Results 41 to 50 of about 19,165 (241)

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

HLoOP—Hyperbolic 2-Space Local Outlier Probabilities

IEEE Access
Hyperbolic geometry has recently garnered considerable attention in machine learning due to its ability to embed hierarchical graph structures with low distortions for further downstream processing.
Clemence Allietta, Jean-Philippe Condomines, Jean-Yves Tourneret, Emmanuel Lochin   +3 more
doaj   +1 more source

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

An Outlier Detection Algorithm Based on Local Density Feedback Outlier Factor

Abstract Neighborhood similarity assumes that adjacent objects should exhibit similar characteristics. To address the issue that previous density-based algorithms neglected neighborhood similarity and global information, resulting in low accuracy for outlier detection, we propose an outlier detection algorithm based on local density feedback ...
Zhongping Zhang, Yuehan Hou, Yin Jia, Ruibo Zhang   +3 more
openaire   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Intelligent Analysis and Prediction of Building Energy Consumption in Cloud Computing

Engineering Proceedings
We researched, analyzed and predicted building energy consumption data using cloud computing and constructed an intelligent model. A local outlier factor outlier discovery algorithm was created to monitor abnormal energy consumption.
Lan Huang, Xiaoli Zhu, Xiangfeng Ren
doaj   +1 more source

Multimodal Data‐Driven Microstructure Characterization

Advanced Engineering Materials, EarlyView.
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source

Unsupervised Machine Learning for Detecting Seismic Anomalies: Local Outlier Factor Algorithm on Indonesian Ring Fire Data

Journal of Computation Physics and Earth Science (JoCPES), 2023
The Indonesian Ring of Fire, known for its intense seismic and volcanic activity, poses significant challenges for hazard mitigation and risk management. This study applies an unsupervised machine learning approach using the Local Outlier Factor (LOF) algorithm to detect seismic anomalies in historical earthquake data.
openaire   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Anomaly detection and clustering‐based identification method for consumer–transformer relationship and associated phase in low‐voltage distribution systems

Energy Conversion and Economics, 2022
The identification accuracy of low‐voltage distribution consumer–transformer relationship and phase are crucial to three‐phase unbalanced regulation and error correction in consumer–transformer relationships.
Zhenyue Chu, Xueyuan Cui, Xingli Zhai, Shengyuan Liu, Weiqiang Qiu, Muhammad Waseem, Tarique Aziz, Qin Wang, Zhenzhi Lin   +8 more
doaj   +1 more source