58 research outputs found
Fault Diagnosis for Power Electronics Converters based on Deep Feedforward Network and Wavelet Compression
A fault diagnosis method for power electronics converters based on deep
feedforward network and wavelet compression is proposed in this paper. The
transient historical data after wavelet compression are used to realize the
training of fault diagnosis classifier. Firstly, the correlation analysis of
the voltage or current data running in various fault states is performed to
remove the redundant features and the sampling point. Secondly, the wavelet
transform is used to remove the redundant data of the features, and then the
training sample data is greatly compressed. The deep feedforward network is
trained by the low frequency component of the features, while the training
speed is greatly accelerated. The average accuracy of fault diagnosis
classifier can reach over 97%. Finally, the fault diagnosis classifier is
tested, and final diagnosis result is determined by multiple-groups transient
data, by which the reliability of diagnosis results is improved. The
experimental result proves that the classifier has strong generalization
ability and can accurately locate the open-circuit faults in IGBTs.Comment: Electric Power Systems Researc
Federated Learning and Meta Learning:Approaches, Applications, and Directions
Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference management, autonomy, and decision-making in wireless networks. Traditional ML approaches rely on centralized methods, where data is collected at a central server for training. However, this approach poses a challenge in terms of preserving the data privacy of devices. To address this issue, federated learning (FL) has emerged as an effective solution that allows edge devices to collaboratively train ML models without compromising data privacy. In FL, local datasets are not shared, and the focus is on learning a global model for a specific task involving all devices. However, FL has limitations when it comes to adapting the model to devices with different data distributions. In such cases, meta learning is considered, as it enables the adaptation of learning models to different data distributions using only a few data samples. In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta). Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks. We also analyze the relationships among these learning algorithms and examine their advantages and disadvantages in real-world applications.</p
AI meets CRNs : a prospective review on the application of deep architectures in spectrum management
The spectrum low utilization and high demand conundrum created a bottleneck towards
ful lling the requirements of next-generation networks. The cognitive radio (CR) technology was advocated
as a de facto technology to alleviate the scarcity and under-utilization of spectrum resources by exploiting
temporarily vacant spectrum holes of the licensed spectrum bands. As a result, the CR technology became
the rst step towards the intelligentization of mobile and wireless networks, and in order to strengthen
its intelligent operation, the cognitive engine needs to be enhanced through the exploitation of arti cial
intelligence (AI) strategies. Since comprehensive literature reviews covering the integration and application
of deep architectures in cognitive radio networks (CRNs) are still lacking, this article aims at lling the
gap by presenting a detailed review that addresses the integration of deep architectures into the intricacies
of spectrum management. This is a prospective review whose primary objective is to provide an in-depth
exploration of the recent trends in AI strategies employed in mobile and wireless communication networks.
The existing reviews in this area have not considered the relevance of incorporating the mathematical
fundamentals of each AI strategy and how to tailor them to speci c mobile and wireless networking
problems. Therefore, this reviewaddresses that problem by detailing howdeep architectures can be integrated
into spectrum management problems. Beyond reviewing different ways in which deep architectures can be
integrated into spectrum management, model selection strategies and how different deep architectures can
be tailored into the CR space to achieve better performance in complex environments are then reported in
the context of future research directions.The Sentech Chair in Broadband Wireless Multimedia Communications (BWMC) at the University of Pretoria.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639am2022Electrical, Electronic and Computer Engineerin
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