检索结果-内蒙古大学图书馆

Integrated damage detection and time-series data augmentation for floating offshore mooring systems via variational semi-supervised learning

引用

OCEAN ENGINEERING 2025年 330卷

作者： Tamuly, Pranjal Sharma, Smriti Nava, Vincenzo Basque Ctr Appl Math Alameda Mazarredo 14 Bilbao 48009 Spain Politecn Torino Corso Duca Abruzzi 24 Turin Italy

The dynamics and stability of the semi-submersible offshore platforms are significantly impacted by the degradation of the mooring system. Identifying structural integrity issues in mooring systems through a data-driven approach is challenging due to the infrequency of damage events and the difficulties in recording them. To address these challenges, this study proposes the Time-Series variational Semi-Supervised Learning (TSVSSL) framework, which effectively bridges the gap between supervised and unsupervised learning by leveraging unlabelled data for damage detection. The proposed framework features a distinctive training procedure in which the encoder-decoder and classifier components are trained concurrently. This process produces a well-clustered latent representation that enhances damage detection and supports class-specific artificial data generation. A numerical study using simulated responses of a 5 MW semi-submersible FOWT under varying metocean conditions demonstrated that the proposed framework outperformed existing deep learning methods in damage detection, achieving superior accuracy, precision, recall, and F1 score. Further, a rejection sampling technique is also introduced to effectively generates artificial data that closely aligns with actual time series displacement response. The novelty of the proposed framework lies in its dual focus on damage detection and artificial data generation marking a significant advancement in the data-driven assessment of mooring systems.

关键词： Offshore structures Damage diagnosis Floating wind turbines Mooring systems Semi-supervised learning variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Piecewise convolutional neural network relation extraction with self-attention mechanism

引用

PATTERN RECOGNITION 2025年 159卷

作者： Zhang, Bo Xu, Li Liu, Ke-Hao Yang, Ru Li, Mao-Zhen Guo, Xiao-Yang Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 200234 Peoples R China Shanghai Normal Univ Inst Artificial Intelligence Educ Shanghai 200234 Peoples R China Brunel Univ London Dept Elect & Elect Engn Uxbridge UB8 3PH England Shanghai Normal Univ Shanghai Engn Res Ctr Intelligent Educ & Bigdata Shanghai 200234 Peoples R China Shanghai Newtouch Software Co Ltd Shanghai 200127 Peoples R China

The task of relation extraction in natural language processing is to identify the relation between two specified entities in a sentence. However, the existing model methods do not fully utilize the word feature information and pay little attention to the influence degree of the relative relation extraction results of each word. In order to address the aforementioned issues, we propose a relation extraction method based on self-attention mechanism (SPCNN-VAE) to solve the above problems. First, we use a multi-head self-attention mechanism to process word vectors and generate sentence feature vector representations, which can be used to extract semantic dependencies between words in sentences. Then, we introduce the word position to combine the sentence feature representation with the position feature representation of words to form the input representation of piecewise convolutional neural network (PCNN). Furthermore, to identify the word feature information that is most useful for relation extraction, an attention-based pooling operation is employed to capture key convolutional features and classify the feature vectors. Finally, regularization is performed by a variational autoencoder (VAE) to enhance the encoding ability of model word information features. The performance analysis is performed on SemEval 2010 task 8, and the experimental results show that the proposed relation extraction model is effective and outperforms some competitive baselines.

关键词： Relation extraction Multi-head attention PCNN variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Combining variational autoencoders and physical bias for improved microscopy data analysis

引用

MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第4期4卷 045004页

作者： Biswas, Arpan Ziatdinov, Maxim Kalinin, Sergei, V Ctr Nanophase Mat Sci Oak Ridge Natl Lab Oak Ridge TN 37831 USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37831 USA Univ Tennessee Mat Sci & Engn Knoxville TN 37996 USA

Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as electron energy loss spectroscopy or 4D scanning transmission electron microscope, that contain information on a wide range of structural, physical, and chemical properties of materials. To extract valuable insights from these data, it is crucial to identify physically separate regions in the data, such as phases, ferroic variants, and boundaries between them. In order to derive an easily interpretable feature analysis, combining with well-defined boundaries in a principled and unsupervised manner, here we present a physics augmented machine learning method which combines the capability of variational autoencoders to disentangle factors of variability within the data and the physics driven loss function that seeks to minimize the total length of the discontinuities in images corresponding to latent representations. Our method is applied to various materials, including NiO-LSMO, BiFeO3, and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data.

关键词： ferroic variants physics driven loss variational autoencoder latent space unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Drift Detection for Black-Box Deep Learning Models

引用

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

来源：评论

学校读者我要写书评

暂无评论

STDNet: Rethinking Disentanglement Learning With Information Theory

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：