Improved Wasserstein Generative Adversarial Networks Research Articles

Electricity and hydrogen fuel generation based on wind, solar energies and alkaline fuel cell: A hybrid IWGAN–AVOA approach

Addressing the pressing need for sustainable energy solutions, this paper proposes a novel hybrid energy system integrating wind, alkaline fuel cells and solar energies. This paper introduces a pioneering hybrid energy system designed to produce electricity and hydrogen fuel through the integration of wind, solar energies, and an alkaline fuel cell. The proposed approach, coined as IWGAN–AVOA technique, synergizes the improved Wasserstein generative adversarial network (IWGAN) and the African vultures optimization algorithm (AVOA). The primary aim is to delineate a distinctive energy cycle reliant on renewable sources, featuring a Stirling engine, electrolyzer, alkaline fuel cell, wind turbine, and solar photovoltaic system, with the objective of generating hydrogen fuel and energy. The AVOA enhances fuel cell capabilities, simulates optimal system component sizes, and rectifies erroneous configurations based on geographical specifics. By validating the efficacy of IWGAN, the study achieves optimal configurations for device capacities and enhances power flow efficiency. Comparative analyses reveals that the IWGAN–AVOA approach surpasses existing techniques, like particle swarm optimization (PSO), wild horse optimizer (WHO), and heap based optimizer (HBO), with a remarkable efficiency rate of 98%, outperforming current methods. The last figure illustrates the effectiveness comparison, showcasing efficiencies of 62%, 79%, and 85% for HBO, PSO, and WHO, respectively, against the superior 98% efficiency achieved by the IWGAN–AVOA approach, affirming its superiority in energy system optimization.

Research on a Bearing Fault Diagnosis Method Based on an Improved Wasserstein Generative Adversarial Network

In this paper, an advanced Wasserstein generative adversarial network (WGAN)-based bearing fault diagnosis approach is proposed to bolster the diagnostic efficacy of conventional WGANs and tackle the challenge of selecting optimal hyperparameters while reducing the reliance on sample labeling. Raw vibration signals undergo continuous wavelet transform (CWT) processing to generate time–frequency images that align with the model’s input dimensions. Subsequently, these images are incorporated into a region-based fully convolutional network (R-FCN), substituting the traditional discriminator for feature capturing. The WGAN model is refined through the utilization of the Bayesian optimization algorithm (BOA) to optimize the generator and discriminator’s semi-supervised learning loss function. This approach is verified using the Case Western Reserve University (CWRU) dataset and a centrifugal pump failure experimental dataset. The results showed improvements in data input generalization and fault feature extraction capabilities. By avoiding the need to label large quantities of sample data, the diagnostic accuracy was improved to 98.9% and 97.4%.

Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification

Objective.Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG) data and ensuring its quality, insufficient training data often leads to overfitting and inadequate generalization capabilities of deep learning-based classification networks. Therefore, we propose a novel data augmentation method and deep learning classification model to enhance the decoding performance of MI-EEG further.Approach.The raw EEG signals were transformed into the time-frequency maps as the input to the model by continuous wavelet transform. An improved Wasserstein generative adversarial network with gradient penalty data augmentation method was proposed, effectively expanding the dataset used for model training. Additionally, a concise and efficient deep learning model was designed to improve decoding performance further.Main results.It has been demonstrated through validation by multiple data evaluation methods that the proposed generative network can generate more realistic data. Experimental results on the BCI Competition IV 2a and 2b datasets and the actual collected dataset show that classification accuracies are 83.4%, 89.1% and 73.3%, and Kappa values are 0.779, 0.782 and 0.644, respectively. The results indicate that the proposed model outperforms state-of-the-art methods.Significance.Experimental results demonstrate that this method effectively enhances MI-EEG data, mitigates overfitting in classification networks, improves MI classification accuracy, and holds positive implications for MI tasks.

