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Design and Implementation of UNet for Offroad Rover Navigation 2

Design and Implementation of UNet for Offroad Rover Navigati...

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2nd international conference on Recent Trends in Microelectronics, automation, computing, and communications Systems, ICMACC 2024

作者： Sugumar, D. Siddharth, S. Staines, Rohith Graham K. Anoj Antony, P.J. Karunya Institute of Technology and Sciences Electronics and Communication Engineering Coimbatore641114 India Karunya Institute of Technology and Sciences Electronics and Computer Engineering Coimbatore641114 India

ISBN: (纸本)9798350366570

Significant logistical obstacles arise when delivering supplies to deep forest and off-road trails, which frequently cause access to isolated and deep forest areas to be delayed. An autonomous off-road delivery rover outfitted with UNet. An U-shaped Convolutional Neural Network (CNN), for navigation and obstacle avoidance, is the novel approach to put out this research. The rover's autonomous navigation of rough terrain guarantees that medical supplies reach far-off places on schedule. With an emphasis on UNet's function in navigation and obstacle and route detection, this paper describes the autonomous rover's development, integration and evaluation. Details are provided regarding the rover's hardware, software, and UNet-based navigation module architecture. Furthermore, the concepts of UNet and how it was modified for off-road navigation, such as data gathering and preprocessing. © 2024 IEEE.

关键词： Semantic Segmentation

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Enhancing Workplace Efficiency: An Intelligent Office automation System Leveraging IoT

Enhancing Workplace Efficiency: An Intelligent Office Automa...

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2024 international conference on Innovations in Science, Engineering and Technology, ICISET 2024

作者： Kabir, Md. Humyun Hasnat, Arif Haque, Mirza Umidul Mahdi, Ahmed Jaser Hoque, Md. Jiabul Ahmed, Md. Razu International Islamic University Dept. of Computer and Communication Engineering Chattogram Chittagong4318 Bangladesh

ISBN: (纸本)9798350355499

Traditional office automation systems generally depend on human procedures and obsolete technology, leading to inefficiencies and heightened costs. Since the advent of Internet of Things (IoT) technology, substantial transformations have transpired in both personal and professional domains. By incorporating IoT devices into their work environments, businesses may obtain real-time data on many operational aspects. This enhances organizational decision-making using data and automates processes. This research introduces a novel framework for workplace environments, utilizing the revolutionary capabilities of IoT technology. We provide a distinctive framework for intelligent workplace settings to replace manual switches with mobile control, improve security measures, and raise safety standards. This framework utilizes the capabilities of the IoT. The paper highlights the prospective benefits of IoT-enabled smart workplaces through research investigations and simulations employing Cisco Packet Tracer. The essential components of the proposed framework include access control that is based on RFID technology, intelligent sensor devices, technologies that are efficient with energy, tight security processes, and automation mechanisms. The findings provide evidence that the IoT is effective in managing administrative tasks, improving network connectivity, reducing latency, and protecting workplace safety and security. © 2024 IEEE.

关键词： Office automation

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Machines Thinking Fast: AI-Powered Automating of Information Capture and Retrieval 3

Machines Thinking Fast: AI-Powered Automating of Information...

引用

3rd international conference on Smart Generation computing, communication and Networking, SMART GENCON 2023

作者： Sable, Nilesh P. Singh, Shweta Sharma, Mohit Kumar Patil, Nagaraj Mishra, Abhinav Latha, B. Vishwakarma Institute of Information Technology Department of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Electrical Engineering Jaipur India Mechanical Engineering Faculty of Engineering and Technology Bangalore Karnataka India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India

ISBN: (纸本)9798350319125

The Machines Questioning Fast: AI-powered automation of information capture and Retrieval devices is a technology that utilizes artificial Intelligence (AI) to capture, store, and analyze virtual information. Via gadget getting to know algorithms, this gadget can become aware of styles and determine practical know-how from records sources. It can be implemented for numerous use instances, which include clinical analysis, SEO, and customer support. This gadget works by leveraging herbal Language Processing (NLP) and device getting to know (ML) to extract facts from textual content documents, photographs, and videos and convert them into dependent data. Additionally, it makes use of computer imaginative and prescient (CV) and know-how Graphs (KGs) to better examine and interpret the statistics. Furthermore, the gadget can engage energy-wise retailers in natural conversations, examine patron feedback, and provide personalized shopping guidelines. ultimately, the system includes an intuitive interface that permits customers to control and manipulate their statistics easily. © 2023 IEEE.

