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Data Calibration of Binocular Vision Pulse Acquisition System Based on CNN+GRU 24

Data Calibration of Binocular Vision Pulse Acquisition Syste...

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8th international conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Lin, Dongmei Gan, Yuanlong Wang, Xiangling Chen, Fuming Chou, Yongxin College of Electrical and Information Engineering Lanzhou University of Technology Gansu Lanzhou China Medical Support Center The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army Gansu Lanzhou China School of Electrical and Automation Engineering Changshu Institute of Technology Jiangsu Changshu China

ISBN: (纸本)9798400710094

To address potential calibration errors that may occur during the acquisition of pulse images based on binocular vision, as well as inaccuracies in three-dimensional(3D) reconstruction, a data calibration model based on Convolutional Neural Networks and Gated Recurrent Units (CNN+GRU) is proposed as a method of calibrating the pulse wave data acquired by the binocular vision pulse acquisition system. The CNN is used to retrieve the error characteristics of the pulse wave and the GRU is used to learn the time-dependent characteristics of the monitoring data and the occurrence of data drift. The precise data produced by the laser profile sensor serves as a reference for calibration. Finally, the fusion module is used to collect the calibration data. The results of both the calibration and ablation experiments show that the calibration model and its component modules are effective. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Unscented Kalman Filter for State of Charge Estimation of Lithium Titanate Battery 20

Unscented Kalman Filter for State of Charge Estimation of Li...

引用

20th international conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Dardchuntuk, Joshua Chun-Ken Banjerdpongchai, David Faculty of Engineering Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Pathumwan 254 Phayathai Road Bangkok10330 Thailand

ISBN: (纸本)9798350310467

An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO's electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems. © 2023 IEEE.

关键词： Battery management systems

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'Vulnerabilities in Smart Contracts: A Detailed Survey of Detection and Mitigation Methodologies'

'Vulnerabilities in Smart Contracts: A Detailed Survey of De...

引用

2024 IEEE international conference on Emerging Technologies in computer Science for Interdisciplinary Applications, ICETCS 2024

作者： Kumar, Nayantara K Honnungar, Niranjan V Sharwari Prakash, M. Lohith, J.J. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350372502

In the ever-changing world of blockchain technology, the emergence of smart contracts has completely transformed the way agreements are executed, offering the potential for automation and trust in decentralized systems. Despite their built-in security features, smart contracts still face persistent vulnerabilities, resulting in significant financial losses. While existing studies often approach smart contract security from specific angles, such as development cycles or vulnerability detection tools, this paper adopts a comprehensive, multidimensional perspective. It delves into the intricacies of smart contract security by examining vulnerability detection mechanisms and defense strategies. The exploration begins by conducting a detailed analysis of the current security challenges and issues surrounding smart contracts. It then delves into established frameworks for classifying vulnerabilities and common security flaws. The paper examines existing methods for detecting, and repairing contract vulnerabilities, evaluating their effectiveness. Additionally, it provides a comprehensive overview of the existing body of knowledge in smart contract security-related research. Through this systematic examination, the paper aims to serve as a valuable reference and provide a comprehensive understanding of the multifaceted landscape of smart contract security. © 2024 IEEE.

关键词： Smart contract

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Machine Learning for Intricate Detection in 6G Networks 2

Machine Learning for Intricate Detection in 6G Networks

引用

2nd IEEE international conference on IoT, Communication and automation Technology, ICICAT 2024

作者： Tiwari, Annapurna Nand Revathi, N. Almusawi, Muntather Prabha, Sakthi Karpagam, J. Hafeena, Md. IES College of Technology Department of Electronics & Communication Engineering M. P Bhopal India Nehru Institute of Technology T.M Department of Electronics and Communication Engineering Tamil Nadu Palayam India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Academy of Higher Education Department of Electrical and Electronics Engineering Karpagam TN India CMR Technical Campus Medchal Hyderabad India

ISBN: (纸本)9798350368109

In the world of 6G networks, the merging of micro-services-oriented designing and intelligent network management demonstrates the central role of neural networks (AI), machine learning (ML), and supervised learning (DL). This study looks into strengthening the security of 6G communications networks through the development of a new anomaly recognition System for 6G Connections (AD6GNs) utilizing ensemble learning (EL). The EL-ADCN workflow comprises prior processing, feature selection utilizing the hybrid methodology CFS-RF, and intruder detection applying hybrid EL methods. The system shows impressive performance on datasets with names like NSL-KDD, UNSW-NB2015, CIC-IDS2017, when CICDDOS2019, getting accuracy of 99.5%, 99.9%, 99.8%, while 99.95426%, respectively, with low false alarm rates. The combination of assistance vector machines (SVM) particularly random forest computing (RF) as EL techniques further improves the efficiency of packing and adaboosting in guaranteeing network that self-sustainability and preventive threat prevention in the envisioned 6G model. © 2024 IEEE.

