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Journal articles on the topic 'Granular Ensembles'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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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 (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 (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 parti

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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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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 (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 mode

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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 (2004): 545–51. http://dx.doi.org/10.1209/epl/i2003-10301-2.

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Đekić, Maja, Ajla Karić, Amra Salčinović Fetić, et al. "THE EFECT OF DIMENSIONALITY AND CURRENT STRENGHT ON CONDUCTIVITY OF GRANULAR METALS." Journal of Sustainable Technologies and Materials 4, no. 6 (2024): 8–14. http://dx.doi.org/10.57131/jstm.2024.6.2.

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&lt;p&gt;Metallic materials in granular packings show different electrical properties from their bulk counterparts. In this paper, we investigate the temporal evolution of the electrical conductivity of granular metals. We use metallic beads arranged in different one-, two- and three-dimensional ensembles through which different constant currents are injected. The conductivity behavior in all three types of systems is qualitatively similar. The results show the rise of conductivity which is more pronounced in the earlier stages of the time evolution. We investigate the influence of the

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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 (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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BADRI, Yousif, Andrew HALL, George DODD, et al. "The use of ensembles trees machine learning method in estimating damping of granular materials." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 270, no. 10 (2024): 1420–31. http://dx.doi.org/10.3397/in_2024_2970.

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Granular Materials (GM) employed within the mechanism of particle dampers attenuate the vibration energy due to their interparticle and particle-wall interactions. Estimating their damping effect using the analytical equivalent single mass approach overlooked the particles' individual losses that are built into the total damping. Alternatively, the numerical techniques (e.g., Discrete Element) are time-inefficient and computationally demanding. Therefore, this study explores the implementation of Machine learning (ML) algorithms to estimate the damping effect of GM. The ML model in this study

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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 (2018): 031004. http://dx.doi.org/10.1088/2399-6528/aab386.

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12

Demin, A. V., and V. A. Khlus. "Fluctuation effects in multicontact Josephson systems: magnetic susceptibility in a weak ac field and magnetic flux noise." Soviet Journal of Low Temperature Physics 18, no. 11 (1992): 836–42. https://doi.org/10.1063/10.0033306.

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A model of superconducting granular systems with weak links between the grains is considered. The model presumes the existence of N-contact closed loops and statistical ensembles of such loops. The dynamics of thermally activated decays of metastable current states of a loop, which are similar to a phase slip, is investigated. The temperature dependence of the imaginary component χ″ of the magnetic susceptibility is calculated for different frequencies of the external magnetic field. The importance of two-level states of the potential energy of a loop is demonstrated. The spectral density of t

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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 (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 correspondi

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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 (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 o

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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 (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 s

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17

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 (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,

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18

Idzhilova, O. S., N. A. Simonova, M. G. Minlebaev, and A. Yu Malyshev. "Infragranular excitatory projection to granular neurons in neonatal rodent somatosensory neocortex." Genes & Cells 18, no. 4 (2023): 771–73. http://dx.doi.org/10.17816/gc623337.

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Though the principles of central nervous system development are genetically encoded, the cortical activity is also critically involved in these processes. While this question is quite important, up to now we are limited in our understanding of the role that neuronal activity plays in formation of functionally linked cellular ensembles in the developing cortex. Recently, transient inhibitory neuronal projections were shown in the barrel cortex during the critical period of its development. Expression of interneuronal connections from infragranular to other cortical layers exactly during the per

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Bhat, Mohd Ilyas, Bhupendra Chand, and Tejas Gorur Murthy. "On the Initial Fabric of Naturally Occurring and Reconstituted Weakly Cemented Geomaterials." Minerals 14, no. 10 (2024): 1000. http://dx.doi.org/10.3390/min14101000.

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The understanding of naturally occurring materials such as clay, sand, hard and soft rocks under a common theoretical framework has been a topic of persistent research interest. Over the past few decades, various sample reconstitution techniques have been developed in the literature to mimic in situ conditions, and to parse carefully the influence of various components in a cohesive-frictional geomaterial such that their behavior can be folded into the broad ambit of a continuum mechanics framework. The initial fabric of natural rock specimens is compared with reconstituted cemented sand sampl

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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 pla

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21

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 (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

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22

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 (2020): 735–39. http://dx.doi.org/10.18178/ijmlc.2020.10.6.998.

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23

Artiemjew, Piotr, and Krzysztof Ropiak. "A Novel Ensemble Model - The Random Granular Reflections." Fundamenta Informaticae 179, no. 2 (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 re

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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 (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, 0.2

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25

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 (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

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Glagolev, M. V., D. V. Il’yasov, A. F. Sabrekov, Irina E. Terentieva, and D. V. Karelin. "Multi-model ensemble successfully predicted atmospheric methane consumption in soils across the complex landscape." Environmental Dynamics and Global Climate Change 14, no. 4 (2024): 209–36. http://dx.doi.org/10.18822/edgcc625761.

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Methane consumption by soils is a crucial component of the CH4 and carbon cycle. It is essential to thoroughly investigate CH4 uptake by soils, particularly considering its anticipated increase by the end of the century [Zhuang et al., 2013]. Numerous mathematical models, both empirical and detailed biogeochemical [Glagolev et al., 2023], have been developed to quantify methane consumption by soils from the atmosphere. These models are instrumental in handling spatio-temporal variability and can offer reliable estimates of regional and global methane consumption by soils. Furthermore, they enh

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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 though

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28

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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29

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

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30

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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31

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

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32

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

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33

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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34

Chand, Bhupendra, and Tejas G. Murthy. "Evolving Deformation Fields in Shallow Indentation of Granular Materials." IOP Conference Series: Earth and Environmental Science 1480, no. 1 (2025): 012085. https://doi.org/10.1088/1755-1315/1480/1/012085.