关键词： AI-Powered automation Gadget Guidelines Language Machines

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IoT Enabled Diabetic Health Surveillance 3

IoT Enabled Diabetic Health Surveillance

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3rd international conference on automation, computing and Renewable Systems, ICACRS 2024

作者： Murugan, Kalpana Shaheen, Nagayapalli Prathyusha, Palle Ashalatha, Malle Sahithi, Akula Kalasalingam Academy of Research and Education Department of Electronics Communication Engineering Virudhunagar Tamil Nadu Krishnan Koil626126 India

ISBN: (纸本)9798331532420

The rising incidence of diabetes among children and adults presents considerable health challenges, particularly for young patients navigating the complexities of blood glucose management. Traditional glucose monitoring devices, while effective, often come with drawbacks such as high costs and the inconvenience of frequent sensor replacements. To address these limitations, we have developed the "Diabetic Health Surveillance" System, which employs advanced IoT technology. This innovative system utilizes GSR(Galvanic Skin Response) sensors to monitor physiological indicators relevant to diabetes management, including stress levels and physical activity. The inclusion of an ESP32 camera enhances functionality by recognizing different images of retinopathy, helping to determine whether a patient has diabetes. The system's design focuses on providing non-intrusive and continuous monitoring, making it particularly beneficial for pediatric and elderly patients. By integrating these features, users can gain deeper insights into their condition and manage their diabetes more effectively. With cloud-based data storage, the system ensures a user-friendly experience and reduces the cost and maintenance burdens of traditional solutions. This system contributes to United Nations Sustainable Development by promoting well-being for all ages through a cost-effective, efficient solution for diabetes management. © 2024 IEEE.

关键词： Glucose sensors

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An Intelligent Innovation of Electrochemical Energy Based Smart Industrial automation 2

An Intelligent Innovation of Electrochemical Energy Based Sm...

引用

2nd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Kumar, N. Shantha Karthikeyan, S. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Electrochemical energy based smart industrial automation uses electrochemical energy generated from chemical reactions to power and control industrial processes. This energy is generated through the use of electrolyte and electrodes, which convert chemical energy into electrical energy. This energy can then be used to power and control industrial machines, such as industrial robots and automated assembly lines. Additionally, this energy can be used to monitor, regulate, and control processes, such as temperature and pressure, within an industrial environment. This technology offers numerous advantages over traditional methods of powering and controlling industrial operations. Firstly, it is more efficient and cost effective than traditional methods. Secondly, it has the potential to reduce energy consumption, improve efficiency, and increase productivity. Finally, it is environmentally friendly, as it does not produce harmful emissions or waste materials. The use of electrochemical energy based smart industrial automation is the way of the future for industrial operations. The potential of this technology is immense, and the advantages it can bring to industrial operations are numerous. With the right implementation and application, this technology has the potential to revolutionize how industrial processes are operated and managed. © 2023 IEEE.

关键词： Electrolytes

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Fraction Order PID controlled Single Phase Electric Vehicle Charger with Quadratic Boost Converter 3

Fraction Order PID Controlled Single Phase Electric Vehicle ...

引用

3rd international conference on automation, computing and Renewable Systems, ICACRS 2024

作者： Balaji, B. Hemalatha, P. Krishnaraj, S. Ashraf, Z. Yaseen Dept. of EEE IFET College of Engineering Villupuram India TIFAC-CORE in Cyber Security Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331532420

This research study proposes a novel strategy for charging Electric Vehicles through a combination of a Fractional Order Proportional-Integral-Derivative (FOPID) control scheme, a Single-Phase Electric Vehicle Charger (SPEVC) and a Quadratic Boost Converter. Towards the sustainable development of the nation, there is a growing demand for efficient charging mechanisms for electric vehicles. This work foresees an EV charging system that ensures high performance, reliability, and efficiency. It intermingles fractional calculus principles into control strategies. Also, it has the advantages of quadratic boost conversion which plays the primary role in the charging system. Additionally, it offers enhanced voltage gain and minimal stress on the components. This integration, in turn, increases the reliability and efficiency of the charging process. The proposed system overcomes the shortcomings of conventional integer-order PID controllers by giving maximal accuracy, enhanced robustness, and interference avoidance during the regulation of current and output voltage of converters’ output. This work highlights the wide-ranging experimental simulations and validations to verify SPEVC’s feasibility and validity. With these results, the complete assessment of FOPIC- the FOPIC-controlled charging system is carried out under various payload factors and input voltage oscillations. Various performance metrics are considered for validating the proposed system against convergence time, peak, and residual error. Experimental results show that the proposed system outperforms traditional systems. Moreover, the findings are composed to enhance the EV charging mechanism by unveiling an efficient, dynamic, and resilient control strategy. Finally, this system promises to break through EV charging technology by coping with the challenges in charging architectures, accordingly promoting diversified EV adoption through electrical grid resilience and energy optimization. © 2024 IEEE.

关键词： Three term control systems

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Enhanced Plagiarism Detection in Online Assignments by Natural Language Processing and Machine Learning Techniques 3

Enhanced Plagiarism Detection in Online Assignments by Natur...

