Results 231 to 240 of about 57,740 (314)

An adaptive coverage method for dynamic wireless sensor network deployment using deep reinforcement learning. [PDF]

Sci Rep
Zhou P, Kan M, Chen W, Wang Y, Cao B.
europepmc   +1 more source

Deep Reinforcement Learning for Resource Management in Network Slicing

, 2018
Rongpeng Li, Zhifeng Zhao, Qi Sun, I Chi-Lin, Chenyang Yang, Xianfu Chen, Minjian Zhao, Honggang Zhang   +7 more
openalex   +2 more sources

A Sleeve Alters the Pressure‐Stretch Curve of a Hyperelastic Balloon to Enable Pre‐Programmed Sequencing

Advanced Materials Technologies, Volume 10, Issue 6, March 18, 2025.
This article demonstrates that encasing a hyperelastic balloon in an inextensible sleeve greatly increases its burst pressure. This simple mechanical behavior can be used to produce an asymmetric inflation‐deflation sequence for coupled balloons with different thicknesses so they could serve as a soft robot's rear and front anchors when driven from a ...
Ifat Gertler, Katherine J. Kuchenbecker
wiley   +1 more source

Using Deep Reinforcement Learning to Decide Test Length. [PDF]

Educ Psychol Meas
Zoucha J, Himelfarb I, Tang NE.
europepmc   +1 more source

Hygromnemics: Programmable Material Memory Matter Actuators via Wet Pre‐Constraining

Advanced Materials Technologies, EarlyView.
Humidity‐based pre‐constraining creates hygromnemics‐shape memory matter. From identical biocomposites actuation in dry and wet states is obtained via hygromnemic and hygromorphic actuation, respectively. Hygromnemic actuators present long‐ and short‐term shape memory abilities.
Charles de Kergariou, Findlay S. G. Smith, Richard S. Trask, Adam W. Perriman, Fabrizio Scarpa, David Correa   +5 more
wiley   +1 more source

The analysis of deep reinforcement learning for dynamic graphical games under artificial intelligence. [PDF]

Sci Rep
Yan Y, Li J, Zaggia C.
europepmc   +1 more source

ANS: Adaptive Network Scaling for Deep Rectifier Reinforcement Learning Models

, 2018
Yueh-Hua Wu, Fan-Yun Sun, Yen‐Yu Chang, Shou-De Lin   +3 more
openalex   +2 more sources

Electrolyte‐Jet 3D Printing of Copper‐Based Strain Sensors for Physiological Signal Monitoring and Robotic Manipulation

Advanced Materials Technologies, EarlyView.
Electrolyte‐jet (EJ) 3D printing turns copper ions into precisely arranged copper microspheres, which combine with flexible polymers and carbon nanomaterials to create durable, high‐sensitivity sensors. These sensors accurately monitor human motion and physiological signals. Real‐time robotic hand control via gesture mirroring demonstrates an intuitive
Wending Gu, David Payne, Binesh Puthen Veettil   +2 more
wiley   +1 more source

Deep Reinforcement Learning-Based Self-Optimization of Flow Chemistry. [PDF]

ACS Eng Au
Yewale A, Yang Y, Nazemifard N, Papageorgiou CD, Rielly CD, Benyahia B.   +5 more
europepmc   +1 more source

Relative Importance Sampling For Off-Policy Actor-Critic in Deep Reinforcement Learning

, 2018
Mahammad Humayoo, Xueqi Cheng
openalex   +2 more sources