检索结果-内蒙古大学图书馆

Petri-net-based deep reinforcement learning for real-time scheduling of automated manufacturing systems

JOURNAL OF MANUFACTURING SYSTEMS 2024年 74卷 995-1008页

作者： Luo, Jiliang Yi, Sijia Lin, Zexuan Zhang, Hongbin Zhou, Jiazhong Huaqiao Univ Coll Informat Sci & Engn Xiamen 361021 Peoples R China Huaqiao Univ Coll Mech Engn & Automat Xiamen 361021 Peoples R China Fujian Engn Res Ctr Motor Control & Syst Optimal S Xiamen 361021 Peoples R China

This work focuses on real-time and joint optimizing issues to handle the routing of automated guided vehicles and allocating processing tasks for automated manufacturing systems. A real-time scheduling approach is proposed based on Petri nets and deep reinforcement learning. First, the considered system is modeled with a placed -timed Petri net, which plays the role of the environment for deep reinforcement learning. Second, a novel graph convolutional network (GCN), called Petri-net-GCN, is designed by using the topological structures of a Petri net model such that it can adapt to changes in Petri -net structures caused by abrupt events such as new orders and failures of devices. Third, a deep Q -network method is presented to train Petri-net-GCN to predict the minimal time to lead a Petri net to its goal if an action (transition) is taken at a present state. Consequently, Petri-net-GCN can replace scheduling rules to schedule an automated manufacturing system in real time. Finally, numerical experiments are carried out where there exist failures of devices and new orders. The results show that Petri-net-GCN can adapt to the Petri -net changes caused by abrupt events and performs much better than the first -in -first -out and longest -processing -time -first rules, which are applicable for real-time scheduling issues in practice.

关键词： deep reinforcement learning Petri net real-time scheduling Graph convolutional network Automated manufacturing system

来源：评论

学校读者我要写书评

暂无评论

Enhanced image processing for Automobile Accident Detection Using deep learning

Enhanced Image Processing for Automobile Accident Detection ...

引用

Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： S. Usharani J. Dhanushkumar P. Manju Bala A. Balachandar Department of Artificial Intelligence and Machine Learning IFET College of Engineering (An Autonomous Institution) Villupuram India Department of Computer Science and Engineering IFET College of Engineering (An Autonomous Institution) Villupuram India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Road safety can be creatively increased by utilizing systems for reporting and detecting accidents use the YOLO algorithm. Yolo, which stands for "You Only Look Once," is a sophisticated object recognition system that can identify and pinpoint objects in live video streams. By identifying accidents and notifying emergency services, the system lowers the reaction times and increases the possibility of saving lives by utilizing the YOLO algorithm. The object detection and alarm systems are the two primary parts of the proposed system. The YOLO method is employed by the object recognition module to look for accidents in live video broadcasts. A module of accident photos was used to teach the system to accurately identify incidents. When an accident is discovered, an alert system is activated. The location of the accident and a brief account of what transpired are communicated to the emergency services by the alert system. This information is communicated to emergency services through a wireless communication network, which expedites response times and increases the likelihood of saving lives. Positive results were obtained by testing the system using the images and an accident module. It was shown that the warning system could react in a couple of seconds and that the YOLO algorithm could identify accidents having a precision of around 94. Highways, busy intersections, and other high-risk areas may have systems in place to increase traffic safety and lower the number of accidents. Developing crash detection and warning systems using deep learning using the YOLO technique is one way to potentially increase road safety. With the use of technology, problems may be precisely identified in real-time video feeds, alerting emergency services and potentially increasing survival rates and reaction times.

关键词： YOLO deep learning Accuracy Alarm systems Streaming media Emergency services Road safety

来源：评论

学校读者我要写书评

暂无评论

Advancing Underwater Vision: A Survey of deep learning Models for Underwater Object Recognition and Tracking

引用

IEEE ACCESS 2025年 13卷 17830-17867页

作者： Elmezain, Mahmoud Saad Saoud, Lyes Sultan, Atif Heshmat, Mohamed Seneviratne, Lakmal Hussain, Irfan Khalifa Univ Khalifa Univ Ctr Autonomous Robot Syst Abu Dhabi U Arab Emirates

