Results 111 to 120 of about 391,078 (263)

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai   +3 more
wiley   +1 more source

A biologically plausible decision-making model based on interacting neural populations. [PDF]

open access: yesPLoS One
Baspinar E   +8 more
europepmc   +1 more source

VAE+DDPG: An Attention‐Enhanced Variational Autoencoder for Deep Reinforcement Learning‐Based Autonomous Navigation in Low‐Light Environments

open access: yesAdvanced Intelligent Systems, EarlyView.
Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee   +7 more
wiley   +1 more source

Context Awareness and Human–Robot Interaction Optimization for Museum Intelligent Guide Robot

open access: yesAdvanced Intelligent Systems, EarlyView.
This study presents a context‐aware human–robot interaction framework designed for intelligent museum guide robots. The system features a three‐layer architecture—perception, understanding, and behavior execution—that enables adaptive and meaningful interactions with museum visitors.
Anna Zou, Yue Meng, Shijing Tong
wiley   +1 more source

Adaptive Control of Run‐and‐Tumble Escape in Pursuit‐Evasion Dynamics of Intelligent Active Particles

open access: yesAdvanced Intelligent Systems, EarlyView.
The pursuit‐evasion game is studied for two adversarial active agents, modeled as deterministic self‐steering pursuer and stochastic, cognitive evader. For a successful evasion strategy, the motile target has to exploit all available pursuer information, e.g., by tuning the tumbling frequency with the pursuer distance.
Segun Goh   +2 more
wiley   +1 more source

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