Image based analysis of meibomian gland dysfunction using conditional generative adversarial neural network

Conditional Generative Adversarial Network (GAN) is a GAN that generates data with the desired condition from the latent vector. The auxiliary classifier GAN is the most used among the variations of conditional GANs. In this study, we explain the problem of auxiliary classifier GAN and propose conditional activation GAN that can replace auxiliary classifier GAN to reduce the number of hyperparameters and improve training speed. The loss function of conditional activation GAN is defined as the sum of the loss of each GAN created for each condition. Since each GAN shares all hidden layers, the GANs can be considered as a single GAN and it does not increase the amount of computation much. Also, in order to prevent ignorance of conditions in the discriminator of conditional GANs with batch normalization, we propose a mixed batch training, in which each batch for discriminator is always configured to have the same ratio of real data and generated data so that each batch always has the similar condition distribution

Load validity discrimination for telescopic boom of rock drilling jumbo

Due to the real working conditions, the collected mechanical fault datasets are actually limited and always highly imbalanced, which restricts the diagnosis accuracy and stability. To solve these problems, we present an imbalanced fault diagnosis method based on the generative model of conditional-deep convolutional generative adversarial network (C-DCGAN) and provide a study in detail. Deep convolutional generative adversarial network (DCGAN), based on traditional generative adversarial networks (GAN), introduces convolutional neural network into the training for unsupervised learning to improve the effect of generative networks. Conditional generative adversarial network (CGAN) is a conditional model obtained through introducing conditional extension into GAN. C-DCGAN is a combination of DCGAN and CGAN. In C-DCGAN, based on the feature extraction ability of convolutional networks, through the structural optimization, conditional auxiliary generative samples are used as augmented data and applied in machine fault diagnosis. Two datasets (Bearing dataset and Planetary gear box dataset) are carried out to validate. The simulation experiments showed that the improved performance is mainly due to the generated signals from C-DCGAN to balance the dataset. The proposed method can deal with imbalanced fault classification problem much more effectively. This model could improve the accuracy of fault diagnosis and the generalization ability of the classifier in the case of small samples and display better fault diagnosis performance.

A conditional variational autoencoding generative adversarial networks with self-modulation for rolling bearing fault diagnosis

Regular sampled seismic data is important for seismic data processing. However, seismic data is often missing due to natural or economic reasons. Especially, when encountering big obstacles, the seismic data will be missing in big gaps, which is more difficult to be reconstructed. Conditional generative adversarial networks (cGANs) are deep-learning models learning the characteristics of the seismic data to reconstruct the missing data. In this paper, we use a conditional Wasserstein generative adversarial network (cWGAN) to interpolate the missing seismic data in big gaps. We use the Wasserstein loss function to train the network and use a gradient penalty in the WGAN (cWGAN-GP) to enforce the Lipschitz constraint. We use a pre-stack seismic dataset to assess the performance. The interpolated results and the calculated recovered signal-to-noise ratios indicate that the cWGAN-GP can recover the missing seismic traces in portions or the entire regions, and the cWGAN-GP based interpolation is more accurate than the cGAN.

This project is intended to gift the ability of expression to the speech deprived by building a module that extracts thoughts from the human brain and presents it to the outside world as a digital image. This novel approach reconstructs same images of different colours from the EEG brain waves. Standalone datasets are being used to train the model which is obtained by using Enobio8 headset. EEG signals are acquired by providing six different shapes of three unique colors as the visual stimuli. After processing these signals EEG features are extracted using a convolutional Neural Network (CNN). The extracted features alongside noise are passed as an input to conditional Deep Convolutional Generative Adversarial Networks (cDCGANs) and conditional Generative Adversarial Networks (cGANs) which reconstruct the visual stimuli corresponding to the EEG data. The results obtained by the same are noted and analysed. The paper explicitly compares the working and performance of cGAN and cDCGAN models.

