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PubMed Publications| Keyword search (4,163 papers available) |  |
"neural networks" Keyword-tagged Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Tuning Deep Learning for Predicting Aluminum Prices Under Different Sampling: Bayesian Optimization Versus Random Search | Alicia Estefania Antonio Figueroa | 41751647 CONCORDIA |
| 2 | Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm | Lahmiri S; Tadj C; Gargour C; | 41294952 ENCS |
| 3 | Efficient neural encoding as revealed by bilingualism | Moore C; Donhauser PW; Klein D; Byers-Heinlein K; | 40828024 PSYCHOLOGY |
| 4 | Personalizing brain stimulation: continual learning for sleep spindle detection | Sobral M; Jourde HR; Marjani Bajestani SE; Coffey EBJ; Beltrame G; | 40609549 PSYCHOLOGY |
| 5 | Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting | Ahmed U; Mahmood A; Khan AR; Kuhlmann L; Alimgeer KS; Razzaq S; Aziz I; Hammad A; | 40185800 PHYSICS |
| 6 | Large language models deconstruct the clinical intuition behind diagnosing autism | Stanley J; Rabot E; Reddy S; Belilovsky E; Mottron L; Bzdok D; | 40147442 ENCS |
| 7 | MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle | McKay MJ; Weber KA; Wesselink EO; Smith ZA; Abbott R; Anderson DB; Ashton-James CE; Atyeo J; Beach AJ; Burns J; Clarke S; Collins NJ; Coppieters MW; Cornwall J; Crawford RJ; De Martino E; Dunn AG; Eyles JP; Feng HJ; Fortin M; Franettovich Smith MM; Galloway G; Gandomkar Z; Glastras S; Henderson LA; Hides JA; Hiller CE; Hilmer SN; Hoggarth MA; Kim B; Lal N; LaPorta L; Magnussen JS; Maloney S; March L; Nackley AG; O' Leary SP; Peolsson A; Perraton Z; Pool-Goudzwaard AL; Schnitzler M; Seitz AL; Semciw AI; Sheard PW; Smith AC; Snodgrass SJ; Sullivan J; Tran V; Valentin S; Walton DM; Wishart LR; Elliott JM; | 39590726 HKAP |
| 8 | A protocol for trustworthy EEG decoding with neural networks | Borra D; Magosso E; Ravanelli M; | 39549492 ENCS |
| 9 | Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks | Adcock B; Brugiapaglia S; Dexter N; Moraga S; | 39454372 MATHSTATS |
| 10 | Deep neural network-based robotic visual servoing for satellite target tracking | Ghiasvand S; Xie WF; Mohebbi A; | 39440297 ENCS |
| 11 | Generalization limits of Graph Neural Networks in identity effects learning | D' Inverno GA; Brugiapaglia S; Ravanelli M; | 39426036 ENCS |
| 12 | The immunomodulatory effect of oral NaHCO3 is mediated by the splenic nerve: multivariate impact revealed by artificial neural networks | Alvarez MR; Alkaissi H; Rieger AM; Esber GR; Acosta ME; Stephenson SI; Maurice AV; Valencia LMR; Roman CA; Alarcon JM; | 38549144 CSBN |
| 13 | Reinforcement learning for automatic quadrilateral mesh generation: A soft actor-critic approach | Pan J; Huang J; Cheng G; Zeng Y; | 36375347 ENCS |
| 14 | Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposome Size in a Periodic Disturbance Micromixer | Ocampo I; López RR; Camacho-León S; Nerguizian V; Stiharu I; | 34683215 ENCS |
| 15 | X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech | Jeancolas L; Petrovska-Delacrétaz D; Mangone G; Benkelfat BE; Corvol JC; Vidailhet M; Lehéricy S; Benali H; | 33679361 PERFORM |
| Title: | Deep neural network-based robotic visual servoing for satellite target tracking | ||||
| Authors: | Ghiasvand S, Xie WF, Mohebbi A | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/39440297/ | ||||
| DOI: | 10.3389/frobt.2024.1469315 | ||||
| Publication: | Frontiers in robotics and AI | ||||
| Keywords: | deep learning; deep neural networks; pose estimation; robot vision systems; visual servoing; | ||||
| PMID: | 39440297 | Category: | Date Added: | 2024-10-23 | |
| Dept Affiliation: |
ENCS
1 Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montréal, QC, Canada. 2 Department of Mechanical Engineering, Polytechnique Montréal, Montréal, QC, Canada. |
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Description: |
In response to the costly and error-prone manual satellite tracking on the International Space Station (ISS), this paper presents a deep neural network (DNN)-based robotic visual servoing solution to the automated tracking operation. This innovative approach directly addresses the critical issue of motion decoupling, which poses a significant challenge in current image moment-based visual servoing. The proposed method uses DNNs to estimate the manipulator's pose, resulting in a significant reduction of coupling effects, which enhances control performance and increases tracking precision. Real-time experimental tests are carried out using a 6-DOF Denso manipulator equipped with an RGB camera and an object, mimicking the targeting pin. The test results demonstrate a 32.04% reduction in pose error and a 21.67% improvement in velocity precision compared to conventional methods. These findings demonstrate that the method has the potential to improve efficiency and accuracy significantly in satellite target tracking and capturing. |
