamilare Harazeem Tchakoucht, Taha Ait Saheed, Yakub Kayode Ahmed, Hilali Alaoui Euro Mediterranean Univ Fes Euromed Res Ctr Sch Digital Engn & Artificial Intelligence UEMF Fes Morocco Amer Univ Nigeria Sch IT & Comp Yola Nigeria

Intrusion Detection Systems (IDS) face significant challenges in detecting minority class attacks within imbalanced network traffic, where traditional methods often struggle to maintain high accuracy without sacrificing key performance metrics like precision and recall. This study introduces the XIDINTFL-VAE framework, which leverages Class-Wise Focal Loss (CWFL) and variational autoencoder (VAE) integrated with XGBoost to effectively address this imbalance. Our approach is designed to enhance the detection of minority class intrusions while maintaining robust overall performance. Current techniques, such as SMOTE and its variants, often fail to balance precision and recall adequately in highly imbalanced datasets, resulting in either high false positives or missed detections. The proposed method directly addresses this gap by generating synthetic data tailored to the most challenging cases within the minority class, thereby improving the classifier's ability to detect these rare but critical instances. A comparative analysis of the proposed XIDINTFL-VAE method was conducted against various oversampling techniques, including SMOTE, Borderline-SMOTE, and Adaptive Synthetic Sampling (ADASYN), as well as traditional classifiers like Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree. While existing techniques focus on balancing datasets or generating realistic data, the CWFL-VAE method takes a more targeted approach by generating synthetic data that specifically enhances the classifier's ability to handle challenging minority class instances. Experimental evaluations using the NSL-KDD and CSE-CIC-IDS2018 datasets demonstrate that the XIDINTFL-VAE model outperforms traditional methods, achieving a precision of 99.67% and an F1 score of 94.74%, with a slight trade-off in recall at 89.41%. These results underscore the model's capability to reduce false positives while maintaining high detection rates, which is crucial for real-

关键词： Intrusion detection system Imbalance network traffic Focal loss variational autoencoder Extreme gradient boosting

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Solving the chemical master equation for stochastic biochemical systems: A variational autoencoder approach with effective reactions

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CHEMICAL ENGINEERING SCIENCE 2025年 311卷

作者： Zhou, Xinyi Lu, Jingyi Gao, Furong Cao, Zhixing East China Univ Sci & Technol State Key Lab Bioreactor Engn Shanghai 200237 Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Hong Kong Peoples R China Queens Univ Dept Chem Engn Kingston ON K7L 3N6 Canada

The stochasticity of biochemical reactions at mesoscopic scale presents significant challenges to our understanding of the underpinnings. The Chemical Master Equation (CME) serves as a crucial tool for describing the dynamics of biochemical reactions, but solving the CME is notoriously difficult and often faces the curse of dimensionality. In this paper, we propose an efficient CME solver for stochastic biochemical dynamics leveraging the power of variational autoencoder (VAE). VAE is employed to find effective reactions from a group of elementary reactions, reducing the complexity of the system. By using VAE to find equivalent propensity for a set of complex reactions, we simplify the intermediate reactions, reducing the sample size and time for training. The trained VAE can predict the distributions of unseen time points, different reaction structures and unobserved species. We show a robust method that simplifies the resolution of CME, providing a powerful tool for analyzing stochastic biochemical systems.

关键词： variational autoencoder Stochastic biochemical dynamics Effective reaction

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DHHNN: A Dynamic Hypergraph Hyperbolic Neural Network based on variational autoencoder for multimodal data integration and node classification

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INFORMATION FUSION 2025年 119卷

作者： Mei, Zhangyu Bi, Xiao Li, Dianguo Xia, Wen Yang, Fan Wu, Hao Shandong Univ Sch Software Jinan 250100 Shandong Peoples R China Qufu Normal Univ Sch Comp Sci Rizhao 276827 Shandong Peoples R China Shandong Univ Sch Math Jinan 250100 Shandong Peoples R China Shandong Univ Hosp 2 Dept Pediat Surg Jinan 250033 Shandong Peoples R China Shandong Univ Qilu Hosp Dept Neurol Jinan 250012 Shandong Peoples R China Shandong Univ Cheeloo Coll Med Sch Publ Hlth Dept Biostat Jinan 250000 Shandong Peoples R China Shandong Univ Shenzhen Res Inst Shenzhen 518063 Guangdong Peoples R China

