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32nd European Safety and Reliability conference, ESREL 2022

32nd European Safety and Reliability Conference, ESREL 2022

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32nd European Safety and Reliability conference, ESREL 2022

ISBN: (纸本)9789811851834

The proceedings contain 438 papers. The special focus in this conference is on Safety and Reliability. The topics include: Towards a Harmonized Framework for Vessel Inspection via Remote Techniques;Probabilistic Risk Analysis in the Overhaul of Aero-engines using a combination of Bayesian Networks and Fuzzy Logic aiming at meeting Civil Aviation Agency Regulations and the requirements AS9100 - A Case Study;comparing distributed and integrated Hazard Analysis Environments;influence of Cargo Arrangement on Aircraft Mass Center of Gravity;designing reliable, data-driven maintenance for aircraft systems with applications to the aircraft landing gear brakes;introducing the Structured Exploration of Complex Adaptations to learn from operations in an Air Navigation Service Provider;application of Monte Carlo simulation to the reliability estimation of an experimental Data Communication System;FBG-based optical sensor networks for thermal measurements in aerospace applications;risk Analysis of Unmanned Air Vehicle and Beyond Visual Line of Sight Flights: How Does systems Thinking Add to the Specific Operations Risk Assessment Method?;estimation of industrial assets ageing and maintenance efficiency with interval censored data;application of industry standards and management commitment to asset management in a petrochemical company;a general framework proposal for modelling complex production systems considering maintenance and operative factors under uncertain. Case study in Mining Industry;managing Asset and Supply Chain Resilience Using A Digital Risk Twin;Exploring how automated technology and advanced driver-assistance systems (ADAS) are taught in the Norwegian driver-training industry. A qualitative study;reliability of fault-tolerant system architectures for automated driving systems;optimizing Multiple Reinsurance Contracts.

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Toxic Gas Monitoring Detector

Toxic Gas Monitoring Detector

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Networking and Communications (ICNWC), International conference on

作者： Kishore. J R. Anirudh Reddy A. Aakash Reddy CH. Rithik Reddy B. Pranay Varma CH. Yogendar Dept. of ECE B. V. Raju Institute of Technology Medak Telangana India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

This project deals with Real-time monitoring of gas concentrations is provided by the system, which also notifies users when concentrations rise over acceptable bounds. The system may be used in a variety of contexts since it is affordable and simple to setup. Due to its advanced technology and ability to identify many hazardous gases, the Gas sensor is an excellent option for simultaneous multi-gas detection. Temperature and humidity are two other critical parameters for gas detection that are measured by the temperature sensor. For remote monitoring and control, the wifi module facilitates wireless connection and data transfer. The gas sensor's accuracy and the wifi module's numerous capabilities improve the accuracy and dependability of the system. Real-time monitoring is made simple by the system's user-friendly interface and adjustable threshold alerting *** system has several benefits over other current systems since it makes use of the temperature sensor and wifi module. Real-time data transfer and remote monitoring are made possible by the wifi module's dual-core design and wireless communication technologies, which makes it a great option for Internet of Things applications.

关键词： Temperature sensors Wireless communication Temperature measurement Gases Detectors Data transfer Real-time systems

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A Wireless sensor Network Application in Forest Fire Early Detection: A Smart and Secure Approach

A Wireless Sensor Network Application in Forest Fire Early D...

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Intelligent systems and Machine Learning conference (ISML)

作者： Hamdy Soliman Ahshanul Haque Department of Computer Science New Mexico Tech

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

Forest fires have devastating effects on the environment and human lives, making early prediction and mitigation crucial. In this paper, we introduce a novel approach for a smart and secure forest fire early detection and also sense of prediction system that integrates wireless sensor networks (WSN), artificial neural networks (ANNs), and advanced security measures. Drawing insights and continuing to advance our previous related research in the field, we propose an innovative and comprehensive forest fire prediction system that addresses the challenges posed by these natural disasters. Our approach leverages the capabilities of WSNs for data collection, employs ANNs for real-time smart fire prediction, and incorporates robust and intelligence security measures to protect such vulnerable sensory systems from external threats. We discuss the system’s architecture, data preprocessing techniques, experimental results, and potential applications, highlighting its significance in forest fire management and beyond. Compared to other peer works in the field, we have the advantage of obtaining much more fire scenarios, rich enough to sense not only early detection but also predication, with very promising accuracy. Moreover, our approach will help providing great control to extinguish existing fire.

关键词： Wireless sensor networks Accuracy Transfer learning Training data Forestry Artificial neural networks Predictive models Vectors Trajectory Security

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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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Enhanced In-Home Human Activity Recognition Using Multimodal Sensing and Spatiotemporal Machine Learning Architecture

Enhanced In-Home Human Activity Recognition Using Multimodal...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Seyyed Mahdi Torabi Mohammad Narimani Edward J. Park School of Mechatronic Systems Engineering Simon Fraser University Surrey Canada WearTech Labs Simon Fraser University Surrey Canada

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In this research, we present an enhanced human activity recognition (HAR) framework using advanced machine learning models incorporating temporal dynamics, leveraging multimodal sensor data. Data from wearable wristbands and real-time location systems (RTLS) were used to detect human activities within a home environment. A key advancement is the development of a spatiotemporal machine learning model combining convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and neural structured learning (NSL), which significantly surpasses traditional machine learning baseline models like RF, SVM. We also highlight the efficacy of sensor fusion achieving an accuracy of 86.21% and an F1 score of 87.40% for routine daily activities by combining IMU and RTLS sensors, compared to using each sensor modality independently. The proposed model paves the way for developing smart environments that can intelligently adapt to the varying routines and behaviors of daily life. Our investigation shows potential applications across diverse domains, including elderly care, smart home technologies, and healthcare monitoring, suggesting the broad applicability and benefits of the developed HAR system.

