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检索条件"任意字段=Proceedings of the 3rd International Conference on Mechatronics and Robotics Engineering"

共 6212 条记录，以下是81-90 订阅

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A Critical Review on the Future of robotics in Manufacturing 3

A Critical Review on the Future of Robotics in Manufacturing

引用

3rd IEEE international conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Agarwal, Neeraj Yadav, U.S. Singh, Gurjeet Singh, Rohit Kumar Pachorkar, Padmakar Gupta, D.K. IES College of Technology Department of Mechanical Engineering Bhopal India Directorate General of Aeronautical Quality Assurance ORDAQA Kanpur India

ISBN: (纸本)9798350390728

robotics has been an integral part of manufacturing for decades, revolutionizing industries with automation and efficiency. As technology advances, the future of robotics in manufacturing holds tremendous promise, with innovations such as collaborative robots (cobots), artificial intelligence (AI), and advanced sensor technologies reshaping the landscape. This review paper provides an in-depth analysis of current trends, challenges, and prospects of robotics in manufacturing, exploring how these technologies will continue to transform production processes and enhance competitiveness in the global market. © 2024 IEEE.

关键词： Smart manufacturing

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Experimental on Virtual Simulation Communication of Digital Twin Based on mechatronics Concept Designer 24

Experimental on Virtual Simulation Communication of Digital ...

引用

3rd international Symposium on Control engineering and robotics, ISCER 2024

作者： Wu, Yan Zhao, Xiaoqi Yan, Shixiao School of Mechanical Engineering Shanghai Institute of Technology Shanghai201408 China

ISBN: (纸本)9798400709951

The test principle of data interaction performance and the test model of virtual reality between mechatronics Concept Designer (MCD) and external controller is constructed in allusion to large difference in data transmission speed and low data exchange stability between virtual model of digital twins and external device. In the meantime, the experimental study on software-in-loop and hardware-in-loop are designed to test the transmission rate and stability of data exchange between MCD and external controller under the four communication protocols: OPC DA, OPC UA, TCP/IP and PLCSIM Adv, using a single set of cyclic CPU runtime as the standard. The test results manifest that the time required by PLCSIM Adv and TCP/IP to transmit 10, 100, 200, 500, 1 000 signals in 10, 100, 1 000, 10 000 cycles can remain at about 30ms, which is faster and more stable than other transmission plans. As a result, PLCSIM Adv is selected as the software-in-loop communication protocol, TCP/IP as the hardware-in-loop communication protocol. © 2024 ACM.

关键词： Digital elevation model

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Construction of an AI Course Cluster System for robotics Specialization Based on PBL Framework 24

Construction of an AI Course Cluster System for Robotics Spe...

引用

3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Wang, Xiao Chu, Hao Ji, Peng Yu, Xiaosheng College Of Information Science and Engineering Northeastern University Liaoning Shenyang China Faculty of Robot Science and Engineering Northeastern University Liaoning Shenyang China

ISBN: (纸本)9798400712692

Traditional AI-related courses in robotics major are not related closely to the latest technological advancements. In order to explore a better approach in the teaching system, this paper introduces a new AI course cluster system. The system conducts the PBL teaching structure and designs a tailored AI course cluster for robotics students. An organized and project-based teaching model is established too. We also conduct case studies to the students in the robotics major. The experimental results verify the effective of the proposed cluster system. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Students

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State-of-the-art Research in Perception and Control of Lower Limb Exoskeletons: A Systematic Review 3

State-of-the-art Research in Perception and Control of Lower...

引用

3rd international conference on Service robotics, ICoSR 2024

作者： Luo, Xiaofeng Ma, Yang Cai, Zhibin Zhou, Tianqi Liu, Xiaoyu Long, Yi Weapon Equipment Research Institute China Ordnance Industries Group Beijing China School of Mechanical Engineering and Automation Northeastern University Shenyang China Northeastern University Faculty of Robot Science and Engineering Shenyang China Foshan Graduate School of Innovation Northeastern University Foshan China

ISBN: (纸本)9798350367119

The lower limb exoskeleton is an intelligent robot worn by the human body and moves in coordination with the human limbs. It can enhance the human body's weight-bearing capacity, maneuverability and operation capabilities. Perception and control, as the brain of the exoskeleton robot, directly determine its collaborative performance and auxiliary effect with the main body. However, the current lower limb exoskeleton has not been widely adopted. One of the reasons is that the 'brain' of the exoskeleton robot is not intelligent enough. Therefore, this article reviewed relevant literature on lower limb exoskeleton sensing and control technology in the past five years, and studied the impact of different sensing methods and control strategies on exoskeleton performance. In addition, this article discusses the current challenges of lower limb exoskeletons and provides some innovative insights into future research directions to promote the further development of lower limb exoskeleton technology. © 2024 IEEE.

