Matching Network Efficiency: The New Old Challenge for Millimeter-Wave Silicon Power Amplifiers

Lauritano, Mario; Baumgartner, P.; Ulusoy, A C; Aghassi‐Hagmann, Jasmin

doi:10.1109/mmm.2021.3109682

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…The metric-based meta-learning method is a non-parametric learning model, so its complexity is less than other methods. The idea is to learn the meta-knowledge of how to measure the similarity of samples between the support set and the query set from the embedding space by using feature embedding, such as matching network [23], relation network [24]. Generally, deep neural networks are used to map samples into the feature space, and cosine similarity [25] is used to measure the similarity of features, predict the category labels, calculate the loss and then back propagate to optimize the network.…”

Section: Preliminary Knowledgementioning

confidence: 99%

Few shot cross equipment fault diagnosis method based on parameter optimization and feature mertic

Tao

Chen

Qiu

et al. 2022

Meas. Sci. Technol.

138

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With the rapid development of industrial informatization and deep learning technology, modern data-driven fault diagnosis (MIFD) methods based on deep learning have been continuously emphasized by the industry. However, most of these methods require sufficient training samples to achieve the desired diagnostic effect, but the scarcity of fault samples in the actual industrial environment leads to the limited development of MIFD methods. In addition, due to the changes of equipment operating conditions and production requirements, data-driven fault diagnosis methods often need to face the cross domain problem of cross load or even cross different equipment. In this paper, a parameter optimization and feature metric-based fault diagnosis method with few samples, called model agnostic matching network model, is designed for the problem of sparse fault samples and cross-domain between data sets in real industrial environments. The method combines both a parameter-based optimization meta-learning network, which extracts optimization information adapted to different domains, and a metric-based meta-learning network, which extracts metric information for similarity discriminations. The experimental result show that the method outperforms the current baseline method for the 5-shot fault diagnosis problem of rolling bearings under limited data conditions and achieves an accuracy of up to 94.4% in cross-equipment diagnosis experiments from rolling bearings to gas regulators, indicating the feasibility of the method. The features are visualized by T-SNE to show the validity of the model.

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Section: Preliminary Knowledgementioning

confidence: 99%