In response to the issues of processing speed and storage associated with the quality map-guided method in classical spatial phase unwrapping techniques, this paper proposes a spatial phase unwrapping method guided by...
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Brain tumors are one of the deadliest diseases and require quick and accurate methods of detection. Finding the optimum image for research goals is the first step in optimizing MRI images for pre- and post-processing....
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This manuscript studies the finite time prescribed performance control problem for a class of switched stochastic nonlinear systems with input saturation. Firstly, utilizing the prescribed performance control, the tra...
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With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictiv...
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With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictive results. However, existing time prediction methods based on deep learning have poor interpretability, an explainable business process remaining time prediction method is proposed using reachability graph,which consists of prediction model construction and visualization. For prediction models, a Petri net is mined and the reachability graph is constructed to obtain the transition occurrence vector. Then, prefixes and corresponding suffixes are generated to cluster into different transition partitions according to transition occurrence vector. Next,the bidirectional recurrent neural network with attention is applied to each transition partition to encode the prefixes, and the deep transfer learning between different transition partitions is performed. For the visualization of prediction models, the evaluation values are added to the sub-processes of a Petri net to realize the visualization of the prediction models. Finally, the proposed method is validated by publicly available event logs.
Existing end-to-end quality of service (QoS) prediction methods based on deep learning often use one-hot encodings as features, which are input into neural networks. It is difficult for the networks to learn the infor...
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Aiming at the trajectory tracking control issue of the autonomous surface vehicle (ASV) subject to unknown actuator failures, a composite learning adaptive intelligent self-triggered fault-tolerant control (FTC) desig...
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The rapid development of Internet of Things technology and the continuous improvement of consumer demand have spawned the emergence of intelligent products based on Internet of Things technology. The existing research...
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With the rapid growth of network bandwidth,traffic identification is currently an important challenge for network management and *** recent years,packet sampling has been widely used in most network management *** thi...
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With the rapid growth of network bandwidth,traffic identification is currently an important challenge for network management and *** recent years,packet sampling has been widely used in most network management *** this paper,in order to improve the accuracy of network traffic identification,sampled NetFlow data is applied to traffic identification,and the impact of packet sampling on the accuracy of the identification method is *** study includes feature selection,a metric correlation analysis for the application behavior,and a traffic identification *** analysis and experimental results show that the significance of behavior characteristics becomes lower in the packet sampling ***,in this paper,the correlation analysis results in different trends according to different ***,as long as the flow number meets the statistical requirement,the feature selection and the correlation degree will be independent of the sampling *** in a high sampling ratio,where the effective information would be less,the identification accuracy is much lower than the unsampled ***,in order to improve the accuracy of the identification,we propose a Deep Belief Networks Application Identification(DBNAI)method,which can achieve better classification performance than other state-of-the-art methods.
Copy-move forgery is a common audio tampering technique in which users copy the contents of one speech and paste them into another region of the same speech signal, thus achieving the effect of tampering with the sema...
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The rapid advancement of technology has given rise to medical cyber-physical systems (MCPS), a subset of cyber-physical systems (CPS) specifically tailored for patient care and healthcare providers. MCPS generate subs...
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