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An Improved YOLOv5 Detection Method for Strip Surface Defect 28

An Improved YOLOv5 Detection Method for Strip Surface Defect

引用

28th International Conference on Automation and Computing, ICAC 2023

作者： Liu, Yefeng Yu, Longfei Zhang, Qichun School of Automation and Electrical Dept. Liaoning Key Laboratory of Information Physical Fusion and Intelligent Cnc Machine Tool Manufacturing Shenyang Institute of Technology Dept. Fushun China Shenyang Ligong University Dept. School of Automation and Electrica Engineering China University of Bradford Bradford Dept. Department of Computer Science United Kingdom

ISBN: (纸本)9798350335859

Traditional steel surface defect detection methods have issues such as low detection accuracy and efficiency. It is suggested a technique based on enhanced YOLOv5 steel surface fault identification. In order to improve the network's capacity to handle feature information, this study suggests adding SE attention mechanisms to the YOLOv5s network's Backbone, Neck, and SPPF and replacing the C3 structure at the end of the Backbone with C3TR. To achieve network model lightweight, it is proposed to replace the Neck part in the YOLOv5s model and the convolution in SPPF with Ghost convolution. Then, to improve network feature fusion, extend the improved SPPF to the Neck part. Introduce the SPD convolution in the Backbone section to reduce the information loss caused by the neural network's convolution. The tested mAP is 75.3%, which is 5.3% higher than the original model, 24.6% higher than the SSD method, and 4.6% higher than the YOLOv6 algorithm after the upgraded model has been trained on the NEU-DET dataset. The observed FPS is 81.3, which is 6.3 more than the relative literature algorithm and 18.0 more than the SSD algorithm. © 2023 IEEE.

关键词： Convolution

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DCP: a TCP-Inspired Method for Online Domain Adaptation under Dynamic Data Drift

DCP: a TCP-Inspired Method for Online Domain Adaptation unde...

引用

IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Alessandro Buratto Marco Levorato Leonardo Badia Dept. of Information Engineering (DEI) University of Padova Italy Donald Bren School of Information and Computer Science Irvine California

ISBN: (数字)9798350394665

ISBN: (纸本)9798350394672

Mobile computing faces challenges due to the resource constraints of mobile devices, such as limited computing power, energy, and connectivity. These limitations hinder the use of high-complexity classifiers and wireless transmissions. To address this issue, we propose a novel collaboration paradigm between mobile devices and edge servers, where the edge server assists the mobile devices by dynamically retraining a low-complexity classifier to adapt to temporal changes in data distribution. We propose a novel approach called drift control protocol (DCP) which is inspired by TCP congestion control mechanism. DCP aims to strike a balance between low-complexity classifier retraining frequency and communication costs with the edge server. It adjusts the update rate of the classifier on the mobile device based on distribution drift characteristics and controls the number of input samples sent to the edge server to improve accuracy. We evaluate and study different versions of DCP using synthetic and real datasets We demonstrate that DCP keeps the error bound, while reducing the burden of the communication cost by 90% for the mobile nodes, which makes our proposal suitable for online domain adaptation.

关键词： Wireless communication Costs Protocols Multimedia computing Mobile handsets Mobile nodes Servers

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Age of information in a Two User MISO Interference Channel Under Rayleigh Fading

Age of Information in a Two User MISO Interference Channel U...

引用

National Conference on Communications (NCC)

作者： J Deeraj Kumar Parthajit Mohapatra Nikolaos Pappas Dept. of Electrical Engineering Indian Institute of Technology Tirupati Tirupati India Dept. of Computer and Information Science Linköping University Sweden

ISBN: (数字)9798331536015

ISBN: (纸本)9798331536022

This work explores the age of information (AoI) in a two-user Rayleigh faded multiple input single output (MISO) Gaussian interference channel. Each transmitter samples the data from a sensor and transmits the data with some random probability. The evolution of AoI is modeled using a Discrete Time Markov Chain (DTMC). Average AoI is characterized in the case of two prominent interference mitigation schemes namely treating interference as noise (TIN) and successive interference cancellation (SIC). Additionally, the impact of multiple transmit antennas and random access probability on the average AoI at the receiver is analyzed.

关键词： Interference cancellation Prevention and mitigation Transmitting antennas Receiving antennas Rayleigh channels Tin information age Interference channels Decoding MISO

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Note on ”RAKS: robust authentication and key agreement scheme for satellite infrastructure“

Note on ”RAKS: robust authentication and key agreement sche...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Chun-Chieh Tsai Chin-Yu Sun Shih-Hsuan Yang Dept. of Computer Science and Information Engineering National Taipei University of Technology

Low Earth orbit (LEO) satellites play an important role in modern communication systems. Recently, Khan et al. [1] designed a robust authentication and key agreement scheme (RAKS) for LEO satellite communication. They also performed security analysis of RAKS to verify the declared safety. Upon careful examination, however, we discovered several security flaws in RAKS, including insufficient information for user identification, vulnerability to elaborate DoS attacks, and inaccurate estimation of computational costs. In this paper, we highlight these flaws and suggest potential modifications to improve the scheme.