An oil wear particle identification method based on Wasserstein generative adversarial network and improved CNN using a custom-built optical imaging sensor

The accurate classification of wear particles in lubricating oil holds significant importance for understanding the wear characteristics of lubricated components. However, there are prominent issues, such as blurred particle images, limited identification efficiency, and low generalization capabilities. To solve such issues, this research initially devises a novel online monitoring sensor to collect particle images based on the optical method. Subsequently, an improved Wasserstein Generative Adversarial Network (WGAN) is developed to ameliorate the categorization performance, which is hampered by data imbalance. Lastly, a MobileNetV2-SENet-based Convolutional Neural Network (MSCNN) is proposed to autonomously discover the associations between images and types of wear particles. Experimental results illustrate that our method achieves a recognition accuracy rate of 92.5%. The analysis outcomes between several comparative networks show that our approach can effectively improve recognition performance when limited samples are available. This work offers an effective solution for automatically identifying wear particles in oil and can be a powerful predictive maintenance tool for rotating mechanical equipment.

Research on an Improved Wasserstein Generative Adversarial Network Early Fault Warning Method for Rotating Machinery

Research on an Improved Wasserstein Generative Adversarial Network Early Fault Warning Method for Rotating Machinery

Synthetic aperture radar ground target image generation based on improved Wasserstein generative adversarial networks with gradient penalty

Synthetic aperture radar ground target image generation based on improved Wasserstein generative adversarial networks with gradient penalty

Partial Discharge Data Augmentation Based on Improved Wasserstein Generative Adversarial Network With Gradient Penalty

The partial discharge (PD) classification for electric power equipment based on machine learning algorithms often leads to insufficient generalization ability and low recognition accuracy. To solve the problem, this paper develops an improved Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based data augmentation model. The improved WGAN-GP model can generate data samples to supplement the low-data input set in PD source classification. Firstly, an improved WGAN-GP model with conditional generation is trained and various new data samples are generated. Then the new data samples are utilized to expand the raw data set. Finally, the expanded data set is trained to get a new PD classifier. Experimental results demonstrate the proposed model can generate new high-quality data samples more stably. Moreover, the proposed method can suppress the over-fitting risk caused by low-data or imbalanced data distributions and the classification accuracy is effectively improved.

Image Reconstruction for Electrical Impedance Tomography (EIT) With Improved Wasserstein Generative Adversarial Network (WGAN)

The image reconstruction of electrical impedance tomography (EIT) is highly ill-posed and nonlinear. Because of the poor nonlinear fitting ability of analytical algorithms, reconstructed images of these algorithms are blurry and lack detailed features. Although high-quality EIT images can be obtained by applying deep-learning networks to image reconstruction, the interpretability and generalization ability of the network are difficult to guarantee. A deep-learning structure, namely conditional Wasserstein generative adversarial network with attention mechanism (CWGAN-AM), is proposed for EIT image reconstruction. CWGAN-AM consists of an imaging module, a generator, and a discriminator. The initial conductivity image obtained by the imaging module is added to both the generator and the discriminator as a constraint to improve the stability of reconstruction. The enhanced residual blocks (ERBs), the structure of residual in residual (RIR), and the attention unit are used in the generator to further improve the reconstruction accuracy for the inclusion boundary. The imaging results indicate that CWGAN-AM can accurately recover the irregular boundaries of inclusions, and effective reconstruction can be accomplished for the new conductivity distribution (inclusions with size/shape variations) and noisy samples.

IWGAN: Anomaly Detection in Airport Based on Improved Wasserstein Generative Adversarial Network

Anomaly detection is an important research topic in the field of artificial intelligence and visual scene understanding. The most significant challenge in real-world anomaly detection problems is the high imbalance of available data (i.e., non-anomalous versus anomalous data). This limits the use of supervised learning methods. Furthermore, the abnormal—and even normal—datasets in the airport field are relatively insufficient, causing them to be difficult to use to train deep neural networks when conducting experiments. Because generative adversarial networks (GANs) are able to effectively learn the latent vector space of all images, the present study adopted a GAN variant with autoencoders to create a hybrid model for detecting anomalies and hazards in the airport environment. The proposed method, which integrates the Wasserstein-GAN (WGAN) and Skip-GANomaly models to distinguish between normal and abnormal images, is called the Improved Wasserstein Skip-Connection GAN (IWGAN). In the experimental stage, we evaluated different hyper-parameters—including the activation function, learning rate, decay rate, training times of discriminator, and method of label smoothing—to identify the optimal combination. The proposed model’s performance was compared with that of existing models, such as U-Net, GAN, WGAN, GANomaly, and Skip-GANomaly. Our experimental results indicate that the proposed model yields exceptional performance.