引用

3rd international conference on automation, computing and Renewable Systems, ICACRS 2024

作者： Hemandkumar, E. Varun Kumar, A. Gracewell, Jeffin Dept. of Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9798331532420

As online projects become more common in the educational setting, ensuring academic integrity is a growing concern. Traditional plagiarism detection algorithms often have trouble meeting some of the more sophisticated practices that students adopt for avoiding detection such as paraphrasing or word substitution. We present a machine learning based approach that was designed to enhance the effectiveness and accuracy of online assignment plagiarism detection, the proposed system can also detect minor plagiarism cases like paraphrased information and cross-linguistically similar. Due to the fact that the model proposed is capable of learning complex patterns and it was trained on a large dataset of a marginally homogenous dataset (academic publications), it maintains relatively high rates of recall and precision. The system uses multiple approaches of information retrieval to combine the interactions of textual relationships and elements such that it captures the text semantics of how person is feeling through word embedding. Later, it trains supervised learning models to classify possible plagiarism cases, like neural networks and Support Vector Machines (SVM). Also, the model can learn continuously from new data to improve its results. Our method improves the detection of traditional plagiarism and is also effective for more complex and subtle types of content reuse based on preliminary evaluation results. © 2024 IEEE.

关键词： Contrastive Learning

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Lightweight fire detection algorithm based on improved YOLOv8

Lightweight fire detection algorithm based on improved YOLOv...

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2024 international Workshop on automation, control, and communication Engineering, IWACCE 2024

作者： Wu, Di Ning, Jinzheng Wang, Yuanzhi School of Computer and Information Anqing Normal University Anqing Anhui246133 China University Key Laboratory of Intelligent Perception and Computing of Anhui Province China

ISBN: (数字)9781510685420

ISBN: (纸本)9781510685413

Aiming at the limitations of traditional fire detection algorithms in terms of accuracy and real-time detection, a lightweight fire detection algorithm based on improved YOLOv8 is proposed. The Slim-neck is used to improve the Neck network, reduce the number of parameters and computation of the model, and improve the detection performance of the model. The C2f-Star module is designed, and the Star block is introduced to replace the bottleneck structure of the C2f module in the Backbone network, to better capture the information of the image, and further reduce the complexity of the model. The Focaler WIoU boundary loss function is used instead of the original loss function, which reduces the influence of low-quality samples and increases the regressivity of the network bounding box. The experimental results show that the number of parameters and the computational volume of the improved model are reduced by 14.0% and 16.0%, while the precision and the mean average precision are improved by 3.0% and 1.2%, compared with the original model, which can help real-time monitoring and early warning of fire. © 2024 SPIE. All rights reserved.

关键词： Fire Detection and tracking algorithms Performance modeling Education and training Target detection Feature extraction Convolution Mathematical optimization Stars Statistical modeling

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Machine Learning for Intricate Detection in 6G Networks 2

Machine Learning for Intricate Detection in 6G Networks

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2nd IEEE international conference on IoT, communication and automation Technology, ICICAT 2024

作者： Tiwari, Annapurna Nand Revathi, N. Almusawi, Muntather Prabha, Sakthi Karpagam, J. Hafeena, Md. IES College of Technology Department of Electronics & Communication Engineering M. P Bhopal India Nehru Institute of Technology T.M Department of Electronics and Communication Engineering Tamil Nadu Palayam India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Academy of Higher Education Department of Electrical and Electronics Engineering Karpagam TN India CMR Technical Campus Medchal Hyderabad India

ISBN: (纸本)9798350368109

In the world of 6G networks, the merging of micro-services-oriented designing and intelligent network management demonstrates the central role of neural networks (AI), machine learning (ML), and supervised learning (DL). This study looks into strengthening the security of 6G communications networks through the development of a new anomaly recognition System for 6G Connections (AD6GNs) utilizing ensemble learning (EL). The EL-ADCN workflow comprises prior processing, feature selection utilizing the hybrid methodology CFS-RF, and intruder detection applying hybrid EL methods. The system shows impressive performance on datasets with names like NSL-KDD, UNSW-NB2015, CIC-IDS2017, when CICDDOS2019, getting accuracy of 99.5%, 99.9%, 99.8%, while 99.95426%, respectively, with low false alarm rates. The combination of assistance vector machines (SVM) particularly random forest computing (RF) as EL techniques further improves the efficiency of packing and adaboosting in guaranteeing network that self-sustainability and preventive threat prevention in the envisioned 6G model. © 2024 IEEE.

关键词： Network intrusion

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Research on domestic technology of substation automation equipment 3

Research on domestic technology of substation automation equ...

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3rd international conference on Electronics Technology and Artificial Intelligence, ETAI 2024

作者： Song, Xinxin Dong, Naizhou Wang, Zhiqiang He, Xiaoyu State Grid of China Technology College Shandong Jinan250000 China State Grid Shandong Electric Power Company Shandong Jinan253000 China

ISBN: (纸本)9781510683143

To proactively address the disruptions in chip supply, the State Grid Corporation of China (SGCC) has undertaken comprehensive measures in line with the requirements set forth in the Regulations on the Protection of Critical Information Infrastructure by the State Council. Starting from November 2019, the National Power Dispatch and communication Center initiated efforts to localize and replace substation automation systems. This involved conducting research on domestic chip options, achieving breakthroughs in key alternative technologies, developing equipment with entirely domestic components, conducting specialized testing, integrating with the power grid, and carrying out large-scale deployment. These endeavours have significantly enhanced the autonomy and controllability of the substation automation system, thereby comprehensively improving the level of prevention and control of grid security risks. © 2024 SPIE.

关键词： controllability

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