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30th International Conference on mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Wang, Boran Wu, Han Department of Computer Science and Technology Shenzhen China Department of Mechatronics Engineering Shenzhen China

ISBN: (纸本)9798350391916

The rapid advancement of industrial automation has underscored the significance of sophisticated control mechanisms for robotic manipulators, especially seven-axis manipulators, known for their flexibility and capability in handling complex tasks. Traditional control methods often struggle with adaptability and managing nonlinear, uncertain environments. To address these challenges, this paper proposes an adaptive imitation learning-based control strategy for seven-axis manipulators. First, the adaptive imitation learning algorithm is introduced and its applicability to manipulator control is analyzed. Then, a reinforcement learning based model of the seven-axis manipulator is established and simulated in a virtual environment to verify the algorithm's effectiveness. Finally, the limitations of the adaptive imitation learning algorithm in the context of manipulator control are discussed, and future research directions are outlined. This study not only offers a novel control strategy for seven-axis manipulators but also serves as a reference for the broader application of adaptive imitation learning algorithms in various domains. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Analysis of PID and LQR Control for Autonomous Vehicle Platoons 3rd

Comparative Analysis of PID and LQR Control for Autonomous V...

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3rd International Conference on Emerging Electronics and Automation, E2A 2023

作者： Nandhini, M. Rabik, M. Mohamed Department of Mechatronics Engineering SRM Institute of Science and Technology KTR Tamil Nadu Chennai India

ISBN: (纸本)9789819768011

The advent of autonomous driving technology has spurred interest in autonomous vehicle platooning to enhance traffic efficiency and road safety. This research conducts a comparative analysis of two prominent control techniques, Proportional-Integral-Derivative (PID) control and Linear Quadratic Regulator (LQR) control, in the context of autonomous vehicle platooning. The study aims to assess the significance of these control strategies in achieving and maintaining desired inter-vehicle distances and overall platoon coordination. The analysis considers several critical factors, including control performance, adaptability, robustness, and continuity, to comprehensively evaluate PID and LQR controllers. While PID controllers offer simplicity and ease of tuning, LQR controllers are known for their precision and optimization capabilities. The research investigates how these control methods perform in various scenarios, considering factors such as changing vehicle dynamics and external disturbances. Consequently, this research aims to assess how effective PID and LQR control systems are in achieving and maintaining desired inter-vehicle distances and overall platoon coordination. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Two term control systems

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Performance Evaluation of Machine Learning Models to Predict Heart Attack

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Machine Graphics and Vision 2023年第1期32卷 99-114页

作者： Khan, Majid Husnain, Ghassan Ahmad, Waqas Shaukat, Zain Jan, Latif Ul Haq, Ihtisham Ul Islam, Shahab Ishtiaq, Atif Department of Computer Science Iqra National University Peshawar25100 Pakistan Department of Mechatronics Engineering University of Engineering and Technology Peshawar25100 Pakistan

Coronary Artery Disease is the type of cardiovascular disease (CVD) that happens when the blood vessels which stream the blood toward the heart, either become tapered or blocked. Of this, the heart is incapable to push sufficient blood to encounter its requirements. This would lead to angina (chest pain). CVDs are the leading cause of mortality worldwide. According to WHO, in the year 2019 17.9 million people deceased from CVD. Machine Learning is a type of artificial intelligence that uses algorithms to help analyse large datasets more efficiently. It can be used in medical research to help process large amounts of data quickly, such as patient records or medical images. By using Machine Learning techniques and methods, scientists can automate the analysis of complex and large datasets to gain deeper insights into the data. Machine Learning is a type of technology that helps with gathering data and understanding patterns. Recently, researchers in the healthcare industry have been using Machine Learning techniques to assist with diagnosing heart-related diseases. This means that the professionals involved in the diagnosis process can use Machine Learning to help them figure out what is wrong with a patient and provide appropriate treatment. This paper evaluates different machine learning models performances. The Supervised Learning algorithms are used commonly in Machine Learning which means that the training is done using labelled data, belonging to a particular classification. Such classification methods like Random Forest, Decision Tree, K-Nearest Neighbour, XGBoost algorithm, Naive Bayes, and Support Vector Machine will be used to assess the cardiovascular disease by Machine Learning. © 2023 Faculty of Applied Informatics and Mathematics - WZIM, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Large dataset

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Design and Resonance Analysis of Tubular Structured Dual Stator Linear Oscillating Actuator

Design and Resonance Analysis of Tubular Structured Dual Sta...

