Results 81 to 90 of about 3,896 (184)

Improving Recognition of Defective Epoxy Images in Integrated Circuit Manufacturing by Data Augmentation

Sensors
This paper discusses the problem of recognizing defective epoxy drop images for the purpose of performing vision-based die attachment inspection in integrated circuit (IC) manufacturing based on deep neural networks.
Lamia Alam, Nasser Kehtarnavaz
doaj   +1 more source

Variational Autoencoder to Obtain High Resolution Wind Fields from Reanalysis Data

Wind
Accurate wind flow prediction is essential for various applications, including the placement of wind turbines and a multitude of environmental assessments.
Bernhard Rösch, Konstantin Zacharias, Luca Fabian Schlaug, Daniel Westerfeld, Stefan Geißelsöder, Alexander Buchele   +5 more
doaj   +1 more source

Model Validation for Multivariate Functional Responses via Autoencoder-Based Dual-Layer Feature Extraction

Mathematics
Model validation for complex simulation models with multivariate functional responses poses significant challenges, as it involves the dual coupling of physical correlations among variables and field correlations in time-series data.
Dengyu Wu, Xiaodong Zhang, Daobo Sun, Haidong Lin, Jinhui Li, Baoqiang Zhang   +5 more
doaj   +1 more source

AE-MCDM: an autoencoder-based multi-criteria decision-making approach for unsupervised feature selection

The Journal of Supercomputing
Feature selection is a fundamental technique for reducing the dimensionality of high-dimensional data by identifying the most relevant features while discarding redundant or irrelevant ones. In unsupervised settings, where labeled data are unavailable and labeling is costly, effective feature selection becomes even more challenging. This paper proposes
Hashemi, Amin, Dowlatshahi, Mohammad Bagher, Farshidi, Siamak, Moradi, Parham   +3 more
openaire   +1 more source

Data Augmentation for Text Classification Using Autoencoders

IEEE Access
Deep learning models have greatly improved various natural language processing tasks. However, their effectiveness depends on large data sets, which can be difficult to acquire.
Mustafa Cataltas, Ilyas Cicekli, Nurdan Akhan Baykan   +2 more
doaj   +1 more source

Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis

Scientific Reports
Periodontitis, a chronic inflammatory condition of the periodontium, is associated with over 60 systemic diseases. Despite advancements, precision medicine approaches have had limited success, emphasizing the need for deeper insights into cellular ...
Pradeep Kumar Yadalam, Prabhu Manickam Natarajan, Carlos M. Ardila   +2 more
doaj   +1 more source

DSFC-AE: A New Hyperspectral Unmixing Method Based on Deep Shared Fully Connected Autoencoder

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The pervasive presence of mixed pixels in hyperspectral remote sensing imagery poses a substantial constraint on the quantitative progress of remote sensing technology. Hyperspectral unmixing (HU) techniques serve as effective means to address this issue.
Hao Chen 0192, Tao Chen 0004, Yuxiang Zhang 0001, Bo Du 0001, Antonio Plaza   +4 more
openaire   +2 more sources

Method for Detecting Disorder of a Nonlinear Dynamic Plant

Sensors
This paper proposes a new disorder detection method CCF-AE for a scalar dynamic plant based only on its input–output relation using a cross-correlation function and neural network autoencoder.
Xuechun Wang, Vladimir Eliseev
doaj   +1 more source

NSP-AE: Neuro-symbolic process autoencoder for time anomaly detection

Science Progress
Temporal anomaly detection is a critical task in business process management, aiming to identify process instances whose temporal behavior deviates from expected specifications.
Na Fang, Ke Lu, Xianwen Fang
doaj   +1 more source

MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
The challenges of applying self-supervised learning to 3D mesh data include difficulties in explicitly modeling and leveraging geometric topology information and designing appropriate pretext tasks and augmentation methods for irregular mesh topology. In this paper, we propose a novel approach for pre-training models on large-scale, unlabeled datasets ...
Zhangsihao Yang, Kaize Ding, Huan Liu 0001, Yalin Wang 0001   +3 more
openaire   +3 more sources