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Journal articles on the topic 'Granular Ensembles'

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Author: Grafiati

Published: 6 September 2023

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1

Puckett, James G., Frédéric Lechenault, and Karen E. Daniels. "Generating ensembles of two-dimensional granular configurations." Chaos: An Interdisciplinary Journal of Nonlinear Science 19, no. 4 (December 2009): 041108. http://dx.doi.org/10.1063/1.3207830.

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2

Puzyrev, Dmitry, Kirsten Harth, Torsten Trittel, and Ralf Stannarius. "Machine Learning for 3D Particle Tracking in Granular Gases." Microgravity Science and Technology 32, no. 5 (July 18, 2020): 897–906. http://dx.doi.org/10.1007/s12217-020-09800-4.

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Abstract Dilute ensembles of granular matter (so-called granular gases) are nonlinear systems which exhibit fascinating dynamical behavior far from equilibrium, including non-Gaussian distributions of velocities and rotational velocities, clustering, and violation of energy equipartition. In order to understand their dynamic properties, microgravity experiments were performed in suborbital flights and drop tower experiments. Up to now, the experimental images were evaluated mostly manually. Here, we introduce an approach for automatic 3D tracking of positions and orientations of rod-like particles in a dilute ensemble, based on two-view video data analysis. A two-dimensional (2D) localization of particles is performed using a Mask R-CNN neural network trained on a custom data set. The problem of 3D matching of the particles is solved by minimization of the total reprojection error, and finally, particle trajectories are tracked so that ensemble statistics are extracted. Depending on the required accuracy, the software can work fully self-sustainingly or serve as a base for subsequent manual corrections. The approach can be extended to other 3D and 2D particle tracking problems.

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3

Singh, Saurabh, and Tejas G. Murthy. "Evolution of structure of cohesive granular ensembles in compression." International Journal of Solids and Structures 238 (March 2022): 111359. http://dx.doi.org/10.1016/j.ijsolstr.2021.111359.

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4

Tighe, Brian P., and Thijs J. H. Vlugt. "Force balance in canonical ensembles of static granular packings." Journal of Statistical Mechanics: Theory and Experiment 2010, no. 01 (January 25, 2010): P01015. http://dx.doi.org/10.1088/1742-5468/2010/01/p01015.

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5

Medini, Chaitanya, Bipin Nair, Egidio D'Angelo, Giovanni Naldi, and Shyam Diwakar. "Modeling Spike-Train Processing in the Cerebellum Granular Layer and Changes in Plasticity Reveal Single Neuron Effects in Neural Ensembles." Computational Intelligence and Neuroscience 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/359529.

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The cerebellum input stage has been known to perform combinatorial operations on input signals. In this paper, two types of mathematical models were used to reproduce the role of feed-forward inhibition and computation in the granular layer microcircuitry to investigate spike train processing. A simple spiking model and a biophysically-detailed model of the network were used to study signal recoding in the granular layer and to test observations like center-surround organization and time-window hypothesis in addition to effects of induced plasticity. Simulations suggest that simple neuron models may be used to abstract timing phenomenon in large networks, however detailed models were needed to reconstruct population coding via evoked local field potentials (LFP) and for simulating changes in synaptic plasticity. Our results also indicated that spatio-temporal code of the granular network is mainly controlled by the feed-forward inhibition from the Golgi cell synapses. Spike amplitude and total number of spikes were modulated by LTP and LTD. Reconstructing granular layer evoked-LFP suggests that granular layer propagates the nonlinearities of individual neurons. Simulations indicate that granular layer network operates a robust population code for a wide range of intervals, controlled by the Golgi cell inhibition and is regulated by the post-synaptic excitability.

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Spahn, F., N. Albers, M. Sremčević, and C. Thornton. "Kinetic description of coagulation and fragmentation in dilute granular particle ensembles." Europhysics Letters (EPL) 67, no. 4 (August 2004): 545–51. http://dx.doi.org/10.1209/epl/i2003-10301-2.

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7

ARÉVALO, ROBERTO, IKER ZURIGUEL, and DIEGO MAZA. "TOPOLOGICAL PROPERTIES OF THE CONTACT NETWORK OF GRANULAR MATERIALS." International Journal of Bifurcation and Chaos 19, no. 02 (February 2009): 695–702. http://dx.doi.org/10.1142/s0218127409023056.

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The force networks of different granular ensembles are defined and their topological properties studied using the tools of complex networks. In particular, for each set of grains compressed in a square box, a force threshold is introduced that determines which contacts conform the network. Hence, the topological characteristics of the network are analyzed as a function of this parameter. The characterization of the structural features thus obtained, may be useful in the understanding of the macroscopic physical behavior exhibited by this class of media.

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8

Singh, Saurabh, Ramesh Kannan Kandasami, Rupesh Kumar Mahendran, and Tejas Murthy. "System size effects on the mechanical response of cohesive-frictional granular ensembles." EPJ Web of Conferences 140 (2017): 08007. http://dx.doi.org/10.1051/epjconf/201714008007.

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9

Kishore, Raj, R. Krishnan, Manoranjan Satpathy, Zohar Nussinov, and Kisor K. Sahu. "Abstraction of meso-scale network architecture in granular ensembles using ‘big data analytics’ tools." Journal of Physics Communications 2, no. 3 (March 21, 2018): 031004. http://dx.doi.org/10.1088/2399-6528/aab386.

