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

共 6216 条记录，以下是71-80 订阅

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A Companion Robot for Reducing Stress and Increasing Workability 3

A Companion Robot for Reducing Stress and Increasing Workabi...

引用

3rd international conference on Image Processing and robotics, ICIPRoB 2024

作者： Mayurawasala, Nihara B. Manawadu, Udaka A. Kulugammana, Dilmi P. De Silva, P. Ravindra S. University of Sri Jayewardenepura Department of Computer Science Nugegoda Sri Lanka University of Aizu Graduate School of Computer Science and Engineering Fukushima Aizu-Wakamatsu Japan

ISBN: (纸本)9798350374766

This research explores the efficacy of a companion robot as a stress intervention for professionals working from home. The robot, designed to observe and engage in friendly conversations, offers stress-reducing suggestions based on continuous monitoring of user behavior. Our findings demonstrate that the stress reduction companion robot effectively reduced stress and improved workability by accurately detecting facial emotions, stress levels, and speech. The system's ability to minimize distractions and interpret facial expressions highlights its success in enhancing user well-being and overall experience. Further research is needed to assess long-term effectiveness and address ethical considerations for widespread implementation. This study presents a novel approach to address stress-related issues and enhance workability in various settings. © 2024 IEEE.

关键词： Human robot interaction

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A Novelty of Face Recognition using Hybrid Artificial Intelligence Method 3

A Novelty of Face Recognition using Hybrid Artificial Intell...

引用

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

作者： Pattaranichakul, Chalida Kunarak, Sunisa Srinakharinwirot University Department of Electrical Engineering Nakhon-Nayok Thailand

ISBN: (纸本)9798350385694

In the context of the COVID-19 pandemic that occurred in the past year, people have had to adjust their daily lives to minimize physical contact, including the shift towards online learning, as traditional classroom settings became impractical. Educational institutions have heavily relied on online learning platforms, leading to challenges in measuring and maintaining students' engagement and attendance. Consequently, there has been a surge in research on facial recognition technology to aid in monitoring attendance and participation in online education. However, existing facial recognition systems often face limitations, particularly in terms of excessive data usage for training. The objective of this study is to enhance the efficiency of existing facial recognition systems and reduce resource consumption. In this research, we employed feature extraction techniques in conjunction with GoogLeNet Convolutional Neural Networks and Support Vector Machine, specifically focusing on extracting crucial facial features that remain consistent for training. The achieved facial recognition accuracy in this study reached up to 93%. © 2024 IEEE.

关键词： Support vector machines

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Periodic Event-Triggered Containment Control for Multirobot Systems 3

Periodic Event-Triggered Containment Control for Multirobot ...

引用

3rd international conference on Service robotics, ICoSR 2024

作者： Zhang, Shikun Liu, Dan Zhang, Bin Wang, Binrui Qin, Feifei College of Mechanical and Electrical Engineering China Jiliang University Hangzhou China

ISBN: (纸本)9798350367119

This paper aims to study the periodic eventtriggered (PET) containment control for multirobot systems (MRSs). To save communication resources, a PET mechanism is proposed to realize containment control. Different from the traditional event-triggered mechanisms where the triggering functions require to be monitored continuously, the proposed PET mechanism allows each robot to monitor the triggering function only at preset periodic sampling instants, thereby reducing the updates of controllers. Under this mechanism, all the followers are able to reach a convex hull formed by leaders. Finally, the obtained theoretical results are verified by a numerical simulation example. © 2024 IEEE.

关键词： Microrobots

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Research on the Structural Optimization of the Vortex Generator in a Piezoelectric Vortex Street Flowmeter Driven by Fluid-Solid Coupling Simulation 3

Research on the Structural Optimization of the Vortex Genera...

引用

3rd international conference on Service robotics, ICoSR 2024

作者： Guo, Bang Yang, Qihua Yuan, Yuefeng Li, Ruipeng Lei, Xiong Wang, Qian Engineering China Jiliang University Faculty of Mechanical and Electrical Hangzhou China

ISBN: (纸本)9798350367119

To address the issues of large nonlinear errors and low sensitivity in vortex flowmeters with trapezoidal vortex generator structures, a three-dimensional fluid-structure interaction simulation model for the piezoelectric vortex-shedding flowmeter is established with the aim of improving the performance of the instrument. The simulation models the fluid dynamics behavior and structural response of the flowmeter under actual working conditions, analyzing the vortex shedding pattern, the stress of the vortex generator, and the vibration characteristics of the probe sensor. A new type of vortex generator with an airfoil sharp edge is proposed to improve the fluid separation characteristics, enhancing the stability of vortex shedding. Simulation analysis shows that under the working conditions of steam medium and flow velocity range of 10m/s to 40m/s, the new structure has improved linearity and sensitivity by 11.3% and 10.7% compared to the traditional structure. © 2024 IEEE.

