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

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Digital Twin-Driven Bearing-Fault Detection in Induction Motor and Drives using Graph Sampling and Aggregation Network

Digital Twin-Driven Bearing-Fault Detection in Induction Mot...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haraprasad Badajena Suryanarayan Majhi Bivash Chakraborty Mamata Jenamani Aurobinda Routray Ronit Dutta Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Industrial and Systems Engineering Indian Institute of Technology Kharagpur India Dept. of Electrical Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Bearing fault diagnosis is crucial for ensuring the reliability and safety of industrial systems, particularly in preventing operational failures and maintaining product quality. Traditional signal processing methods and deep learning algorithms, while useful, often overlook the complex structural relationships within sensor data, limiting their diagnostic effectiveness. To address this, we present a novel Digital Twin-Driven Fault Diagnosis Framework that integrates graph-based learning techniques with advanced signal analysis. Our approach employs XGBoost and GraphSAGE embeddings to capture both spatial and temporal correlations within the current signals. The raw sensor data is then processed using a sliding window technique and time-frequency domain features are extracted then transformed into a graph structure that represents the intricate relationship in the signal. GraphSAGE is then applied to these graph structures, generating embeddings that enhance fault detection accuracy. Additionally, XGBoost is utilized for classification, improving the overall robustness of the system. The proposed method, deployed on edge devices, delivers real-time diagnostics, providing a scalable and efficient solution for industrial applications. Experimental results using real-world datasets demonstrate that our method significantly outperforms state-of-the-art algorithms, improving detection accuracy and Area under the curve (AUC) scores up to 97% and 99%, respectively.

关键词： Fault diagnosis Time-frequency analysis Accuracy Correlation Fault detection Image edge detection Signal processing algorithms Feature extraction Real-time systems Speech processing

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Advancing Real-Time Object Detection for Autonomous Vehicles with YOLOv11

Advancing Real-Time Object Detection for Autonomous Vehicles...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Manish Mahala Manisha Pattanaik Gaurav Pandey ABV-Indian Institute of Information Technology and Management (IIITM) Gwalior India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The safe and efficient operation of autonomous vehicles relies heavily on robust and real-time object detection systems. This paper presents YOLOv11, the latest advancement in the YOLO (You Only Look Once) series, optimized for detecting critical objects in autonomous driving scenarios, including vehicles, pedestrians, and road signs. YOLOv11 incorporates novel architectural enhancements, such as the C3k2 block for efficient feature extraction and the Cross-Stage Partial with Spatial Attention (C2PSA) module for enhanced spatial attention, enabling state-of-the-art accuracy and real-time performance. Evaluations conducted on the KITTI dataset, augmented with advanced data augmentation and multi-scale training techniques, demonstrate substantial improvements in precision, recall, and mean Average Precision (mAP) compared to its predecessors. Furthermore, the integration of YOLOv11 into autonomous systems leverages sensor fusion to deliver comprehensive perception capabilities, establishing its potential for real-world deployment. This study highlights YOLOv11’s contributions to advancing autonomous vehicle technology and provides valuable insights for developing robust, scalable, and efficient object detection frameworks for future applications.

关键词： YOLO Training Technological innovation Accuracy Computational modeling Roads sensor fusion Real-time systems Usability Autonomous vehicles

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IoT-Based Worker Safety and Environmental Monitoring

IoT-Based Worker Safety and Environmental Monitoring

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Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： Vijay Gaikwad Jay Paunikar Arya Kale Jagruti Supe Sahil Kakad Kabir Khake Department of Multidisciplinary Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The system ensures worker safety in hazardous environments like mines through real-time monitoring using NodeMCU. It incorporates sensors such as MQ7 for detecting carbon monoxide, MQ135 for carbon dioxide and other harmful gases, and DHT22 for temperature and humidity. With an accuracy of 97%, the system delivers reliable performance in 90% of typical conditions. The NodeMCU's built-in Wi-Fi facilitates efficient data transmission to a mobile app, maintaining real-time updates with minimal delays of 1–3 %. Under normal conditions, workers can carry out tasks without concern, but when hazardous gases or unsafe temperatures are detected, the system sends instant alerts to the app, enabling prompt corrective actions. This advanced monitoring solution provides effective safety measures, ensuring both protection and operational efficiency in high-risk workplaces. Its seamless operation and proactive alert system make it a dependable tool for safeguarding workers' well-being

关键词： Temperature sensors Temperature measurement Gases Accuracy Real-time systems sensor systems Safety sensors Mobile applications Reliability

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A Flexible Hybrid Resource Allocation Model for Enhanced Efficiency using AI in Wireless sensor Networks

A Flexible Hybrid Resource Allocation Model for Enhanced Eff...

