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2024 International Conference on System, Computation, automation and Networking, ICSCAN 2024

作者： Dharani, D. Pradaap, B. Hemavandhana, H. Raja, V. Mohan Manakula Vinayagar Institute of Technology Department of Robotics and Automation Pondicherry India

ISBN: (纸本)9798331510022

Electric Vehicles (EVs) have revolutionized transportation, providing eco-friendly alternatives to traditional internal combustion engine vehicles. As the number of Electric Vehicles on the road increases, there is a critical need for systems that ensure both safety and operational efficiency. The proposed Adaptive Dynamic Speed Limiter system adjusts a vehicle's speed dynamically based on external control signals transmitted wirelessly. The system uses a transmitter to send predefined commands corresponding to different speed levels or stop conditions. A receiver integrated into the EV decodes these signals and modifies the vehicle's motor speed accordingly. An LCD provides real-time feedback to the driver and displays the vehicle's current status. The adaptive dynamic model ensures compliance with traffic regulations, reduces energy consumption, and enhances safety for both drivers and pedestrians. By integrating advanced control mechanisms, the project contributes to developing smarter, safer EVs. © 2024 IEEE.

关键词： Feedback

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Multi-class Bearing Fault Classification using Deep Learning and Transfer Learning Models 4

Multi-class Bearing Fault Classification using Deep Learning...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Shimpi, Avanish Suhalka, Gunjay Chitr Sayyad, Sameer Kumar, Satish Bongale, Arunkumar Symbiosis Institute of Technology Dept.Robotics and Automation Pune India

ISBN: (纸本)9798350354218

In the modern age of industry 4.0 and state-of-the-art manufacturing, automated inspection plays an essential role. The bearing in the rotary machine is a vital and critical component as it provides support and stability to the rotary elements of machines. The defect in the bearing is responsible for the increase in vibrations, noise and reduction in reliability and safety of the rotating machines. It is very challenging to humanly detect different faults in these bearings. This work highlights the methods of multi-class bearing fault classification using Deep Learning (DL) and Transfer Learning (TL) models. The different DL models such as Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) are used along with the TL models such as VGG16 and MobileNet. The models perform with good classification accuracy ranging from 88% to 98%. The Transfer learning models perform better for both binary and multi-class fault classification of the bearings. © 2024 IEEE.

关键词： Convolutional neural networks

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Tool Wear Estimation Using Few-Shot Learning in Deep Neural Network Architectures

Tool Wear Estimation Using Few-Shot Learning in Deep Neural ...

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2024 MIT Art, Design and technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Pandey, Aarin Shetty, Ananya Sayyad, Sameer Warke, Vivek Kumar, Satish Bongale, Arunkumar Symbiosis Institute of Technology Dept. of Robotics and Automation Pune India

ISBN: (纸本)9798350362879

This research leverages the specialized machine learning technique known as few-shot learning to make precise predictions of cutting tool wear during milling, even when provided with limited training data. Employing a meta-learning approach, the model demonstrates exceptional generalization capabilities from a constrained dataset, enabling seamless adaptation to new tasks with minimal examples. Utilizing a foundational deep neural network trained on a small dataset, novel models are constructed, showcasing remarkable accuracy when presented with previously unseen data. These findings underscore the distinct advantages of few-shot learning over traditional methodologies, delivering highly accurate tool wear predictions while significantly reducing computational time. Furthermore, this approach shows potential for dealing with data-intensive circumstances in real-world applications. Ultimately, the research has the potential to revolutionize machining processes by increasing tool longevity, reducing downtime, and lowering tool replacement costs. © 2024 IEEE.