In recent years, the integration of hyperbolic geometry with Graph Neural Networks (GNNs) has garnered significant attention due to its effectiveness in capturing complex hierarchical structures, particularly within real-world graphs and scale-free networks. Although hyperbolic neural networks have shown strong performance across various domains, most existing models rely on static graph structures, limiting their adaptability to dynamic data. Previous studies have primarily focused on improving the modeling capacity of hyperbolic space for latent space data during training, often neglecting the preservation of high-order intrinsic features before training. To address this, we propose a novel Dynamic Hypergraph Hyperbolic Neural Network (DHHNN) based on a variational autoencoder for multimodal data integration. This model combines the advantages of hyperbolic geometry, dynamic hypergraphs, and the self-attention mechanism to enhance multimodal data representation learning. DHHNN introduces a dynamic hypergraph framework that continuously adjusts the relationships between hypernodes and hyperedges during training, effectively capturing higher-order dependencies within complex networks. Furthermore, the self-attention mechanism dynamically regulates the dependency levels between hypernodes and hyperedges, enhancing the model's ability to capture long-range dependencies and complex feature interactions. Leveraging the negative curvature of hyperbolic space, DHHNN compactly and accurately represents complex scale-free networks. Experimental results on seven benchmark datasets and latent space visualizations demonstrate that DHHNN provides compact and effective data representation, outperforming existing models and achieving state-of-the-art performance in node classification tasks.

关键词： Hyperbolic hypergraph Dynamic hypergraph variational autoencoder

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Attenuation Tomography Using Low-Frequency Ultrasound With variational autoencoder for Thorax Imaging: Experimental Study

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2025年第1期72卷 238-248页

作者： Zhang, Tong Shi, Haokang Lin, Zhichao Cao, Yeyu Guo, Rui Zhang, Haolin Li, Maokun Yang, Fan Xu, Shenheng Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Inst Precis Med Dept Elect Engn Beijing 100084 Peoples R China Johns Hopkins Univ Whiting Sch Engn Dept Comp Sci Baltimore MD 21218 USA Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China

In this paper, we introduce a novel inversion methodology employing the variational autoencoder (VAE) for human thorax attenuation tomography using low-frequency ultrasound. The VAE is trained to assimilate the structural priors of the human thorax, utilizing training samples generated from computed tomography (CT) scans. This approach enables the compression of high-dimensional attenuation distributions into a lower-dimensional latent space. During the inversion process, the latent code is optimized, and then the reconstructed model is generated by the decoder of the VAE. This process can effectively integrate prior information of the domain of interest (DOI) into the inversion through coding and decoding, which would mitigate the ill-posedness of the inverse problem and facilitate better outcomes. Our method demonstrates robust generalization capabilities and noise resilience in numerical simulations, outperforming the conventional pixel-based Gauss-Newton method. Human subject experiment further corroborates the effectiveness of our approach. This is also the first experimental validation of the feasibility of low-frequency ultrasound functional imaging of the human thorax. Although the current study presents certain limitations, it underscores the potential of low-frequency ultrasound in the continuous monitoring of the human respiratory system.

关键词： Thorax Ultrasonic imaging Imaging Attenuation Codes Decoding Training Attenuation tomography low-frequency ultrasound respiratory system monitoring thorax functional imaging variational autoencoder

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Explainable hybrid tabular variational autoencoder and feature Tokenizer Transformer for depression prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 265卷

作者： Tran, Vinh Quang Byeon, Haewon Inje Univ Dept Digital Antiaging Healthcare BK21 Gimhae 50834 South Korea