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10

Kruggel-Emden, Harald, Erdem Simsek, Siegmar Wirtz, and Viktor Scherer. "A Comparative Numerical Study of Particle Mixing on Different Grate Designs Through the Discrete Element Method." Journal of Pressure Vessel Technology 129, no. 4 (August 18, 2006): 593–600. http://dx.doi.org/10.1115/1.2767338.

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Based on LEAT’s discrete element codes, granular flow and mixing on conveying equipment are studied in two and three dimensions. Discrete element simulations, which are briefly introduced, provide detailed information on particle positions and velocities over time. This information is used to derive quantities characterizing the dynamic process of mixing. The main focus of the study presented is the mixing process of inhomogeneous particle ensembles on different grate types. For this purpose, the introduced mixing parameters are used to compare the mixing in a 3D situation with the corresponding 2D approximation on identical grates and to compare different grate designs in two dimensions.

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11

Bär, Markus, Robert Großmann, Sebastian Heidenreich, and Fernando Peruani. "Self-Propelled Rods: Insights and Perspectives for Active Matter." Annual Review of Condensed Matter Physics 11, no. 1 (March 10, 2020): 441–66. http://dx.doi.org/10.1146/annurev-conmatphys-031119-050611.

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A wide range of experimental systems including gliding, swarming and swimming bacteria, in vitro motility assays, and shaken granular media are commonly described as self-propelled rods. Large ensembles of those entities display a large variety of self-organized, collective phenomena, including the formation of moving polar clusters, polar and nematic dynamic bands, mobility-induced phase separation, topological defects, and mesoscale turbulence, among others. Here, we give a brief survey of experimental observations and review the theoretical description of self-propelled rods. Our focus is on the emergent pattern formation of ensembles of dry self-propelled rods governed by short-ranged, contact mediated interactions and their wet counterparts that are also subject to long-ranged hydrodynamic flows. Altogether, self-propelled rods provide an overarching theme covering many aspects of active matter containing well-explored limiting cases. Their collective behavior not only bridges the well-studied regimes of polar self-propelled particles and active nematics, and includes active phase separation, but also reveals a rich variety of new patterns.

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Lakkimsetti, Balaji, and Gali Madhavi Latha. "Role of grain size and shape on undrained monotonic shear, liquefaction, and post-liquefaction behaviour of granular ensembles." Soil Dynamics and Earthquake Engineering 173 (October 2023): 108086. http://dx.doi.org/10.1016/j.soildyn.2023.108086.

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Hurley, Ryan C., Eric B. Herbold, and Darren C. Pagan. "Characterization of the crystal structure, kinematics, stresses and rotations in angular granular quartz during compaction." Journal of Applied Crystallography 51, no. 4 (June 28, 2018): 1021–34. http://dx.doi.org/10.1107/s1600576718006957.

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Three-dimensional X-ray diffraction (3DXRD), a method for quantifying the position, orientation and elastic strain of large ensembles of single crystals, has recently emerged as an important tool for studying the mechanical response of granular materials during compaction. Applications have demonstrated the utility of 3DXRD and X-ray computed tomography (XRCT) for assessing strains, particle stresses and orientations, inter-particle contacts and forces, particle fracture mechanics, and porosity evolution in situ. Although past studies employing 3DXRD and XRCT have elucidated the mechanics of spherical particle packings and angular particle packings with a small number of particles, there has been limited effort to date in studying angular particle packings with a large number of particles and in comparing the mechanics of these packings with those composed of a large number of spherical particles. Therefore, the focus of the present paper is on the mechanics of several hundred angular particles during compaction using in situ 3DXRD to study the crystal structure, kinematics, stresses and rotations of angular quartz grains. Comparisons are also made between the compaction response of angular grains and that of spherical grains, and stress-induced twinning within individual grains is discussed.

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Pais-Vieira, Miguel, Carolina Kunicki, Po-He Tseng, Joel Martin, Mikhail Lebedev, and Miguel A. L. Nicolelis. "Cortical and thalamic contributions to response dynamics across layers of the primary somatosensory cortex during tactile discrimination." Journal of Neurophysiology 114, no. 3 (September 2015): 1652–76. http://dx.doi.org/10.1152/jn.00108.2015.

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Tactile information processing in the rodent primary somatosensory cortex (S1) is layer specific and involves modulations from both thalamocortical and cortico-cortical loops. However, the extent to which these loops influence the dynamics of the primary somatosensory cortex while animals execute tactile discrimination remains largely unknown. Here, we describe neural dynamics of S1 layers across the multiple epochs defining a tactile discrimination task. We observed that neuronal ensembles within different layers of the S1 cortex exhibited significantly distinct neurophysiological properties, which constantly changed across the behavioral states that defined a tactile discrimination. Neural dynamics present in supragranular and granular layers generally matched the patterns observed in the ventral posterior medial nucleus of the thalamus (VPM), whereas the neural dynamics recorded from infragranular layers generally matched the patterns from the posterior nucleus of the thalamus (POM). Selective inactivation of contralateral S1 specifically switched infragranular neural dynamics from POM-like to those resembling VPM neurons. Meanwhile, ipsilateral M1 inactivation profoundly modulated the firing suppression observed in infragranular layers. This latter effect was counterbalanced by contralateral S1 block. Tactile stimulus encoding was layer specific and selectively affected by M1 or contralateral S1 inactivation. Lastly, causal information transfer occurred between all neurons in all S1 layers but was maximal from infragranular to the granular layer. These results suggest that tactile information processing in the S1 of awake behaving rodents is layer specific and state dependent and that its dynamics depend on the asynchronous convergence of modulations originating from ipsilateral M1 and contralateral S1.