Underwater computer vision plays a vital role in ocean research, enabling autonomous navigation, infrastructure inspections, and marine life monitoring. However, the underwater environment presents unique challenges, including color distortion, limited visibility, and dynamic light conditions, which hinder the performance of traditional image processing methods. Recent advancements in deep learning (DL) have demonstrated remarkable success in overcoming these challenges by enabling robust feature extraction, image enhancement, and object recognition. This review provides a comprehensive analysis of cutting-edge deep learning architectures designed for underwater object detection, segmentation, and tracking. State-of-the-art (SOTA) models, including AGW-YOLOv8, Feature-Adaptive FPN, and Dual-SAM, have shown substantial improvements in addressing occlusions, camouflaging, and small underwater object detection. For tracking tasks, transformer-based models like SiamFCA and FishTrack leverage hierarchical attention mechanisms and convolutional neural networks (CNNs) to achieve high accuracy and robustness in dynamic underwater environments. Beyond optical imaging, this review explores alternative modalities such as sonar, hyperspectral imaging, and event-based vision, which provide complementary data to enhance underwater vision systems. These approaches improve performance under challenging conditions, enabling richer and more informative scene interpretation. Promising future directions are also discussed, emphasizing the need for domain adaptation techniques to improve generalizability, lightweight architectures for real-time performance, and multi-modal data fusion to enhance interpretability and robustness. By critically evaluating current methodologies and highlighting gaps, this review provides insights for advancing underwater computer vision systems to support ocean exploration, ecological conservation, and disaster management.

关键词： Reviews Object recognition Computer vision Computational modeling Imaging image segmentation deep learning Surveys Optical imaging Oceans Underwater computer vision deep learning underwater robotics ocean research underwater image enhancement object tracking object detection

来源：评论

学校读者我要写书评

暂无评论

deep-learning-Based Analysis of Electronic Skin Sensing Data

引用

SENSORS 2025年第5期25卷 1615-1615页

作者： Guo, Yuchen Sun, Xidi Li, Lulu Shi, Yi Cheng, Wen Pan, Lijia Nanjing Univ Collaborat Innovat Ctr Adv Microstruct Sch Elect Sci & Engn Nanjing 210093 Peoples R China

E-skin is an integrated electronic system that can mimic the perceptual ability of human skin. Traditional analysis methods struggle to handle complex e-skin data, which include time series and multiple patterns, especially when dealing with intricate signals and real-time responses. Recently, deep learning techniques, such as the convolutional neural network, recurrent neural network, and transformer methods, provide effective solutions that can automatically extract data features and recognize patterns, significantly improving the analysis of e-skin data. deep learning is not only capable of handling multimodal data but can also provide real-time response and personalized predictions in dynamic environments. Nevertheless, problems such as insufficient data annotation and high demand for computational resources still limit the application of e-skin. Optimizing deep learning algorithms, improving computational efficiency, and exploring hardware-algorithm co-designing will be the key to future development. This review aims to present the deep learning techniques applied in e-skin and provide inspiration for subsequent researchers. We first summarize the sources and characteristics of e-skin data and review the deep learning models applicable to e-skin data and their applications in data analysis. Additionally, we discuss the use of deep learning in e-skin, particularly in health monitoring and human-machine interactions, and we explore the current challenges and future development directions.

关键词： deep learning electronic skin data processing health monitoring human-machine interaction

来源：评论

学校读者我要写书评

暂无评论

Dynamic Tactical image Recognition and Analysis in Football Matches Using Convolutional Neural Networks

引用

TRAITEMENT DU SIGNAL 2025年第1期42卷 583-592页

作者： Xie, Qi Baoji Univ Arts & Sci Sch Phys Educ Baoji 721000 Peoples R China

With the increasing complexity of modern football tactics, how to intelligently and accurately analyze tactical changes in real-time during matches has become an important research direction. Traditional manual tactical analysis methods are inefficient and susceptible to subjective bias. Therefore, using computer vision and deep learning technologies for tactical image recognition and analysis in football matches has gradually become a research hotspot. Convolutional Neural Networks (CNNs), as a powerful image processing tool, have been widely applied in video analysis and player detection. However, multi-target motion prediction and tracking management in dynamic football match scenes still face significant challenges. Existing research mainly focuses on static image analysis or simple player tracking, but the high-frequency image updates, player interactions, and occlusion issues in football matches complicate multi-target tracking. While some deep learning-based methods for multi-target detection and tracking have made progress, challenges remain, such as handling high-density player targets and improving motion trajectory prediction accuracy. To address these shortcomings, this study proposes two core techniques based on CNNs: first, multi-target motion prediction, which accurately forecasts players' future positions based on historical motion data;second, multi-target tracking management, which uses deep learning to track and manage each player's movement trajectory in real-time. Through these two techniques, this research aims to improve the realtime and accuracy of tactical analysis in football matches, providing coaches and analysts with more scientific and efficient tactical decision-making support.

