AIMC Topic: Aircraft

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Whole-Field Reinforcement Learning: A Fully Autonomous Aerial Scouting Method for Precision Agriculture.

Sensors (Basel, Switzerland)
Unmanned aerial systems (UAS) are increasingly used in precision agriculture to collect crop health related data. UAS can capture data more often and more cost-effectively than sending human scouts into the field. However, in large crop fields, fligh...

A Cyber-Physical-Human System for One-to-Many UAS Operations: Cognitive Load Analysis.

Sensors (Basel, Switzerland)
The continuing development of avionics for Unmanned Aircraft Systems (UASs) is introducing higher levels of intelligence and autonomy both in the flight vehicle and in the ground mission control, allowing new promising operational concepts to emerge....

Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning.

Sensors (Basel, Switzerland)
Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the...

Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications.

Sensors (Basel, Switzerland)
Unmanned aerial vehicles (UAVs) have become popular in surveillance, security, and remote monitoring. However, they also pose serious security threats to public privacy. The timely detection of a malicious drone is currently an open research issue fo...

Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines.

Neural networks : the official journal of the International Neural Network Society
In this work, a novel data-driven fault diagnostic framework is developed by using hybrid multi-mode machine learning strategies to monitor system health status. The coexistence of multi-mode and concurrent faults and their adverse coupling effects p...

Predicting dengue importation into Europe, using machine learning and model-agnostic methods.

Scientific reports
The geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and i...

Automatic wheat ear counting using machine learning based on RGB UAV imagery.

The Plant journal : for cell and molecular biology
In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield-determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is ...

Dynamic performances of a bird-like flapping wing robot under randomly uncertain disturbances.

PloS one
The nonlinear dynamics of a bird-like flapping wing robot under randomly uncertain disturbances was studied in this study. The bird-like flapping wing robot was first simplified into a two-rod model with a spring connection. Then, the dynamic model o...

Exact flow of particles using for state estimations in unmanned aerial systems` navigation.

PloS one
The navigation is a substantial issue in the field of robotics. Simultaneous Localization and Mapping (SLAM) is a principle for many autonomous navigation applications, particularly in the Global Navigation Satellite System (GNSS) denied environments...

Human Factors Contributions to the Development of Standards for Displays of Unmanned Aircraft Systems in Support of Detect-and-Avoid.

Human factors
OBJECTIVE: The aim is to provide a high-level synthesis of human factors research that contributed to the development of detect-and-avoid display requirements for unmanned aircraft systems (UAS).