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Artiemjew, Piotr, and Krzysztof Ropiak. "A Novel Ensemble Model - The Random Granular Reflections." Fundamenta Informaticae 179, no. 2 (March 10, 2021): 183–203. http://dx.doi.org/10.3233/fi-2021-2020.

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One of the most popular families of techniques to boost classification are Ensemble methods. Random Forests, Bagging and Boosting are the most popular and widely used ones. This article presents a novel Ensemble Model, named Random Granular Reflections. The algorithm used in this new approach creates an ensemble of homogeneous granular decision systems. The first step of the learning process is to take the training system and cover it with random homogeneous granules (groups of objects from the same decision class that are as little indiscernible from each other as possible). Next, granular reflection is created, which is finally used in the classification process. Results obtained by our initial experiments show that this approach is promising and comparable with other tested methods. The main advantage of our new method is that it is not necessary to search for optimal parameters while looking for granular reflections in the subsequent iterations of our ensemble model.

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16

PENG, J., and J. O. DABIRI. "Transport of inertial particles by Lagrangian coherent structures: application to predator–prey interaction in jellyfish feeding." Journal of Fluid Mechanics 623 (March 6, 2009): 75–84. http://dx.doi.org/10.1017/s0022112008005089.

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We use a dynamical systems approach to identify coherent structures from often chaotic motions of inertial particles in open flows. We show that particle Lagrangian coherent structures (pLCS) act as boundaries between regions in which particles have different kinematics. They provide direct geometric information about the motion of ensembles of inertial particles, which is helpful to understand their transport. As an application, we apply the methodology to a planktonic predator–prey system in which moon jellyfish Aurelia aurita uses its body motion to generate a flow that transports small plankton such as copepods to its vicinity for feeding. With the flow field generated by the jellyfish measured experimentally and the dynamics of plankton described by a modified Maxey–Riley equation, we use the pLCS to identify a capture region in which prey can be captured by the jellyfish. The properties of the pLCS and the capture region enable analysis of the effect of several physiological and mechanical parameters on the predator–prey interaction, such as prey size, escape force, predator perception, etc. The methods developed here are equally applicable to multiphase and granular flows, and can be generalized to any other particle equation of motion, e.g. equations governing the motion of reacting particles or charged particles.

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17

Li, Wei, Youmeng Luo, Chao Tang, Kaiqiang Zhang, and Xiaoyu Ma. "Boosted Fuzzy Granular Regression Trees." Mathematical Problems in Engineering 2021 (July 21, 2021): 1–16. http://dx.doi.org/10.1155/2021/9958427.

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The regression problem is a valued problem in the domain of machine learning, and it has been widely employed in many fields such as meteorology, transportation, and material. Granular computing (GrC) is a good approach of exploring human intelligent information processing, which has the superiority of knowledge discovery. Ensemble learning is easy to execute parallelly. Based on granular computing and ensemble learning, we convert the regression problem into granular space equivalently to solve and proposed boosted fuzzy granular regression trees (BFGRT) to predict a test instance. The thought of BFGRT is as follows. First, a clustering algorithm with automatic optimization of clustering centers is presented. Next, in terms of the clustering algorithm, we employ MapReduce to parallelly implement fuzzy granulation of the data. Then, we design new operators and metrics of fuzzy granules to build fuzzy granular rule base. Finally, a fuzzy granular regression tree (FGRT) in the fuzzy granular space is presented. In the light of these, BFGRT can be designed by parallelly combing multiple FGRTs via random sampling attributes and MapReduce. Theory and experiments show that BFGRT is accurate, efficient, and robust.

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18

Kang, Jaewoong, Wooseong Yang, and Mye Sohn. "Homogeneous Ensemble Instance Intervals Determination Method of Time Series Data Based on Granular Computing." International Journal of Machine Learning and Computing 10, no. 6 (December 2020): 735–39. http://dx.doi.org/10.18178/ijmlc.2020.10.6.998.

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19

Gopalakrishnan, Kasthurirangan, and Konstantina Nadia Gkritza. "FORECASTING TRANSPORTATION INFRASTRUCTURE IMPACTS OF RENEWABLE ENERGY INDUSTRY USING NEURAL NETWORKS." Technological and Economic Development of Economy 19, Supplement_1 (January 28, 2014): S157—S175. http://dx.doi.org/10.3846/20294913.2013.876690.

