scholarly journals High-throughput, high-resolution deep learning microscopy based on registration-free generative adversarial network

2019 ◽  
Vol 10 (3) ◽  
pp. 1044 ◽  
Author(s):  
Hao Zhang ◽  
Chunyu Fang ◽  
Xinlin Xie ◽  
Yicong Yang ◽  
Wei Mei ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
High Throughput ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Generative Design of Stable Semiconductor Materials Using Deep Learning And DFT

2022 ◽  
Author(s):  
Edirisuriya Siriwardane ◽  
Yong Zhao ◽  
Indika Perera ◽  
Jianjun Hu
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Density Functional ◽  
Semiconductor Device ◽  
Wide Bandgap ◽  
Generative Adversarial Network ◽  
Functional Theory ◽  
Adversarial Network ◽  
Bandgap Semiconductors ◽  
Learning Data

Semiconductor device technology has exceptionally developed in complexity since discovering the bipolar transistor. With the rapid advancement of various technologies, semiconductors with distinct properties are essential. Recently, deep-learning, data-mining, and density functional theory (DFT)- based high-throughput calculations were widely performed to discover potential semiconductors for diverse applications. CubicGAN is a generative adversarial network where high-throughput analyses were done to uncover mechanically and dynamically stable materials with the assistance of DFT. In our work, we screened the semiconductors using a binary classifier from materials found from the CubicGAN. Next, we performed DFT computations to study their thermodynamic stability based on energy-above-hull and formation energy. According to our studies, 12 stable semiconductors were found with a particular class of materials, which we label as AA′MH6. Those are BaNaRhH6, BaSrZnH6, BaCsAlH6, SrTlIrH6, KNaNiH6, NaYRuH6, CsKSiH6, CaScMnH6, YZnMnH6, NaZrMnH6, AgZrMnH6, AgZrMnH6, and ScZnMnH6. It could be shown that AA′MH6 with M=Mn and NaYRuH6 semiconductors have considerably different structural, mechanical, and thermodynamic properties compared to the rest of the AA′MH6 semiconductors. In this study, The maximum bandgap found was approximately 3.3 eV from KNaNiH6, while the minimum bandgap was about 1.3 eV from CaScMnH6. BaNaRhH6, BaCsAlH6, CsKSiH6, KNaNiH6, and NaYRuH6 were identified as wide-bandgap semiconductors, where bandgaps are greater than 2 eV. Furthermore, BaSrZnH6 and KNaNiH6 are a direct bandgap semiconductors, whereas other AA′MH6 semiconductors exhibit indirect bandgaps.

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

AN AI-BASED APPROACH TO ENHANCED FRACTURE RESOLUTION IN IMAGE LOGS

2021 ◽  
Author(s):  
James Howard ◽  
◽  
Joe Tracey ◽  
Mike Shen ◽  
Shawn Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Nearest Neighbor ◽  
Rock Fracture ◽  
Short Interval ◽  
Acoustic Properties ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Deep Learning Neural Network ◽  
Borehole Image

Borehole image logs are used to identify the presence and orientation of fractures, both natural and induced, found in reservoir intervals. The contrast in electrical or acoustic properties of the rock matrix and fluid-filled fractures is sufficiently large enough that sub-resolution features can be detected by these image logging tools. The resolution of these image logs is based on the design and operation of the tools, and generally is in the millimeter per pixel range. Hence the quantitative measurement of actual width remains problematic. An artificial intelligence (AI) -based workflow combines the statistical information obtained from a Machine-Learning (ML) segmentation process with a multiple-layer neural network that defines a Deep Learning process that enhances fractures in a borehole image. These new images allow for a more robust analysis of fracture widths, especially those that are sub-resolution. The images from a BHTV log were first segmented into rock and fluid-filled fractures using a ML-segmentation tool that applied multiple image processing filters that captured information to describe patterns in fracture-rock distribution based on nearest-neighbor behavior. The robust ML analysis was trained by users to identify these two components over a short interval in the well, and then the regression model-based coefficients applied to the remaining log. Based on the training, each pixel was assigned a probability value between 1.0 (being a fracture) and 0.0 (pure rock), with most of the pixels assigned one of these two values. Intermediate probabilities represented pixels on the edge of rock-fracture interface or the presence of one or more sub-resolution fractures within the rock. The probability matrix produced a map or image of the distribution of probabilities that determined whether a given pixel in the image was a fracture or partially filled with a fracture. The Deep Learning neural network was based on a Conditional Generative Adversarial Network (cGAN) approach where the probability map was first encoded and combined with a noise vector that acted as a seed for diverse feature generation. This combination was used to generate new images that represented the BHTV response. The second layer of the neural network, the adversarial or discriminator portion, determined whether the generated images were representative of the actual BHTV by comparing the generated images with actual images from the log and producing an output probability of whether it was real or fake. This probability was then used to train the generator and discriminator models that were then applied to the entire log. Several scenarios were run with different probability maps. The enhanced BHTV images brought out fractures observed in the core photos that were less obvious in the original BTHV log through enhanced continuity and improved resolution on fracture widths.

scholarly journals Generative Adversarial Networks-Based Semi-Supervised Automatic Modulation Recognition for Cognitive Radio Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3913 ◽  
Author(s):  
Mingxuan Li ◽  
Ou Li ◽  
Guangyi Liu ◽  
Ce Zhang
Keyword(s):  
Deep Learning ◽  
Cognitive Radio ◽  
Supervised Learning ◽  
Rapid Development ◽  
Generative Adversarial Networks ◽  
Modulation Recognition ◽  
Learning Methods ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Automatic Modulation Recognition

With the recently explosive growth of deep learning, automatic modulation recognition has undergone rapid development. Most of the newly proposed methods are dependent on large numbers of labeled samples. We are committed to using fewer labeled samples to perform automatic modulation recognition in the cognitive radio domain. Here, a semi-supervised learning method based on adversarial training is proposed which is called signal classifier generative adversarial network. Most of the prior methods based on this technology involve computer vision applications. However, we improve the existing network structure of a generative adversarial network by adding the encoder network and a signal spatial transform module, allowing our framework to address radio signal processing tasks more efficiently. These two technical improvements effectively avoid nonconvergence and mode collapse problems caused by the complexity of the radio signals. The results of simulations show that compared with well-known deep learning methods, our method improves the classification accuracy on a synthetic radio frequency dataset by 0.1% to 12%. In addition, we verify the advantages of our method in a semi-supervised scenario and obtain a significant increase in accuracy compared with traditional semi-supervised learning methods.

scholarly journals Generating High-Resolution Climate Prediction through Generative Adversarial Network

2020 ◽  
Vol 174 ◽  
pp. 123-127
Author(s):  
Jianxin Cheng ◽  
Jin Liu ◽  
Zhou Xu ◽  
Chenkai Shen ◽  
Qiuming Kuang
Keyword(s):  
High Resolution ◽  
Climate Prediction ◽  
Generative Adversarial Network ◽  
Adversarial Network
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