关键词： Vortex shedding

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Implementation of Hector SLAM Algorithm for Mapping Indoor Environments with Obstacles 3

Implementation of Hector SLAM Algorithm for Mapping Indoor E...

引用

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

作者： Hossain, Amreen Chowdhury, Raian Haider Brac University Department of Computer Science and Engineering Dhaka Bangladesh Bangladesh University of Engineering and Technology Department of Mechanical Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

Autonomous navigation in obstacle-rich indoor environments is crucial for both industrial and domestic robotic applications. A central aspect of this process is Simultaneous Localization and Mapping (SLAM). This paper presents a comprehensive methodology for implementing Hector SLAM, a widely used SLAM algorithm that operates without relying on external odometry and pose data from wheel encoders or Inertial Measurement Unit (IMU) sensors. The experimental setup involves a custom-built two-wheeled mobile robotic platform, equipped with essential components including a 2D Light Detection and Ranging (LiDAR) sensor, wheel encoders, IMU, motor drivers, an Arduino UNO, and a Raspberry Pi 4B, with Robot Operating System (ROS) serving as the primary software framework. The robot navigates within a predefined rectangular indoor environment, where real-time mapping results are captured at different time intervals to assess mapping accuracy. Additionally, a randomly placed object is introduced to test the obstacle detection capability during operation. Results indicate that Hector SLAM demonstrates high accuracy, achieving precision within a few centimeters, particularly in environments with distinct structural features. © 2024 IEEE.

关键词： Robot Operating System

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MSrdN: A Super-Resolution Network for Human Body 3

MSRDN: A Super-Resolution Network for Human Body

引用

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

作者： Wang, Huan School of Information Science and Engineering Xinjiang University of Science and Technology Korla China

ISBN: (纸本)9798350385694

Recent advances in human parsing estimation implicitly assume the input image is of sufficiently high resolution (HR). However, person images are everywhere especially in the surveillance applications. This paper aims at super resolving a person image with different resolution. First of all, a multi-scale residual dense block (MSrdB) is proposed to completely gather sufficient features for human body image super resolution (SR) task. And then, a multi-scale residual dense network (MSrdN) is developed, which consists of a cascaded MSrdBs, to reconstruct HR outputs in a way that goes from coarse to fine. Experiments on human body benchmark datasets show that our method performs better than other SOTA methods in terms of PSNR and SSIM metrics. Further, body landmark estimation task is introduced as novel evaluation metrics for human body. Performance on the task further proves the effectiveness of our method. © 2024 IEEE.

关键词： Optical resolving power

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A non Invasive Brain-Computer-Interface for Service robotics 3

A non Invasive Brain-Computer-Interface for Service Robotics

引用

3rd IEEE international conference on Artificial Intelligence, ICAI 2023

作者： Ahmed, Fatima Iqbal, Hashim Nouman, Ahmed Maqbool, Hafiz Farhan Zafar, Saqib Saleem, Muhammad Khurram University of Engineering & Technology Mechatronics Engineering Department Lahore Pakistan AASS Computer Science Department Orebro University Örebro Sweden University of Engineering & Technology Mechatronics Engineering Department Taxila Pakistan

ISBN: (纸本)9798350322125

A Brain-Computer Interface (BCI) enables individuals to control a system solely through their brain activity, without relying on physical movement. These interfaces have numerous applications, particularly in assisting individuals with paralysis. Our research paper details a BCI interface that can classify and control seven wheelchair movements: forward, backward, left, right, stair climbing upwards, stair climbing downwards, and stop. We collected raw signal data using the electroencephalog-raphy (EEG) technique from healthy volunteers, which we then filter before feeding into the feature extraction and classification stages. We evaluated our approach using three classification algorithms: Convolution Neural Network (CNN), Support Vector Machines (SVM), and Random Forest Classifier, and compared their performance. Our experimental results demonstrate that our proposed approach is highly promising for implementing BCI, with a classification accuracy of 99% using a Random Forest Classifier. © 2023 IEEE.

