An Incremental Phase Mapping Approach for X-ray Diffraction Patterns
using Binary Peak Representations
- URL: http://arxiv.org/abs/2211.04011v1
- Date: Tue, 8 Nov 2022 05:05:21 GMT
- Title: An Incremental Phase Mapping Approach for X-ray Diffraction Patterns
using Binary Peak Representations
- Authors: Dipendra Jha, K.V.L.V. Narayanachari, Ruifeng Zhang, Justin Liao,
Denis T. Keane, Wei-keng Liao, Alok Choudhary, Yip-Wah Chung, Michael Bedzyk,
Ankit Agrawal
- Abstract summary: We introduce an incremental phase mapping approach based on binary peak representations using a new threshold based fuzzy dissimilarity measure.
We evaluate our method on the composition space of two ternary alloy systems- Co-Ni-Ta and Co-Ti-Ta.
- Score: 3.3323560120870988
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Despite the huge advancement in knowledge discovery and data mining
techniques, the X-ray diffraction (XRD) analysis process has mostly remained
untouched and still involves manual investigation, comparison, and
verification. Due to the large volume of XRD samples from high-throughput XRD
experiments, it has become impossible for domain scientists to process them
manually. Recently, they have started leveraging standard clustering
techniques, to reduce the XRD pattern representations requiring manual efforts
for labeling and verification. Nevertheless, these standard clustering
techniques do not handle problem-specific aspects such as peak shifting,
adjacent peaks, background noise, and mixed phases; hence, resulting in
incorrect composition-phase diagrams that complicate further steps. Here, we
leverage data mining techniques along with domain expertise to handle these
issues. In this paper, we introduce an incremental phase mapping approach based
on binary peak representations using a new threshold based fuzzy dissimilarity
measure. The proposed approach first applies an incremental phase computation
algorithm on discrete binary peak representation of XRD samples, followed by
hierarchical clustering or manual merging of similar pure phases to obtain the
final composition-phase diagram. We evaluate our method on the composition
space of two ternary alloy systems- Co-Ni-Ta and Co-Ti-Ta. Our results are
verified by domain scientists and closely resembles the manually computed
ground-truth composition-phase diagrams. The proposed approach takes us closer
towards achieving the goal of complete end-to-end automated XRD analysis.
Related papers
- Unsupervised Semantic Segmentation in Synchrotron Computed Tomography with Self-Correcting Pseudo Labels [2.3100447881717345]
Deep learning has emerged as a powerful tool capable of providing a wide range of purely data-driven solutions.<n>We introduce a novel framework that enables automatic segmentation of large, high-resolution SR-CT datasets.<n>We find our approach improves pixel-wise accuracy and mIoU by 13.31% and 15.94%, respectively, over the baseline pseudo labels.
arXiv Detail & Related papers (2026-02-27T23:15:41Z) - HomoFM: Deep Homography Estimation with Flow Matching [2.0260360833154913]
HomoFM is a new framework that introduces the flow matching technique from generative modeling into the homography estimation task.<n>We show that HomoFM outperforms state-of-the-art methods in both estimation accuracy and robustness on standard benchmarks.
arXiv Detail & Related papers (2026-01-26T07:17:32Z) - Manifold Learning for Hyperspectral Images [0.0]
We propose a method that approximates the dataset topology by constructing adjacency graphs using the Uniform Manifold Approximation and Projection.
This approach captures nonlinear correlations within the data, significantly improving the performance of machine learning algorithms.
arXiv Detail & Related papers (2025-03-19T09:12:56Z) - A First-order Generative Bilevel Optimization Framework for Diffusion Models [57.40597004445473]
Diffusion models iteratively denoise data samples to synthesize high-quality outputs.
Traditional bilevel methods fail due to infinite-dimensional probability space and prohibitive sampling costs.
We formalize this challenge as a generative bilevel optimization problem.
Our first-order bilevel framework overcomes the incompatibility of conventional bilevel methods with diffusion processes.
arXiv Detail & Related papers (2025-02-12T21:44:06Z) - Progressive Multi-Level Alignments for Semi-Supervised Domain Adaptation SAR Target Recognition Using Simulated Data [3.1951121258423334]
We develop an instance-prototype alignment (AIPA) strategy to push the source domain instances close to the corresponding target prototypes.
