scholarly journals An improved generative adversarial network with modified loss function for crack detection in electromagnetic nondestructive testing

2021 ◽  
Author(s):  
Lulu Tian ◽  
Zidong Wang ◽  
Weibo Liu ◽  
Yuhua Cheng ◽  
Fuad E. Alsaadi ◽  
...  
Keyword(s):  
Nondestructive Testing ◽  
Loss Function ◽  
Real World ◽  
Crack Detection ◽  
Contrast Ratio ◽  
Detection Problem ◽  
Generative Adversarial Network ◽  
The Real ◽  
Adversarial Network ◽  
Optical Images

AbstractIn this paper, an improved generative adversarial network (GAN) is proposed for the crack detection problem in electromagnetic nondestructive testing (NDT). To enhance the contrast ratio of the generated image, two additional regulation terms are introduced in the loss function of the underlying GAN. By applying an appropriate threshold to the segmentation of the generated image, the real crack areas and the fake crack areas (which are affected by the noises) are accurately distinguished. Experiments are carried out to show the superiority of the improved GAN over the original one on crack detection tasks, where a real-world NDT dataset is exploited that consists of magnetic optical images obtained using the electromagnetic NDT technique.

scholarly journals Empower parameterized generative adversarial networks using a novel particle swarm optimizer: algorithms and applications

2021 ◽  
Author(s):  
Lulu Tian ◽  
Zidong Wang ◽  
Weibo Liu ◽  
Yuhua Cheng ◽  
Fuad E. Alsaadi ◽  
...  
Keyword(s):  
Crack Detection ◽  
Contrast Ratio ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Generative Adversarial Networks ◽  
Destructive Testing ◽  
Particle Swarm Optimizer ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Thermal Data

AbstractIn this paper, a novel parameterized generative adversarial network (GAN) is proposed where the parameters are introduced to enhance the performance of image segmentation. The developed algorithm is applied to the image-based crack detection problem on the thermal data obtained through the non-destructive testing process. A new regularization term, which contains three tunable hyperparameters, embedded into the objective function of the GAN in order to improve the contrast ratio of certain areas of the image so as to benefit the crack detection process. To automate the selection of the optimal hyperparameters of the GAN, a new particle swarm optimization (PSO) algorithm is put forward where a neighborhood-based velocity updating strategy is developed for the purpose of thoroughly exploring the problem space. The proposed PSO-based GAN algorithm is shown to 1) work well in detecting cracks on the thermal data generated by the eddy current pulsed thermography technique; and 2) outperforms other conventional GAN algorithms.

scholarly journals Generative adversarial network for glioblastoma ensures morphologic variations and improves diagnostic model for isocitrate dehydrogenase mutant type

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ji Eun Park ◽  
Dain Eun ◽  
Ho Sung Kim ◽  
Da Hyun Lee ◽  
Ryoung Woo Jang ◽  
...  
Keyword(s):  
Isocitrate Dehydrogenase ◽  
Synthetic Data ◽  
Diagnostic Model ◽  
Generative Adversarial Network ◽  
Classification Rate ◽  
Mutant Type ◽  
Clinical Model ◽  
The Real ◽  
Adversarial Network ◽  
Synthetic Images

AbstractGenerative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic variations to improve molecular-based prediction, as a rare disease of isocitrate dehydrogenase (IDH)-mutant glioblastomas. GAN was initially trained on 500 normal brains and 110 IDH-mutant high-grade astocytomas, and paired contrast-enhanced T1-weighted and FLAIR MRI data were generated. Diagnostic models were developed from real IDH-wild type (n = 80) with real IDH-mutant glioblastomas (n = 38), or with synthetic IDH-mutant glioblastomas, or augmented by adding both real and synthetic IDH-mutant glioblastomas. Turing tests showed synthetic data showed reality (classification rate of 55%). Both the real and synthetic data showed that a more frontal or insular location (odds ratio [OR] 1.34 vs. 1.52; P = 0.04) and distinct non-enhancing tumor margins (OR 2.68 vs. 3.88; P < 0.001), which become significant predictors of IDH-mutation. In an independent validation set, diagnostic accuracy was higher for the augmented model (90.9% [40/44] and 93.2% [41/44] for each reader, respectively) than for the real model (84.1% [37/44] and 86.4% [38/44] for each reader, respectively). The GAN-based synthetic images yield morphologically variable, realistic-seeming IDH-mutant glioblastomas. GAN will be useful to create a realistic training set in terms of morphologic variations and quality, thereby improving diagnostic performance in a clinical model.

