The robust feature extraction capability of deep learning (DL) has rendered it a popular choice for fault diagnosis in various industries. However, the accuracy of fault diagnosis models is often compromised due to an...
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Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health. However, current commercial products of polysomnography are cumbersome with connecting wires...
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Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health. However, current commercial products of polysomnography are cumbersome with connecting wires and state-of-the-art flexible sensors are still interferential for being attached to the body. Herein, we develop a flexible-integrated multimodal sensing patch based on hydrogel and its application in unconstraint sleep monitoring. The patch comprises a bottom hydrogel-based dualmode pressure–temperature sensing layer and a top electrospun nanofiber-based non-contact detection layer as one integrated device. The hydrogel as core substrate exhibits strong toughness and water retention, and the multimodal sensing of temperature, pressure, and non-contact proximity is realized based on different sensing mechanisms with no crosstalk interference. The multimodal sensing function is verified in a simulated real-world scenario by a robotic hand grasping objects to validate its practicability. Multiple multimodal sensing patches integrated on different locations of a pillow are assembled for intelligent sleep monitoring. Versatile human–pillow interaction information as well as their evolution over time are acquired and analyzed by a one-dimensional convolutional neural network. Track of head movement and recognition of bad patterns that may lead to poor sleep are achieved, which provides a promising approach for sleep monitoring.
The quality of features has a great impact on machine learning tasks. Feature selection obtains a high-quality feature subset from data, which has been widely studied because of high interpretability. In this paper, w...
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Multi-hop Knowledge Graph Question Answering (KGQA) requires reasoning about multi-hop inference relations between topic entities and answers on the knowledge graph(KG) and returning correct answers. The difficulty of...
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The article studies the efficiency of optical radiation input/output between a photonic integrated circuit (PIC) and an edge-coupled tapered silicon nitride waveguide with dimensions 350 nm x 850 nm at a wavelength of...
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Electric properties of thin films are depended on its structure. The more uniform structure of thin film, the more uniform distribution has electric field across it. This work determines the optimal mode of magnetron ...
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This study proposes a Bayesian optimization method that combines thread pooling with Gaussian process acceleration to optimize the hyperparameters of convolutional neural networks. In the experiment, this method showe...
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Face spoofing detection technology can accurately distinguish the truth of the face captured by the camera, and it has been applied in many security fields. In view of the problems of the existing face spoofing detect...
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In recent years,deep learning has been applied to a variety of scenarios in Industrial Internet of Things(IIoT),including enhancing the security of ***,the existing deep learning methods utilised in IIoT security are ...
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In recent years,deep learning has been applied to a variety of scenarios in Industrial Internet of Things(IIoT),including enhancing the security of ***,the existing deep learning methods utilised in IIoT security are manually designed by heavily relying on the experience of the *** authors have made the first contribution concerning the joint optimisation of neural architecture search and hyper-parameters optimisation for securing IIoT.A novel automated deep learning method called synchronous optimisation of parameters and architectures by GA with CNN blocks(SOPA-GA-CNN)is proposed to synchronously optimise the hyperparameters and block-based architectures in convolutional neural networks(CNNs)by genetic algorithms(GA)for the intrusion detection issue of *** efficient hybrid encoding strategy and the corresponding GA-based evolutionary operations are designed to characterise and evolve both the hyperparameters,including batch size,learning rate,weight optimiser and weight regularisation,and the architectures,such as the block-based network topology and the parameters of each CNN *** experimental results on five intrusion detection datasets in IIoT,including secure water treatment,water distribution,Gas Pipeline,Botnet in Internet of Things and Power System Attack Dataset,have demonstrated the superiority of the proposed SOPA-GA-CNN to the state-of-the-art manually designed models and neuron-evolutionary methods in terms of accuracy,precision,recall,F1-score,and the number of parameters of the deep learning models.
Accurate and interpretable estimation of battery state of health (SOH) is critical for ensuring the reliability, safety, and longevity of energy storage systems. Existing SOH estimation methods often struggle to gener...
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