Results 51 to 60 of about 24,804 (235)
AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling
Advanced Science, EarlyView.This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi +4 more
wiley +1 more source
An agent-based implementation of hidden Markov models for gas turbine condition monitoring
, 2014 This paper considers the use of a multi-agent system (MAS) incorporating hidden Markov models (HMMs) for the condition monitoring of gas turbine (GT) engines.
Twiddle, John +3 more
core +1 more source
Inferenza statistica per Hidden Markov Models [PDF]
, 2022 L’oggetto di questa tesi consiste nello studio degli Hidden Markov Models, processi doppiamente stocastici che possono essere definiti in diversi modi ma tutti con la caratteristica comune di presentare un processo di Markov a cui non si può accedere ...
Ruzza, Marco
core
Advanced Science, EarlyView.Employing a digital single‐molecule activity tracker (dSMAT), this research demonstrates that high‐photon‐flux irradiation drives progressive oxidative scarring in polymerases. Unlike simple thermal denaturation, real‐time kinetic tracking dynamically visualizes enzymes degrading into multiple impaired subpopulations.
Anran Zheng +11 more
wiley +1 more source
The Hierarchical Dirichlet Process Hidden Semi-Markov Model
CoRR, 2010 There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the traditional HMM. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations.
Johnson, Matthew James, Willsky, Alan S
openaire +4 more sources
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 +3 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
IEEE Access, 2020 In this paper, we design a mathematical model for performance and reliability evaluation of the IEEE 802.11p Enhanced Distributed Channel Access (EDCA) broadcast scheme in Dedicated Short-Range Communication (DSRC) with the presence of hidden terminals ...
Lin Hu, Zhijian Dai
doaj +1 more source
Robot introspection through learned hidden Markov models
, 2006 In this paper we describe a machine learning approach for acquiring a model of a robot behaviour from raw sensor data. We are interested in automating the acquisition of behavioural models to provide a robot with an introspective capability.
Maria Fox +11 more
core +1 more source
PPO‐Based Reinforcement Learning for the Semi‐Active Vibration Control of MDOF Platform
AI &Innovation, EarlyView.ABSTRACT Aiming at the coupled vibration problem of a multi‐degree‐of‐freedom (MDOF) vibration isolation platform under eccentric excitation, this paper proposes a semi‐active vibration control strategy based on Proximal Policy Optimization (PPO) ‐based reinforcement learning (PPO RL).
Wei Huang, Jian Xu
wiley +1 more source