检索结果-内蒙古大学图书馆

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

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

Fund transfer fraud detection: Analyzing irregular transactions and customer with self-attention and neural networks

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 259卷

作者： Shih, Yi-Cheng Dai, Tian-Shyr Chen, Ying-Ping Ti, Yen-Wu Wang, Wun-Hao Kuo, Yun Yang Ming Chiao Tung Univ Dept Comp Sci 1001 Daxue Rd Hsinchu 300093 Taiwan Yang Ming Chiao Tung Univ Dept Informat Management & Finance 1001 Daxue Rd Hsinchu 300093 Taiwan Natl Chengchi Univ Risk & Insurance Res Ctr 64Sec 2Zhi Nan Rd Taipei 11605 Taiwan Xiamen Ocean Vocat Coll Sch Informat Engn 61 Tiyu Rd Xiamen 361012 Fujian Peoples R China

This paper presents a method for identifying fraudulent fund transfers using real bank data, analyzing customer information, transactional activities, and customer relationships. The preprocessing step transforms high- dimensional, irregular transaction time series into regular time series, then further compresses them in a latent space using a self-attention-based autoencoder. To address the scarcity of fraudulent data samples and mitigate training issues caused by data imbalance, various deep generative models, including the conditional variational autoencoder and Wasserstein generative adversarial network, are applied to generate additional fraudulent raw data and augment fraud samples in latent space. The reparameterization trick is integrated into the encoder-decoder structure to boost the model's generative capabilities. Additionally, a Graph Neural Network (GNN) is used to model customer relationships. The proposed approach utilizes end-to-end learning, integrating the autoencoder's reconstruction loss, KL divergence loss (when reparameterization trick is applied), and classification loss for fraud detection. To optimize computational resources, neighborhood sampling for GNN is combined with mini-batch training for the autoencoder, improving both training efficiency and model reliability. Comprehensive experiments demonstrate the effectiveness of the proposed fusion network, highlighting the importance of each component and preprocessing step. For example, the areas under the precision-recall curves for fraud detection show notable improvements in our model. For suspicious transactions identified by the bank's rules, other models range from 0.66% to 22.15%, while our model reached 27%. For non-suspicious transactions, other models range from 2.53% to 22.00%, with our model achieving 22.90%. This model has potential for wider applications in anomaly detection, particularly in datasets with irregular time series and complex customer relationships.

关键词： Fraud detection Irregular time series Self-attention mechanism Graph neural networks variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

VSC-ACGAN: bearing fault diagnosis model applied to imbalanced samples

引用

MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第3期36卷 036212-036212页

作者： Yang, Zhenyu Mao, Runze Ye, Linchang Liu, Yun Hu, Xiaoxi Li, Yuanjiang Jiangsu Univ Sci & Technol Sch Oceanog Mengxi Rd Zhenjiang 212013 Peoples R China 711 Res Inst 400 Newton Rd Shanghai 201108 Peoples R China China Australian AI Res Inst 238 Nanxu Ave Zhenjiang 212021 Peoples R China Beijing Jiaotong Univ State Key Lab Adv Rail Autonomous Operat 3 Shangyuancun Beijing 100044 Peoples R China

The rapid development of deep learning has promoted the application of rolling bearing fault diagnosis techniques. However, in practical applications, the researchers often faces the challenge of a serious imbalance in the proportion of normal and fault states. This imbalance greatly affects the accuracy of diagnosis. Therefore, this paper proposes a novel fault diagnosis framework based on an auxiliary classifier generative adversarial network (ACGAN). Firstly, the stacked contractive autoencoder is cleverly combined with the discriminator network to improve its feature extraction capability and fault diagnosis accuracy. Subsequently, the original algorithm's focus on different types of samples is optimised to improve the generalisation of the diagnostic network. Finally, the stability of the generator network training is optimised with the help of the metric properties of the Kullback-Leibler scatter, and thus the variational stacked contractive-ACGAN model is proposed. The experimental results show that the fault diagnosis accuracy reaches 99.75 % with 200 training samples of each class on the Case Western Reserve University (CWRU) bearing dataset, which is significantly better than other algorithms. Under the same conditions, on the Jiangnan University bearing dataset, the accuracy reaches 99.25 %, which shows good generalization and provides a broad prospect for future applications.

关键词： fault diagnosis imbalanced samples auxiliary classifier generative adversarial network stacked contractive autoencoder variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

State-of-health estimation and remaining useful life prediction of lithium-ion batteries using DnCNN-CNN

引用

JOURNAL OF ENERGY STORAGE 2025年 106卷

作者： Chae, Sun Geu Bae, Suk Joo Oh, Ki-Yong Hanyang Univ Dept Ind Engn Seoul South Korea Hanyang Univ Sch Mech Engn Seoul South Korea

Accurate evaluation of state-of-health (SoH) and prediction of remaining useful life (RUL) are crucial to sustain the reliability of lithium-ion batteries (LIBs) via timely maintenance actions. However, ambient noises under various operating conditions hinder accurate diagnosis of dynamic status for LIBs in real-world applications. To overcome this difficulty, an allied denoising convolutional neural network (DnCNN) and convolutional neural network (CNN) model is proposed as a new framework for estimating SoH and predicting RUL of LIBs under various operating environments. In the presence of unknown ambient noises, DnCNN is applied to improve prediction accuracy of SoH to eliminate the noises using a residual learning technique. To verify denoising abilities and resulting SoH prediction performance under real-life scenarios, multi-physics feature degradation testing data collected from custom test benches are used to evaluate its performance over competitive denoising techniques. Results from the experiments under various operating environments demonstrate that the proposed allied framework results in a higher accuracy and robustness than other state-of-the-art denoising methods in estimating SoH and predicting RUL of LIBs.

