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Advancements in Computing (ICAC), International Conference on

作者： S.M.R.B. Senanayake H.M.K.K.M.B. Herath S.L.P. Yasakethu B.G.D.A. Madhusanka Department of Mechatronics Engineering Sri Lanka Technology Campus Padukka Sri Lanka Computational Intelligence and Robotics Research Lab Sri Lanka Technology Campus Padukka Sri Lanka Department of Mechanical Engineering The Open University of Sri Lanka Nugegoda Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Urban greening is becoming increasingly important, which has increased interest in tracking and analyzing urban vegetation and highlighted the necessity for an accurate and practical evaluation of greenery inside cities. Unmanned Aerial Vehicles (UAVs) have become an essential tool for mapping vegetation at a broad scale, frequently using orthographic views to manage the heavy workload. This research examines semantic segmentation and a particular issue with tree species identification brought on by perspective views from different heights. The methodology involved the semantic segmentation methods used without orthorectification on a single low-altitude (<100 m) UAV RGB image. It suggested that an innovative mathematical model be used to successfully reduce GPS inaccuracies, which are known to impact the precision of tree species identification. The proposed system demonstrated an overall accuracy of 89.81 (±1.45) %. Furthermore, the mathematical model exhibits the potential to significantly reduce GPS inaccuracies, leading to enhanced precision in tree species identification.

关键词： Accuracy Semantic segmentation Surveillance Green products Urban areas Vegetation mapping Vegetation Autonomous aerial vehicles Mathematical models Global Positioning System

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A Review on Image Processing Tecniques for In-Pipe Robots 5th

A Review on Image Processing Tecniques for In-Pipe Robots

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Suryawanshi, Shubhangi Chavan, Amol Bhosale, Digvijay G. Mahakal, Shivam Jakate, Govind Kanojiya, Abhay Department of Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Maharashtra Pune411 018 India Department of Mechatronics Engineering Rajarambapu Institute of Technology Shivaji University RajaramnagarKolhapur Maharashtra 415 414 India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Maharashtra Pune411 018 India Department of Automation and Robotics Dr. D. Y Patil Institute of Technology Maharashtra Pune411 018 India

ISBN: (纸本)9789819788644

Pipelines are vital infrastructure for the transportation of water, sewage, oil, and gas because they offer a better safety option and fit into the minimum environmental hazardous than other modes. But pipelines are aging and getting damaged all the time, which can lead to significant loss of resources and contamination of the environment. Regular maintenance and inspections are necessary for pipelines to operate efficiently and prevent overspending. This paper reviews the core techniques used in each of the four missions—localization, mapping, navigation, and inspection—that these robots have completed. Additionally, because image processing has become a widely used and crucial strategy for completing all of the aforementioned missions, we made the decision to review the thorough classification of techniques for image processing in a separate section. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Navigation

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Backpropagation Techniques in SNN and Application in Image Segmentation

Backpropagation Techniques in SNN and Application in Image S...

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IEEE Southeastcon

作者： Sumit Chakravarty M. Hassan Tanveer Razvan C. Voicu Madhushri Banerjee Ghulam Mahdi Department of Electrical Engineering Kennesaw State University Marietta Georgia USA Department of Robotics & Mechatronics Engineering Kennesaw State University Marietta Georgia USA Department of IT Georgia Gwinett College Atlanta USA Kimberly Clark Coorporation Roswell Innovation Center Atlanta GA USA

Recent advancements in Spiking Neural Networks (SNNs) have begun to bridge the performance gap with Artificial Neural Networks (ANNs), particularly through the use of approximated gradients. This paper delves into modern backpropagation techniques in SNNs, focusing on surrogate or approximate gradient methods. Traditionally, SNNs have been predominantly applied to image classification challenges, incor-porating fully connected dense layers. Our research extends the application of SNNs to the realm of image segmentation. We propose and implement a modified U-net architecture within an SNN framework, tailored to operate without dense layers, producing segmented images as output. Experiments conducted on datasets such as “EM segmentation 2015” and “Data Science Bowl 2018” demonstrate the efficacy of our approach, yielding results that closely rival those achieved by ANN-based methods. This exploration into SNNs for image segmentation not only broadens the scope of SNN applications but also underscores the potential of these networks in tasks traditionally dominated by ANNs.