Convolutional Two-Stream Generative Adversarial Network-Based Hyperspectral Feature Extraction

Hyperspectral image processing is faced with difficulties considering its redundant features and complex information. Studies on hyperspectral feature extraction in the deep learning domain have become increasingly popular. The mainstream techniques fully consider the spatial information in local neighborhoods when extracting spectral features by constructing deep neural networks. Deep generative models simulate the intrinsic structure of samples by adequately training, showing their potential values for signal processing. In this article, a convolutional two-stream network (cs <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GAN-FE) based on the improved Wasserstein generative adversarial network (WGAN) is proposed for unsupervised hyperspectral spatial–spectral feature extraction. The improved WGAN is composed of one generator and one discriminator; the former perceives real data distributions, and the latter determines the attribution of generated data. The designed two-stream strategy is not a simple extension of a one-stream strategy and considers both the static spectral–spatial information and the dynamic spectral reflectance variation in multiple bands. Intrinsic spatial–spectral features are extracted by the trained discriminator considering sample distributions and feature relationships. The loss function is also improved for the unique structure of cs <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GAN-FE. Various state-of-the-art techniques are chosen for comparison. Experimental results show the feasibility and potential of this network. Besides, experiments with the random split and the disjointed split both show that the proposed method can outperform other comparison techniques.

Speech Dereverberation Based on Improved Wasserstein Generative Adversarial Networks

In reality, the sound we hear is not only disturbed by noise, but also the reverberant, whose effects are rarely taken into account. Recently, deep learning has shown great advantages in speech signal processing. But among the existing dereverberation approaches, very few methods apply deep learning at the waveform level. In addition, in the case of sever reverberation, the conventional speech dereverberation methods perform poorly, such as MCLP (multi-channel linear prediction). We proposed a new speech dereverberation method in this paper, which is based on improved WGAN (Wasserstein Generative Adversarial Networks), called WGAN-GP, whose generator uses strided-convolutional networks and the discriminator is structured on DNNs. Due to the addition of the gradient penalty item, WGAN-GP improves the stability of training and the generalization of the model. In the case of severe reverberation, according to the experimental results, the proposed system can perform better than MCLP. As the proposed method based on WAGN-GP can improve speech quality, speech signal processing systems may be able to apply it to pre-processing stage.

Channel Estimation Enhancement With Generative Adversarial Networks

Improving the accuracy of channel estimation is a significant topic in the context of wireless communications. For training-based channel estimations, increasing the length of a training sequence may improve the accuracy of channel estimation but causes a higher overhead. Nevertheless, this paper shows that benefiting from generative adversarial networks (GANs), which is an emerging deep learning framework, the accuracy of channel estimation can be improved without transmitting a longer training sequence. To this end, this paper proposes a GAN-based channel estimation enhancement algorithm, where GANs are trained online with receive sequence so as to obtain a longer mimic sequence and enhance channel estimation. In order to address the problem of improving the training stability and the learning ability of GANs, this paper proposes a novel framework by integrating a conditional GAN with an improved Wasserstein GAN. Furthermore, a strategy based on a lookup table is proposed to alleviate overfitting that may occur during the training of GANs. Simulation results indicate that the proposed GAN-based channel estimation enhancement algorithm can benefit the conventional training-based channel estimation, yielding lower relative error performance, especially in the low SNR regions.

