Rotating Machinery Fault Diagnosis Method Based on Improved Semisupervised Generative Confrontation Network

Breast cancer is the leading cause of death in women worldwide; prevention of possible death from breast cancer can be decreased by early identification ultrasound image analysis by classifying ultrasound images into three classes (Normal, Benign, and Malignant), where the dataset used has imbalanced data. Imbalanced data cause the classification system only to recognize the majority class, so it is necessary to handle imbalanced data. In this study, imbalanced data can be handled by implementing the Deep Convolution Generative Adversarial Network (DCGAN) method as the addition of synthetic images to the training data. The DCGAN method generates synthetic images with feature learning on a Convolutional Neural Network (CNN), making DCGAN more stable than the basic generative adversarial network method. Synthetic and original images were further classified using the CNN GoogleNet method, which performs well in image classification and with reasonable computation cost. Synthetic ultrasound images were generated using a tuning hyperparameter in the DCGAN method to adjust the input size on GoogleNet for imbalanced data handling. From the experiment result, the implementation of DCGAN-GoogleNet has a higher accuracy in handling imbalanced data than conventional augmentation and other previous research, with an accuracy value reaching 91.61%, which is 1% to 4% higher than the accuracy value in the previous method.

Deep convolutional generative adversarial networks for the generation of numerous artificial spectrum‐compatible earthquake accelerograms using a limited number of ground motion records

Generative models have been applied in many fields and can be evaluated with many methods. In the evaluation of generative models, the proper evaluation metric varies with the application field. Therefore, the evaluation of generative adversarial networks is inherently challenging. In this study, conditional deep convolutional generative adversarial networks were applied in mechanical fault diagnosis and then evaluated. We proposed three evaluation metrics of conditional deep convolutional generative adversarial networks: Jensen-Shannon divergence, kernel maximum mean discrepancy, and the 1-nearest neighbor classifier which were used to distinguish generated samples from real samples, test mode collapsing and detect overfitting based on the dataset of Electronic Engineering Laboratory of Case Western Reserve University and the planetary gearbox dataset measured in the laboratory. The Jensen-Shannon divergence could not well distinguish generated samples from real samples. However, the two metrics (maximum mean discrepancy and 1-nearest neighbor classifier) well-distinguished generated samples from real samples, thus verifying the applicability of conditional deep convolutional generative adversarial networks in the field of mechanical diagnosis.

Inferring particle distributions in two-dimensional space with numerical features based on generative adversarial networks (GANs)

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.

Generative Adversarial Network is the topic of interest in today’s research in the field of image processing and computer vision. A basic GAN model was introduced by Ian Goodfellow et al. in 2014. After that advancement in the field of research in GAN models has been application specific. In computer vision and image to image translation GANs are playing very effective role either in the case of face detection and recognition or in image resolution enhancement and image augmentation. This paper represents a concise overview of various GAN models along with their features and applications. Pix2Pix and conditional GAN models work upon paired datasets while other models like cycle GAN, discover GAN, dual GAN, info GAN, deep convolutional GAN etc. work upon unpaired datasets. Various image datasets which are commonly used for training of generator and discriminator networks are also discussed in this paper. Since partial mode collapse is a common problem to occur during training process for all models, therefore various normalization techniques are also preferred during the training of generator and discriminator networks. As the advancements in GAN models are increasing at a very fast rate, soon these models will also be preferred in commercial applications.

Arabic calligraphy is one of the most aesthetic art forms in the world due to its variety and long history. However, generating calligraphic style is mainly done by human expert calligrapher (also known as Khattat) and has not been carried out by machine learning techniques. Generative adversarial networks (GAN) are deep learning tools that achieved outstanding results in the field of style transfer and generation. In this paper, various GAN architectures were investigated such as CycleGAN, Pix2pix, and deep convolutional generative adversarial networks (DCGAN) within Arabic calligraphy in two aspects: generation and style transfer. The results show that CycleGAN can transfer skeleton letters to both Naskh and Thulth styles, Pix2Pix can denoise the calligraphy papers, and DCGAN can generate realistic Arabic calligraphy letters. The proposed approaches are applicable for other calligraphy styles besides Naskh and Thulth. Finally, the models are evaluated qualitatively using a preference judgment technique survey.

