Wearable sensors advance toward the goal of multifunction and integration. Most previous researches focus on the pressure or temperature sensibility, while moisture perception enabling proximity sense and noncontact c...
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Wearable sensors advance toward the goal of multifunction and integration. Most previous researches focus on the pressure or temperature sensibility, while moisture perception enabling proximity sense and noncontact control of soft robotic and human-machine interfacing still remains a challenge. Herein, we reported a flexible self-powered multifunctional sensor (FSMS) based on fluoropolymer composites for simultaneously and actively detecting the moist, thermal and pressure stimuli. The inclusion of lithium chloride (LiCl) and polyvinyl alcohol (PVA) endows the fluoropolymer composites with excellent humidity sensing capabilities of low hysteresis (2.1%) and short response time (9 s). The as-prepared FSMS can be easily worn on versatile places of human body for wearable biomonitoring of arterial pulse and respiration as well as human-machine interaction. A moist -thermal coupling (MTC) mode is invented to boost the sensitivity for respiratory monitoring and noncontact human-machine interaction. This work opens up a new paradigm to develop multifunctional biomonitoring devices and artificial skin for next-generation wearable electronics.
We propose urethane-foam-embedded silicon pressure sensors, including a stress-concentration packaging structure, for integration into a car seat to monitor the driver's cognitive state, posture, and driving behav...
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We propose urethane-foam-embedded silicon pressure sensors, including a stress-concentration packaging structure, for integration into a car seat to monitor the driver's cognitive state, posture, and driving behavior. The technical challenges of embedding silicon pressure sensors in urethane foam are low sensitivity due to stress dispersion of the urethane foam and non-linear sensor response caused by the non-uniform deformation of the foam. Thus, the proposed package structure includes a cover to concentrate the force applied over the urethane foam and frame to eliminate this non-linear stress because the outer edge of the cover receives large non-linear stress concentration caused by the geometric non-linearity of the uneven height of the sensor package and ground substrate. With this package structure, the pressure sensitivity of the sensors ranges from 0 to 10 kPa. The sensors also have high linearity with a root mean squared error of 0.049 N in the linear regression of the relationship between applied pressure and sensor output, and the optimal frame width is more than 2 mm. Finally, a prototype 3 x 3 sensor array included in the proposed package structure detects body movements, which will enable the development of sensor-integrated car seats.
MEMS inertial sensors are used in handheld smart devices such as smartphones and smart watches to detect and monitor physical activities. The most common transduction techniques used are based on piezoelectric [1], pi...
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Printed electronics offer ultra-low manufacturing costs and the potential for on-demand fabrication of flexible hardware. However, significant intrinsic constraints stemming from their large feature sizes and low inte...
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In cases with a large number of sensors and complex spatial distribution, correctly learning the spatial characteristics of the sensors is vital for structural damage identification. Graph convolutional neural network...
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In cases with a large number of sensors and complex spatial distribution, correctly learning the spatial characteristics of the sensors is vital for structural damage identification. Graph convolutional neural networks (GCNs), unlike other methods, have the ability to learn the spatial characteristics of the sensors, which is targeted at the above problems in structural damage identification. However, under the influence of environmental interference, sensor instability, and other factors, part of the vibration signal can easily change its fundamental characteristics, and there is a possibility of misjudging structural damage. Therefore, on the basis of building a high-performance graphical convolutional deep learning model, this paper considers the integration of data fusion technology in the model decision-making layer and proposes a single-model decision-making fusion neural network (S_DFNN) model. Through experiments involving the frame model and the self-designed cable-stayed bridge model, it is concluded that this method has a better performance of damage recognition for different structures, and the accuracy is improved based on a single model and has good damage recognition performance. The method has better damage identification performance in different structures, and the accuracy rate is improved based on the single model, which has a very good damage identification effect. It proves that the structural damage diagnosis method proposed in this paper with data fusion technology combined with deep learning has a strong generalization ability and has great potential in structural damage diagnosis.
Pressure sensors are widely employed in various fields from biomedical to aerospace engineering. Each field requires pressure sensors with specific sensitivity, response time, recovery time, durability, and detection ...
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Wireless sensor Networks (WSNs) continue to experience rapid developments and integration into modern-day applications. Overall, WSNs collect and process relevant data through sensors or nodes and communicate with dif...
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The exponential growth of population is leading to the need for the integration of Information and Communication Technology (ICT) and advanced equipment in farming. Either excess or lack of water leads to uneven crop ...
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The paper discusses the deployment of UHF RFID receptors equipped with various sensors to enhance the maintenance of railway infrastructure. These receptors provide continuous, real-time data on safety parameters, add...
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ISBN:
(数字)9798331539511
ISBN:
(纸本)9798331539528
The paper discusses the deployment of UHF RFID receptors equipped with various sensors to enhance the maintenance of railway infrastructure. These receptors provide continuous, real-time data on safety parameters, addressing the challenge of limited revision times in subway systems. Wireless sensor Networks (WSNs) are emphasized for their cost-effectiveness, high resolution, robustness, and low power consumption. WSNs consist of small-scale sensors capable of data processing and communication, offering advantages like self-organization, short-range broadcasting, dense deployment, and continuous topology changes. Effective management of these networks involves addressing energy consumption, data transmission, and signal processing to ensure stability and efficiency. The proposed system utilizes UHF RFID receptors, RFID tags and infrared sensors to monitor rail integrity and environmental conditions. Data is centrally processed and transmitted via GSM with GPS positioning for real-time event localization. The study examines energy consumption, distance measurement, and information transmission delays, proposing the deployment of WSNs in UHF RFID receptors to improve railway safety. Potential applications extend to civilian, military, and emergency response contexts.
This study introduces a novel approach by combining burning number analysis and supervised machine learning, specifically linear regression, to optimize sensor placement in large agricultural fields modeled as a 3-ary...
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ISBN:
(数字)9798350364088
ISBN:
(纸本)9798350364095
This study introduces a novel approach by combining burning number analysis and supervised machine learning, specifically linear regression, to optimize sensor placement in large agricultural fields modeled as a 3-ary n-cube network. In this unique integration, each node represents a location equipped with environmental sensors, and edges signify wireless communication links. The burning number analysis identifies critical sensor nodes, strategically activating them for efficient information propagation throughout the network. Simultaneously, linear regression is employed to predict and optimize the activation of key sensors, maximizing coverage and data dissemination. This approach aims to enhance network efficiency, providing valuable insights for precision agriculture practices such as crop management, irrigation scheduling, and resource allocation. Through this innovative method, the study not only contributes to the development of adaptive solutions for sustainable farming but also showcases the potential of combining graph theory-based burning number analysis with linear regression for optimizing sensor networks in complex agricultural landscapes.
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