Results 61 to 70 of about 5,083,495 (228)

Continuum Mechanics Modeling of Flexible Spring Joints in Surgical Robots

Advanced Robotics Research, EarlyView.
A new mechanical model of a tendon‐actuated helical extension spring joint in surgical robots is built using Cosserat rod theory. The model can implicitly handle the unknown contacts between adjacent coils and numerically predict spring shapes from straight to significantly bent under actuation forces.
Botian Sun, Liang Zhang, Jianwen Zeng, Xuefeng Wang   +3 more
wiley   +1 more source

Implementation and testing of selected optimization methods for the parameter estimation of simulation models [PDF]

, 2016
Tato práce se zabývá návrhem vhodných optimalizačních algoritmů pro potřeby nově vyvíjeného nástroje Mechlab’s parameter estimation, který slouží pro odhad parametrů simulačních modelů v prostředí Matlab/Simulink.
Zapletal, Marek
core  

A Concurrent Fuzzy-Neural Network Approach for Decision Support Systems

, 2003
Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved.
Abraham, Ajith, Jain, Lakhmi, Tran, Cong
core   +1 more source

Second-order Shape Optimization for Geometric Inverse Problems in Vision [PDF]

, 2014
We develop a method for optimization in shape spaces, i.e., sets of surfaces modulo re-parametrization. Unlike previously proposed gradient flows, we achieve superlinear convergence rates through a subtle approximation of the shape Hessian, which is ...
Balzer, J., Soatto, S.
core   +1 more source

Data‐Driven Feedback Identifies Focused Ultrasound Exposure Regimens for Improved Nanotheranostic Targeting of the Brain

Advanced Science, EarlyView.
Machine learning models predict in real time the onset of harmful microbubble collapse during microbubble‐enhanced focused ultrasound (MB‐FUS) and enable dynamic adjustment of sonication to prevent cavitation‐induced damage. This predictive control expands the safe operating window for bloodbrain barrier opening, enhancing nanoparticle delivery and ...
Hohyun Lee, Victor Menezes, Shiqin Zeng, Chulyong Kim, Cynthia M. Baseman, Jae Hyun Kim, Samhita Padmanabhan, Pranav Premdas, Naima Djeddar, Anton Bryksin, Nikhil Pandey, Pavlos Anastasiadis, Anthony J. Kim, Tobey J. MacDonald, Chetan Bettegowda, Graeme F. Woodworth, Felix J. Herrmann, Costas Arvanitis   +17 more
wiley   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

An accelerated adaptive two-step Levenberg–Marquardt method with the modified Metropolis criterion

AIMS Mathematics
In this paper, aiming at the nonlinear equations, a new two-step Levenberg–Marquardt method was proposed. We presented a new Levenberg–Marquardt parameter to obtain the trial step. A new modified Metropolis criterion was used to adjust the upper bound of
Dingyu Zhu, Yueting Yang, Mingyuan Cao
doaj   +1 more source

Modeling Leachate Generation Using Artificial Neural Networks [PDF]

آب و فاضلاب, 2011
In this study, a neural network model is proposed for modeling leachate flow-rate in a municipal solid waste landfill site. After training, the neural network model predicts leachate generation based on meteorological data and leachate characteristics ...
Mohammad Javad Zoqi, Mohsen Saeedi
doaj  

Parameter identification problems in the modelling of cell motility [PDF]

, 2014
We present a novel parameter identification algorithm for the estimation of parameters in models of cell motility using imaging data of migrating cells.
A Friedman, A Mogilner, AM Stuart, AS Ackleh, AS Ackleh, B Onken, Björn Stinner, C Hogea, C Jiang, C Weston, Cathryn Weston, Chandrasekhar Venkataraman, Charles M. Elliott, CT Kelley, D Bray, D Dormann, DW Marquardt, F Haußer, FD Kelly, G Dziuk, G Dziuk, G Dziuk, G Dziuk, Graham Ladds, H Tatebe, I Hecht, IF Sbalzarini, J Alamo Del, J Barrett, J Barrett, J Nocedal, J Satulovsky, JF Monahan, K Deckelnick, K Deckelnick, L Bosgraaf, M Burger, M Herant, M Lombardi, M Mos, M Neilson, MA Dewar, MA Iglesias, MJ Pittet, MR Garvie, O Lakkis, R Lefever, T Drake, TF Chan, V Isakov, Wayne Croft   +50 more
core   +1 more source

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

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, Bairong Zhu, Haoyu Liu, Tingting Yu   +3 more
wiley   +1 more source