Direct Unsupervised Super-Resolution using Generative Adversarial Network (DUS-GAN) for Real-World Data

2021 ◽  
pp. 1-1
Author(s):  
Kalpesh Prajapati ◽  
Vishal Chudasama ◽  
Heena Patel ◽  
Kishor Upla ◽  
Kiran Raja ◽  
...  
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Real World Data ◽  
Generative Adversarial Network ◽  
World Data ◽  
Adversarial Network

scholarly journals Cognitive Covert Traffic Synthesis Method Based on Generative Adversarial Network

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhangguo Tang ◽  
Junfeng Wang ◽  
Huanzhou Li ◽  
Jian Zhang ◽  
Junhao Wang
Keyword(s):  
Loss Function ◽  
Learning Algorithm ◽  
Synthesis Method ◽  
Covert Channel ◽  
Covert Communication ◽  
New Paradigm ◽  
Generative Adversarial Network ◽  
Good Convergence ◽  
Adversarial Network ◽  
Activity Loss

In the intelligent era of human-computer symbiosis, the use of machine learning method for covert communication confrontation has become a hot topic of network security. The existing covert communication technology focuses on the statistical abnormality of traffic behavior and does not consider the sensory abnormality of security censors, so it faces the core problem of lack of cognitive ability. In order to further improve the concealment of communication, a game method of “cognitive deception” is proposed, which is aimed at eliminating the anomaly of traffic in both behavioral and cognitive dimensions. Accordingly, a Wasserstein Generative Adversarial Network of Covert Channel (WCCGAN) model is established. The model uses the constraint sampling of cognitive priors to construct the constraint mechanism of “functional equivalence” and “cognitive equivalence” and is trained by a dynamic strategy updating learning algorithm. Among them, the generative module adopts joint expression learning which integrates network protocol knowledge to improve the expressiveness and discriminability of traffic cognitive features. The equivalent module guides the discriminant module to learn the pragmatic relevance features through the activity loss function of traffic and the application loss function of protocol for end-to-end training. The experimental results show that WCCGAN can directly synthesize traffic with comprehensive concealment ability, and its behavior concealment and cognitive deception are as high as 86.2% and 96.7%, respectively. Moreover, the model has good convergence and generalization ability and does not depend on specific assumptions and specific covert algorithms, which realizes a new paradigm of cognitive game in covert communication.

scholarly journals Best Selection of Generative Adversarial Networks Hyper-Parameters using Genetic Algorithm

2020 ◽  
Author(s):  
Fajr Alarsan ◽  
Mamoon Younes
Keyword(s):  
Genetic Algorithm ◽  
Programming Language ◽  
Loss Function ◽  
Generative Adversarial Networks ◽  
Accuracy Function ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Parameters Selection ◽  
Selection Of

Abstract Generative Adversarial Networks (GANs) are most popular generative frameworks that have achieved compelling performance. They follow an adversarial approach where two deep models generator and discriminator compete with each other In this paper, we propose a Generative Adversarial Network with best hyper-parameters selection to generate fake images for digits number 1 to 9 with generator and train discriminator to decide whereas the generated images are fake or true. Using Genetic Algorithm technique to adapt GAN hyper-parameters, the final method is named GANGA:Generative Adversarial Network with Genetic Algorithm. Anaconda environment with tensorflow library facilitates was used, python as programming language also used with needed libraries. The implementation was done using MNIST dataset to validate our work. The proposed method is to let Genetic algorithm to choose best values of hyper-parameters depending on minimizing a cost function such as a loss function or maximizing accuracy function. GA was used to select values of Learning rate, Batch normalization, Number of neurons and a parameter of Dropout layer.

scholarly journals Using the Generative Adversarial Network to Generate Recommendations

2020 ◽  
Author(s):  
A.V. Prosvetov
Keyword(s):  
Real World ◽  
Recommendation System ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Theoretical Comparison ◽  
New Methods ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Recommendation Algorithms ◽  
Cold Start Problem

Widely used recommendation systems do not meet all industry requirements, so the search for more advanced methods for creating recommendations continues. The proposed new methods based on Generative Adversarial Networks (GAN) have a theoretical comparison with other recommendation algorithms; however, real-world comparisons are needed to introduce new methods in the industry. In our work, we compare recommendations from the Generative Adversarial Network with recommendation from the Deep Semantic Similarity Model (DSSM) on real-world case of airflight tickets. We found a way to train the GAN so that users receive appropriate recommendations, and during A/B testing, we noted that the GAN-based recommendation system can successfully compete with other neural networks in generating recommendations. One of the advantages of the proposed approach is that the GAN training process avoids a negative sampling, which causes a number of distortions in the final ratings of recommendations. Due to the ability of the GAN to generate new objects from the distribution of the training set, we assume that the Conditional GAN is able to solve the cold start problem.

