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

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Design a Touch sensor Using Conductive Spandex Fabric 7th

Design a Touch Sensor Using Conductive Spandex Fabric

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7th International conference on Green Technology and Sustainable Development

作者： Minh Ky Tran Thi Tuyet Nga Ly Bao Hy Nguyen Chi Cuong Vu HCMC Univ Technol & Educ Dept Facil Elect & Elect Engn 01 Vo Ngan St Ho Chi Minh City Vietnam

ISBN: (纸本)9783031762314;9783031762321

Recent advancements in sensor technology have propelled the e-textile industry to the forefront of innovation. E-textiles, also known as smart fabrics, possess physical and dimensional flexibility, making them suitable for wearable sensor technology. These technologies are developed to cater to the needs of advanced daily life, which aligns with the IoT development trend. This paper introduces an applied resistive sensor that uses an electronic fabric made from elastic fabric and carbon nanotubes (CNTs) fabric as the electrical layer. The sensor achieves high sensitivity (2.6241 N-1 with force less than 1 N), thickness (similar to 3 mm), and versatility make it ideal for load performance and fast resistance switching without waiting for capacitor charging and discharging as capacitive sensors, facilitating faster interaction with e-touch. The study provides an overview of the sensor's characteristics and future directions, emphasizing the creation of soft sensors based on pyralux for medical devices and health monitoring applications in the future.

关键词： Touch sensor Carbon nanotubes (CNTs) Conductive spandex fabric Force-sensing resistor (FSR)

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Evaluating the Impact of Lossy Compression on ADAS Deep Learning Models using Fisheye Cameras 26

Evaluating the Impact of Lossy Compression on ADAS Deep Lear...

引用

26th Irish Machine Vision and Image Processing conference, IMVIP 2024

作者： Simha, Srinidhi Mukanahallipatna Molloy, Dara Fahy, Darren Valeo Vision Systems Ireland University of Galway Ireland

ISBN: (纸本)9781837242672

The increasing deployment of advanced Driver Assistance systems (ADAS) alongside the continual rise in camera sensor resolution has led to high bandwidth, and generally high cost, computation, and intra-vehicle communication. While the sensor bandwidth impacts the vehicle architecture, it also affects the data collection, storage, deep learning model training, and validation infrastructures. However, if the bandwidth was low, while still achieving the goal of high accuracy ADAS perception, the time and cost associated with creating and deploying the system would be greatly reduced. This study investigates the influence of lossy compression on multi-task deep learning models for real-time perception in ADAS employing fisheye cameras. We leverage a large-scale dataset and train a representative multi-task ADAS perception model for pedestrian, kerb, line, and soiling classification. The testing dataset is subjected to compression using the popular H.264 video codec at varying compression ratios. Through rigorous evaluation, we analyse the effects of compression on model performance, providing insights into the feasibility of employing lossy compression techniques in ADAS applications. Our results reveal that lossy compression could be deployed in automotive perception applications and that a compression ratio of up to 98% (720Mb/s to 12Mb/s), could be utilised with negligible performance degradation. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： advanced driver assistance systems

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Quantitative Analysis and Optimization of sensor Deployment for Enhanced Activity Recognition in Smart Home Environments: A Multidimensional Approach to Data-Driven Intelligence and Efficiency in Universal Computing Spaces

Quantitative Analysis and Optimization of Sensor Deployment ...

引用

2024 International conference on Advancements in Power, Communication and Intelligent systems, APCI 2024

作者： Aziz, Khalid Dua, Sakshi Gupta, Prabal Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350363289

This paper presents a comprehensive study on the enhancement of smart home automation and monitoring systems through the application of advanced activity recognition algorithms and sensor deployment optimization. We start by taking a CASAS dataset capturing a wide range of daily living activities, detected through an array of environmental sensors. This study conducts an in-depth analysis of activity recognition performance, leveraging precision, recall, and f1score metrics to identify areas for improvement in existing machine learning models. Further, we investigate sensor activation data to propose optimizations in sensor deployment, aimed at increasing the accuracy of activity recognition while reducing unnecessary sensor data processing. Through temporal analysis of activity patterns, we develop predictive models that forecast future activities with significant accuracy, facilitating smarter resource management and personalized automation strategies. This paper also explores the implications of our findings on user experience enhancement. Our methodology showcases the integration of machine learning techniques, statistical analysis, and practical considerations for deploying smart home technologies. The results indicate a promising avenue for future research and development in smart environments, with potential applications extending beyond residential settings to include healthcare facilities and office spaces. The findings underscore the importance of adaptive and intelligent systems in enhancing the quality of life through technology. © 2024 IEEE.

