Results 191 to 200 of about 531,665 (271)

Machine Learning‐Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of image quality metrics in combination with machine learning enables automatic image quality assessment for fluorescence microscopy images. The method can be integrated into the experimental pipeline for optical microscopy and utilized to classify artifacts in experimental images and to build quality rankings with a reference‐free approach ...
Elena Corbetta, Thomas Bocklitz
wiley   +1 more source

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence‐Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study

Advanced Intelligent Systems, EarlyView.
The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez, Estefania Ascencio, Jose M. Ageitos, Lucia Feijoo, Shan He, Amirreza Daghighi, Gerardo M. Casanola‐Martin, Cristian R. Munteanu, Santiago Rodriguez Yañez, Alejandro Pazos, Harbil Bediaga, Brenda Durand Ordiales, Raquel Esteban, Ana‐Maria Heres, Jorge Curiel Yuste, Lur Epelde, Bakhtiyor Rasulev, Tomas Gonzalez, Sonia Arrasate, Humberto González‐Diaz   +19 more
wiley   +1 more source

Characteristics, Management, and Utilization of Muscles in Musculoskeletal Humanoids: Empirical Study on Kengoro and Musashi

Advanced Intelligent Systems, EarlyView.
Musculoskeletal humanoids exhibit rich biomechanical properties that remain insufficiently unified in prior discussions. This article systematically categorizes muscle characteristics into five properties: redundancy, independency, anisotropy, variable moment arm, and nonlinear elasticity, and analyzes their combined effects on control.
Kento Kawaharazuka, Kei Okada, Masayuki Inaba   +2 more
wiley   +1 more source

A Fully Soft Sensing Suit With Optimal Sensor Placement for Real‐Time Motion Tracking

Advanced Intelligent Systems, EarlyView.
A fully soft, skin‐conformable sensing suit integrating stretchable sensors, liquid metal wiring, and soft electrodes was developed using direct ink writing, with sensor placement optimized through an automated algorithmic pipeline. This system enables accurate and unobtrusive real‐time motion tracking, providing a scalable, material‐based solution to ...
Jinhyeok Oh, Joonbum Bae
wiley   +1 more source

Machine Learning‐Driven Variability Analysis of Process Parameters for Semiconductor Manufacturing

Advanced Intelligent Systems, EarlyView.
This research presents a machine learning approach that integrates nonlinear variation decomposition (NLVD) with statistical techniques to quantify the contribution of individual unit processes to performance and variance of figure of merit (FoM) at the LOT level.
Sinyeong Kang, Hyeok Yun, Kyeongrae Cho, Seungjoon Eom, Soomin Kim, Seongsoo Jeong, Rockhyun Baek   +6 more
wiley   +1 more source

Voxel‐SLAM: A Complete, Accurate, and Versatile Light Detection and Ranging‐Inertial Simultaneous Localization and Mapping System

Advanced Intelligent Systems, EarlyView.
: In this work, Voxel‐SLAM (simultaneous localization and mapping) is introduced: a complete, accurate, and versatile LiDAR (light detection and ranging) ‐inertial SLAM system consisting of five modules: initialization, odometry, local mapping (LM), loop closure (LC), and global mapping (GM), all employing the same map representation, an adaptive voxel
Zheng Liu, Haotian Li, Chongjian Yuan, Xiyuan Liu, Jiarong Lin, Rundong Li, Chunran Zheng, Bingyang Zhou, Wenyi Liu, Fu Zhang   +9 more
wiley   +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

Enabling Stochastic Dynamic Games for Robotic Swarms

Advanced Intelligent Systems, EarlyView.
This paper scales stochastic dynamic games to large swarms of robots through selective agent modeling and variable partial belief space planning. We formulate these games using a belief space variant of iterative Linear Quadratic Gaussian (iLQG). We scale to teams of 50 agents through selective modeling based on the estimated influence of agents ...
Kamran Vakil, Alyssa Pierson
wiley   +1 more source

Research on the Control Algorithm for a Brushless DC Motor Based on an Adaptive Extended Kalman Filter. [PDF]

Sensors (Basel)
Jinwu T, Lifan Z, Xinyun L, Peng L, Jin S, Shujun L.   +5 more
europepmc   +1 more source

Subsampling Cumulative Covariance Estimator

Subsampling Cumulative Covariance Estimator
In this paper subsampling Cumulative Covariance (CC) estimatoris investigated specially on the optimal numbers of its subgrids.Although Hayashi and Yoshida (2005) proposed CC estimator to solvenonsynchronous bias problem under non-noise assumption, recent studiessuggest that subsampling of CC estimator works for microstructurenoise. However, it is also
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