Journal ArticleDOI
Deep learning for gas sensing using MOFs coated weakly-coupled microbeams
TLDR
In this paper , the authors proposed a novel MEMS gas sensor made of two mechanically-coupled microbeams coated with metal organic frameworks and subject to electric actuation, which exploited the dynamic features of the microstructure to simultaneously detect the presence of two gases, namely CO2 and CH4, and estimate their concentrations.About:
This article is published in Applied Mathematical Modelling.The article was published on 2022-01-01. It has received 4 citations till now. The article focuses on the topics: Nonlinear system & Microelectromechanical systems.read more
Citations
More filters
Journal ArticleDOI
On the equivalence between mass perturbation and DC voltage bias in coupled MEMS resonators: Theoretical and experimental investigation
TL;DR: In this article , the authors investigated the effect of voltage bias on the frequency response of mechanically coupled microbeams under electric actuation and developed and validated a mathematical model to simulate the response of the device.
Journal ArticleDOI
Theoretical and Experimental Investigation of Using Multidegree of Freedom Electrostatically Actuated Microstructures in Performing Classification Problems
TL;DR: In this paper , the authors demonstrate the use of micro-electromechanical systems (MEMS) to perform an efficient classification problem, which involves distinguishing between a ramp signal and a step input signal.
Journal ArticleDOI
DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining
Lawson T. Glasby,Kristian Gubsch,Rama Oktavian,Seyed Mohamad Moosavi,Joan Cordiner,Jason C. Cole,Peyman Z. Moghadam +6 more
TL;DR: In this paper , the authors adapted the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), to generate an open-source database of metal-organic frameworks (MOFs) focused on their synthetic properties: the DigiMOF database.
Journal ArticleDOI
Theoretical and Experimental Investigation of Using Multidegree of Freedom Electrostatically Actuated Microstructures in Performing Classification Problems
TL;DR: In this paper , the authors demonstrate the use of micro-electromechanical systems (MEMS) to perform an efficient classification problem, which involves distinguishing between a ramp signal and a step input signal.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Journal ArticleDOI
A concordance correlation coefficient to evaluate reproducibility.
TL;DR: A new reproducibility index is developed and studied that is simple to use and possesses desirable properties and the statistical properties of this estimate can be satisfactorily evaluated using an inverse hyperbolic tangent transformation.
Journal ArticleDOI
Compilation of Henry's law constants (version 4.0) for water as solvent
TL;DR: According to Henry's law, the equilibrium ratio between the abundances in the gas phase and in the aqueous phase is constant for a dilute solution as discussed by the authors, and a compilation of 17 350 values of Henry's Law constants for 4632 species, collected from 689 references is available at http://wwwhenrys-law.org
Journal ArticleDOI
Practical selection of SVM parameters and noise estimation for SVM regression
Vladimir Cherkassky,Yunqian Ma +1 more
TL;DR: This work describes a new analytical prescription for setting the value of insensitive zone epsilon, as a function of training sample size, and compares generalization performance of SVM regression under sparse sample settings with regression using 'least-modulus' loss (epsilon=0) and standard squared loss.