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Electronics and Renewable systems (ICEARS), International conference on

作者： Purushotham Endla R. Mohandas Achinta Saikia C. Lakshmi G Karthikeyan Tejashree Tejpal Moharekar Department of Physics School of Sciences and Humanities SR University Warangal Telangana India Department of ECE Chennai Institute of Technology Chennai Department of Statistics Mariani College Jorhat Assam Saveetha Engineering College Chennai Department of Artificial Intelligence & Data Science Karpagam College of Engineering Coimbatore Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Wireless sensor Networks (WSNs) have become a cornerstone technology in various fields, including environmental monitoring, industrial automation, and healthcare. This paper presents a novel hybrid resource allocation model designed to enhance efficiency in Wireless sensor Networks (WSNs) using advanced Artificial Intelligence (AI) techniques. The proposed model integrates static and dynamic resource allocation strategies with machine learning and reinforcement learning to optimize key network resources such as energy, bandwidth, and processing power. Comprehensive evaluations demonstrate that the proposed model outperforms nine existing models across multiple metrics, including energy efficiency, network lifetime, throughput, delay, latency, and accuracy of resource allocation. The proposed model achieved an energy efficiency of 0.98 J/bit, extended network lifetime to 620 days, and maintained a high throughput of 620 Kbps. Notably, the model demonstrated the lowest delay (90 ms) and latency (180 ms) while achieving the highest resource allocation accuracy of 97.8%. These results highlight the model’s capability to dynamically adjust resource allocation in response to real-time network conditions, ensuring optimal performance and resource utilization. The proposed model’s success in efficiently managing WSN resources marks a significant advancement in the field, offering a robust solution for various WSN applications requiring high efficiency and reliability.

关键词： Wireless sensor networks Renewable energy sources Accuracy Reinforcement learning Throughput Energy efficiency Real-time systems Delays Resource management Reliability

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1.2 From Chips to Thoughts: Building Physical Intelligence into Robotic systems

1.2 From Chips to Thoughts: Building Physical Intelligence i...

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IEEE International conference on Solid-State Circuits (ISSCC)

作者： Daniela Rus CSAIL Massachusetts Institute of Technology Cambridge MA

ISBN: (数字)9798331541019

ISBN: (纸本)9798331541026

The rapid growth of AI technologies has brought unprecedented advancements across numerous domains, from healthcare to autonomous systems, yet this progress has been accompanied by substantial energy demands. Generative AI has revolutionized the accessibility of advanced machine learning models, putting powerful AI capabilities directly into the hands of everyday users. Tools that generate text, images, video, and audio have made AI more democratic, fueling creativity, productivity, and innovation across industries. This explosion of generative AI applications, from chatbots to art generators, has transformed AI from a niche tool into an indispensable asset in our digital lives, as many can now access these technologies through their phones or computers. However, this widespread accessibility comes with significant trade-offs, particularly in terms of the energy required to train and deploy these enormous models. Large generative models such as GPT, DALL-E, and other multi-modal AI systems demand immense computational resources, translating to high energy consumption both during the extensive training process and when they are used in practice.

关键词： Energy consumption Technological innovation Liquids Generative AI Computational modeling Energy efficiency State-space methods Smart grids Artificial intelligence sensor arrays

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AI-Powered Soil Stability Assessment and Landslide Forecasting with Multi-sensor Networks, Edge Computing, and Jetson Nano Integration

AI-Powered Soil Stability Assessment and Landslide Forecasti...