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2024 International Conference on Electrical Machines, ICEM 2024

作者： Ahmad, Zahoor Kallaste, Ants Vaimann, Toomas Sardar, Muhammad Usman Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn19086 Estonia

ISBN: (纸本)9798350370607

This paper examines an innovative dual stator (DS) tubular moving magnet linear oscillating actuator (LOA) design that integrates a second stator within the tubular-shaped mover. Two axially magnetized permanent magnets (PMs) are employed on the moving part (mover), whereas each stator comprises winding and core materials. An outer stator (OS) LOA is first developed, and then the radius of the mover bore is optimized using a parametric sweep method. Then, another stator named inner stator (IS) is housed inside the tubular mover bore space, which improves the electromagnetic (EM) force per Ampere current (motor constant) with an amount of 12.73 percent. The performance indicators, such as stroke and EM force, are analyzed and discussed. The principles of resonance phenomena and their effects on coil current, stroke-to-current ratio (SCR), input and output powers, and efficiency are examined. The performance characteristics of the proposed LOA in connection to geometrical design factors like stroke, motor constant (MC), MC per mass of the PM, and MC per volume of the design are compared to traditional LOA design. The proposed DS-LOA performs significantly in terms of MC per PM mass while having a simple geometric structure. Additionally, the proposed design has characteristics of better design space utilization and convenience for manufacturing. © 2024 IEEE.

关键词： Stators

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Study and Fabrication of Fan Blade Using Coconut Leaf Sheath Fibre/Epoxy-Reinforced Composite Materials

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Journal of The Institution of Engineers (India): Series D 2024年第1期105卷 405-412页

作者： Karthik, A. Jeyakumar, R. Sampath, P.S. Soundararajan, R. Manikandan, G.K. Department of Mechatronics Engineering Akshaya College of Engineering and Technology Tamil Nadu Coimbatore India Department of Mechanical Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India Department of Mechanical Engineering K.S Rangasamy College of Technology Tamil Nadu Tiruchengode India Department of Mechanical Engineering SSM College of Engineering Tamil Nadu Komarapalayam India

Natural fibres have improved their availability in an extensive range of engineering applications at a lesser cost because of their enhanced mechanical properties. Natural fibre from coconut leaf sheath is capable of enhancing the characteristics of composite materials. In this work coconut leaf sheath strengthened with epoxy is woven into composites with different fibre weight ratios (20%, 25%, 30% and 35%) to determine the mechanical strength of the laminates. Sample test materials were prepared under ASTM guidelines by compression moulding. The outcomes of the tests showed that the composite with 30% fibre content exhibited the highest tensile and modulus of rupture of 25.76 and 23.27 MPa, respectively. The maximum energy absorbed by the specimen on sudden impact was 0.272 J. A scanning electron microscope was used to thoroughly investigate the fractures and failures in the laminates. Finally, the specimen that yielded the highest strength was chosen to fabricate a fan blade by the hand layup method. © The Institution of Engineers (India) 2023.

关键词： Scanning electron microscopy

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Unsupervised anomaly detection in the textile texture database

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Microsystem Technologies 2024年第12期30卷 1609-1621页

作者： Chu, Wen-Lin Chang, Qun-Wei Jian, Bo-Lin Department of Bio-industrial Mechatronics Engineering National Chung Hsing University Taichung402202 Taiwan Department of Electrical Engineering National Chin-Yi University of Technology Taichung411030 Taiwan

Anomaly detection in textile images poses significant challenges due to the scarcity of defective samples and the complex nature of textile textures. This study presents a novel image processing workflow that enhances the unsupervised Variational Autoencoder’s (VAE) ability to identify anomalies in textile images, addressing the limitation of insufficient defective samples in real-world manufacturing scenarios. The primary motivation behind this research is to develop a robust anomaly detection method that can be trained using only normal samples, overcoming the common imbalance between normal and defective samples in the textile industry. Our proposed method introduces domain-specific techniques to preprocess images, assess the adequacy of training samples, and employ intuitive visual methods to differentiate between normal and abnormal samples. A key strength of our approach lies in strategically cropping original images into smaller blocks, increasing training samples and computational efficiency. However, this cropping step introduces abrupt boundary issues that can hinder accurate anomaly detection. To mitigate this problem, we developed a refined image processing approach that effectively resolves boundary artifacts, enabling precise localization of abnormal regions. We trained, tested, and validated our VAE model using the TILDA textile texture database. The experimental results highlight the robustness of our method, achieving high identification rates of 74% for normal samples and 76.9% for abnormal samples, even when trained solely on normal samples. The insights gained from this study have significant implications for the textile industry, paving the way for more efficient and reliable quality control processes. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Textures

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Community Battery Sizing for Distribution Level RES Hosting Capacity Improvement 65

Community Battery Sizing for Distribution Level RES Hosting ...