关键词： Network intrusion

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Application of machine vision based foreign object recognition in conveyor protection

Application of machine vision based foreign object recogniti...

引用

2023 international conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Yu, Zhiqiang Automation Co. Ltd. Changzhou Jiangsu China Middling Coal Science and Engineering Group Changzhou Research Institute Co. Ltd. Changzhou Jiangsu China

ISBN: (纸本)9781510677685

The current low level of intelligence in the main transportation process of coal mines, traditional protection devices can only passively shut down after accidents occur. Although they prevent further losses from expanding, they cannot provide early warning and fundamentally avoid accidents. This article designs a foreign object recognition system based on machine vision and applies it to the protection of the main transportation belt conveyor. Use YOLOv5s as the deep learning model, and deploy the trained model to the edge computing module. Real time video is captured by an industrial intrinsic safety camera and transmitted to the edge computing module to identify the foreign matters in the coal flow. Finally, only the recognition results are transmitted externally. When a high level of foreign object danger is detected, an alarm signal will be sent to the collaborative controller, which will issue a shutdown command to the specific single controller. Simultaneously upload the processed real-time video and alarm information to the control platform for display. This design improves the intelligence, stability, and reliability of the protection of the current main conveyor belt conveyor, achieving preventive measures. © 2024 SPIE.

关键词： computer vision

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Dirt Video Anomaly Detection Method Based on Automatic Identification of the Exit Port 5

Dirt Video Anomaly Detection Method Based on Automatic Ident...

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5th international conference on Machine Learning and computer Application, ICMLCA 2024

作者： Zheng, Ziyuan Gao, Xinwen School of Mechatronic Engineering and Automation Shanghai University Shanghai China Shanghai University SHU-SUCG Research Center of Building Industrialization Shanghai China

ISBN: (纸本)9798331530334

This study proposes an online approach for detecting abnormalities in slag discharged from the shield machine using video anomaly detection and automatic identification of the soil outlet. By analyzing various soil anomalies, the method characterizes these irregularities. An automatic identification system based on You Only Look Once v3 is developed to process surveillance video while mitigating environmental interference. An adaptive frame sampling method is introduced to accurately identify normal and abnormal slag output speeds. Additionally, the 3DResnet50 network model is modified to effectively capture slag motion information. Experimental results show that this approach increases video classification accuracy to 98.81%, meeting online detection requirements for short video segments. © 2024 IEEE.

关键词： Automatic identification

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Research on automatic segmentation and recognition of oracle bone script images based on deep learning technology 4

Research on automatic segmentation and recognition of oracle...

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4th international conference on computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Ruan, Qiaoqiao Chang, Xinyu Cui, Ziyi School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781510683242

Oracle bone script is an important form of ancient Chinese writing, and its study is significant for understanding ancient society, culture, and the development of language. With technological advancements, particularly in computer vision and deep learning, the digital study of oracle bone script has become feasible. This research aims to enhance the efficiency of automatic segmentation and recognition of oracle bone script images through deep learning technology. First, this paper investigates the unique challenges presented by oracle bone script images, such as dotted noise, artificial textures, and intrinsic patterns, and applies image preprocessing techniques such as gray-scaling, binarization, and noise reduction to optimize image quality. Second, convolutional neural networks (CNNs) are employed for image segmentation to accurately isolate individual characters within the oracle bone script images. Then, recurrent neural networks (RNNs) are used to automatically recognize the segmented characters, achieving the conversion from images to text. Finally, tests on original rubbings validate the model's high accuracy and reliability in automatic single-character segmentation and recognition. This study not only improves the level of automation in the image processing of oracle bone script but also provides methods and references for the image processing of other ancient artifacts. © 2024 SPIE.

关键词： Convolutional neural networks

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Erythemato-Squamous Diseases Diagnosis Based on Support Vector Machine Optimized by Black-Winged Kite Algorithm 5

Erythemato-Squamous Diseases Diagnosis Based on Support Vect...

