Results 111 to 120 of about 28,317 (304)

Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude Control

Applied Sciences
Remotely operated vehicles (ROVs) face challenges in achieving optimal trajectory tracking performance during underwater movement due to external disturbances and parameter uncertainties. To address this issue, this paper proposes a position and attitude
Gaosheng Luo, Dong Zhang, Wei Feng, Zhe Jiang, Xingchen Liu   +4 more
doaj   +1 more source

Multivariable adaptive variable structure disturbance rejection control for DFIG system

, 2016
A novel variable structure and disturbance rejection control strategy for a wind turbines equipped with a double fed induction generator based on stator-flux-oriented vector control is presented.
Tohidi, Akbar, Ojaroudi, Nasser, Oveis Abedinia, Akbar Tohidi, Abedinia, Oveis, Masoud Bekravi, Nasser Ojaroudi, Bekravi, Masoud   +7 more
core   +1 more source

Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

Advanced Intelligent Discovery, EarlyView.
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser, Haseeb Muhammad, Junaid Younas, Gillian Lovell, Bianca Migliori, Andreas Dengel, Sheraz Ahmed   +6 more
wiley   +1 more source

Data-driven Approach for Disturbance Rejection for Dual-Stage Actuator Hard Disk Drives

Disturbance rejection is a crucial problem in contemporary Hard Disk Drives (HDDs) to increase data storage capacities in an information-centric society. This paper presents a control method to mitigate steady-state oscillations of a dual-stage HDD.
Oddon, Rémi Tanguy, Schuchert, Philippe Louis, Karimi, Alireza, Gupta, Vaibhav   +3 more
core  

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Set-point tracking and disturbance rejection capability analysis of controllers designed for SOPDT model

, 2017
Disturbance rejection is one of the key performance benchmark for a controller. For an industrial process, disturbance can be internal or external and it is an important that it can be rejected effectively by an optimal controller design.
Chaturvedi, M, Juneja, PK, Upreti, G
core  

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

Active Disturbance Rejection Control for diving motion of Autonomous Underwater Vehicles

, 2016
This paper presents the dynamic model and control scheme of an active variable buoyancy system (VBS) actuator for Autonomous Underwater Vehicles (AUVs).
Jiang ZB(姜志斌), Liu TJ(刘铁军), Li S(李硕)   +2 more
core  

Investigation of Periodic-Disturbance Identification and Rejection in Spacecraft [PDF]

, 2006
The article of record as published may be found at http://dx.doi.org/10.2514/1.17341Spacecraft periodic-disturbance rejection using a realistic spacecraft hardware simulator and its associated models is investigated.
Kim, J.J., Joshi, S., Lau, J., Agrawal, B.N.   +3 more
core  

A Critical Assessment of Bonding Descriptors for Predicting Materials Properties

Advanced Intelligent Discovery, EarlyView.
The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik, Nidal Dhamrait, Katharina Ueltzen, Christina Ertural, Philipp Benner, Gian‐Marco Rignanese, Janine George   +6 more
wiley   +1 more source