Results 51 to 60 of about 14,298 (230)

Stable Gaussian Process based Tracking Control of Lagrangian Systems

, 2018
High performance tracking control can only be achieved if a good model of the dynamics is available. However, such a model is often difficult to obtain from first order physics only. In this paper, we develop a data-driven control law that ensures closed
Beckers, Thomas, Hirche, Sandra, Kulić, Dana, Umlauft, Jonas   +3 more
core   +1 more source

Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

Advanced Functional Materials, EarlyView.
A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou, Yunsik Ohm, Ren‐Mian Chin, Olivia Kerr, Krithika Manohar, Mohammad H. Malakooti   +5 more
wiley   +1 more source

Deck motion prediction using neural kernel network Gaussian process regression

Xibei Gongye Daxue Xuebao
Deck motion prediction and compensation are critical technologies for carrier-based aircraft automatic landing. Traditional deck motion prediction methods rely on precision of motion models and parameter adjustments, facing challenges in adaptability to ...
QIN Peng, LUO Jianjun, MA Weihua, WU Liming   +3 more
doaj   +1 more source

Novel Hybridized Computational Paradigms Integrated with Five Stand-Alone Algorithms for Clinical Prediction of HCV Status among Patients: A Data-Driven Technique

Life, 2022
The emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the ...
Zachariah Madaki, Nurettin Abacioglu, A. G. Usman, Neda Taner, Ahmet. O. Sehirli, S. I. Abba   +5 more
doaj   +1 more source

A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems

, 2018
We develop a method for the evaluation of extreme event statistics associated with nonlinear dynamical systems, using a small number of samples. From an initial dataset of design points, we formulate a sequential strategy that provides the 'next-best ...
Mohamad, Mustafa A., Sapsis, Themistoklis P.   +1 more
core   +1 more source

AI–Guided 4D Printing of Carnivorous Plants–Inspired Microneedles for Accelerated Wound Healing

Advanced Materials, EarlyView.
This work presents an artificial intelligence (AI)‐guided 4D‐printed microneedle platform inspired by carnivorous plants for wound healing. A thermo‐responsive shape memory polymer enables body temperature–triggered self‐coiling for autonomous wound closure.
Hyun Lee, Moon‐Jo Kim, DongEung Kim, Chan Ho Moon, Seojoon Bang, Hyeong Seok Kang, Ju Yeong Gwon, Jong Hwa Seo, Junhyub Jeon, Junhyuk Son, Munwon Lim, Minho Kang, Dong Yun Lee, Donghyun Lim, Jung‐Hoon Park, Gi Doo Cha, Soo‐Hong Lee, Tae‐Sik Jang, Kisuk Yang, Yunho Jeong, Youngho Eom, Hyun‐Do Jung   +21 more
wiley   +1 more source

Invariances of random fields paths, with applications in Gaussian Process Regression [PDF]

, 2013
We study pathwise invariances of centred random fields that can be controlled through the covariance. A result involving composition operators is obtained in second-order settings, and we show that various path properties including additivity boil down ...
Durrande, Nicolas, Ginsbourger, David, Roustant, Olivier   +2 more
core   +2 more sources

A nonparametric Bayesian approach toward robot learning by demonstration

, 2012
In the past years, many authors have considered application of machine learning methodologies to effect robot learning by demonstration. Gaussian mixture regression (GMR) is one of the most successful methodologies used for this purpose.
Antoniak, Argall, Argall, Billard, Billard, Billard, Billard, Bishop, Blackwell, Blei, Celeux, Chandler, Chatzis, Demiris, Dimitrios Korkinof, Ferguson, Ghahramani, Jordan, Leroux, Lopes, Lopes, Muller, Myersand, Neal, Pearlmutter, Qi, Rasmussen, Schwarz, Sethuraman, Skoglund, Sotirios P. Chatzis, Ude, Vapnik, Walker, Yiannis Demiris, Zegers   +35 more
core   +1 more source

Machine Learning for Green Solvents: Assessment, Selection and Substitution

Advanced Science, EarlyView.
Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta, Janhavi Nistane, Abhishek Sose, Harikrishna Sahu, Rampi Ramprasad   +4 more
wiley   +1 more source

Machine learning regression tools for erosion prediction of WC-10Co4Cr thermal spray coating

Results in Surfaces and Interfaces, 2023
The prediction of erosion in WC-10Co4Cr thermal spray coating is predicted using regression machine learning technique. A pot tester helped to examine the erosion rate of WC-10Co4Cr thermal spray coatings. WC-10Co4Cr thermal spray powder was sprayed onto
Jashanpreet Singh, Satish Kumar, Ranvijay Kumar, S.K. Mohapatra   +3 more
doaj   +1 more source