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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications VII"

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Cloud-Based Monitoring of Lithium-Ion Battery Management systems for Health Estimation in Manufacturing Industries

Cloud-Based Monitoring of Lithium-Ion Battery Management Sys...

引用

2025 International conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility, ADMMS 2025

作者： Zeeshan, Mohammad Akre, Vineet Wardwizard Innovations and Mobility India

The increasing reliance on lithium-ion batteries in manufacturing necessitates advanced monitoring techniques to ensure their longevity and reliability. Cloud technology offers a solution by enabling real-time data collection, analysis, and accessibility, facilitating thorough monitoring and predictive maintenance. Digital twin technology, creating a virtual replica of the physical battery system, provides a platform for simulating real-world conditions and predicting potential issues before they arise. By integrating sensor data and historical usage patterns, the digital twin model can accurately predict battery degradation, aiding in timely maintenance strategies. This proactive approach enhances battery operational efficiency and extends lifespan, leading to cost savings and improved safety. The paper explores using cloud-based monitoring systems to enhance the health estimation and management of lithium-ion batteries. A comprehensive feasibility study on adopting battery digital twin technology for electric two-wheeler and three-wheeler manufacturers examines creating a digital twin model for batteries and validating corresponding tests. Furthermore, the research discusses the technical challenges and solutions associated with implementing digital twin technology in manufacturing. Key metrics such as state of charge (SoC) and state of health (SoH) are analyzed to showcase the effectiveness of the digital twin model in real-world applications. © 2025 SAE International. All Rights Reserved.

关键词： Predictive maintenance

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Forest Fire Prevention and Detection System

Forest Fire Prevention and Detection System

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Power Electronics, Computer applications (ICPECA), IEEE International conference on

作者： Lasitha Jayakody Chamathka Edirisinghe Tharush Senasinghe Shaminda Dissanayake Narmada Gamage Dinithi Pandithage Jaliya L. Wijayaraja Dept. of Computer Systems Engineering Sri Lanka Institute of Information Malabe Sri Lanka

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires.

关键词： Temperature sensors Technological innovation Wildfires Prevention and mitigation Forestry sensor phenomena and characterization sensor systems Real-time systems Gas detectors Resilience

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A Hybrid Optimization Strategy for k-Coverage Maximization in Wireless sensor and IoT Networks 3

A Hybrid Optimization Strategy for k-Coverage Maximization i...

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3rd International conference on Intelligent systems, advanced Computing, and Communication, ISACC 2025

作者： Manna, Prithwish Majumder, Sudipta Singh, N. Hemarjit Dibrugarh University Dept. of CSE DUIET Assam Dibrugarh India Dibrugarh University Dept. of ECE DUIET Assam Dibrugarh India

ISBN: (纸本)9798331523893

In this paper, we propose a combination of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to create a hybrid optimization technique which meets k-coverage criteria in Wireless sensor Network (WSN) and Internet of Things (IoT). The goal of this approach is to increase the coverage area alongside minimizing the deployment cost of the sensors. Another aim is to reduce the power consumption. This technique is helpful to determine the optimal position of the sensors. It helps to ensure that each target point in the network is getting the coverage of at least k sensors. In this way we can enhance the reliability and robustness of the network. From experimental results, we can conclude that with optimal sensor utilization and minimized redundancy, our proposed approach achieves 97.14% coverage ratio with k-coverage equals 3. According to performance metrics, it is clear that there is consistent energy efficiency through the cycles. The approach converges in less than 3 second. So, this approach is applicable for real time applications. It is a hybrid metaheuristic approach which is very efficient in managing various challenges associated with sensor deployment. From this approach we can get solutions of coverage enhancement, energy efficiency and improved sensor utilization. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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A Hybrid Optimization Strategy for k-Coverage Maximization in Wireless sensor and IoT Networks

A Hybrid Optimization Strategy for k-Coverage Maximization i...

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International Symposium on advanced Computing and Communication (ISACC)

作者： Prithwish Manna Sudipta Majumder N. Hemarjit Singh Dept. of CSE DUIET Dibrugarh University Dibrugarh Assam India Dept. of ECE DUIET Dibrugarh University Dibrugarh Assam India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

关键词： Wireless communication Wireless sensor networks Turning Energy efficiency sensor systems Real-time systems Internet of Things Particle swarm optimization Intelligent sensors Genetic algorithms

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Reinforcement Learning for Mobile Robot's Environment Exploration

Reinforcement Learning for Mobile Robot's Environment Explor...

