Urban 3D Structure Reconstruction Through a Generative Adversarial Network Model

2020 ◽  
Vol 45 (12) ◽  
pp. 10731-10741
Author(s):  
Bharath Haridas Aithal ◽  
Soumya Kanta Das ◽  
Prakash Pilinja Subrahmanya
Keyword(s):  
Network Model ◽  
3D Structure ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Structure Reconstruction

scholarly journals Research on extraction and reproduction of deformation camouflage spot based on generative adversarial network model

2020 ◽  
Vol 16 (3) ◽  
pp. 555-563 ◽  
Author(s):  
Xin Yang ◽  
Wei-dong Xu ◽  
Qi Jia ◽  
Ling Li ◽  
Wan-nian Zhu ◽  
...  
Keyword(s):  
Network Model ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals A Transfer Deep Generative Adversarial Network Model to Synthetic Brain CT Generation from MR Images

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yi Gu ◽  
Qiankun Zheng
Keyword(s):  
Transfer Learning ◽  
Network Model ◽  
Medical Images ◽  
Image Method ◽  
Data Sets ◽  
Generation Process ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Model Based ◽  
Adversarial Network

Background. The generation of medical images is to convert the existing medical images into one or more required medical images to reduce the time required for sample diagnosis and the radiation to the human body from multiple medical images taken. Therefore, the research on the generation of medical images has important clinical significance. At present, there are many methods in this field. For example, in the image generation process based on the fuzzy C-means (FCM) clustering method, due to the unique clustering idea of FCM, the images generated by this method are uncertain of the attribution of certain organizations. This will cause the details of the image to be unclear, and the resulting image quality is not high. With the development of the generative adversarial network (GAN) model, many improved methods based on the deep GAN model were born. Pix2Pix is a GAN model based on UNet. The core idea of this method is to use paired two types of medical images for deep neural network fitting, thereby generating high-quality images. The disadvantage is that the requirements for data are very strict, and the two types of medical images must be paired one by one. DualGAN model is a network model based on transfer learning. The model cuts the 3D image into multiple 2D slices, simulates each slice, and merges the generated results. The disadvantage is that every time an image is generated, bar-shaped “shadows” will be generated in the three-dimensional image. Method/Material. To solve the above problems and ensure the quality of image generation, this paper proposes a Dual3D&PatchGAN model based on transfer learning. Since Dual3D&PatchGAN is set based on transfer learning, there is no need for one-to-one paired data sets, only two types of medical image data sets are needed, which has important practical significance for applications. This model can eliminate the bar-shaped “shadows” produced by DualGAN’s generated images and can also perform two-way conversion of the two types of images. Results. From the multiple evaluation indicators of the experimental results, it can be analyzed that Dual3D&PatchGAN is more suitable for the generation of medical images than other models, and its generation effect is better.

A Generative Adversarial Network Model for Simulating Various Types of Human-Induced Loads

2019 ◽  
Vol 19 (08) ◽  
pp. 1950092 ◽  
Author(s):  
Jiecheng Xiong ◽  
Jun Chen
Keyword(s):  
Network Model ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Real Samples ◽  
Load Model ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Power Spectral ◽  
Physical Features ◽  
Fully Connected

Severe vibrations may occur on slender structures like footbridges and cantilever stands due to human-induced loads such as walking, jumping or bouncing. Currently, to develop a load model for structural design, the main features, such as periodicity and stationarity of experimental load records, are artificially extracted and then mathematically modeled. Different physical features have been included in different load models, i.e. no unified load model exists for different individual activities. The recently emerged generative adversarial networks can be used to model high-dimensional random variables. The probability distribution of these variables learned from real samples can be used to generate new samples, avoiding extracting features artificially. In this paper, a new model is proposed which combines the conditional generative adversarial networks and Wasserstein generative adversarial networks with gradient penalty to generate individual walking, jumping and bouncing loads. The generator of the model has five fully connected layers and a one-dimensional convolutional layer, and the discriminator has five fully connected layers. After one million training steps using the experimental records, the generator can generate high-quality samples similar to real samples in waveform. Finally, by comparing the power spectral densities and single degree of freedom system’s responses of the generated samples with real samples, it is further proved that the proposed generative adversarial network model can be used to simulate various human-induced loads. Source code of the model along with its trained weights is provided to the readers to further analysis and application.

Sign in / Sign up

Export Citation Format

Share Document