Results 161 to 170 of about 90,320 (221)

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

Advanced Intelligent Systems, EarlyView.
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi, Bairong Zhu, Haoyu Liu, Tingting Yu   +3 more
wiley   +1 more source

Research on High-Precision Localization Method of Curved Surface Feature Points Based on RGB-D Data Fusion. [PDF]

Sensors (Basel)
Wang E, Zou R, Su C.
europepmc   +1 more source

Interactive Tool for Customizing Hydrogel Properties in Practical Applications

Advanced Intelligent Systems, EarlyView.
This research provides an open‐access tool that enables the scientific community to optimally synthesize hydrogels without requiring expert knowledge, thereby reducing experimental costs. Soft materials represent an interdisciplinary frontier in modern science, combining theoretical and experimental knowledge from diverse fields in both fundamental ...
Ricardo Negrete‐Gallego, Carlos M. Andreu, Sergio Pozuelo‐Campos, Raúl Martín‐Martín, Irene García‐Camacha Gutiérrez, Ester Vázquez   +5 more
wiley   +1 more source

Upsampling DINOv2 Features for Unsupervised Vision Tasks and Weakly Supervised Materials Segmentation

Advanced Intelligent Systems, EarlyView.
Feature from recent image foundation models (DINOv2) are useful for vision tasks (segmentation, object localization) with little or no human input. Once upsampled, they can be used for weakly supervised micrograph segmentation, achieving strong results when compared to classical features (blurs, edge detection) across a range of material systems.
Ronan Docherty, Antonis Vamvakeros, Samuel J. Cooper   +2 more
wiley   +1 more source

Sub‐Micrometer‐Precision Path Following of Piezo‐Actuated Mobile Robot

Advanced Intelligent Systems, EarlyView.
This article reports on the Holonomic‐Beetle (HB), a palm‐sized robot that achieves sub‐micrometer (sub‐µm) precision path tracking across spatial ranges from 100 µm to 10 mm. Using proportional‐integral‐derivative (PID) control, the HB accurately tracks both complex and straight paths with sub‐µm path errors, surpassing existing robots.
Eiji Kusui, Chihiro Sekine, Taiki Goto, Ryosuke Kinoshita, Yuko Nishimura, Shogen Sekiguchi, Satoshi Ando, Yuto Oishi, Yusuke Matsui, Ohmi Fuchiwaki   +9 more
wiley   +1 more source

Effects of dietary selenium yeast supplementation on the production performance, egg quality, antioxidant and plasma biochemical parameters of laying hens. [PDF]

Sci Rep
Wang T, Chen S, Lu S, Li H, Ni M, Hao J, Cheng W, Li J, Cai Y, An J, Qin L.   +10 more
europepmc   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source

Multi-Threshold Image Segmentation Based on the Hybrid Strategy Improved Dingo Optimization Algorithm. [PDF]

Biomimetics (Basel)
Zhu Q, Gong M, Wang Y, Yang Z.
europepmc   +1 more source

A Flexible and Energy‐Efficient Compute‐in‐Memory Accelerator for Kolmogorov–Arnold Networks

Advanced Intelligent Systems, EarlyView.
This article presents KA‐CIM, a compute‐in‐memory accelerator for Kolmogorov–Arnold Networks (KANs). It enables flexible and efficient computation of arbitrary nonlinear functions through cross‐layer co‐optimization from algorithm to device. KA‐CIM surpasses CPU, ASIC, VMM‐CIM, and prior KAN accelerators by 1–3 orders of magnitude in energy‐delay ...
Chirag Sudarshan, Oliver Artner, Paul‐Philipp Manea, Sebastian Siegel, Susanne Hoffmann‐Eifert, Regina Dittmann, John Paul Strachan   +6 more
wiley   +1 more source