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Abstract The problem of indentation has myriad applications in the infrastructure industry, especially in design of shallow foundations. The framework offered by plasticity theory and metal working have been used to understand the mechanics of indentation. The results of a series of experiments on indentation of granular ensemble conducted inside a XRCT set up are presented here. The indentation was carried out in 3 stages – in both intrusion and extrusion directions and the evolving displacement was mapped. The region of deformation of the ensemble is localized around the indenter and reduces

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35

Janthakal, Sheetal, and Girisha Hosalli. "An ensemble image augmentation approach to enhance granular parakeratosis dataset." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 1 (2025): 312. https://doi.org/10.11591/ijeecs.v38.i1.pp312-320.

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The study discusses the revolutionizing impact of deep convolutional neural network (CNN) techniques on medical image classification, particularly in identifying skin lesions. It addresses the challenge of limited datasets for granular parakeratosis (GP) and paraneoplastic pemphigus (PNP) by employing traditional and advanced ensemble data augmentation techniques. These techniques include geometric transformations, generative adversarial networks (GANs), Cutout, and keep augment. GP affects keratinization in the groin and other regions, while PNP is associated with malignancies. The study’s re

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Sheetal, Janthakal Girisha Hosalli. "An ensemble image augmentation approach to enhance granular parakeratosis dataset." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 1 (2025): 312–20. https://doi.org/10.11591/ijeecs.v38.i1.pp312-320.

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The study discusses the revolutionizing impact of deep convolutional neural network (CNN) techniques on medical image classification, particularly in identifying skin lesions. It addresses the challenge of limited datasets for granular parakeratosis (GP) and paraneoplastic pemphigus (PNP) by employing traditional and advanced ensemble data augmentation techniques. These techniques include geometric transformations, generative adversarial networks (GANs), Cutout, and keep augment. GP affects keratinization in the groin and other regions, while PNP is associated with malignancies. The study&rsqu

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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 (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

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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 (2015): P02005. http://dx.doi.org/10.1088/1742-5468/2015/02/p02005.

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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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40

Burney, S. M. Aqil, Tahseen Jilani, Humera Tariq, Zeeshan Asim, Usman Amjad, and Syed Shah Mohammad. "A Portfolio Optimization Algorithm Using Fuzzy Granularity Based Clustering." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 10, no. 2 (2019): 159. https://doi.org/10.70594/brain/v10.i2/16.

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<p>Clustering algorithms are applied to numerous problems in multiple domains including historic data analysis, financial markets analysis for portfolio optimization and image processing. Recent years have witnessed a surge in use of nature inspired computing (NIC) techniques for data clustering to solve various real world optimization problems. Granular Computing (GC) is an emerging technique to handle pieces of information, known as information granules. In this paper, an ensemble of fuzzy clustering using Particle Swarm Optimization and Granular computing for stock market portfolio op

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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 sourc

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Tertytchny, Georgios, Georgios L. Stavrinides, and Maria K. Michael. "Rare Event Detection in Imbalanced Multi-Class Datasets Using an Optimal MIP-Based Ensemble Weighting Approach." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 20867–75. https://doi.org/10.1609/aaai.v39i19.34300.

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To address the challenges of imbalanced multi-class datasets typically used for rare event detection in critical cyber-physical systems, we propose an optimal, efficient, and adaptable mixed integer programming (MIP) ensemble weighting scheme. Our approach leverages the diverse capabilities of the classifier ensemble on a granular per class basis, while optimizing the weights of classifier-class pairs using elastic net regularization for improved robustness and generalization. Additionally, it seamlessly and optimally selects a predefined number of classifiers from a given set. We evaluate and

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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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44

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 propose

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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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Kalyan, NSSP, P. Badu, and RK Kandasami. "Numerical Study on the Influence of Rubber Content on the Mechanical Behaviour of Angular Aggregates." IOP Conference Series: Earth and Environmental Science 1480, no. 1 (2025): 012058. https://doi.org/10.1088/1755-1315/1480/1/012058.

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Abstract Railway tracks endure significant loads, with the ballast layer playing a crucial role in both damping and safely transmitting these forces to the subgrade. However, ballast degradation significantly impacts track maintenance costs. To mitigate this, tyre rubber crumbs could be mixed with the coarse aggregates. This study employs a coupled 2D - Discrete Element Method (DEM) - Element Free Galerkin (EFG) framework to evaluate the effect of including rubber with angular aggregates on the micromechanics during simple shear. The addition of deformable rubber particles to the granular ense

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Nijhuis, Michiel, and Iman van Lelyveld. "Outlier Detection with Reinforcement Learning for Costly to Verify Data." Entropy 25, no. 6 (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 app

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Dmitriev, V. M., A. L. Solov’ev, and A. I. Dmitrenko. "Fluctuation conductivity of granular vanadium films." Soviet Journal of Low Temperature Physics 11, no. 7 (1985): 374–83. https://doi.org/10.1063/10.0031320.

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The effect of fluctuations on the resistive transitions in thin granular vanadium films at temperatures T ≥ Tc is studied. As the temperature is increased and as a result of the change in the ratio of the coherence length ξ(T) and the average size of the granules g, the dimensionality of the fluctuations changes in all films studied from two [ξ (T) &gt;g] to zero [ξ (T) &gt;g] .Studies of the region of the two-dimensional behavior of the fluctuations showed that the Maki–Thompson mechanism makes the main contribution to the fluctuation conductivity σ′ of such films. The study of critic

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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 (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 (2018): 715–24. http://dx.doi.org/10.1007/s41066-018-0122-5.

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