AIMC Topic: Predatory Behavior
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Testing the equivalency of human "predators" and deep neural networks in the detection of cryptic moths.
Journal of evolutionary biology
Feb 1, 2025
Researchers have shown growing interest in using deep neural networks (DNNs) to efficiently test the effects of perceptual processes on the evolution of colour patterns and morphologies. Whether this is a valid approach remains unclear, as it is unkn...
A controllable dual-catapult system inspired by the biomechanics of the dragonfly larvae's predatory strike.
Science robotics
Jan 20, 2021
The biomechanics underlying the predatory strike of dragonfly larvae is not yet understood. Dragonfly larvae are aquatic ambush predators, capturing their prey with a strongly modified extensible mouthpart. The current theory of hydraulic pressure be...
Ionic spiderwebs.
Science robotics
Jul 15, 2020
Spiders use adhesive, stretchable, and translucent webs to capture their prey. However, sustaining the capturing capability of these webs can be challenging because the webs inevitably invite contamination, thus reducing its adhesion force. To overco...
Machine learning gets to grips with plankton challenge.
Nature
Sep 1, 2018
Information theory and robotics meet to study predator-prey interactions.
Chaos (Woodbury, N.Y.)
Jul 1, 2017
Transfer entropy holds promise to advance our understanding of animal behavior, by affording the identification of causal relationships that underlie animal interactions. A critical step toward the reliable implementation of this powerful information...
Three-Dimensional Path Planning for Uninhabited Combat Aerial Vehicle Based on Predator-Prey Pigeon-Inspired Optimization in Dynamic Environment.
IEEE/ACM transactions on computational biology and bioinformatics
Jan 1, 2017
Three-dimension path planning of uninhabited combat aerial vehicle (UCAV) is a complicated optimal problem, which mainly focused on optimizing the flight route considering the different types of constrains under complex combating environment. A novel...
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