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deep learning model for early prediction of material fracture in tensile testing

NEURAL COMPUTING & APPLICATIONS 2024年第18期36卷 10461-10474页

作者： Jubair, Fahed Alhamayel, Ahmad Aljaiose, Raed Darabkh, Khalid A. Univ Jordan Sch Engn Comp Engn Dept Amman 11942 Jordan

Tensile testing (aka tension testing) is a widely employed mechanical testing technique for analyzing materials' properties and behavior under applied stress. Tensile testing plays a pivotal role in helping engineers to make informed decision about material selection and usage. Despite its importance, there is a limited numbers of studies that explored the potential of AI techniques for real time monitoring and material behavior prediction in tensile testing. To this end, this work presents a deep learning model designed to predict the material's condition throughout tensile testing and provide an early warning prior to fracture. By leveraging a comprehensive dataset of tension test video samples, the proposed model utilizes both convolution and recurrent neural networks to extract pertinent spatial and temporal visual features, thereby predicting the frames at which material deformation and fracture occur. The evaluation results of our research showed that the proposed model achieved a predictive ability with an F1-score of 97%, on average. The implications of our research are significant for industries and researchers in the field of materials science and engineering. By accurately predicting material status, our model enables automounts, real time analysis of material behavior during tensile testing, leading to better time and cost efficiency in various applications.

关键词： Tensile testing Material deformation Material fracture deep learning image processing EfficientNet

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Needle tracking in low-resolution ultrasound volumes using deep learning

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2024年第10期19卷 1975-1981页

作者： Grube, Sarah Latus, Sarah Behrendt, Finn Riabova, Oleksandra Neidhardt, Maximilian Schlaefer, Alexander Hamburg Univ Technol Inst Med Technol & Intelligent Syst Hamburg Germany

PurposeClinical needle insertion into tissue, commonly assisted by 2D ultrasound imaging for real-time navigation, faces the challenge of precise needle and probe alignment to reduce out-of-plane movement. Recent studies investigate 3D ultrasound imaging together with deep learning to overcome this problem, focusing on acquiring high-resolution images to create optimal conditions for needle tip detection. However, high-resolution also requires a lot of time for image acquisition and processing, which limits the real-time capability. Therefore, we aim to maximize the US volume rate with the trade-off of low image resolution. We propose a deep learning approach to directly extract the 3D needle tip position from sparsely sampled US *** design an experimental setup with a robot inserting a needle into water and chicken liver tissue. In contrast to manual annotation, we assess the needle tip position from the known robot pose. During insertion, we acquire a large data set of low-resolution volumes using a 16 x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} 16 element matrix transducer with a volume rate of 4 Hz. We compare the performance of our deep learning approach with conventional needle *** experiments in water and liver show that deep learning outperforms the conventional approach while achieving sub-millimeter accuracy. We achieve mean position errors of 0.54 mm in water and 1.54 mm in liver for deep *** study underlines the strengths of deep learning to predict the 3D needle positions from low-resolution ultrasound volumes. This is an important milestone for real-time needle navigation, simplifying the alignment of needle and ultrasound probe and enabling a 3D motion analysis.

关键词： Volumetric ultrasound imaging deep learning Needle tip detection real-time Sparse feature learning

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real-time monitoring system for power distribution network faults based on deep learning technology

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International Journal of Information and Communication Technology 2024年第5期25卷 18-39页

作者： Zhao, Qiaoni Yang, Li Hunan Railway Professional Technology College ZhuZhou412001 China

This article aims to propose a reliable real-time monitoring system for distribution network defects, improve intelligent monitoring technology by combining deep learning technology, and analyse the drawbacks of traditional real-time monitoring of distribution network defects in real-time, by improving the algorithm, the basic structure of the algorithm model is constructed. Based on experimental analysis, the data processing of this system is based on deep learning technology. Multiple monitoring modules are used in the system to improve the accuracy and real-time performance of data collection, providing more reliable data support for fault detection. From the simulation experiment, it can be seen that the real-time monitoring system for distribution network defects based on deep learning proposed in this article can play an important role in fault diagnosis and troubleshooting in the distribution network. Copyright © The Author(s) 2024.

关键词： Defects

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deep learning for Traffic Scene Understanding: A Review

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IEEE ACCESS 2025年 13卷 13187-13237页

作者： Dolatyabi, Parya Regan, Jacob Khodayar, Mahdi Univ Tulsa Dept Comp Sci Tulsa OK 74104 USA

