检索结果-内蒙古大学图书馆

International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Hubert Milanowski Adam Krzysztof Piłat Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics AGH University of Krakow Krakow Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

This report explores the experimental identification of magnetic flux density within the motor air gap, focusing on the development of a robot-based approach to automate the scanning procedure. Emphasis is placed on analyzing the distribution of magnetic flux within the motor’s spatial air gap, as well as the amplification of harmonics resulting from changes in air gap orientation. Drawing upon experimental findings, a model is proposed to illustrate the three-dimensional distribution of magnetic flux within the gap.

关键词： Solid modeling Magnetic flux density Automation Atmospheric modeling Air gaps Rotors Drives

来源：评论

学校读者我要写书评

暂无评论

Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries 13

Exponential Weighted Moving Average-Based Variational Mode D...

引用

13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wu, Penghao Tao, Hongfeng Tian, Engang Yin, Tangwen Chen, Hongtian Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214122 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350361674

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods. © 2024 IEEE.

关键词： Variational mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Multi-Task Lane-Free Driving Strategy for Connected and Automated Vehicles: A Multi-Agent Deep Reinforcement Learning Approach

arXiv

引用

arXiv 2024年

作者： Berahman, Mehran Rostami-Shahrbabaki, Majid Bogenberger, Klaus Department of Electrical and Computer Engineering Shiraz University Shiraz Iran Chair of Traffic Engineering and Control Technical University of Munich Munich Germany

Deep reinforcement learning has shown promise in various engineering applications, including vehicular traffic control. The non-stationary nature of traffic, especially in the lane-free environment with more degrees of freedom in vehicle behaviors, poses challenges for decision-making since a wrong action might lead to a catastrophic failure. In this paper, we propose a novel driving strategy for Connected and Automated Vehicles (CAVs) based on a competitive Multi-Agent Deep Deterministic Policy Gradient approach. The developed multi-agent deep reinforcement learning algorithm creates a dynamic and non-stationary scenario, mirroring real-world traffic complexities and making trained agents more robust. The algorithm’s reward function is strategically and uniquely formulated to cover multiple vehicle control tasks, including maintaining desired speeds, overtaking, collision avoidance, and merging and diverging maneuvers. Moreover, additional considerations for both lateral and longitudinal passenger comfort and safety criteria are taken into account. We employed inter-vehicle forces, known as nudging and repulsive forces, to manage the maneuvers of CAVs in a lane-free traffic environment. The proposed driving algorithm is trained and evaluated on lane-free roads using the Simulation of Urban Mobility platform. Experimental results demonstrate the algorithm’s efficacy in handling different objectives, highlighting its potential to enhance safety and efficiency in autonomous driving within lane-free traffic environments. © 2024, CC BY-NC-SA.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Nonsingular Fast Terminal Sliding Mode Attitude control Design for a Coaxial Tilt-Rotor UAV

Nonsingular Fast Terminal Sliding Mode Attitude Control Desi...

引用

SICE Annual Conference

作者： Zheng Hou Longlong Chen Zongyang Lv Yuhu Wu Huijuan Song Man Wang School of Control Science and Engineering Dalian University of Technology Dalian China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (数字)9784907764838

ISBN: (纸本)9798331544461

This paper proposes an attitude controller utilizing the sliding mode technique for a coaxial tilt-rotor unmanned aerial vehicle (CTRUAV) in the presence of external disturbances. The CTRUAV features two servos that empower two pairs of coaxial rotors to tilt independently, along with a rear rotor. This sophisticated configuration sets it apart from regular unmanned aerial vehicles (UAVs) and imposes stringent requirements on the attitude controller design. A nonsingular fast terminal sliding mode controller (NFTSMC) is designed to achieve high speed, accuracy, and robust attitude tracking performance to address these challenges. The finite-time convergence property of NFTSMC is theoretically proved, and the singularity issue is addressed. The effectiveness of the developed control system is validated through comparative simulations and real-time experiments.

关键词： Accuracy Attitude control Rotors Autonomous aerial vehicles control systems Real-time systems Iron Servomotors Convergence

来源：评论

学校读者我要写书评

暂无评论

A Tube-Based Reinforcement Learning Approach for Optimal Motion Planning in Unknown Workspaces

A Tube-Based Reinforcement Learning Approach for Optimal Mot...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Department of Electrical and Computer Engineering University of Patras

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this work, a tube-based nearly optimal solution to motion planning in unknown workspaces is presented. The advantages of reactive motion planning are combined with a Policy Iteration Reinforcement Learning scheme to yield a novel solution for unknown workspaces that inherits provable safety, convergence and optimality. Moreover, in simply-connected workspaces, our method is proven to asymptotically provide the globally optimal path. Our method is compared against a provably asymptotically optimal RRT ⋆ method, as well as a relevant reactive method and provides satisfactory performance, closely matching or outperforming the former.

关键词： Reinforcement learning Planning Safety Robotics and automation Convergence

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Reliability of Integer Multipliers With Respect to Permanent Faults

Evaluating the Reliability of Integer Multipliers With Respe...