关键词： CNN football matches dynamic tactical image multi-target motion prediction multi-target tracking management computer vision

来源：评论

学校读者我要写书评

暂无评论

A deep learning enhanced inverse scattering framework for microwave imaging of piece-wise homogeneous targets

引用

INVERSE PROBLEMS 2024年第4期40卷 045001-045001页

作者： Ruiz, Alvaro Yago Stevanovic, Maria Nikolic Cavagnaro, Marta Crocco, Lorenzo Inst Electromagnet Sensing Environm Natl Res Council Italy CNR IREA Naples Italy Singidunum Univ Belgrade Serbia Sapienza Univ Rome DIET Rome Italy

In this paper, we present a framework for the solution of inverse scattering problems that integrates traditional imaging methods and deep learning. The goal is to image piece-wise homogeneous targets and it is pursued in three steps. First, raw-data are processed via orthogonality sampling method to obtain a qualitative image of the targets. Then, such an image is fed into a U-Net. In order to take advantage of the implicitly sparse nature of the information to be retrieved, the network is trained to retrieve a map of the spatial gradient of the unknown contrast. Finally, such an augmented shape is turned into a map of the unknown permittivity by means of a simple post-processing. The framework is computationally effective, since all processing steps are performed in real-time. To provide an example of the achievable performance, Fresnel experimental data have been used as a validation.

关键词： inverse scattering microwave imaging deep learning qualitative methods orthogonality sampling method

来源：评论

学校读者我要写书评

暂无评论

Classification of real and bogus transients using active learning and semi-supervised learning

引用

ASTRONOMY & ASTROPHYSICS 2025年 693卷

作者： Liu, Yating Fan, Lulu Hu, Lei Lu, Junqiang Lu, Yan Xu, Zelin Zhu, Jiazheng Wang, Haochen Kong, Xu Univ Sci & Technol China Sch Artificial Intelligence & Data Sci Hefei 230026 Peoples R China Univ Sci & Technol China Dept Astron CAS Key Lab Res Galaxies & Cosmol Hefei 230026 Peoples R China Univ Sci & Technol China Sch Astron & Space Sci Hefei 230026 Peoples R China Deep Space Explorat Lab Hefei 230088 Peoples R China Carnegie Mellon Univ McWilliams Ctr Cosmol Dept Phys 5000 Forbes Ave Pittsburgh PA 15213 USA Shanghai AI Lab Shanghai 200232 Peoples R China Purple Mt Observ Nanjing 210023 Peoples R China

Context. The mounting data stream of large time-domain surveys renders the visual inspections of a huge set of transient candidates impractical. Techniques based on deep learning-based are popular solutions for minimizing human intervention in the time domain community. The classification of real and bogus transients is a fundamental component in real-time data processing systems and is critical to enabling rapid follow-up observations. Most existing methods (supervised learning) require sufficiently large training samples with corresponding labels, which involve costly human labeling and are challenging in the early stages of a time-domain survey. One method that can make use of training samples with access to only a limited amount of labels is highly desirable for future large time-domain surveys. These include the forthcoming 2.5-meter Wide-Field Survey Telescope (WFST) six-year survey and the Vera C. Rubin Observatory Legacy Survey of Space and time (LSST). Aims. deep-learning-based methods have been favored in astrophysics owing to their adaptability and remarkable performance. They have been applied to the task of the classification of real and bogus transients. Unlike most existing approaches, which necessitate massive and expensive annotated data, we aim to leverage training samples with only 1000 labels and discover real sources that vary in brightness over time in the early stages of the WFST six-year survey. Methods. We present a novel deep learning method that combines active learning and semi-supervised learning to construct a competitive real-bogus classifier. Our method incorporates an active learning stage, where we actively select the most informative or uncertain samples for annotation. This stage aims to achieve higher model performance by leveraging fewer labeled samples, thus reducing annotation costs and improving the overall learning process efficiency. Furthermore, our approach involves a semi-supervised learning stage that exploits the unlab

关键词： techniques: image processing

来源：评论

学校读者我要写书评

暂无评论

Integrating YOLOv8 and CSPBottleneck based CNN for enhanced license plate character recognition

引用

JOURNAL OF real-time image processing 2024年第5期21卷 168页

作者： Khokhar, Sahil Kedia, deepak GJUS&T Dept ECE Hisar India

The paper introduces an integrated methodology for license plate character recognition, combining YOLOv8 for segmentation and a CSPBottleneck-based CNN classifier for character recognition. The proposed approach incorporates pre-processing techniques to enhance the recognition of partial plates and augmentation methods to address challenges arising from colour diversity. Performance analysis demonstrates YOLOv8's high segmentation accuracy and fast processing time, complemented by precise character recognition and efficient processing by the CNN classifier. The integrated system achieves an overall accuracy of 99.02% with a total processing time of 9.9 ms, offering a robust solution for automated license plate recognition (ALPR) systems. The integrated approach presented in the paper holds promise for the practical implementation of ALPR technology and further development in the field of license plate recognition systems.

