Results 141 to 150 of about 298,220 (292)

Real‐time fault detection in multicomponent nuclear‐waste slurries through data fusion of spectroscopic sensors

AIChE Journal, EarlyView.
Abstract Three instruments–Raman spectroscopy, attenuated total reflectance–Fourier transform infrared spectroscopy, and focused beam reflectance measurement–were used to detect sensor faults, mixing faults, and unanticipated chemistry in a system of multicomponent slurries.
Steven H. Crouse, Ronald W. Rousseau, Martha A. Grover   +2 more
wiley   +1 more source

Correction: SSNdesign-An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks. [PDF]

PLoS One
Pearse AR, McGree JM, Som NA, Leigh C, Maxwell P, Ver Hoef JM, Peterson EE.   +6 more
europepmc   +1 more source

On the smallest eigenvalues of covariance matrices of multivariate\n spatial processes [PDF]

, 2016
François Bachoc, Reinhard Furrer
openalex   +1 more source

Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution

Advanced Intelligent Discovery, EarlyView.
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren, Qian Jiang, Xin Jin, Puming Wang, Xing Chu, Shaowen Yao, Shengfa Miao   +6 more
wiley   +1 more source

Variational adaptive Gaussian approximation filter for nonlinear systems with generalized unknown disturbances. [PDF]

iScience
Qin Y, Lv J, Li S, Hou Y.
europepmc   +1 more source

On infinite covariance expansions [PDF]

, 2019
Marie Ernst, Gesine Reinert, Yvik Swan
openalex   +1 more source

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Advanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang, Jiayi Tan, Qizhen Song, Tian Du, Jens Hauch, Christoph J. Brabec   +5 more
wiley   +1 more source

Reconstructing Spatial Localization Error Maps via Physics-Informed Tensor Completion for Passive Sensor Systems. [PDF]

Sensors (Basel)
Zhang Z, Huang Z, Wang C, Jiang Q.
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Estimating the additive genetic variance for relative fitness from changes in allele frequency. [PDF]

Genetics
Geeta Arun M, Angus-Henry A, Obbard DJ, Hadfield JD.   +3 more
europepmc   +1 more source