Predicting the fluid behavior of random microfluidic mixers using convolutional neural networks

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Junchao Wang ◽  
Naiyin Zhang ◽  
Jinkai Chen ◽  
Guodong Su ◽  
Hailong Yao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Microfluidic Channel ◽  
Channel Networks ◽  
Fluid Behavior ◽  
Microfluidic Mixers

Predicting the fluid behavior of complex microfluidic channel networks using convolutional neural networks.

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Analysis of Material Representation of Manga Line Drawings using Convolutional Neural Networks

2017 ◽  
Vol 2017 (25) ◽  
pp. 48-57
Author(s):  
Takahiko Horiuchi ◽  
Yuma Saito ◽  
Keita Hirai
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Line Drawings

Convolutional Neural Networks for Underwater Pipeline Segmentation using Imperfect Datasets

2021 ◽  
Author(s):  
Edgar Medina ◽  
Roberto Campos ◽  
Jose Gabriel R. C. Gomes ◽  
Mariane R. Petraglia ◽  
Antonio Petraglia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Underwater Pipeline

Progressive Training Of Convolutional Neural Networks For Acoustic Events Classification

2021 ◽  
Author(s):  
Federico Colangelo ◽  
Federica Battisti ◽  
Alessandro Neri
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks

Early vs Late Fusion in Multimodal Convolutional Neural Networks

2020 ◽  
Author(s):  
Konrad Gadzicki ◽  
Razieh Khamsehashari ◽  
Christoph Zetzsche
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Late Fusion
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