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Modelling, Analysis and Control of OmniMorph: an Omnidirectional Morphing Multi-rotor UAV

arXiv

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arXiv 2023年

作者： Aboudorra, Youssef Gabellieri, Chiara Brantjes, Ralph Sablé, Quentin Franchi, Antonio Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy

This paper introduces for the first time the design, modelling, and control of a novel morphing multirotor Unmanned Aerial Vehicle (UAV) that we call the OmniMorph. The morphing ability allows the selection of the configuration that optimizes energy consumption while ensuring the needed maneuverability for the required task. The most energy-efficient uni-directional thrust (UDT) configuration can be used, e.g., during standard point-to-point displacements. Fully-actuated (FA) and omnidirectional (OD) configurations can be instead used for full pose tracking, such as, e.g., constant attitude horizontal motions and full rotations on the spot, and for full wrench 6D interaction control and 6D disturbance rejection. Morphing is obtained using a single servomotor, allowing possible minimization of weight, costs, and maintenance complexity. The actuation properties are studied, and an optimal controller that compromises between performance and control effort is proposed and validated in realistic simulations. Preliminary tests on the prototype are presented to assess the propellers' mutual aerodynamic interference. © 2023, CC BY.

关键词： Maneuverability

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Evaluating the Viscoelastic Behavior of Elastomers Using a Piezoelectric Tactile Sensor and an Optimization Approach

Evaluating the Viscoelastic Behavior of Elastomers Using a P...

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SICE Annual Conference

作者： Abdallah M. Elsahy Victor Parque Abdelhady M. Esmaeel Mohsen A. Hassan Ahmed M. R. Fath El-Bab Mechatronics and Robotics Department Egypt-Japan University of Science and Technology Alexandria Egypt Production and Mechanical Design Department Menofia University Menofia Egypt Department of Modern Mechanical Engineering Waseda University Tokyo Japan Mechatronics Engineering Department Assiut University Assiut Egypt Industrial and Manufacturing Engineering Department Egypt-Japan University of Science and Technology Alexandria Egypt

Elastomers, polymers, and human tissues are well-known viscoelastic materials whose behavior are hard to grasp. Several mathematical models, being linear and nonlinear, have been proposed to describe the elastic and viscous properties of viscoelastic materials. In this paper, we aim to characterize the dynamic properties of silicon rubber viscoelastic material with different mixing ratios. To this end, an experimental setup using a piezoelectric cantilever was used to apply force to the viscoelastic specimens, and an eddy current sensor was used to measure the displacement. We used the Standard Linear Solid (SLS) model and the hysteresis loop methods to formulate the relations between applied force, displacement, damping, and stiffness. Furthermore, an optimization approach was proposed to find the SLS model parameters by searching functions over the frequency domain. The obtained results agree with the related research. In addition, our approach differentiated between the tested specimens depending on damping and stiffness dynamic properties. The results have shown that the damping of the PDMS tested specimes decreases with increasing the frequency and the harder the elastomer the more damping it shows. Also, the stiffnesses of the SLS model increase by increasing the frequency and by increasing the hardness of the elastomer.

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Precision healthcare: A deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction

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Computers in Biology and Medicine 2024年 176卷 108432-108432页

作者： Islam, Md. Ariful Majumder, Md. Ziaul Hasan Miah, Md. Sohel Jannaty, Sumaia Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka1000 Bangladesh Institute of Electronics Bangladesh Atomic Energy Commision Dhaka Bangladesh Department of Computer Science and Technology Moulvibazar Polytechnic Institute Bangladesh Gonoshasthaya Samaj Vittik Medical College Savar Dhaka Bangladesh

This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for early detection. MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precision and recall metrics emphasized for robust predictions. Our study addresses challenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques—Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate feature selection—play a crucial role in identifying relevant features and reducing data dimensionality. Our findings showcase the impact of these techniques on improving prediction accuracy. Optimized models for each dataset have been achieved through grid search hyperparameter tuning, with configurations meticulously outlined. Notably, a remarkable 99.12 % accuracy was achieved on the first Kaggle dataset, showcasing the potential for accurate HDP. Model robustness across diverse datasets was highlighted, with caution against overfitting. The study emphasizes the need for validation of unseen data and encourages ongoing research for generalizability. Serving as a practical guide, this research aids researchers and practitioners in HDP model development, influencing clinical decisions and healthcare resource allocation. By providing insights into effective algorithms and techniques, the paper contributes to reducing heart disease-related morbidity and mortality, supporting the healthcare community's ongoing efforts. © 2024 Elsevier Ltd

关键词： Forecasting

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Design and Characterization of a Flexure-Based Hybrid Mechanism for Precise Micro/Nano Positioning

Design and Characterization of a Flexure-Based Hybrid Mechan...

