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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Patil, Trushant Nandan, Durgesh Sharma, Kanhaiya Parashar, Deepak Department of Robotics and Automation Pune India Department of Electronics and Telecommunication Engg. Pune India Department of Computer Science & Engineering Pune India Faculty of Engineering Pune India

ISBN: (纸本)9798350329186

Drone-based predictive structure analysis is a rapidly growing field that has the potential to revolutionize infrastructure and building inspection. In this review of the research, we give a general overview of how drones could increase the effectiveness and precision of structural analysis, particularly for keeping an eye on difficult-to-reach locations. The usage of drones for 3D mapping, inspection, and monitoring of diverse constructions, such as concrete buildings, bridges, tunnels, and dams. The investigations show that drone-based inspection can produce 3D models and high-resolution photos, allowing for remote monitoring and the early identification of possible problems. The findings show that drones may greatly boost infrastructure management's safety and effectiveness, making them an important tool for predictive structural research. © 2023 IEEE.

关键词： Drones

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60 GHz Compact Microstrip Antenna Design with Inset-Fed Technique for Millimetre-Wave Applications 1

60 GHz Compact Microstrip Antenna Design with Inset-Fed Tech...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Badrinathan, B. Anitha, G. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Automation and Robotics Engineering Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

Millimeter-wave (mmWave) frequencies play a critical role in next-generation communication systems by supporting high-speed data transfer and improved bandwidth utilization. This study focuses on developing a compact 60 GHz microstrip patch antenna using an inset-fed technique with a gallium arsenide (GaAs) substrate. The methodology integrates precision micromachining and electromagnetic simulations conducted with Ansys HFSS 2021. Key results include a return loss of -50.18 dB, a gain of 4.8 dBi, and a VSWR of 1.585, demonstrating efficient signal transmission and excellent impedance matching. The antenna's small footprint of 1.8 mm × 1.2 mm ensures its suitability for telecommunications, radar systems, and other mmWave technologies applications. By effectively addressing bandwidth and signal attenuation challenges, the optimized design highlights significant advancements in performance and scalability. The study concludes with insights into the potential for cost-effective manufacturing, marking a step forward in mmWave antenna technology. © 2025 IEEE.

关键词： Gallium arsenide

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AI-Powered Real-Time Detection of Wheel Defects in Railways Using Yolov8 7

AI-Powered Real-Time Detection of Wheel Defects in Railways ...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Shaikh, Muhammad Zakir Baro, Enrique Nava Baloch, Yaqoob Ali Hussain, Tanweer Uqaili, Mohammad Aslam Chowdhry, Bhawani Shankar School of Industrial Engineering University of Malaga Malaga Spain Jamshoro Pakistan Universidad de Malaga Departamento de Ingeniería de Comunicaciones Campus de Teatinos Malaga Spain National Center of Robotics and Automation Cms Lab Jamshoro Pakistan Mehran University of Engineering and Technology National Center of Robotics and Automation Cms Lab Department of Electrical Engineering Jamshoro Pakistan

ISBN: (纸本)9798331534271

Railway wheels are one of the most critical components of the railway infrastructure, functioning as the load carrier. They are exposed to various forms of damage, caused due to intense sliding friction and inadequate inspection procedures. This research introduces a real-time wheel fault detection system using YOLOv8, leveraging computer vision and AI-based object detection. The main contribution of this research is the utilization of a custom data acquisition setup for capturing wheel images and detecting faults with high accuracy and speed. Our results demonstrate an overall F1 score of 0.80, with precision and recall curves indicating strong wheel detection and moderate defect detection capabilities. This approach aims the reduction of inspection costs and the improvement of railway safety through automation and digitization. © 2024 IEEE.

关键词： Railroads

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Transformers Unveiled: A Comprehensive Evaluation of Emotion Detection in Text Transcription 7

Transformers Unveiled: A Comprehensive Evaluation of Emotion...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Ali, Sara Naz, Bushra Narejo, Sanam Ali, Sahil Pabani, Jitander Kumar Mehran University of Engineering and Technology National Center of Robotics and Automation Cms Lab Jamshoro Pakistan Mehran University of Engineering and Technology Department of Computer Systems Engineering Jamshoro Pakistan Mehran University of Engineering and Technology National Center of Rorotics and Automation Cms Lab Jamshoro Pakistan Dawood University of Engineering and Technology Department of Telecommunication Engineering Karachi Pakistan