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Iowa is a state rich in renewable energy resources, especially biomass. The successful development of renewable energy industry in Iowa is concomitant with increase in freight traffic and is likely to have significant impacts on transportation infrastructure condition and increased maintenance expenses for the state and local governments. The primary goal of this paper is to investigate the feasibility of employing the Neural Networks (NN) methodology to forecast the impacts of Iowa's biofuels and wind power industries on Iowa's secondary and local road condition and maintenance-related costs in a panel data framework. The data for this study were obtained from a number of sources and for a total of 24 counties in clusters in Northern, Western, and Southern Iowa over a period of ten years. Back-Propagation NN (BPNN) using a Quasi-Newton secondorder training algorithm was chosen for this study owing to its very fast convergence properties. Since the size of the training set is relatively small, ensembles of well-trained NNs were formed to achieve significant improvements in generalization performance. The developed NN forecasting models could identify the presence of biofuel plants and wind farms as well as large-truck traffic as the most sensitive inputs influencing pavement condition and granular and blading maintenance costs. Pavement deterioration resulting from traffic loads was found to be associated with the presence of both biofuel plants and wind farms. The developed NN forecasting models can be useful in identifying and properly evaluating future transportation infrastructure impacts resulting from the renewable energy industry development and thus help Iowa maintain its competitive edge in the rapidly developing bioeconomy.

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20

Bi, Dapeng, and Bulbul Chakraborty. "Rheology of granular materials: dynamics in a stress landscape." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1909 (December 28, 2009): 5073–90. http://dx.doi.org/10.1098/rsta.2009.0193.

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We present a framework for analysing the rheology of dense driven granular materials, based on a recent proposal of a stress-based ensemble. In this ensemble, fluctuations in a granular system near jamming are controlled by a temperature-like parameter, the angoricity, which is conjugate to the stress of the system. In this paper, we develop a model for slowly driven granular materials based on the stress ensemble and the idea of a landscape in stress space. The idea of an activated process driven by the angoricity has been shown by Behringer et al . (Behringer et al. 2008 Phys. Rev. Lett. 101 , 268301) to describe the logarithmic strengthening of granular materials. Just as in the soft glassy rheology (SGR) picture, our model represents the evolution of a small patch of granular material (a mesoscopic region) in a stress-based trap landscape. The angoricity plays the role of the fluctuation temperature in the SGR. We determine (i) the constitutive equation, (ii) the yield stress, and (iii) the distribution of stress dissipated during granular shearing experiments, and compare these predictions with the experiments of Hartley & Behringer (Hartley & Behringer 2003 Nature 421 , 928–931.).

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Pandit, Pallavi, Matthias Schwartzkopf, André Rothkirch, Stephan V. Roth, Sigrid Bernstorff, and Ajay Gupta. "Structure–Function Correlations in Sputter Deposited Gold/Fluorocarbon Multilayers for Tuning Optical Response." Nanomaterials 9, no. 9 (September 3, 2019): 1249. http://dx.doi.org/10.3390/nano9091249.

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A new strategy to nanoengineer gold/fluorocarbon multilayer (ML) nanostructures is reported. We have investigated the morphological changes occurring at the metal–polymer interface in ML structures with varying volume fraction of gold (Au) and the kinetic growth aspect of the microscale properties of nano-sized Au in plasma polymer fluorocarbon (PPFC). Investigations were carried out at various temperatures and annealing times by means of grazing incidence small-angle and wide-angle X-ray scattering (GISAXS and GIWAXS). We have fabricated a series of MLs with varying volume fraction (0.12, 0.27, 0.38) of Au and bilayer periodicity in ML structure. They show an interesting granular structure consisting of nearly spherical nanoparticles within the polymer layer. The nanoparticle (NP) morphology changes due to the collective effects of NPs diffusion within ensembles in the in-plane vicinity and interlayer with increasing temperature. The in-plane NPs size distinctly increases with increasing temperature. The NPs become more spherical, thus reducing the surface energy. Linear growth of NPs with temperature and time shows diffusion-controlled growth of NPs in the ML structure. The structural stability of the multilayer is controlled by the volume ratio of the metal in polymer. At room temperature, UV-Vis shows a blue shift of the plasmon peak from 560 nm in ML Au/PTFE_1 to 437 nm in Au/PTFE_3. We have identified the fabrication and postdeposition annealing conditions to limit the local surface plasmon resonance (LSPR) shift from Δ λ L S P R = 180 nm (Au/PTFE_1) to Δ λ L S P R = 67 nm (Au/PTFE_3 ML)) and their optical response over a wide visible wavelength range. A variation in the dielectric constant of the polymer in presence of varying Au inclusion is found to be a possible factor affecting the LSPR frequency. Our findings may provide insights in nanoengineering of ML structure that can be useful to systematically control the growth of NPs in polymer matrix.

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22

Tighe, Brian P., Jacco H. Snoeijer, Thijs J. H. Vlugt, and Martin van Hecke. "The force network ensemble for granular packings." Soft Matter 6, no. 13 (2010): 2908. http://dx.doi.org/10.1039/b926592a.

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23

Edwards, S. F. "The full canonical ensemble of a granular system." Physica A: Statistical Mechanics and its Applications 353 (August 2005): 114–18. http://dx.doi.org/10.1016/j.physa.2005.01.045.

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24

Rey, Juan Sebastián, Jose Daniel Muñoz, and William Fernando Oquendo. "A procedure to join the force and volume ensemble statistical descriptions of granular media." EPJ Web of Conferences 249 (2021): 14002. http://dx.doi.org/10.1051/epjconf/202124914002.