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

作者： N. Muthukumaran A. Vignesh S. Aruna M. Jenish T. Vinoth Kumar Shanthi Madasamy Department of ECE Sri Eshwar College of Engineering Coimbatore India Department of EEE Dr. N.G.P Institute of Technology Coimbatore India Department of EEE RVS College of Engineering and Technology Coimbatore India Department of EEE JCT College Of Engineering And Technology Coimbatore India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Landslides pose a severe threat to mountainous regions, often resulting in catastrophic damage and loss of life. Existing prediction methods rely on limited data sources and infrequent monitoring, leading to delays in early warnings. Previous technologies have explored individual sensor applications, such as Ground-Penetrating Radar (GPR) and seismic sensors, but lack a comprehensive multi-sensor approach with real-time processing. Notable cases, like the Wayanad landslides in Kerala, India, highlight the urgent need for advanced predictive systems. This study proposes an AI-driven landslide prediction system using a network of 40 sensor-equipped slave kits. Each kit integrates GPR, seismic, Electromagnetic Induction (EMI), ultrasonic, LiDAR, capacitance probes, gravimeters, Electrical Resistivity Tomography (ERT), and magnetometers onto a Teensy 4.1 microcontroller with an ESP8266 for wireless data transmission. Data is processed at a central master unit featuring a Jetson Nano module, employing the ALEXNET+BILSTM model for real-time analysis of soil stability and landslide risk. By leveraging edge computing, this system enables rapid decision-making and early warnings, addressing the limitations of existing methods. Its scalable, cost-effective architecture enhances environmental monitoring, offering a robust solution for landslide-prone communities and significantly improving disaster preparedness.

关键词： Wireless communication Landslides Wireless sensor networks Soft sensors Soil Tomography Stability analysis Real-time systems Terrain factors Edge computing

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Ceramic-Based High-Q Retroreflectors for Sub-mm Localization in High-Temperature Environments

Ceramic-Based High-Q Retroreflectors for Sub-mm Localization...

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International Workshop on Mobile Terahertz systems (IWMTS)

作者： Alejandro Jiménez-Sáez Masoud Sakaki Jesús Sánchez-Pastor Martin Schüßler Rolf Jakoby Niels Benson Technical University of Darmstadt Darmstadt Germany University of Duisburg-Essen Duisburg Germany

ISBN: (数字)9798350365832

ISBN: (纸本)9798350365849

This scientific paper is an outcome of the MARIE project (for more information, see ***) C09. This research addresses the development of innovative frequency-coded tags for localization, sensing, and ranging in harsh environments. The study focuses on enhancing orientation-insensitive THz retroreflectors through advanced approaches, including multi-notch high Radar Cross-Section (RCS) Corner Reflectors (CR) and Quasi Conformal Transformation Optics (QCTO)-enabled designs. Core innovations involve the integration of Luneburg lenses with Photonic Crystal (PhC)-based coding particles, fabricated using advanced materials such as Deep Reactive Ion Etched (DRIE) High-Resistive Silicon (HR-Si) and Lithography-based Ceramic Additive Manufacturing (LCM) with Alumina (Al₂O₃). These designs enable high-Q resonators for effective signal detection in extreme conditions up to 1200 °C, as in an indoor fire scenario. Achievements include wide-field-of-view (FoV) retroreflectors, frequency-coded lens reflectors, and wireless readouts capable of sub-mm localization. Future work will explore multi-material additive manufacturing and Liquid Crystal (LC)-based Reconfigurable Intelligent Surfaces (RIS) to advance state-of-the-art localization systems for autonomous rescue applications. These findings establish a foundation for robust, high-temperature sensor fusion technologies.

关键词： Location awareness Wireless communication Wireless sensor networks Ultraviolet sources Reconfigurable intelligent surfaces Three-dimensional printing sensors Retroreflectors Resonators Lenses

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Enhanced Clustering Framework for Unveiling Hidden Patterns in IoT-Based Structured and Unstructured Data

Enhanced Clustering Framework for Unveiling Hidden Patterns ...

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International Symposium on advanced Computing and Communication (ISACC)

作者： Prabhat Das Karthik Kovuri Sajal Saha Department of IT The Assam Kaziranga University Department of CSE Adamas University Department of CSE The Assam Kaziranga University

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Clustering algorithms play a pivotal role in uncovering hidden patterns and relationships within diverse datasets. This study discusses about an enhanced real-time clustering approach based on the Birch methodology, optimized for analyzing both structured and unstructured data. Structured data, consisting of IoT sensor readings, and unstructured image data, captured using Raspberry Pi devices deployed in various locations, were analyzed to identify meaningful patterns. For structured data, the algorithm effectively clustered environmental parameters, revealing correlations and anomalies. For unstructured data, high-dimensional image features extracted using a pre-trained VGG16 model were clustered, uncovering patterns in texture, color, and structure. Experimental results demonstrate the algorithm’s scalability and efficiency, achieving high-quality clusters with average Silhouette Scores of 0.87 and 0.81 for structured and unstructured datasets, respectively. These findings highlight the algorithm’s adaptability and its potential for real-time analytics in diverse applications such as environmental monitoring and image-based decision-making systems.