First arrival picking of microseismic signals based on nested U-Net and Wasserstein Generative Adversarial Network

Picking the first arrival of microseismic signals, quickly and accurately, is the key for real-time data processing of microseismic monitoring. The traditional method cannot meet the high-accuracy and high-efficiency requirements for the firstarrival microseismic picking, in a low SNR environment. Concentrating on the problem of relatively low microseismic SNR, this paper proposes the Residual Link Nested U-Net Network (RLU-Net), which can not only retain the spatial position information of input signal and profile, but also realize the first arrival picking from end-to-end. The added number of layers with the residual block is important to extract more features and better distinguish the signal and noise in low SNR environments, which improves the accuracy and efficiency of first arrival picking. We also use the improved Wasserstein Generative Adversarial Network (WGAN) generated adversarial sample sets and conducted adversarial training on the network to enhance the generalization ability of the model. The resulting model then accurately and efficiently identified the first arrival of effective signals. The testing of forward modeling microseismic signals and measured microseismic data show that, compared with the Short Term Average/Long Term Average (STA/LTA) algorithm, the proposed algorithm not only has a high accuracy, but also can pick the first arrival time of microseismic signals in a low SNR environment.

Recurrent GANs Password Cracker For IoT Password Security Enhancement.

Text-based passwords are a fundamental and popular means of authentication. Password authentication can be simply implemented because it does not require any equipment, unlike biometric authentication, and it relies only on the users’ memory. This reliance on memory is a weakness of passwords, and people therefore usually use easy-to-remember passwords, such as “iloveyou1234”. However, these sample passwords are not difficult to crack. The default passwords of IoT also are text-based passwords and are easy to crack. This weakness enables free password cracking tools such as Hashcat and JtR to execute millions of cracking attempts per second. Finally, this weakness creates a security hole in networks by giving hackers access to an IoT device easily. Research has been conducted to better exploit weak passwords to improve password-cracking performance. The Markov model and probabilistic context-free-grammar (PCFG) are representative research results, and PassGAN, which uses generative adversarial networks (GANs), was recently introduced. These advanced password cracking techniques contribute to the development of better password strength checkers. We studied some methods of improving the performance of PassGAN, and developed two approaches for better password cracking: the first was changing the convolutional neural network (CNN)-based improved Wasserstein GAN (IWGAN) cost function to an RNN-based cost function; the second was employing the dual-discriminator GAN structure. In the password cracking performance experiments, our models showed 10–15% better performance than PassGAN. Through additional performance experiments with PCFG, we identified the cracking performance advantages of PassGAN and our models over PCFG. Finally, we prove that our models enhanced password strength estimation through a comparison with zxcvbn.

DPIR-Net: Direct PET Image Reconstruction Based on the Wasserstein Generative Adversarial Network

Positron emission tomography (PET) is an advanced medical imaging technique widely used in various clinical applications, such as tumor detection and neurologic disorders. Reducing the radiotracer dose is desirable in PET imaging because it decreases the patient’s radiation exposure. However, reducing the dose can also increase noise, affecting the image quality. Therefore, an advanced image reconstruction algorithm based on low-dose PET data is needed to improve the quality of the reconstructed image. In this article, we propose the use of a direct PET image reconstruction network (DPIR-Net) using an improved Wasserstein generative adversarial network (WGAN) framework to enhance image quality. This article provides two main findings. First, our network uses sinogram data as input and outputs high-quality PET images direct, resulting in shorter reconstruction times compared with traditional model-based reconstruction networks. Second, we combine perceptual loss, mean square error, and the Wasserstein distance as the loss function, which effectively solves the problem of excessive smoothness and loss of detailed information in traditional network image reconstruction. We performed a comparative study using maximum-likelihood expectation maximization (MLEM) with a post-Gaussian filter, a total variation (TV)-norm regularization, a nonlocal means (NLMs) denoising method, a neural network denoising method, a traditional deep learning PET reconstruction network, and our proposed DPIR-Net method and evaluated the proposed method using both human and mouse data. The mouse data were obtained from a small animal PET prototype system developed by our laboratory. The quantitative and qualitative results show that our proposed method outperformed the conventional methods.