关键词： Computer vision Optical character recognition deep learning Artificial intelligence ALPR

来源：评论

学校读者我要写书评

暂无评论

real-time diabetic foot ulcer classification based on deep learning & parallel hardware computational tools

引用

MULtimeDIA TOOLS AND APPLICATIONS 2024年第27期83卷 70369-70394页

作者： Fadhel, Mohammed A. Alzubaidi, Laith Gu, Yuantong Santamaria, Jose Duan, Ye Univ Sumer Coll Comp Sci & Informat Technol Thi Qar 64005 Iraq Queensland Univ Technol Sch Mech Med & Proc Engn Brisbane Qld 4000 Australia Queensland Univ Technol ARC Ind Transformat Training Ctr Joint Biomech Brisbane Qld 4000 Australia Akunah Med Technol Pty Ltd Co Brisbane Qld 4120 Australia Univ Jaen Dept Comp Sci Jaen 23071 Spain Clemson Univ Sch Comp Clemson SC 29631 USA

Meeting the rising global demand for healthcare diagnostic tools is crucial, especially with a shortage of medical professionals. This issue has increased interest in utilizing deep learning (DL) and telemedicine technologies. DL, a branch of artificial intelligence, has progressed due to advancements in digital technology and data availability and has proven to be effective in solving previously challenging learning problems. Convolutional neural networks (CNNs) show potential in image detection and recognition, particularly in healthcare applications. However, due to their resource-intensiveness, they surpass the capabilities of general-purpose CPUs. Therefore, hardware accelerators such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and graphics processing units (GPUs) have been developed. With their parallelism efficiency and energy-saving capabilities, FPGAs have gained popularity for DL networks. This research aims to automate the classification of normal and abnormal (specifically Diabetic Foot Ulcer-DFU) classes using various parallel hardware accelerators. The study introduces two CNN models, namely DFU_FNet and DFU_TFNet. DFU_FNet is a simple model that extracts features used to train classifiers like SVM and KNN. On the other hand, DFU_TFNet is a deeper model that employs transfer learning to test hardware efficiency on both shallow and deep models. DFU_TFNet has outperformed AlexNet, VGG16, and GoogleNet benchmarks with an accuracy 99.81%, precision 99.38% and F1-Score 99.25%. In addition, the study evaluated two high-performance computing platforms, GPUs and FPGAs, for real-time system requirements. The comparison of processing time and power consumption revealed that while GPUs outpace FPGAs in processing speed, FPGAs exhibit significantly lower power consumption than GPUs.

关键词： deep learning Double transfer learning FPGA GPU DFU real-time Parallel hardware Medical imaging

来源：评论

学校读者我要写书评

暂无评论

deep learning using a residual deconvolutional network enables real-time high-density single-molecule localization microscopy

引用

BIOMEDICAL OPTICS EXPRESS 2023年第4期14卷 1833-1847页

作者： Zhou, Zhiwei Wu, Junnan Wang, Zhengxia Huang, Zhen-li Huazhong Univ Sci & Technol Britton Chance Ctr Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Engn Sci MoE Key Lab Biomed Photon Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Hainan Univ Sch Biomed Engn Key Lab Biomed Engn Hainan Prov Haikou 570228 Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China

High-density localization based on deep learning is a very effective method to accelerate single molecule localization microscopy (SMLM). Compared with traditional highdensity localization methods, deep learning-based methods enable a faster data processing speed and a higher localization accuracy. However, the reported high-density localization methods based on deep learning are still not fast enough to enable real time data processing for large batches of raw images, which is probably dueto the heavy computational burden and computation complexity in the U-shape architecture used in these models. Here we propose a high-density localization method called FID-STORM, which is based on an improved residual deconvolutional network for the real-time processing of raw images. In FID-STORM, we use a residual network to extract the features directly from low-resolution raw images rather than the U-shape network from interpolated images. We also use a model fusion from TensorRT to further accelerate the inference of the model. In addition, we process the sum of the localization images directly on GPU to obtain an additional speed gain. Using simulated and experimental data, we verified that the FID-STORM method achieves a processing speed of 7.31 ms/frame at 256 x 256 pixels @ Nvidia RTX 2080 Ti graphic card, which is shorter than the typical exposure time of 10-30 ms, thus enabling real-time data processing in high-density SMLM. Moreover, compared with a popular interpolated image-based method called deep-STORM, FID-STORM enables a speed gain of -26 times, without loss of reconstruction accuracy. We also provided an imageJ plugin for our new method.

关键词： deep learning Imaging techniques Neural networks Spatial resolution Super resolution imaging Wavefront distortions

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：