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Academic literature on the topic 'Granular Ensembles'

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Published: 6 September 2023

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Journal articles on the topic "Granular Ensembles"

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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Granular Ensembles"

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Metzger, Philip. "DERIVING THE DENSITY OF STATES FOR GRANULAR CONTACT FORCES." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2809.

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The density of single grain states in static granular packings is derived from first principles for an idealized yet fundamental case. This produces the distribution of contact forces P_f(f) in the packing. Because there has been some controversy in the published literature over the exact form of the distribution, this dissertation begins by reviewing the existing empirical observations to resolve those controversies. A method is then developed to analyze Edwards' granular contact force probability functional from first principles. The derivation assumes Edwards' flat measure -- a density of states (DOS) that is uniform within the metastable regions of phase space. A further assumption, supported by physical arguments and empirical evidence, is that contact force correlations arising through the closure of loops of grains may be neglected. Then, maximizing a state-counting entropy results in a transport equation that can be solved numerically. For the present it has been solved using the "Mean Structure Approximation," projecting the DOS across all angular coordinates to more clearly identify its predominant features in the remaining stress coordinates. These features are: (1) the Grain Factor related to grain stability and strong correlation between the contact forces on the same grain, and (2) the Structure Factor related to Newton's third law and strong correlation between neighboring grains. Numerical simulations were then performed for idealized granular packings to permit a direct comparison with the theory, and the data including P_f(f) were found to be in excellent agreement. Where the simulations and theory disagree, it is primarily due to the coordination number Z because the theory assumes Z to be a constant whereas in disordered packings it is not. The form of the empirical DOS is discovered to have an elegant, underlying pattern related to Z. This pattern consists entirely of the functional forms correctly predicted by the theory, but with only slight parameter changes as a function of Z. This produces significant physical insight and suggests how the theory may be generalized in the future.
Ph.D.
Department of Physics
Arts and Sciences
Physics
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Books on the topic "Granular Ensembles"

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Dominik, Ślęzak, ed. Rough sets, fuzzy sets, data mining, and granular computing: 10th international conference, RSFDGrC 2005, Regina, Canada, August 31-September 3, 2005 : proceedings. Berlin: Springer, 2005.

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Duentsch, Ivo. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (vol. # 3641): 10th International Conference, RSFDGrC 2005, Regina, Canada, August 31 - September 3, 2005, Proceedings, Part I. Berlin Heidelberg: Springer-Verlag., 2005.

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(Editor), Dominik Slezak, Guoyin Wang (Editor), Marcin Szczuka (Editor), Ivo Duentsch (Editor), and Yiyu Yao (Editor), eds. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 10th International Conference, RSFDGrC 2005, Regina, Canada, August 31 - September 3, 2005, ... / Lecture Notes in Artificial Intelligence). Springer, 2005.

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Oklopcic, Zoran. Nephos, Scopos, Algorithm. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799092.003.0005.

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Chapter 5 moves beyond the two most politically consequential understandings of the right to self-determination: attributed to Demos and Ethnos respectively. While normative theorists are not sure how to evoke these figures, this chapter treats them as ensembles that are extracted from Nephos; an even fuzzier and more granular political ‘aerosol’. Against it as a backdrop, the discrete locations of territorial rights will also appear more fuzzified—not as identifiable locations, but rather as Scopos; visual effects of concealed, but nevertheless contestable scopic regimes. Once its holders and objects appear in that light, otherwise incommensurable accounts of the right to self-determination will reveal a denominator they secretly share: a Kelsenian ‘tendency’—an aspiration to increase the degree of constituent attachments across the entirety of the spacetime of a constitutional order whose legitimacy is put in question by a demand for ‘self-determination’.
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Book chapters on the topic "Granular Ensembles"

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Mireles de Villafranca, Alonso Espinosa, Richard D. Connors, and R. Eddie Wilson. "Static Traffic Assignment on Ensembles of Synthetic Road Networks." In Traffic and Granular Flow '17, 29–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11440-4_4.

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Frye, Robert H., and David C. Wilson. "Granular Emotion Detection in Social Media Using Multi-Discipline Ensembles." In Lecture Notes in Computer Science, 3–12. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16564-1_1.

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Wang, Xin, and Rong Zhong. "A New Weighted Ensemble Classifier Based on Granular Model." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 866–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_93.

