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An Automated Floor Cleaner Robot for Domestic and Industrial applications

An Automated Floor Cleaner Robot for Domestic and Industrial...

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Decision Aid Sciences and Application (DASA), International conference on

作者： Molla Rashied Hussein Abu Salman Shaikat Rumana Tasnim Md Zonayed Abdullah Al Amin Shahed Rahman Sayma Sultana Jhara Dept. of Computer Science & Engineering University of Asia Pacific Dhaka Bangladesh Bharti School of Engineering and Computer Science Laurentian University Canada Dept. of Mechatronics Engineering World University of Bangladesh Dhaka Bangladesh Dept of Mechanical Engineering Anglia Ruskin University Essex United Kingdom

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Smart home technologies have significantly enhanced comfort and convenience in our daily lives by facilitating several domestic tasks. Household robots utilized in smart homes are designed to maintain clean floor spaces. The manually operated floor cleaning machines are exceedingly labor-intensive and time-consuming. Furthermore, fuel-operated machinery is prohibitively expensive as well as detrimental to the environment. This paper presents the design and development of an automated floor cleaning robot that eliminates previous limitations, thereby conserving both time and financial resources by reducing human labor costs. Recently, several household robots, particularly floor-cleaning robots, have been developed. Nonetheless, only a limited number of advanced robots were dedicated to the wet cleaning of floors. This study presents an efficient automated vacuum cleaning robot that can perform both vacuuming and wet cleaning of floor surfaces while navigating the area. This eliminates the labor and necessity of designating separate time for cleaning the premises. The automated robot was initially designed, followed by the construction of an effective chassis. Subsequently, IR sensors, an Arduino Nano, motor drivers, and a motor shield were incorporated into the system. The economical and user-friendly robot is designed for autonomous cleaning of various surfaces in both household and industrial settings.

关键词： Costs Service robots Navigation Smart homes Robot sensing systems Motors Cleaning sensor systems sensors Floors

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AI-Driven Predictive Maintenance for Industrial IoT with Real-Time Fault Detection and Prediction

AI-Driven Predictive Maintenance for Industrial IoT with Rea...

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International conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： A.Hency Juliet Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Predictive maintenance for industrial machinery uses data analysis and advanced techniques to forecast equipment failures. This proactive approach schedules maintenance to minimize downtime and optimize operational efficiency. Achieving accurate, consistent, and well-integrated IoT sensor data is difficult, as poor data quality can result in inaccurate predictions and false alerts. Scaling real-time fault detection and prediction requires substantial computational power and optimized algorithms to process the continuous influx of data from multiple machines. To address data quality and integration issues while enhancing real-time processing and scalability, a Custom Neural Network technique can be used to improve IoT sensor data accuracy and consistency, and efficiently manage the computational demands of continuous data influx for fault detection and prediction. The proposed system, leveraging an extended Neural Network technique, enhances prediction accuracy by efficiently managing diverse industrial datasets and improves real-time fault detection through optimized data processing and reduced latency. The model demonstrated a remarkable accuracy of 92.86%, showcasing its ability to accurately identify both faulty and non-faulty equipment. With a precision of 90.91%, a perfect recall of 100%, and an F1 score of 95.24%, the model exhibited a well-balanced fault detection performance, minimizing false positives while ensuring thorough fault identification, all while processing in real-time with an efficient time complexity of 2.49 seconds.

关键词： Fault diagnosis Accuracy Fault detection Scalability Data integrity Neural networks Real-time systems Time complexity Predictive maintenance Industrial Internet of Things

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Intelligent Wireless Self-Sustained Sensing Cubic Node Toward AIOT Ready Smart City

Intelligent Wireless Self-Sustained Sensing Cubic Node Towar...

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Micro and Nanotechnology for Power Generation and Energy Conversion applications (PowerMEMS)

作者： Manjuan Huang Tingting Zhao Guoqing Jin Xiaojing Mu Huicong Liu Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electrical Engineering Soochow University Suzhou CHINA Key Laboratory of Optoelectronic Technology & Systems Ministry of Education International R&D Center of Micro-nano Systems and New Materials Technology Chongqing University Chongqing CHINA

This paper presents an intelligent wireless self-sustained sensing cubic node, consisting of a rotational electromagnetic generator (R-EMG) for power supply and a versatile triaxial piezoelectric sensor (TPS) composed of three piezoelectric beams. This system enhances internet of things (IoT) sensing through cloud-based artificial intelligence (AI) computing. The cubic node provides power generation and multi-functional sensing capabilities, including vibration acceleration, frequency, and tilting angle. The design of the R-EMG ensures stable output at different rotational speeds, reaching a maximum output power of 66 mW at 550 rpm with an external load of 1.1 kΩ, allowing for self-sustaining energy generation for the node. Additionally, the cubic design enables the orthogonal placement of the three piezoelectric beams, enabling a broad response to vibrational energy from various directions. This flexibility allows for diverse applications across different frequencies and scenarios.

