Results 71 to 80 of about 34,284 (266)
Defect Inspection in Display Panel Using Concentrated Auto Encoder
International Journal of Optics, 2019 In this paper, concentrated auto encoder (CAE) is proposed for aligning photo spacer (PS) and for local inspection of PS. The CAE method has two characteristics. First, unaligned images can be moved to the same alignment position, which makes it possible
DongHun Ku
doaj +1 more source
Future Internet, 2023 In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV power production could limit the influence of uncertainties on ...
Ganapathy Ramesh +3 more
doaj +1 more source
Variational Graph Auto-Encoders
, 2016 Bayesian Deep Learning Workshop (NIPS 2016)
Kipf, T.N., Welling, M.
openaire +3 more sources
Metachromatic Butterfly Bile Pigments for Multi‐Level Optical Security Films
Advanced Functional Materials, EarlyView.Bio‐derived optical security materials are manufactured by embedding butterfly‐based pigments in polymer films. Tunable color and fluorescence responses arise from concentration‐controlled metachromasy, enabling spatially encoded patterns with distinct visible, UV‐active, and spectral signatures.
Limin Wang, Bodo D. Wilts
wiley +1 more source
Image Geo-Site Estimation Using Convolutional Auto-Encoder and Multi-Label Support Vector Machine
Information, 2023 The estimation of an image geo-site solely based on its contents is a promising task. Compelling image labelling relies heavily on contextual information, which is not as simple as recognizing a single object in an image.
Arpit Jain +5 more
doaj +1 more source
Variational Recurrent Auto-Encoders
, 2014 In this paper we propose a model that combines the strengths of RNNs and SGVB: the Variational Recurrent Auto-Encoder (VRAE). Such a model can be used for efficient, large scale unsupervised learning on time series data, mapping the time series data to a latent vector representation. The model is generative, such that data can be generated from samples
Fabius, Otto, van Amersfoort, Joost R.
openaire +2 more sources
Advanced Healthcare Materials, EarlyView.A robust method to generate functional human iPSC‐derived endothelial cells using inducible ETV2 expression. These cells self‐organize into stable, lumenized microvascular networks within microfluidic chips, surpassing conventional differentiation methods.
Shun Zhang +12 more
wiley +1 more source
Respiratory Organ‐on‐a‐Chip for Disease Modeling: From Architecture to Functional Integration
Advanced Healthcare Materials, EarlyView.Respiratory organ‐on‐a‐chip (ROC) models capture key mechanical and cellular cues of the human respiratory system, enabling quantitative dissection of disease mechanisms. This review links ROC architectures to disease modeling, functional integration, and commercialization, and proposes a decision framework that aligns model complexity with mechanistic
Jinzhuo Hu +4 more
wiley +1 more source
Tessellated Wasserstein Auto-Encoders
, 2020 Non-adversarial generative models such as variational auto-encoder (VAE), Wasserstein auto-encoders with maximum mean discrepancy (WAE-MMD), sliced-Wasserstein auto-encoder (SWAE) are relatively easy to train and have less mode collapse compared to Wasserstein auto-encoder with generative adversarial network (WAE-GAN).
Gai, Kuo, Zhang, Shihua
openaire +2 more sources