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Robotics and Technologies for Industrial Automation (ROBOTHIA), IEEE International conference on

作者： Saif Alahdal Nasr Abdalmanan Kamarulzaman Kamarudin Muhammad Aizat Abu Bakar Mohd Rizal Manan Ahmad Shakaff Ali Yeon Syed Muhammad Mamduh Faculty of Electrical Engineering & Technology Universiti Malaysia Perlis Arau Malaysia Center of Excellence for Advanced Sensor Technology Faculty of Electrical Engineering & Technology Universiti Malaysia Perlis Arau Malaysia Faculty of Electrical Engineering & Technology Universiti Malaysia Perlis Perlis Malaysia Center of Excellence for Advanced Sensor Technology Universiti Malaysia Perlis Arau Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

The field of mobile robotics has become increasingly significant in today's technology-driven world, with applications ranging from autonomous vehicles to automated delivery systems. This project aims to enhance the capabilities of mobile robots in navigating and exploring unfamiliar environments by developing and implementing a Reinforcement Learning (RL) model. The core objective is to enable these robots to autonomously explore, navigate, and adapt to new surroundings efficiently. By integrating the RL model into a mobile robot using the Robot Operating System (ROS), the project evaluates the system's efficiency through comprehensive ground experiments. The research compares the RL-based approach with established exploration methods, emphasizing the effectiveness of the Value Iteration algorithm, a dynamic programming algorithm for Markov Decision Processes (MDPs). Controlled experiments conducted in a 6×3 meter flat space with static obstacles and a 2D lidar sensor provide valuable insights into the RL model's performance and potential real-world applications. The findings highlight the advantages of the Value Iteration algorithm over traditional exploration methods, demonstrating its potential for improving autonomous robotic navigation and exploration.

关键词： Adaptation models Accuracy Service robots Heuristic algorithms Reinforcement learning Robot sensing systems Robustness Dynamic programming Mobile robots Robots

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Nanostructure-Based Highly Sensitive and Reliable Piezo-Transmittance Strain sensor and Integrated System

Nanostructure-Based Highly Sensitive and Reliable Piezo-Tran...

引用

IEEE International conference on Micro Electro Mechanical systems

作者： Myung-Kun Chung Su-Min Jeon Jae-Soon Yang Jae-Young Yoo Min-Uk Kim Beom-Jun Kim Tae-Yeon Lee Min-Seung Jo Jun-Bo Yoon School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Republic of Korea Department of Semiconductor Convergence Engineering Sungkyunkwan University (SKKU) Republic of Korea Center for Bio-Integrated Electronics Northwestern University Evanston IL USA

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

Advancements in human-machine interaction, health monitoring, and robotics have led to increasing demand for reliable strain sensors. Of the various flexible sensors, piezo-transmittance strain sensors adjust their optical transmittance in response to strain and can overcome limitations seen in other sensor types. However, current piezo-transmittance strain sensors face challenges related to controllability and applicability, as well as constraints from bulky designs and complex wired connections. This paper reports a highly sensitive, reliable, and mechanically durable piezo-transmittance strain sensor that addresses these challenges. In addition, we propose a simple, wireless integrated sensor system that eliminates the need for bulky and complex wiring, offering a practical solution for human motion detection applications.

关键词： Wireless communication Micromechanical devices Human computer interaction Wireless sensor networks Materials reliability Capacitive sensors sensor systems Optical sensors Monitoring Strain

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Sensitivity of the Rolling Bearings Diagnostic Method Depending on the Number of Measuring Points of a Multipoint Temperature sensor

Sensitivity of the Rolling Bearings Diagnostic Method Depend...

引用

Digital Signal Processing and its applications (DSPA), International conference on

作者： Denis K. Lebedev Laboratory of Self-Validating Sensors Systems and Advanced Instrumentation South Ural State University Chelyabinsk Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

This work is aimed at research in the field of developing methods for the diagnosis of rolling bearings. This paper evaluates the influence of a number of measuring points of a multipoint temperature sensor on the performance of a rolling bearing diagnostic method. The study considers a rolling bearing diagnosis method based on analyzing a bearing temperature field and applying a neural network with an “autoencoder” architecture. The research of efficiency of the method is made on the temperature field data of the rolling bearing, which is a part of the technical equipment of the metal rolling line. As a result of this work, the hypothesis that the efficiency of the diagnostic model increases with the increase in the number of measuring points was confirmed.

关键词： Temperature measurement Temperature sensors Analytical models Temperature distribution Sensitivity Neural networks Autoencoders Rolling bearings Metals Battery charge measurement

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Leveraging Machine Learning Models for Proactive Disaster Forecasting

Leveraging Machine Learning Models for Proactive Disaster Fo...

