Mobile edge computing (MEC) extends cloud computing capabilities to the network edge, enabling efficient processing of compute-intensive tasks from resource-constrained devices. However, static edge servers can lead t...
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This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, ...
This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory.
The UNet architecture, which is widely used for biomedical image segmentation, has limitations like blurred feature maps and over- or under-segmented regions. To overcome these limitations, we propose a novel network ...
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Sidechain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration solutions for Internet of Things (IoT) data across various domains. However, cur...
In this paper, we consider the physical layer security (PLS) problem for integrated sensing and communication (ISAC) systems in the presence of hybrid-colluding eavesdroppers, where an active eavesdropper (AE) and a p...
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With the development of economic globalization, information technology has made great progress, and artificial intelligence technology has been widely used, such as car driving, robot automatic steering, rescue and di...
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ISBN:
(纸本)9781665478748
With the development of economic globalization, information technology has made great progress, and artificial intelligence technology has been widely used, such as car driving, robot automatic steering, rescue and disaster relief robot, underwater robot and so on. However, in the laboratory management of colleges and universities, the application of artificial intelligence is insufficient, and the traditional management mode is backward. Some universities still adopt manual management mode, which not only has high labor cost, but also has a large management gap, resulting in too many potential safety hazards, which is not conducive to the safety management of university laboratories. The market urgently needs to realize the combination of laboratory management and artificial intelligence. As a keylaboratory of engineering, the electrical laboratory carries out and sets up multiple experimental courses according to the discipline training requirements. As an Engineering University, the course content spans multiple grades, faces more students and covers multiple majors in two colleges. It can be imagined that the experimental courses opened in the electrical laboratory basically take the power supply as the carrier, in this way, the requirements for students are very high. If the electrical experiment is misoperated or caused by emergencies, the risk is great and the accidents are very sudden. Based on the above situation, it fully shows that higher requirements are put forward for the safety management of the electrical laboratory and the management will be more difficult. In recent years, discipline construction has been paid more and more attention, but with the frequent occurrence of laboratory accidents, the fire safety related to the laboratory has attracted more and more attention. For the application-oriented laboratory in Colleges and universities, we must raise awareness, strengthen safety prevention, and strengthen the management of laboratory fire safet
Dynamic metasurface antennas (DMAs) represent a novel transceiver array architecture for extremely large-scale (XL) communications, offering the advantages of reduced power consumption and lower hardware costs compare...
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Liver disease diagnosis is pivotal for effective patient management, and machine learning techniques have shown promise in this domain. In this study, we investigate the impact of Polynomial-SHapley Additive exPlanati...
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The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has ...
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The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.
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