English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In radar high-resolution range profile (HRRP) recognition, the recognition accuracy will decline when the training samples in some classes (majority classes) greatly outnumbers other classes (minority classes). To alleviate the above imbalanced problem, an HRRP data augmentation framework is proposed. A one-dimensional (1-D) deep convolutional generative adversarial network (DCGAN) is developed to generate artificial HRRPs. The fidelity of the generated HRRPs is evaluated subjectively in the raw data domain and quantitatively by the similarity in the feature domain. The experimental results show that the generated data are similar to the true HRRPs and demonstrate that the proposed framework outperforms the state-of-the-art oversampling methods when handling the imbalanced problem.

Data Augmentation for Imbalanced HRRP Recognition Using Deep Convolutional Generative Adversarial Network