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AIML-applications for Engineering & Technology (ICAET), International conference on

作者： Daimi Syed Affan Askari Arslaan Ahmed Ahmed Syed Maaz Iqbal Asma Computer Science and Engineering Muffakham Jah College of Engineering and Technology Hyderabad India Electronics and Communication Engineering Deccan College of Engineering and Technology Hyderabad India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

The increasing frequency and intensity of natural disasters, such as earthquakes, tsunamis, floods, and forest fires, necessitate the development of advanced early warning systems. Current disaster prediction systems are hampered by limitations in sensor technology, which is often expensive and primarily effective for large-scale disasters. This project aims to enhance disaster prediction accuracy and timeliness by utilizing real-time data and advanced machine learning algorithms, including XGBoost, Voting classifier, and Random Forest classifier. Additionally, recognizing the long-term health impacts of poor air quality, the project extends its scope to predict the Air Quality Index (AQI). The project seeks to enable proactive risk management and effective disaster response by integrating real-time environmental data and machine learning models. We aim to improve proactive risk management and disaster response by employing real-time data. The outcomes of this project promise significant advancements in disaster preparedness, ultimately safeguarding human lives and infrastructure.

关键词： Disasters Forestry Air quality Tsunami Real-time systems Risk management Indexes Floods Forecasting Random forests

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Design and Simulation of Surface Acoustic Wave sensor in COMSOL Multiphysics for Detection of Acetone

Design and Simulation of Surface Acoustic Wave Sensor in COM...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Praneeth Borra Prasanti Korapati Vamsi Sravanam Shanmuk Bala Dikishitha Sree Tikkisetty Department of Electronics and Instrumentation Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The increasing demand for efficient and reliable gas sensors has propelled the exploration of novel materials and advanced simulation techniques. This study presents the design and development of a gas sensor that detects acetone using molybdenum disulfide $(\mathbf{MoS}_sensor)$ as the sensing material. Acetone is a major volatile organic compound that has applications in a variety of industries, including healthcare and environmental monitoring. Acetone gas is a respiratory symptom that indicates diabetes. The high sensitivity, stability and cost of $\mathbf{MoS}_{\mathbfsensor}$ make it a good sensor material. The sensor design involves the integration of $\mathbf{MoS}_{\mathbfsensor}$ thin films onto a substrate, with interdigitated electrodes facilitating the detection mechanism. COMSOL Multiphysics, a versatile simulation software, is employed to model the sensor's performance under different operating conditions and geometrical configurations. The simulation framework incorporates electrochemical processes, mass transport phenomena, and electrical properties to accurately represent the sensor's *** was found that the resistance of the sensor layer was reduced in the presence of acetone gas. The simulation was carried out using COMSOL Multiphysics.

关键词： Volatile organic compounds Transducers Surface resistance Surface acoustic waves Titanium Software Molybdenum Gas detectors Substrates Surface treatment

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IoT based Real Time and Remote Fuel Tank and CNG Gas Tank Monitoring System

IoT based Real Time and Remote Fuel Tank and CNG Gas Tank Mo...

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Trends in Material Science and Inventive Materials (ICTMIM), International conference on

作者： Arulkarthick E K Srivarshini S Suvathi K Yazhini S Department Of ECE Nandha Engineering College (Autonomous) Erode India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Efficient and safe monitoring of fuel and gas levels is critical in modern systems, particularly for vehicles and industrial applications. This project, titled "Fuel and Gas Level Monitoring," introduces an advanced IoT-enabled system that tracks fuel and Compressed Natural Gas (CNG) levels in real-time. By utilizing a combination of sensors—including a level sensor, pressure sensor, and gas sensor— integrated with an Arduino microcontroller, the system ensures accurate measurements and robust safety features. The primary objectives are twofold: real-time monitoring to prevent unexpected shortages and optimize refueling schedules, and enhanced safety by detecting gas leaks or abnormal pressure levels to mitigate potential hazards. Data from the sensors is displayed on an LCD for local monitoring and uploaded to the cloud via Wi-Fi for remote access and analytics. Alerts, both visual and audible, are triggered through the LCD and buzzer for immediate attention in case of critical *** to existing systems that rely on basic fuel-level tracking and limited connectivity, this proposed solution incorporates IoT capabilities for seamless cloud integration, making it more reliable and scalable. It leverages wireless communication for real- time data accessibility, enhancing operational efficiency and safety standards. This innovative system has significant implications for ensuring safety, efficiency, and proactive maintenance in fuel and gas monitoring applications.

关键词： Wireless communication Cloud computing Schedules Fuel storage Real-time systems Liquid crystal displays Hazards Fuels Monitoring Wireless fidelity

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