关键词： Bayesian optimization Deep learning Feature fusion Health monitoring variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

An adaptive performance map generation method through shape feature fusion for the gas turbine compressor

引用

ENERGY 2025年 320卷

作者： Huang, Qinni Gu, Xiwen Zhang, Hongwei Sun, Jiahao Yang, Shixi Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310058 Peoples R China Zhejiang Univ Sch Mech Engn Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou 310058 Peoples R China Hangzhou Turbine Power Grp Co Ltd Hangzhou 310022 Peoples R China

Accurate generation of the compressor performance map (CPM) is necessary for building high-precision physical models of gas turbines. The applicability and generalization ability of the current CPM generation method are relatively poor under off-design operating conditions, and the extrapolability of the generated curves is debatable. This paper proposes an adaptive performance map generation method through shape feature fusion (SFFAG) for the gas turbine compressor. A framework for the generation and optimization of the CPM curve database is developed. The nonlinear features of the CPM curves are extracted and fused by the variational autoencoder. Different CPM curves conforming to the physical shape are reconstructed to build a high-quality CPM curve database. Correction factors are introduced to adaptively correct the CPM curves by genetic algorithm. The proposed method is verified on an aircraft engine and an in-service industrial gas turbine and compared with other methods. The results show that the method can generate CPM with higher accuracy, better generalization ability and extrapolability, and better capture the nonlinear behavior of different types of gas turbines. With the continuous updating and improvement of the initial CPM curve database with richer curve shape features, the CPM curve database constructed by SFF-AG will be more complete.

关键词： Compressor performance map Adaptive generation method variational autoencoder Gas turbine Shape feature fusion

来源：评论

学校读者我要写书评

暂无评论

Ground-motion generations using Multi-label Conditional Embedding-conditional Denoising Diffusion Probabilistic Model (ML-cDDPM)

引用

SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2025年 191卷

作者： Huang, Yanwen Sun, Xiaodan You, Junjie Yang, Cheng Lu, Dagang Zhang, Jie Southwest Jiaotong Univ Dept Civil Engn Chengdu 610093 Peoples R China Natl Engn Res Ctr Technol Geol Disaster Prevent La Chengdu 610031 Peoples R China Harbin Inst Technol Dept Civil Engn Harbin 150090 Peoples R China Tongji Univ Key Lab Geotech & Underground Engn Minist Educ Shanghai 200092 Peoples R China Tongji Univ Dept Geotech Engn Shanghai 200092 Peoples R China

Current studies on ground motion prediction equations require the categorization of earthquake records by regions with different attenuation patterns. Conversely, machine learning based on entire datasets provides an opportunity to explore the factors that dominate ground motion at a site and their complex interactions. In this study, Multi-label Conditional Embedding is proposed to modify a conditional Denoising Diffusion Probabilistic Model (cDDPM) and achieve ground motion prediction of earthquake scenarios. A database was created for neural network training, consisting of 7154 horizontal ground motion records from 105 earthquakes selected from the Next Generation Attenuation (NGA)-West2 database of the Pacific Earthquake Engineering Research Center (PEER). Each record was labeled using four conditional parameters: VS30, F, MW, and Rrup. To embed multi-label and location information into the neural network, One-hot Encoding and Positional Encoding techniques were integrated. Hence, a Multi-label Conditional Embedding-conditional Denoising Diffusion Probabilistic Model (ML-cDDPM) was constructed for ground motion prediction. The model was used to simulate ground motions of past earthquakes and was compared with recorded motions. The model was also compared with two other neural-network-based prediction models. The comparisons demonstrate the reliability of ML-cDDPM in simulating ground motions for earthquake scenarios and its superiority over the other two models in representing the complexity of ground motion.

关键词： Ground motion generation Denoising diffusion probabilistic model Earthquake scenarios Multi-label conditional embedding variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Developing an Urban Landscape Fumigation Service Robot: A Machine-Learned, Gen-AI-Based Design Trade Study

引用

APPLIED SCIENCES-BASEL 2025年第4期15卷 2061-2061页

作者： Chittoor, Prithvi Krishna Dandumahanti, Bhanu Priya Veerajagadheswar, Prabakaran Samarakoon, S. M. Bhagya P. Muthugala, M. A. Viraj J. Elara, Mohan Rajesh Singapore Univ Technol & Design Engn Prod Dev Pillar Singapore 487372 Singapore

Generative AI (Gen-AI) revolutionizes design by leveraging machine learning to generate innovative solutions. It analyzes data to identify patterns, creates tailored designs, enhances creativity, and allows designers to explore complex possibilities for diverse industries. This study uses a Gen-AI design generation process to develop an urban landscape fumigation service robot. This study proposes a machine-learned multimodal and feedback-based variational autoencoder (MMF-VAE) model that incorporates a readily available spraying robot dataset and includes design considerations from various research efforts to ensure real-time deployability. The objective is to demonstrate the effectiveness of data-driven and feedback-based approaches in generating design specifications for a fumigation robot with the targeted requirements of autonomous navigation, precision spraying, and an extended runtime. The design generation process comprises three stages: (1) parameter fixation, emphasizing functionality-based and aesthetic-based specifications;(2) design specification generation using the proposed MMF-VAE model with and without a spraying robot dataset;and (3) robot development based on the generated specifications. A comparative analysis evaluated the impact of the dataset-driven design generation. The design generated with the dataset proved more feasible and optimized for real-world deployment with the integration of multimodal inputs and iterative feedback refinement. A real-time prototype was then constructed using the model's parametric constraints and tested in actual fumigation scenarios to validate operational viability. This study highlights the transformative potential of Gen-AI in robotic design workflows.

关键词： generative design variational autoencoder multimodal design fumigation robot urban landscape

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：