检索结果-内蒙古大学图书馆

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

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

Deep autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration

引用

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2024年第11期71卷 3111-3122页

作者： Xing, Le Casson, Alexander J. Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL England Henry Royce Inst Adv Mat Manchester M13 9PL England

Objective: To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channel count independent manner to enable portable Brain-Computer Interface (BCI) applications. Methods: We propose a Deep autoencoder (DAE) neural network for single-channel EEG artifact removal, and implement it on a smartphone via TensorFlow Lite. Delegate based acceleration is employed to allow real-time, low computational resource operation. Artifact removal performance is quantified by comparing corrupted and ground-truth clean EEG data from public datasets for a range of artifact types. The on-phone computational resources required are also measured when processing pre-saved data. Results: DAE cleaned EEG shows high correlations with ground-truth clean EEG, with average Correlation Coefficients of 0.96, 0.85, 0.70 and 0.79 for clean EEG reconstruction, and EOG, motion, and EMG artifact removal respectively. On-smartphone tests show the model processes a 4 s EEG window within 5 ms, substantially outperforming a comparison FastICA artifact removal algorithm. Conclusion: The proposed DAE model shows effectiveness in single-channel EEG artifact removal. This is the first demonstration of a low-computational resource deep learning model for mobile EEG in smartphones with hardware/software acceleration. Significance: This work enables portable BCIs which require low latency real-time artifact removal, and potentially operation with a small number of EEG channels for wearability. It makes use of the artificial intelligence accelerators found in modern smartphones to improve computational performance compared to previous artifact removal approaches.

关键词： Deep learning convolutional autoencoder EEG artifact removal EEG artifact removal smartphone smartphone tensorflow lite tensorflow lite smartphone tensorflow lite

来源：评论

学校读者我要写书评

暂无评论

Robustness evaluation of large-scale machine learning-based reduced order models for reproducing flow fields

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 159卷 243-254页

作者： Higashida, Aito Ando, Kazuto Ruettgers, Mario Lintermann, Andreas Tsubokura, Makoto Kobe Univ Grad Sch Syst Informat Dept Computat Sci 1-1Rokkodai-choNada-ku Kobe Hyogo 6578501 Japan RIKEN Ctr Computat Sci Complex Phenomena Unified Simulat Res Team 7-1-26Minatojima-minami-machiChuo-ku Kobe Hyogo 6500047 Japan RIKEN Ctr Computat Sci Operat & Comp Technol Div 7-1-26Minatojima-minami-machiChuo-ku Kobe Hyogo 6500047 Japan Forschungszentrum Julich Julich Supercomp Ctr Wilhelm Johnen Str D-52428 Julich Germany

The robustness of an artificial neural network that performs model order reduction for flow field data is studied. The network is trained with a large-scale distributed learning approach using up to 6259 nodes of the supercomputer Fugaku. Flow around two square cylinders with a varying distance between their centers is investigated. The network is trained and tested with data from numerical simulations. First, the capability to reproduce flow fields with 2, 12, and 24 modes is investigated by comparing the reconstructed flow data to simulated data. It is shown, that reconstructions based on 2 modes cannot capture both, low- and high -frequency flow structures correctly, whereas predictions based on 12 and 24 modes yield improved flow fields, especially in the case of high -frequency waves in the vicinity of the square cylinders. Reconstructions with 24 modes provide smooth velocity fields that reproduce all relevant low- and high -frequency waves for all variations of the distance between the two square cylinders. Second, the performance of the machine learning-based reconstructions are compared to proper orthogonal decomposition, which is a commonly used reduced order model technique. The comparison only includes flow fields based on 24 modes. For all geometric variations, the mean squared errors of the reconstructions by the conventional method are higher than those of the machine learning model. This underlines the advantage of artificial neural networks over linear methods like proper orthogonal decomposition for tasks like reconstructing flow fields that are characterized by non -linear governing equations.

