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

To improve the intelligence and accuracy of the Situation Assessment (SA) in complex scenes, this work develops an improved fuzzy deep neural network approach to the situation assessment for multiple Unmanned Aerial Vehicle(UAV)s. Firstly, this work normalizes the scene data based on time series and use the normalized data as the input for an improved fuzzy deep neural network. Secondly, adaptive momentum and Elastic SGD (Elastic Stochastic Gradient Descent) are introduced into the training process of the neural network, to improve the learning performance. Lastly, in the real-time situation assessment task for multiple UAVs, conventional methods often bring inaccurate results for the situation assessment because these methods don’t consider the fuzziness of task situations. This work uses an improved fuzzy deep neural network to calculate the results of situation assessment and normalizes these results. Then, the degree of trust of the current result, relative to each situation label, is calculated with the normalized results using fuzzy logic. Simulation results show that the proposed method outperforms competitors.

A Situation Assessment Method with an Improved Fuzzy Deep Neural Network for Multiple UAVs