A tropical cyclone intensity prediction model using conditional generative adversarial network

We demonstrate the feasibility of solving atmospheric remote sensing problems with machine learning using conditional generative adversarial networks (CGANs), implemented using convolutional neural networks. We apply the CGAN to generating two‐dimensional cloud vertical structures that would be observed by the CloudSat satellite‐based radar, using only the collocated Moderate‐Resolution Imaging Spectrometer measurements as input. The CGAN is usually able to generate reasonable guesses of the cloud structure and can infer complex structures such as multilayer clouds from the Moderate‐Resolution Imaging Spectrometer data. This network, which is formulated probabilistically, also estimates the uncertainty of its own predictions. We examine the statistics of the generated data and analyze the response of the network to each input parameter. The success of the CGAN in solving this problem suggests that generative adversarial networks are applicable to a wide range of problems in atmospheric science, a field characterized by complex spatial structures and observational uncertainties.

Bangla handwritten character recognition is known to be one of the most classical problem in the field of machine learning. In order to solve a machine learning problem one must thing is dataset. The more varied data a model sees the better it learns. Generative adversarial networks (GANs) are a group of neural networks that are used in unsupervised machine learning. It helps to resolve many difficult operations such as image generation from description, transforming low resolution image into high resolution, retrieving image contents given a small pattern etc. GAN's have many other promising applications in machine learning. There are many variations available for GAN. One of the variation of GAN is Conditional Generative Adversarial Networks(cGAN). This kind of GAN is used for generating a specific type of image. In this work we have used cGAN for generating Class Based Character Generation. This work can help researchers to generate handwritten characters to enhance the perfomance of deep learning models. We have trained this model to generate 50 Basic Bangla Characters, 10 Bangla Numerals and 24 Compound characters.

In adversarial learning, the discriminator often fails to guide the generator successfully since it distinguishes between real and generated images using silly or nonrobust features. To alleviate this problem, this brief presents a simple but effective way that improves the performance of the generative adversarial network (GAN) without imposing the training overhead or modifying the network architectures of existing methods. The proposed method employs a novel cascading rejection (CR) module for discriminator, which extracts multiple nonoverlapped features in an iterative manner using the vector rejection operation. Since the extracted diverse features prevent the discriminator from concentrating on nonmeaningful features, the discriminator can guide the generator effectively to produce images that are more similar to the real images. In addition, since the proposed CR module requires only a few simple vector operations, it can be readily applied to existing frameworks with marginal training overheads. Quantitative evaluations on various data sets, including CIFAR-10, CelebA, CelebA-HQ, LSUN, and tiny-ImageNet, confirm that the proposed method significantly improves the performance of GAN and conditional GAN in terms of the Frechet inception distance (FID), indicating the diversity and visual appearance of the generated images.

High-entropy alloys (HEAs) have emerged as promising candidates for solid-state hydrogen storage, yet the vast compositional space poses a major challenge for targeted materials discovery. Here, we develop a deep generative framework based on generative adversarial networks (GANs) and conditional GANs (CGANs) to enable the inverse design of HEAs with optimized hydride formation enthalpy and hydrogen storage capacity. The GAN accurately captures the underlying compositional distributions, achieving high novelty and diversity while reproducing known chemical trends and empirical phase-formation rules. By incorporating property labels as conditional inputs, the CGAN allows directional control over the generation process, steering the model toward alloys with targeted enthalpy and capacity ranges. Density functional theory (DFT) calculations are performed to validate a representative GAN-generated composition, Mn18V2Fe16Ti18, confirming its structural stability and hydrogen uptake, in good agreement with machine-learning predictions. These results demonstrate that GAN model can autonomously learn distribution characteristics of data set and efficiently explore unexplored regions of the HEAs design space, providing a powerful strategy for the inverse design of advanced hydrogen storage materials.