ISBN: (纸本)9798331534271

The fast growth of digital communication via smartphones and social media has made textual data as one of the most essential forms of people's expressions. Accurate emotion discovery from text is thus important for applications in customer sentiment analysis and interactive technologies, be it a web, desktop, or mobile application, an intelligent robot, or minor connected devices. This paper is intended to evaluate the performance of five state-of-the-art transformer models, including BERT, ALBERT, DistilBERT, Transformer XL, and RoBERTa, for multiclass emotion detection using the IEMOCAP dataset. From the results, it is observed that DistilBERT has the highest accuracy of 79.2%, implying an effective approach in balancing model size and performance. These research show that smaller models like DistilBERT often outperform larger models by reducing their overfitting and computational demands. The findings also highlights the need for task-specific fine-tuning and optimization in emotion detection. © 2024 IEEE.

关键词： DistilBERT IEMOCAP NLP Text Emotion Detection Transformers

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A Computational Model for Estimating NMDA Properties from Local Field Potential Spectra 17th

A Computational Model for Estimating NMDA Properties from ...

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17th International Conference on Brain Informatics, BI 2024

作者： Mancini, Gabriele Martínez-Cañada, Pablo Panzeri, Stefano Hamburg Germany Istituto Italiano di Tecnologia Genoa Italy Department of Pharmacy and Biotechnology University of Bologna Bologna Italy Department of Computer Engineering Automation and Robotics University of Granada Granada Spain

ISBN: (纸本)9789819632930

The balance between excitatory (E) and inhibitory (I) neural activity is crucial for brain function and is often disrupted in brain disorders. Recent model studies suggest that the slope of the frequency spectra of aggregate measures of neural activity, such as local field potentials (LFPs) or electroencephalograms (EEGs), can be used to predict the E/I ratio. However, these existing models overlook NMDA receptors, which are critical for cognitive functions like working memory and are implicated in disorders such as schizophrenia and Alzheimer’s. Here we use neural network simulations to show that the presence and strength of NMDA receptors have a major influence on the spectral slope of aggregate neural activity, particularly for lower frequencies, suggesting that spectral slopes of aggregate neural activity can be used to estimate NMDA receptor strength. We also find that estimates are better when using slopes computed from persistent activity after stimulus offset, a time when NMDA receptors affect most network dynamics. Moreover, when gamma oscillations are generated by the network, NMDA modulation affects the gamma peak spectral properties. The results suggest that the spectral slope of aggregate measures of neural activity such as LFPs or EEGs may be used as a biomarker of NMDA function or dysfunction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neural network models

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A deep learning model-based framework for abnormal state monitoring of blast furnace heat loads 14

A deep learning model-based framework for abnormal state mon...

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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2024

作者： Xu, Hong-Wei Sun, Yan-Ning Liu, Li-Lan Department of Mechanical and Automation Engineering Shanghai University Shanghai200444 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China

ISBN: (纸本)9781837242672

Heat load is a pivotal metric for ensuring the safety of blast furnace equipment in the iron and steel smelting industry, crucial for monitoring the heat exchange process of blast furnace cooling walls and evaluating the erosion of furnace bodies. The variability of blast furnace heat load, influenced by factors such as feed and oxygen supply, often results in abnormal internal furnace temperatures and drastic heat load fluctuations, hindering effective preventive maintenance. This study introduces a deep-learning-based framework for monitoring the abnormal state of blast furnace heat load. Initially, a two-stage data preprocessing method for monitoring ironmaking parameters is proposed: the first stage eliminates missing and abnormal values from the monitoring parameters, while the second stage employs a feature selection model to identify key influencing parameters. Subsequently, a real-time prediction model of blast furnace heat load is designed, integrating one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks. This model uses a two-layer 1DCNN for automatic feature extraction from time series data, complemented by a single-layer BiLSTM network to accurately predict anomalous trends in heat load. This deep learning framework has demonstrated superior performance in prediction accuracy and abnormal trend identification compared to alternative models. © The Institution of engineering & Technology 2024.