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Granular media consist of a large number of discrete particles interacting mostly through contact forces that, being dissipative, jeopardizes a classical statistical equilibrium approach based on energy. Instead, two independent equilibrium statistical descriptions have been proposed: the Volume Ensemble and the Force Network Ensemble. Hereby, we propose a procedure to join them into a single description, using Discrete Element simulations of a granular medium of monodisperse spheres in the limit state of isotropic compression as testing ground. By classifying grains according to the number of faces of the Voronoï cells around them, our analysis establishes an empirical relationship between that number of faces and the number of contacts on the grain. In addition, a linear relationship between the number of faces of each Voronoï cell and the number of elementary cells proposed by T. Aste and T. Di Matteo in 2007 is found. From those two relations, an expression for the total entropy (volumes plus forces) is written in terms of the contact number, an entropy that, when maximized, gives an equation of state connecting angoricity (the temperature-like variable for the force network ensemble) and compactivity (the temperature-like variable for the volume ensemble). So, the procedure establishes a microscopic connection between geometry and mechanics and, constitutes a further step towards building a complete statistical theory for granular media in equilibrium.

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Sheng, Chunyang, Haixia Wang, Xiao Lu, Zhiguo Zhang, Wei Cui, and Yuxia Li. "Distributed Gaussian Granular Neural Networks Ensemble for Prediction Intervals Construction." Complexity 2019 (July 3, 2019): 1–17. http://dx.doi.org/10.1155/2019/2379584.

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To overcome the weakness of generic neural networks (NNs) ensemble for prediction intervals (PIs) construction, a novel Map-Reduce framework-based distributed NN ensemble consisting of several local Gaussian granular NN (GGNNs) is proposed in this study. Each local network is weighted according to its contribution to the ensemble model. The weighted coefficient is estimated by evaluating the performance of the constructed PIs from each local network. A new evaluation principle is reported with the consideration of the predicting indices. To estimate the modelling uncertainty and the data noise simultaneously, the Gaussian granular is introduced to the numeric NNs. The constructed PIs can then be calculated by the variance of output distribution of each local NN, i.e., the summation of the model uncertainty variance and the data noise variance. To verify the effectiveness of the proposed model, a series of prediction experiments, including two classical time series with additive noise and two industrial time series, are carried out here. The results indicate that the proposed distributed GGNNs ensemble exhibits a good performance for PIs construction.

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26

Xu, Li, and Shifei Ding. "A novel clustering ensemble model based on granular computing." Applied Intelligence 51, no. 8 (January 9, 2021): 5474–88. http://dx.doi.org/10.1007/s10489-020-01979-8.

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Chakraborty, Bulbul. "Statistical ensemble approach to stress transmission in granular packings." Soft Matter 6, no. 13 (2010): 2884. http://dx.doi.org/10.1039/b927435a.

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DEAN, DAVID S., and ALEXANDRE LEFEVRE. "THE STEADY STATE OF THE TAPPED ISING MODEL." Advances in Complex Systems 04, no. 04 (December 2001): 333–43. http://dx.doi.org/10.1142/s0219525901000255.

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We consider a tapping dynamics, analogous to that in experiments on granular media, on the simple one-dimensional ferromagnetic Ising model. When unperturbed, the system undergoes a single spin flip falling dynamics where only energy lowering moves occur. With this dynamics the system has an exponentially large number of metastable states and gets stuck in blocked or jammed configurations as do granular media. When stuck, the system is tapped, in order to make it evolve, by flipping in parallel each spin with probability p (corresponding to the strength of the tapping). Under this dynamics the system reaches a steady state regime characterized by an asymptotic energy per spin E(p), which is determined analytically. Within the steady state regime we compare certain time averaged quantities with the ensemble average of Edwards based on a canonical measure over metastable states of fixed average energy. The ensemble average yields results in excellent agreement with the dynamical measurements.

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29

Slobinsky, D., and Luis A. Pugnaloni. "Arch-based configurations in the volume ensemble of static granular systems." Journal of Statistical Mechanics: Theory and Experiment 2015, no. 2 (February 3, 2015): P02005. http://dx.doi.org/10.1088/1742-5468/2015/02/p02005.

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30

Li, Hui, Xuesong Wang, and Shifei Ding. "Research of multi-sided multi-granular neural network ensemble optimization method." Neurocomputing 197 (July 2016): 78–85. http://dx.doi.org/10.1016/j.neucom.2016.02.013.

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31

Levy dit Vehel, Victor, Ange Haddjeri, and Osvanny Ramos. "Acoustic localisation in a two-dimensional granular medium." EPJ Web of Conferences 249 (2021): 15005. http://dx.doi.org/10.1051/epjconf/202124915005.

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We focus on localizing the source of acoustic emissions within a compressed two-dimensional granular ensemble of photoelastic disks, having as main information the arrival times of the acoustic signal to 6 sensors located in the boundaries of the system. By estimating, thanks to the photoelasticity of the grains, the wave speed at every point of the structure, we are able to compute the arrival times from every point of the system to the sensors. A comparison between the arrival time differences between every set of computed values to those from the actual measurements allows finding the source of the acoustic emissions.

