Results 61 to 70 of about 44,296 (265)

A Knowledge‐Based Approach for Understanding and Managing Additive Manufacturing Data

Advanced Engineering Materials, EarlyView.
Additive manufacturing processes generate a large amount of data. Effectively managing, understanding, and retrieving information from this data remains a major challenge. Therefore, we propose an ontology‐based approach to integrate heterogeneous data, enable semantic queries, and support decision‐making.
Mina Abd Nikooie Pour, Prithwish Tarafder, Anton Wiberg, Huanyu Li, Johan Moverare, Patrick Lambrix   +5 more
wiley   +1 more source

A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning

Sensors
The annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image ...
Shaolong Chen, Zhiyong Zhang
doaj   +1 more source

Semi-supervised Learning with Bidirectional GANs [PDF]

, 2019
In this work we introduce a novel approach to train Bidirectional Generative Adversarial Model (BiGAN) in a semi-supervised manner. The presented method utilizes triplet loss function as an additional component of the objective function used to train discriminative data representation in the latent space of the BiGAN model.
Maciej Zamorski, Maciej Zieba
openaire   +2 more sources

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

Advanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier, Moritz Blum, Luana Caron, Alisa Chirkova, Philipp Cimiano, Basil Ell, Inga Ennen, Michael Feige, Maik Gaerner, Thomas Hilbig, Andreas Hütten, Günter Reiss, Tapas Samanta, Sonja Schöning, Christian Schröder, Lennart Schwan, Chris Taake, Martin Wortmann   +17 more
wiley   +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

Active learning for deep object detection by fully exploiting unlabeled data

Connection Science, 2023
Object detection is a challenging task that requires a large amount of labeled data to train high-performance models. However, labeling huge amounts of data is expensive, making it difficult to train a good detector with limited labeled data.
Feixiang Tan, Guansheng Zheng
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

Deceptive Reviews Detection Based on Semi supervised Learning Algorithm

工程科学与技术, 2014
:Machine learning methods were presented to identify deceptive reviews.With the integration of knowledge from computational linguistics and psycholinguistics,supervised method was developed to evaluate the performance of different feature modelings,and ...
任亚峰, 姬东鸿, 尹兰
doaj  

CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW

Iraqi Journal for Computers and Informatics, 2021
Semi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data.
Aska Ezadeen Mehyadin, Adnan Mohsin Abdulazeez   +1 more
doaj   +1 more source

Augmentation Learning for Semi-Supervised Classification

, 2022
Recently, a number of new Semi-Supervised Learning methods have emerged. As the accuracy for ImageNet and similar datasets increased over time, the performance on tasks beyond the classification of natural images is yet to be explored. Most Semi-Supervised Learning methods rely on a carefully manually designed data augmentation pipeline that is not ...
Tim Frommknecht, Pedro Alves Zipf, Quanfu Fan, Nina Shvetsova, Hilde Kuehne   +4 more
openaire   +2 more sources