Results 71 to 80 of about 301,921 (265)

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

IEEE Access, 2020
The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation. Various studies are examined through a survey of existing literature and investigation of various aspects, namely,
Marwan Qaid Mohammed, Kwek Lee Chung, Chua Shing Chyi   +2 more
doaj   +1 more source

Playing Atari with Deep Reinforcement Learning [PDF]

, 2013
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.
Antonoglou, Ioannis, Graves, Alex, Kavukcuoglu, Koray, Mnih, Volodymyr, Riedmiller, Martin, Silver, David, Wierstra, Daan   +6 more
core   +1 more source

Zein‐Based Adhesives: Sustainable Extraction and Application in Bioadhesive Technologies

Advanced Engineering Materials, EarlyView.
Zein is extracted from corn gluten meal using a simple and scalable process with high yield (~90%). The resulting protein is applied in bioadhesives modified with Ca2+ and Fe3+ ions, exhibiting substrate‐dependent adhesion. The findings demonstrate competitive bonding performance and highlight the role of ionic interactions in tuning adhesion ...
Paula Bertolino Sanvezzo, Daniela Winsley Valereto Friozi, Vinícius de Oliveira, Marcia Cristina Branciforti   +3 more
wiley   +1 more source

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

Deep Reinforcement Learning Approach for Traffic Light Control and Transit Priority

Future Transportation
This study investigates the use of deep reinforcement learning techniques to improve traffic signal control systems through the integration of deep learning and reinforcement learning approaches.
Saeed Mansouryar, Chiara Colombaroni, Natalia Isaenko, Gaetano Fusco   +3 more
doaj   +1 more source

Thermodynamic Pathways of Nonequilibrium Solidification in Wire‐Arc Additive Manufacturing Fe‐Based Multicomponent Alloy Structures

Advanced Engineering Materials, EarlyView.
Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios, Anthony Gonzalez, Aleksander Aleman, Tyler Dolmetsch, Tanaji Paul, Arvind Agarwal   +5 more
wiley   +1 more source

Effect of Laser Deoxidation on Adhesive‐Bonded Aluminum in an Oxygen‐Free Atmosphere

Advanced Engineering Materials, EarlyView.
This study investigates laser ablation of aluminum under oxygen‐free conditions. The goal is to produce oxide‐free substrates that enable improved adhesive bonding with epoxy. Optimized laser parameters (90% overlap, 300 µJ) combined with oxide‐free substrates result in the highest tensile strength of the adhesive bond.
Sandra Gerland, Finn‐Lennard Janthur, Sarah Seffer, Jörg Hermsdorf, Ludger Overmeyer, Annika Raatz   +5 more
wiley   +1 more source

Dynamic optimization of stand structure in Pinus yunnanensis secondary forests based on deep reinforcement learning and structural prediction

Frontiers in Plant Science
IntroductionThe rational structure of forest stands plays a crucial role in maintaining ecosystem functions, enhancing community stability, and ensuring sustainable management.
Jian Zhao, Jianming Wang, Jiting Yin, Yuling Chen, Baoguo Wu   +4 more
doaj   +1 more source

A Biologically Plausible Learning Rule for Deep Learning in the Brain [PDF]

, 2018
Researchers have proposed that deep learning, which is providing important progress in a wide range of high complexity tasks, might inspire new insights into learning in the brain. However, the methods used for deep learning by artificial neural networks
Bohté, Sander, Pozzi, Isabella, Roelfsema, Pieter   +2 more
core   +2 more sources

Additive Manufacturing of Continuous Fibre Reinforced Composites: Process, Characterisation, Modelling, and Sustainability

Advanced Engineering Materials, EarlyView.
Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley   +1 more source