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Guest Editor

Netherlands Organisation for Applied Scientific Research, Den Haag, The Netherlands
Interests: Artificial Intelligence; self-organization; nature-inspired optimization; emergent behavior; logic; intelligent autonomous systems; UAVs; planning; scheduling

Prof. Dr. Fabrice Saffre

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Guest Editor

Technical Research Centre of Finland, 02150 Espoo, Finland
Interests: distributed systems; cloud computing; computational intelligence; distribution; environment; distributed computing; computer networks; network; Artificial Intelligence; drones

Special Issue Information

Dear Colleagues,

As unmanned aerial vehicles become an increasingly mainstream technology and their utilisation more ubiquitous, the opportunities afforded by their becoming more autonomous and “intelligent” multiply. There is a wide variety of applications in which an ability for drones to operate semi-independently from a human pilot could bring substantial advantages. Autonomous decision-making based on information collected in real time and the ability for several drones to communicate and cooperate in the execution of a complex mission would open countless new possibilities.

For this Special Issue, we invite original contributions presenting advances in Artificial Intelligence (machine learning, visual pattern recognition, swarm intelligence, etc.) that significantly increase the autonomy of unmanned systems, whether operating individually or as a team. Rigorous theoretical or simulation-based studies, prototype implementation reports, and experimental results are equally welcome, as are comprehensive literature surveys and reviews. Innovative research on the interaction between humans and “intelligent” drones (interface design, concepts of operation, etc.) or the control of autonomous platforms is also of interest.

Dr. Hanno Hildmann
Prof. Dr. Fabrice Saffre
Guest Editors

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Keywords

Artificial Intelligence
autonomy
swarm behaviour
co-ordination
communication
navigation
interaction
decision making
self-organisation
spatial organization

Published Papers (1 paper)

Research

Open AccessArticle

Anomaly Detection for Data from Unmanned Systems via Improved Graph Neural Networks with Attention Mechanism

and

Drones 2023, 7(5), 326; https://doi.org/10.3390/drones7050326 - 19 May 2023

Viewed by 391

Abstract

Anomaly detection has an important impact on the development of unmanned aerial vehicles, and effective anomaly detection is fundamental to their utilization. Traditional anomaly detection discriminates anomalies for single-dimensional factors of sensing data, which often performs poorly in multidimensional data scenarios due to weak computational scalability and the problem of dimensional catastrophe, ignoring potential correlations between sensing data and some important information of certain characteristics. In order to capture the correlation of multidimensional sensing data and improve the accuracy of anomaly detection effectively, GTAF, an anomaly detection model for multivariate sequences based on an improved graph neural network with a transformer, a graph attention mechanism and a multi-channel fusion mechanism, is proposed in this paper. First, we added a multi-channel transformer structure for intrinsic pattern extraction of different data. Then, we combined the multi-channel transformer structure with GDN’s original graph attention network (GAT) to attain better capture of features of time series, better learning of dependencies between time series and hence prediction of future values of adjacent time series. Finally, we added a multi-channel data fusion module, which utilizes channel attention to integrate global information and upgrade anomaly detection accuracy. The results of experiments show that the average accuracies of GTAF, the anomaly detection model proposed in this paper, are 92.83% and 96.59% on two datasets from unmanned systems, respectively, which has higher accuracy and computational efficiency compared with other methods. Full article

(This article belongs to the Special Issue Advances in AI for Intelligent Autonomous Systems)

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