Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication
- Publication Type
Articles 1 - 6 of 6
Full-Text Articles in Engineering
High-Throughput Discovery Of Novel Cubic Crystal Materials Using Deep Generative Neural Networks, Yong Zhao, Mohammed Al-Fahdi, Ming Hu, Edirisuriya M.D. Siriwardane, Yuqi Song, Alireza Nasiri, Jianjun Hu
High-Throughput Discovery Of Novel Cubic Crystal Materials Using Deep Generative Neural Networks, Yong Zhao, Mohammed Al-Fahdi, Ming Hu, Edirisuriya M.D. Siriwardane, Yuqi Song, Alireza Nasiri, Jianjun Hu
Faculty Publications
High-throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of sufficient and diverse materials in current materials repositories such as the open quantum materials database (OQMD). Recent progress in deep learning have enabled generative strategies that learn implicit chemical rules for creating hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generative design …
Creating Synthetic Satellite Cloud Data Based On Gan Method, Wencong Cheng, Xiaokang Shi, Zhigang Wang
Creating Synthetic Satellite Cloud Data Based On Gan Method, Wencong Cheng, Xiaokang Shi, Zhigang Wang
Journal of System Simulation
Abstract: To create the synthetic satellite cloud data in the domain of Meteorology, a method based on Generative Adversarial Networks (GAN) is proposed. Depending on ability of the nonlinear mapping and the information extraction of raster data with the deep learning network, a deep generative adversarial network model is proposed to extract the corresponding information between the numerical weather prediction(NWP) products and the satellite cloud data, and then the appropriate elements of the NWP product are chosen as the input to synthesize the corresponding satellite cloud data. The experiments are conducted on the re-analysis products of the European Centre …
Research Of Super-Resolution Processing Of Invoice Image Based On Generative Adversarial Network, Xinli Li, Changming Zou, Guotian Yang, Liu He
Research Of Super-Resolution Processing Of Invoice Image Based On Generative Adversarial Network, Xinli Li, Changming Zou, Guotian Yang, Liu He
Journal of System Simulation
Abstract: Automatic identification of invoices can effectively improve financial efficiency. But low-resolution invoice image reduces the accuracy of automatic identification, an ESRGAN (Encoder Super-resolution Generative Adversarial Network) network for super-resolution processing of invoice images is proposed. The ESRGAN network is based on a conditional generative adversarial network. An auxiliary encoder is designed to guide the network to generate a more realistic super-resolution image. Based on the actual invoice image, the ESRGAN network and the conventional image processing, SRCNN (Super-resolution Convolutional Neural Networks) network and SRGAN (Super-resolution Generative Adversarial Network) network. The model is evaluated through two evaluation indicators of peak …
Can Generative Adversarial Networks Help Us Fight Financial Fraud?, Sean Mciver
Can Generative Adversarial Networks Help Us Fight Financial Fraud?, Sean Mciver
Dissertations
Transactional fraud datasets exhibit extreme class imbalance. Learners cannot make accurate generalizations without sufficient data. Researchers can account for imbalance at the data level, algorithmic level or both. This paper focuses on techniques at the data level. We evaluate the evidence of the optimal technique and potential enhancements. Global fraud losses totalled more than 80 % of the UK’s GDP in 2019. The improvement of preprocessing is inherently valuable in fighting these losses. Synthetic minority oversampling technique (SMOTE) and extensions of SMOTE are currently the most common preprocessing strategies. SMOTE oversamples the minority classes by randomly generating a point between …
Determining Overfitting And Underfitting In Generative Adversarial Networksusing Fréchet Distance, Enes Eken
Determining Overfitting And Underfitting In Generative Adversarial Networksusing Fréchet Distance, Enes Eken
Turkish Journal of Electrical Engineering and Computer Sciences
Generative adversarial networks (GANs) can be used in a wide range of applications where drawing samples from a data probability distribution without explicitly representing it is essential. Unlike the deep convolutional neural networks (CNNs) trained for mapping an input to one of the multiple outputs, monitoring the overfitting and underfitting in GANs is not trivial since they are not classifying but generating a data. While training set and validation set accuracy give a direct sense of success in terms of overfitting and underfitting for CNNs during the training process, evaluating the GANs mainly depends on the visual inspection of the …
Ship Deck Segmentation In Engineering Document Using Generative Adversarial Networks, Mohammad Shahab Uddin, Raphael Pamie-George, Daron Wilkins, Andres Sousa Poza, Mustafa Canan, Samuel Kovacic, Jiang Li
Ship Deck Segmentation In Engineering Document Using Generative Adversarial Networks, Mohammad Shahab Uddin, Raphael Pamie-George, Daron Wilkins, Andres Sousa Poza, Mustafa Canan, Samuel Kovacic, Jiang Li
Engineering Management & Systems Engineering Faculty Publications
Generative adversarial networks (GANs) have become very popular in recent years. GANs have proved to be successful in different computer vision tasks including image-translation, image super-resolution etc. In this paper, we have used GAN models for ship deck segmentation. We have used 2D scanned raster images of ship decks provided by US Navy Military Sealift Command (MSC) to extract necessary information including ship walls, objects etc. Our segmentation results will be helpful to get vector and 3D image of a ship that can be later used for maintenance of the ship. We applied the trained models to engineering documents provided …