引用

5th international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2024

作者： Lin, Meijin Qiu, Zhirong Dai, Zibin Luo, Yuliang School of Mechatronic Engineering and Automation Foshan University Foshan China

ISBN: (纸本)9798350368284

In response to the difficulty in distinguishing different types of erythemato-squamous diseases, this paper uses support vector machine (SVM) to construct a diagnostic classifier for erythemato-squamous diseases, and optimizes SVM classifier hyperparameters by using black-winged kite algorithm (BKA). Finally, the BKA-SVM classifier for diagnosing erythemato-squamous diseases is established. To test the effectiveness of the classifier, a comparative experiment was conducted with two SVM classifiers optimized by tuna swarm algorithm (TSO) and Osprey optimization algorithm (OOA) respectively. The experimental result indicates that the BKA-SVM classifier has higher accuracy rate in the diagnosis of erythemato-squamous diseases. © 2024 IEEE.

关键词： Support vector machines

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AI-Driven Digital Mirror Technology for Securing IoT-Enabled Smart Infrastructures 2

AI-Driven Digital Mirror Technology for Securing IoT-Enabled...

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2nd IEEE international conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Chaudhary, Jyoti Jain, Upma Sudarshan, N. Pai, Vasudeva Sutaria, Kamal Natrayan, L. Department of Computer Engineering The Technological Institute of Textile and Sciences Bhiwani India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Mechanical Engineering The National Institute of Engineering Mysuru India Department of Information Science and Engineering NMAM Institute of Technology NITTE Deemed to be University Karkala India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India Department of Mechanical Engineering Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331504960

The expansion of the Internet of Things (IoT) is driven by the proliferation of interconnected wireless gadgets, made possible by ever-improving computer and Internet infrastructure. The IoT is an extensive system of interconnected sensors and gateways. In the course of their constant environmental interaction, IoT devices produce a wealth of data resources, including images, audio, video, digital signals, and more. Connectivity to the Internet allows all Internet of Things (IoT) equipment and apps to exchange data and resources more effectively. The proposed AI-Driven Digital Mirror Technology effectively enhances the security of IoT-enabled smart infrastructures by integrating real-time monitoring, anomaly detection, and adaptive response mechanisms. By employing advanced AI algorithms such as Graph Neural Networks (GNNs) and Reinforcement Learning (RL), the technology mirrors IoT environments digitally, enabling dynamic modeling and threat assessment. The system demonstrated a significant improvement in threat detection accuracy, achieving a precision rate of 95.6% and a recall rate of 93.2% during simulations. Additionally, the adaptive response mechanism reduced the average response time to security breaches by 40%, ensuring minimal disruption to infrastructure operations. Comparative analysis with traditional security solutions highlighted the system's superiority in mitigating complex, multi-layered attacks, and its scalability makes it suitable for various smart infrastructure applications, including smart cities, industrial automation, and intelligent transportation systems. The results underscore the potential of AI-Driven Digital Mirror Technology in securing IoT ecosystems against evolving cyber threats while maintaining operational efficiency and resilience. © 2024 IEEE.

关键词： Gateways (computer networks)

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IoT and AI Synergy in Society: Robotics and automation Perspectives 7

IoT and AI Synergy in Society: Robotics and Automation Persp...

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7th international conference on Contemporary Computing and Informatics, IC3I 2024

作者： Geetha, B.T. Jothi, J. Salomi Backia Pal, Shaunak Islam, Imam Taqi Deen Raghuram, K.S. Soni, Mukesh Simats Saveetha University Saveetha School of Engineering Department of Ece Tamil Nadu India Srm Institute of Science and Technology Faculty of Science and Humanities Department of Commerce Ramapuram campus Chennai600089 India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India University of Electrical Science and Technology of China Sichuan Chengdu China Department of Mechanical Engineering Visakhapatnam India Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science& Technology Tathawade Pune India

ISBN: (纸本)9798350350067

The Internet of Things, or IoT, is a relatively new Internet revolution that is being successfully adopted by the corporate, industrial, healthcare, and economic sectors as well as other areas of the modern information society. Specifically, IoT with artificial intelligence (AI) support improves a great deal the performance in a wide range of daily applications with a focus on automation systems, robotics, transportation, and business. Smart automation in the manufacturing sector is now possible thanks to the development of Industry 4.0. The BRDT algorithm is used in this work to track how much energy appliances and machinery use, forecast behaviour, and identifies unusual conduct. When the suggested system's efficiency is compared to current approaches, it provides an efficiency of 85%. The application of this technology raises a number of standardisation, security, resource planning, legal, and paradigm-shifting business challenge problems. The whole supply chain and all of its actors, from producers to end users, are crucial to the success or failure of Industry 4.0 deployment. © 2024 IEEE.

关键词： Supply chains

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