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32

Zhang Xing-Gang, Long Zheng-Wen, and Hu Lin. "The scalar force network ensemble model for force distribution in granular system." Acta Physica Sinica 58, no. 1 (2009): 90. http://dx.doi.org/10.7498/aps.58.90.

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33

Wang, Ye-Cheng, Xu-Qing Tang, and Honglin Xu. "Clustering Ensemble Technology Based on Granular Computing to Extract Cervical Cancer Predictors." Mathematical Problems in Engineering 2022 (May 26, 2022): 1–13. http://dx.doi.org/10.1155/2022/4465007.

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Background. Cervical cancer is the most common gynecological malignancy, and its incidence has tended to be younger in recent years. Through the analysis of high-throughput expression data, the identification of key genes in cancer and healthy individuals as predictors of cervical cancer is of great significance for the early detection and early treatment of cervical cancer. Method. Granular computing is a concept and computing paradigm to deal with problems through information granulation, and the process of granulation can be realized by means of clustering. Based on this, this paper proposes an AB method to obtain representative elements in a multiattribute data system. First, the evaluation index FHEI of the clustering structure is introduced, and Algorithm 1 is designed to obtain the optimal clustering structure of each attribute of the data system and use it as the base cluster. Secondly, based on the clustering ensemble technology of granular computing, Algorithm 2 is designed with the help of the concept of information entropy. The algorithm takes the base cluster as the input to obtain the optimal ensemble clustering structure. Finally, using the nearest center principle, the representative elements of each class in the optimal ensemble clustering structure are obtained. Results. In this paper, the differentially expressed genes (DEGs) are screened out by using the gene expression data of cervical cancer, and the scores of the four interaction relationships among the DEGs are used as a multiattribute data system and input into the AB method. The five representative elements obtained are RTTN, SAMD10, ZNF207, WAC, and METTL14, which are the predictors of cervical cancer. The classification accuracy of these predictors is as high as 98.82%. This paper also conducts a comparative study between the AB method and other classical methods on six independent gene expression datasets. The results show that the number of predictors obtained by the AB method is small but has a high classification accuracy in the classification of patient samples.

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Leite, Daniel, and Igor Škrjanc. "Ensemble of evolving optimal granular experts, OWA aggregation, and time series prediction." Information Sciences 504 (December 2019): 95–112. http://dx.doi.org/10.1016/j.ins.2019.07.053.

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Nijhuis, Michiel, and Iman van Lelyveld. "Outlier Detection with Reinforcement Learning for Costly to Verify Data." Entropy 25, no. 6 (May 25, 2023): 842. http://dx.doi.org/10.3390/e25060842.

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Outliers are often present in data and many algorithms exist to find these outliers. Often we can verify these outliers to determine whether they are data errors or not. Unfortunately, checking such points is time-consuming and the underlying issues leading to the data error can change over time. An outlier detection approach should therefore be able to optimally use the knowledge gained from the verification of the ground truth and adjust accordingly. With advances in machine learning, this can be achieved by applying reinforcement learning on a statistical outlier detection approach. The approach uses an ensemble of proven outlier detection methods in combination with a reinforcement learning approach to tune the coefficients of the ensemble with every additional bit of data. The performance and the applicability of the reinforcement learning outlier detection approach are illustrated using granular data reported by Dutch insurers and pension funds under the Solvency II and FTK frameworks. The application shows that outliers can be identified by the ensemble learner. Moreover, applying the reinforcement learner on top of the ensemble model can further improve the results by optimising the coefficients of the ensemble learner.

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Liu, Han, and Mihaela Cocea. "Granular computing-based approach for classification towards reduction of bias in ensemble learning." Granular Computing 2, no. 3 (November 11, 2016): 131–39. http://dx.doi.org/10.1007/s41066-016-0034-1.

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Liu, Han, and Mihaela Cocea. "Nature-inspired framework of ensemble learning for collaborative classification in granular computing context." Granular Computing 4, no. 4 (July 30, 2018): 715–24. http://dx.doi.org/10.1007/s41066-018-0122-5.

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Старовойтов, А. А., А. А. Фандеев, И. Ю. Никитин, И. А. Гладских, and Д. Р. Дададжанов. "Мониторинг локализованного плазмонного резонанса неоднородного ансамбля серебряных наночастиц на границе двух сред." Оптика и спектроскопия 131, no. 7 (2023): 1005. http://dx.doi.org/10.21883/os.2023.07.56138.4895-23.

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Optical properties of an inhomogeneous ensemble of plasmonic silver nanoparticles obtained by thermal vacuum deposition at the interface between two media as this interface moves relative to the nanoparticles themselves have been studied. The movement was carried out due to thermally stimulated diffusion of nanoparticles into the polystyrene layer and due to the deposition of an additional layer of quartz on a granular film with nanoparticles on a quartz substrate. The frequency shifts of the inhomogeneous plasmon resonance of nanoparticles moving through the interface was to about two tens of nanometers.

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Meher, S. K., and D. Arun Kumar. "Ensemble of Adaptive Rule-Based Granular Neural Network Classifiers for Multispectral Remote Sensing Images." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 5 (May 2015): 2222–31. http://dx.doi.org/10.1109/jstars.2015.2403297.