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Unleashing Machine Learning for Hepatitis C Prediction: A Holistic Exploration of Clinical Insights

Unleashing Machine Learning for Hepatitis C Prediction: A Ho...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Al-Amain Fatema Tuj Janin Faizul Ahmed Robin Shifat Ahmed Khandaker Mohammad Mohi Uddin Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Information and Electrical Engineering Hamburg University of Applied Sciences Hamburg Germany

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Chronic diseases, characterized by long-term effects and persistent symptoms, pose significant challenges to human health and well-being. The hepatitis C virus (HCV) is the cause of chronic hepatitis C, which affects 58 million people globally. It also causes 290,000 deaths annually due to liver cancer and cirrhosis, two conditions connected to HCV. Taking advantage of machine learning (ML) algorithms' ability to analyze large datasets and identify patterns and relationships that aid in diagnosis, this effort focuses on employing algorithms for machine learning to identify and forecast hepatitis C precisely. Various techniques were utilized, including Random Forest, Cat Boost, SVM, and others to create predictive models using blood values from 615 patients, including both hepatitis patients and healthy blood donors. The dataset underwent comprehensive preprocessing, including handling missing values, scaling, and dimensionality reduction via Principal Component Analysis (PCA). Various ML algorithms were evaluated, and the Cat Boost classifier achieved the highest accuracy of 99.17%, recall of 92.00%, precision of 100.00% and F1-score is 96.00%. This study demonstrates the potential of ML in enhancing HCV diagnosis, offering less invasive and cost-effective alternatives to traditional methods, and providing predictive insights for personalized treatment plans. The findings underscore the importance of advanced data preprocessing and feature selection in improving model efficiency. This study adds to the ongoing efforts to lessen the prevalence of hepatitis C worldwide, aligning with the World Health Organization's goal to significantly decrease new HCV cases and related deaths by 2030.

关键词： Support vector machines Machine learning algorithms Accuracy Liver diseases Predictive models Prediction algorithms Classification algorithms Viruses (medical) Medical diagnostic imaging Principal component analysis

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Musical Metaverse Playgrounds: exploring the design of shared virtual sonic experiences on web browsers

Musical Metaverse Playgrounds: exploring the design of share...

引用

Internet of Sounds, International Symposium on the

作者： Alberto Boem Luca Turchet Dept. of Information Engineering and Computer Science University of Trento Trento Italy

The "Musical Metaverse" (MM) promises a new dimension of musical expression, creation, and education through shared virtual environments. However, the research on the MM is in its infancy. Little work has been done to understand the MM musical capabilities and its user experience. One cause can be found in the lack of technologies capable of providing high-quality audio streaming, and complex enough musical interactions within shared virtual worlds. Two promising candidates for bridging these gaps are web technologies such as WebXR and Web Audio, whose combination can potentially allow more accessible and interoperable networked immersive musical experiences. To explore this possibility, we developed two prototypes of MM playgrounds. We leveraged WebXR through Networked-AFrame, and Web Audio with *** and *** to create and test immersive sonic experiences that conveniently run on the web browsers integrated into commercially available standalone Head-Mounted Displays. The first playground focuses on facilitating musical creation in a multi-user immersive application through real-time sound synthesis and binaural rendering. The second explores real-time audio analysis and music information retrieval for creating audio-reactive virtual shared environments. A preliminary evaluation of these two playgrounds is also presented, which revealed some usability issues such as: accessing the immersive experiences through URLs on a headset, the ambiguity in ownership of shared musical tools, and the impact of real-time audio analysis algorithms on perceived audio-visual latency. Finally, the paper outlines future work and discusses possible developments and applications of web-based MM environments.

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An Introduction of Explainable Artificial Intelligence to Winter Sports Performance Analysis

An Introduction of Explainable Artificial Intelligence to Wi...

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Sport, Technology and Research (STAR), IEEE International Workshop on

作者： Lawrence A. Odong Paolo Bouquet dept. of Information Engineering and Computer Science University of Trento Trento Italy

In recent years, the utilization of machine learning techniques has revolutionized performance analysis in the field of sports. However, a crucial drawback of these algorithms is their inherent black box nature, which hinders stakeholders' ability to comprehend the underlying rationale behind their decisions. This lack of transparency presents challenges for coaches, trainers, and athletes who heavily rely on analysis outcomes to shape their strategies and training approaches. To address this limitation, this paper introduces the concept of Explainable Artificial Intelligence (XAI) to sports performance analysis. We employ SHAP (SHapley Additive exPlanations), a model-agnostic explainable method, to provide a clear and interpretable representation of the contribution of different features towards the final model prediction. Ski jumping is chosen as the initial test case to illustrate the effectiveness of this approach.