关键词： Computational fluid dynamics Distributed machine learning convolutional autoencoder Reduced order model Model robustness evaluation

来源：评论

学校读者我要写书评

暂无评论

TriDeepRec: a hybrid deep learning approach to content- and behavior-based recommendation systems

引用

USER MODELING AND USER-ADAPTED INTERACTION 2024年第5期34卷 2085-2114页

作者： Ghadami, Amirhossein Tran, Thomas Univ Ottawa Sch Elect Engn & Comp Sci EECS 800 King Edward Ave Ottawa ON K1N 6N5 Canada

Hybrid recommendation systems are increasingly crucial for businesses aiming to boost revenue and customer engagement. These systems integrate various algorithms, each with unique strengths, to outperform traditional recommendation methods. Our study introduces a novel hybrid recommendation system, TriDeepRec, which effectively combines content-based and behavior-based data to enhance recommendation accuracy. We first introduce a convolutional autoencoder-based Recommendation System (CAERS), designed to process content data and extract complex, meaningful patterns, translating these into predictive ratings. Notably, CAERS tackles the cold-start problem by leveraging content information alone, making it robust in scenarios where historical user interaction data are sparse or unavailable. Next, we incorporate Neural Collaborative Filtering (NCF), a deep learning approach, to analyze past user behavior and predict ratings. The outputs from CAERS and NCF are then integrated using a Multilayer Perceptron (MLP), a type of neural network, to generate the final recommendations. Our methodology employs three deep learning techniques to create TriDeepRec, a system capable of utilizing both past interactions and content attributes. We evaluate our system using two datasets, focusing on both error-based metrics, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), and ranking quality through Normalized Discounted Cumulative Gain (NDCG). These metrics highlight the system's performance in both prediction accuracy and ranking relevance. The results indicate improvements over both the individual components and other leading models in the field. This demonstrates that TriDeepRec, by harnessing the strengths of both content and behavior data, provides a more accurate, reliable, and effective recommendation system.

关键词： Recommendation system Hybrid recommendation system Deep learning convolutional autoencoder Cold start

来源：评论

学校读者我要写书评

暂无评论

Domain Generalized Open-Set Fault Detection and Diagnosis for Belt Conveyor Systems With Prototype Learning

引用

IEEE ACCESS 2025年 13卷 59959-59976页

作者： Kim, Jinyou Yi, Ilkyu Suh, Young-Joo Pohang Univ Sci & Technol POSTECH Dept Comp Sci & Engn Pohang 37673 South Korea Pohang Univ Sci & Technol POSTECH Grad Sch Artificial Intelligence Pohang 37673 South Korea

Belt conveyor systems are essential across various industries but are prone to faults due to their distinctive design and challenging operational environments. Various approaches have been explored for fault detection and diagnosis (FDD) in belt conveyor systems, but research specifically addressing the unique features of these systems and providing a comprehensive analysis remains limited. In this paper, we propose a deep learning-based two-stage FDD approach specifically tailored to address the challenges associated with belt conveyor systems. Challenges in applying deep learning-based FDD include managing the vast number of features collected from large-scale systems, the scarcity of diverse fault data, and distribution shifts between training and testing environments. To address these issues, we developed a FDD approach that integrates open-set recognition and domain generalization, leveraging multivariate sensor data. Our two-stage design effectively mitigates the limitations posed by insufficient faulty samples while enhancing overall FDD performance. To properly assess the proposed method, we utilized datasets collected from a testbed that simulates the actual operational conditions of belt conveyor systems. The evaluation results demonstrate that the proposed FDD method exhibits robust performance in real-world applications on belt conveyor systems.

关键词： Belts Prototypes Fault diagnosis Pulleys Training Feature extraction Fault detection Deep learning Robustness Motors Fault detection and diagnosis (FDD) belt conveyor system open-set recognition domain generalization convolutional autoencoder prototype learning

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised learning for laser directed energy deposition monitoring via co-axial dynamic imaging

引用

ADDITIVE MANUFACTURING 2025年 97卷

作者： Zheng, Fenglei Xie, Luofeng Bai, Qingsong Zhu, Yangyang Yin, Ming Zhang, Yuhang Niu, Kaiyu Sichuan Univ Sch Mech & Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China