关键词： Exoskeleton (robotics)

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CNN-XGBoost Based Ensemble Model for Short-Term Load Forecasting 3

CNN-XGBoost Based Ensemble Model for Short-Term Load Forecas...

引用

3rd IEEE international conference on robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hossain, Kazi Reyad Islam, Md Samin Safayet Islam, Md Rashidul Sheikh, Md. Rafiqul Islam Faruque, Md. Omer Rajshahi University of Engineering & Technology Department of EEE Rajshahi6204 Bangladesh Dhaka University of Engineering & Technology Gazipur Bangladesh

ISBN: (纸本)9798331534400

Precise short-term load forecasting is crucial in the electric power industry, which serves a vital purpose in maintaining a balance between demand and supply. Maintaining this equilibrium is essential for ensuring the stability and efficiency of power systems. This forecasting task, however, poses significant challenges due to its inherent complexity. This paper presents a new approach for short-term load forecasting through the combination of convolutional neural networks (CNN) and extreme gradient boosting (XGBoost) algorithms. The model incorporates the feature extraction skills of CNN with the sequential learning strength of XGBoost. Both the techniques are ensembled with gradient boosting, which functions as a meta-learner. Gradient boosting combines the individual predictions done by the CNN and XGBoost and enhances the accuracy. The research used the daily load consumption data of Queensland, Australia. This proposed ensemble method is evaluated on the dataset and compared with other forecasting techniques. The outcomes indicated that the suggested ensemble model surpasses the existing forecasting algorithms in terms of various evaluation metrics. © 2024 IEEE.

关键词： Electric load forecasting

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Heart Disease Classification with XAI and Kernel SHAP 3

Heart Disease Classification with XAI and Kernel SHAP

引用

3rd IEEE international conference on robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Vasker, Nishat Nafisa, Anika Tabassum Rahman, Md Arifur Hasan, Mahamudul East West University Department of Computer Science and Engineering Dhaka Bangladesh Trine University Department of Information Studies 1 University avenue AngolaIN United States University of Minnesota Department of Computer Science and Engineering Twin Cities United States

ISBN: (纸本)9798331534400

Heart disease is a major worldwide health concern, necessitating advances in precise and cost-effective diagnostics. While machine learning (ML) models have shown promise in this field, their opaque nature frequently impedes clinical use due to a lack of interpretability. This work investigates the effectiveness of Explainable AI (XAI) approaches, specifically Kernel SHAP, to increase the transparency and trustworthiness of machine learning models for heart disease categorization. We evaluated the interpretability and performance of a logistic regression model using a well-known heart disease dataset. Our logistic regression model has an accuracy of 85.2%, precision of 82.5%, recall of 84.7%, and F1-score of 83.6%. Kernel SHAP analysis gave useful insights, demonstrating that characteristics like 'ca' (number of main arteries) and 'thal' (thalassemia) have the high influence on predictions. These findings suggest that adding XAI approaches might significantly increase the confidence and acceptance of ML models in healthcare contexts, eventually leading to better patient outcomes. © 2024 IEEE.

关键词： Logistic regression

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Fault-Tolerant Control for Delayed PDE-ODE Coupled Systems 3

Fault-Tolerant Control for Delayed PDE-ODE Coupled Systems

引用

3rd international conference on robotics, Automation and Intelligent Control, ICRAIC 2023

作者： Shi, Xi-Dong Wang, Zi-Peng Yan, Xue-Hua Zhang, Xiao-Wei University of Jinan School of Electrical Engineering Jinan China Beijing University of Technology Faculty of Information Technology Beijing China Tiangong University School of Control Science and Engineering Tianjin China

ISBN: (纸本)9798350360813

This paper introduces a fault-tolerant control strategy for delayed PDE-ODE coupled systems. By using a Lyapunov functional (LF), a fault-tolerant control design is developed by space-dependent linear matrix inequality (SDL-MI) to ensure the exponential stability (ES) for the closed-loop delayed PDE-ODE coupled system. Finally, the effectiveness of the control approach is demonstrated through its application to a hypersonic rocket car (HRC). © 2023 IEEE.