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International Conference on mechatronics Technology (ICMT)

作者： Kim Sang Tran Kai Zhong Lai Bijan Shirinzadeh Armin Ehrampoosh Yanling Tian Dawei Zhang Yanding Qin Department of Mechanical and Aerospace Engineering Monash University Robotics and Mechatronics Research Laboratory (RMRL) Melbourne Australia School of Engineering University of Warwick Conventry UK Control and Systems Engineering Tianjin University Tianjin China

This paper presents a hybrid mechanism for micro/nano positioning and manipulation by combining a piezoelectric actuator (PEA) and a voice coil motor (VCM) using a flexure bridge. The research investigates the behavior of the hybrid mechanism, designs the amplification mechanism, and characterizes and compares the dominant parameters through modal and finite element analysis. The models were designed using Solidworks software and optimized using response surface in ANSYS. The hybrid mechanism provides both long-range displacement and precision movement capabilities, making it suitable for micro-assembly, micro-manipulation, and microfluidics applications. The results illustrate that the amplification ratio of the flexure mechanism obtained by modal analysis was 4.38, which was 3.42% larger than that simulated by finite element model. The input stiffness of the flexure mechanism was 2231.81 N/mm, which is slightly lower than the figure calculated by Finite element analysis (FEA) at 2325.58 N/mm. However, the difference between the two output stiffnesses of this mechanism obtained by the two methods is close, standing at 1.60%.

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Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery Classification

arXiv

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arXiv 2024年

作者： An, Sion Kang, Myeongkyun Kim, Soopil Chikontwe, Philip Shen, Li Park, Sang Hyun Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu Korea Republic of Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PA United States Department of Biomedical Informatics Harvard Medical School MA United States

Electroencephalography (EEG) motor imagery (MI) classification is a fundamental, yet challenging task due to the variation of signals between individuals i.e., inter-subject variability. Previous approaches try to mitigate this using task-specific (TS) EEG signals from the target subject in training. However, recording TS EEG signals requires time and limits its applicability in various fields. In contrast, resting state (RS) EEG signals are a viable alternative due to ease of acquisition with rich subject information. In this paper, we propose a novel subject-adaptive transfer learning strategy that utilizes RS EEG signals to adapt models on unseen subject data. Specifically, we disentangle extracted features into task- and subject-dependent features and use them to calibrate RS EEG signals for obtaining task information while preserving subject characteristics. The calibrated signals are then used to adapt the model to the target subject, enabling the model to simulate processing TS EEG signals of the target subject. The proposed method achieves state-of-the-art accuracy on three public benchmarks, demonstrating the effectiveness of our method in cross-subject EEG MI classification. Our findings highlight the potential of leveraging RS EEG signals to advance practical brain-computer interface systems. The code is available at https://***/SionAn/MICCAI2024-ResTL. Copyright © 2024, The Authors. All rights reserved.

关键词： Transfer learning

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Robot-Assisted Navigation for Visually Impaired through Adaptive Impedance and Path Planning

arXiv

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arXiv 2023年

作者： Balatti, Pietro Ozdamar, Idil Sirintuna, Doganay Fortini, Luca Leonori, Mattia Gandarias, Juan M. Ajoudani, Arash HRI2 Lab Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and System Engineering University of Genoa Genoa Italy Robotics and Mechatronics lab Systems Engineering and Automation Department University of Malaga Malaga Spain

This paper presents a framework to navigate visually impaired people through unfamiliar environments by means of a mobile manipulator. The Human-Robot system consists of three key components: a mobile base, a robotic arm, and the human subject who gets guided by the robotic arm via physically coupling their hand with the cobot’s end-effector. These components, receiving a goal from the user, traverse a collision-free set of waypoints in a coordinated manner, while avoiding static and dynamic obstacles through an obstacle avoidance unit and a novel human guidance planner. With this aim, we also present a legs tracking algorithm that utilizes 2D LiDAR sensors integrated into the mobile base to monitor the human pose. Additionally, we introduce an adaptive pulling planner responsible for guiding the individual back to the intended path if they veer off course. This is achieved by establishing a target arm end-effector position and dynamically adjusting the impedance parameters in real-time through a impedance tuning unit. To validate the framework we present a set of experiments both in laboratory settings with 12 healthy blindfolded subjects and a proof-of-concept demonstration in a real-world scenario. Copyright © 2023, The Authors. All rights reserved.

关键词： Robotic arms

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Control Characteristics of Zero-Current-Switching Quasi-Resonant Boost Converter under Variation of Resonant Circuit and Load Parameters

Control Characteristics of Zero-Current-Switching Quasi-Reso...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Andrii V. Dymerets Roman D. Yershov Alexey N. Gorodny Serhii A. Stepenko Anatoliy S. Revko Department of Electronics Automation Robotics and Mechatronics National University “Chernihiv Polytechnic” Chernihiv Ukraine Department of Electrical Engineering Information and Measurement Technologies National University “Chernihiv Polytechnic” Chernihiv Ukraine

The investigation of resonant circuit parameters influence on the control characteristics behavior in a boost half-wave zero-current switching quasi-resonant converter is presented. The dependences of the control pulses duration and operating frequency as well as electric characteristics under variation of resonant circuit time constant and control pulses duty cycle are established by the numeric simulation and are represented as 3D-surfaces. The dependences of these characteristics under variation of load impedance and control pulses duty cycle are also obtained. The obtained results can be used in the process of developing and constructing a table-based, robust multi-loop or nonlinearity compensated control systems for quasi-resonant converters.

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