scholarly journals Automatic Melody Composition Using Enhanced GAN

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 883 ◽  
Author(s):  
Shuyu Li ◽  
Sejun Jang ◽  
Yunsick Sung
Keyword(s):  
Evaluation Method ◽  
Short Term Memory ◽  
Traditional Music ◽  
Musical Instrument ◽  
Generative Adversarial Network ◽  
Digital Interface ◽  
The Real ◽  
Adversarial Network ◽  
Creative Experience ◽  
Special Knowledge

In traditional music composition, the composer has a special knowledge of music and combines emotion and creative experience to create music. As computer technology has evolved, various music-related technologies have been developed. To create new music, a considerable amount of time is required. Therefore, a system is required that can automatically compose music from input music. This study proposes a novel melody composition method that enhanced the original generative adversarial network (GAN) model based on individual bars. Two discriminators were used to form the enhanced GAN model: one was a long short-term memory (LSTM) model that was used to ensure correlation between the bars, and the other was a convolutional neural network (CNN) model that was used to ensure rationality of the bar structure. Experiments were conducted using bar encoding and the enhanced GAN model to compose a new melody and evaluate the quality of the composition melody. In the evaluation method, the TFIDF algorithm was also used to calculate the structural differences between four types of musical instrument digital interface (MIDI) file (i.e., randomly composed melody, melody composed by the original GAN, melody composed by the proposed method, and the real melody). Using the TFIDF algorithm, the structures of the melody composed were compared by the proposed method with the real melody and the structure of the traditional melody was compared with the structure of the real melody. The experimental results showed that the melody composed by the proposed method had more similarity with real melody structure with a difference of only 8% than that of the traditional melody structure.

scholarly journals Generative Adversarial Networks for Visible to Infrared Video Conversion

2020 ◽  
Author(s):  
Mohammad Shahab Uddin ◽  
Jiang Li
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Infrared Image ◽  
Image Databases ◽  
Learning Models ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Optical Images ◽  
Infrared Video ◽  
Image Pairs

Deep learning models are data driven. For example, the most popular convolutional neural network (CNN) model used for image classification or object detection requires large labeled databases for training to achieve competitive performances. This requirement is not difficult to be satisfied in the visible domain since there are lots of labeled video and image databases available nowadays. However, given the less popularity of infrared (IR) camera, the availability of labeled infrared videos or image databases is limited. Therefore, training deep learning models in infrared domain is still challenging. In this chapter, we applied the pix2pix generative adversarial network (Pix2Pix GAN) and cycle-consistent GAN (Cycle GAN) models to convert visible videos to infrared videos. The Pix2Pix GAN model requires visible-infrared image pairs for training while the Cycle GAN relaxes this constraint and requires only unpaired images from both domains. We applied the two models to an open-source database where visible and infrared videos provided by the signal multimedia and telecommunications laboratory at the Federal University of Rio de Janeiro. We evaluated conversion results by performance metrics including Inception Score (IS), Frechet Inception Distance (FID) and Kernel Inception Distance (KID). Our experiments suggest that cycle-consistent GAN is more effective than pix2pix GAN for generating IR images from optical images.

scholarly journals A NEPHOGRAM PREDICTION METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 925-930
Author(s):  
Y. Xun ◽  
W. Q. Yu
Keyword(s):  
Loss Function ◽  
Internal Representation ◽  
Prediction Method ◽  
Ground Truth ◽  
Generative Adversarial Network ◽  
Multi Scale ◽  
Adversarial Network ◽  
Basic Characteristics ◽  
Cloud Evolution

Abstract. As one of the important sources of meteorological information, satellite nephogram is playing an increasingly important role in the detection and forecast of disastrous weather. The predictions about the movement and transformation of cloud with certain timeliness can enhance the practicability of satellite nephogram. Based on the generative adversarial network in unsupervised learning, we propose a prediction model of time series nephogram, which construct the internal representation of cloud evolution accurately and realize nephogram prediction for the next several hours. We improve the traditional generative adversarial network by constructing the generator and discriminator used the multi-scale convolution network. After the scale transform process, different scales operate convolutions in parallel and then merge the features. This structure can solve the problem of long-term dependence in the traditional network, and both global and detailed features are considered. Then according to the network structure and practical application, we define a new loss function combined with adversarial loss function to accelerate the convergence of model and sharpen predictions which keeps the effectivity of predictions further. Our method has no need to carry out the stack mathematics calculation and the manual operations, has greatly enhanced the feasibility and the efficiency. The results show that this model can reasonably describe the basic characteristics and evolution trend of cloud cluster, the prediction nephogram has very high similarity to the ground-truth nephogram.

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