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Development of Automatic Vehicle Accident Detection and Rescue System (AVADRS)

Development of Automatic Vehicle Accident Detection and Resc...

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International conference on Computer applications and Industrial Electronics (ICCAIE)

作者： Hasliza bini Hassan Muhamad Fitri Bin Salim Tan Mun Mun Department of Electrical Engineering Technology Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia ST Microelectronics Malaysia Sdn. Bhd Tanjung Agas Industrial Area Johor Malaysia

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

Despite the progress made in driving assistance and safety systems, vehicular accidents continue to be a major concern in contemporary society. The primary aims of this study are to develop a dependable and precise accident detection and rescue system, assess its efficacy in mitigating response time, and guarantee timely collaboration with emergency services. The paper addresses challenges related to accurate accident detection, precise location identification, and efficient emergency service coordination. The A V ADRS prototype is equipped with advanced functionalities, including GPS-based location transmission and seamless data communication with nearby hospitals and police stations. This study focuses on the system's scope, which pertains to major accidents and high-speed collisions. Therefore, the result shows the system can detect accidents and send a notification to the rescue team. This proj ect emphasizes cost-effectiveness, low power consumption, and extended battery life. The A V ADRS system is deemed a significant advancement in expediting vehicular accident assistance and rescue services, as per the study's findings. The recommendations are to enhance the system's functionality, dependability, and overall efficacy in detecting and resolving vehicular accidents.

关键词： Accuracy Manuals System integration Emergency services sensor systems Road safety sensors

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Revolutionizing Military Operational Awareness and Intelligence Gathering Through the Integration of advanced Internet of Things (IoT) Technologies

Revolutionizing Military Operational Awareness and Intellige...

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Frontier Technologies and Solutions (ICFTS), International conference on

作者： Sakthi Prabha R Jerrin Simla A Gomathi T Aranganathan A Sivasundarapandian S Vedanarayanan V Department of ECE Sathyabama Institute of Science and Technology Chennai India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of CSE HKBK College of Engineering Bangalore India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

IoT (Internet of Things)-based military monitoring has the potential to have a dramatic impact on troops and increase national security. Although we have deployed a variety of technology to assist troops during war, these can be also used to reduce their risk of injury and decrease the casualties. Tracking the health and tracing the soldier's location continuously would provide a significant change and help to them in the battle field. This project aims in combining some significant idea and technology which could save and reduce the number of casualties in some basic mission. On the battlefield soldiers are exposed to a variety of traumatic blasts that can cause fatal Traumatic brain damage (TBI), these are left unnoticed till the soldier reaches the base station. The primary goal of the work described is to create and deploy a soldier surveillance system that uses a wearable health sensor as a central hub to read real-time location, activity data. health information and blast sensor to detect the intensity of the blast and which contains a integrated circuit with gyro scope and accelerometer is interfaced with the system along with the other sensor and send notification to the control room to update about the soldier's well-being through IoT based correspondence framework for troopers. This system is An IoT- based wearable gadget concept framework for a military use. This concept has numerous military applications. This project aims to bring an effective communication between the soldier and base control by proving the base about the soldier's current health parameters, live location and several other parameters. Through cloud application the data can be accessed by the control room immediately and by the control room at any time of the mission/post mission. Each soldier must turn on his own device through cloud application the data can be accessed by the control room immediately.

关键词： Cloud computing Surveillance Real-time systems Safety Internet of Things Biomedical monitoring National security Monitoring Brain injuries Video recording

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Leveraging Trustworthy AI and IIoT for Cost-Efficient Multimodal Quality Control in Metallurgic Additive Manufacturing

Leveraging Trustworthy AI and IIoT for Cost-Efficient Multim...