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Zaghloul, Mohamed Sherif, Oliver Terna Iorhemen, Rania Ahmed Hamza, Joo Hwa Tay, and Gopal Achari. "Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors." Water Research 189 (February 2021): 116657. http://dx.doi.org/10.1016/j.watres.2020.116657.

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Liu, Han, and Li Zhang. "Advancing Ensemble Learning Performance through data transformation and classifiers fusion in granular computing context." Expert Systems with Applications 131 (October 2019): 20–29. http://dx.doi.org/10.1016/j.eswa.2019.04.051.

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Fuxreiter, Monika. "Towards a Stochastic Paradigm: From Fuzzy Ensembles to Cellular Functions." Molecules 23, no. 11 (November 17, 2018): 3008. http://dx.doi.org/10.3390/molecules23113008.

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The deterministic sequence → structure → function relationship is not applicable to describe how proteins dynamically adapt to different cellular conditions. A stochastic model is required to capture functional promiscuity, redundant sequence motifs, dynamic interactions, or conformational heterogeneity, which facilitate the decision-making in regulatory processes, ranging from enzymes to membraneless cellular compartments. The fuzzy set theory offers a quantitative framework to address these problems. The fuzzy formalism allows the simultaneous involvement of proteins in multiple activities, the degree of which is given by the corresponding memberships. Adaptation is described via a fuzzy inference system, which relates heterogeneous conformational ensembles to different biological activities. Sequence redundancies (e.g., tandem motifs) can also be treated by fuzzy sets to characterize structural transitions affecting the heterogeneous interaction patterns (e.g., pathological fibrillization of stress granules). The proposed framework can provide quantitative protein models, under stochastic cellular conditions.

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Gao, Yuan, Xiangjian Chen, Xibei Yang, Pingxin Wang, and Jusheng Mi. "Ensemble-Based Neighborhood Attribute Reduction: A Multigranularity View." Complexity 2019 (November 11, 2019): 1–17. http://dx.doi.org/10.1155/2019/2048934.

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Recently, multigranularity has been an interesting topic, since different levels of granularity can provide different information from the viewpoint of Granular Computing (GrC). However, established researches have focused less on investigating attribute reduction from multigranularity view. This paper proposes an algorithm based on the multigranularity view. To construct a framework of multigranularity attribute reduction, two main problems can be addressed as follows: (1) The multigranularity structure can be constructed firstly. In this paper, the multigranularity structure will be constructed based on the radii, as different information granularities can be induced by employing different radii. Therefore, the neighborhood-based multigranularity can be constructed. (2) The attribute reduction can be designed and realized from the viewpoint of multigranularity. Different from traditional process which computes reduct through employing a fixed granularity, our algorithm aims to obtain reduct from the viewpoint of multigranularity. To realize the new algorithm, two main processes are executed as follows: (1) Considering that different decision classes may require different key condition attributes, the ensemble selector is applied among different decision classes; (2) to accelerate the process of attribute reduction, only the finest and the coarsest granularities are employed. The experiments over 15 UCI data sets are conducted. Compared with the traditional single-granularity approach, the multigranularity algorithm can not only generate reduct which can provide better classification accuracy, but also reduce the elapsed time. This study suggests new trends for considering both the classification accuracy and the time efficiency with respect to the reduct.

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Kozub, V. I., and A. V. Shumilin. "Giant magnetoresistance for ensembles of ferromagnetic granules in variable range hopping conductivity regime." Solid State Communications 171 (October 2013): 55–58. http://dx.doi.org/10.1016/j.ssc.2013.07.031.

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Chen, Yi Lan, and Huan Bao Wang. "A Combinatorial Classifier for Error-Data in Joining Processes with Diverse-Granular Computing." Advanced Materials Research 548 (July 2012): 740–43. http://dx.doi.org/10.4028/www.scientific.net/amr.548.740.

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In this paper, we present a novel hybrid classification model with fuzzy clustering and design a newly combinatorial classifier for error-data in joining processes with diverse-granular computing, which is an ensemble of a naïve Bayes classifier with fuzzy c-means clustering. And we apply it to improve classification performance of traditional hard classifiers in more complex real-world situations. The fuzzy c-means clustering is applied to a fuzzy partition based on a given propositional function to augment the combinatorial classifier. This strategy would work better than a conventional hard classifier without fuzzy clustering. Proper scale granularity of objects contributes to higher classification performance of the combinatorial classifier. Our experimental results show the newly combinatorial classifier has improved the accuracy and stability of classification.

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Wang, Chaoyi, Derek Elsworth, and Yi Fang. "Ensemble Shear Strength, Stability, and Permeability of Mixed Mineralogy Fault Gouge Recovered From 3D Granular Models." Journal of Geophysical Research: Solid Earth 124, no. 1 (January 2019): 425–41. http://dx.doi.org/10.1029/2018jb016066.

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Irastorza, Ramiro M., C. Manuel Carlevaro, and Luis A. Pugnaloni. "Exact predictions from the Edwards ensemble versus realistic simulations of tapped narrow two-dimensional granular columns." Journal of Statistical Mechanics: Theory and Experiment 2013, no. 12 (December 20, 2013): P12012. http://dx.doi.org/10.1088/1742-5468/2013/12/p12012.