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65th IEEE Annual International Scientific Conference on Power and Electrical engineering of Riga Technical University, RTUCON 2024

作者： Peterson, Kristjan Shabbir, Noman Astapov, Victor Kutt, Lauri Daniel, Kamran Tallinn University of Technology Dept. of Electrical Power Engineering and Mechatronics Tallinn Estonia

ISBN: (纸本)9798350365771

Electric energy storage is the key term for the establishment of successful RES (renewable energy systems) implementation on the residential customer level. However, battery storage costs are defining both the installation capacity, flexibility provision availability and final cost of electricity served to a customer. In this paper the analysis targets the comparison of private household batteries installation capacities and community storage availability capacity for the best availability of the RES infeed hosting during the year. Analysis provides the information on the balance levels of battery sizes and available congestion duration throughout the year. An explanation of selection of the community substation central battery sizing will be provided for rural or suburban distribution network capacities considerations. © 2024 IEEE.

关键词： Battery storage

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Simulation and experimental investigation of hydro-pneumatic energy harvesting suspension system

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International Journal of Vehicle Noise and Vibration 2023年第1-2期19卷 32-54页

作者： Awad, Magdy Naeem El-Arabi, M.E. Rabbo, Saber A. Sokar, Mohamed I.B. Department of Mechanical Engineering Mechatronics Engineering Arab Academy for Science Technology and Maritime Transport Smart Village Campus Giza Egypt Mechatronics Engineering Department Canadian International College 5th Settlement Cairo Egypt Department of Design and Production Engineering Faculty of Engineering Cairo University Cairo Egypt Department of Mechanical Engineering Faculty of Engineering at Shoubra Benha University Egypt Department of Mechanical Engineering Mechatronics Engineering Faculty of Engineering at Shoubra Benha University Egypt

This article describes a promising hydro-pneumatic regenerative suspension system (HPEHS) that converts the linear oscillation of the suspension system into rotational motion via a hydraulic rectifier to maintain a unidirectional rotational motion of a hydraulic motor connected to an electric generator to generate electricity. The simulation model using the block diagram-oriented environment of MATLAB-Simulink has been created based on the governing equations to investigate the behaviour of the system and validate it. The effect of the sprung mass and the accumulator on the system performance and harvested power of the HPEHS system was investigated. It is found that the motor speed and harvested power increase with increasing sprung mass while the displacement of the sprung mass reduces. The existence of the accumulator in the system minimises the fluctuation in system pressure causing more stability to the suspension system. The average power of the regenerative suspension system is approximately 7.5 W. © 2023 Inderscience Enterprises Ltd.

关键词： Energy harvesting

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Molecular dynamic analysis of pristine single layered graphene for mass sensor

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Materials Today: Proceedings 2023年 72卷 729-735页

作者： Makwana, Manisha V. Patel, Ajay M. Department of Mechanical Engineering A D Patel Institute of Technology Gujarat Vallabh Vidyanagar India Mechatronics Engineering Department G.H.Patel College of Engineering & Technology Gujarat Vallabh Vidyanagar India

The impact of mass on the resonance frequency of single-layered pristine graphene is examined in this research paper. Graphene is viewed as a fascinating material in modern era. In mass sensing, graphene has a significant potential. The authors of this work explored the appliance of single layer graphene (SLG) as a sensing device. The dynamic analysis of SLG is carried out with various boundary conditions. The atomistic finite element technique is used to simulate SLG. SLG sheets have been modelled. The mechanical properties of covalently bound carbon atoms in a hexagonal structure are also used to compute the physical properties of beam elements. At nodes that correspond with carbon atoms, each beam element's mass is represented as a point mass. Simulations were done to see how SLG responded to different conditions of boundary and when used as a mass sensor. Changing the length and applied mass of SLGs reveals the frequency variation. The obtained results show that the length of the sheet and varied mass values have a substantial influence on the dynamic properties. The results reveal that when mass increase, the sensitivity of the SLGS-based mass sensor increases. © 2022

关键词： Graphene

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

A non Invasive Brain-Computer-Interface for Service Robotics

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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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