Machine learning of lubrication correction based on GPR for the coupled DPD-DEM simulation of colloidal suspensions

Soft Matter ◽  
2021 ◽  
Author(s):  
Yi Wang ◽  
Jie Ouyang ◽  
Xiaodong Wang
Keyword(s):  
Machine Learning ◽  
Colloidal Suspensions ◽  
Hydrodynamic Interactions ◽  
Dem Simulation ◽  
Molecular Fluids ◽  
Close Range

Hydrodynamic interactions have a major impact on the suspension properties, but they are absent in atomic and molecular fluids due to a lack of intervening medium at close range. To...

scholarly journals Unravelling the role of phoretic and hydrodynamic interactions in active colloidal suspensions

Soft Matter ◽  
2020 ◽  
Vol 16 (38) ◽  
pp. 8893-8903
Author(s):  
Andrea Scagliarini ◽  
Ignacio Pagonabarraga
Keyword(s):  
Colloidal Suspensions ◽  
Hydrodynamic Interactions ◽  
Aggregation Kinetics ◽  
Cluster Growth ◽  
Cluster Morphology

We study numerically suspensions of self-diffusiophoretic colloids for various colloid–solute affinities. We show that hydrodynamics affects the aggregation kinetics and the cluster morphology, significantly hindering cluster growth.

scholarly journals MACHINE LEARNING FOR CLASSIFICATION OF AN ERODING SCARP SURFACE USING TERRESTRIAL PHOTOGRAMMETRY WITH NIR AND RGB IMAGERY

2020 ◽  
Vol V-3-2020 ◽  
pp. 431-437
Author(s):  
H. Bernsteiner ◽  
N. Brožová ◽  
I. Eischeid ◽  
A. Hamer ◽  
S. Haselberger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Single Point ◽  
Kappa Index ◽  
River Bank ◽  
Commission Errors ◽  
Machine Learning Classification ◽  
Machine Learning Model ◽  
Dense Image ◽  
Close Range

Abstract. Increasingly advanced and affordable close-range sensing techniques are employed by an ever-broadening range of users, with varying competence and experience. In this context a method was tested that uses photogrammetry and classification by machine learning to divide a point cloud into different surface type classes. The study site is a peat scarp 20 metres long in the actively eroding river bank of the Rotmoos valley near Obergurgl, Austria. Imagery from near-infra red (NIR) and conventional (RGB) sensors, georeferenced with coordinates of targets surveyed with a total station, was used to create a point cloud using structure from motion and dense image matching. NIR and RGB information were merged into a single point cloud and 18 geometric features were extracted using three different radii (0.02 m, 0.05 m and 0.1 m) totalling 58 variables on which to apply the machine learning classification. Segments representing six classes, dry grass, green grass, peat, rock, snow and target, were extracted from the point cloud and split into a training set and a testing set. A Random Forest machine learning model was trained using machine learning packages in the R-CRAN environment. The overall classification accuracy and Kappa Index were 98% and 97% respectively. Rock, snow and target classes had the highest producer and user accuracies. Dry and green grass had the highest omission (1.9% and 5.6% respectively) and commission errors (3.3% and 3.4% respectively). Analysis of feature importance revealed that the spectral descriptors (NIR, R, G, B) were by far the most important determinants followed by verticality at 0.1 m radius.

scholarly journals Identifying Mangrove Species Using Field Close-Range Snapshot Hyperspectral Imaging and Machine-Learning Techniques

2018 ◽  
Vol 10 (12) ◽  
pp. 2047 ◽  
Author(s):  
Jingjing Cao ◽  
Kai Liu ◽  
Yuanhui Zhu ◽  
Jun Li ◽  
Zhi He
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Reflectance Spectra ◽  
Machine Learning Techniques ◽  
Order Derivative ◽  
Mangrove Species ◽  
First Order ◽  
Learning Techniques ◽  
Close Range