关键词： Smart homes

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Salp Swarm Algorithm for Drift Compensation in E-nose 15

Salp Swarm Algorithm for Drift Compensation in E-nose

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15th International conference on advanced Computational Intelligence (ICACI)

作者： Rehman, Atiq Ur Kabir, Md Alamgir Ijaz, Muhammad Al-Mohsin, Hanadi M. Bermak, Amine Malardalen Univ Sch Innovat Design & Engn Hogskoleplan 1 S-72220 Vasteras Sweden Hamad Bin Khalifa Univ Coll Sci & Engn Doha Qatar

ISBN: (纸本)9798350321456

E-nose technology relies on the proper functioning of sensors to identify and discriminate between different chemicals and odors. The long-term reliability of e-nose technology is hindered by the phenomenon of sensor drift. The effect of sensor drift is seen as a random and unpredictable shift in the data domain. This random shift in data deteriorates the performance of machine learning algorithms used in e-nose technology. Swarm intelligence based optimization has been successfully applied in different domains to deal with NP-hard optimization problems. In this paper, a swarm intelligence-based metaheuristic is proposed to deal with the sensors drift issue in e-nose technology. The proposed framework is validated using a benchmark dataset of sensor drift, and a significant improvement is observed in terms of the classification accuracy of different industrial gases. The proposed framework has the following benefits over conventional approaches: (i) there is no need for sensor re-calibration;(ii) there is no need for sensor replacement;(iii) there is no need for target domain data;and (iv) there is no need for domain transformation. Instead, the proposed work relies only on the source domain data and optimizes the feature space to deal with sensor drift. This makes the proposed framework more suitable for real applications of E-nose technology.

关键词： E-nose technology Heuristic optimization Salp swarm optimization sensor drift swarm intelligence

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The Optimal Design of a Rotor System with Elastic Ring 10

The Optimal Design of a Rotor System with Elastic Ring

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10th IEEE International conference on Dependability in sensor, Cloud and Big Data systems and applications, DependSys 2024

作者： Shi, Yihang Yang, Zhongyu Feng, Yinli Liu, Di University of Chinese Academy of Sciences Chinese Academy of Sciences School of Aeronautics and Astronautics Natl. Key Lab. of Sci. and Technol. on Adv. Light-duty Gasturbine Inst. of Engineering Thermophysics Beijing China University of Chinese Academy of Sciences Chinese Academy of Sciences School of Engineering Sciences Natl. Key Lab. of Sci. and Technol. on Adv. Light-duty Gasturbine Inst. of Engineering Thermophysics Beijing China Chinese Academy of Sciences Institute of Engineering Thermophysics National Key Laboratory of Science and Technology on Advanced Light-duty Gasturbine Beijing China

ISBN: (纸本)9798331540500

Elastic ring (ER) is widely adopted for its compact size and ease of assembly and have become a prevalent type of elastic support within rotor-support systems. However, it may exhibit stiffness nonlinearity under specific operating conditions, potentially compromising the safety of the rotor system. This paper examines the influence of elastic ring stiffness on the critical speed of a specific aero-engine rotor support structure, focusing on related nonlinear phenomena. The Levenberg-Marquardt method was employed to optimize key parameters, including the stiffness and clearance of the elastic ring, with the second-order critical speed as the optimization goal. Finite element analysis was performed to validate the optimization results, which were found to be both effective and practically applicable. The finding provides a significant reference for the design and analysis of aero-engine rotor systems and similar applications. © 2024 IEEE.

关键词： Optimal systems

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Investigation of Hardware-Software Solutions for an Energy-Efficient Wireless sensor Network 12

Investigation of Hardware-Software Solutions for an Energy-E...

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12th IEEE International conference on Intelligent Data Acquisition and advanced Computing systems: Technology and applications, IDAACS 2023

作者： Yatsenko, Anastasiia Parkhomenko, Anzhelika Wolff, Carsten Tulenkov, Artem Parkhomenko, Andriy National University Zaporizhzhia Polytechnic Zaporizhzhia Ukraine Institute for the Digital Transformation of Application and Living Domains University of Applied Sciences and Arts Dortmund Germany

ISBN: (纸本)9798350358056

This work is devoted to the study of methods and tools for minimizing power consumption of wireless sensor networks. The results of processing experimental data for various operating modes of network nodes, as well as options for organizing their power supply and software settings are given. Practical recommendations have been developed to improve the energy efficiency of wireless sensors networks for the Smart Home systems. © 2023 IEEE.

关键词： communication module energy efficiency Smart Home system solar panel wireless sensors network

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A Predictive Maintenance Platform for a Conveyor Motor sensor System Using Recurrent Neural Networks 5th

A Predictive Maintenance Platform for a Conveyor Motor Senso...