关键词： Deep neural networks

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Corrosion Image Classification using Deep Learning and Transfer Learning Approaches 4

Corrosion Image Classification using Deep Learning and Trans...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Misal, Aarya Sayyad, Sameer Symbiosis Institute of Technology Dept. of Robotics and Automation Pune India

ISBN: (纸本)9798350354218

Corrosion, a pervasive challenge across numerous industries, undermines material integrity and incurs substantial economic costs. Traditional methods of corrosion detection are often labour-intensive and prone to human error. Innovative solutions for timely and accurate identification are needed. This study explores applying Deep Learning (DL) and Transfer Learning (TL) techniques for classifying corrosion images into five distinct classes. This work uses the Worcester Polytechnic Institute's dataset, which consists of 600 corrosion image samples expertly rated according to the ASTM D1654 standard. This dataset is used to develop and compare the performance of custom Convolutional Neural Networks (CNNs) with TL approaches using VGG16 and ResNet50 models. The results indicate that TL models significantly outperform the custom CNN in training and validation accuracy, with VGG16 achieving the highest validation accuracy of 90% after fine-tuning. This work highlights the efficacy of leveraging pre-trained models and fine-tuning them for specific tasks. The results lay the groundwork for developing robust and generalized models capable of excelling in diverse real-world scenarios, enhancing maintenance strategies, improving safety, and significantly reducing costs across various industries. © 2024 IEEE.

关键词： Convolutional neural networks

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Robust control of isopropyl benzene production process using H_(∞) loop shaping control scheme

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Journal of Control and Decision 2024年第1期11卷 153-163页

作者： Vinila Mundakkal Lakshmanan Aparna Kallingal Sreepriya Sreekumar Department of Chemical Engineering National Institute of Technology CalicutKozhikodeKeralaIndia Department of Robotics and Automation Adi Shankara Institute of Engineering and TechnologyKaladyIndia

This paper discusses robust control strategy for isopropyl benzene production process using the method of loop shaping H_(∞) *** cumene production process is a part of phenol plant in HOCL,Kochi.H_(∞) control of the propylene concentration is done *** H_(∞) controller is derived from the linearised model of the *** optimal H_(∞) controller is obtained by simplifying two algebraic Riccati *** proposed PID-like H_(∞) controller provides a single tuning parameter which makes the controller design more *** proposed controller has been compared with other robust controllers like H_(2) and *** H_(∞) controller is found to be superior in a wide frequency range and has a feature of low distortion and good regulating *** reactor model has been developed in COMSOL Multiphysics with the parameters obtained from HOCL plant,*** model extracted is reduced using model order reduction for the controller design.

关键词： Cumene reactor H_(∞)loop shaping model order reduction Riccati equation PID control stability robustness

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Healthcare Monitoring Companion: The Social Robot 3

Healthcare Monitoring Companion: The Social Robot

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Goraksha, Rahul Bongale, Arunkumar Sayyad, Sameer Kumar, Satish Symbiosis Institute of Technology Department of Robotics and Automation Pune412115 India Symbiosis Centre for Applied Artificial Intelligence Department of Robotics and Automation Pune412115 India

ISBN: (纸本)9798350372120

There is a rising scarcity of healthcare professionals to meet the demand of taking care of aged people. Social robots are becoming more and more popular because of the benefits they provide;during the pandemic, in particular, several of their primary applications are advantageous. The purpose of this work is to build the prototype of social robots and carry out the integration of those robots into healthcare monitoring using Thing Speak and Raspberry Pi. This prototype of social robots with Thing Speak and Raspberry Pi hosts an array of sensors (for measuring a multitude of data from the body of humans) like heartbeat sensor, Dallas temperature sensor, Radio-Frequency Identification (RFID), ultrasonic sensor, and Infrared (IR) sensor along with other components like motor controlling module and DC motor. The data from these sensors are transmitted to the cloud platform Thing Speak, offering remote monitoring and data analysis capabilities and the Raspberry Pi acts as the central processing element in our design, which additionally helps in triggering corresponding alerts regarding the patient status. Finally, we experimentally validate our design using 2 cases and then we present the assessment of the patient alerting feature. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Learning to Avoid Collisions in Mobile Manipulator Reaching Task 7

Learning to Avoid Collisions in Mobile Manipulator Reaching ...