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Taghizadeh, Kianoosh, Rohit Shrivastava, and Stefan Luding. "Stochastic Model for Energy Propagation in Disordered Granular Chains." Materials 14, no. 7 (April 6, 2021): 1815. http://dx.doi.org/10.3390/ma14071815.

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Energy transfer is one of the essentials of mechanical wave propagation (along with momentum transport). Here, it is studied in disordered one-dimensional model systems mimicking force-chains in real systems. The pre-stressed random masses (other types of disorder lead to qualitatively similar behavior) interact through (linearized) Hertzian repulsive forces, which allows solving the deterministic problem analytically. The main goal, a simpler, faster stochastic model for energy propagation, is presented in the second part, after the basic equations are re-visited and the phenomenology of pulse propagation in disordered granular chains is reviewed. First, the propagation of energy in space is studied. With increasing disorder (quantified by the standard deviation of the random mass distribution), the attenuation of pulsed signals increases, transiting from ballistic propagation (in ordered systems) towards diffusive-like characteristics, due to energy localization at the source. Second, the evolution of energy in time by transfer across wavenumbers is examined, using the standing wave initial conditions of all wavenumbers. Again, the decay of energy (both the rate and amount) increases with disorder, as well as with the wavenumber. The dispersive ballistic transport in ordered systems transits to low-pass filtering, due to disorder, where localization of energy occurs at the lowest masses in the chain. Instead of dealing with the too many degrees of freedom or only with the lowest of all the many eigenmodes of the system, we propose a stochastic master equation approach with reduced complexity, where all frequencies/energies are grouped into bands. The mean field stochastic model, the matrix of energy-transfer probabilities between bands, is calibrated from the deterministic analytical solutions by ensemble averaging various band-to-band transfer situations for short times, as well as considering the basis energy levels (decaying with the wavenumber increasing) that are not transferred. Finally, the propagation of energy in the wavenumber space at transient times validates the stochastic model, suggesting applications in wave analysis for non-destructive testing, underground resource exploration, etc.

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Gonzalez-Ondina, Jose M., Luigi Fraccarollo, and Philip L. F. Liu. "Two-level, two-phase model for intense, turbulent sediment transport." Journal of Fluid Mechanics 839 (January 26, 2018): 198–238. http://dx.doi.org/10.1017/jfm.2017.920.

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The study of sediment transport requires in-depth investigation of the complex effects of sediment particles in fluid turbulence. In this paper we focus on intense sediment transport flows. None of the existing two-phase models in the literature properly replicates the liquid and solid stresses in the near bed region of high concentration of sediment. The reason for this shortcoming is that the physical processes occurring at the length scale of the particle collisions are different from those occurring at larger length scales and therefore, they must be modelled independently. We present here a two-level theoretical derivation of two-phase, Favre averaged Navier–Stokes equations (FANS). This approach treats two levels of energy fluctuations independently, those associated with a granular spatial scale (granular temperature and small-scale fluid turbulence) and those associated with the ensemble average (turbulent kinetic energy for the two phases). Although similar attempts have been made by other researchers, the two level approach ensures that the two relevant length scales are included independently in a more consistent manner. The model is endowed with a semi-empirical formulation for the granular scale fluid turbulence, which is important even in the dense collisional shear layer, as has been recently recognized. As a result of the large and small scale modelling of the liquid and solid fluctuations, predictions are promising to be reliable in a wide range of flow conditions, from collisional to turbulent suspensions. This model has been validated for steady state flows with intense, collisional or mixed collisional–turbulent sediment transport, using various sources of detailed experimental data. It compares well with the experimental results in the whole experimental range of Shields parameters, better than previous models, although at the cost of increased complexity in the equations. Further experiments on turbulent suspensions would be necessary to definitely assess the model capabilities.

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Shrivastava, Rohit Kumar, and Stefan Luding. "Effect of disorder on bulk sound wave speed: a multiscale spectral analysis." Nonlinear Processes in Geophysics 24, no. 3 (August 9, 2017): 435–54. http://dx.doi.org/10.5194/npg-24-435-2017.

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Abstract. Disorder of size (polydispersity) and mass of discrete elements or particles in randomly structured media (e.g., granular matter such as soil) has numerous effects on the materials' sound propagation characteristics. The influence of disorder on energy and momentum transport, the sound wave speed and its low-pass frequency-filtering characteristics is the subject of this study. The goal is understanding the connection between the particle-microscale disorder and dynamics and the system-macroscale wave propagation, which can be applied to nondestructive testing, seismic exploration of buried objects (oil, mineral, etc.) or to study the internal structure of the Earth. To isolate the longitudinal P-wave mode from shear and rotational modes, a one-dimensional system of equally sized elements or particles is used to study the effect of mass disorder alone via (direct and/or ensemble averaged) real time signals, signals in Fourier space, energy and dispersion curves. Increase in mass disorder (where disorder has been defined such that it is independent of the shape of the probability distribution of masses) decreases the sound wave speed along a granular chain. Energies associated with the eigenmodes can be used to obtain better quality dispersion relations for disordered chains; these dispersion relations confirm the decrease in pass frequency and wave speed with increasing disorder acting opposite to the wave acceleration close to the source.

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