Recent advancements in machine learning and deep learning have significantly improved the diagnosis, detection, prediction, and prognosis of depressive disorders. However, these methodologies have issues related to generalizability, transparency, data scarcity, privacy concerns, and class imbalance. This study develops a robust and interpretable model for predicting depression in South Korea, addressing these limitations. We employed a hybrid deep learning approach that integrates the Feature Tokenizer Transformer model, specifically designed for tabular data, to effectively tokenize and process categorical and numerical features, alongside synthetic data generated by the tabular variational autoencoder (TVAE). TVAE is an adaptation of variational autoencoders that uses a specialized loss function for tabular data. generated high-quality synthetic data from the Korea National Health and Nutrition Examination Survey (KNHANES) dataset. The efficacy of the TVAE-generated data was validated using non-parametric statistical tests, achieving 86.30% on the Kolmogorov-Smirnov test and 76.65% on the Chi-squared test. Performance evaluation metrics, such as accuracy, recall, F1-score, and AUC, demonstrated our model's effectiveness, yielding an accuracy 0.7783, a recall score of 0.5310, an F1-score of 0.4657, and an AUC of 0.6822, outperforming state-of-the-art models. Additionally, SHapley Additive exPlanations analysis was incorporated to explain feature importance, offering valuable insights for healthcare professionals. This research highlights the potential of deep learning and synthetic data generation techniques to enhance depression prediction, addressing critical challenges such as generalizability, class imbalance, data privacy, and interpretability over existing models.

关键词： variational autoencoder Feature tokenizer Transformer Depression Explainable

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Univariate conditional variational autoencoder for morphogenic pattern design in frontal polymerization-based manufacturing

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2025年 438卷

作者： Liu, Qibang Cai, Pengfei Abueidda, Diab Vyas, Sagar Koric, Seid Gomez-Bombarelli, Rafael Geubelle, Philippe Univ Illinois Natl Ctr Supercomp Applicat Urbana IL 61801 USA Univ Illinois Beckman Inst Adv Sci & Technol Urbana IL 61801 USA Univ Illinois Grainger Coll Engn Dept Aerosp Engn Urbana IL 61801 USA Univ Illinois Grainger Coll Engn Dept Mech Sci & Engn Urbana IL 61801 USA New York Univ Abu Dhabi Civil & Urban Engn Dept Abu Dhabi U Arab Emirates Kansas State Univ Dept Ind & Mfg Syst Engn Manhattan KS 66506 USA MIT Dept Mat Sci & Engn Cambridge MA 02139 USA

Under some initial and boundary conditions, the rapid reaction-thermal diffusion process taking place during frontal polymerization (FP) destabilizes the planar mode of front propagation, leading to spatially varying, complex hierarchical patterns in thermoset polymeric materials. Although modern reaction-diffusion models can predict the patterns resulting from unstable FP, the inverse design of patterns, which aims to retrieve process conditions that produce a desired pattern, remains an open challenge due to the non-unique and non-intuitive mapping between process conditions and manufactured patterns. In this work, we propose a probabilistic generative model named univariate conditional variational autoencoder (UcVAE) for the inverse design of hierarchical patterns in FP-based manufacturing. Unlike the cVAE, which encodes both the design space and the design target, the UcVAE encodes only the design space. In the encoder of the UcVAE, the number of training parameters is significantly reduced compared to the cVAE, resulting in a shorter training time while maintaining comparable performance. Given desired pattern images, the trained UcVAE can generate multiple process condition solutions that produce high-fidelity hierarchical patterns.

关键词： Frontal polymerization Manufacturing Inverse design variational autoencoder Deep learning Deep generative model

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Spatiotemporal subspace variational autoencoder with repair mechanism for traffic data imputation

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NEUROCOMPUTING 2025年 617卷

作者： Qian, Jialong Zhang, Shiqi Pian, Yuzhuang Chen, Xinyi Liu, Yonghong Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 510275 Guangdong Peoples R China Guangdong Prov Key Lab Intelligent TransportationS Guangzhou 510275 Guangdong Peoples R China Guangdong Prov Engn Res Ctr Traff Environm Monitor Guangzhou 510275 Guangdong Peoples R China

