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Signal generation for bolt loosening detection with unbalanced datasets based on the CBAM-VAE

MEASUREMENT 2025年 240卷

作者： You, Zengying Wang, Xian Xu, Jiawen Wang, Hui Yan, Ruqiang Southeast Univ Sch Instrument Sci & Engn Jiangsu Key Lab Remote Measurement & Control Nanjing 210096 Jiangsu Peoples R China Shanghai Aerosp Elect Technol Inst Shanghai 201109 Peoples R China Jiaotong Univ Int Machinery Ctr Sch Mech Engn Xian 710049 Shaanxi Peoples R China

Bolt looseness has adverse influences on the stability and safety of engineering structures. Neural network algorithms can effectively monitor health conditions using impedance signals. However, impedance data of engineering structures in damaged conditions is challenging to obtain. The data would also exhibit an imbalanced distribution, yielding deterioration of the accuracy of health monitoring. In this study, we propose a data augmentation method based on a variational autoencoder model with a convolutional block attention module. This method addressed the issue of imbalanced data by generating new data. A Transformer model was adopted for training and fault classification. Without employing data augmentation methods, the max accuracy is 85.71%. However, experimental results demonstrate the remarkable effectiveness of this approach in enhancing and classifying imbalanced datasets, with an average accuracy of 89.35% and the highest accuracy of 94.81% after enhancement. The proposed method can be applied to health conditions identification of buildings, bridges, and trusses.

关键词： Bolt looseness variational autoencoder Convolutional Block Attention Module Transformer Data Augmentation

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Generative AI-Enabled Blockchain Networks: Fundamentals, Applications, and Case Study

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IEEE NETWORK 2025年第2期39卷 232-241页

作者： Nguyen, Cong T. Liu, Yinqiu Du, Hongyang Hoang, Dinh Thai Niyato, Dusit Nguyen, Diep N. Mao, Shiwen Duy Tan Univ Inst Fundamental & Appl Sci Ho Chi Minh City 70000 Vietnam Duy Tan Univ Fac Informat Technol Da Nang 50000 Vietnam Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Univ Technol Sydney Sch Elect & Data Engn Ultimo NSW 2007 Australia Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA

Generative Artificial Intelligence (GAI) has recently emerged as a promising solution to address critical challenges of blockchain technology, including scalability, security, privacy, and interoperability. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains. Then, we discuss emerging solutions that demonstrate the effectiveness of GAI in addressing various challenges of blockchain, such as detecting unknown blockchain attacks and smart contract vulnerabilities, designing key secret sharing schemes, and enhancing privacy. Moreover, we present a case study to demonstrate that GAI, specifically the generative diffusion model, can be employed to optimize blockchain network performance metrics. Experimental results clearly show that, compared to a baseline traditional AI approach, the proposed generative diffusion model approach can converge faster, achieve higher rewards, and significantly improve the throughput and latency of the blockchain network. Additionally, we highlight future research directions for GAI in blockchain applications, including personalized GAI-enabled blockchains, GAI-blockchain synergy, and privacy and security considerations within blockchain ecosystems.

关键词： Blockchains Smart contracts Security Consensus protocol Scalability Privacy Data privacy Generative Artificial Intelligence Blockchain variational autoencoder Generative Adversarial Network Generative Diffusion Model Large Language Model

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Privacy-preserving healthcare data in IoT: a synergistic approach with deep learning and blockchain

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-30页

作者： Bezanjani, Behnam Rezaei Ghafouri, Seyyed Hamid Gholamrezaei, Reza Islamic Azad Univ Dept Comp Engn Kerman Branch Kerman Iran

The integration of Internet of Things (IoT) devices in healthcare has revolutionized patient care by enabling real-time monitoring, personalized treatments, and efficient data management. However, this technological advancement introduces significant security risks, particularly concerning the confidentiality, integrity, and availability of sensitive medical data. Traditional security measures are often insufficient to address the unique challenges posed by IoT environments, such as heterogeneity, resource constraints, and the need for real-time processing. To tackle these challenges, we propose a comprehensive three-phase security framework designed to enhance the security and reliability of IoT-enabled healthcare systems. In the first phase, the framework assesses the reliability of IoT devices using a reputation-based trust estimation mechanism, which combines device behavior analytics with off-chain data storage to ensure scalability. The second phase integrates blockchain technology with a lightweight proof-of-work mechanism, ensuring data immutability, secure communication, and resistance to unauthorized access. The third phase employs a lightweight long short-term memory (LSTM) model for anomaly detection and classification, enabling real-time identification of cyber-threats. Simulation results demonstrate that the proposed framework outperforms existing methods, achieving a 2% increase in precision, accuracy, and recall, a 5% higher attack detection rate, and a 3% reduction in false alarm rate. These improvements highlight the framework's ability to address critical security concerns while maintaining scalability and real-time performance.