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5th International conference on Neural Computing for advanced applications (NCAA)

作者： Kiangala, Kahiomba Sonia Wang, Zenghui Univ South Africa UNISA Dept Elect & Smart Syst Engn ZA-1709 Johannesburg Gauteng South Africa

ISBN: (纸本)9789819770007;9789819770014

Since the effusion of Industry 4.0 (I40) and smart manufacturing, predictive maintenance (PdM) has become critical to prevent severe system breakdowns and costly production downtime in various industries. Several state-of-the-art Artificial Intelligence (AI) approaches, such as machine learning models (ML), empower the PdM design concept to produce more accurate outcomes. In this study, we propose the development of a PdM platform that effectively detects future possible warnings and failures in the vibration speed sensor of a conveyor motor. We build the platform backend using recurrent neural networks (RNN) regression ML models to predict vibration speed values and their trends. We train our RNN models using univariate time-series historical vibration sensor data recorded over the years. The experimental results demonstrate the robustness of our platform built on RNN models compared to other traditional regression machine learning models such as Extreme Gradient Boosting (XGboost). We compare the effectiveness of the PdM platform with two RNN models: a basic RNN model and a long short-term memory (LSTM) model. Unlike other regression PdM systems focusing on predicting devices Remaining Useful Life (RUL), our PdM platform forecasts and generates new vibration sensor data based on the desired future time range. It also pinpoints set warning and failure values and indicates when these faults are more likely to occur. Factories can utilize this feature for analytics purposes.

关键词： Conveyor Motor sensor Predictive Maintenance (PdM) Recurrent Neural Networks (RNN) Regression Models time-series data

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Wearable Electronic Textiles for Healthcare, Wellbeing, and Protective applications

Wearable Electronic Textiles for Healthcare, Wellbeing, and ...

引用

CHI conference on Human Factors in Computing Sytems (CHI)

作者： Hughes-Riley, Theo Shahidi, Arash M. Marasinghe, Kalana Rahemtulla, Zahra Ehelagasthenna, Malindu Ebrahimi, Parvin Arm, Richard Oliveira, Carlos Holmquist, Lars Erik Dias, Tilak Nottingham Trent Univ Nottingham Sch Art & Design Nottingham England

ISBN: (纸本)9798400703317

Electronic textiles (E-textiles) can act as an exceptional substrate for incorporating sensing devices for monitoring physiological parameters, as they are comfortable to wear close to the skin. This makes them excellent for a variety of applications in the healthcare, wellbeing, and personal protection sectors. One method of creating E-textiles with good textile properties is to integrate an electronic component into a yarn to create an electronic yarn (or E-yarn), which can then be used to construct a textile. This Interactivity Lab Demonstration will present electronic textiles developed by the advanced Textiles Research Group (ATRG) from Nottingham Trent University (UK). The group has conducted a wealth of research into the development of E-yarns and the incorporation of these into textiles and garment. At CHI, users will be able to interact with textiles capable of fall and near-fall detection, temperature sensing, acoustic sensing, giving haptic feedback, and harvesting solar energy.

关键词： Electronic textiles E-textiles wearables sensor systems health wellbeing

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Smart systems for Precision Agriculture, Environmental Protection and Healthcare 12

Smart Systems for Precision Agriculture, Environmental Prote...

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12th IEEE International conference on Intelligent Data Acquisition and advanced Computing systems: Technology and applications, IDAACS 2023

作者： Romanov, Volodymyr Galelyuka, Igor Hrusha, Volodymyr Voronenko, Oleksandr Kovyrova, Oleksandra Antonova, Hanna Kedych, Anna V.M. Glushkov Institute of Cybernetics National Academy of Sciences of Ukraine Kyiv03187 Ukraine

The paper represents the result of developments of the Institute of Cybernetics of NAS of Ukraine in the field of smart systems for precision agriculture, environmental protection and healthcare. Main conceptions, str... 详细信息

ISBN: (纸本)9798350358056

关键词： digital agriculture environmental protection healthcare wireless sensor network

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Designing A Fall Detection System for Elderly People in Smart Healthcare IoT Using Multi sensor Data Fusion

Designing A Fall Detection System for Elderly People in Smar...

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2024 International conference on Artificial Intelligence and Quantum Computation-Based sensor applications, ICAIQSA 2024

作者： Krishna, Awantika Yadav, Brijkant Prakash, Devansh Dwivedi, Rajendra Kumar University of Technology Madan Mohan Malaviya Department of ITCA Gorakhpur India

ISBN: (纸本)9798331517953

This study presents a novel approach to deal with prototyping a smart healthcare IoT framework for precise fall detection in older adults using multi-sensor data fusion. The proposed framework utilizes advanced filtering and context-aware analysis to enhance the accuracy of fall detection, addressing key challenges in current systems such as high false positives and slow reaction times. A MPU6050 accelerometer and gyrator, incorporated with an ESP32 microcontroller, is used to distinguish falls while limiting false-alarms precisely. The framework utilizes Blynk, an IoT platform, to work with continuous observing and warnings. The methodology includes gathering information from the MPU6050 sensor, handling it with the ESP32, and utilizing AI calculations to separate falls from different exercises of everyday living. The system includes coordinating wearable sensors with lightweight AI calculations to guarantee continuous location and similarity with low-power gadgets. The discoveries exhibit improved reliability, sensibility, and real-world applicability of the system in elderly care settings. This research has critical ramifications for improving the wellbeing and freedom of older adults by giving convenient mediation in case of a fall. © 2024 IEEE.

关键词： Wearable sensors

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