关键词： Iron and steel industry

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Editorial:special issue on frontiers of control and automation,dedicated to *** *** 60th birthday

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Control Theory and Technology 2023年第3期21卷 263-264页

作者： Jie Chen Jie Huang Lihua Xie Department of Control Science and Engineering Tongji UniversityShanghaiChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Center for Advanced Robotics Technology Innovation School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore

It is our great pleasure and honor to organize this special issue"Frontiers of Control and automation"in honor of the 60th birthday of our long-time colleague and friend Professor Ben *** Chen obtained his *** in mathemat-ics and computer science in 1983 from Xiamen University,China;*** in electrical engineering in 1988 from Gonzaga University,Spokane,Washington,USA;and *** in electrical and computer engineering in 1991 from Washington State University,Pullman,Washington,USA.

关键词： computer Xiamen frontier

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Controllability Analysis and Stabilization of Untethered Tumbler Floating ROV

Controllability Analysis and Stabilization of Untethered Tum...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Huang, Junda Wu, Haiwen Cao, Hanwen Zhou, Jianshu Liu, Yunhui The Chinese University of Hong Kong T Stone Robotics Institute The Department of Mechanical and Automation Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350325706

Floating ROVs have extensive applications on oceanographic research, environmental monitoring, and search and rescue operations. In this work, two levels of floating ROV stabilization is realized. First, the intrinsic stability is induced by the design of center of mass and moment of inertia based on the stability analysis, such that the ROV can always keep its upwards standing configuration. Besides, an underactuated control method for this novel untethered tumbler floating (TF) ROV is introduced for stabilization, which effectively enhances the stabilization efficiency based on the attitude controller. The model considering rigid body dynamics based on Euler equations, and nonlinear controllability with one, two, or three torques are analyzed serving for the moment of inertia design and underactuated controller. Simulation experiments are conducted to validate the efficacy of our approach, and the results demonstrate its effectiveness for stabilizing the TF-ROV. © 2023 IEEE.

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SmartDrive: The Ultimate EV-IoT Control Hub 5

SmartDrive: The Ultimate EV-IoT Control Hub

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Elavarasi, K. Rajasekar, P. Chandra Mouli, B. Siva Subramanian, P. Bharanidharan, R. Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Dhanalakshmi Srinivasan College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Dhaanish Ahmed Institute of Technology Department of Robotics and Automation India SSM Institute of Engineering and Technology Department of Electrical and Electronics Engineering Engineering India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

IoT technology is crucial for advancing smart automation in electric vehicles (EVs). Interest in smart grids has surged due to the growth of distributed generation, the aging infrastructure of the existing grid, and the push for network modernization. As accurate usage predictions gain importance, so does the need for efficient energy storage systems, particularly for electrical energy. Green electrical energy storage systems are common in EVs due to their excellent electrical properties and adaptability, but the grid faces challenges from potential battery damage caused by overcharging, deep discharging, and high load surges. In this project, hardware utilizing GPS and IoT technology was developed to monitor and manage both the EV and its battery systems. A Battery Management System (BMS) with real-time monitoring and GPS tracking capabilities estimates the State of Charge (SoC) using coulomb counting techniques, alongside an MQTT protocol for message-based communication. The proposed BMS is implemented on a hardware platform utilizing the Arduino environment, a central processor, suitable sensing technologies, and interface devices. Project code was developed in the Arduino IDE and applied to the hardware system. © 2024 IEEE.

关键词： Battery management systems

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Development of a portable and compact robotic ankle rehabilitation system 68

Development of a portable and compact robotic ankle rehabili...

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68th International Conference on Vibroengineering

作者： Zhetenbayev, Nursultan Sergazin, Gani Jamwal, Prashant Uzbekbayev, Arman Sagidoldin, Daulet Department of Electronics and Robotics Almaty University of Power Engineering and Telecommunications Almaty050013 Kazakhstan Academy of Logistics and Transport Almaty050012 Kazakhstan Nazarbayev University School of Engineering and Digital Sciences Astana010000 Kazakhstan Department of Robotics and Technical Means of Automation Satbayev University Almaty050013 Kazakhstan

As part of this study, an ankle rehabilitation device was developed for patients suffering from motor disorders caused by incomplete spinal cord injury (ISCI), stroke, or cerebral palsy. First, the research models and dynamic equations of the device were considered. Secondly, the prototype ankle rehabilitation device is made of PLA plastic and uses linear actuators to simulate outward and inward flexion exercises (dorsiflexion and plantar flexion) for the patient. The advantages of the device are its simple design, low cost, and the possibility of installation on recovery equipment. The device can work quickly in experiments, in addition, the rehabilitation robot can work stably and is convenient to use for patients. © 2024 Nursultan Zhetenbayev, et al.

关键词： Linear actuators

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