关键词： Blockchain Privacy-preserving Health care Machine learning variational autoencoder

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Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using autoencoder Models

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APPLIED SCIENCES-BASEL 2025年第5期15卷 2662-2662页

作者： Melo, Renato Finotti, Rafaelle Guedes, Antonio Goncalves, Vitor Meixedo, Andreia Ribeiro, Diogo Barbosa, Flavio Cury, Alexandre Univ Fed Juiz de Fora Grad Program Civil Engn BR-36036900 Juiz De Fora Brazil Univ Porto Fac Engn CONSTRUCT LESE P-4200465 Porto Portugal Polytech Porto Sch Engn CONSTRUCT LESE P-4200465 Porto Portugal

This study presents a comparative analysis of three autoencoder (AE) models-variational autoencoder (VAE), Sparse autoencoder (SAE), and Convolutional autoencoder (CAE)-to detect and quantify structural anomalies in railway vehicle wheels, such as polygonization. Vertical acceleration data from a virtual wayside monitoring system serve as input for training the AE models, which are coupled with Hotelling's T2 Control Charts to differentiate normal and abnormal railway component behaviors. The results indicate that the SAE-T2 model outperforms its counterparts, achieving 16.67% higher accuracy than the CAE-T2 model in identifying distinct structural conditions, although with a 35.78% higher computational cost. Conversely, the VAE-T2 model is outperformed in 100% of the analyzed scenarios when compared to SAE-T2 in identifying distinct structural conditions while also exhibiting a 21.97% higher average computational cost. Across all scenarios, the SAE-T2 methodology consistently provided better classifications of wheel damage, showing its capability to extract relevant features from dynamic signals for Structural Health Monitoring (SHM) applications. These findings highlight SAE's potential as an interesting tool for predictive maintenance, offering improved efficiency and safety in railway operations.

关键词： structural health monitoring railways damage detection out of roundness sparse autoencoder convolutional autoencoder variational autoencoder

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Causal Discovery and Deep Learning Algorithms for Detecting Geochemical Patterns Associated with Gold-Polymetallic Mineralization: A Case Study of the Edongnan Region

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MATHEMATICAL GEOSCIENCES 2025年第1期57卷 193-220页

作者： Luo, Zijing Zuo, Renguang China Univ Geosci State Key Lab Geol Proc & Mineral Resources Wuhan 430074 Peoples R China

The identification of mineral deposit footprints by processing geochemical survey data constitutes a crucial stage in mineral exploration because it provides valuable and substantial information for future prospecting endeavors. However, the selection of appropriate pathfinder elements and the recognition of their anomalous patterns for determining metallogenic favorability based on geochemical survey data remain challenging tasks because of the complex interactions among different geochemical elements and the highly nonlinear and heterogeneous characteristics of their spatial distribution patterns. This study investigated the application of a causal discovery algorithm and deep learning models to identify geochemical anomaly patterns associated with mineralization. Using gold-polymetallic deposits in the Edongnan region of China as a case study, stream sediment samples containing concentrations of 39 elements were collected and preprocessed using a centered log-ratio transformation, addressing the closure effect of compositional data. The combination of the synthetic minority oversampling technique, Tomek link algorithm, and causal discovery algorithm to explore the potential associations and influences among geochemical elements provides new insights into the selection of pathfinder elements. Regarding the problem of identifying anomalous spatial distribution patterns in pathfinder elements and considering that the formation of mineral deposits is the result of various geological processes interacting under specific spatiotemporal conditions, we proposed a hybrid deep learning model called VAE-CAPSNET-GAN, which combines a variational autoencoder (VAE), capsule network (CAPSNET), and generative adversarial network (GAN). The model was designed to capture the spatial distribution characteristics of pathfinder elements and the spatial coupling relationships between mineral deposits and geochemical anomalies, enabling the recognition of geochemical anomaly patterns r