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Evaluating Datagram Congestion Control Protocol (DCCP) for Real-Time Applications: A Comparative Study with TCP in Multi-Node Networks 10

Evaluating Datagram Congestion Control Protocol (DCCP) for R...

引用

10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Hassan, Aqib Memon, Vengas Shaikh, Farhan Bashir Alahmari, Saad Iqbal, Mansoor Azam, Farooq School of Computing and Mathematical Sciences University of Greenwich London United Kingdom Universiti Tunku Faculty of Information and Communication Tech. Abdul Rahman Perak Malaysia Northern Border University Department of Computer Science Arar Saudi Arabia University of Wah Department of Computer Science Wah Cantt Pakistan Nust Dept. of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798331532895

The rise in demand for real-time applications, such as live streaming, online gaming, and Internet telephony, has highlighted the necessity for transport protocols that offer low latency and network stability. Traditional protocols, namely Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), have inherent limitations. TCP ensures reliable data delivery but suffers from latency, making it unsuitable for real-time needs, while UDP provides fast data transmission but lacks congestion control, which can lead to network instability. This gap underscores the need for a protocol that combines high-speed data transfer with effective congestion management. To address this issue, the Internet engineering Task Force (IETF) introduced the Datagram Congestion Control Protocol (DCCP), designed to optimize data delivery speed while managing congestion. This study evaluates DCCP's performance against TCP in network scenarios of 8 and 16 nodes using the NS2 simulation environment. By comparing throughput and delay across various bandwidth and latency settings, the study finds that DCCP offers up to 35% higher throughput in 8-node setups and reduces packet loss by 20% in high-bandwidth scenarios. These results suggest that DCCP is well-suited for real-time applications, achieving a crucial balance between fast data transmission and network stability. © 2024 IEEE.

关键词： Congestion control (communication)

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Industrial 3D Printing Quality Control System Based on Machine Vision

Industrial 3D Printing Quality Control System Based on Machi...

引用

International Russian Smart Industry Conference (SmartIndustryCon)

作者： Klara Tagirova Alexey Vulfin Polina Kachkaeva Dept. of Technical Cybernetics Ufa University of Science and Technology Ufa Russia Dept. of Computer Engineering and Information Security Ufa University of Science and Technology Ufa Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The work is devoted to improving the efficiency of the 3D printing quality control using machine learning and network technologies. Its relevance stems from the inherent limitations of quality control systems in additive manufacturing and the need to refine machine vision methods, models, and algorithms to automate and improve the control of the additive manufacturing process for technical products. Significant practical results have been obtained, demonstrating the effectiveness of a machine learning-based web service for 3D printing quality control. According to the results of calculating metrics for assessing the effectiveness of the proposed solution to classification and detection problems within the framework of a computational experiment, the F1-measure indicator is 0.94.

关键词： Measurement Web services Machine vision Semantic segmentation Neural networks Process control Quality control Machine learning Three-dimensional printing Software

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Real-Time Embedded Deep Learning on Elk Audio OS

Real-Time Embedded Deep Learning on Elk Audio OS

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Internet of Sounds, International Symposium on the

作者： Domenico Stefani Luca Turchet Dept. of Information Engineering and Computer Science University of Trento Trento Italy

Recent years have witnessed significant advancements in deep learning architectures for music, along with the availability of more powerful embedded computing platforms specific to low-latency audio processing tasks. These recent developments have opened promising avenues for new Smart Musical Instruments and audio devices that rely on the execution of deep learning models on small embedded computers. Despite these new opportunities, there is a lack of instructions on how to deploy neural networks to many promising embedded audio platforms, including the embedded real-time Elk Audio OS. In this paper, we introduce a procedure for deploying audio deep learning models on embedded systems utilizing the Elk Audio OS. The procedure covers the entire process, from creating a compatible code project to executing and diagnosing it on a Raspberry Pi. Moreover, we discuss different approaches for the real-time execution of deep learning inference on embedded devices and provide alternatives for handling larger neural network models. To facilitate implementation and support future updates, we provide an online repository with a detailed guide, code templates, functional examples, and precompiled library binaries for the TensorFlow Lite and ONNX Runtime inference engines. This work aims to bridge the gap between deep learning model development and real-world deployment on embedded systems, fostering the development of self-contained digital musical instruments and other audio devices equipped with real-time deep learning capabilities. By promoting the deployment of neural networks to embedded devices, we contribute to the development of Smart Musical Instruments that are capable of providing musicians and audiences with unprecedented services.

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