关键词： Machine learning Smart homes sensor fusion Data models sensor systems Spatiotemporal phenomena Human activity recognition Convolutional neural networks Biomedical monitoring Monitoring

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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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Predictive Vigilance: Harnessing Internet of Things and Machine Learning for Smoke Detection

Predictive Vigilance: Harnessing Internet of Things and Mach...

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International conference on Information, Intelligence, systems and applications (IISA)

作者： Gregory Davrazos George Raftopoulos Theodor Panagiotakopoulos Sotiris Kotsiantis Achilles Kameas Dept. of Mathematics University of Patras Patras Greece Hellenic Open University Greece School of Business University of Nicosia Cyprus

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

The increasing prevalence of fire incidents in various environments underscores the critical need for advanced smoke detection systems. This paper presents a novel approach, termed “Predictive Vigilance”, which integrates Internet of Things (IoT) sensors with state-of-the-art machine learning techniques to enhance smoke detection capabilities. By leveraging the inter-connectedness of IoT devices and the computational power of machine learning algorithms, a system can achieve real-time monitoring and predictive analysis of smoke presence. We propose a framework that combines sensor data acquisition, preprocessing, feature extraction, and classification using machine learning models. Through extensive experimentation and validation, we demonstrate the effectiveness and efficiency of our approach in detecting smoke accurately while minimizing false alarms. Furthermore, we discuss potential applications and implications of Predictive Vigilance in diverse settings, including residential, commercial, and industrial environments. Overall, this paper contributes to the advancement of proactive fire safety measures by harnessing the synergy between IoT technology and machine learning for smoke detection.

关键词： Surveillance Scalability Data acquisition Machine learning Real-time systems sensors Internet of Things Predictive analytics Fire safety Testing

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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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Human Activity Recognition Using sensor Fusion and Deep Learning for Ergonomics in Logistics applications

Human Activity Recognition Using Sensor Fusion and Deep Lear...

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IEEE International conference on Internet of Things and Intelligence System (IOTAIS)

作者： Ahmad Arab Antonius Schmidt Dominik Aufderheide Faculty of Electrical Engineering Industrial Measurement Section (iMe) South Westphalia University of Applied Sciences Soest Germany

The rapid growth within the e-commerce industry and the necessity of hiring more inexperienced labor in logistics made the warehouse safety more critical than ever. Monitoring the workers' activity during warehouse operations is a fundamental task to create an ergonomic workplace and to reduce operational costs. Therefore, an advanced Human Activity Recognition (HAR) solution is needed to monitor such industrial environments. In this paper, the development of an accurate and reliable HAR classifier using sensor fusion methods is investigated. Three Inertial Measurement Units (IMUs) are used to capture body movement while performing typical warehouse tasks. Deep Learning (DL) algorithms are employed to model the raw time series data and two sensor fusion techniques are applied to combine the data from the different sensors. Overall, the optimal HAR classifier was found to be a stacked model, being more practical and less computationally expensive for industrial applications.

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An AI-Assisted Autonomous IoRT Agent for Smart Spaces

An AI-Assisted Autonomous IoRT Agent for Smart Spaces

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International conference on Computer Science and Engineering (UBMK)

作者： Yakup Kayataş Sanem Kabadayı Department of Computer Engineering Istanbul Technical University Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

The Internet of Robotic Things (IoRT) integrates autonomous robots into smart environments and enables advanced applications in various domains such as smart cities, warehouses, and factories. In this study, we implement a modular IoRT system that can be adapted to many application domains. Our system uses a differential drive autonomous robot with ROS (Robot Operating System) for localization and navigation along with an orchestrator for interoperability with IoT devices and processes environmental sensor data to dynamically assist the robot's actions. To enhance the robot's autonomy and decision- making, we use artificial intelligence (AI) in two areas: (i) real-time human detection using AI computer vision to improve spatial awareness, and (ii) determining and following the shortest path for navigation. When a human is detected at the smart station (designated area equipped with an IoT ultrasonic sensor and an IoT camera), the robot autonomously navigates there. This implementation illustrates an exemplary use of IoRT in the transportation domain. The novel contributions of this work are the integration of IoT devices, an autonomous robot, and AI-based methods in an IoRT system, modularity that supports adaptation to different applications, and real-time system monitorina throuah WebSockets.

关键词： Computer vision Navigation Smart cities Robot sensing systems Real-time systems Internet of Things Robots Monitoring Smart spaces Autonomous robots

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