关键词： Visualization Temperature distribution Correlation Image color analysis Scalability Clustering algorithms Feature extraction Real-time systems Environmental monitoring Object recognition

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GIIDS: Generalized Intelligent Intrusion Detection System for Heterogeneous UAVs in UAM

GIIDS: Generalized Intelligent Intrusion Detection System fo...

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International conference on advanced Communication Technology (ICACT)

作者： Fahmina Kabir Nishat I Mowla Inshil Doh Division of Artificial Intelligence and Software Ewha Womans University Seoul Korea Department of Industrial Systems RISE Research Institutes of Sweden Sundsvall Sweden Department of Cyber Security Ewha Womans University Seoul Korea

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

Unmanned Aerial Vehicles (UAVs) are increasingly integral in various sectors, simultaneously encountering rising security threats as UAV and Urban Air Mobility (UAM) networks continue to expand. This paper addresses the challenge of securing UAM networks while also emphasizing generalizability of the security solution to protect heterogeneous UAVs against threats that compromise their stability, reliability and can cause catastrophic failures such as a crash landing. The deployment of traditional machine learning (ML) based intrusion detection systems (IDSs) is often hampered in real-world applications due to a lack of generalizability of the security solution. As a result, the system fails to provide adequate security across the varying models and platforms of UAVs, each with its unique statistical properties and data distributions. To address these challenges, we focus on employing a comprehensive set of UAV sensor parameters, tailored feature engineering and selection to develop multi-stage cross-validated ensemble learning systems to facilitate generalized detection of attack and non-attack cases. For additional analysis, we cross-validate the models using two different cross-validation techniques. The proposed stacking ensemble systems provide the overall best performance, with AUC within the range of 92% to 100% across different cross-validations.

关键词： Analytical models Scalability Stacking Intrusion detection Performance gain Autonomous aerial vehicles Robustness Data models Stability analysis Thermal stability

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Smart Drying Technologies: A Case Study on Red Flesh Apple (Kissabel®) Using Computer Vision and Deep Learning

Smart Drying Technologies: A Case Study on Red Flesh Apple (...

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International Mid-Term conference of the Italian Association of Agricultural Engineering, MID-TERM AIIA 2024

作者： Benelli, Alessandro Pandolfi, Flavia Bandiera, Andrea Taormina, Eleonora Russo, Paola Adiletta, Giuseppina Massantini, Riccardo Moscetti, Roberto Department for Innovation in Biological Agro-Food and Forest Systems University of Tuscia Viterbo Italy Department of Chemical Engineering Materials Environment Sapienza University of Rome Rome Italy

ISBN: (纸本)9783031842115

Food drying is a crucial process that helps in enhancing the shelf-life of food at ambient temperature, also lowering transportation costs. However, erroneous applications can degrade product quality and lead to a significant energy consumption as common dryers use fossil fuels. The present study aims to develop a "smart" prototype hot-air dryer augmented with Computer Vision (CV) and Deep Learning (DL) technologies to monitor quality and drying status of red-flesh sliced apples (Malus domestica B. - cultivar Kissabel®) during the process. Both CV and DL represent Process Analytical Technology (PAT) tools that, combined with a Quality by Design (QbD) approach, can lead to real-time data from the process, which are potentially vital for understanding and controlling the drying dynamics of products. The experimentation was conducted on apple slices (approx. 5-mm thick) using the following drying parameters: drying time, 24 h;temperature, 35 ℃;R.H., 35%;and airflow, 3 m s−1. The "smart" drying system consisted of (i) a CMOS digital camera;(ii) a LED illumination setup (4200 K);(iii) a load cell;(iv) an Arduino UNO microcontroller (master);(v) a Jumo Dicon Touch controller (slave);(vi) a temperature and R.H. sensor;and (vii) a Raspberry Pi 4 microcomputer. The open-source software Node-Red was used as an orchestrator for controlling devices, as well as acquiring and storing product features in real-time, such as weight, color, size, and shape of slices. All acquired images of apple slices were subjected to semantic segmentation using a deep learning model. With the slice shrinkage data obtained from semantic segmentation and the moisture data, time-independent moisture ratio prediction models were developed. The approach opens new perspectives for the use of CV and DL technologies in food drying. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer vision deep learning moisture red flesh apples shrinkage smart drying

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