This review paper presents an in-depth analysis of deep learning (DL) models applied to traffic scene understanding, a key aspect of modern intelligent transportation systems. It examines fundamental techniques such as classification, object detection, and segmentation, and extends to more advanced applications like action recognition, object tracking, path prediction, scene generation and retrieval, anomaly detection, image-to-image Translation (I2IT), and person re-identification (Person Re-ID). The paper synthesizes insights from a broad range of studies, tracing the evolution from traditional image processing methods to sophisticated DL techniques, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). The review also explores three primary categories of domain adaptation (DA) methods: clustering-based, discrepancy-based, and adversarial-based, highlighting their significance in traffic scene understanding. The significance of Hyperparameter Optimization (HPO) is also discussed, emphasizing its critical role in enhancing model performance and efficiency, particularly in adapting DL models for practical, real-world use. Special focus is given to the integration of these models in real-world applications, including autonomous driving, traffic management, and pedestrian safety. The review also addresses key challenges in traffic scene understanding, such as occlusions, the dynamic nature of urban traffic, and environmental complexities like varying weather and lighting conditions. By critically analyzing current technologies, the paper identifies limitations in existing research and proposes areas for future exploration. It underscores the need for improved interpretability, real-time processing, and the integration of multi-modal data. This review serves as a valuable resource for researchers and practitioners aiming to apply or advance DL techniques in traffic scene understanding.

关键词： Reviews Adaptation models Object detection deep learning real-time systems Accuracy Feature extraction Synthetic data Data models Autonomous vehicles traffic scene understanding discriminative models generative models domain adaptation classification object detection segmentation

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Towards explainable artificial intelligence in deep vision-based odometry

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 115卷

作者： Ghasemieh, Alireza Kashef, Rasha Toronto Metropolitan Univ Dept Elect Comp & BioMed Engn Toronto ON Canada

Visual Odometry (VO) is a crucial process for estimating camera motion in real-time based on visual information captured. The emergence of deep learning has significantly transformed VO and Explainable Artificial Intelligence (XAI) in deep Vision-Based Odometry. This survey paper explores the latest advancements in VO facilitated by deep learning methods, focusing on explainability and interpretability. It provides an overview of state-of-the-art deep learning techniques and dissects each model into its elemental building blocks to understand their explainable and interpretable aspects. The survey also highlights research gaps in optical flow robustness, occlusion and dynamic objects, real-time processing, drift correction, semantic awareness, and sensor integration. The aim is to catalyze future innovations in deep learningbased VO and stimulate dialogue about potential directions for the next wave of research, emphasizing explainability and interpretability as integral components of advanced systems.

关键词： Explainable artificial intelligence (XAI) Visual Odometry Self-localization Pose estimation deep learning

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A Hardware Accelerator for real-time processing Platforms Used in Synthetic Aperture Radar Target Detection Tasks

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MICROMACHINES 2025年第2期16卷 193-193页

作者： Zhang, Yue Tang, Yunshan Cao, Yue Yu, Zhongjun Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 101408 Peoples R China

The deep learning object detection algorithm has been widely applied in the field of synthetic aperture radar (SAR). By utilizing deep convolutional neural networks (CNNs) and other techniques, these algorithms can effectively identify and locate targets in SAR images, thereby improving the accuracy and efficiency of detection. In recent years, achieving real-time monitoring of regions has become a pressing need, leading to the direct completion of real-time SAR image target detection on airborne or satellite-borne real-time processing platforms. However, current GPU-based real-time processing platforms struggle to meet the power consumption requirements of airborne or satellite applications. To address this issue, a low-power, low-latency deep learning SAR object detection algorithm accelerator was designed in this study to enable real-time target detection on airborne and satellite SAR platforms. This accelerator proposes a Process Engine (PE) suitable for multidimensional convolution parallel computing, making full use of Field-Programmable Gate Array (FPGA) computing resources to reduce convolution computing time. Furthermore, a unique memory arrangement design based on this PE aims to enhance memory read/write efficiency while applying dataflow patterns suitable for FPGA computing to the accelerator to reduce computation latency. Our experimental results demonstrate that deploying the SAR object detection algorithm based on Yolov5s on this accelerator design, mounted on a Virtex 7 690t chip, consumes only 7 watts of dynamic power, achieving the capability to detect 52.19 512 x 512-sized SAR images per second.

关键词： synthetic aperture radar (SAR) imagery convolutional neural networks (CNNs) target detection tasks

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Face recognition using deep learning on Raspberry Pi

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COMPUTER JOURNAL 2024年第10期67卷 3020-3030页

作者： Aboluhom, Abdulatif Ahmed Ali Kandilli, Ismet Kocaeli Univ Engn Fac Elect & Commun Dept TR-41380 Kocaeli Turkiye Kocaeli Univ Elect & Commun Dept TR-41380 Kocaeli Turkiye

Facial recognition on resource-limited devices such as the Raspberry Pi poses a challenge due to inherent processing limitations. For real-time applications, finding efficient and reliable solutions is critical. This study investigated the feasibility of using transfer learning for facial recognition tasks on the Raspberry Pi and evaluated transfer learning that leverages knowledge from previously trained models. We compared two well-known deep learning (DL) architectures, InceptionV3 and MobileNetV2, adapted to face recognition datasets. MobileNetV2 outperformed InceptionV3, achieving a training accuracy of 98.20% and an F1 score of 98%, compared to InceptionV3's training accuracy of 86.80% and an F1 score of 91%. As a result, MobileNetV2 emerges as a more powerful architecture for facial recognition tasks on the Raspberry Pi when integrated with transfer learning. These results point to a promising direction for deploying efficient DL applications on edge devices, reducing latency, and enabling real-time processing.