Data-driven approaches have emerged as a promising solution for monitoring the quality of metal parts produced by Laser Directed Energy Deposition (LDED). Current methods often require specialized knowledge for extensive data labeling, while defect characterization experiments can be costly and time-consuming. To overcome these issues, we propose a semi-supervised autoencoder framework capable of real-time prediction of local quality attributes during the LDED process. Our method significantly diverges from traditional semi-supervised learning approaches, which primarily rely on data augmentation to enforce consistency regularization. First, domain-level samples are constructed based on a series of coaxial melt pool images, providing a comprehensive assessment of the quality state of local regions by analyzing the local porosity of the samples. Second, a semi-supervised convolutional autoencoder (SCAE) is designed to develop a robust understanding of melt pool features and latent knowledge through unsupervised training. Residual connections and L2 normalization are utilized to enhance the extraction of melt pool morphological features and effectively handle the multi-scale nature of pore samples, respectively. Finally, a limited amount of labeled data is employed to activate the class space of the samples. Experimental results validate the feasibility and efficacy of the proposed model in monitoring the local quality of the LDED process. With only 60 % of the original labeled data, our model's accuracy is comparable to the maximum accuracy achieved by the fully supervised model. Even with a reduction of labeled data to 20 %, our model still maintains a prediction accuracy of 83.8 %, significantly reducing the time and costs associated with data labeling. These findings contribute to improving the quality of LDED final products and promote its broader industrial application.

关键词： Laser directed energy deposition Semi-supervised Real-time monitoring convolutional autoencoder

来源：评论

学校读者我要写书评

暂无评论

Development of weighted residual RNN model with hybrid heuristic algorithm for movement recognition framework in ambient assisted living

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-25页

作者： Abidi, Mustufa Haider Alkhalefah, Hisham Almutairi, Zeyad King Saud Univ Adv Mfg Inst POB 800 Riyadh 11421 Saudi Arabia King Saud Univ Coll Engn Dept Mech Engn POB 800 Riyadh 11421 Saudi Arabia

In healthcare applications, automatic and intelligent movement recognition systems in Ambient Assisted Living (AAL) are designed for elderly and disabled persons. The AAL provides assistance as well as secure feelings to disabled persons and elderly individuals. In AAL, the movement recognition process has been emerging in recent days. The automatic and safe living of the disabled person is ensured by performing movement recognition in AAL. Movement recognition in the AAL is developed for disabled and elderly people and is also performed to provide healthcare assistance to the elderly and disabled person. The weighted deep learning model and a hybrid heuristic algorithm are proposed to achieve this goal. The required input data is initially gathered from the standard data sources. Subsequently, the essential deep features are extracted from the input data using a convolutional autoencoder. Finally, the resultant features are subjected to the movement recognition model, termed as Weighted Residual Recurrent Neural Network. For achieving a better training and testing process, the weights in the RRNN model are optimally selected by using the hybrid algorithm named Hybrid Rat Swarm with Coati Optimization Algorithm, which is developed with the integration of the Rat Warm Optimization and Coati Optimization. The movement recognition results are used for providing medical assistance to elderly and disabled persons. Lastly, the efficacy of the suggested strategy is validated with different measures. From the experiments, the proposed system attains standard results in terms of improved system performance and accuracy that can aid in significantly recognizing human movements.

关键词： Human movement recognition Ambient assisted living convolutional autoencoder Weighted residual recurrent neural network Hybrid rat swarm with coati optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

An undercarriage image driven anomaly detection method for metro vehicle based on adversarial memory enhancement

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT 2024年第5期238卷 489-500页

作者： Wang, Lei Zhang, Kai Zheng, Qing Ding, Guofu Zhang, Weihua Chen, Dejun Liu, Bin Southwest Jiaotong Univ Tangshan Inst Tangshan Peoples R China Southwest Jiaotong Univ Sch Mech Engn Chengdu Peoples R China Southwest Jiaotong Univ Key Lab Sichuan Prov Technol & Equipment Rail Transit Operat & Maintena Chengdu Peoples R China Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Chengdu Peoples R China Tangshan Baichuan Intelligent Machine Co Ltd Rail vehicle operat Dept Tangshan Peoples R China Southwest Jiaotong Univ Sch Mech Engn 111 North SectSecond Ring Rd Chengdu 610031 Peoples R China Southwest Jiaotong Univ Key Lab Sichuan Prov Technol & Equipment Rail Transit Operat & Maintena 111 North SectSecond Ring Rd Chengdu 610031 Peoples R China Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst 111 North SectSecond Ring Rd Chengdu 610031 Peoples R China