Investigating mangrove species composition is a basic and important topic in wetland management and conservation. This study aims to explore the potential of close-range hyperspectral imaging with a snapshot hyperspectral sensor for identifying mangrove species under field conditions. Specifically, we assessed the data pre-processing and transformation, waveband selection and machine-learning techniques to develop an optimal classification scheme for eight mangrove species in Qi'ao Island of Zhuhai, Guangdong, China. After data pre-processing and transformation, five spectral datasets, which included the reflectance spectra R and its first-order derivative d(R), the logarithm of the reflectance spectra log(R) and its first-order derivative d[log(R)], and hyperspectral vegetation indices (VIs), were used as the input data for each classifier. Consequently, three waveband selection methods, including the stepwise discriminant analysis (SDA), correlation-based feature selection (CFS), and successive projections algorithm (SPA) were used to reduce dimensionality and select the effective wavebands for identifying mangrove species. Furthermore, we evaluated the performance of mangrove species classification using four classifiers, including linear discriminant analysis (LDA), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). Application of the four considered classifiers on the reflectance spectra of all wavebands yielded overall classification accuracies of the eight mangrove species higher than 80%, with SVM having the highest accuracy of 93.54% (Kappa=0.9256). Using the selected wavebands derived from SPA, the accuracy of SVM reached 93.13% (Kappa=0.9208). The addition of hyperspectral VIs and d[log(R)] spectral datasets further improves the accuracies to 93.54% (Kappa=0.9253) and 96.46% (Kappa=0.9591), respectively. These results suggest that it is highly effective to apply field close-range snapshot hyperspectral images and machine-learning classifiers to classify mangrove species.

Colloidal Dynamics from Forced Rayleigh Scattering

1991 ◽  
Vol 248 ◽  
Author(s):  
C. Gochanour ◽  
S. Mazur ◽  
M.S. Wolfe
Keyword(s):  
Rayleigh Scattering ◽  
Volume Fraction ◽  
Translational Motion ◽  
Colloidal Suspensions ◽  
High Volume ◽  
Close Packing ◽  
Correlation Spectroscopy ◽  
Molecular Fluids ◽  
Characteristic Features ◽  
Diffusive Modes

AbstractColloidal suspensions are remarkable analogues of molecular fluids. In particular, at high volume fraction (Φv) they share two characteristic features with super-cooled molecular liquids: the appearance of two distinct modes of translational motion (fast and slow diffusive modes), and a critical retardation of the latter as Φv approaches random close packing (a colloidal “glass transition”). These phenomena have been studied extensively by photon correlation spectroscopy (PCS) [1-4] and are the subject of many theoretical analyses [5-12]. This paper concerns the use of forced Rayleigh scattering (FRS) to address questions not resolved by existing data or theory. We report: 1) properties of a hydrophobic silica colloid bearing photoactive azo-dye groups suitable for FRS studies, and 2) preliminary results from FRS measurements which reveal some unanticipated features regarding the transition from short-time to long-time self-diffusion at small k.

Hydrodynamic interactions in quasi-two-dimensional colloidal suspensions

2010 ◽  
Vol 165 (17-18) ◽  
pp. 941-945 ◽  
Author(s):  
Angeles Ramírez-Saito ◽  
Jesús Santana-Solano ◽  
Beatriz Bonilla-Capilla ◽  
José Luis Arauz-Lara
Keyword(s):  
Colloidal Suspensions ◽  
Hydrodynamic Interactions ◽  
Two Dimensional

scholarly journals Combining machine learning and close-range photogrammetry for infant’s head 3D measurement: a smartphone-based solution

Measurement ◽  
2021 ◽  
pp. 109686
Author(s):  
Inés Barbero-García ◽  
Roberto Pierdicca ◽  
Marina Paolanti ◽  
Andrea Felicetti ◽  
José Luis Lerma
Keyword(s):  
Machine Learning ◽  
3D Measurement ◽  
Close Range Photogrammetry ◽  
Close Range

scholarly journals Tracer diffusion in colloidal suspensions under dilute and crowded conditions with hydrodynamic interactions

2012 ◽  
Vol 137 (1) ◽  
pp. 014503 ◽  
Author(s):  
A. Tomilov ◽  
A. Videcoq ◽  
T. Chartier ◽  
T. Ala-Nissilä ◽  
I. Vattulainen
Keyword(s):  
Colloidal Suspensions ◽  
Tracer Diffusion ◽  
Hydrodynamic Interactions
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