This article explores the application of Generative Adversarial Networks (GANs) in improving subsurface rock analysis. GANs generate realistic images of rock features, enhancing the accuracy and efficiency of estimating rock properties. The objective is to advance the field of subsurface rock analysis, offering insights into the potential of GAN techniques. The article reviews recent developments in data-driven rock reconstruction technology, specifically focusing on GANs. It highlights GANs’ ability to generate and estimate rock features like porosity and permeability. These GAN methods are complemented by discriminators to select or reject features, making them integral to the reconstruction model. The article provides a framework for engineers and researchers to effectively utilize these techniques for rock reconstruction, offering an assessment of their strengths and limitations. Digital rock reconstruction, aided by GANs, has promising implications for site selection and recovery. GANs enhance image resolution, improving the field of view and dynamism of parameters in rock analysis. However, GANs heavily rely on high-quality data, which poses a limitation. To address this, conditional GANs have been proposed. The article comprehensively reviews the latest developments in GAN methods for digital rock reconstruction, offering valuable insights for engineers and researchers using GANs for accurate subsurface rock analysis. It also proposes ways to enhance these methods and advance the field. The novelty of this article lies in its exploration of Generative Adversarial Networks (GANs) in the context of subsurface rock analysis. It emphasizes the potential of GAN techniques to generate realistic rock images and estimate properties like porosity and permeability. Additionally, the article discusses the integration of GANs into the digital rock concept, highlighting their role in improving predictive models and supporting decision-making in the oil and gas industry's resource extraction and production strategies.

A Generative Adversarial Network (GAN) based asphalt pavement crack image generation method was proposed to improve the dataset size of the road images. Five open-source road crack datasets were leveraged to construct an image dataset, which contained two labels - transverse cracks and longitudinal cracks. The constructed dataset was used to facilitate crack detection and classification research by providing a diverse collection of labeled crack images derived from multiple public sources. The network structure of fully connected, convolutional and attention mechanisms based on the Conditional Generative Adversarial Network (CGAN) was used in this project. The purpose of this study was to train a generative model on selected categories of input pavement crack images and generate realistic crack images of those categories. We aim to tune the parameters of the GAN and optimize hyperparameters to improve the realism possibility of generated images. It also explored the generated images with different sizes and evaluated the performance of networks with different architectures. In particular, we analyzed the structural characteristics of conditional GAN. Results demonstrated that the Self-Attention Generative Adversarial Networks (SAGAN) model, which combines self-attention mechanisms with CGAN, can effectively address challenges related to limited crack image data and the inability to selectively generate images from specific categories. By conditioning the generator on category information, the SAGAN model was able to generate high-quality images while focusing on the target categories. Overall, the self-attention and conditional aspects of the SAGAN framework helped improve the generation of realistic pavement crack images.

The effect of loss function on conditional generative adversarial networks

To characterize the innervation of the cynomolgus monkey (Macaca fascicularis) Meibomian (tarsal) glands, upper lids of six cynomolgus monkeys were investigated with electronmicroscopical and double-labeling immunocytochemical methods. Antibodies against calcitonin gene-related peptide (CGRP), dopamine-beta-hydroxylase (DBH), neuropeptide Y (NPY), nitric oxide synthase (NOS), protein gene product 9.5 (PGP 9.5), substance P (SP), tyrosine hydroxylase (TH), and vasoactive intestinal peptide (VIP) were used. In addition, sections were processed for NADPH-diaphorase (NADPH-d) histochemistry. Staining for PGP 9.5 and electron microscopy showed that Meibomian gland acini were surrounded by a network of unmyelinated nerves and terminal varicose axons. The terminals contained small agranular (30-60 nm) and large granular vesicles (65-110 nm), and were observed in close contact with the basal lamina of the acini, but never internally to the basal lamina. Meibomian axons showed like-immunoreactivity (LI) for the neuropeptides SP, CGRP, NPY, and VIP. In addition, the axons stained for TH, DBH, NOS, and NADPH-d. VIP-LI, NOS- and NADPH-d-positive axons appeared to be more numerous, TH- and DBH-positive axons more rare than others. Most SP-LI axons were double-labelled for CGRP-LI, some for VIP-LI or NPY-LI. In addition, some VIP-LI axons were double-labeled for NPY-LI. NPY/VIP-LI and NPY/SP-LI axons were only observed close to the Meibomian acini. Conversely, NPY-LI colocalized with TH-IR or DBH-IR predominated in perivascular nerves of Meibomian gland vasculature. The close association of varicose axons with the acini of Meibomian glands indicates that nervous signals modulate meibomian secretion. Meibomian gland nerve fibers in the cynomolgus monkey appear to utilize various neuropeptides, catecholamines and nitric oxide as transmitter substances, and seem to derive from the pterygopalatine, superior cervical and trigeminal ganglion respectively.