We also develop an instance-prototype alignment (AIPA) strategy to push the source domain instances close to the corresponding target prototypes.
arXiv Detail & Related papers (2024-11-07T13:53:13Z) - Implicit neural representation for change detection [15.741202788959075]
Most commonly used approaches to detecting changes in point clouds are based on supervised methods.
We propose an unsupervised approach that comprises two components: Implicit Neural Representation (INR) for continuous shape reconstruction and a Gaussian Mixture Model for categorising changes.
We apply our method to a benchmark dataset comprising simulated LiDAR point clouds for urban sprawling.
arXiv Detail & Related papers (2023-07-28T09:26:00Z) - Reflected Diffusion Models [93.26107023470979]
We present Reflected Diffusion Models, which reverse a reflected differential equation evolving on the support of the data.
Our approach learns the score function through a generalized score matching loss and extends key components of standard diffusion models.
arXiv Detail & Related papers (2023-04-10T17:54:38Z) - Enhancing Generative Networks for Chest Anomaly Localization through Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data Translation [4.562196564569076]
generative adversarial network (GAN-IT) is a promising method for precise localization of abnormal regions in chest X-ray images (AL-CXR)
We propose an improved two-stage GAN-IT involving registration and data augmentation.
arXiv Detail & Related papers (2022-07-21T06:42:12Z) - Interpolation-based Correlation Reduction Network for Semi-Supervised
Graph Learning [49.94816548023729]
We propose a novel graph contrastive learning method, termed Interpolation-based Correlation Reduction Network (ICRN)
In our method, we improve the discriminative capability of the latent feature by enlarging the margin of decision boundaries.
By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discnative representation learning.
arXiv Detail & Related papers (2022-06-06T14:26:34Z) - Tracking perovskite crystallization via deep learning-based feature
detection on 2D X-ray scattering data [137.47124933818066]
We propose an automated pipeline for the analysis of X-ray diffraction images based on the Faster R-CNN deep learning architecture.
We demonstrate our method on real-time tracking of organic-inorganic perovskite structure crystallization and test it on two applications.
arXiv Detail & Related papers (2022-02-22T15:39:00Z) - Distribution Agnostic Symbolic Representations for Time Series
Dimensionality Reduction and Online Anomaly Detection [8.00114449574708]
This paper proposes two novel data-driven SAX-based symbolic representations, distinguished by their discretization steps.
The proposed representations possess all the attractive properties of the conventional SAX method.
arXiv Detail & Related papers (2021-05-20T08:35:50Z) - Detecting micro fractures with X-ray computed tomography [4.855026133182103]
We present a data-set produced by the successful visualization of a fracture network in Carrara marble with XRCT.
Three conventional and two machine-learning-based methods are evaluated.
The output of the 2D U-net model is one of the adopted machine-learning-based segmentation methods.
arXiv Detail & Related papers (2021-03-23T20:20:24Z) - Ensemble and Random Collaborative Representation-Based Anomaly Detector
for Hyperspectral Imagery [133.83048723991462]
We propose a novel ensemble and random collaborative representation-based detector (ERCRD) for hyperspectral anomaly detection (HAD)
Our experiments on four real hyperspectral datasets exhibit the accuracy and efficiency of this proposed ERCRD method compared with ten state-of-the-art HAD methods.
arXiv Detail & Related papers (2021-01-06T11:23:51Z) - Progressive Spatio-Temporal Graph Convolutional Network for
Skeleton-Based Human Action Recognition [97.14064057840089]
We propose a method to automatically find a compact and problem-specific network for graph convolutional networks in a progressive manner.
Experimental results on two datasets for skeleton-based human action recognition indicate that the proposed method has competitive or even better classification performance.
arXiv Detail & Related papers (2020-11-11T09:57:49Z) - MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT
Prostate Segmentation via Online Sampling [66.01558025094333]
We propose a two-stage framework, with the first stage to quickly localize the prostate region and the second stage to precisely segment the prostate.
We introduce a novel online metric learning module through voxel-wise sampling in the multi-task network.
Our method can effectively learn more representative voxel-level features compared with the conventional learning methods with cross-entropy or Dice loss.
arXiv Detail & Related papers (2020-05-15T10:37:02Z)
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.