Convolutional neural networks-based anti-weapon detection for safe 3D printing

2019 ◽  
Author(s):  
Giao Pham ◽  
Suk-Hwan Lee ◽  
Ki-Ryong Kwon
Keyword(s):  
Neural Networks ◽  
3D Printing ◽  
Convolutional Neural Networks

scholarly journals Anti-3D Weapon Model Detection for Safe 3D Printing Based on Convolutional Neural Networks and D2 Shape Distribution

Symmetry ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 90 ◽  
Author(s):  
Giao Pham ◽  
Suk-Hwan Lee ◽  
Oh-Heum Kwon ◽  
Ki-Ryong Kwon
Keyword(s):  
Neural Networks ◽  
3D Printing ◽  
Convolutional Neural Networks ◽  
Shape Distribution

scholarly journals DEMOCRATISING DESIGN THROUGH SURROGATE MODEL CONVOLUTIONAL NEURAL NETWORKS OF COMPUTER AIDED DESIGN REPOSITORIES

2020 ◽  
Vol 1 ◽  
pp. 1285-1294
Author(s):  
J. Gopsill ◽  
S. Jennings
Keyword(s):  
Neural Networks ◽  
3D Printing ◽  
Convolutional Neural Networks ◽  
Real World ◽  
Surrogate Model ◽  
Computer Aided Design ◽  
Computer Aided ◽  
Cad Models ◽  
Knowledge Intensive ◽  
Aided Design

AbstractThe capability to manufacture at home is continually increasing with technologies, such as 3D printing. However, the ability to design products suitable for manufacture and use remains a highly-skilled and knowledge intensive activity. This has led to ‘content creators’ providing vast repositories of manufacturable products for society, however challenges remain in the search & retrieval of models. This paper presents the surrogate model convolutional neural networks approach to search and retrieve CAD models by mapping them directly to their real-world photographed counterparts.

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.

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
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