We present a mathematically well-founded approach for the synthetic modeling of turbulent flows using generative adversarial networks (GAN). Based on the analysis of chaotic, deterministic systems in terms of ergodicity, we outline a mathematical proof that GAN can actually learn to sample state snapshots from the invariant measure of the chaotic system. Based on this analysis, we study a hierarchy of chaotic systems starting with the Lorenz attractor and then carry on to the modeling of turbulent flows with GAN. As training data, we use fields of velocity fluctuations obtained from large-eddy simulations (LES). Two architectures are investigated in detail: we use a deep, convolutional GAN (DCGAN) to synthesize the turbulent flow around a cylinder. We furthermore simulate the flow around a low-pressure turbine stator using the pix2pixHD architecture for a conditional DCGAN being conditioned on the position of a rotating wake in front of the stator. The settings of adversarial training and the effects of using specific GAN architectures are explained. We thereby show that GAN are efficient in simulating turbulence in technically challenging flow problems on the basis of a moderate amount of training data. GAN training and inference times significantly fall short when compared with classical numerical methods, in particular, LES, while still providing turbulent flows in high resolution. We furthermore analyze the statistical properties of the synthesized and LES flow fields, which agree excellently. We also show the ability of the conditional GAN to generalize over changes of geometry by generating turbulent flow fields for positions of the wake that are not included in the training data.

Thanks to the availability of large-scale data, deep Convolutional Neural Networks (CNNs) have witnessed success in various applications of computer vision. However, the performance of CNNs on Synthetic Aperture Radar (SAR) image classification is unsatisfactory due to the lack of well-labeled SAR data, as well as the differences in imaging mechanisms between SAR images and optical images. Therefore, this paper addresses the problem of SAR image classification by employing the Generative Adversarial Network (GAN) to produce more labeled SAR data. We propose special GANs for generating SAR images to be used in the training process. First, we incorporate the quadratic operation into the GAN, extending the convolution to make the discriminator better represent the SAR data; second, the statistical characteristics of SAR images are integrated into the GAN to make its value function more reasonable; finally, two types of parallel connected GANs are designed, one of which we call PWGAN, combining the Deep Convolutional GAN (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP) together in the structure, and the other, which we call CNN-PGAN, applying a pre-trained CNN as a discriminator to the parallel GAN. Both PWGAN and CNN-PGAN consist of a number of discriminators and generators according to the number of target categories. Experimental results on the TerraSAR-X single polarization dataset demonstrate the effectiveness of the proposed method.

Introduction: The advancement of artificial intelligence (AI) in medicine has exposed critical challenges, particularly the shortage of high-quality medical data and associated ethical concerns. Frontotemporal dementia (FTD) is a group of neurodegenerative conditions where early diagnosis is crucial. However, the development of AI-assisted diagnostic systems faces challenges due to the scarcity of labeled magnetic resonance imaging (MRI) data. Objective: To develop, compare, and evaluate two Generative Adversarial Network (GAN) models, Deep Convolutional GAN (DCGAN) and Progressive Growing GAN (PGGAN), for generating synthetic brain MRI slices, assessing image-level similarity and visual plausibility. Methods: We trained both models on a limited dataset of FTD brain MRIs. Our evaluation encompassed quantitative and qualitative assessments. We used three metrics for quantitative analysis: Structural Similarity Index (SSIM), Normalized Mutual Information (NMI), and Peak Signal-to-Noise Ratio (PSNR). Results: Both models generated synthetic MRIs. SSIM values were similar (≈0.215) with no practical difference; PGGAN surpassed DCGAN in NMI and PSNR (0.976 versus 0.798; 14.900 versus 12.426). Qualitative analysis corroborated these findings, with PGGAN producing images featuring enhanced detail. Conclusion: This research contributes to the field of synthetic medical image generation, offering a potential solution to data scarcity while maintaining patient privacy. The observed performance of PGGAN holds promise for augmenting limited datasets in neurological research. Furthermore, this study lays the groundwork for developing robust AI-assisted diagnostic tools in neurology, particularly for conditions like FTD, where extensive, diverse datasets are crucial yet challenging. Keywords: Artificial intelligence; frontotemporal dementia; magnetic resonance imaging; deep learning (Source: MeSH NLM)

The purpose of this study is to develop a novel computer-aided diagnosis (CAD) scheme to facilitate breast mass classification, which is based on the latest transferring generative adversarial networks (GAN) technology. Although GAN is one of the most popular techniques for image augmentation, it requires a relatively large original dataset to achieve satisfactory results, which may not be available for most of the medical imaging tasks. To address this challenge, we developed a novel transferring GAN, which was built based on the deep convolutional generative adversarial networks (DCGAN). This novel model was first pre-trained on a dataset of non-mass mammogram patches. Then the generator and the discriminator were fine-tuned on the mass dataset. A supervised loss was integrated with the discriminator, such that it can be used to directly classify the benign/malignant masses. We retrospectively assembled a total of 25,000 non-mass patches and 1024 mass images to assess this model, using classification accuracy and receiver operating characteristic (ROC) curve. The results demonstrated that our proposed approach improved the accuracy and area under the ROC curve (AUC) by 6.0% and 3.5% respectively, when compared with the classifiers trained without conventional data augmentation. This investigation may provide a new perspective for researchers to effectively train the GAN models on medical imaging tasks with limited datasets.