关键词： Causal discovery Deep learning variational autoencoder Generative adversarial network Capsule network Geochemical anomalies

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Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation

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BMC BIOINFORMATICS 2024年第1期25卷 1页

作者： Liu, Renjing Qian, Kun He, Xinwei Li, Hongwei China Univ Geosci Wuhan Sch Math & Phys Wuhan 430074 Peoples R China

BackgroundThe integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular *** expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar single-cell RNA data. It constructs a condition-specific domain-adaptive network founded on variational autoencoders. scDisco not only effectively reduces batch effects but also successfully disentangles biological effects and condition-specific effects, and further augmenting condition-specific representations through the utilization of condition-specific Domain-Specific Batch Normalization layers. This enhancement enables the identification of genes specific to particular conditions. The effectiveness and robustness of scDisco as an integration method were analyzed using both simulated and real datasets, and the results demonstrate that scDisco can yield high-quality visualizations and quantitative outcomes. Furthermore, scDisco has been validated using real datasets, affirming its proficiency in cell clustering quality, retaining batch-specific cell types and identifying condition-specific *** is an effective integration method based on variational autoencoders, which improves analytical tasks of reducing batch effects, cell clustering, retaining batch-specific cell types and identifying condition-specific genes.

关键词： ScRNA-seq Cellular heterogeneity Integration variational autoencoder Disentangled representation learning Condition domain adaptation

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Drift Detection for Black-Box Deep Learning Models

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IT PROFESSIONAL 2024年第2期26卷 24-31页

作者： Piano, Luca Garcea, Fabio Cavallone, Andrea Vazquez, Ignacio Aparicio Morra, Lia Lamberti, Fabrizio Polytech Univ Turin Dept Control & Comp Engn I-10129 Turin Italy Reale Mutua Assicurazioni Data Sci Ctr Excellence I-10122 Turin Italy Reale Seguros Data Sci Ctr Excellence Madrid 28002 Spain

Dataset drift is a common challenge in machine learning, especially for models trained on unstructured data, such as images. In this article, we propose a new approach for the detection of data drift in black-box models, which is based on Hellinger distance and feature extraction methods. The proposed approach is aimed at detecting data drift without knowing the architecture of the model to monitor, the dataset on which it was trained, or both. The article analyzes three different use cases to evaluate the effectiveness of the proposed approach, encompassing a variety of tasks including document segmentation, classification, and handwriting recognition. The use cases considered for the drift are adversarial assaults, domain shifts, and dataset biases. The experimental results show the efficacy of our drift detection approach in identifying changes in distribution under various training settings.

关键词： Training Degradation Computational Modeling Closed Box Training Data Computer Architecture Detection Algorithms Data Models Machine Learning Deep Learning Data Integrity Deep Learning Models Drift Detection Machine Learning Training Set Data Distribution Training Dataset Changes In Distribution Image Classification Domain Shift Model Architecture Application Programming Interface Inference Time Unstructured Data Artificial Intelligence Systems Dataset Bias Hellinger Distance Auxiliary Model Compact Embedding Artificial Intelligence Solutions Machine Learning Challenges Adversarial Examples variational autoencoder Latent Space Fast Gradient Sign Method Adversarial Attacks Source Task variational autoencoder Model Time Window Systematic Bias Training Data

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STDNet: Rethinking Disentanglement Learning With Information Theory

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第8期35卷 10407-10421页

作者： Liu, Ziwen Li, Mingqiang Han, Congying Tang, Siqi Guo, Tiande Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China China Elect Technol Grp Corp Informat Sci Acad Beijing 100144 Peoples R China