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International conference on Technology, Informatics, Management, Engineering, and Environment (TIME-E)

作者： Jan Buescher Jonas Zajaczkowski Christian Blecking Ingor Baumann Roman Möhle David Wagstyl Hans-Georg Rademacher Wolfgang Tillmann Jochen Deuse Institute of Production Systems TU-Dortmund Dortmund Germany Institute of Materials Engineering TU-Dortmund Dortmund Germany Department of Industrial Applications Kupferverband e.V. Duesseldorf Germany Production Systems RIF e.V. Dortmund Germany Materials Engineering RIF e.V. Dortmund Germany Centre for Advanced Manufacturing UTS Sydney Sydney Australia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

Ensuring consistent quality in metallurgic Additive Manufacturing (AM) processes, such as Cold Gas Spraying (CGS), is a significant challenge due to the inherent process variability in part quality. This study explores the integration of trustworthy Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT) to multimodal quality control, with the goal of increasing in-situ test coverage of manufactured parts and process stability. An open-source software solution for data engineering and analysis was developed using the Design Science Research methodology. Experiments were conducted on a CGS system utilizing CuZn30, a copper-zinc alloy material, as a representative case study. The developed AI models demonstrated high efficiency in identifying quality deviations across multiple quality characteristics, including oxidation, geometric inaccuracies and porosity (F1-Score=0.998). The results suggest that the combination of IIoT-measurement components and AI is a cost-effective way to significantly improve test coverage, enabling the control of multiple quality attributes and process reliability in metallurgical AM using low-cost sensors and open-source software. This research contributes to the advancement of cost-effective quality control systems and provides a case study of how AI can be used to improve the reliability and stability of advanced industrial manufacturing processes with respect to multiple quality characteristics and data modalities, paving the way for mass customization as proposed in the Industry 4.0 vision.

关键词： Process control Spraying Quality control sensor phenomena and characterization Three-dimensional printing Stability analysis Software reliability Artificial intelligence Open source software Industrial Internet of Things

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Efficient Node Reduction Heuristic for GNN-Based Traffic Speed Forecasting

Efficient Node Reduction Heuristic for GNN-Based Traffic Spe...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Yuto Inokuchi Pedro Henrique González Masayoshi Aritsugi Israel Mendonça Graduate School of Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy.

关键词： Graph convolutional networks Noise Memory management Estimation Computational efficiency Forecasting Optimization Intelligent transportation systems Faces Genetic algorithms

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Edge AI in 6G: Vision, Technologies, and applications

Edge AI in 6G: Vision, Technologies, and Applications

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Pervasive Computational Technologies (ICPCT), International conference on

作者： Vanessa Azucena Monzón Centeno Jesús Emilio Agustín Padilla Caballero Claudia Rossana Poma García Jonathan Orihuela Flores Wilmer Amado Huamán Malca Carlos Alberto Ferrando Gómez Escuela de Posgrado Universidad César Vallejo Lima Perú Pregrado Universidad Nacional de Educación Enrique Guzmán y Valle Lima Perú Pregrado Universidad Nacional del Callao Callao Perú

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The development of wireless connections is being pushed by an unprecedented rise of artificial intelligence (AI) software, with 6G becoming a game-changing technology. However advanced artificial intelligence (AI) needs a lot of connectivity and processing power, which causes delays, energy waste, network traffic, and privacy breaches. By combining sensing, interaction, computing, and intellect, Edge AI—which incorporates model learning and reasoning capacities at the connection's edge—offers an innovative approach for 6G network deployments. With an emphasis on distributed machine learning algorithms and incorporated wireless networking protocols, this article offers a vision for sustainable and reliable edge artificial intelligence (AI) systems. To fit edge AI, the article also covers novel design concepts, service-driven utilization techniques for optimizing, and a complete end-to-end system framework.

关键词： 6G mobile communication Wireless communication Wireless sensor networks Edge AI Telecommunication traffic Software sensors Reliability Internet of Things Artificial intelligence

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An IoT Based Greenhouse Remote Monitoring System for Automation of Supervision for Optimal Plant Growth

An IoT Based Greenhouse Remote Monitoring System for Automat...