引用

IEEE Design and Diagnostics of Electronic Circuits and Systems (DDECS)

作者： Nikolaos I. Deligiannis Riccardo Cantoro Matteo Sonza Reorda S. E. D. Habib Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy Department of Electronics and Electrical Communications Engineering Cairo University Giza Egypt

Arithmetic circuits form the foundation of modern digital computation, enabling us to conduct precise mathematical operations and drive the digital age. They are integral components in nearly every digital circuit, such as processors' arithmetic and logic units. Especially in safety-critical domains like automotive and aviation, the flawless operation of these circuits is of paramount importance. This paper presents a case study involving two variants of Dadda multipliers and assesses their intrinsic reliability when affected by permanent hardware faults. We conducted extensive fault injection campaigns on the circuit models under various datasets, presenting the aggregated statistical errors in the form of the mean absolute error (MAE) for each case. Specifically, we performed fault injection campaigns in which the operands are sourced from trained quantized weights of a convolutional neural network, as well as randomly generated sets of integers. The results not only reveal differences between the two circuits but also show significant variations when different datasets are used in the fault injection campaigns.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced Robotic Vision System Based on Deep Learning and Image Fusion

引用

computers, Materials & Continua 2022年第10期73卷 1845-1861页

作者： E.A.Alabdulkreem Ahmed Sedik Abeer D.Algarni Ghada M.El Banby Fathi E.Abd El-Samie Naglaa F.Soliman Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafrelsheikh33511Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt

Image fusion has become one of the interesting fields that attract researchers to integrate information from different image *** is involved in several *** of the recent applications is the robotic *** application necessitates image enhancement of both infrared(IR)and visible *** paper presents a Robot Human Interaction System(RHIS)based on image fusion and deep *** basic objective of this system is to fuse visual and IR images for efficient feature extraction from the captured ***,an enhancement model is carried out on the fused image to increase its *** image enhancement models such as fuzzy logic,Convolutional Neural Network(CNN)and residual network(ResNet)pre-trained model are utilized on the fusion results and they are compared with each other and with the state-of-the-art *** results prove that the fuzzy logic enhancement gives the best results from the image quality ***,the proposed system can be considered as an efficient solution for the robotic vision problem with multi-modality images.

关键词： Deep learning fuzzy logic image fusion IR images

来源：评论

学校读者我要写书评

暂无评论

An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

引用

2022 IEEE International Conference on Big Data, Big Data 2022

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Mrozek, Dariusz National Taiwan University Department of Computer Science and Information Engineering Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Norway Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Poland

ISBN: (纸本)9781665480451

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems. © 2022 IEEE.

关键词： Energy management

来源：评论

学校读者我要写书评

暂无评论

Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

引用

engineering 2023年第1期20卷 77-95页

作者： Jun Liu Kangli Dong Yi Sun Ioannis Kakkos Fan Huang Guozheng Wang Peng Qi Xing Chen Delin Zhang Anastasios Bezerianos Yu Sun Key Laboratory for Biomedical Engineering of Ministry of Education of China Zhejiang UniversityHangzhou 310007China Department of Neurology Sir Run Run Shaw HospitalSchool of MedicineZhejiang UniversityHangzhou 310020China School of Electrical and Computer Engineering National Technical University of AthensAthens 15780Greece Department of Control Science and Engineering College of Electronics and Information EngineeringTongji UniversityShanghai 200092China Department of Anesthesiology The First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou 310003China The N1 Institute for Health Center for Life SciencesNational University of SingaporeSingapore 117456Singapore

Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.

关键词： Anesthesia Brain network Connectivity Graph theoretical analysis Clinical monitoring system

来源：评论

学校读者我要写书评

暂无评论

EOG acquisition system based on ATmega AVR microcontroller

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16589-16605页

作者： Abdel-Samei, Abdel-Gawad A. Shaaban, Abdel-Salam Brisha, Ayman M. El-Samie, Fathi E. Abd Ali, Ahmed S. Department of Electronics Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Department of Process of Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Department of Mechanical Engineering Mechatronics Division Faculty of Engineering Assiut University Assiut Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

This paper presents a hardware and software of an electrooculogram (EOG) acquisition system based on ATmega AVR microcontroller for the acquisition of vertical and horizontal eye movements. The presented system is used to acquire a dataset of eye movements for volunteers. This system gives two channels representing vertical and horizontal EOG signals. The frequency range of the EOG signal is known to be 0.1 to 10 Hz, and hence this frequency range is isolated with a High-Pass Filter (HPF) with a cutoff frequency of 0.1 Hz followed by a Low-Pass Filter (LPF) with a cutoff frequency of 10 Hz. The EOG acquisition system is interfaced with an ATmega AVR microcontroller to acquire a dataset that can be used for controlling hardware such as Light Emitting Diodes (LEDs), wheelchair, and robot arm. The presented system is composed of EOG signal acquisition, Ag/AgCl electrodes, analog-to-digital converter through Arduino Mega 2560 board microcontroller unit, trainer board, laptop, keypad, and Liquid Crystal Display (LCD). The eye movement is detected by measuring the potential difference between cornea and retina using five Ag–Agcl disposable electrodes. Different volunteers of different ages at different times have been treated with the presented system to obtain data. Classified vertical and horizontal EOG signals and the basic eye movements e.g., open eye, left, right, up, and down can be used to control robots and wheelchairs for rehabilitation purposes. © 2023, The Author(s).

关键词： Electrodes

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：