Disentangled representation learning is typically achieved by a generative model, variational encoder (VAE). Existing VAE-based methods try to disentangle all the attributes simultaneously in a single hidden space, while the separation of the attribute from irrelevant information varies in complexity. Thus, it should be conducted in different hidden spaces. Therefore, we propose to disentangle the disentanglement itself by assigning the disentanglement of each attribute to different layers. To achieve this, we present a stair disentanglement net (STDNet), a stair-like structure network with each step corresponding to the disentanglement of an attribute. An information separation principle is employed to peel off the irrelevant information to form a compact representation of the targeted attribute within each step. Compact representations, thus, obtained together form the final disentangled representation. To ensure the final disentangled representation is compressed as well as complete with respect to the input data, we propose a variant of the information bottleneck (IB) principle, the stair IB (SIB) principle, to optimize a tradeoff between compression and expressiveness. In particular, for the assignment to the network steps, we define an attribute complexity metric to assign the attributes by the complexity ascending rule (CAR) that dictates a sequencing of the attribute disentanglement in ascending order of complexity. Experimentally, STDNet achieves state-of-the-art results in representation learning and image generation on multiple benchmarks, including Mixed National Institute of Standards and Technology database (MNIST), dSprites, and CelebA. Furthermore, we conduct thorough ablation experiments to show how the strategies employed here contribute to the performance, including neurons block, CAR, hierarchical structure, and variational form of SIB.

关键词： Complexity theory Generative adversarial networks Stairs Task analysis Automobiles Training Periodic structures Disentangled representation information bottleneck (IB) principle information theory variational autoencoder

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Multimodal protein representation learning and target-aware variational auto-encoders for protein-binding ligand generation

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第2期5卷 025021页

作者： Khang Ngo, Nhat Son Hy, Truong FPT Software AI Ctr Hanoi Vietnam Indiana State Univ Dept Math & Comp Sci Terre Haute IN USA

Without knowledge of specific pockets, generating ligands based on the global structure of a protein target plays a crucial role in drug discovery as it helps reduce the search space for potential drug-like candidates in the pipeline. However, contemporary methods require optimizing tailored networks for each protein, which is arduous and costly. To address this issue, we introduce TargetVAE, a target-aware variational auto-encoder that generates ligands with desirable properties including high binding affinity and high synthesizability to arbitrary target proteins, guided by a multimodal deep neural network built based on geometric and sequence models, named Protein Multimodal Network (PMN), as the prior for the generative model. PMN unifies different representations of proteins (e.g. primary structure-sequence of amino acids, 3D tertiary structure, and residue-level graph) into a single representation. Our multimodal architecture learns from the entire protein structure and is able to capture their sequential, topological, and geometrical information by utilizing language modeling, graph neural networks, and geometric deep learning. We showcase the superiority of our approach by conducting extensive experiments and evaluations, including predicting protein-ligand binding affinity in the PBDBind v2020 dataset as well as the assessment of generative model quality, ligand generation for unseen targets, and docking score computation. Empirical results demonstrate the promising and competitive performance of our proposed approach. Our software package is publicly available at https://***/HySonLab/Ligand_Generation.

关键词： protein representation learning protein-ligand binding ligand generation geometric deep learning large language models multimodal architecture variational autoencoder

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FLPM: A property modification scheme for data protection in federated learning

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 154卷 151-159页

作者： Xu, Shuo Xia, Hui Liu, Peishun Zhang, Rui Chi, Hao Gao, Wei Ocean Univ China Coll Comp Sci & Technol 1299 Sansha Rd Qingdao 266000 Shandong Peoples R China Ocean Univ China Coll Ocean Technol 1299 Sansha Rd Qingdao 266000 Shandong Peoples R China

Federated learning (FL) is a critical technology for implementing time -critical computing systems in the Internet of Things (IoT). It allows for continuous updates to machine learning (ML) models across IoT devices. However, the vulnerability of ML models and the complexity of IoT pose significant threats to device data security and privacy, affecting the robustness of time -critical computing systems constructed through FL. Recent research on FL data protection has made progress, but challenges remain in balancing privacy protection with model availability. For example, cryptography -based defense schemes increase time overhead in time -critical computing systems, while differential privacy negatively impacts system performance. This paper proposes the FL properties modification scheme (FLPM) for data preprocessing to resist property inference attacks and data poisoning attacks. FLPM modifies training data properties using algorithms for property separation, selection, and control based on continuous latent variables. While this sacrifices a small amount of classification accuracy, it significantly improves data protection capabilities. Detailed experimental results demonstrate that FLPM successfully separates and controls image property vectors. In the FL classification task, the property modification data achieve a precision of 94.44%. This scheme effectively prevents property inference attacks and data poisoning attacks. FLPM can reduce the AUC score for property inference attacks from 0.94 to 0.56 and reduce the success rate of data poisoning attacks to 5.13%, 7.07%, and 4.60%.

关键词： Federated learning variational autoencoder Privacy protection Property inference attack Data poisoning attack

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