Results 61 to 70 of about 48,260 (274)

Optimal FOC-PID Parameters of BLDC Motor System Control Using Parallel PM-PSO Optimization Technique

International Journal of Computational Intelligence Systems, 2021
This paper proposes a parallelization method for meta-heuristic particle swarm optimization algorithm to obtain a convincingly fast execution and stable global solution result.
Nguyen Tien Dat, Cao Van Kien, Ho Pham Huy Anh   +2 more
doaj   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source

FastCat: Autonomous Discovery of Multielement Layered Double Hydroxide Alloy Catalysts for Alkaline Oxygen Evolution Reaction

Advanced Intelligent Discovery, EarlyView.
A machine learning‐guided self‐driving laboratory screened over 500 nickel‐based layered double‐hydroxide catalysts for alkaline oxygen evolution. Out of the eight metals, the robot uncovered a quaternary Ni–Fe–Cr–Co catalysts requiring only 231 mV overpotential to reach 20 mA cm−2.
Nis Fisker‐Bødker, Enzo Raffaele Moretti, Jin Hyun Chang, Tejs Vegge   +3 more
wiley   +1 more source

Application of predator-prey optimization for task scheduling in cloud computing [PDF]

Journal of Mahani Mathematical Research
Cloud computing environments require scheduling to allocate resources efficiently and ensure optimal performance. It is possible to maximize resource utilization and minimize execution time by scheduling cloud systems effectively.
Zahra Jalali Khalil Abadi, Behnam Mohammad Hasani zade, Najme Mansouri, Mohammad Masoud Javidi   +3 more
doaj   +1 more source

Slow and steady wins the race: Approximating Nash equilibria in nonlinear quadratic tracking games [PDF]

, 2015
We propose a meta-heuristic approach for solving nonlinear dynamic tracking games. In contrast to more "traditional" methods based on linear-quadratic (LQ) techniques, this derivative-free method is very flexible (e.g. to introduce inequality constraints)
Blueschke, Dmitri, Blueschke-Nikolaeva, Victoria, Savin, Ivan   +2 more
core  

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

A Virtual Machine Consolidation Algorithm Based on Ant Colony System and Extreme Learning Machine for Cloud Data Center

IEEE Access, 2020
The energy consumption issue of large-scale data centers is attracting more and more attention. Virtual machine consolidation can significantly reduce energy consumption by migrating virtual machines from one physical machine to another.
Fagui Liu, Zhenjiang Ma, Bin Wang, Weiwei Lin   +3 more
doaj   +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

A Meta-Heuristic Algorithm-Based Feature Selection Approach to Improve Prediction Success for Salmonella Occurrence in Agricultural Waters

Journal of Agricultural Sciences
The presence of Salmonella in agricultural waters may be a source of produce contamination. Recently, the performances of various algorithms have been tested for the prediction of indicator bacteria population and pathogen occurrence in agricultural ...
Murat Canayaz, Murat Demir, Zeynal Topalcengiz   +2 more
doaj   +1 more source