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International conference on advanced Computing and Communication systems (ICACCS)

作者： M. Pradeep Dhruva R Rinku P. Swapna V. Jyothi A. Athiraja G. Prasannakumar Department of ECE Shri Vishnu Engineering College for Women Bhimavaram Andhrapradesh India Department of ECE CVR College of Engineering Hyderabad Telangana State India Deprtment of IT MalIa Reddy Engg. College Secunderabad Telanagana State India Department of ECE Vardhaman college of Engineering Hyderabad Telangana State India Department of CSE Malla Reddy College of Engg. Secunderabad Telanagana State India Department of ECE Malla Reddy Engg. College Secunderabad Telanagana State India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

A greenhouse regulates both indoor and outdoor characteristics to create a controlled environment that fosters ideal circumstances for plant development. This research presents a comprehensive remote monitoring system for greenhouses that is intended to identify and control important environmental factors. The system uses a variety of sensors to measure air pressure, soil moisture, pH, temperature, humidity, light, and carbon dioxide levels indoors. Rainfall and other outdoor factors, such as wind direction and speed, are also tracked. sensors such as the DHT11 for temperature and humidity, the LM393 for light, the MG811 for carbon dioxide, the LM293 for soil moisture, the ES-PH-NOI-TR-l for soil pH, the IC-RK120-03 for wind speed and direction, and the ACS72981 for precipitation are all used by the greenhouse remote monitoring system. At predetermined places, these sensors continuously assess particular environmental parameters. They then use machine learning algorithms to send measurement data to the control platform. The system sends a signal to the controller of the associated sensor when it detects a change in value. This enables prompt modifications to be made using terminal valves (such as water valves, heating, droppers, and spray irrigation). applications in agriculture, horticulture, and animal husbandry make use of the Internet of Things (IoT)-enabled greenhouse remote monitoring system, which offers vital information and makes real-time modifications for the proper growth of ecological crops possible. The system ensures scientific monitoring and management while automating supervision. Based on parameters like accuracy, sensitivity, specificity, fl-score, recall, and precision in relation to current techniques, a performance analysis of the suggested system is carried out. The goal of this research is to further greenhouse technology by providing a dependable and effective method for managing and cultivating plants in controlled conditions.

关键词： Temperature sensors Temperature measurement Soil measurements Wind speed Green products Soil moisture Humidity sensor systems Internet of Things Remote monitoring

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Sub-1 GHz Band Wireless Coexistence Study for OFDM systems in IEEE 802.19.3a

Sub-1 GHz Band Wireless Coexistence Study for OFDM Systems i...

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International conference on Ubiquitous and Future Networks, ICUFN

作者： Yukimasa Nagai Jianlin Guo Benjamin A. Rolfe Kazuto Yano Takenori Sumi Kieran Parsons Philip Orlik Pu Wang Information Technology R & D Cetner Mitsubishi Electric Corporation Kamakura JAPAN Mitsubishi Electric Research Laboratories Cambridge USA Blind Creak Associates Los Gatos USA Advanced Telecommunications Research Institute International Kyoto JAPAN

ISBN: (数字)9798350385298

ISBN: (纸本)9798350385304

With the dramatic increase in the number of IoT devices, wireless communication technologies in the Sub-1 GHz frequency bands featuring LPWAN are in disarray. These tech-nologies include standardized IEEE 802.15.4g/Wi-SUN, IEEE 802.11ah/Wi-Fi HaLow, and proprietary SigFox, LoRaWAN and others. These communication technologies use lower frequencies than conventional Wi-Fi in the 2.4/5/6 GHz bands and are used in critical infrastructure such as smart meters in addition to applications such as sensor data collection, environmental monitoring and surveillance cameras for security. However, performance degradation has been observed on IEEE 802.15.4g in environments where IEEE 802.15.4g devices are densely deployed with other interfering devices. IEEE 802.19.3 specifies recommended best practices and coexistence mechanisms which enable IEEE 802.15.4g-FSK PHY and IEEE 802.11ah OFDM PHY based systems to effectively coexist in Sub-1 GHz bands. IEEE 802.19.3 was published in April 2021. Subsequently, new functions have been specified in the standards, frequency regu-lations have been updated and new market requirements have emerged. Accordingly, new coexistence mechanisms are needed. This paper describes the status and future prospects of the IEEE 802.19.3a Task Group started in March 2024 towards spectrum resource efficiency and higher network performance as an amendment of IEEE 802.19.3-2021 standard.

关键词： IEEE 802.15 Standard Wireless communication Wireless sensor networks OFDM Surveillance Simulation Smart meters

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