关键词： face recognition deep learning transfer learning Raspberry Pi

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A self-validation Noise2Noise training framework for image denoising

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IMAGING SCIENCE JOURNAL 2024年第7期72卷 855-870页

作者： Limsuebchuea, Asavaron Duangsoithong, Rakkrit Jaruenpunyasak, Jermphiphut Prince Songkla Univ Fac Engn Dept Elect Engn Hat Yai Thailand Prince Songkla Univ Fac Med Dept Biomed Sci & Biomed Engn Hat Yai Thailand Prince Songkla Univ Fac Engn Dept Elect Engn Hat Yai 90110 Thailand

image denoising is a crucial algorithm in image processing that aims to enhance image quality. deep learning-based image denoising methods can be categorized into supervised and unsupervised approaches. Supervised learning requires pairs of noisy and noise-free training data, which is impractical in real-world scenarios. Unsupervised learning uses pairs of noisy images for training, but it may yield lower accuracy. Additionally, deep learning-based methods often require a large amount of training data. To overcome these challenges, this research proposes a self-validation Noise2Noise (SV-N2N) framework that generates validation sets using only noisy images without requiring noise-free pairs. The proposed SV-N2N method effectively reduces noise, comparable to supervised and unsupervised methods, without requiring a noise-free ground truth, which is efficient for solving real-world scenarios.

关键词： image denoising single image denoising self-supervised image denoising deep learning noise2clean noise2noise blind noise image restoration >

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An efficient deep learning-based workflow for real-time CO2 plume visualization in saline aquifer using distributed pressure and temperature measurements

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 239卷

作者： Yao, Changqing Nagao, Masahiro Datta-Gupta, Akhil Mishra, Srikanta Texas A&M Univ College Stn TX 77843 USA Battelle Mem Inst 505 King Ave Columbus OH 43201 USA

Underground carbon dioxide (CO2) sequestration is widely accepted as a proven and established technology to respond to global warming from greenhouse gas emissions. It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficacy. We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site. Our proposed workflow integrates field measurements, including distributed pressure and temperature at wells to visualize spatial and temporal migration of the CO2 plume in the subsurface. The deep learning framework has two key concepts that enhance the efficiency of the training and robustness of the CO2 plume prediction: first, instead of using multiple saturation images as output of the deep-learning model, a single time of Flight (TOF) image are used to represent CO2 plume propagation;second, we use variational autoencoderdecoder (VAE) for high dimensional image data compression considering uncertainties in the predicted images. time-of-Flight (TOF) is the travel time of a neural particle from the injection point, which is calculated along streamline based on the velocity field by considering reservoir heterogeneity and driving forces. The latent variables of VAE are estimated through a deep neural network model with inputs of distributed pressure and temperature measurements. Subsequently, the decoder part of the VAE expands the estimated latent variables to the original dimension of the TOF images. We demonstrate the efficacy of the proposed method by comparison with the more traditional pareto-based multi-objective Genetic Algorithm (MOGA) for the assimilation of the field measurements and forecasting of the CO2 plume migration. The power and utility of our proposed workflow are demonstrated by application to the Illinois Basin-Decatu

关键词： CO 2 plume visualization time -of -Flight deep learning Variational autoencoder

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Accelerating Disaster Response with deep learning and image processing Techniques

Accelerating Disaster Response with Deep Learning and Image ...

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International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Çağrı Karakaş İlhan Aydin Erhan Akin Dept. of Computer Engineering Firat University Elaziğ Türkiye

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

This study examines the use of artificial intelligence-based image segmentation and image processing techniques in disaster management. The aim of the study is to integrate artificial intelligence and image processing techniques for fast and effective response in disaster areas to facilitate disaster management. Our hypothesis is that fast and accurate analysis can be performed with high-performance and fast AI-based segmentation models and image processing techniques on images taken from UAVs and satellites in disaster areas. In this process, important regions in the images are identified by applying segmentation processes and masks are created. Then, using these masks, numerical results can be obtained with image processing techniques with low computational cost. Thus, it is aimed to make fast and accurate decisions in disaster management. In this study, popular segmentation models were compared and analyzed using 2343 images obtained from Floodnet dataset. The results show that the SegFormer model provides detailed damage analysis and contour area or connected component analysis, which can provide both detailed and numerically accurate results for disaster management. This study reveals that the use of image processing and artificial intelligence in disaster management can improve response processes by increasing operational efficiency.

关键词： image segmentation Analytical models Accuracy Disasters Computational modeling Disaster management Numerical models Computational efficiency time factors Artificial intelligence

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