Wearable ultraviolet sensor based on convolutional neural network image processing method

Chen, Yan; Cao, Zimei; Zhang, Jiejian; Liu, Yuanqing; Yu, Duli; Guo, Xiaoliang

doi:10.1016/j.sna.2022.113402

Cited by 11 publications

(3 citation statements)

References 24 publications

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“…The CNN has been used for the automatic classification, acquisition, and analysis of ECG signals from wearable devices, which provides higher accuracy than other algorithms [ 130 ]. The CNN has been used to process the ultraviolet signals to quantify ultraviolet intensity for real-time ultraviolet monitoring [ 131 ]. Other signal-processing methods also have been utilized to analyze and process sensing signals [ 132 , 133 ].…”

Section: Signal Processingmentioning

confidence: 99%

Flexible and Wearable Biosensors for Monitoring Health Conditions

et al. 2023

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Flexible and wearable biosensors have received tremendous attention over the past decade owing to their great potential applications in the field of health and medicine. Wearable biosensors serve as an ideal platform for real-time and continuous health monitoring, which exhibit unique properties such as self-powered, lightweight, low cost, high flexibility, detection convenience, and great conformability. This review introduces the recent research progress in wearable biosensors. First of all, the biological fluids often detected by wearable biosensors are proposed. Then, the existing micro-nanofabrication technologies and basic characteristics of wearable biosensors are summarized. Then, their application manners and information processing are also highlighted in the paper. Massive cutting-edge research examples are introduced such as wearable physiological pressure sensors, wearable sweat sensors, and wearable self-powered biosensors. As a significant content, the detection mechanism of these sensors was detailed with examples to help readers understand this area. Finally, the current challenges and future perspectives are proposed to push this research area forward and expand practical applications in the future.

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Section: Signal Processingmentioning

confidence: 99%

Flexible and Wearable Biosensors for Monitoring Health Conditions

et al. 2023

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“…Chen and Cao et al [62] use image processing and a CNN-based technique to determine UV intensity. They created a wearable UV sensor out of PDMS and photochromic material.…”

Section: Deep Learningmentioning

confidence: 99%

Advances in Machine Learning for Sensing and Condition Monitoring

Ao¹,

Gelman²,

Karimi³

et al. 2022

Applied Sciences

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In order to overcome the complexities encountered in sensing devices with data collection, transmission, storage and analysis toward condition monitoring, estimation and control system purposes, machine learning algorithms have gained popularity to analyze and interpret big sensory data in modern industry. This paper put forward a comprehensive survey on the advances in the technology of machine learning algorithms and their most recent applications in the sensing and condition monitoring fields. Current case studies of developing tailor-made data mining and deep learning algorithms from practical aspects are carefully selected and discussed. The characteristics and contributions of these algorithms to the sensing and monitoring fields are elaborated.

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“…In order to solve the limitations of the algorithm template, scholars at home and abroad have carried out a series of studies. As one of the important algorithms in the field of Web GUI image recognition, deep learning can describe the attributes and features of objects more abstractly and deeply, and has great potential in many fields [2].…”

Section: Introductionmentioning

confidence: 99%

Research on Web GUI Image Recognition System Based on Improved Convolutional Neural Network

Zhao

2022

JWE

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In this paper, a MobileNet V2 convolutional neural network depending on L2 regularization method, a amended particle swarm model and Dropout method are constructed in a bid for enhancing the accuracy and speed of Web GUI image recognition for establishing Web Web GUI image recognition system of Web GUI test. Firstly, an improved MobileNet V2 convolutional neural network is constructed. Secondly, the basic models of L2 regularization method, improved particle swarm method and Dropout method are studied, the improved MobileNet V2 convolution neural network optimization algorithm is proposed, and the basic model of image processing is designed. Finally, simulation analysis is carried out on Web GUI image recognition through using the amended convolutional neural network on basis of MINIST data set as the research object. The simulation results illustrate that the amended convolutional neural network proposed in this study is more accurate and efficient in Web GUI image recognition.

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Wearable ultraviolet sensor based on convolutional neural network image processing method

Cited by 11 publications

References 24 publications

Flexible and Wearable Biosensors for Monitoring Health Conditions

Flexible and Wearable Biosensors for Monitoring Health Conditions

Advances in Machine Learning for Sensing and Condition Monitoring

Research on Web GUI Image Recognition System Based on Improved Convolutional Neural Network

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