Anomaly detection is essential to ensure metro vehicles' safe operation. Error reconstruction-based anomaly detection methods have been widely studied because they only need to be trained by normal data and do not require much anomaly data, which is challenging to obtain. However, sometimes the auto-encoder network for error reconstructing "generalizes" so well that it also rebuilds the anomaly well, leading to missed anomaly detection. Therefore, this paper proposes an undercarriage image-driven anomaly detection method for metro vehicles based on adversarial memory enhancement. Firstly, this study performs component segmentation based on YOLOv5 detection results and constructs a component anomaly detection dataset. Secondly, an anomaly detection method based on memory enhancement and adversarial training of encoding-decoding-encoding structure is proposed for component anomaly detection. It enables the auto-encoder to reconstruct the image better. Thirdly, the combined indicator of the difference between potential features and reconstruction error is used as an anomaly indicator for anomaly detection of metro components, reducing the rate of fault misses. The experimental results on the established dataset demonstrate that the proposed method reduces false negative rates of 92.4%, 92.6%, 74.6%, and 59.1% compared with AE(L2), AE(SSIM),GANomaly, and MemAE, respectively.

关键词： Metro vehicle anomaly detection convolutional autoencoder memory enhancement adversarial training

来源：评论

学校读者我要写书评

暂无评论

A deep-learning-based surrogate modeling method with application to plasma processing

引用

CHEMICAL ENGINEERING RESEARCH & DESIGN 2024年 211卷 299-317页

作者： Liu, Pengwei Wu, Qinxin Ren, Xingyu Wang, Yian Ni, Dong Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Polytech Inst Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

Rapid and accurate system evolution predictions are crucial in scientific and engineering research. However, the complexity of processing systems, involving multiple physical field couplings and slow convergence of iterative numerical algorithms, leads to low computational efficiency. Hence, this paper introduces a systematic deep-learning-based surrogate modeling methodology for multi-physics-coupled process systems with limited data and long-range time evolution, accurately predicting physics dynamics and considerably improving computational efficiency and generalization. The methodology comprises three main components: (1) generating datasets using a sequential sampling strategy, (2) modeling multi-physics spatio-temporal dynamics by designing a heterogeneous convolutional autoencoder and Recurrent Neural Network, and (3) training high-precision models with limited data and long-range time evolution via a dual-phase training strategy. A holistic evaluation using a 2D low-temperature plasma processing example demonstrates the methodology's superior computational efficiency, accuracy, and generalization capabilities. It predicts plasma dynamics approximately 105 5 times faster than traditional numerical solvers, with a consistent 2% relative error across different generalization tasks. Furthermore, the potential for transferability across various geometries is explored, and the model's transfer capability is demonstrated with two distinct geometric domain examples.

关键词： Data-driven surrogate modeling Process systems Plasma processing convolutional autoencoder Recurrent neural network Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based integrated attack detection framework to protect distance relays against cyberattacks

引用

ELECTRIC POWER SYSTEMS RESEARCH 2024年 231卷

作者： Narang, Jagendra Kumar Bag, Baidyanath Natl Inst Technol Raipur Dept Elect Engn GE Rd Raipur 492010 Chhattisgarh India

The cyber security of communication -assisted intelligent relays at the IEC61850-enabled modern digital substations has become a prime concern among power companies and regulatory bodies. Particularly, distance relay IEDs are easy to manipulate to send false trip command to circuit breakers through random false data injection and replay attacks. With these attacks evolving and becoming more sophisticated, existing intrusion detection techniques can be easily compromised, resulting in unwanted line outages. This work proposes a convolutional -based autoencoder and Siamese neural network deep learning integrated attack detection framework (IADF) to differentiate these attacks from actual faults. The framework works in conjunction with distance relay IEDs to issue genuine trip commands to circuit breakers. The autoencoder, trained on fault voltage and current signals, detects the false data injection attacks using reconstruction error, while the siamese neural network trained on similar and dissimilar pairs of the fault voltage and current signals detects the fully synchronized replay attacks using similarity estimation with fault data stored on database. The validity of the proposed framework is assessed using IEEE 39 and 118 bus test system simulated on PSCAD/EMTDC software. The validation results suggest that the proposed IADF can accurately discriminate the faults from attacks and is resilient to various system parameter changes.

关键词： False data injection attack on distance relays Replay attacks False tripping command Deep learning detection of cyber attacks convolutional autoencoder convolutional siamese neural network

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：