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Artiemjew, Piotr. "Ensemble of Classifiers Based on Simple Granules of Knowledge." In Communications in Computer and Information Science, 343–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67642-5_28.

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Brnawy, Rahmah, and Nematollaah Shiri. "Improving Quality of Ensemble Technique for Categorical Data Clustering Using Granule Computing." In Lecture Notes in Computer Science, 261–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86472-9_24.

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Sarkar, Jaya, and Sua Myong. "Single-Molecule and Ensemble Methods to Probe Initial Stages of RNP Granule Assembly." In Methods in Molecular Biology, 325–38. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8591-3_19.

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Kandasami, R., and T. Murthy. "Effect of particle shape on the mechanical response of a granular ensemble." In Geomechanics from Micro to Macro, 1093–98. CRC Press, 2014. http://dx.doi.org/10.1201/b17395-196.

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Conference papers on the topic "Granular Ensembles"

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Saluena, Clara, Sergei E. Esipov, Thorsten Poeschel, and Stephan S. Simonian. "Dissipative properties of granular ensembles." In 5th Annual International Symposium on Smart Structures and Materials, edited by L. Porter Davis. SPIE, 1998. http://dx.doi.org/10.1117/12.310696.

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Puckett, James G., Frédéric Lechenault, Karen E. Daniels, Masami Nakagawa, and Stefan Luding. "Generating ensembles and measuring mixing in a model granular system." In POWDERS AND GRAINS 2009: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MICROMECHANICS OF GRANULAR MEDIA. AIP, 2009. http://dx.doi.org/10.1063/1.3180016.

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Jianhua Dai, Xiaochun Liu, Shaomin Zhang, Huaijian Zhang, Yu Yi, Qingbo Wang, Yu Su, Weidong Chen, and Xiaoxiang Zheng. "Analysis of neuronal ensembles encoding model in invasive brain-computer interface study using Radial-Basis-Function networks." In 2008 IEEE International Conference on Granular Computing (GrC-2008). IEEE, 2008. http://dx.doi.org/10.1109/grc.2008.4664720.

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Snezhko, Alexey, Igor Aranson, Maxim Belkin, Masami Nakagawa, and Stefan Luding. "Emergent Phenomena in Far-From-Equilibrium Magnetic Granular Ensembles at a Liquid-Air Interface." In POWDERS AND GRAINS 2009: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MICROMECHANICS OF GRANULAR MEDIA. AIP, 2009. http://dx.doi.org/10.1063/1.3179849.

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Kruggel-Emden, Harald, Erdem Simsek, Siegmar Wirtz, and Viktor Scherer. "A Numerical Study of Particle Flow and Mixing on Conveying Equipment in Two and Three Dimensions by the Discrete Element Method (DEM)." In ASME 2006 Pressure Vessels and Piping/ICPVT-11 Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/pvp2006-icpvt-11-93367.

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Based on LEAT’s discrete element codes, granular flow and mixing on conveying equipment is 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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Boyle, Edward J. "Flowing Granular Materials and the Maxwell-Boltzmann Velocity Distribution." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2001.

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Abstract The single-granule velocity distribution function is shown to be Maxwell-Boltzmann for hard-sphere granular flows at steady-state exhibiting no gradients and absent a body-force. This is accomplished by approximating the two-granule velocity distribution function as the product of two single-granule velocity distribution functions and a correlating function and by applying to a canonical ensemble a function analogous to Boltzmann’s H-function.
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Mei, S. Y., Y. Liu, G. F. Wu, and B. F. Zhang. "Rough reducts based SVM ensemble." In 2005 IEEE International Conference on Granular Computing. IEEE, 2005. http://dx.doi.org/10.1109/grc.2005.1547356.

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Cheng, Yong, and Ruilian Zhao. "Multiview spectral clustering via ensemble." In 2009 IEEE International Conference on Granular Computing (GRC). IEEE, 2009. http://dx.doi.org/10.1109/grc.2009.5255152.

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Li, Xuzhou. "Tackle three practical classification problems via Ensemble Learning." In 2012 IEEE International Conference on Granular Computing (GrC-2012). IEEE, 2012. http://dx.doi.org/10.1109/grc.2012.6468566.

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Zhou, Tao, and Huiling Lu. "Multi-features prostate tumor aided diagnoses based on ensemble-svm." In 2013 IEEE International Conference on Granular Computing (GrC). IEEE, 2013. http://dx.doi.org/10.1109/grc.2013.6740425.

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