关键词： Lyapunov functions

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SLRNet: Shallow Convolutional Neural Network for Efficient Sign Language Recognition 3

SLRNet: Shallow Convolutional Neural Network for Efficient S...

引用

3rd IEEE international conference on robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Mostafa, Lamia Brishti, Faria Jerin Akther, Eishita Sultana Khaliluzzaman, M.D. Hoque, MD Jiabul Department of Computer Science and Engineering Chattogram4318 Bangladesh Department of Computer and Communication Engineering Chattogram4318 Bangladesh

ISBN: (纸本)9798331534400

In recent times, recognizing hand gestures from a sign image has become a vital research field in the areas of computer vision and human-computer interaction. Many researchers have taken advantage of various features and deep learning architectures (DLA) to update the performance of existing sign recognition systems. In small datasets such as ASL, shallow DLAs such as convolutional neural networks (CNN) work well. However, the deeper deep learning architectures are overfitted on the small datasets. To overcome this problem, in this work, two shallow CNN models, SLRNet1 and SLRNet2, are proposed. The proposed models use four convolutional layers to extract the features from the sign image and two fully connected layers to recognize the sign. The first and second models gain 98.94% and 99.14% accuracy, respectively. Both models achieve significant improvements with respect to the present state of the art. Contribution - Two regularized hand gesture recognition models, SLRNet1 and SLRNet2, were developed to work efficiently on small datasets without overfitting and perform fine-tuning to achieve better accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Fault Prediction Method for SINS/GNSS Integrated Navigation System Based on SD-RBF Neural Network 3

Fault Prediction Method for SINS/GNSS Integrated Navigation ...

引用

3rd international conference on Innovations and Development of Information Technologies and robotics, IDITR 2024

作者： Zhang, Shuaishuai Zhang, Tao Hu, Bin Wang, Zijian The Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of Education School of Instrument Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350385694

In response to the problem of difficult prediction of slowly changing faults in Strapdown Intertial Navigation System / Global Navigation Satellite System(SINS/GNSS) integrated navigation system, this paper proposes a sliding window based double-layer Radial Basis Function(SD-RBF) fault prediction model when the system model and data distribution characteristics are difficult to obtain. This algorithm combines the explanatory model with the RBF based time series model. It uses system residuals to establish a time series prediction model and extracts fault trend information through residual analysis, achieving the prediction of slowly changing faults in SINS/GNSS integrated navigation. The simulation results show that the algorithm can predict slowly changing faults in 3 seconds, and the accuracy can reach 88%. © 2024 IEEE.

关键词： Radial basis function networks

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Tracking System for Heavy Transport with Sensors and IOT 10

Tracking System for Heavy Transport with Sensors and IOT

引用

10th international conference on mechatronics and robotics engineering (ICMRE)

作者： Watson, Piyabut Pengwang, Eakkachai King Mongkuts Univ Technol Thonburi KMUTT Inst FIeld roBOt FIBO 126 Pracha Uthid RdBangmodThrung Kru Bangkok Thailand

ISBN: (纸本)9798350394283;9798350394276

The purpose of this project is to create a system that could assist land reclamation business owners to monitor their trucks during operations, by implementing IoT system to maintain and improve their business structure. Thus, this device will help to track the vehicle and alert suspicious acts for the owner. In the traditional way of monitoring their vehicle visually or by mobile communication, this creates an opportunity for lorries driver of the vehicles to conduct an unlawful act of selling company's fuel or delivering unscheduled payloads without company's knowledge. The system will help to reduce the possibilities of conducting such acts and reduce the workload from the company in process of monitoring vehicle. Furthermore, this apparatus reduces operating time for monitoring fuel for heavy transport and will comfort the users for both business owners and workers. By this method, we implement the fusion of inductive proximity sensor (Model LJ12A3-4-Z/BY PNP) with IOT system to analyze the data with GPS information. The results from the tracking are averagely within 5 meters error, which is sufficient for the business owners.

关键词： GPS/GSM System IoT Truck Tracking Fuel Cap

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