Results 81 to 90 of about 17,544 (260)
Covariance Matrix Adaptation MAP-Annealing: Theory and Experiments
Single-objective optimization algorithms search for the single highest quality solution with respect to an objective. Quality diversity (QD) optimization algorithms, such as Covariance Matrix Adaptation MAP-Elites (CMA-ME), search for a collection of solutions that are both high quality with respect to an objective and diverse with respect to specified
Shihan Zhao +3 more
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Deep learning‐based denoising models are applied to DNA data storage systems to enhance error reduction and data fidelity. By integrating DnCNN with DNA sequence encoding methods, the study demonstrates significant improvements in image quality and correction of substitution errors, revealing a promising path toward robust and efficient DNA‐based ...
Seongjun Seo +5 more
wiley +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation
Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem, where most of these algorithms could be grouped into one of the following ...
Deniz Erdogmus +4 more
doaj +1 more source
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source
Data-Efficient Reinforcement Learning for Variable Impedance Control
One of the most crucial steps toward achieving human-like manipulation skills in robots is to incorporate compliance into the robot controller. Compliance not only makes the robot’s behaviour safe but also makes it more energy efficient.
Akhil S. Anand +3 more
doaj +1 more source
A parallel implementation of the covariance matrix adaptation evolution strategy
In many practical optimization problems, the derivatives of the functions to be optimized are unavailable or unreliable. Such optimization problems are solved using derivative-free optimization techniques. One of the state-of-the-art techniques for derivative-free optimization is the covariance matrix adaptation evolution strategy (CMA-ES) algorithm ...
openaire +2 more sources
A laser pointer‐guided robotic grasping method for arbitrary objects based on promptable segment anything model and force‐closure analysis is presented. Grasp generation methods based on force‐closure analysis can calculate the optimal grasps for objects through their appearances. However, the limited visual perception ability makes robots difficult to
Yan Liu +5 more
wiley +1 more source
This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire
Aymen Alshawi +8 more
doaj +1 more source
It is a fact that slippage causes tracking errors in both longitudinal and lateral directions which results to have less travel distance in tracking a reference trajectory. Less travel distance means having energy loss of the battery and carrying loads less than planned.
Gokhan Bayar +2 more
wiley +1 more source