Hyperspectral image classification with unsupervised feature extraction

ABSTRACTDeep learning models have been widely applied to extract the high-level features of hyperspectral images (HSIs) due to their strong data mining ability, and some encouraging results have been obtained. Sufficient labelled samples are the guarantee of the network performance. However, the collection of labelled hyperspectral data is time consuming and labour consuming. To get rid of the limitation of labelled samples, an unsupervised feature extractor is proposed based on transfer learning and an improved Wasserstein generative adversarial network (WGAN-GP) in this paper. In addition, considering the spectral and spatial characteristics of HSIs, the framework is designed in three-dimensional (3D) form which helps fully exploit the information in hyperspectral data. Thanks to transfer learning, the well-trained discriminator of WGAN-GP is used as a feature extractor for learning the spectral-spatial features without involving labelled samples. Moreover, in order to facilitate the optimization of WGAN-GP, we propose a novel dimensionality reduction (DR) method using convolutions to get lower-dimensional target data. Experimental results on real hyperspectral datasets demonstrate the efficiency of the proposed method combining the proposed DR and WGAN-GP(DR+WGAN-GP), which has great potential prospects in HSI classification.

Wasserstein Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis

Ground-based weather radar can observe a wide range with a high spatial and temporal resolution. They are beneficial to meteorological research and services by providing valuable information. Recent weather radar data related research has focused on applying machine learning and deep learning to solve complicated problems. It is a well-known fact that an adequate amount of data is a positively necessary condition in machine learning and deep learning. Generative adversarial networks (GANs) have received extensive attention for their remarkable data generation capacity, with a fascinating competitive structure having been proposed since. Consequently, a massive number of variants have been proposed; which model is adequate to solve the given problem is an inevitable concern. In this paper, we propose exploring the problem of radar image synthesis and evaluating different GANs with authentic radar observation results. The experimental results showed that the improved Wasserstein GAN is more capable of generating similar radar images while achieving higher structural similarity results.

Person-independent facial expression recognition method based on improved Wasserstein generative adversarial networks in combination with identity aware

Since the distinction between two expressions is fairly vague, usually a subtle change in one part of the human face is enough to change a facial expression. Most of the existing facial expression recognition algorithms are not robust enough because they rely on general facial features or algorithms without considering differences between facial expression and facial identity. In this paper, we propose a person-independent recognition method based on Wasserstein generative adversarial networks for micro-facial expressions, where a facial expression recognition network and a facial identity recognition network are established to improve the accuracy and robustness of facial expression recognition via inhibition of intra-class variation. Extensive experimental results demonstrate that 90% average recognition accuracy of facial expression has been reached on a mixed dataset composed of CK+, Multi-PIE, and JAFFE. Moreover, our method achieves 96% accuracy of person-independent recognition on CK+. A 4.5% performance gain is achieved with the novel identity-inhibited expression feature. The proposed algorithm in this paper has been successfully applied to Haikang Visual Integrated Management Platform (iVMS-8700). At present, it runs well and can effectively recognize facial expressions.

Typical wind power scenario generation for multiple wind farms using conditional improved Wasserstein generative adversarial network

Because of environmental benefits, wind power is taking an increasing role meeting electricity demand. However, wind power tends to exhibit large uncertainty and is largely influenced by meteorological conditions. Apart from the variability, when multiple wind farms have geographical adjacency, their power generation also displays strong correlation. Thus, scenario generation considering the spatiotemporal relationships is a useful tool to model the stochastic process. In this work, we propose a wind power scenario generation framework based on the conditional improved Wasserstein generative adversarial network (WGAN). The framework includes a cluster analysis to establish the classification rule, a support vector classifier (SVC) to predict labels, a conditional scenario generation process based on improved WGAN, and finally a scenario reduction procedure. We demonstrate that the clustering analysis and SVC based labeling model can provide accurate classification results and the scenarios conditioned on input labels can not only follow the marginal distribution of each category but also capture the spatiotemporal relationships. We also illustrate that by adding a gradient penalty term to the discriminator’s loss function to enforce the Lipschitz constraint, the quality of scenarios is better than that of the existing method.