Application of machine learning in grain-related clustering of Laue spots in a polycrystalline energy dispersive Laue pattern

• URL: http://arxiv.org/abs/2412.12224v1
• Date: Mon, 16 Dec 2024 09:28:17 GMT
• Title: Application of machine learning in grain-related clustering of Laue spots in a polycrystalline energy dispersive Laue pattern
• Authors: Amir Tosson, Mohammad Shokr, Mahmoud Al Humaidi, Eduard Mikayelyan, Christian Gutt, Ulrich Pietsch,
• Abstract summary: Grain-corresponding Laue reflections in energy dispersive Laue diffraction experiments are identified using unsupervised machine learning (ML)<n>We employ a combination of clustering algorithms, namely hierarchical clustering (HC) and K-means, to identify grains in a Laue pattern.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We address the identification of grain-corresponding Laue reflections in energy dispersive Laue diffraction (EDLD) experiments by formulating it as a clustering problem solvable through unsupervised machine learning (ML). To achieve reliable and efficient identification of grains in a Laue pattern, we employ a combination of clustering algorithms, namely hierarchical clustering (HC) and K-means. These algorithms allow us to group together similar Laue reflections, revealing the underlying grain structure in the diffraction pattern. Additionally, we utilise the elbow method to determine the optimal number of clusters, ensuring accurate results. To evaluate the performance of our proposed method, we conducted experiments using both simulated and experimental datasets obtained from nickel wires. The simulated datasets were generated to mimic the characteristics of real-world EDLD experiments, while the experimental datasets were obtained from actual measurements.

Related papers

• A Hybrid Mixture of $t$-Factor Analyzers for Clustering High-dimensional Data [0.07673339435080444]
  This paper develops a novel hybrid approach for estimating the mixture model of $t$-factor analyzers (MtFA) The effectiveness of our approach is demonstrated through simulations showcasing its superior computational efficiency compared to the existing method. Our method is applied to cluster the Gamma-ray bursts, reinforcing several claims in the literature that Gamma-ray bursts have heterogeneous subpopulations and providing characterizations of the estimated groups.
  arXiv  Detail & Related papers  (2025-04-29T18:59:58Z)
• An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance [0.0]
  We present a novel agglomerative clustering algorithm that utilizes the regularized Wasserstein distance to cluster simulation outputs. This framework has several important use cases, including anomaly detection, pre-optimization, and online monitoring.
  arXiv  Detail & Related papers  (2024-07-16T18:07:32Z)
• GCC: Generative Calibration Clustering [55.44944397168619]
  We propose a novel Generative Clustering (GCC) method to incorporate feature learning and augmentation into clustering procedure. First, we develop a discrimirative feature alignment mechanism to discover intrinsic relationship across real and generated samples. Second, we design a self-supervised metric learning to generate more reliable cluster assignment.
  arXiv  Detail & Related papers  (2024-04-14T01:51:11Z)
• GFDC: A Granule Fusion Density-Based Clustering with Evidential Reasoning [22.526274021556755]
  density-based clustering algorithms are widely applied because they can detect clusters with arbitrary shapes. This paper proposes a granule fusion density-based clustering with evidential reasoning (GFDC) Both local and global densities of samples are measured by a sparse degree metric first. Then information granules are generated in high-density and low-density regions, assisting in processing clusters with significant density differences.
  arXiv  Detail & Related papers  (2023-05-20T06:27:31Z)
• Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls [67.33923111887933]
  In this paper, the data is fuzzy iterated using granular-balls, and the membership degree of data only considers the two granular-balls where it is located. The formed fuzzy granular-balls set can use more processing methods in the face of different data scenarios.
  arXiv  Detail & Related papers  (2023-03-07T01:52:55Z)
• Machine-Learned Exclusion Limits without Binning [0.0]
  We extend the Machine-Learned Likelihoods (MLL) method by including Kernel Density Estimators (KDE) to extract one-dimensional signal and background probability density functions. We apply the method to two cases of interest at the LHC: a search for exotic Higgs bosons, and a $Z'$ boson decaying into lepton pairs.
  arXiv  Detail & Related papers  (2022-11-09T11:04:50Z)
• Flow-based sampling in the lattice Schwinger model at criticality [54.48885403692739]
  Flow-based algorithms may provide efficient sampling of field distributions for lattice field theory applications. We provide a numerical demonstration of robust flow-based sampling in the Schwinger model at the critical value of the fermion mass.
  arXiv  Detail & Related papers  (2022-02-23T19:00:00Z)
• A Robust and Flexible EM Algorithm for Mixtures of Elliptical Distributions with Missing Data [71.9573352891936]
  This paper tackles the problem of missing data imputation for noisy and non-Gaussian data. A new EM algorithm is investigated for mixtures of elliptical distributions with the property of handling potential missing data. Experimental results on synthetic data demonstrate that the proposed algorithm is robust to outliers and can be used with non-Gaussian data.
  arXiv  Detail & Related papers  (2022-01-28T10:01:37Z)
• Grain segmentation in atomistic simulations using orientation-based iterative self-organizing data analysis [0.0]
  We propose a method for grain segmentation of an atomistic configuration using an unsupervised machine learning algorithm. The proposed method, called the Orisodata algorithm, is based on the iterative self-organizing data analysis technique and is modified to work in the orientation space. The results show that the Orisodata algorithm is able to correctly identify deformation twins as well as regions separated by low angle grain boundaries.
  arXiv  Detail & Related papers  (2021-12-06T20:44:39Z)
• Density-Based Clustering with Kernel Diffusion [59.4179549482505]
  A naive density corresponding to the indicator function of a unit $d$-dimensional Euclidean ball is commonly used in density-based clustering algorithms. We propose a new kernel diffusion density function, which is adaptive to data of varying local distributional characteristics and smoothness.
  arXiv  Detail & Related papers  (2021-10-11T09:00:33Z)
• A Multiscale Environment for Learning by Diffusion [9.619814126465206]
  We introduce the Multiscale Environment for Learning by Diffusion (MELD) data model. We show that the MELD data model precisely captures latent multiscale structure in data and facilitates its analysis. To efficiently learn the multiscale structure observed in many real datasets, we introduce the Multiscale Learning by Unsupervised Diffusion (M-LUND) clustering algorithm.
  arXiv  Detail & Related papers  (2021-01-31T17:46:19Z)
• A Novel Granular-Based Bi-Clustering Method of Deep Mining the Co-Expressed Genes [76.84066556597342]
  Bi-clustering methods are used to mine bi-clusters whose subsets of samples (genes) are co-regulated under their test conditions. Unfortunately, traditional bi-clustering methods are not fully effective in discovering such bi-clusters. We propose a novel bi-clustering method by involving here the theory of Granular Computing.
  arXiv  Detail & Related papers  (2020-05-12T02:04:40Z)
• RelDenClu: A Relative Density based Biclustering Method for identifying non-linear feature relations [0.1843404256219181]
  The proposed method, RelDenClu uses the local variations in marginal and joint densities for each pair of features to find the subset of observations. It then finds the set of features connected by a common set of observations, resulting in a bicluster. Experiments have been carried out on fifteen types of simulated datasets and six real-life datasets.
  arXiv  Detail & Related papers  (2018-11-12T11:11:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.