Diffusion models, variational autoencoders, and generative adversarial networks (GANs) are three common types of generative artificial intelligence models for image generation. Among these, GANs are the most frequently used for medical image generation and are often employed for data augmentation in various studies. However, due to the adversarial nature of GANs, where the generator and discriminator compete against each other, the training process can sometimes end with the model unable to generate meaningful images or even producing noise. This phenomenon is rarely discussed in the literature, and no studies have proposed solutions to address this issue. Such outcomes can introduce significant bias when GANs are used for data augmentation in medical image training. Moreover, GANs often require substantial computational power and storage, adding to the challenges. In this study, we used deep convolutional GANs for chest X-raygeneration, and three typical training outcomes were found. Two scenarios generated meaningful medical images and one failed to produce usable images. By analyzing the loss history during training, we observed that the regression line of the overall losses tends to diverge slowly. After excluding outlier losses, we found that the slope of the regression line within the stable loss segment indicates the optimal point to terminate training, ensuring the generation of meaningful medical images.

As a promising generative modeling method, Generative Adversarial Networks are a deep-learning-based generative model, in which two networks, namely the generative network and the discriminant network, contest with each other in a game during which the generator “learns” and get trained to be able to fool the discriminator of believing a specific image is (superficially) authentic. It is widely used to generate pictures based on existing data. Based on the MNIST data set, this paper studies and analyzes the application of Generative Adversarial Networks in the generation of handwritten digital images. In addition, by studying the Deep Convolutional Generative Adversarial Network and the Conditional Generation Adversarial Network model, we compared the connections and differences between these three models in training the MNIST dataset to generate handwritten digits algorithms, and gave an optimization method for generating handwritten digits.

Generative Adversarial Networks, as a powerful generative model, show great potential in generating adversarial samples and defending against adversarial attacks. In this paper, using Generative Adversarial Networks (GANs) as the basic framework, we design a network attack sample generation method based on Deep Convolutional Generative Adversarial Networks (DCGANs) and an adversarial sample defence method based on multi-scale GANs, and verify the practicality of the two methods through experiments, respectively. Compared with the three adversarial sample generation methods of AE-CDA, AE-DEEP and AE-ATTACK, the DCGAN-based adversarial sample generation method in this paper can interfere with the detection function of the anomaly detection model more effectively, and has better stability and versatility, and can maintain a relatively stable attack effect on a wide range of models and datasets. On the MNIST dataset, the classification accuracy of the adversarial sample defence method proposed in this paper is only slightly lower than that of the APE-GAN defence method on the JSMA adversarial samples, with a maximum classification accuracy of 98.69%. The maximum classification accuracy reaches 98.69%, and the time consumption is 1.5 s, which is only slightly larger than that of the APE-GAN defence method of 1.2 s. Thus, the time consumption of this paper’s multi-scale GAN-based adversarial sample defense method is smaller or equal to that of other comparative defense methods when systematic errors are ignored. The purpose of this paper is to provide a technical reference on how to eliminate adversarial perturbations using generative adversarial networks.

Convolutional Neural Network (CNN) tend to have better results on large data sets and poor performance on small data sets, so the data augmentation is crucial for a CNN to get better performance based on the dataset with limited size. In this paper, Deep Convolution Generative Adversarial Network (DCGAN) was used to augment data to make the AlexNet perform better on an image classification task with small data sets. AlexNet was trained on a small anime face training set with only 160 samples to determine whether the anime face was male or female, and then tested its accuracy on a test set with 240 sample. Then, a pre-trained DCGAN was transferred to train on the male and female training sets respectively. And 2 DCGANs were obtained, one could generate male cartoon faces and another could generate female cartoon faces. The images generated by DCGANs were put in train set, which was used to train AlexNet again and the result was recorded. Other data augmentation methods such as cutout, cutmix and Noise Injection were compared as well. Finally, it is found that AlexNet has the best performance when using the DCGAN augmentation method, which can significantly improve the verification accuracy of the model.