The proceedings contain 82 papers. The topics discussed include: rogue nodes detection using idle parking resources;analysis of node importance of satellite network based on triangular motif;research on web configurat...
ISBN:
(纸本)9798400716683
The proceedings contain 82 papers. The topics discussed include: rogue nodes detection using idle parking resources;analysis of node importance of satellite network based on triangular motif;research on web configuration software interactivity enhancement technology based on Vue and component library;design and implementation of power communication protocol plug-in based on hot loading;cross polarization interference cancellation algorithm based on maximum likelihood for satellite communication;research on machinelearning and state grid business risk prevention and control based on the Internet;design and implementation of intelligent radar anti-jamming simulation system;application of artificial bee colony algorithm in power communicationnetwork routing optimization simulation;and research on intelligent recommendation model for application systems.
The proceedings contain 28 papers. The topics discussed include: building of a convolutional neuronal network for the prediction of mood states through face recognition based on object detection with YOLOV8 and Python...
ISBN:
(纸本)9798350303919
The proceedings contain 28 papers. The topics discussed include: building of a convolutional neuronal network for the prediction of mood states through face recognition based on object detection with YOLOV8 and Python;comparative analysis of LSTM and ensemble LSTM approaches for gene mutation classification in cancer;efficiency analysis of microservices based on queueing models;time series prediction using convolutional neural networks;ECG anomaly detection using an interpretable autoencoder model;innovative approaches to neurosurgical planning: virtual reality integration in Honduran secondary care;evaluation and analysis of standard security techniques in V2X communication: exploring the cracking of ECQV implicit certificates;homomorphic encryption based on post-quantum cryptography;and implementation of a computer vision system for fault and component analysis of computer PCBs.
This paper introduces an end-to-end trainable power line communication (PLC) system based on a deep neural network (DNN). PLC channels are challenging to be modeled and estimated because of uncertain loads in utility ...
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ISBN:
(纸本)9781450398329
This paper introduces an end-to-end trainable power line communication (PLC) system based on a deep neural network (DNN). PLC channels are challenging to be modeled and estimated because of uncertain loads in utility / power grids, heterogeneous network topologies, and many equipment such as switching gears, relays, transformers, etc., which are usually not considered in traditional communication systems. With a trainable transmitter and receiver, this paper designs a PLC system not requiring any power line channel information. The designed PLC system can achieve similar bit error rate (BER) performances to the cases harnessing perfect power line channel information. Since the proposed design can be end-to-end trained without any PLC channel information in diverse power line environments, it will be a promising PLC communication design even for internet of things (IoT) applications targeting for smart grids, smart homes / factories etc.
With the rapid development of technology, the network environment is becoming increasingly complex, and security issues are becoming more prominent. network security situational awareness is an important method for as...
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With the integration and development of computer and communication technologies, intelligent communication is playing an increasingly important role in various fields. In response to the intelligent, flexible, and rob...
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Smart grid communicationnetworks are facing the increasing challenge of heterogeneous facilities and diverse communication requirements. Traditional communication technologies without the ability to adapt cannot meet...
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ISBN:
(纸本)9798400702006
Smart grid communicationnetworks are facing the increasing challenge of heterogeneous facilities and diverse communication requirements. Traditional communication technologies without the ability to adapt cannot meet these requirements. A data-driven and machinelearning-based multi-class traffic management scheme is proposed in this study, which classifies network traffic into different service levels for better transmission provision. Numerical experiments with a real-world dataset are conducted to validate the effectiveness of the multi-class traffic management scheme, in which XGBoost achieves an accuracy of 0.9842 and an F1 score of 0.9914.
Bilevel optimization has been applied to a wide variety of machinelearning models and numerous stochastic bilevel optimization algorithms have been developed in recent years. However, most existing algorithms restric...
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Bilevel optimization has been applied to a wide variety of machinelearning models and numerous stochastic bilevel optimization algorithms have been developed in recent years. However, most existing algorithms restrict their focus on the single -machine setting so that they are incapable of handling the distributed data. To address this issue, under the setting where all participants compose a network and perform peer-to peer communication in this network, we developed two novel decentralized stochastic bilevel optimization algorithms based on the gradient tracking communication mechanism and two different gradient estimators. Additionally, we established their convergence rates for nonconvex strongly-convex problems with novel theoretical analysis strategies. To our knowledge, this is the first work achieving these theoretical results. Finally, we applied our algorithms to practical machinelearning models, and the experimental results confirmed the efficacy of our algorithms.
communicationnetwork is an important part of social life, but it is extremely vulnerable to various network attacks, and the energy of data nodes in the network is limited, and abnormal behavior in the network cannot...
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In the digital age, the protection of data assets in the enterprise cybersecurity space has become critical. This paper explores a deep learning-based data asset protection technique that aims to solve the challenges ...
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Decentralized federated learning (DFL) uses peer-to-peer communication to avoid the single point of failure problem in federated learning and has been considered an attractive solution for machinelearning tasks on di...
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ISBN:
(纸本)9798350351712;9798350351729
Decentralized federated learning (DFL) uses peer-to-peer communication to avoid the single point of failure problem in federated learning and has been considered an attractive solution for machinelearning tasks on distributed devices. We provide the first solution to a fundamental network problem of DFL: what overlay network should DFL use to achieve fast training of highly accurate models, low communication, and decentralized construction and maintenance? Overlay topologies of DFL have been investigated, but no existing DFL topology includes decentralized protocols for network construction and topology maintenance. Without these protocols, DFL cannot run in practice. This work presents an overlay network, called FedLay, which provides fast training and low communication cost for practical DFL. FedLay is the first solution for constructing near-random regular topologies in a decentralized manner and maintaining the topologies under node joins and failures. Experiments based on prototype implementation and simulations show that FedLay achieves the fastest model convergence and highest accuracy on real datasets compared to existing DFL solutions while incurring small communication costs and being resilient to node joins and failures.
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