关键词： Brain computer interface

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Epileptic Seizure Prediction Using a Deep Hybrid CNN-GAN Model on EEG Data 3

Epileptic Seizure Prediction Using a Deep Hybrid CNN-GAN Mod...

引用

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

作者： Hasan, Nowshad Sharif, Md. Maskat Rahman, Miskatur Islam, Md Saiful Khan, Md Tohedur Rahaman Hoque, MD Jiabul Bahar, Anamul Chittagong University of Engineering and Technology Department of Electronics and Telecommunication Engineering Chittagong Bangladesh International Islamic University Chittagong Department of Computer Science Enginnering Chittagong Bangladesh International Islamic University Chittagong Department of Computer and Communication Engineering Chittagong Bangladesh University of London Department of Computer Science Enginnering London United Kingdom

ISBN: (纸本)9798331534400

Epileptic seizure detection from EEG signals is essential for the diagnosis and treatment of neurological conditions. However, this task is difficult because of the complex and fluctuating characteristics of EEG data. To overcome these difficulties, this research employs a specialized CNN-GAN hybrid model designed to improve seizure prediction by generating realistic synthetic data. This approach addresses class imbalance and enhances feature learning. The process begins with preprocessing EEG datasets to remove noise and artifacts. Data augmentation follows, expanding the dataset and reducing the risk of overfitting. Segmentation then isolates important features to boost prediction accuracy. Feature extraction is achieved using a custom CNN model, and classification is performed with a GAN model. The model demonstrates high accuracy, with results of 99.95% on the BONN Dataset, 98.93% on the CHB-MIT Dataset and 98.80% on the SWEC-ETHZ Dataset. Overall, the custom CNN-GAN hybrid model significantly improves the accuracy of predicting epileptic seizures from EEG signals across various datasets. © 2024 IEEE.

关键词： Prediction models

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Near Infrared Therapy: A Non-invasive Device for Arteriovenous Fistula Patency in Dialysis Treatment 3

Near Infrared Therapy: A Non-invasive Device for Arterioveno...

引用

3rd international conference on Image Processing and robotics, ICIPRoB 2024

作者： Sheba, Jaichandar Kulandaidaasan Tianyu, Sun Qiang, Cai Zhi Singapore Polytechnic School of Electrical and Electronics Engineering Singapore

ISBN: (纸本)9798350374766

A novel, non-invasive device that provides simple method for arteriovenous fistula (AVF) maintenance. This device performs near infrared (NIR) therapy on dialysis patients with AVF created on them. The objective of this device is to improve the AVF patency by implementing light with combination of different NIR wavelengths. According to studies, this would be able to deliver energy to AVF, stimulate and activate cell activities that leads to higher AVF patency and maturation rate. Here we have designed a device that can be used in the experimental study to increase the flow rate across AVF. © 2024 IEEE.

关键词： Dialysis

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Neural-Network-Based Inverse Dynamic Solutions of an Asymmetric Parallel Robot 3

Neural-Network-Based Inverse Dynamic Solutions of an Asymmet...

引用

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

作者： Zou, Qi Zhang, Dan Zhang, Shuo Shi, Yuancheng Huang, Guanyu Li, Lijian Robotics Engineering Columbus State University ColumbusGA United States The Hong Kong Polytechnic University Department of Mechanical Engineering Hong Kong Lassonde School of Engineering York University Toronto Canada School of Electrical Engineering Hanyang University Ansan-si Korea Republic of Intelligent Robotics Research Center Zhejiang Lab Hangzhou China School of Mechanical Engineering North China University of Water Resources and Electric Power Zhengzhou China

ISBN: (纸本)9798350385694

The dynamic models of parallel robots are more complicated due to the highly coupled kinematic chains compared with those of their counterparts-serial robot. The approximate dynamic models can be considered if the analytical solutions high cost in time due to the sophisticated calculations. To address this issue, this paper presents an alternative approach-the dynamic model based on the neural network. A three degrees-of-freedom parallel manipulator is introduced, followed by a detailed mathematical inverse dynamic model, which is replicated by three feedforward neural networks with various performance goals. A cascade forward neural network is also constructed to be compared with the feedforward neural network. The simulation results demonstrate the effectiveness of these proposed neural networks. © 2024 IEEE.

关键词： Feedforward neural networks

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