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7th International Conference on Intelligent robotics and Control Engineering, IRCE 2024

作者： Zheng, Zunchao Karlsruhe Institute of Technology Intelligent Process Automation and Robotics Lab Karlsruhe Germany

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352399

Planning a trajectory for a mobile manipulator to complete a task challenges extremely as the robot has high degrees of freedom (DoF), especially in an unstructured environment. In recent years deep reinforcement learning (RL) achieves excellent performance in control fields, this paper applies the state-of-the-art deep RL algorithms to solve whole-body motion planning and control problem for mobile manipulator reaching in different environments. The method generalizes the problem from collision-free environment to those with static or dynamic obstacles by using transfer learning. The experiments are evaluated in different environments and the results verify the feasibility. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Robotic Manipulator with Conveyor System for Visual Sorting of Work Pieces

A Robotic Manipulator with Conveyor System for Visual Sortin...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Adarsh, V.S. Mathew, Jedin Jebi Thuruthiyil, Joseph Mathew Sunil, Sidharth Joseph, Georgina Binoy Toc H Institute of Science and Technology Robotics and Automation Kerala Ernakulum India

ISBN: (纸本)9798350394399

The proposed system for sorting work pieces integrates a four-degree-of-freedom robotic manipulator with computer vision and conveyor transportation capabilities for automated object handling and sorting. Utilizing a conveyor system and inverse kinematics, the robotic arm ensures efficient movements. Real-time images of work pieces are captured and processed using computer vision techniques, allowing for size and color-based sorting. The integration of machine learning with robotic control can ensure dynamic and accurate sorting. The integrated system reduces labor costs, minimizes errors, and finds applications in manufacturing, logistics, and assembly lines improving productivity and product quality. © 2024 IEEE.

关键词： Inverse kinematics

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Characterization and Mechanical Behaviour of Nano-Al2O3 Reinforced Polymer Composites

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Journal of The Institution of Engineers (India): Series D 2024年 1-12页

作者： Mahesh, K.S. Satheesh, J. Department of Robotics & amp Automation JSS Academy of Technical Education Bengaluru India Department of Mechanical Engineering SJB Institute of Technology Bengaluru India

This study presents an experimental investigation into the mechanical behaviour and characterisation of Nano-Al2O3 reinforced polymer composites, aiming to enhance the understanding of their structural performance. The incorporation of nanoscale Al2O3 particles into polymer matrices has gained attention for its potential to improve the mechanical properties of the resulting composites. In this research, 1 wt% Nano-Al2O3 was dispersed within a polymer matrix through a systematic fabrication process. The mechanical behaviour of the developed composites was evaluated through a comprehensive set of experiments, including compression testing, flexural testing, and impact testing. The results provided insights into the effects of Nano-Al2O3 content on the strength, stiffness, and impact resistance of the composites. Microstructural characterisation using techniques such as scanning electron microscopy and X-ray diffraction were employed to analyse the dispersion of nanoparticles and the resulting changes in the composite structure. The findings from this experimental investigation contribute to the current knowledge base on Nano-Al2O3 reinforced polymer composites and offer valuable information for optimising the fabrication process to achieve desired mechanical properties. © The Institution of Engineers (India) 2024.

关键词： Compression testing

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Enhancing retinal fundus image classification through Active Gradient Deep Convolutional Neural Network and Red Spider Optimization

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Neural Computing and Applications 2024年第26期36卷 16607-16619页

作者： Subramaniam, Krishnakumar Naganathan, Archana Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore India Department of Electrical and Electronics Engineering PSG College of Technology Coimbatore India

Retinal fundus image classification is pivotal for the early detection of various eye disorders. Leveraging advancements in deep learning, this research introduces a robust framework for retinal fundus image classification. In this research paper, we introduce a novel methodology that integrates the Active Gradient Deep Convolutional Neural Network (AG-DCNN) architecture with the Red Spider Optimization (RSO) algorithm. Extensive evaluations conducted on well-established retinal image datasets such as DRIVE, STARE, CHASE DB1, HRF, DRISHTI-GS, and RFMiD illustrate substantial enhancements in classification accuracy, sensitivity, and specificity when compared to conventional approaches. The AG-DCNN with RSO exhibits superior performance and remarkable generalization abilities across diverse datasets. This novel approach not only enhances the latest advances in retinal image classification but also holds promise for early disease diagnosis and improved patient care. Through extensive examinations, our proposed method consistently outperforms established techniques, establishing itself as the benchmark in retinal fundus image classification. The integration of AG-DCNN and RSO showcases its efficacy and potential impact in advancing the field of medical image analysis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image classification

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