High-quality spatial-temporal traffic data is crucial for the functioning of modern smart transportation systems. However, the collection and storage of traffic data in real-world scenarios are often hindered by many factors, causing data loss that greatly affects decision-making. Different modes of data absence result in varying degrees of information loss, which introduces considerable challenges to the precise imputation of traffic data. Many existing studies are concentrate on two main aspects: the examination of data distribution and the extraction of spatiotemporal relationships. On the one hand, methods that focus on distribution fitting do not require a large volume of observational data but often fail to capture spatial-temporal relationships, leading to overly smooth results. On the other hand, methods that aim to identify spatial-temporal relationships, while offering higher accuracy in fitting, demand a substantial amount of high-quality historical data. Taking into account the merits and demerits of both two paradigm, we developed a novel unsupervised two-stage model simultaneously takes into account the spatiotemporal distribution and relationships, termed Spatiotemporal Subspace variational autoencoder with Repair Mechanism (SVAE-R). In stage one, we introduced the concept of spatiotemporal subspace, which not only mitigates the noise impact caused by data sparsity but also reduces the cost for the model to find the distribution. In stage two, we designed a simple repair structure to capture spatial-temporal relationships among data through graph convolution network(GCN) and gated recurrent units(GRU), revising the details of the data. We have evaluated our model on two authentic datasets, and it has exhibited a high degree of robustness, maintaining effective performance even under extreme data loss conditions.

关键词： Spatiotemporal traffic data Missing data imputation variational autoencoder Spatiotemporal subspace Graph neural networks

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Dual-granularity multi-instance multi-label learning with variational autoencoder

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KNOWLEDGE-BASED SYSTEMS 2025年 316卷

作者： Wang, Meixia Zhao, Yuhai Wang, Yejiang Huang, Miaomiao Liu, Xuze Wang, Xingwei Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

Multi-instance multi-label learning (MIML) is a weakly supervised approach that models relationships between complex objects and multiple labels, where each object is represented as a bag of instances. A key advantage of MIML is its ability to perform both bag-level and instance-level multi-label predictions, relying solely on bag-level labels. However, a significant performance gap persists between instance-level MIML algorithms and fully supervised learning approaches due to the lack of instance-level labels. Existing MIML algorithms address this challenge by treating bag labels as ambiguous and attempting to reduce supervision imprecision. Moreover, they often assume that instances are independent and identically distributed (i.i.d.) and rely on prior knowledge to learn label correlations, which is impractical in real-world scenarios. To address these challenges, we propose MIMLVAE, a novel dual-granularity MIML algorithm based on a variational autoencoder. MIMLVAE employs a graph attention network to dynamically capture label correlations and instance dependencies, eliminating the i.i.d. assumption and prior knowledge. By treating all instances within a bag equally, it infers effective bag-level and instance-level representations for dual-granularity prediction. At the same time, the label encoder captures label-specific prototype representations, facilitating prototype-based classification at both the bag and instance levels without requiring label disambiguation. Furthermore, MIMLVAE integrates Gaussian mixture model into the shared latent space of features and labels, mitigating posterior collapse and over-regularization. Experiments on six standard MIML datasets demonstrate that MIMLVAE significantly outperforms state-of-the-art methods in both bag-level and instance-level multi-label classification tasks.

关键词： variational autoencoder Correlation learning Prototype classifier

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Detecting and clustering structural damages using modal information and variational autoencoder with triple loss

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年

作者： Huang, Shieh-Kung Li, You-Jing Lin, Yi-Xun Natl Chung Hsing Univ Dept Civil Engn 145 Xingda Rd Taichung 40227 Taiwan

Among vibration-based structural health monitoring (SHM), methods based on modal analysis are the most popular and efficient ones for assessing the inherent states of structures. The primary techniques involve tracking changes in modal parameters regularly during the monitoring period. Numerous studies have utilized both modal frequencies and mode shapes for damage detection, as mode shapes help identify damage locations, and the combination is more robust against environmental effects. Obviously, recent advances in machine learning (ML) and artificial intelligence (AI) have revolutionized SHM based on modal analysis. This study aims to develop a clustering technique using variational autoencoder (VAE) for classifying structural damages. To fully expand the entangled spaces that may arise from lower-dimensional representation, the loss function incorporates triplet loss (known as triplet VAE or tVAE) while preserving all dimensions in the encoded space. The proposed approach is thoroughly developed and evaluated using both numerical simulations and experimental investigations of multistory building structures. The effectiveness of detecting changes via VAE-based and tVAE-based modal clustering has been demonstrated under the consideration of environmental variations and uncertainties. Additionally, a procedure is suggested for the field applications and the proposed approach's capability for structural inspection during long-term monitoring is showcased using a practical scenario.

关键词： structural health monitoring damage evaluation modal parameter machine learning variational autoencoder triplet loss

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